From 3f26adda3008c6597dfaed25e42a5315676abb83 Mon Sep 17 00:00:00 2001
From: rgtjf <rgtjf1@163.com>
Date: Sat, 19 Jan 2019 21:33:26 +0800
Subject: [PATCH 001/100] feature/bimpm-2.0 init commit from feature/bimpm

---
 matchzoo/contrib/layers/attention_layer.py    | 154 +++++++
 .../contrib/layers/multi_perspective_layer.py | 419 ++++++++++++++++++
 matchzoo/contrib/models/bimpm_model.py        | 187 ++++++++
 requirements.txt                              |   2 +-
 4 files changed, 761 insertions(+), 1 deletion(-)
 create mode 100644 matchzoo/contrib/layers/attention_layer.py
 create mode 100644 matchzoo/contrib/layers/multi_perspective_layer.py
 create mode 100644 matchzoo/contrib/models/bimpm_model.py

diff --git a/matchzoo/contrib/layers/attention_layer.py b/matchzoo/contrib/layers/attention_layer.py
new file mode 100644
index 00000000..a3bd0ec6
--- /dev/null
+++ b/matchzoo/contrib/layers/attention_layer.py
@@ -0,0 +1,154 @@
+"""An implementation of Attention Layer for Bimpm model."""
+
+from keras import backend as K
+from keras.engine import Layer
+
+
+class AttentionLayer(Layer):
+    """
+    Layer that compute attention for Bimpm model.
+
+    For detailed information, see Bilateral Multi-Perspective Matching for
+    Natural Language Sentences, section 3.2.
+
+    Reference:
+    https://github.com/zhiguowang/BiMPM/blob/master/src/layer_utils.py#L145-L196
+
+    Examples:
+        >>> import matchzoo as mz
+        >>> layer = mz.layers.AttentionLayer(att_dim=50)
+
+    """
+
+    def __init__(self,
+                 att_dim: int,
+                 att_type: str = 'default',
+                 remove_diagonal: bool = False,
+                 dropout_rate: float = 0.0):
+        """
+        class: `AttentionLayer` constructor.
+
+        :param att_dim: int
+        :param att_type: int
+        :param remove_diagonal: bool
+        """
+        super(AttentionLayer, self).__init__()
+        self._att_dim = att_dim
+        self._att_type = att_type
+        self._remove_diagonal = remove_diagonal
+        self._dropout_rate = dropout_rate
+
+    @property
+    def att_dim(self):
+        """Get the attention dimension."""
+        return self._att_dim
+
+    @property
+    def att_type(self):
+        """Get the attention type."""
+        return self._att_type
+
+    def build(self, input_shapes):
+        """
+        Build the layer.
+
+        :param input_shapes: input_shapes
+        """
+        if not isinstance(input_shapes, list):
+            raise ValueError('A attention layer should be called '
+                             'on a list of inputs.')
+        # input_shapes[0]: batch, time_steps, d
+        hidden_dim_lt = input_shapes[0][2]
+        hidden_dim_rt = input_shapes[1][2]
+        self.attn_w1 = self.add_weight(name='attn_w1',
+                                       shape=(hidden_dim_lt,
+                                              self._att_dim),
+                                       initializer='uniform',
+                                       trainable=True)
+        if hidden_dim_lt == hidden_dim_rt:
+            self.attn_w2 = self.attn_w1
+        else:
+            self.attn_w2 = self.add_weight(name='attn_w2',
+                                           shape=(hidden_dim_rt,
+                                                  self._att_dim),
+                                           initializer='uniform',
+                                           trainable=True)
+
+        self.diagonal_W = self.add_weight(name='diagonal_W',
+                                          shape=(1,
+                                                 1,
+                                                 self._att_dim),
+                                          initializer='uniform',
+                                          trainable=True)
+        self.built = True
+
+    def call(self, x: list, **kwargs):
+        """
+        Calculate attention.
+
+        :param x: [reps_lt, reps_rt]
+        :return attn_prob: [batch_size, len_1, len_2]
+        """
+
+        if not isinstance(x, list):
+            raise ValueError('A attention layer should be called '
+                             'on a list of inputs.')
+
+        reps_lt, reps_rt = x
+
+        # [1, 1, d, 20]
+        attn_w1 = self.attn_w1
+        attn_w1 = K.expand_dims(K.expand_dims(attn_w1, axis=0), axis=0)
+        # [b, s, d, -1]
+        reps_lt = K.expand_dims(reps_lt, axis=-1)
+        attn_reps_lt = K.sum(reps_lt * attn_w1, axis=2)
+
+        # [1, 1, d, 20]
+        attn_w2 = self.attn_w2
+        attn_w2 = K.expand_dims(K.expand_dims(attn_w2, axis=0), axis=0)
+        # [b, s, d, -1]
+        reps_rt = K.expand_dims(reps_rt, axis=-1)
+        attn_reps_rt = K.sum(reps_rt * attn_w2, axis=2)
+
+        # Tanh
+        attn_reps_lt = K.tanh(attn_reps_lt)  # [b, s, 20]
+        attn_reps_rt = K.tanh(attn_reps_rt)
+
+        # diagonal_W
+        attn_reps_lt = attn_reps_lt * self.diagonal_W  # [b, s, 20]
+        attn_reps_rt = K.permute_dimensions(attn_reps_rt, (0, 2, 1))
+
+        # [batch_size, s, s]
+        attn_value = K.batch_dot(attn_reps_lt, attn_reps_rt)
+
+        # TODO(tjf) or remove: normalize
+        # if self.remove_diagonal:
+        #     diagonal = K.ones([len_1], tf.float32)  # [len1]
+        #     diagonal = 1.0 - tf.diag(diagonal)  # [len1, len1]
+        #     diagonal = tf.expand_dims(diagonal, axis=0)  # ['x', len1, len1]
+        #     attn_value = attn_value * diagonal
+
+        if len(x) == 4:
+            mask_lt = x[2]
+            mask_rt = x[3]
+            attn_value = attn_value * K.expand_dims(mask_lt, axis=2)
+            attn_value = attn_value * K.expand_dims(mask_rt, axis=1)
+
+        # softmax
+        attn_prob = K.softmax(attn_value)  # [batch_size, len_1, len_2]
+
+        # if remove_diagonal: attn_value = attn_value * diagonal
+        if len(x) == 4:
+            mask_lt = x[2]
+            mask_rt = x[3]
+            attn_prob = attn_prob * K.expand_dims(mask_lt, axis=2)
+            attn_prob = attn_prob * K.expand_dims(mask_rt, axis=1)
+
+        return attn_prob
+
+    def compute_output_shape(self, input_shapes):
+        """Calculate the layer output shape."""
+        if not isinstance(input_shapes, list):
+            raise ValueError('A attention layer should be called '
+                             'on a list of inputs.')
+        return input_shapes[0][0], input_shapes[0][1], input_shapes[1][1]
diff --git a/matchzoo/contrib/layers/multi_perspective_layer.py b/matchzoo/contrib/layers/multi_perspective_layer.py
new file mode 100644
index 00000000..6e5851de
--- /dev/null
+++ b/matchzoo/contrib/layers/multi_perspective_layer.py
@@ -0,0 +1,419 @@
+"""An implementation of MultiPerspectiveLayer for Bimpm model."""
+
+from keras import backend as K
+from keras.engine import Layer
+
+from matchzoo.contrib.layers.attention_layer import AttentionLayer
+
+
+class MultiPerspectiveLayer(Layer):
+    """
+    A keras implementation of Bimpm multi-perspective layer.
+
+    For detailed information, see Bilateral Multi-Perspective
+    Matching for Natural Language Sentences, section 3.2.
+
+    Examples:
+        >>> import matchzoo as mz
+        >>> layer = mz.layers.MultiPerspectiveLayer(50, 20, None)
+
+    """
+
+    def __init__(self,
+                 att_dim: int,
+                 mp_dim: int,
+                 perspective: dict):
+        """Class initialization."""
+        super(MultiPerspectiveLayer, self).__init__()
+        self._att_dim = att_dim
+        self._mp_dim = mp_dim
+        self._perspective = perspective
+
+    @classmethod
+    def list_available_perspectives(cls) -> list:
+        """List available strategy for multi-perspective matching."""
+        return ['full', 'max-pooling', 'attentive', 'max-attentive']
+
+    @property
+    def num_perspective(self):
+        """Get the number of perspectives that is True."""
+        return sum(self._perspective.values())
+
+    def build(self, input_shape: list):
+        """Input shape."""
+        # The shape of the weights is l * d.
+        if self._perspective.get('full'):
+            self.full_match = MpFullMatch(self._mp_dim)
+
+        if self._perspective.get('max-pooling'):
+            self.max_pooling_match = MpMaxPoolingMatch(self._mp_dim)
+
+        if self._perspective.get('attentive'):
+            self.attentive_match = MpAttentiveMatch(self._att_dim,
+                                                    self._mp_dim)
+
+        if self._perspective.get('max-attentive'):
+            self.max_attentive_match = MpMaxAttentiveMatch(self._att_dim)
+
+        self.built = True
+
+    def call(self, x: list, **kwargs):
+        """Call."""
+        seq_lt, seq_rt = x[:5], x[5:]
+        # unpack seq_left and seq_right
+        # all hidden states, last hidden state of forward pass,
+        # last cell state of forward pass, last hidden state of
+        # backward pass, last cell state of backward pass.
+        lstm_reps_lt, forward_h_lt, _, backward_h_lt, _ = seq_lt
+        lstm_reps_rt, forward_h_rt, _, backward_h_rt, _ = seq_rt
+
+        match_tensor_list = []
+        match_dim = 0
+        if self._perspective.get('full'):
+            # Each forward & backward contextual embedding compare
+            # with the last step of the last time step of the other sentence.
+            h_rt = K.concatenate([forward_h_rt, backward_h_rt], axis=-1)
+            full_match_tensor = self.full_match([lstm_reps_lt, h_rt])
+            match_tensor_list.append(full_match_tensor)
+            match_dim += self._mp_dim + 1
+
+        if self._perspective.get('max-pooling'):
+            # Each contextual embedding compare with each contextual embedding.
+            # retain the maximum of each dimension.
+            max_match_tensor = self.max_pooling_match([lstm_reps_lt,
+                                                       lstm_reps_rt])
+            match_tensor_list.append(max_match_tensor)
+            match_dim += self._mp_dim
+
+        if self._perspective.get('attentive'):
+            # Each contextual embedding compare with each contextual embedding.
+            # retain sum of weighted mean of each dimension.
+            attentive_tensor = self.attentive_match([lstm_reps_lt,
+                                                     lstm_reps_rt])
+            match_tensor_list.append(attentive_tensor)
+            match_dim += self._mp_dim + 1
+
+        if self._perspective.get('max-attentive'):
+            # Each contextual embedding compare with each contextual embedding.
+            # retain max of weighted mean of each dimension.
+            relevancy_matrix = _calc_relevancy_matrix(lstm_reps_lt, lstm_reps_rt)
+            max_attentive_tensor = self.max_attentive_match([lstm_reps_lt,
+                                                             lstm_reps_rt,
+                                                             relevancy_matrix])
+            match_tensor_list.append(max_attentive_tensor)
+            match_dim += self._mp_dim + 1
+
+        mp_tensor = K.concatenate(match_tensor_list, axis=-1)
+        self.output_dim = match_dim
+
+        return mp_tensor
+
+    def compute_output_shape(self, input_shape: list):
+        """Compute output shape."""
+        shape_a = input_shape[0]
+        return shape_a[0], shape_a[1], self.output_dim
+
+
+class MpFullMatch(Layer):
+    """Mp Full Match Layer."""
+
+    def __init__(self, mp_dim):
+        """Init."""
+        super(MpFullMatch, self).__init__()
+        self.mp_dim = mp_dim
+
+    def build(self, input_shapes):
+        """Build."""
+        input_shape = input_shapes[0]
+        self.built = True
+
+    def call(self, x, **kwargs):
+        """Call."""
+        reps_lt, rep_rt = x
+        reps_rt = K.expand_dims(rep_rt, 1)
+        # match_tensor: [batch, steps_lt, mp_dim+1]
+        match_tensor, match_dim = _multi_perspective_match(self.mp_dim,
+                                                           reps_lt,
+                                                           reps_rt)
+        return match_tensor
+
+    def compute_output_shape(self, input_shape):
+        """Compute output shape."""
+        return input_shape[0][0], input_shape[0][1], self.mp_dim + 1
+
+
+class MpMaxPoolingMatch(Layer):
+    """MpMaxPoolingMatch."""
+
+    def __init__(self, mp_dim):
+        """Init."""
+        super(MpMaxPoolingMatch, self).__init__()
+        self.mp_dim = mp_dim
+
+    def build(self, input_shapes):
+        """Build."""
+        self.kernel = self.add_weight(name='kernel',
+                                      shape=(1, 1, 1,
+                                             self.mp_dim, input_shapes[0][-1]),
+                                      initializer='uniform',
+                                      trainable=True)
+        self.built = True
+
+    def call(self, x, **kwargs):
+        """Call."""
+        reps_lt, reps_rt = x
+
+        # kernel: [1, 1, 1, mp_dim, d]
+        # lstm_lt -> [batch, steps_lt, 1, 1, d]
+        reps_lt = K.expand_dims(reps_lt, axis=2)
+        reps_lt = K.expand_dims(reps_lt, axis=2)
+        reps_lt = reps_lt * self.kernel
+
+        # lstm_rt -> [batch, 1, steps_rt, 1, d]
+        reps_rt = K.expand_dims(reps_rt, axis=2)
+        reps_rt = K.expand_dims(reps_rt, axis=1)
+
+        # match_tensor -> [batch, steps_lt, steps_rt, mp_dim]
+        match_tensor = _cosine_distance(reps_lt, reps_rt, cosine_norm=False)
+        max_match_tensor = K.max(match_tensor, axis=2)
+        return max_match_tensor
+
+    def compute_output_shape(self, input_shape):
+        """Compute output shape."""
+        return input_shape[0][0], input_shape[0][1], self.mp_dim
+
+
+class MpAttentiveMatch(Layer):
+    """
+    MpAttentiveMatch Layer.
+
+    Reference:
+    https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L188-L193
+
+    """
+
+    def __init__(self, att_dim, mp_dim):
+        """Init."""
+        super(MpAttentiveMatch, self).__init__()
+        self.att_dim = att_dim
+        self.mp_dim = mp_dim
+
+    def build(self, input_shapes):
+        """Build."""
+        input_shape = input_shapes[0]
+        self.built = True
+
+    def call(self, x, **kwargs):
+        """Call."""
+        reps_lt, reps_rt = x[0], x[1]
+        # attention prob matrix
+        attention_layer = AttentionLayer(self.att_dim)
+        attn_prob = attention_layer([reps_rt, reps_lt])
+        # attention reps
+        reps_lt = K.batch_dot(attn_prob, reps_lt)
+        # mp match
+        attn_match_tensor, match_dim = _multi_perspective_match(self.mp_dim,
+                                                                reps_lt,
+                                                                reps_rt)
+        return attn_match_tensor
+
+    def compute_output_shape(self, input_shape):
+        """Compute output shape."""
+        return input_shape[0][0], input_shape[0][1], self.mp_dim
+
+
+class MpMaxAttentiveMatch(Layer):
+    """MpMaxAttentiveMatch."""
+
+    def __init__(self, mp_dim):
+        """Init."""
+        super(MpMaxAttentiveMatch, self).__init__()
+        self.mp_dim = mp_dim
+
+    def build(self, input_shapes):
+        """Build."""
+        input_shape = input_shapes[0]
+        self.built = True
+
+    def call(self, x):
+        """Call."""
+        reps_lt, reps_rt = x[0], x[1]
+        relevancy_matrix = x[2]
+        max_att_lt = cal_max_question_representation(reps_lt, relevancy_matrix)
+        max_attentive_tensor, match_dim = _multi_perspective_match(self.mp_dim,
+                                                                   reps_rt,
+                                                                   max_att_lt)
+        return max_attentive_tensor
+
+
+def cal_max_question_representation(reps_lt, attn_scores):
+    """Return [batch_size, passage_len]."""
+    attn_positions = K.argmax(attn_scores, axis=2)
+    max_reps_lt = collect_representation(reps_lt, attn_positions)
+    return max_reps_lt
+
+
+def collect_representation(representation, positions):
+    """
+    Collect_representation.
+
+    :param representation: [batch_size, node_num, feature_dim]
+    :param positions: [batch_size, neigh_num]
+    :return:
+    """
+    return collect_probs(representation, positions)
+
+
+def collect_final_step_of_lstm(lstm_representation, lengths):
+    """
+    Collect final step of lstm.
+
+    :param lstm_representation: [batch_size, passsage_length, dim]
+    :param lengths: [batch_size]
+    :return: [batch_size, dim]
+    """
+    lengths = K.maximum(lengths, K.zeros_like(lengths))
+
+    batch_size = K.shape(lengths)[0]
+    # shape (batch_size)
+    batch_nums = K.tf.range(0, limit=batch_size)
+    # shape (batch_size, 2)
+    indices = K.stack((batch_nums, lengths), axis=1)
+    result = K.tf.gather_nd(lstm_representation, indices,
+                            name='last-forwar-lstm')
+    # [batch_size, dim]
+    return result
+
+
+def collect_probs(probs, positions):
+    """
+    Collect Probs.
+
+    :param probs: [batch_size, chunks_size]
+    :param positions: [batch_size, pair_size]
+    :return:
+    """
+    batch_size = K.shape(probs)[0]
+    pair_size = K.shape(positions)[1]
+    # shape (batch_size)
+    batch_nums = K.arange(0, batch_size)
+    # [batch_size, 1]
+    batch_nums = K.reshape(batch_nums, shape=[-1, 1])
+    # [batch_size, pair_size]
+    batch_nums = K.tile(batch_nums, [1, pair_size])
+
+    # shape (batch_size, pair_size, 2)
+    # alert: to solve error message
+    positions = K.tf.to_int32(positions)
+    indices = K.stack([batch_nums, positions], axis=2)
+
+    pair_probs = K.tf.gather_nd(probs, indices)
+    # pair_probs = tf.reshape(pair_probs, shape=[batch_size, pair_size])
+    return pair_probs
+
+
+def _multi_perspective_match(mp_dim, reps_lt, reps_rt,
+                             with_cosine=True, with_mp_cosine=True):
+    """
+    The core function of zhiguowang's implementation.
+
+    reference:
+    https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L207-L223
+    :param mp_dim: about 20
+    :param reps_lt: [batch, len, dim]
+    :param reps_rt: [batch, len, dim]
+    :param with_cosine: True
+    :param with_mp_cosine: True
+    :return: [batch, len, feature_dim*2]
+    """
+    input_shape = K.shape(reps_lt)
+    batch_size = input_shape[0]
+    seq_length = input_shape[1]
+
+    match_dim = 0
+    match_result_list = []
+    if with_cosine:
+        cosine_value = _cosine_distance(reps_lt, reps_rt, False)
+        cosine_value = K.reshape(cosine_value, [batch_size, seq_length, 1])
+        match_result_list.append(cosine_value)
+        match_dim += 1
+
+    if with_mp_cosine:
+        mp_cosine_layer = MpCosineLayer(mp_dim)
+        match_tensor = mp_cosine_layer([reps_lt, reps_rt])
+        match_result_list.append(match_tensor)
+        match_dim += mp_cosine_layer.mp_dim
+
+    match_result = K.concatenate(match_result_list, 2)
+    return match_result, match_dim
+
+
+class MpCosineLayer(Layer):
+    """
+    Implementation of Multi-Perspective Cosine Distance.
+
+    Reference:
+    https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L121-L129
+    """
+
+    def __init__(self, mp_dim, **kwargs):
+        """Init."""
+        self.mp_dim = mp_dim
+        super(MpCosineLayer, self).__init__(**kwargs)
+
+    def build(self, input_shape):
+        """Build."""
+        self.kernel = self.add_weight(name='kernel',
+                                      shape=(1, 1, self.mp_dim,
+                                             input_shape[0][-1]),
+                                      initializer='uniform',
+                                      trainable=True)
+        super(MpCosineLayer, self).build(input_shape)
+
+    def call(self, x, **kwargs):
+        """Call."""
+        v1, v2 = x
+        v1 = K.expand_dims(v1, 2) * self.kernel
+        v2 = K.expand_dims(v2, 2)
+        return _cosine_distance(v1, v2, False)
+
+    def compute_output_shape(self, input_shape):
+        """Compute output shape."""
+        return input_shape[0][0], input_shape[0][1], self.mp_dim
+
+
+def _calc_relevancy_matrix(reps_lt, reps_rt):
+    # -> [batch_size, 1, len_lt, dim]
+    reps_lt = K.expand_dims(reps_lt, 1)
+    # -> [batch_size, len_rt, 1, dim]
+    in_passage_repres_tmp = K.expand_dims(reps_rt, 2)
+    relevancy_matrix = _cosine_distance(reps_lt, reps_rt)
+    return relevancy_matrix
+
+
+def _mask_relevancy_matrix(relevancy_matrix, question_mask, passage_mask):
+    # relevancy_matrix: [batch_size, passage_len, question_len]
+    # question_mask: [batch_size, question_len]
+    # passage_mask: [batch_size, passsage_len]
+    if question_mask is not None:
+        relevancy_matrix = relevancy_matrix * K.expand_dims(question_mask, 1)
+    relevancy_matrix = relevancy_matrix * K.expand_dims(passage_mask, 2)
+    return relevancy_matrix
+
+
+def _cosine_distance(v1, v2, cosine_norm=True, eps=1e-6):
+    """
+    Only requires `K.sum(v1 * v2, axis=-1)`.
+
+    :param v1: [batch, time_steps(v1), 1, l, d]
+    :param v2: [batch, 1, time_steps(v2), l, d]
+    :param cosine_norm: True
+    :param eps: 1e-6
+    :return: [batch, time_steps(v1), time_steps(v2), l]
+    """
+    cosine_numerator = K.sum(v1 * v2, axis=-1)
+    if not cosine_norm:
+        return K.tanh(cosine_numerator)
+    v1_norm = K.sqrt(K.maximum(K.sum(K.square(v1), axis=-1), eps))
+    v2_norm = K.sqrt(K.maximum(K.sum(K.square(v2), axis=-1), eps))
+    return cosine_numerator / v1_norm / v2_norm
diff --git a/matchzoo/contrib/models/bimpm_model.py b/matchzoo/contrib/models/bimpm_model.py
new file mode 100644
index 00000000..44ef2f18
--- /dev/null
+++ b/matchzoo/contrib/models/bimpm_model.py
@@ -0,0 +1,187 @@
+"""Bimpm."""
+
+from keras.models import Model
+from keras.layers import Dense, Concatenate, Dropout
+from keras.layers import Bidirectional, LSTM
+
+from matchzoo import engine
+from matchzoo.contrib.layers.multi_perspective_layer import MultiPerspectiveLayer
+
+
+class BimpmModel(engine.BaseModel):
+    """
+    BimpmModel.
+
+    Examples:
+        >>> import matchzoo as mz
+        >>> model = mz.models.BimpmModel()
+        >>> model.guess_and_fill_missing_params(verbose=0)
+        >>> model.build()
+
+    """
+
+    @classmethod
+    def get_default_params(cls) -> engine.ParamTable:
+        """:return: model default parameters."""
+        params = super().get_default_params(with_embedding=True)
+        params['optimizer'] = 'adam'
+
+        params.add(engine.Param('dim_word_embedding', 50))
+        # TODO(tjf): remove the unused params in the final version
+        params.add(engine.Param('dim_char_embedding', 50))
+        params.add(engine.Param('word_embedding_mat'))
+        params.add(engine.Param('char_embedding_mat'))
+        params.add(engine.Param('embedding_random_scale', 0.2))
+        params.add(engine.Param('activation_embedding', 'softmax'))
+
+        # Bimpm Setting
+        params.add(engine.Param('perspective', {'full': True,
+                                                'max-pooling': True,
+                                                'attentive': True,
+                                                'max-attentive': True}))
+        params.add(engine.Param('mp_dim', 20))
+        params.add(engine.Param('att_dim', 20))
+        params.add(engine.Param('hidden_size', 128))
+        params.add(engine.Param('dropout_rate', 0.0))
+        params.add(engine.Param('w_initializer', 'glorot_uniform'))
+        params.add(engine.Param('b_initializer', 'zeros'))
+        params.add(engine.Param('activation_hidden', 'linear'))
+
+        params.add(engine.Param('with_match_highway', False))
+        params.add(engine.Param('with_aggregation_highway', False))
+
+        return params
+
+    # @classmethod
+    # def get_default_preprocessor(cls):
+    #     """:return: Instance of :class:`NaivePreprocessor`."""
+    #     return preprocessors.NaivePreprocessor()
+    #
+    # @property
+    # def word_embedding_mat(self) -> np.ndarray:
+    #     """Get pretrained embedding for ArcI model."""
+    #     # Check if provided embedding matrix
+    #     if self._params['word_embedding_mat'] is None:
+    #         raise TypeError('No pre-trained word embeddings provided.')
+    #
+    # @word_embedding_mat.setter
+    # def word_embedding_mat(self, embedding_mat: np.ndarray):
+    #     """
+    #     Set pretrained embedding for ArcI model.
+    #
+    #     :param embedding_mat: pretrained embedding in numpy format.
+    #     """
+    #     self._params['word_embedding_mat'] = embedding_mat
+    #     self._params['vocab_size'], self._params['dim_word_embedding'] = \
+    #         embedding_mat.shape
+    #
+    # @property
+    # def char_embedding_mat(self) -> np.ndarray:
+    #     """Initialize character level embedding."""
+    #     s = self._params['embedding_random_scale']
+    #     self._params['char_embedding_mat'] = \
+    #         np.random.uniform(-s, s, (self._params['vocab_size'],
+    #                                   self._params['dim_char_embedding']))
+    #     return self._params['char_embedding_mat']
+    #
+    # def char_embedding(self, char_input_shape, char_vocab_size):
+    #     """Create a character level embedding model."""
+    #     input_char = Input(shape=char_input_shape)
+    #     embed_char = LSTM(self._params['dim_char_embedding'],
+    #                       kernel_initializer=self._params['w_initializer'],
+    #                       bias_initializer=self._params['b_initializer'])(
+    #                           input_char)
+    #     embed_char = Dense(char_vocab_size,
+    #                        activation=self._params['activation_embedding'])(
+    #                            embed_char)
+    #     return Model(input_char, embed_char)
+
+    def build(self):
+        """Build model structure."""
+        # ~ Input Layer
+        input_left, input_right = self._make_inputs()
+
+        # Word Representation Layer
+        # TODO: concatenate word level embedding and character level embedding.
+        embedding = self._make_embedding_layer()
+        embed_left = embedding(input_left)
+        embed_right = embedding(input_right)
+
+        embed_left = Dropout(self._params['dropout_rate'])(embed_left)
+        embed_right = Dropout(self._params['dropout_rate'])(embed_right)
+
+        # ~ Word Level Matching Layer
+        # Reference:
+        # https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L207-L223
+        # TODO
+        pass
+
+        # ~ Encoding Layer
+        # Note: When merge_mode = None, output will be [forward, backward],
+        # The default merge_mode is concat, and the output will be [lstm].
+        # If with return_state, then the output would append [h,c,h,c].
+        bi_lstm = Bidirectional(
+            LSTM(self._params['hidden_size'],
+                 return_sequences=True,
+                 return_state=True,
+                 dropout=self._params['dropout_rate'],
+                 kernel_initializer=self._params['w_initializer'],
+                 bias_initializer=self._params['b_initializer']),
+            merge_mode='concat')
+        # x_left = [lstm_lt, forward_h_lt, _, backward_h_lt, _ ]
+        x_left = bi_lstm(embed_left)
+        x_right = bi_lstm(embed_right)
+
+        # ~ Multi-Perspective Matching layer.
+        # Output is two sequence of vectors.
+        # Cons: Haven't support multiple context layer
+        multi_perspective = MultiPerspectiveLayer(self._params['att_dim'],
+                                                  self._params['mp_dim'],
+                                                  self._params['perspective'])
+        # Note: input to `keras layer` must be list of tensors.
+        mp_left = multi_perspective(x_left + x_right)
+        mp_right = multi_perspective(x_right + x_left)
+
+        # ~ Dropout Layer
+        mp_left = Dropout(self._params['dropout_rate'])(mp_left)
+        mp_right = Dropout(self._params['dropout_rate'])(mp_right)
+
+        # ~ Highway Layer
+        # reference:
+        # https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L289-L295
+        if self._params['with_match_highway']:
+            # the input is left matching representations (question / passage)
+            pass
+
+        # ~ Aggregation layer
+        # TODO: mask the above layer
+        aggregation = Bidirectional(
+            LSTM(self._params['hidden_size'],
+                 return_sequences=False,
+                 return_state=False,
+                 dropout=self._params['dropout_rate'],
+                 kernel_initializer=self._params['w_initializer'],
+                 bias_initializer=self._params['b_initializer']),
+            merge_mode='concat')
+        rep_left = aggregation(mp_left)
+        rep_right = aggregation(mp_right)
+
+        # Concatenate the concatenated vector of left and right.
+        x = Concatenate()([rep_left, rep_right])
+
+        # ~ Highway Network
+        # reference:
+        # https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L289-L295
+        if self._params['with_aggregation_highway']:
+            pass
+
+        # ~ Prediction layer.
+        # reference:
+        # https://github.com/zhiguowang/BiMPM/blob/master/src/SentenceMatchModelGraph.py#L140-L153
+        x = Dense(self._params['hidden_size'],
+                  activation=self._params['activation_hidden'])(x)
+        x = Dense(self._params['hidden_size'],
+                  activation=self._params['activation_hidden'])(x)
+        x_out = self._make_output_layer()(x)
+        self._backend = Model(inputs=[input_left, input_right],
+                              outputs=x_out)
diff --git a/requirements.txt b/requirements.txt
index db793e1c..9b04fcd7 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
 keras >= 2.2.4
 tabulate >= 0.8.2
-tensorflow >= 1.1.0
+tensorflow >= 1.8.0
 nltk >= 3.2.3
 numpy >= 1.14
 tqdm >= 4.19.4

From 504503a72141ba9d19471e0590f81f66ae6dd3da Mon Sep 17 00:00:00 2001
From: rgtjf <rgtjf1@163.com>
Date: Sun, 20 Jan 2019 21:12:24 +0800
Subject: [PATCH 002/100] hotfix: fix CI

---
 matchzoo/contrib/layers/__init__.py                |  2 ++
 matchzoo/contrib/layers/attention_layer.py         |  2 +-
 matchzoo/contrib/layers/multi_perspective_layer.py | 13 +++++++------
 matchzoo/contrib/models/__init__.py                |  1 +
 matchzoo/contrib/models/bimpm_model.py             |  4 ++--
 5 files changed, 13 insertions(+), 9 deletions(-)

diff --git a/matchzoo/contrib/layers/__init__.py b/matchzoo/contrib/layers/__init__.py
index e69de29b..a3fa4931 100644
--- a/matchzoo/contrib/layers/__init__.py
+++ b/matchzoo/contrib/layers/__init__.py
@@ -0,0 +1,2 @@
+from .attention_layer import AttentionLayer
+from .multi_perspective_layer import MultiPerspectiveLayer
\ No newline at end of file
diff --git a/matchzoo/contrib/layers/attention_layer.py b/matchzoo/contrib/layers/attention_layer.py
index a3bd0ec6..74bb1331 100644
--- a/matchzoo/contrib/layers/attention_layer.py
+++ b/matchzoo/contrib/layers/attention_layer.py
@@ -16,7 +16,7 @@ class AttentionLayer(Layer):
 
     Examples:
         >>> import matchzoo as mz
-        >>> layer = mz.layers.AttentionLayer(att_dim=50)
+        >>> layer = mz.contrib.layers.AttentionLayer(att_dim=50)
 
     """
 
diff --git a/matchzoo/contrib/layers/multi_perspective_layer.py b/matchzoo/contrib/layers/multi_perspective_layer.py
index 6e5851de..37368fd0 100644
--- a/matchzoo/contrib/layers/multi_perspective_layer.py
+++ b/matchzoo/contrib/layers/multi_perspective_layer.py
@@ -15,7 +15,7 @@ class MultiPerspectiveLayer(Layer):
 
     Examples:
         >>> import matchzoo as mz
-        >>> layer = mz.layers.MultiPerspectiveLayer(50, 20, None)
+        >>> layer = mz.contrib.layers.MultiPerspectiveLayer(50, 20, None)
 
     """
 
@@ -96,7 +96,8 @@ def call(self, x: list, **kwargs):
         if self._perspective.get('max-attentive'):
             # Each contextual embedding compare with each contextual embedding.
             # retain max of weighted mean of each dimension.
-            relevancy_matrix = _calc_relevancy_matrix(lstm_reps_lt, lstm_reps_rt)
+            relevancy_matrix = _calc_relevancy_matrix(lstm_reps_lt,
+                                                      lstm_reps_rt)
             max_attentive_tensor = self.max_attentive_match([lstm_reps_lt,
                                                              lstm_reps_rt,
                                                              relevancy_matrix])
@@ -124,7 +125,7 @@ def __init__(self, mp_dim):
 
     def build(self, input_shapes):
         """Build."""
-        input_shape = input_shapes[0]
+        # input_shape = input_shapes[0]
         self.built = True
 
     def call(self, x, **kwargs):
@@ -200,7 +201,7 @@ def __init__(self, att_dim, mp_dim):
 
     def build(self, input_shapes):
         """Build."""
-        input_shape = input_shapes[0]
+        # input_shape = input_shapes[0]
         self.built = True
 
     def call(self, x, **kwargs):
@@ -232,7 +233,7 @@ def __init__(self, mp_dim):
 
     def build(self, input_shapes):
         """Build."""
-        input_shape = input_shapes[0]
+        # input_shape = input_shapes[0]
         self.built = True
 
     def call(self, x):
@@ -386,7 +387,7 @@ def _calc_relevancy_matrix(reps_lt, reps_rt):
     # -> [batch_size, 1, len_lt, dim]
     reps_lt = K.expand_dims(reps_lt, 1)
     # -> [batch_size, len_rt, 1, dim]
-    in_passage_repres_tmp = K.expand_dims(reps_rt, 2)
+    # in_passage_repres_tmp = K.expand_dims(reps_rt, 2)
     relevancy_matrix = _cosine_distance(reps_lt, reps_rt)
     return relevancy_matrix
 
diff --git a/matchzoo/contrib/models/__init__.py b/matchzoo/contrib/models/__init__.py
index 6faa60bb..f7970f85 100644
--- a/matchzoo/contrib/models/__init__.py
+++ b/matchzoo/contrib/models/__init__.py
@@ -1 +1,2 @@
 from .match_lstm import MatchLSTM
+from .bimpm_model import BimpmModel
diff --git a/matchzoo/contrib/models/bimpm_model.py b/matchzoo/contrib/models/bimpm_model.py
index 44ef2f18..e67cfbe3 100644
--- a/matchzoo/contrib/models/bimpm_model.py
+++ b/matchzoo/contrib/models/bimpm_model.py
@@ -5,7 +5,7 @@
 from keras.layers import Bidirectional, LSTM
 
 from matchzoo import engine
-from matchzoo.contrib.layers.multi_perspective_layer import MultiPerspectiveLayer
+from matchzoo.contrib.layers import MultiPerspectiveLayer
 
 
 class BimpmModel(engine.BaseModel):
@@ -14,7 +14,7 @@ class BimpmModel(engine.BaseModel):
 
     Examples:
         >>> import matchzoo as mz
-        >>> model = mz.models.BimpmModel()
+        >>> model = mz.contrib.models.BimpmModel()
         >>> model.guess_and_fill_missing_params(verbose=0)
         >>> model.build()
 

From 580178f894c02478a929f4b6d5af99cac240efd8 Mon Sep 17 00:00:00 2001
From: rgtjf <rgtjf1@163.com>
Date: Tue, 26 Mar 2019 20:21:59 +0800
Subject: [PATCH 003/100] hotfix: fix errors

fix Incompatible shapes: [36,40,256] vs. [36,36,256]
---
 matchzoo/contrib/layers/multi_perspective_layer.py | 12 +++++++++---
 matchzoo/contrib/models/bimpm_model.py             |  5 ++++-
 2 files changed, 13 insertions(+), 4 deletions(-)

diff --git a/matchzoo/contrib/layers/multi_perspective_layer.py b/matchzoo/contrib/layers/multi_perspective_layer.py
index 37368fd0..6440f53a 100644
--- a/matchzoo/contrib/layers/multi_perspective_layer.py
+++ b/matchzoo/contrib/layers/multi_perspective_layer.py
@@ -248,7 +248,12 @@ def call(self, x):
 
 
 def cal_max_question_representation(reps_lt, attn_scores):
-    """Return [batch_size, passage_len]."""
+    """
+    cal_max_question_representation
+    :param reps_lt: [batch_size, passage_len, hidden_size]
+    :param attn_scores: []
+    :return: [batch_size, passage_len, hidden_size].
+    """
     attn_positions = K.argmax(attn_scores, axis=2)
     max_reps_lt = collect_representation(reps_lt, attn_positions)
     return max_reps_lt
@@ -289,10 +294,10 @@ def collect_final_step_of_lstm(lstm_representation, lengths):
 def collect_probs(probs, positions):
     """
     Collect Probs.
-
+    Reference: https://github.com/zhiguowang/BiMPM/blob/master/src/layer_utils.py#L128-L140
     :param probs: [batch_size, chunks_size]
     :param positions: [batch_size, pair_size]
-    :return:
+    :return: [batch_size, pair_size]
     """
     batch_size = K.shape(probs)[0]
     pair_size = K.shape(positions)[1]
@@ -386,6 +391,7 @@ def compute_output_shape(self, input_shape):
 def _calc_relevancy_matrix(reps_lt, reps_rt):
     # -> [batch_size, 1, len_lt, dim]
     reps_lt = K.expand_dims(reps_lt, 1)
+    reps_rt = K.expand_dims(reps_rt, 2)
     # -> [batch_size, len_rt, 1, dim]
     # in_passage_repres_tmp = K.expand_dims(reps_rt, 2)
     relevancy_matrix = _cosine_distance(reps_lt, reps_rt)
diff --git a/matchzoo/contrib/models/bimpm_model.py b/matchzoo/contrib/models/bimpm_model.py
index e67cfbe3..f7f93eb6 100644
--- a/matchzoo/contrib/models/bimpm_model.py
+++ b/matchzoo/contrib/models/bimpm_model.py
@@ -11,7 +11,8 @@
 class BimpmModel(engine.BaseModel):
     """
     BimpmModel.
-
+    Reference:
+        - https://github.com/zhiguowang/BiMPM/blob/master/src/SentenceMatchModelGraph.py#L43-L186
     Examples:
         >>> import matchzoo as mz
         >>> model = mz.contrib.models.BimpmModel()
@@ -107,6 +108,8 @@ def build(self):
         embed_left = embedding(input_left)
         embed_right = embedding(input_right)
 
+        # L119-L121
+        # https://github.com/zhiguowang/BiMPM/blob/master/src/SentenceMatchModelGraph.py#L119-L121
         embed_left = Dropout(self._params['dropout_rate'])(embed_left)
         embed_right = Dropout(self._params['dropout_rate'])(embed_right)
 

From e000688ca9d8387238072ec2ea880c3c7f4bb15c Mon Sep 17 00:00:00 2001
From: rgtjf <rgtjf1@163.com>
Date: Wed, 27 Mar 2019 10:25:27 +0800
Subject: [PATCH 004/100] hotfix: fix CI

---
 matchzoo/contrib/layers/multi_perspective_layer.py | 7 +++++--
 matchzoo/contrib/models/bimpm_model.py             | 3 ++-
 2 files changed, 7 insertions(+), 3 deletions(-)

diff --git a/matchzoo/contrib/layers/multi_perspective_layer.py b/matchzoo/contrib/layers/multi_perspective_layer.py
index 6440f53a..212da13c 100644
--- a/matchzoo/contrib/layers/multi_perspective_layer.py
+++ b/matchzoo/contrib/layers/multi_perspective_layer.py
@@ -249,7 +249,8 @@ def call(self, x):
 
 def cal_max_question_representation(reps_lt, attn_scores):
     """
-    cal_max_question_representation
+    Calculate max_question_representation.
+
     :param reps_lt: [batch_size, passage_len, hidden_size]
     :param attn_scores: []
     :return: [batch_size, passage_len, hidden_size].
@@ -294,7 +295,9 @@ def collect_final_step_of_lstm(lstm_representation, lengths):
 def collect_probs(probs, positions):
     """
     Collect Probs.
-    Reference: https://github.com/zhiguowang/BiMPM/blob/master/src/layer_utils.py#L128-L140
+
+    Reference:
+    https://github.com/zhiguowang/BiMPM/blob/master/src/layer_utils.py#L128-L140
     :param probs: [batch_size, chunks_size]
     :param positions: [batch_size, pair_size]
     :return: [batch_size, pair_size]
diff --git a/matchzoo/contrib/models/bimpm_model.py b/matchzoo/contrib/models/bimpm_model.py
index f7f93eb6..9bc01e9d 100644
--- a/matchzoo/contrib/models/bimpm_model.py
+++ b/matchzoo/contrib/models/bimpm_model.py
@@ -11,8 +11,9 @@
 class BimpmModel(engine.BaseModel):
     """
     BimpmModel.
+
     Reference:
-        - https://github.com/zhiguowang/BiMPM/blob/master/src/SentenceMatchModelGraph.py#L43-L186
+    https://github.com/zhiguowang/BiMPM/blob/master/src/SentenceMatchModelGraph.py#L43-L186
     Examples:
         >>> import matchzoo as mz
         >>> model = mz.contrib.models.BimpmModel()

From 3a75e3ebc23211df699f1c7a59e742cb0591f7e5 Mon Sep 17 00:00:00 2001
From: rgtjf <rgtjf1@163.com>
Date: Mon, 1 Apr 2019 13:50:50 +0800
Subject: [PATCH 005/100] hotfix: fix wrong package path

---
 matchzoo/contrib/models/bimpm_model.py | 42 ++++++++++++++------------
 1 file changed, 22 insertions(+), 20 deletions(-)

diff --git a/matchzoo/contrib/models/bimpm_model.py b/matchzoo/contrib/models/bimpm_model.py
index 9bc01e9d..105e58fb 100644
--- a/matchzoo/contrib/models/bimpm_model.py
+++ b/matchzoo/contrib/models/bimpm_model.py
@@ -4,11 +4,13 @@
 from keras.layers import Dense, Concatenate, Dropout
 from keras.layers import Bidirectional, LSTM
 
-from matchzoo import engine
+from matchzoo.engine.param import Param
+from matchzoo.engine.param_table import ParamTable
+from matchzoo.engine.base_model import BaseModel
 from matchzoo.contrib.layers import MultiPerspectiveLayer
 
 
-class BimpmModel(engine.BaseModel):
+class BimpmModel(BaseModel):
     """
     BimpmModel.
 
@@ -23,34 +25,34 @@ class BimpmModel(engine.BaseModel):
     """
 
     @classmethod
-    def get_default_params(cls) -> engine.ParamTable:
+    def get_default_params(cls) -> ParamTable:
         """:return: model default parameters."""
         params = super().get_default_params(with_embedding=True)
         params['optimizer'] = 'adam'
 
-        params.add(engine.Param('dim_word_embedding', 50))
+        params.add(Param('dim_word_embedding', 50))
         # TODO(tjf): remove the unused params in the final version
-        params.add(engine.Param('dim_char_embedding', 50))
-        params.add(engine.Param('word_embedding_mat'))
-        params.add(engine.Param('char_embedding_mat'))
-        params.add(engine.Param('embedding_random_scale', 0.2))
-        params.add(engine.Param('activation_embedding', 'softmax'))
+        params.add(Param('dim_char_embedding', 50))
+        params.add(Param('word_embedding_mat'))
+        params.add(Param('char_embedding_mat'))
+        params.add(Param('embedding_random_scale', 0.2))
+        params.add(Param('activation_embedding', 'softmax'))
 
         # Bimpm Setting
-        params.add(engine.Param('perspective', {'full': True,
+        params.add(Param('perspective', {'full': True,
                                                 'max-pooling': True,
                                                 'attentive': True,
                                                 'max-attentive': True}))
-        params.add(engine.Param('mp_dim', 20))
-        params.add(engine.Param('att_dim', 20))
-        params.add(engine.Param('hidden_size', 128))
-        params.add(engine.Param('dropout_rate', 0.0))
-        params.add(engine.Param('w_initializer', 'glorot_uniform'))
-        params.add(engine.Param('b_initializer', 'zeros'))
-        params.add(engine.Param('activation_hidden', 'linear'))
-
-        params.add(engine.Param('with_match_highway', False))
-        params.add(engine.Param('with_aggregation_highway', False))
+        params.add(Param('mp_dim', 20))
+        params.add(Param('att_dim', 20))
+        params.add(Param('hidden_size', 128))
+        params.add(Param('dropout_rate', 0.0))
+        params.add(Param('w_initializer', 'glorot_uniform'))
+        params.add(Param('b_initializer', 'zeros'))
+        params.add(Param('activation_hidden', 'linear'))
+
+        params.add(Param('with_match_highway', False))
+        params.add(Param('with_aggregation_highway', False))
 
         return params
 

From b72d250682538a01588802f2cbc0252a788758ba Mon Sep 17 00:00:00 2001
From: Chriskuei <chenjiangui@outlook.com>
Date: Mon, 1 Apr 2019 15:37:34 +0800
Subject: [PATCH 006/100] [Feature] Add Match-SRNN model

---
 matchzoo/contrib/layers/__init__.py           |   7 +
 .../contrib/layers/matching_tensor_layer.py   | 137 +++++++++
 matchzoo/contrib/layers/spatial_gru.py        | 267 ++++++++++++++++++
 matchzoo/contrib/models/__init__.py           |   1 +
 matchzoo/contrib/models/match_srnn.py         |  93 ++++++
 5 files changed, 505 insertions(+)
 create mode 100644 matchzoo/contrib/layers/matching_tensor_layer.py
 create mode 100644 matchzoo/contrib/layers/spatial_gru.py
 create mode 100644 matchzoo/contrib/models/match_srnn.py

diff --git a/matchzoo/contrib/layers/__init__.py b/matchzoo/contrib/layers/__init__.py
index e69de29b..74d3db05 100644
--- a/matchzoo/contrib/layers/__init__.py
+++ b/matchzoo/contrib/layers/__init__.py
@@ -0,0 +1,7 @@
+from .matching_tensor_layer import MatchingTensorLayer
+from .spatial_gru import SpatialGRU
+
+layer_dict = {
+    "MatchingTensorLayer": MatchingTensorLayer,
+    "SpatialGRU": SpatialGRU
+}
diff --git a/matchzoo/contrib/layers/matching_tensor_layer.py b/matchzoo/contrib/layers/matching_tensor_layer.py
new file mode 100644
index 00000000..44de9f2f
--- /dev/null
+++ b/matchzoo/contrib/layers/matching_tensor_layer.py
@@ -0,0 +1,137 @@
+"""An implementation of Matching Tensor Layer."""
+import typing
+
+import numpy as np
+
+from keras import backend as K
+from keras.engine import Layer
+from keras.initializers import constant
+
+
+class MatchingTensorLayer(Layer):
+    """
+    Layer that captures the basic interactions between two tensors.
+
+    :param channels: Number of word interaction tensor channels
+    :param normalize: Whether to L2-normalize samples along the
+        dot product axis before taking the dot product.
+        If set to True, then the output of the dot product
+        is the cosine proximity between the two samples.
+    :param init_diag: Whether to initialize the diagonal elements
+        of the matrix.
+    :param kwargs: Standard layer keyword arguments.
+
+    Examples:
+        >>> import matchzoo as mz
+        >>> layer = mz.contrib.layers.MatchingTensorLayer(channels=10,
+        ...                                               normalize=True,
+        ...                                               init_diag=True)
+        >>> num_batch, left_len, right_len, num_dim = 5, 3, 2, 10
+        >>> layer.build([[num_batch, left_len, num_dim],
+        ...              [num_batch, right_len, num_dim]])
+
+    """
+
+    def __init__(self, channels: int = 10, normalize: bool = False,
+                 init_diag: bool = True, **kwargs):
+        """:class:`MatchingTensorLayer` constructor."""
+        super(MatchingTensorLayer, self).__init__(**kwargs)
+        self._channels = channels
+        self._normalize = normalize
+        self._init_diag = init_diag
+        self._shape1 = None
+        self._shape2 = None
+
+    def build(self, input_shape: list):
+        """
+        Build the layer.
+
+        :param input_shape: the shapes of the input tensors,
+            for MatchingTensorLayer we need two input tensors.
+        """
+        # Used purely for shape validation.
+        if not isinstance(input_shape, list) or len(input_shape) != 2:
+            raise ValueError('A `MatchingTensorLayer` layer should be called '
+                             'on a list of 2 inputs.')
+        self._shape1 = input_shape[0]
+        self._shape2 = input_shape[1]
+        for idx in (0, 2):
+            if self._shape1[idx] != self._shape2[idx]:
+                raise ValueError(
+                    'Incompatible dimensions: '
+                    f'{self._shape1[idx]} != {self._shape2[idx]}.'
+                    f'Layer shapes: {self._shape1}, {self._shape2}.'
+                )
+
+        if self._init_diag:
+            if self._shape1[2] != self._shape2[2]:
+                raise ValueError('Use init_diag need same embedding shape.')
+            M_diag = np.float32(
+                np.random.uniform(
+                    -0.05, 0.05,
+                    [self._channels, self._shape1[2], self._shape2[2]]
+                )
+            )
+            for i in range(self._channels):
+                for j in range(self._shape1[2]):
+                    M_diag[i][j][j] = 0.1
+            self.M = self.add_weight(
+                name='M',
+                shape=(self._channels, self._shape1[2], self._shape2[2]),
+                initializer=constant(M_diag),
+                trainable=True
+            )
+        else:
+            self.M = self.add_weight(
+                name='M',
+                shape=(self._channels, self._shape1[2], self._shape2[2]),
+                initializer='uniform',
+                trainable=True
+            )
+        super(MatchingTensorLayer, self).build(input_shape)
+
+    def call(self, inputs: list, **kwargs) -> typing.Any:
+        """
+        The computation logic of MatchingTensorLayer.
+
+        :param inputs: two input tensors.
+        """
+        x1 = inputs[0]
+        x2 = inputs[1]
+        if self._normalize:
+            x1 = K.l2_normalize(x1, axis=2)
+            x2 = K.l2_normalize(x2, axis=2)
+        output = K.tf.einsum('abd,fde,ace->afbc', x1, self.M, x2)
+        return output
+
+    def compute_output_shape(self, input_shape: list) -> tuple:
+        """
+        Calculate the layer output shape.
+
+        :param input_shape: the shapes of the input tensors,
+            for MatchingTensorLayer we need two input tensors.
+        """
+        if not isinstance(input_shape, list) or len(input_shape) != 2:
+            raise ValueError('A `MatchingTensorLayer` layer should be called '
+                             'on a list of 2 inputs.')
+        shape1 = list(input_shape[0])
+        shape2 = list(input_shape[1])
+        if len(shape1) != 3 or len(shape2) != 3:
+            raise ValueError('A `MatchingTensorLayer` layer should be called '
+                             'on 2 inputs with 3 dimensions.')
+        if shape1[0] != shape2[0] or shape1[2] != shape2[2]:
+            raise ValueError('A `MatchingTensorLayer` layer should be called '
+                             'on 2 inputs with same 0,2 dimensions.')
+
+        output_shape = [shape1[0], self._channels, shape1[1], shape2[1]]
+        return tuple(output_shape)
+
+    def get_config(self) -> dict:
+        """Get the config dict of MatchingTensorLayer."""
+        config = {
+            'channels': self._channels,
+            'normalize': self._normalize,
+            'init_diag': self._init_diag,
+        }
+        base_config = super(MatchingTensorLayer, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
diff --git a/matchzoo/contrib/layers/spatial_gru.py b/matchzoo/contrib/layers/spatial_gru.py
new file mode 100644
index 00000000..7676f744
--- /dev/null
+++ b/matchzoo/contrib/layers/spatial_gru.py
@@ -0,0 +1,267 @@
+"""An implementation of Spatial GRU Layer."""
+import typing
+
+from keras import backend as K
+from keras.engine import Layer
+from keras.layers import Permute
+from keras.layers import Reshape
+from keras.layers import activations
+from keras.layers import initializers
+
+from tensorflow.python.ops import control_flow_ops
+from tensorflow.python.ops import tensor_array_ops
+
+
+class SpatialGRU(Layer):
+    """
+    Spatial GRU layer.
+
+    :param units: Number of SpatialGRU units.
+    :param normalize: Whether to L2-normalize samples along the
+        dot product axis before taking the dot product.
+        If set to True, then the output of the dot product
+        is the cosine proximity between the two samples.
+    :param init_diag: Whether to initialize the diagonal elements
+        of the matrix.
+    :param activation: Activation function to use. Default:
+        hyperbolic tangent (`tanh`). If you pass `None`, no
+        activation is applied (ie. "linear" activation: `a(x) = x`).
+    :param recurrent_activation: Activation function to use for
+        the recurrent step. Default: sigmoid (`sigmoid`).
+        If you pass `None`, no activation is applied (ie. "linear"
+        activation: `a(x) = x`).
+    :param use_bias: Boolean, whether the layer uses a bias vector.
+    :param kernel_initializer: Initializer for the `kernel` weights
+        matrix, used for the linear transformation of the inputs.
+    :param recurrent_initializer: Initializer for the `recurrent_kernel`
+        weights matrix, used for the linear transformation of the
+        recurrent state.
+    :param bias_initializer: Initializer for the bias vector.
+    :param direction: Scanning direction. `lt` (i.e., left top)
+        indicates the scanning from left top to right bottom, and
+        `rb` (i.e., right bottom) indicates the scanning from
+        right bottom to left top.
+    :param kwargs: Standard layer keyword arguments.
+
+    Examples:
+        >>> import matchzoo as mz
+        >>> layer = mz.contrib.layers.SpatialGRU(units=50,
+        ...                                      direction='lt')
+        >>> num_batch, channel, left_len, right_len = 5, 10, 3, 2
+        >>> layer.build([num_batch, channel, left_len, right_len])
+
+    """
+
+    def __init__(self,
+                 units: int = 50,
+                 normalize: bool = False,
+                 init_diag: bool = False,
+                 activation: str = 'tanh',
+                 recurrent_activation: str = 'sigmoid',
+                 use_bias: bool = True,
+                 kernel_initializer: str = 'glorot_uniform',
+                 recurrent_initializer: str = 'orthogonal',
+                 bias_initializer: str = 'zeros',
+                 direction: str = 'lr',
+                 **kwargs):
+        """:class:`SpatialGRU` constructor."""
+        super(SpatialGRU, self).__init__(**kwargs)
+        self._units = units
+        self._normalize = normalize
+        self._init_diag = init_diag
+        self._activation = activations.get(activation)
+        self._recurrent_activation = activations.get(recurrent_activation)
+        self._use_bias = use_bias
+
+        self._kernel_initializer = initializers.get(kernel_initializer)
+        self._recurrent_initializer = initializers.get(recurrent_initializer)
+        self._bias_initializer = initializers.get(bias_initializer)
+        self._direction = direction
+
+    def build(self, input_shape: typing.Any):
+        """
+        Build the layer.
+
+        :param input_shape: the shapes of the input tensors.
+        """
+        # input_shape: (batch_size, channel, text1_maxlen, text2_maxlen)
+        self._batch_size = input_shape[0]
+        self._channel = input_shape[1]
+        self._input_dim = self._channel + 3 * self._units
+
+        self._text1_maxlen = input_shape[2]
+        self._text2_maxlen = input_shape[3]
+        self._recurrent_step = self._text1_maxlen * self._text2_maxlen
+
+        self._W = self.add_weight(name='W',
+                                  shape=(self._input_dim, self._units * 7),
+                                  initializer=self._kernel_initializer,
+                                  trainable=True)
+
+        self._U = self.add_weight(name='U',
+                                  shape=(self._units * 3, self._units),
+                                  initializer=self._recurrent_initializer,
+                                  trainable=True)
+
+        self._bias = self.add_weight(name='bias',
+                                     shape=(self._units * 8,),
+                                     initializer='zeros',
+                                     trainable=True)
+
+        # w_rl, w_rt, w_rd
+        self._wr = self._W[:, :self._units * 3]
+        # b_rl, b_rt, b_rd
+        self._br = self._bias[:self._units * 3]
+        # w_zi, w_zl, w_zt, w_zd
+        self._wz = self._W[:, self._units * 3: self._units * 7]
+        # b_zi, b_zl, b_zt, b_zd
+        self._bz = self._bias[self._units * 3: self._units * 7]
+        self._w_ij = self.add_weight(name='Wij',
+                                     shape=(self._channel, self._units),
+                                     initializer=self._recurrent_initializer,
+                                     trainable=True)
+        self._b_ij = self._bias[self._units * 7:]
+        super(SpatialGRU, self).build(input_shape)
+
+    def softmax_by_row(self, z: typing.Any) -> tuple:
+        """Conduct softmax on each dimension across the four gates."""
+
+        z_transform = Permute((2, 1))(Reshape((4, self._units))(z))
+        for i in range(0, self._units):
+            begin1 = [0, i, 0]
+            size = [-1, 1, -1]
+            if i == 0:
+                z_s = K.tf.nn.softmax(K.tf.slice(z_transform, begin1, size))
+            else:
+                z_s = K.tf.concat(
+                    [z_s, K.tf.nn.softmax(
+                        K.tf.slice(z_transform, begin1, size))], 1)
+
+        zi, zl, zt, zd = K.tf.unstack(z_s, axis=2)
+        return zi, zl, zt, zd
+
+    def calculate_recurrent_unit(self,
+                                 inputs_ta: typing.Any,
+                                 states: typing.Any,
+                                 step: int,
+                                 h: typing.Any,
+                                 h0: typing.Any) -> tuple:
+        """
+        Calculate recurrent unit.
+
+        :param inputs: input tensors.
+        """
+        i = K.tf.div(step, K.tf.constant(self._text2_maxlen))
+        j = K.tf.mod(step, K.tf.constant(self._text2_maxlen))
+
+        h_diag = states.read(i * (self._text2_maxlen + 1) + j)
+        h_top = states.read(i * (self._text2_maxlen + 1) + j + 1)
+        h_left = states.read((i + 1) * (self._text2_maxlen + 1) + j)
+
+        s_ij = inputs_ta.read(step)
+        q = K.tf.concat([K.tf.concat([h_top, h_left], 1),
+                        K.tf.concat([h_diag, s_ij], 1)], 1)
+        r = self._recurrent_activation(
+            self._time_distributed_dense(self._wr, q, self._br))
+        z = self._time_distributed_dense(self._wz, q, self._bz)
+        zi, zl, zt, zd = self.softmax_by_row(z)
+
+        hij_1 = self._time_distributed_dense(self._w_ij, s_ij, self._b_ij)
+        hij_2 = K.dot(r * (K.tf.concat([h_left, h_top, h_diag], 1)), self._U)
+        hij_ = self._activation(hij_1 + hij_2)
+        hij = zl * h_left + zt * h_top + zd * h_diag + zi * hij_
+        states = states.write(((i + 1) * (self._text2_maxlen + 1) + j + 1),
+                              hij)
+        hij.set_shape(h_top.get_shape())
+        return inputs_ta, states, step + 1, hij, h0
+
+    def call(self, inputs: list, **kwargs) -> typing.Any:
+        """
+        The computation logic of SpatialGRU.
+
+        :param inputs: input tensors.
+        """
+        batch_size = K.tf.shape(inputs)[0]
+        self._bounder_state_h0 = K.tf.zeros([batch_size, self._units])
+
+        # input_x: (text1_maxlen, text2_maxlen, batch_size, channel)
+        input_x = K.tf.transpose(inputs, [2, 3, 0, 1])
+        if self._direction == 'rb':
+            input_x = K.tf.reverse(input_x, [0, 1])
+        elif self._direction != 'lt':
+            raise ValueError(f"Invalid direction. "
+                             f"`{self._direction}` received. "
+                             f"Must be in `lt`, `rb`.")
+        input_x = K.tf.reshape(input_x, [-1, self._channel])
+        input_x = K.tf.split(
+            axis=0,
+            num_or_size_splits=self._text1_maxlen * self._text2_maxlen,
+            value=input_x)
+        inputs_ta = K.tf.TensorArray(
+            dtype=K.tf.float32,
+            size=self._text1_maxlen * self._text2_maxlen,
+            name='input_ta')
+        states_ta = K.tf.TensorArray(
+            dtype=K.tf.float32,
+            size=(self._text1_maxlen + 1) * (self._text2_maxlen + 1),
+            name='state_ta', clear_after_read=False)
+
+        for i in range(self._text2_maxlen + 1):
+            states_ta = states_ta.write(i, self._bounder_state_h0)
+        for i in range(self._text1_maxlen):
+            states_ta = states_ta.write((i + 1) * (self._text2_maxlen + 1),
+                                        self._bounder_state_h0)
+        inputs_ta = inputs_ta.unstack(input_x)
+        _, _, _, hij, _ = control_flow_ops.while_loop(
+            cond=lambda _0, _1, i, _3, _4: i < self._recurrent_step,
+            body=self.calculate_recurrent_unit,
+            loop_vars=(
+                inputs_ta,
+                states_ta,
+                K.tf.Variable(0, dtype=K.tf.int32),
+                self._bounder_state_h0,
+                self._bounder_state_h0),
+            parallel_iterations=1,
+            swap_memory=True
+        )
+        return hij
+
+    def compute_output_shape(self, input_shape: typing.Any) -> tuple:
+        """
+        Calculate the layer output shape.
+
+        :param input_shape: the shapes of the input tensors.
+        """
+        output_shape = [input_shape[0], self._units]
+        return tuple(output_shape)
+
+    def get_config(self) -> dict:
+        """Get the config dict of SpatialGRU."""
+        config = {
+            'units': self._units,
+            'normalize': self._normalize,
+            'init_diag': self._init_diag,
+            'activation': activations.serialize(self._activation),
+            'recurrent_activation':
+                activations.serialize(self._recurrent_activation),
+            'use_bias': self._use_bias,
+            'kernel_initializer':
+                initializers.serialize(self._kernel_initializer),
+            'recurrent_initializer':
+                initializers.serialize(self._recurrent_initializer),
+            'bias_initializer':
+                initializers.serialize(self._bias_initializer),
+            'direction': self._direction
+        }
+        base_config = super(SpatialGRU, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+    @classmethod
+    def _time_distributed_dense(cls, w, x, b):
+        if K.backend() == 'tensorflow':
+            x = K.dot(x, w)
+            x = K.bias_add(x, b)
+        else:
+            raise Exception("time_distributed_dense doesn't "
+                            "backend tensorflow")
+        return x
diff --git a/matchzoo/contrib/models/__init__.py b/matchzoo/contrib/models/__init__.py
index 6faa60bb..bff26142 100644
--- a/matchzoo/contrib/models/__init__.py
+++ b/matchzoo/contrib/models/__init__.py
@@ -1 +1,2 @@
 from .match_lstm import MatchLSTM
+from .match_srnn import MatchSRNN
diff --git a/matchzoo/contrib/models/match_srnn.py b/matchzoo/contrib/models/match_srnn.py
new file mode 100644
index 00000000..a6251d39
--- /dev/null
+++ b/matchzoo/contrib/models/match_srnn.py
@@ -0,0 +1,93 @@
+"""An implementation of Match-SRNN Model."""
+import typing
+
+import keras
+import keras.backend as K
+
+import matchzoo
+from matchzoo.engine.base_model import BaseModel
+from matchzoo.engine.param import Param
+from matchzoo.engine.param_table import ParamTable
+from matchzoo.engine import hyper_spaces
+
+
+class MatchSRNN(BaseModel):
+    """
+    Match-SRNN Model.
+
+    Examples:
+        >>> model = MatchSRNN()
+        >>> model.params['channels'] = 4
+        >>> model.params['units'] = 10
+        >>> model.params['dropout_rate'] = 0.0
+        >>> model.params['direction'] = 'lt'
+        >>> model.guess_and_fill_missing_params(verbose=0)
+        >>> model.build()
+
+    """
+
+    @classmethod
+    def get_default_params(cls) -> ParamTable:
+        """:return: model default parameters."""
+        params = super().get_default_params(with_embedding=True)
+        params.add(Param(name='channels', value=4,
+                         desc="Number of word interaction tensor channels"))
+        params.add(Param(name='units', value=10,
+                         desc="Number of SpatialGRU units"))
+        params.add(Param(name='direction', value='lt',
+                         desc="Direction of SpatialGRU scanning"))
+        params.add(Param(
+            name='dropout_rate', value=0.0,
+            hyper_space=hyper_spaces.quniform(low=0.0, high=0.8,
+                                              q=0.01),
+            desc="The dropout rate."
+        ))
+        return params
+
+    def build(self):
+        """
+        Build model structure.
+
+        Match-SRNN: Modeling the Recursive Matching Structure
+            with Spatial RNN
+        """
+
+        # Scalar dimensions referenced here:
+        #   B = batch size (number of sequences)
+        #   D = embedding size
+        #   L = `input_left` sequence length
+        #   R = `input_right` sequence length
+        #   C = number of channels
+
+        # Left input and right input.
+        # shape = [B, L]
+        # shape = [B, R]
+        query, doc = self._make_inputs()
+
+        embedding = self._make_embedding_layer()
+        # Process left and right input.
+        # shape = [B, L, D]
+        embed_query = embedding(query)
+        # shape = [B, R, D]
+        embed_doc = embedding(doc)
+
+        # Get matching tensor
+        # shape = [B, C, L, R]
+        matching_tensor_layer = matchzoo.contrib.layers.MatchingTensorLayer(
+            channels=self._params['channels'])
+        matching_tensor = matching_tensor_layer([embed_query, embed_doc])
+        # shape = [B, L, R, C]
+        matching_tensor = keras.layers.Permute((2, 3, 1))(matching_tensor)
+
+        spatial_gru = matchzoo.contrib.layers.SpatialGRU(
+            units=self._params['units'],
+            direction=self._params['direction'])
+
+        h_ij = spatial_gru(matching_tensor)
+
+        x = keras.layers.Dropout(
+            rate=self._params['dropout_rate'])(h_ij)
+
+        x_out = self._make_output_layer()(x)
+
+        self._backend = keras.Model(inputs=[query, doc], outputs=x_out)

From b09d67afeeaf5a84f059ffbb06973acb6f1d1894 Mon Sep 17 00:00:00 2001
From: Crystina <xzhangbx@ust.hk>
Date: Mon, 1 Apr 2019 21:55:19 -0400
Subject: [PATCH 007/100] add esim code and test file into dev branch

---
 matchzoo/models/__init__.py  |   1 +
 matchzoo/models/esim.py      | 251 +++++++++++++++++++++++++++++++++++
 tests/inte_test/test_esim.py | 115 ++++++++++++++++
 3 files changed, 367 insertions(+)
 create mode 100644 matchzoo/models/esim.py
 create mode 100644 tests/inte_test/test_esim.py

diff --git a/matchzoo/models/__init__.py b/matchzoo/models/__init__.py
index 29516ab3..526c33f9 100644
--- a/matchzoo/models/__init__.py
+++ b/matchzoo/models/__init__.py
@@ -12,6 +12,7 @@
 from .drmm import DRMM
 from .anmm import ANMM
 from .mvlstm import MVLSTM
+from .esim import ESIM 
 
 
 def list_available() -> list:
diff --git a/matchzoo/models/esim.py b/matchzoo/models/esim.py
new file mode 100644
index 00000000..04a1a944
--- /dev/null
+++ b/matchzoo/models/esim.py
@@ -0,0 +1,251 @@
+"""KNRM model."""
+import keras
+import tensorflow as tf
+from keras.layers import * # Dense, Dot, Softmax, Concatenate, Embedding, LSTM, Bidirectional
+import keras.backend as K
+
+import matchzoo as mz
+from matchzoo import engine, preprocessors
+
+
+class ESIM(engine.BaseModel):
+    """
+    ESIM model.
+
+    Examples:
+        >>> model = ESIM()
+        >>> task = classification_task = mz.tasks.Classification(num_classes=2)
+        >>> model.params['task'] = task
+        >>> model.params['input_shapes'] = [(20, ), (40, )]
+        >>> model.params['lstm_dim'] = 300
+        >>> model.params['mlp_num_units'] = 300
+        >>> model.params['embedding_input_dim'] =  5000
+        >>> model.params['embedding_output_dim'] =  10
+        >>> model.params['embedding_trainable'] = False
+
+        >>> model.params['mlp_num_layers'] = 0      # zero hidden layer (one output layer)
+        >>> model.params['mlp_num_fan_out'] = 300
+        >>> model.params['mlp_activation_func'] = 'tanh'
+
+        >>> model.params['mask_value'] = 0        
+        >>> model.params['dropout_rate'] = 0.5
+
+        >>> model.guess_and_fill_missing_params()
+        >>> model.build()
+    """
+
+    @classmethod
+    def get_default_params(cls)  -> engine.ParamTable:
+        """Get default parameters."""
+        params = super().get_default_params(with_embedding=True,
+                                            with_multi_layer_perceptron=True)
+
+        params.add(engine.Param(
+            name='dropout_rate',
+            value=0.5,
+            desc="The dropout rate for all fully-connected layer"
+        ))
+
+        params.add(engine.Param(
+            name='lstm_dim',
+            value=300,
+            desc="The dimension of LSTM layer."
+        ))
+
+        params.add(engine.Param(
+            name='mask_value',
+            value=0,
+            desc="The value would be regarded as pad"
+        ))
+
+        return params
+
+    def compile(self, optimizer=None):
+        """
+        Overwrite the engine.BaseModel.compile() to allow self-defined optimizer and learning rate
+
+        Examples:
+            >>> from matchzoo import models
+            >>> model = models.Naive()
+            >>> model.guess_and_fill_missing_params(verbose=0)
+            >>> model.params['task'].metrics = ['mse', 'map']
+            >>> model.params['task'].metrics
+            ['mse', mean_average_precision(0.0)]
+            >>> model.build()
+            >>> model.compile()
+        """
+        if optimizer == None:
+            optimizer = self._params['optimizer']
+
+        self._backend.compile(optimizer=optimizer,
+                              loss=self._params['task'].loss)
+
+
+    def _make_inputs(self) -> list:
+        """
+        prepare input from input dict
+        """        
+        input_left = keras.layers.Input(
+            name='text_left',
+            shape=self._params['input_shapes'][0]
+        )
+        input_right = keras.layers.Input(
+            name='text_right',
+            shape=self._params['input_shapes'][1]
+        )
+        return [input_left, input_right]
+
+    
+    def _make_embedding_layer(self, name: str = 'embedding') -> keras.layers.Layer:
+        """
+        Overwrite the engine.BaseModel._make_embedding_layer to allow specifiying mask_zero
+        """
+        return keras.layers.Embedding(
+            self._params['embedding_input_dim'],
+            self._params['embedding_output_dim'],
+            trainable=self._params['embedding_trainable'],
+            mask_zero = True,
+            name=name
+        )
+
+
+    def _expand_dim(self, inp, axis):
+        """
+        Wrap keras.backend.expand_dims into a Lambda layer
+        """
+        return keras.layers.Lambda(lambda x: K.expand_dims(x, axis=axis))(inp)
+
+
+    def _make_atten_mask_layer(self) -> keras.layers.Layer:
+        """
+        make mask layer for attention weight matrix so that each word won't pay attention to <PAD> timestep
+        """
+        return keras.layers.Lambda(
+            lambda weight_mask: weight_mask[0] + (1.0 - weight_mask[1]) * -1e7,
+            name="atten_mask"
+        )
+
+
+    def _make_BiLSTM_layer(self, lstm_dim) -> keras.layers.Layer:
+        """
+        Bidirectional LSTM layer in ESIM.
+
+        :param lstm_dim: int, dimension of LSTM layer
+        :return: `keras.layers.Layer`.
+        """
+        return Bidirectional(layer=LSTM(lstm_dim, return_sequences=True),
+                             merge_mode='concat')
+
+
+    def _max(self, texts, mask):
+        """
+        Compute the max of each text according to their real length
+
+        :param texts: np.array with shape [B, T, H]
+        :param lengths: np.array with shape [B, T, ], where 1 means valid, 0 means pad
+        """
+        mask = self._expand_dim(mask, axis=2)
+        new_texts = Multiply()([texts, mask])
+
+        text_max = Lambda(
+            lambda x: K.max(x, axis=1),
+        )(new_texts)
+
+        return text_max
+
+
+    def _avg(self, texts, mask):
+        """
+        Compute the mean of each text according to their real length
+
+        :param texts: np.array with shape [B, T, H]
+        :param lengths: np.array with shape [B, T, ], where 1 means valid, 0 means pad
+        """
+        mask = self._expand_dim(mask, axis=2)
+        new_texts = Multiply()([texts, mask])
+
+        # timestep-wise division, exclude the PAD number when calc avg
+        text_avg = keras.layers.Lambda(
+            lambda text_mask: K.sum(text_mask[0], axis=1) / K.sum(text_mask[1], axis=1),
+        )([new_texts, mask])
+
+        return text_avg
+
+    def build(self):
+        """Build model."""
+
+        # parameters
+        lstm_dim = self._params['lstm_dim']
+        dropout_rate = self._params['dropout_rate']
+
+        # layers
+        create_mask = Lambda(lambda x: K.cast(K.not_equal(x, self._params['mask_value']), K.floatx()))
+        embedding = self._make_embedding_layer()
+        lstm_compare = self._make_BiLSTM_layer(lstm_dim)
+        lstm_compose = self._make_BiLSTM_layer(lstm_dim)
+        dense_compare = Dense(units=lstm_dim, activation='relu', use_bias=True)
+        dropout = Dropout(dropout_rate)
+
+
+        # model
+        a, b = self._make_inputs()      # [B, T_a], [B, T_b]
+        a_mask = create_mask(a)         # [B, T_a]
+        b_mask = create_mask(b)         # [B, T_b]
+
+        ########################
+        # encoding
+        ########################
+        a_emb = dropout(embedding(a))   # [B, T_a, E_dim]
+        b_emb = dropout(embedding(b))   # [B, T_b, E_dim]
+
+        a_ = lstm_compare(a_emb)          # [B, T_a, H*2]
+        b_ = lstm_compare(b_emb)          # [B, T_b, H*2]
+
+
+        ########################
+        # local inference
+        ########################
+        # similarity matrix
+        e = Dot(axes=-1)([a_, b_])  # [B, T_a, T_b]
+        _ab_mask = Multiply()([self._expand_dim(a_mask, axis=2),    # [B, T_a, 1]
+                               self._expand_dim(b_mask, axis=1)])   # [B, 1, T_b]
+        # _ab_mask: [B, T_a, T_b]
+
+        pm = Permute((2, 1))
+        mask_layer = self._make_atten_mask_layer()
+        softmax_layer = Softmax(axis=-1)
+
+        e_a = softmax_layer(mask_layer([e, _ab_mask]))          # [B, T_a, T_b]
+        e_b = softmax_layer(mask_layer([pm(e), pm(_ab_mask)]))  # [B, T_b, T_a]
+
+        # alignment (a_t = a~, b_t = b~ )
+        a_t = Dot(axes=(2, 1))([e_a, b_])   # [B, T_a, H*2]
+        b_t = Dot(axes=(2, 1))([e_b, a_])   # [B, T_b, H*2]
+
+        # local inference info enhancement
+        m_a = Concatenate(axis=-1)([a_, a_t, Subtract()([a_, a_t]), Multiply()([a_, a_t])])    # [B, T_a, H*2*4]
+        m_b = Concatenate(axis=-1)([b_, b_t, Subtract()([b_, b_t]), Multiply()([b_, b_t])])    # [B, T_b, H*2*4]
+
+        # project m_a and m_b from 4*H*2 dim to H dim
+        m_a = dropout(dense_compare(m_a))   # [B, T_a, H]
+        m_b = dropout(dense_compare(m_b))   # [B, T_a, H]
+
+
+        ########################
+        # inference composition
+        ########################
+        v_a = lstm_compose(m_a)          # [B, T_a, H*2]
+        v_b = lstm_compose(m_b)          # [B, T_b, H*2]
+
+        # pooling
+        v_a = Concatenate(axis=-1)([self._avg(v_a, a_mask), self._max(v_a, a_mask)]) # [B, H*4]
+        v_b = Concatenate(axis=-1)([self._avg(v_b, b_mask), self._max(v_b, b_mask)]) # [B, H*4]
+        v = Concatenate(axis=-1)([v_a, v_b])                                         # [B, H*8]
+
+        # mlp (multilayer perceptron) classifier
+        output = dropout(self._make_multi_layer_perceptron_layer()(v))               # [B, H]
+        output = self._make_output_layer()(output)                                   # [B, #classes]
+
+        self._backend = keras.Model(inputs=[a, b], outputs=output)
+
+
diff --git a/tests/inte_test/test_esim.py b/tests/inte_test/test_esim.py
new file mode 100644
index 00000000..d72a6552
--- /dev/null
+++ b/tests/inte_test/test_esim.py
@@ -0,0 +1,115 @@
+import shutil
+
+import pytest
+import numpy as np
+import pandas as pd
+import matchzoo as mz
+from keras.utils import to_categorical
+
+
+@pytest.fixture(scope='module')
+def train_data():
+    return mz.datasets.toy.load_data(stage='train')
+    
+
+@pytest.fixture(scope='module')
+def valid_data():
+    return mz.datasets.toy.load_data(stage='dev')
+
+
+@pytest.fixture(scope='module')
+def test_data():
+    return mz.datasets.toy.load_data(stage='test')
+
+
+@pytest.fixture(scope='module')
+def task(request) -> mz.engine.BaseTask:
+    return mz.tasks.Classification(num_classes=2)
+
+
+@pytest.fixture(scope='module')
+def preprocessor():
+    return mz.preprocessors.BasicPreprocessor()
+
+
+@pytest.fixture(scope='module')
+def train_data_processed(train_data, preprocessor) -> mz.DataPack:
+    X, Y = preprocessor.fit_transform(train_data).unpack()
+    Y = to_categorical(Y)
+    df = pd.DataFrame(data = {  'id_left': list(X['id_left']), 
+                                'text_left': list(X['text_left']), 
+                                'id_right': list(X['id_right']), 
+                                'text_right': list(X['text_right']), 
+                                'label': list(Y) })
+    return mz.pack(df)
+
+
+@pytest.fixture(scope='module')
+def valid_data_processed(valid_data, preprocessor) -> mz.DataPack:
+    X, Y = preprocessor.transform(valid_data).unpack()
+    Y = to_categorical(Y)
+    df = pd.DataFrame(data = {  'id_left': list(X['id_left']), 
+                                'text_left': list(X['text_left']), 
+                                'id_right': list(X['id_right']), 
+                                'text_right': list(X['text_right']), 
+                                'label': list(Y) })
+    return mz.pack(df)
+
+
+@pytest.fixture(scope='module')
+def test_data_processed(test_data, preprocessor) -> mz.DataPack:
+    X, Y = preprocessor.transform(test_data).unpack()
+    Y = to_categorical(Y)
+    df = pd.DataFrame(data = {  'id_left': list(X['id_left']), 
+                                'text_left': list(X['text_left']), 
+                                'id_right': list(X['id_right']), 
+                                'text_right': list(X['text_right']), 
+                                'label': list(Y) })
+    return mz.pack(df)
+
+
+@pytest.fixture(scope='module')
+def train_generator(request, train_data_processed):
+    return mz.DataGenerator(train_data_processed)
+
+
+@pytest.mark.slow
+def test_duet(train_data_processed,
+                 task,
+                 train_generator,
+                 valid_data_processed,
+                 test_data_processed,
+                 preprocessor):
+    """Test esim model."""
+    # Create a esim model
+    esim = mz.models.ESIM()
+    input_shapes = preprocessor.context['input_shapes']
+    embed_dimension = preprocessor.context['vocab_size'] + 1
+    esim.params['input_shapes'] = input_shapes
+    esim.params['task'] = task
+    esim.params['embedding_input_dim'] = embed_dimension
+    esim.params['embedding_output_dim'] = 300
+    esim.params['lstm_dim'] =  300
+    esim.params['mlp_num_units'] = 300
+    esim.params['mlp_num_layers'] = 0
+    esim.params['mlp_num_fan_out'] = 300
+    esim.params['mlp_activation_func'] = 'relu'
+    esim.params['dropout_rate'] = 0.5
+    esim.guess_and_fill_missing_params()
+    esim.build()
+    esim.compile()
+    
+    x_valid, y_valid = valid_data_processed.unpack()
+    valid_eval = mz.callbacks.EvaluateAllMetrics(esim,
+                                                 x_valid,
+                                                 y_valid)
+    esim.fit_generator(train_generator, epochs=1, callbacks=[valid_eval])
+    esim.save('.tmpdir')
+    
+    try:
+        esim = mz.load_model('.tmpdir')
+        x, y = test_data_processed.unpack()
+        results = esim.evaluate(x, y)
+        assert len(results) > 0
+    finally:
+        shutil.rmtree('.tmpdir')

From af8878ea1934bc1363e3c9bb1d367b219e449372 Mon Sep 17 00:00:00 2001
From: Crystina <xzhangbx@ust.hk>
Date: Mon, 1 Apr 2019 23:46:52 -0400
Subject: [PATCH 008/100] moved esim to mz/contrib/model, change the import
 path from mz.engine to mz.engine.base_model

---
 matchzoo/{ => contrib}/models/esim.py | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)
 rename matchzoo/{ => contrib}/models/esim.py (99%)

diff --git a/matchzoo/models/esim.py b/matchzoo/contrib/models/esim.py
similarity index 99%
rename from matchzoo/models/esim.py
rename to matchzoo/contrib/models/esim.py
index 04a1a944..2bb0f531 100644
--- a/matchzoo/models/esim.py
+++ b/matchzoo/contrib/models/esim.py
@@ -5,10 +5,10 @@
 import keras.backend as K
 
 import matchzoo as mz
-from matchzoo import engine, preprocessors
+from matchzoo import engine
 
 
-class ESIM(engine.BaseModel):
+class ESIM(engine.base_model.BaseModel):
     """
     ESIM model.
 

From 4dabb6c045eed7559c5a2735331f9529c59e3b0d Mon Sep 17 00:00:00 2001
From: Crystina <xzhangbx@ust.hk>
Date: Tue, 2 Apr 2019 01:26:22 -0400
Subject: [PATCH 009/100] update import path

---
 matchzoo/contrib/models/__init__.py |  1 +
 matchzoo/contrib/models/esim.py     | 21 +++++++++++++--------
 matchzoo/models/__init__.py         |  1 -
 tests/inte_test/test_esim.py        |  7 +++++--
 4 files changed, 19 insertions(+), 11 deletions(-)

diff --git a/matchzoo/contrib/models/__init__.py b/matchzoo/contrib/models/__init__.py
index 6faa60bb..6a81f8f3 100644
--- a/matchzoo/contrib/models/__init__.py
+++ b/matchzoo/contrib/models/__init__.py
@@ -1 +1,2 @@
 from .match_lstm import MatchLSTM
+from .esim import ESIM
\ No newline at end of file
diff --git a/matchzoo/contrib/models/esim.py b/matchzoo/contrib/models/esim.py
index 2bb0f531..1f64a062 100644
--- a/matchzoo/contrib/models/esim.py
+++ b/matchzoo/contrib/models/esim.py
@@ -5,10 +5,15 @@
 import keras.backend as K
 
 import matchzoo as mz
-from matchzoo import engine
 
 
-class ESIM(engine.base_model.BaseModel):
+from matchzoo.engine.param import Param
+from matchzoo.engine.base_model import BaseModel
+from matchzoo.engine.param_table import ParamTable
+
+
+
+class ESIM(BaseModel):
     """
     ESIM model.
 
@@ -35,24 +40,24 @@ class ESIM(engine.base_model.BaseModel):
     """
 
     @classmethod
-    def get_default_params(cls)  -> engine.ParamTable:
+    def get_default_params(cls)  -> ParamTable:
         """Get default parameters."""
         params = super().get_default_params(with_embedding=True,
                                             with_multi_layer_perceptron=True)
 
-        params.add(engine.Param(
+        params.add(Param(
             name='dropout_rate',
             value=0.5,
             desc="The dropout rate for all fully-connected layer"
         ))
 
-        params.add(engine.Param(
+        params.add(Param(
             name='lstm_dim',
             value=300,
             desc="The dimension of LSTM layer."
         ))
 
-        params.add(engine.Param(
+        params.add(Param(
             name='mask_value',
             value=0,
             desc="The value would be regarded as pad"
@@ -62,7 +67,7 @@ def get_default_params(cls)  -> engine.ParamTable:
 
     def compile(self, optimizer=None):
         """
-        Overwrite the engine.BaseModel.compile() to allow self-defined optimizer and learning rate
+        Overwrite the BaseModel.compile() to allow self-defined optimizer and learning rate
 
         Examples:
             >>> from matchzoo import models
@@ -98,7 +103,7 @@ def _make_inputs(self) -> list:
     
     def _make_embedding_layer(self, name: str = 'embedding') -> keras.layers.Layer:
         """
-        Overwrite the engine.BaseModel._make_embedding_layer to allow specifiying mask_zero
+        Overwrite the BaseModel._make_embedding_layer to allow specifiying mask_zero
         """
         return keras.layers.Embedding(
             self._params['embedding_input_dim'],
diff --git a/matchzoo/models/__init__.py b/matchzoo/models/__init__.py
index 526c33f9..29516ab3 100644
--- a/matchzoo/models/__init__.py
+++ b/matchzoo/models/__init__.py
@@ -12,7 +12,6 @@
 from .drmm import DRMM
 from .anmm import ANMM
 from .mvlstm import MVLSTM
-from .esim import ESIM 
 
 
 def list_available() -> list:
diff --git a/tests/inte_test/test_esim.py b/tests/inte_test/test_esim.py
index d72a6552..646b786b 100644
--- a/tests/inte_test/test_esim.py
+++ b/tests/inte_test/test_esim.py
@@ -6,6 +6,8 @@
 import matchzoo as mz
 from keras.utils import to_categorical
 
+from matchzoo.engine.base_task import BaseTask
+from matchzoo.contrib.models import ESIM
 
 @pytest.fixture(scope='module')
 def train_data():
@@ -23,7 +25,7 @@ def test_data():
 
 
 @pytest.fixture(scope='module')
-def task(request) -> mz.engine.BaseTask:
+def task(request) -> BaseTask:
     return mz.tasks.Classification(num_classes=2)
 
 
@@ -82,7 +84,8 @@ def test_duet(train_data_processed,
                  preprocessor):
     """Test esim model."""
     # Create a esim model
-    esim = mz.models.ESIM()
+    # esim = mz.contrib.models.ESIM()
+    esim = ESIM()
     input_shapes = preprocessor.context['input_shapes']
     embed_dimension = preprocessor.context['vocab_size'] + 1
     esim.params['input_shapes'] = input_shapes

From 1e83c2a69f9ffa973877908a6ffaae5a54c9f889 Mon Sep 17 00:00:00 2001
From: Crystina <xzhangbx@ust.hk>
Date: Tue, 2 Apr 2019 08:23:41 -0400
Subject: [PATCH 010/100] update according to the PR comments and flake8

---
 Makefile                        |   2 +-
 matchzoo/contrib/models/esim.py | 147 ++++++++++++++------------------
 tests/inte_test/test_esim.py    |   2 +-
 3 files changed, 68 insertions(+), 83 deletions(-)

diff --git a/Makefile b/Makefile
index a7bd9a58..e2f4382b 100644
--- a/Makefile
+++ b/Makefile
@@ -2,7 +2,7 @@ init:
 	pip install -r requirements.txt
 
 TEST_ARGS = --doctest-modules --doctest-continue-on-failure --cov matchzoo/ --cov-report term-missing --cov-report html --cov-config .coveragerc matchzoo/ tests/ -W ignore::DeprecationWarning
-FLAKE_ARGS = ./matchzoo --exclude=__init__.py,matchzoo/contrib
+FLAKE_ARGS = ./matchzoo --exclude=__init__.py, matchzoo/contrib
 
 test:
 	pytest $(TEST_ARGS)
diff --git a/matchzoo/contrib/models/esim.py b/matchzoo/contrib/models/esim.py
index 1f64a062..185e5154 100644
--- a/matchzoo/contrib/models/esim.py
+++ b/matchzoo/contrib/models/esim.py
@@ -1,18 +1,14 @@
-"""KNRM model."""
+"""ESIM model."""
 import keras
 import tensorflow as tf
-from keras.layers import * # Dense, Dot, Softmax, Concatenate, Embedding, LSTM, Bidirectional
 import keras.backend as K
 
 import matchzoo as mz
-
-
 from matchzoo.engine.param import Param
 from matchzoo.engine.base_model import BaseModel
 from matchzoo.engine.param_table import ParamTable
 
 
-
 class ESIM(BaseModel):
     """
     ESIM model.
@@ -27,20 +23,17 @@ class ESIM(BaseModel):
         >>> model.params['embedding_input_dim'] =  5000
         >>> model.params['embedding_output_dim'] =  10
         >>> model.params['embedding_trainable'] = False
-
-        >>> model.params['mlp_num_layers'] = 0      # zero hidden layer (one output layer)
+        >>> model.params['mlp_num_layers'] = 0
         >>> model.params['mlp_num_fan_out'] = 300
         >>> model.params['mlp_activation_func'] = 'tanh'
-
-        >>> model.params['mask_value'] = 0        
+        >>> model.params['mask_value'] = 0
         >>> model.params['dropout_rate'] = 0.5
-
         >>> model.guess_and_fill_missing_params()
         >>> model.build()
     """
 
     @classmethod
-    def get_default_params(cls)  -> ParamTable:
+    def get_default_params(cls) -> ParamTable:
         """Get default parameters."""
         params = super().get_default_params(with_embedding=True,
                                             with_multi_layer_perceptron=True)
@@ -65,31 +58,7 @@ def get_default_params(cls)  -> ParamTable:
 
         return params
 
-    def compile(self, optimizer=None):
-        """
-        Overwrite the BaseModel.compile() to allow self-defined optimizer and learning rate
-
-        Examples:
-            >>> from matchzoo import models
-            >>> model = models.Naive()
-            >>> model.guess_and_fill_missing_params(verbose=0)
-            >>> model.params['task'].metrics = ['mse', 'map']
-            >>> model.params['task'].metrics
-            ['mse', mean_average_precision(0.0)]
-            >>> model.build()
-            >>> model.compile()
-        """
-        if optimizer == None:
-            optimizer = self._params['optimizer']
-
-        self._backend.compile(optimizer=optimizer,
-                              loss=self._params['task'].loss)
-
-
     def _make_inputs(self) -> list:
-        """
-        prepare input from input dict
-        """        
         input_left = keras.layers.Input(
             name='text_left',
             shape=self._params['input_shapes'][0]
@@ -100,36 +69,38 @@ def _make_inputs(self) -> list:
         )
         return [input_left, input_right]
 
-    
-    def _make_embedding_layer(self, name: str = 'embedding') -> keras.layers.Layer:
+    def _make_embedding_layer(self, name='embedding') -> keras.layers.Layer:
         """
-        Overwrite the BaseModel._make_embedding_layer to allow specifiying mask_zero
+        Overwrite the BaseModel._make_embedding_layer to allow
+        specifiying mask_zero.
+
+        :param name: name for embedding layer, default 'embedding'
         """
         return keras.layers.Embedding(
             self._params['embedding_input_dim'],
             self._params['embedding_output_dim'],
             trainable=self._params['embedding_trainable'],
-            mask_zero = True,
+            mask_zero=True,
             name=name
         )
 
-
-    def _expand_dim(self, inp, axis):
+    def _expand_dim(self, inp, axis) -> keras.layers.Layer:
         """
-        Wrap keras.backend.expand_dims into a Lambda layer
+        Wrap keras.backend.expand_dims into a Lambda layer.
+
+        :param inp: input tensor to expand the dimension
+        :param axis: the axis of new dimension
         """
         return keras.layers.Lambda(lambda x: K.expand_dims(x, axis=axis))(inp)
 
-
     def _make_atten_mask_layer(self) -> keras.layers.Layer:
         """
-        make mask layer for attention weight matrix so that each word won't pay attention to <PAD> timestep
+        Make mask layer for attention weight matrix so that
+        each word won't pay attention to <PAD> timestep.
         """
         return keras.layers.Lambda(
             lambda weight_mask: weight_mask[0] + (1.0 - weight_mask[1]) * -1e7,
-            name="atten_mask"
-        )
-
+            name="atten_mask")
 
     def _make_BiLSTM_layer(self, lstm_dim) -> keras.layers.Layer:
         """
@@ -138,40 +109,42 @@ def _make_BiLSTM_layer(self, lstm_dim) -> keras.layers.Layer:
         :param lstm_dim: int, dimension of LSTM layer
         :return: `keras.layers.Layer`.
         """
-        return Bidirectional(layer=LSTM(lstm_dim, return_sequences=True),
-                             merge_mode='concat')
-
+        return keras.layers.Bidirectional(
+            layer=keras.layers.LSTM(lstm_dim, return_sequences=True),
+            merge_mode='concat')
 
-    def _max(self, texts, mask):
+    def _max(self, texts, mask) -> tf.Tensor:
         """
         Compute the max of each text according to their real length
 
         :param texts: np.array with shape [B, T, H]
-        :param lengths: np.array with shape [B, T, ], where 1 means valid, 0 means pad
+        :param lengths: np.array with shape [B, T, ],
+            where 1 means valid, 0 means pad
         """
         mask = self._expand_dim(mask, axis=2)
-        new_texts = Multiply()([texts, mask])
+        new_texts = keras.layers.Multiply()([texts, mask])
 
-        text_max = Lambda(
+        text_max = keras.layers.Lambda(
             lambda x: K.max(x, axis=1),
         )(new_texts)
 
         return text_max
 
-
-    def _avg(self, texts, mask):
+    def _avg(self, texts, mask) -> tf.Tensor:
         """
         Compute the mean of each text according to their real length
 
         :param texts: np.array with shape [B, T, H]
-        :param lengths: np.array with shape [B, T, ], where 1 means valid, 0 means pad
+        :param lengths: np.array with shape [B, T, ],
+            where 1 means valid, 0 means pad
         """
         mask = self._expand_dim(mask, axis=2)
-        new_texts = Multiply()([texts, mask])
+        new_texts = keras.layers.Multiply()([texts, mask])
 
         # timestep-wise division, exclude the PAD number when calc avg
         text_avg = keras.layers.Lambda(
-            lambda text_mask: K.sum(text_mask[0], axis=1) / K.sum(text_mask[1], axis=1),
+            lambda text_mask:
+                K.sum(text_mask[0], axis=1) / K.sum(text_mask[1], axis=1),
         )([new_texts, mask])
 
         return text_avg
@@ -184,13 +157,17 @@ def build(self):
         dropout_rate = self._params['dropout_rate']
 
         # layers
-        create_mask = Lambda(lambda x: K.cast(K.not_equal(x, self._params['mask_value']), K.floatx()))
+        create_mask = keras.layers.Lambda(
+            lambda x:
+                K.cast(K.not_equal(x, self._params['mask_value']), K.floatx())
+        )
         embedding = self._make_embedding_layer()
         lstm_compare = self._make_BiLSTM_layer(lstm_dim)
         lstm_compose = self._make_BiLSTM_layer(lstm_dim)
-        dense_compare = Dense(units=lstm_dim, activation='relu', use_bias=True)
-        dropout = Dropout(dropout_rate)
-
+        dense_compare = keras.layers.Dense(units=lstm_dim,
+                                           activation='relu',
+                                           use_bias=True)
+        dropout = keras.layers.Dropout(dropout_rate)
 
         # model
         a, b = self._make_inputs()      # [B, T_a], [B, T_b]
@@ -206,36 +183,43 @@ def build(self):
         a_ = lstm_compare(a_emb)          # [B, T_a, H*2]
         b_ = lstm_compare(b_emb)          # [B, T_b, H*2]
 
-
         ########################
         # local inference
         ########################
         # similarity matrix
-        e = Dot(axes=-1)([a_, b_])  # [B, T_a, T_b]
-        _ab_mask = Multiply()([self._expand_dim(a_mask, axis=2),    # [B, T_a, 1]
-                               self._expand_dim(b_mask, axis=1)])   # [B, 1, T_b]
+        e = keras.layers.Dot(axes=-1)([a_, b_])  # [B, T_a, T_b]
+        _ab_mask = keras.layers.Multiply()(
+            [self._expand_dim(a_mask, axis=2),    # [B, T_a, 1]
+             self._expand_dim(b_mask, axis=1)])   # [B, 1, T_b]
         # _ab_mask: [B, T_a, T_b]
 
-        pm = Permute((2, 1))
+        pm = keras.layers.Permute((2, 1))
         mask_layer = self._make_atten_mask_layer()
-        softmax_layer = Softmax(axis=-1)
+        softmax_layer = keras.layers.Softmax(axis=-1)
 
         e_a = softmax_layer(mask_layer([e, _ab_mask]))          # [B, T_a, T_b]
         e_b = softmax_layer(mask_layer([pm(e), pm(_ab_mask)]))  # [B, T_b, T_a]
 
         # alignment (a_t = a~, b_t = b~ )
-        a_t = Dot(axes=(2, 1))([e_a, b_])   # [B, T_a, H*2]
-        b_t = Dot(axes=(2, 1))([e_b, a_])   # [B, T_b, H*2]
+        a_t = keras.layers.Dot(axes=(2, 1))([e_a, b_])   # [B, T_a, H*2]
+        b_t = keras.layers.Dot(axes=(2, 1))([e_b, a_])   # [B, T_b, H*2]
 
         # local inference info enhancement
-        m_a = Concatenate(axis=-1)([a_, a_t, Subtract()([a_, a_t]), Multiply()([a_, a_t])])    # [B, T_a, H*2*4]
-        m_b = Concatenate(axis=-1)([b_, b_t, Subtract()([b_, b_t]), Multiply()([b_, b_t])])    # [B, T_b, H*2*4]
+        m_a = keras.layers.Concatenate(axis=-1)([
+            a_,
+            a_t,
+            keras.layers.Subtract()([a_, a_t]),
+            keras.layers.Multiply()([a_, a_t])])    # [B, T_a, H*2*4]
+        m_b = keras.layers.Concatenate(axis=-1)([
+            b_,
+            b_t,
+            keras.layers.Subtract()([b_, b_t]),
+            keras.layers.Multiply()([b_, b_t])])    # [B, T_b, H*2*4]
 
         # project m_a and m_b from 4*H*2 dim to H dim
         m_a = dropout(dense_compare(m_a))   # [B, T_a, H]
         m_b = dropout(dense_compare(m_b))   # [B, T_a, H]
 
-
         ########################
         # inference composition
         ########################
@@ -243,14 +227,15 @@ def build(self):
         v_b = lstm_compose(m_b)          # [B, T_b, H*2]
 
         # pooling
-        v_a = Concatenate(axis=-1)([self._avg(v_a, a_mask), self._max(v_a, a_mask)]) # [B, H*4]
-        v_b = Concatenate(axis=-1)([self._avg(v_b, b_mask), self._max(v_b, b_mask)]) # [B, H*4]
-        v = Concatenate(axis=-1)([v_a, v_b])                                         # [B, H*8]
+        v_a = keras.layers.Concatenate(axis=-1)(
+            [self._avg(v_a, a_mask), self._max(v_a, a_mask)])   # [B, H*4]
+        v_b = keras.layers.Concatenate(axis=-1)(
+            [self._avg(v_b, b_mask), self._max(v_b, b_mask)])   # [B, H*4]
+        v = keras.layers.Concatenate(axis=-1)([v_a, v_b])       # [B, H*8]
 
         # mlp (multilayer perceptron) classifier
-        output = dropout(self._make_multi_layer_perceptron_layer()(v))               # [B, H]
-        output = self._make_output_layer()(output)                                   # [B, #classes]
+        output = self._make_multi_layer_perceptron_layer()(v)  # [B, H]
+        output = dropout(output)
+        output = self._make_output_layer()(output)             # [B, #classes]
 
         self._backend = keras.Model(inputs=[a, b], outputs=output)
-
-
diff --git a/tests/inte_test/test_esim.py b/tests/inte_test/test_esim.py
index 646b786b..d5d4c39f 100644
--- a/tests/inte_test/test_esim.py
+++ b/tests/inte_test/test_esim.py
@@ -3,9 +3,9 @@
 import pytest
 import numpy as np
 import pandas as pd
-import matchzoo as mz
 from keras.utils import to_categorical
 
+import matchzoo as mz
 from matchzoo.engine.base_task import BaseTask
 from matchzoo.contrib.models import ESIM
 

From 772855174bb5316e0a6f079d6d2864d0449fb61e Mon Sep 17 00:00:00 2001
From: Crystina <xzhangbx@ust.hk>
Date: Tue, 2 Apr 2019 14:26:58 -0400
Subject: [PATCH 011/100] undo the change on makefile, add type hint for each
 function parameters, removed the redundant white line and comment line

---
 Makefile                        |  2 +-
 matchzoo/contrib/models/esim.py | 26 +++++++++-----------------
 2 files changed, 10 insertions(+), 18 deletions(-)

diff --git a/Makefile b/Makefile
index e2f4382b..a7bd9a58 100644
--- a/Makefile
+++ b/Makefile
@@ -2,7 +2,7 @@ init:
 	pip install -r requirements.txt
 
 TEST_ARGS = --doctest-modules --doctest-continue-on-failure --cov matchzoo/ --cov-report term-missing --cov-report html --cov-config .coveragerc matchzoo/ tests/ -W ignore::DeprecationWarning
-FLAKE_ARGS = ./matchzoo --exclude=__init__.py, matchzoo/contrib
+FLAKE_ARGS = ./matchzoo --exclude=__init__.py,matchzoo/contrib
 
 test:
 	pytest $(TEST_ARGS)
diff --git a/matchzoo/contrib/models/esim.py b/matchzoo/contrib/models/esim.py
index 185e5154..d3856c6a 100644
--- a/matchzoo/contrib/models/esim.py
+++ b/matchzoo/contrib/models/esim.py
@@ -69,9 +69,10 @@ def _make_inputs(self) -> list:
         )
         return [input_left, input_right]
 
-    def _make_embedding_layer(self, name='embedding') -> keras.layers.Layer:
+    def _make_embedding_layer(self, 
+                              name: str = 'embedding') -> keras.layers.Layer:
         """
-        Overwrite the BaseModel._make_embedding_layer to allow
+        Overwrite the :meth:`BaseModel._make_embedding_layer` to allow
         specifiying mask_zero.
 
         :param name: name for embedding layer, default 'embedding'
@@ -84,7 +85,7 @@ def _make_embedding_layer(self, name='embedding') -> keras.layers.Layer:
             name=name
         )
 
-    def _expand_dim(self, inp, axis) -> keras.layers.Layer:
+    def _expand_dim(self, inp: tf.Tensor, axis: int) -> keras.layers.Layer:
         """
         Wrap keras.backend.expand_dims into a Lambda layer.
 
@@ -102,7 +103,7 @@ def _make_atten_mask_layer(self) -> keras.layers.Layer:
             lambda weight_mask: weight_mask[0] + (1.0 - weight_mask[1]) * -1e7,
             name="atten_mask")
 
-    def _make_BiLSTM_layer(self, lstm_dim) -> keras.layers.Layer:
+    def _make_BiLSTM_layer(self, lstm_dim: int) -> keras.layers.Layer:
         """
         Bidirectional LSTM layer in ESIM.
 
@@ -113,7 +114,7 @@ def _make_BiLSTM_layer(self, lstm_dim) -> keras.layers.Layer:
             layer=keras.layers.LSTM(lstm_dim, return_sequences=True),
             merge_mode='concat')
 
-    def _max(self, texts, mask) -> tf.Tensor:
+    def _max(self, texts: tf.Tensor, mask: tf.Tensor) -> tf.Tensor:
         """
         Compute the max of each text according to their real length
 
@@ -130,7 +131,7 @@ def _max(self, texts, mask) -> tf.Tensor:
 
         return text_max
 
-    def _avg(self, texts, mask) -> tf.Tensor:
+    def _avg(self, texts: tf.Tensor, mask: tf.Tensor) -> tf.Tensor:
         """
         Compute the mean of each text according to their real length
 
@@ -151,7 +152,6 @@ def _avg(self, texts, mask) -> tf.Tensor:
 
     def build(self):
         """Build model."""
-
         # parameters
         lstm_dim = self._params['lstm_dim']
         dropout_rate = self._params['dropout_rate']
@@ -174,24 +174,18 @@ def build(self):
         a_mask = create_mask(a)         # [B, T_a]
         b_mask = create_mask(b)         # [B, T_b]
 
-        ########################
         # encoding
-        ########################
         a_emb = dropout(embedding(a))   # [B, T_a, E_dim]
         b_emb = dropout(embedding(b))   # [B, T_b, E_dim]
 
         a_ = lstm_compare(a_emb)          # [B, T_a, H*2]
         b_ = lstm_compare(b_emb)          # [B, T_b, H*2]
 
-        ########################
         # local inference
-        ########################
-        # similarity matrix
-        e = keras.layers.Dot(axes=-1)([a_, b_])  # [B, T_a, T_b]
-        _ab_mask = keras.layers.Multiply()(
+        e = keras.layers.Dot(axes=-1)([a_, b_])   # [B, T_a, T_b]
+        _ab_mask = keras.layers.Multiply()(       # _ab_mask: [B, T_a, T_b]
             [self._expand_dim(a_mask, axis=2),    # [B, T_a, 1]
              self._expand_dim(b_mask, axis=1)])   # [B, 1, T_b]
-        # _ab_mask: [B, T_a, T_b]
 
         pm = keras.layers.Permute((2, 1))
         mask_layer = self._make_atten_mask_layer()
@@ -220,9 +214,7 @@ def build(self):
         m_a = dropout(dense_compare(m_a))   # [B, T_a, H]
         m_b = dropout(dense_compare(m_b))   # [B, T_a, H]
 
-        ########################
         # inference composition
-        ########################
         v_a = lstm_compose(m_a)          # [B, T_a, H*2]
         v_b = lstm_compose(m_b)          # [B, T_b, H*2]
 

From fb81d9a5f6e93654abf41efafbfda84c0ac068bf Mon Sep 17 00:00:00 2001
From: rgtjf <rgtjf1@163.com>
Date: Wed, 3 Apr 2019 16:38:41 +0800
Subject: [PATCH 012/100] hotfix: name bimpm -> BiMpM

---
 matchzoo/contrib/models/__init__.py    |  2 +-
 matchzoo/contrib/models/bimpm_model.py | 30 +++++++++++++-------------
 2 files changed, 16 insertions(+), 16 deletions(-)

diff --git a/matchzoo/contrib/models/__init__.py b/matchzoo/contrib/models/__init__.py
index f7970f85..257d11c3 100644
--- a/matchzoo/contrib/models/__init__.py
+++ b/matchzoo/contrib/models/__init__.py
@@ -1,2 +1,2 @@
 from .match_lstm import MatchLSTM
-from .bimpm_model import BimpmModel
+from .bimpm_model import BiMpM
diff --git a/matchzoo/contrib/models/bimpm_model.py b/matchzoo/contrib/models/bimpm_model.py
index 105e58fb..6db88201 100644
--- a/matchzoo/contrib/models/bimpm_model.py
+++ b/matchzoo/contrib/models/bimpm_model.py
@@ -1,4 +1,4 @@
-"""Bimpm."""
+"""BiMpM."""
 
 from keras.models import Model
 from keras.layers import Dense, Concatenate, Dropout
@@ -10,15 +10,15 @@
 from matchzoo.contrib.layers import MultiPerspectiveLayer
 
 
-class BimpmModel(BaseModel):
+class BiMpM(BaseModel):
     """
-    BimpmModel.
+    BiMpM.
 
     Reference:
     https://github.com/zhiguowang/BiMPM/blob/master/src/SentenceMatchModelGraph.py#L43-L186
     Examples:
         >>> import matchzoo as mz
-        >>> model = mz.contrib.models.BimpmModel()
+        >>> model = mz.contrib.models.BiMpM()
         >>> model.guess_and_fill_missing_params(verbose=0)
         >>> model.build()
 
@@ -30,19 +30,19 @@ def get_default_params(cls) -> ParamTable:
         params = super().get_default_params(with_embedding=True)
         params['optimizer'] = 'adam'
 
-        params.add(Param('dim_word_embedding', 50))
-        # TODO(tjf): remove the unused params in the final version
-        params.add(Param('dim_char_embedding', 50))
-        params.add(Param('word_embedding_mat'))
-        params.add(Param('char_embedding_mat'))
-        params.add(Param('embedding_random_scale', 0.2))
-        params.add(Param('activation_embedding', 'softmax'))
+        # params.add(Param('dim_word_embedding', 50))
+        # TODO(tjf): remove unused params in the final version
+        # params.add(Param('dim_char_embedding', 50))
+        # params.add(Param('word_embedding_mat'))
+        # params.add(Param('char_embedding_mat'))
+        # params.add(Param('embedding_random_scale', 0.2))
+        # params.add(Param('activation_embedding', 'softmax'))
 
-        # Bimpm Setting
+        # BiMpM Setting
         params.add(Param('perspective', {'full': True,
-                                                'max-pooling': True,
-                                                'attentive': True,
-                                                'max-attentive': True}))
+                                         'max-pooling': True,
+                                         'attentive': True,
+                                         'max-attentive': True}))
         params.add(Param('mp_dim', 20))
         params.add(Param('att_dim', 20))
         params.add(Param('hidden_size', 128))

From f67df4cb6d32c2cb8fdba553e5f9be263e1d8fd3 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Wed, 3 Apr 2019 17:39:23 +0800
Subject: [PATCH 013/100] Update test models.

The old test_models.py is too time consuming and frequently exceeds the CI time limit. Thus this change.
---
 tests/unit_test/models/test_models.py | 20 ++++++++++----------
 1 file changed, 10 insertions(+), 10 deletions(-)

diff --git a/tests/unit_test/models/test_models.py b/tests/unit_test/models/test_models.py
index ecf3da20..7ecd9c79 100644
--- a/tests/unit_test/models/test_models.py
+++ b/tests/unit_test/models/test_models.py
@@ -31,7 +31,7 @@ def model_class(request):
 
 @pytest.fixture(scope='module')
 def embedding():
-    return mz.datasets.embeddings.load_glove_embedding(dimension=50)
+    return mz.datasets.toy.load_embedding()
 
 
 @pytest.fixture(scope='module')
@@ -65,19 +65,19 @@ def embedding_matrix(setup):
 
 
 @pytest.fixture(scope='module')
-def gen(train_raw, preprocessor, gen_builder):
-    return gen_builder.build(preprocessor.transform(train_raw))
+def data(train_raw, preprocessor, gen_builder):
+    return gen_builder.build(preprocessor.transform(train_raw))[0]
 
 
 @pytest.mark.slow
-def test_model_fit_eval_predict(model, gen):
-    x, y = gen[0]
-    assert model.fit(x, y, verbose=0)
-    assert model.evaluate(x, y)
-    assert model.predict(x) is not None
+def test_model_fit_eval_predict(model, data):
+    x, y = data
+    assert model.fit(x, y, batch_size=len(x), verbose=0)
+    assert model.evaluate(x, y, batch_size=len(x))
+    assert model.predict(x, batch_size=len(x)) is not None
 
 
-@pytest.mark.slow
+@pytest.mark.skip('too time consuming, need a better way to test this')
 def test_save_load_model(model):
     tmpdir = '.matchzoo_test_save_load_tmpdir'
 
@@ -94,7 +94,7 @@ def test_save_load_model(model):
             shutil.rmtree(tmpdir)
 
 
-@pytest.mark.slow
+@pytest.mark.skip('too time consuming, need a better way to test this')
 def test_hyper_space(model):
     for _ in range(8):
         new_params = copy.deepcopy(model.params)

From 52c95e733edfa1d7ed89aa359029c8024ec06961 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Wed, 3 Apr 2019 17:51:51 +0800
Subject: [PATCH 014/100] Fix tuner callback save model must specify path bug.

---
 matchzoo/auto/tuner/callbacks/save_model.py | 7 +++++--
 1 file changed, 5 insertions(+), 2 deletions(-)

diff --git a/matchzoo/auto/tuner/callbacks/save_model.py b/matchzoo/auto/tuner/callbacks/save_model.py
index 808f48a1..e50d5aef 100644
--- a/matchzoo/auto/tuner/callbacks/save_model.py
+++ b/matchzoo/auto/tuner/callbacks/save_model.py
@@ -21,9 +21,12 @@ class SaveModel(Callback):
 
     """
 
-    def __init__(self, dir_path: typing.Union[str, Path]):
+    def __init__(
+        self,
+        dir_path: typing.Union[str, Path] = mz.USER_TUNED_MODELS_DIR
+    ):
         """Init."""
-        self._dir_path = dir_path or mz.USER_TUNED_MODELS_DIR
+        self._dir_path = dir_path
 
     def on_run_end(self, tuner, model: BaseModel, result: dict):
         """Save model on run end."""

From 5fd1e83af56b00aba7fa1502aaea946e668cdc6f Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Wed, 3 Apr 2019 19:25:05 +0800
Subject: [PATCH 015/100] Update test_models.py batch_size.

---
 tests/unit_test/models/test_models.py | 7 ++++---
 1 file changed, 4 insertions(+), 3 deletions(-)

diff --git a/tests/unit_test/models/test_models.py b/tests/unit_test/models/test_models.py
index 7ecd9c79..e4fc35e4 100644
--- a/tests/unit_test/models/test_models.py
+++ b/tests/unit_test/models/test_models.py
@@ -72,9 +72,10 @@ def data(train_raw, preprocessor, gen_builder):
 @pytest.mark.slow
 def test_model_fit_eval_predict(model, data):
     x, y = data
-    assert model.fit(x, y, batch_size=len(x), verbose=0)
-    assert model.evaluate(x, y, batch_size=len(x))
-    assert model.predict(x, batch_size=len(x)) is not None
+    batch_size = len(x['id_left'])
+    assert model.fit(x, y, batch_size=batch_size, verbose=0)
+    assert model.evaluate(x, y, batch_size=batch_size)
+    assert model.predict(x, batch_size=batch_size) is not None
 
 
 @pytest.mark.skip('too time consuming, need a better way to test this')

From 3373f0d3c88ef575456a77807a6d885a76690496 Mon Sep 17 00:00:00 2001
From: wsdm2019-dapa <wsdm2019-dmpa@hotmail.com>
Date: Wed, 3 Apr 2019 20:54:20 +0800
Subject: [PATCH 016/100] rename BiMpM as BiMPM

---
 matchzoo/contrib/models/__init__.py    |  2 +-
 matchzoo/contrib/models/bimpm_model.py | 10 +++++-----
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/matchzoo/contrib/models/__init__.py b/matchzoo/contrib/models/__init__.py
index 257d11c3..645edd16 100644
--- a/matchzoo/contrib/models/__init__.py
+++ b/matchzoo/contrib/models/__init__.py
@@ -1,2 +1,2 @@
 from .match_lstm import MatchLSTM
-from .bimpm_model import BiMpM
+from .bimpm_model import BiMPM
diff --git a/matchzoo/contrib/models/bimpm_model.py b/matchzoo/contrib/models/bimpm_model.py
index 6db88201..d795889e 100644
--- a/matchzoo/contrib/models/bimpm_model.py
+++ b/matchzoo/contrib/models/bimpm_model.py
@@ -1,4 +1,4 @@
-"""BiMpM."""
+"""BiMPM."""
 
 from keras.models import Model
 from keras.layers import Dense, Concatenate, Dropout
@@ -10,15 +10,15 @@
 from matchzoo.contrib.layers import MultiPerspectiveLayer
 
 
-class BiMpM(BaseModel):
+class BiMPM(BaseModel):
     """
-    BiMpM.
+    BiMPM.
 
     Reference:
     https://github.com/zhiguowang/BiMPM/blob/master/src/SentenceMatchModelGraph.py#L43-L186
     Examples:
         >>> import matchzoo as mz
-        >>> model = mz.contrib.models.BiMpM()
+        >>> model = mz.contrib.models.BiMPM()
         >>> model.guess_and_fill_missing_params(verbose=0)
         >>> model.build()
 
@@ -38,7 +38,7 @@ def get_default_params(cls) -> ParamTable:
         # params.add(Param('embedding_random_scale', 0.2))
         # params.add(Param('activation_embedding', 'softmax'))
 
-        # BiMpM Setting
+        # BiMPM Setting
         params.add(Param('perspective', {'full': True,
                                          'max-pooling': True,
                                          'attentive': True,

From f4ef6462de723d1f6ea961a7de55174869b57133 Mon Sep 17 00:00:00 2001
From: Crystina <xzhangbx@ust.hk>
Date: Sat, 6 Apr 2019 01:19:39 -0400
Subject: [PATCH 017/100] add mask on a_ and b_ and test file typo according to
 pr review, yet spatial mask is not added

---
 matchzoo/contrib/models/esim.py |  7 ++++++-
 tests/inte_test/test_esim.py    | 32 ++++++++++++++++----------------
 2 files changed, 22 insertions(+), 17 deletions(-)

diff --git a/matchzoo/contrib/models/esim.py b/matchzoo/contrib/models/esim.py
index d3856c6a..d9cf96ab 100644
--- a/matchzoo/contrib/models/esim.py
+++ b/matchzoo/contrib/models/esim.py
@@ -28,6 +28,7 @@ class ESIM(BaseModel):
         >>> model.params['mlp_activation_func'] = 'tanh'
         >>> model.params['mask_value'] = 0
         >>> model.params['dropout_rate'] = 0.5
+        >>> model.params['optimizer'] = keras.optimizers.Adam(lr=4e-4)
         >>> model.guess_and_fill_missing_params()
         >>> model.build()
     """
@@ -69,7 +70,7 @@ def _make_inputs(self) -> list:
         )
         return [input_left, input_right]
 
-    def _make_embedding_layer(self, 
+    def _make_embedding_layer(self,
                               name: str = 'embedding') -> keras.layers.Layer:
         """
         Overwrite the :meth:`BaseModel._make_embedding_layer` to allow
@@ -181,6 +182,10 @@ def build(self):
         a_ = lstm_compare(a_emb)          # [B, T_a, H*2]
         b_ = lstm_compare(b_emb)          # [B, T_b, H*2]
 
+        # mask a_ and b_, since the <pad> position is no more zero
+        a_ = keras.layers.Multiply()([a_, self._expand_dim(a_mask, axis=2)])
+        b_ = keras.layers.Multiply()([b_, self._expand_dim(b_mask, axis=2)])
+
         # local inference
         e = keras.layers.Dot(axes=-1)([a_, b_])   # [B, T_a, T_b]
         _ab_mask = keras.layers.Multiply()(       # _ab_mask: [B, T_a, T_b]
diff --git a/tests/inte_test/test_esim.py b/tests/inte_test/test_esim.py
index d5d4c39f..6cd86f9e 100644
--- a/tests/inte_test/test_esim.py
+++ b/tests/inte_test/test_esim.py
@@ -12,7 +12,7 @@
 @pytest.fixture(scope='module')
 def train_data():
     return mz.datasets.toy.load_data(stage='train')
-    
+
 
 @pytest.fixture(scope='module')
 def valid_data():
@@ -38,10 +38,10 @@ def preprocessor():
 def train_data_processed(train_data, preprocessor) -> mz.DataPack:
     X, Y = preprocessor.fit_transform(train_data).unpack()
     Y = to_categorical(Y)
-    df = pd.DataFrame(data = {  'id_left': list(X['id_left']), 
-                                'text_left': list(X['text_left']), 
-                                'id_right': list(X['id_right']), 
-                                'text_right': list(X['text_right']), 
+    df = pd.DataFrame(data = {  'id_left': list(X['id_left']),
+                                'text_left': list(X['text_left']),
+                                'id_right': list(X['id_right']),
+                                'text_right': list(X['text_right']),
                                 'label': list(Y) })
     return mz.pack(df)
 
@@ -50,10 +50,10 @@ def train_data_processed(train_data, preprocessor) -> mz.DataPack:
 def valid_data_processed(valid_data, preprocessor) -> mz.DataPack:
     X, Y = preprocessor.transform(valid_data).unpack()
     Y = to_categorical(Y)
-    df = pd.DataFrame(data = {  'id_left': list(X['id_left']), 
-                                'text_left': list(X['text_left']), 
-                                'id_right': list(X['id_right']), 
-                                'text_right': list(X['text_right']), 
+    df = pd.DataFrame(data = {  'id_left': list(X['id_left']),
+                                'text_left': list(X['text_left']),
+                                'id_right': list(X['id_right']),
+                                'text_right': list(X['text_right']),
                                 'label': list(Y) })
     return mz.pack(df)
 
@@ -62,10 +62,10 @@ def valid_data_processed(valid_data, preprocessor) -> mz.DataPack:
 def test_data_processed(test_data, preprocessor) -> mz.DataPack:
     X, Y = preprocessor.transform(test_data).unpack()
     Y = to_categorical(Y)
-    df = pd.DataFrame(data = {  'id_left': list(X['id_left']), 
-                                'text_left': list(X['text_left']), 
-                                'id_right': list(X['id_right']), 
-                                'text_right': list(X['text_right']), 
+    df = pd.DataFrame(data = {  'id_left': list(X['id_left']),
+                                'text_left': list(X['text_left']),
+                                'id_right': list(X['id_right']),
+                                'text_right': list(X['text_right']),
                                 'label': list(Y) })
     return mz.pack(df)
 
@@ -76,7 +76,7 @@ def train_generator(request, train_data_processed):
 
 
 @pytest.mark.slow
-def test_duet(train_data_processed,
+def test_esim(train_data_processed,
                  task,
                  train_generator,
                  valid_data_processed,
@@ -101,14 +101,14 @@ def test_duet(train_data_processed,
     esim.guess_and_fill_missing_params()
     esim.build()
     esim.compile()
-    
+
     x_valid, y_valid = valid_data_processed.unpack()
     valid_eval = mz.callbacks.EvaluateAllMetrics(esim,
                                                  x_valid,
                                                  y_valid)
     esim.fit_generator(train_generator, epochs=1, callbacks=[valid_eval])
     esim.save('.tmpdir')
-    
+
     try:
         esim = mz.load_model('.tmpdir')
         x, y = test_data_processed.unpack()

From 719ba374ff21e4232bf40e65b5a60d551b3916b4 Mon Sep 17 00:00:00 2001
From: Crystina <xzhangbx@ust.hk>
Date: Sun, 7 Apr 2019 11:07:24 -0400
Subject: [PATCH 018/100] remove _make_input, change function name to be all
 lower case, removed test_esim file

---
 matchzoo/contrib/models/esim.py |  17 +----
 tests/inte_test/test_esim.py    | 118 --------------------------------
 2 files changed, 3 insertions(+), 132 deletions(-)
 delete mode 100644 tests/inte_test/test_esim.py

diff --git a/matchzoo/contrib/models/esim.py b/matchzoo/contrib/models/esim.py
index d9cf96ab..b92bfe2f 100644
--- a/matchzoo/contrib/models/esim.py
+++ b/matchzoo/contrib/models/esim.py
@@ -59,17 +59,6 @@ def get_default_params(cls) -> ParamTable:
 
         return params
 
-    def _make_inputs(self) -> list:
-        input_left = keras.layers.Input(
-            name='text_left',
-            shape=self._params['input_shapes'][0]
-        )
-        input_right = keras.layers.Input(
-            name='text_right',
-            shape=self._params['input_shapes'][1]
-        )
-        return [input_left, input_right]
-
     def _make_embedding_layer(self,
                               name: str = 'embedding') -> keras.layers.Layer:
         """
@@ -104,7 +93,7 @@ def _make_atten_mask_layer(self) -> keras.layers.Layer:
             lambda weight_mask: weight_mask[0] + (1.0 - weight_mask[1]) * -1e7,
             name="atten_mask")
 
-    def _make_BiLSTM_layer(self, lstm_dim: int) -> keras.layers.Layer:
+    def _make_bilstm_layer(self, lstm_dim: int) -> keras.layers.Layer:
         """
         Bidirectional LSTM layer in ESIM.
 
@@ -163,8 +152,8 @@ def build(self):
                 K.cast(K.not_equal(x, self._params['mask_value']), K.floatx())
         )
         embedding = self._make_embedding_layer()
-        lstm_compare = self._make_BiLSTM_layer(lstm_dim)
-        lstm_compose = self._make_BiLSTM_layer(lstm_dim)
+        lstm_compare = self._make_bilstm_layer(lstm_dim)
+        lstm_compose = self._make_bilstm_layer(lstm_dim)
         dense_compare = keras.layers.Dense(units=lstm_dim,
                                            activation='relu',
                                            use_bias=True)
diff --git a/tests/inte_test/test_esim.py b/tests/inte_test/test_esim.py
deleted file mode 100644
index 6cd86f9e..00000000
--- a/tests/inte_test/test_esim.py
+++ /dev/null
@@ -1,118 +0,0 @@
-import shutil
-
-import pytest
-import numpy as np
-import pandas as pd
-from keras.utils import to_categorical
-
-import matchzoo as mz
-from matchzoo.engine.base_task import BaseTask
-from matchzoo.contrib.models import ESIM
-
-@pytest.fixture(scope='module')
-def train_data():
-    return mz.datasets.toy.load_data(stage='train')
-
-
-@pytest.fixture(scope='module')
-def valid_data():
-    return mz.datasets.toy.load_data(stage='dev')
-
-
-@pytest.fixture(scope='module')
-def test_data():
-    return mz.datasets.toy.load_data(stage='test')
-
-
-@pytest.fixture(scope='module')
-def task(request) -> BaseTask:
-    return mz.tasks.Classification(num_classes=2)
-
-
-@pytest.fixture(scope='module')
-def preprocessor():
-    return mz.preprocessors.BasicPreprocessor()
-
-
-@pytest.fixture(scope='module')
-def train_data_processed(train_data, preprocessor) -> mz.DataPack:
-    X, Y = preprocessor.fit_transform(train_data).unpack()
-    Y = to_categorical(Y)
-    df = pd.DataFrame(data = {  'id_left': list(X['id_left']),
-                                'text_left': list(X['text_left']),
-                                'id_right': list(X['id_right']),
-                                'text_right': list(X['text_right']),
-                                'label': list(Y) })
-    return mz.pack(df)
-
-
-@pytest.fixture(scope='module')
-def valid_data_processed(valid_data, preprocessor) -> mz.DataPack:
-    X, Y = preprocessor.transform(valid_data).unpack()
-    Y = to_categorical(Y)
-    df = pd.DataFrame(data = {  'id_left': list(X['id_left']),
-                                'text_left': list(X['text_left']),
-                                'id_right': list(X['id_right']),
-                                'text_right': list(X['text_right']),
-                                'label': list(Y) })
-    return mz.pack(df)
-
-
-@pytest.fixture(scope='module')
-def test_data_processed(test_data, preprocessor) -> mz.DataPack:
-    X, Y = preprocessor.transform(test_data).unpack()
-    Y = to_categorical(Y)
-    df = pd.DataFrame(data = {  'id_left': list(X['id_left']),
-                                'text_left': list(X['text_left']),
-                                'id_right': list(X['id_right']),
-                                'text_right': list(X['text_right']),
-                                'label': list(Y) })
-    return mz.pack(df)
-
-
-@pytest.fixture(scope='module')
-def train_generator(request, train_data_processed):
-    return mz.DataGenerator(train_data_processed)
-
-
-@pytest.mark.slow
-def test_esim(train_data_processed,
-                 task,
-                 train_generator,
-                 valid_data_processed,
-                 test_data_processed,
-                 preprocessor):
-    """Test esim model."""
-    # Create a esim model
-    # esim = mz.contrib.models.ESIM()
-    esim = ESIM()
-    input_shapes = preprocessor.context['input_shapes']
-    embed_dimension = preprocessor.context['vocab_size'] + 1
-    esim.params['input_shapes'] = input_shapes
-    esim.params['task'] = task
-    esim.params['embedding_input_dim'] = embed_dimension
-    esim.params['embedding_output_dim'] = 300
-    esim.params['lstm_dim'] =  300
-    esim.params['mlp_num_units'] = 300
-    esim.params['mlp_num_layers'] = 0
-    esim.params['mlp_num_fan_out'] = 300
-    esim.params['mlp_activation_func'] = 'relu'
-    esim.params['dropout_rate'] = 0.5
-    esim.guess_and_fill_missing_params()
-    esim.build()
-    esim.compile()
-
-    x_valid, y_valid = valid_data_processed.unpack()
-    valid_eval = mz.callbacks.EvaluateAllMetrics(esim,
-                                                 x_valid,
-                                                 y_valid)
-    esim.fit_generator(train_generator, epochs=1, callbacks=[valid_eval])
-    esim.save('.tmpdir')
-
-    try:
-        esim = mz.load_model('.tmpdir')
-        x, y = test_data_processed.unpack()
-        results = esim.evaluate(x, y)
-        assert len(results) > 0
-    finally:
-        shutil.rmtree('.tmpdir')

From a03461ec5320bf0c731b64456c738d78c27167ca Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Mon, 8 Apr 2019 18:38:09 +0800
Subject: [PATCH 019/100] Propagate keyword arguments to
 `keras.layers.Embedding` to support things like masking (e.g. #673).

---
 matchzoo/engine/base_model.py | 10 +++++++---
 1 file changed, 7 insertions(+), 3 deletions(-)

diff --git a/matchzoo/engine/base_model.py b/matchzoo/engine/base_model.py
index bc77e41b..f716e33e 100644
--- a/matchzoo/engine/base_model.py
+++ b/matchzoo/engine/base_model.py
@@ -505,13 +505,17 @@ def _make_output_layer(self) -> keras.layers.Layer:
             raise ValueError(f"{task} is not a valid task type."
                              f"Must be in `Ranking` and `Classification`.")
 
-    def _make_embedding_layer(self, name: str = 'embedding'
-                              ) -> keras.layers.Layer:
+    def _make_embedding_layer(
+        self,
+        name: str = 'embedding',
+        **kwargs
+    ) -> keras.layers.Layer:
         return keras.layers.Embedding(
             self._params['embedding_input_dim'],
             self._params['embedding_output_dim'],
             trainable=self._params['embedding_trainable'],
-            name=name
+            name=name,
+            **kwargs
         )
 
     def _make_multi_layer_perceptron_layer(self) -> keras.layers.Layer:

From 537fb4e09a40935057a060a059e0f7fbd6aa7cfa Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Tue, 9 Apr 2019 17:12:10 +0800
Subject: [PATCH 020/100] Add base model doc tests.

---
 matchzoo/engine/base_model.py | 24 ++++++++++++++++++++++++
 1 file changed, 24 insertions(+)

diff --git a/matchzoo/engine/base_model.py b/matchzoo/engine/base_model.py
index f716e33e..9669cec9 100644
--- a/matchzoo/engine/base_model.py
+++ b/matchzoo/engine/base_model.py
@@ -405,6 +405,17 @@ def save(self, dirpath: typing.Union[str, Path]):
         h5 file saved by `keras`.
 
         :param dirpath: directory path of the saved model
+
+        Example:
+
+            >>> import matchzoo as mz
+            >>> model = mz.models.Naive()
+            >>> model.guess_and_fill_missing_params(verbose=0)
+            >>> model.build()
+            >>> model.save('temp-model')
+            >>> import shutil
+            >>> shutil.rmtree('temp-model')
+
         """
         dirpath = Path(dirpath)
         params_path = dirpath.joinpath(self.PARAMS_FILENAME)
@@ -541,6 +552,19 @@ def load_model(dirpath: typing.Union[str, Path]) -> BaseModel:
 
     :param dirpath: directory path of the saved model
     :return: a :class:`BaseModel` instance
+
+    Example:
+
+            >>> import matchzoo as mz
+            >>> model = mz.models.Naive()
+            >>> model.guess_and_fill_missing_params(verbose=0)
+            >>> model.build()
+            >>> model.save('temp-model')
+            >>> model.params.keys() == mz.load_model('temp-model').params.keys()
+            True
+            >>> import shutil
+            >>> shutil.rmtree('temp-model')
+
     """
     dirpath = Path(dirpath)
 

From 6dceb819f86fd469a4d817dec0156646a5f574cf Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Tue, 9 Apr 2019 17:12:31 +0800
Subject: [PATCH 021/100] Update data generator lambda callback docs.

---
 .../data_generator/callbacks/lambda_callback.py   | 15 ++++++++++++---
 1 file changed, 12 insertions(+), 3 deletions(-)

diff --git a/matchzoo/data_generator/callbacks/lambda_callback.py b/matchzoo/data_generator/callbacks/lambda_callback.py
index 27797159..684171ba 100644
--- a/matchzoo/data_generator/callbacks/lambda_callback.py
+++ b/matchzoo/data_generator/callbacks/lambda_callback.py
@@ -8,10 +8,19 @@ class LambdaCallback(Callback):
     See :class:`matchzoo.data_generator.callbacks.Callback` for more details.
 
     Example:
+
+        >>> import matchzoo as mz
         >>> from matchzoo.data_generator.callbacks import LambdaCallback
-        >>> callback = LambdaCallback(on_batch_unpacked=print)
-        >>> callback.on_batch_unpacked('x', 'y')
-        x y
+        >>> data = mz.datasets.toy.load_data()
+        >>> batch_func = lambda x: print(type(x))
+        >>> unpack_func = lambda x, y: print(type(x), type(y))
+        >>> callback = LambdaCallback(on_batch_data_pack=batch_func,
+        ...                           on_batch_unpacked=unpack_func)
+        >>> data_gen = mz.DataGenerator(
+        ...     data, batch_size=len(data), callbacks=[callback])
+        >>> _ = data_gen[0]
+        <class 'matchzoo.data_pack.data_pack.DataPack'>
+        <class 'dict'> <class 'numpy.ndarray'>
 
     """
 

From 7d642a0e5c934ee201fc96c03ea3e9a0259a9625 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Tue, 9 Apr 2019 17:12:57 +0800
Subject: [PATCH 022/100] Add "cron" tests.

---
 Makefile                         | 9 ++++++---
 tests/unit_test/test_datasets.py | 6 +++---
 2 files changed, 9 insertions(+), 6 deletions(-)

diff --git a/Makefile b/Makefile
index a7bd9a58..8333c9df 100644
--- a/Makefile
+++ b/Makefile
@@ -9,10 +9,13 @@ test:
 	flake8 $(FLAKE_ARGS)
 
 quick:
-	pytest -m 'not slow' $(TEST_ARGS)
+	pytest -m 'not slow and not cron' $(TEST_ARGS) ${ARGS}
 
 slow:
-	pytest -m 'slow' $(TEST_ARGS)
+	pytest -m 'slow and not cron' $(TEST_ARGS) ${ARGS}
+
+cron:
+	pytest -m 'cron' $(TEST_ARGS) ${ARGS}
 
 flake:
-	flake8 $(FLAKE_ARGS)
+	flake8 $(FLAKE_ARGS) ${ARGS}
diff --git a/tests/unit_test/test_datasets.py b/tests/unit_test/test_datasets.py
index 8781e444..03b44cc4 100644
--- a/tests/unit_test/test_datasets.py
+++ b/tests/unit_test/test_datasets.py
@@ -3,7 +3,7 @@
 import matchzoo as mz
 
 
-@pytest.mark.slow
+@pytest.mark.cron
 def test_load_data():
     train_data = mz.datasets.wiki_qa.load_data('train', task='ranking')
     assert len(train_data) == 20360
@@ -32,7 +32,7 @@ def test_load_data():
     assert tag == [False, True]
 
 
-@pytest.mark.slow
+@pytest.mark.cron
 def test_load_snli():
     train_data, classes = mz.datasets.snli.load_data('train',
                                                      'classification',
@@ -60,7 +60,7 @@ def test_load_snli():
     assert y.shape == (num_samples, 1)
 
 
-@pytest.mark.slow
+@pytest.mark.cron
 def test_load_quora_qp():
     train_data = mz.datasets.quora_qp.load_data(task='classification')
     assert len(train_data) == 363177

From 2ee217426ddb09dde9959466f99923b724c8e1e1 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Tue, 9 Apr 2019 17:50:16 +0800
Subject: [PATCH 023/100] Update testing mechanism.

Separate tests into three categories: quick (normal), slow, and cron.
---
 Makefile                              | 43 ++++++++++++++++++++++++++-
 tests/unit_test/models/test_models.py |  4 +--
 2 files changed, 44 insertions(+), 3 deletions(-)

diff --git a/Makefile b/Makefile
index 8333c9df..82957121 100644
--- a/Makefile
+++ b/Makefile
@@ -1,3 +1,41 @@
+# Usages:
+#
+# to install matchzoo dependencies:
+# $ make init
+#
+# to run all matchzoo tests, recommended for big PRs and new versions:
+# $	make test
+#
+# there are three kinds of tests:
+#
+# 1. "quick" tests
+# 	- run in seconds
+#   - include all unit tests without marks and all doctests
+#   - for rapid prototyping
+#   - CI run this for all PRs
+#
+# 2. "slow" tests that run in minutes
+#   - run in minutes
+#   - include all unit tests marked "slow"
+#   - CI run this for all PRs
+#
+# 3. "cron" tests that not only slow, but also undeterministic (all tests marked "cron")
+#   - run in minutes and involves underministic behavoirs (e.g. network connection)
+#   - include all unit tests marked "cron"
+#   - CI run this on a daily basis
+#
+# to run quick tests, excluding time consuming tests and crons:
+# $ make quick
+#
+# to run slow tests, excluding normal tests and crons:
+# $ make slow
+#
+# to run crons:
+# $ make cron
+#
+# to run docstring style check:
+# $ make flake
+
 init:
 	pip install -r requirements.txt
 
@@ -5,7 +43,7 @@ TEST_ARGS = --doctest-modules --doctest-continue-on-failure --cov matchzoo/ --co
 FLAKE_ARGS = ./matchzoo --exclude=__init__.py,matchzoo/contrib
 
 test:
-	pytest $(TEST_ARGS)
+	pytest $(TEST_ARGS) ${ARGS}
 	flake8 $(FLAKE_ARGS)
 
 quick:
@@ -19,3 +57,6 @@ cron:
 
 flake:
 	flake8 $(FLAKE_ARGS) ${ARGS}
+
+doc:
+	flake8 $(FLAKE_ARGS) ${ARGS}
diff --git a/tests/unit_test/models/test_models.py b/tests/unit_test/models/test_models.py
index e4fc35e4..3d6c994c 100644
--- a/tests/unit_test/models/test_models.py
+++ b/tests/unit_test/models/test_models.py
@@ -78,7 +78,7 @@ def test_model_fit_eval_predict(model, data):
     assert model.predict(x, batch_size=batch_size) is not None
 
 
-@pytest.mark.skip('too time consuming, need a better way to test this')
+@pytest.mark.cron
 def test_save_load_model(model):
     tmpdir = '.matchzoo_test_save_load_tmpdir'
 
@@ -95,7 +95,7 @@ def test_save_load_model(model):
             shutil.rmtree(tmpdir)
 
 
-@pytest.mark.skip('too time consuming, need a better way to test this')
+@pytest.mark.cron
 def test_hyper_space(model):
     for _ in range(8):
         new_params = copy.deepcopy(model.params)

From e48cb7d29e6b47b582dc52c0e3504294a1dc1f48 Mon Sep 17 00:00:00 2001
From: fanyixing <fanyixing111@hotmail.com>
Date: Tue, 9 Apr 2019 20:01:43 +0800
Subject: [PATCH 024/100] rename bimpm

---
 matchzoo/contrib/models/__init__.py                  | 2 +-
 matchzoo/contrib/models/{bimpm_model.py => bimpm.py} | 0
 2 files changed, 1 insertion(+), 1 deletion(-)
 rename matchzoo/contrib/models/{bimpm_model.py => bimpm.py} (100%)

diff --git a/matchzoo/contrib/models/__init__.py b/matchzoo/contrib/models/__init__.py
index 645edd16..90d19dd2 100644
--- a/matchzoo/contrib/models/__init__.py
+++ b/matchzoo/contrib/models/__init__.py
@@ -1,2 +1,2 @@
 from .match_lstm import MatchLSTM
-from .bimpm_model import BiMPM
+from .bimpm import BiMPM
diff --git a/matchzoo/contrib/models/bimpm_model.py b/matchzoo/contrib/models/bimpm.py
similarity index 100%
rename from matchzoo/contrib/models/bimpm_model.py
rename to matchzoo/contrib/models/bimpm.py

From 5076fea877498e7a97dddf79a59a34350c659392 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Tue, 9 Apr 2019 20:07:32 +0800
Subject: [PATCH 025/100] Fix line width style.

---
 matchzoo/engine/base_model.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/matchzoo/engine/base_model.py b/matchzoo/engine/base_model.py
index 9669cec9..c134bbb2 100644
--- a/matchzoo/engine/base_model.py
+++ b/matchzoo/engine/base_model.py
@@ -559,11 +559,11 @@ def load_model(dirpath: typing.Union[str, Path]) -> BaseModel:
             >>> model = mz.models.Naive()
             >>> model.guess_and_fill_missing_params(verbose=0)
             >>> model.build()
-            >>> model.save('temp-model')
-            >>> model.params.keys() == mz.load_model('temp-model').params.keys()
+            >>> model.save('my-model')
+            >>> model.params.keys() == mz.load_model('my-model').params.keys()
             True
             >>> import shutil
-            >>> shutil.rmtree('temp-model')
+            >>> shutil.rmtree('my-model')
 
     """
     dirpath = Path(dirpath)

From fe97b3b1f7fbf6a71beac1f034697bf4e9ae82e7 Mon Sep 17 00:00:00 2001
From: Crystina <xzhangbx@ust.hk>
Date: Tue, 9 Apr 2019 16:32:51 -0400
Subject: [PATCH 026/100] remove the overriden make_emb

---
 matchzoo/contrib/models/esim.py | 16 ----------------
 matchzoo/engine/base_model.py   | 12 ++++++++----
 2 files changed, 8 insertions(+), 20 deletions(-)

diff --git a/matchzoo/contrib/models/esim.py b/matchzoo/contrib/models/esim.py
index b92bfe2f..4af0371c 100644
--- a/matchzoo/contrib/models/esim.py
+++ b/matchzoo/contrib/models/esim.py
@@ -59,22 +59,6 @@ def get_default_params(cls) -> ParamTable:
 
         return params
 
-    def _make_embedding_layer(self,
-                              name: str = 'embedding') -> keras.layers.Layer:
-        """
-        Overwrite the :meth:`BaseModel._make_embedding_layer` to allow
-        specifiying mask_zero.
-
-        :param name: name for embedding layer, default 'embedding'
-        """
-        return keras.layers.Embedding(
-            self._params['embedding_input_dim'],
-            self._params['embedding_output_dim'],
-            trainable=self._params['embedding_trainable'],
-            mask_zero=True,
-            name=name
-        )
-
     def _expand_dim(self, inp: tf.Tensor, axis: int) -> keras.layers.Layer:
         """
         Wrap keras.backend.expand_dims into a Lambda layer.
diff --git a/matchzoo/engine/base_model.py b/matchzoo/engine/base_model.py
index bc77e41b..9692780b 100644
--- a/matchzoo/engine/base_model.py
+++ b/matchzoo/engine/base_model.py
@@ -505,15 +505,19 @@ def _make_output_layer(self) -> keras.layers.Layer:
             raise ValueError(f"{task} is not a valid task type."
                              f"Must be in `Ranking` and `Classification`.")
 
-    def _make_embedding_layer(self, name: str = 'embedding'
-                              ) -> keras.layers.Layer:
+    def _make_embedding_layer(
+        self,
+        name: str = 'embedding',
+        **kwargs,
+    ) -> keras.layers.Layer:
         return keras.layers.Embedding(
             self._params['embedding_input_dim'],
             self._params['embedding_output_dim'],
             trainable=self._params['embedding_trainable'],
-            name=name
+            name=name,
+            **kwargs
         )
-
+    
     def _make_multi_layer_perceptron_layer(self) -> keras.layers.Layer:
         # TODO: do not create new layers for a second call
         if not self._params['with_multi_layer_perceptron']:

From 2488d9e39b8b83373237803e073c9894ef0cc594 Mon Sep 17 00:00:00 2001
From: faneshion <fanyixing111@hotmail.com>
Date: Wed, 10 Apr 2019 17:10:06 +0800
Subject: [PATCH 027/100] seperate OOV with PAD in the vocabulary (#693)

* seperate OOV with PAD in the vocabulary

* rename _PAD as PAD

* <PAD> and <OOV>

* oov and pad as member
---
 matchzoo/embedding/embedding.py              |  6 +--
 matchzoo/models/mvlstm.py                    |  2 +-
 matchzoo/preprocessors/basic_preprocessor.py |  4 +-
 matchzoo/preprocessors/cdssm_preprocessor.py |  2 +-
 matchzoo/preprocessors/dssm_preprocessor.py  |  2 +-
 matchzoo/preprocessors/units/vocabulary.py   | 49 +++++++++++---------
 matchzoo/preprocessors/units/word_hashing.py | 16 ++++---
 tests/unit_test/test_embedding.py            |  6 +--
 8 files changed, 46 insertions(+), 41 deletions(-)

diff --git a/matchzoo/embedding/embedding.py b/matchzoo/embedding/embedding.py
index 3787f8bc..fb21af36 100644
--- a/matchzoo/embedding/embedding.py
+++ b/matchzoo/embedding/embedding.py
@@ -25,13 +25,13 @@ class Embedding(object):
     To load from a file:
         >>> embedding = mz.embedding.load_from_file(embed_path)
         >>> matrix = embedding.build_matrix(term_index)
-        >>> matrix.shape[0] == len(term_index) + 1
+        >>> matrix.shape[0] == len(term_index)
         True
 
     To build your own:
         >>> data = pd.DataFrame(data=[[0, 1], [2, 3]], index=['A', 'B'])
         >>> embedding = mz.Embedding(data)
-        >>> matrix = embedding.build_matrix({'A': 2, 'B': 1})
+        >>> matrix = embedding.build_matrix({'A': 2, 'B': 1, '_PAD': 0})
         >>> matrix.shape == (3, 2)
         True
 
@@ -70,7 +70,7 @@ def build_matrix(
             `(-0.2, 0.2)`).
         :return: A matrix.
         """
-        input_dim = len(term_index) + 1
+        input_dim = len(term_index)
 
         matrix = np.empty((input_dim, self.output_dim))
         for index in np.ndindex(*matrix.shape):
diff --git a/matchzoo/models/mvlstm.py b/matchzoo/models/mvlstm.py
index dc896d83..fcd5f648 100644
--- a/matchzoo/models/mvlstm.py
+++ b/matchzoo/models/mvlstm.py
@@ -52,7 +52,7 @@ def build(self):
         query, doc = self._make_inputs()
 
         # Embedding layer
-        embedding = self._make_embedding_layer()
+        embedding = self._make_embedding_layer(mask_zero=True)
         embed_query = embedding(query)
         embed_doc = embedding(doc)
 
diff --git a/matchzoo/preprocessors/basic_preprocessor.py b/matchzoo/preprocessors/basic_preprocessor.py
index 0d8415c9..d14af9f2 100644
--- a/matchzoo/preprocessors/basic_preprocessor.py
+++ b/matchzoo/preprocessors/basic_preprocessor.py
@@ -44,7 +44,7 @@ class BasicPreprocessor(BasePreprocessor):
         >>> preprocessor.context['input_shapes']
         [(10,), (20,)]
         >>> preprocessor.context['vocab_size']
-        225
+        226
         >>> processed_train_data = preprocessor.transform(train_data,
         ...                                               verbose=0)
         >>> type(processed_train_data)
@@ -105,7 +105,7 @@ def fit(self, data_pack: DataPack, verbose: int = 1):
         vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
         self._context['vocab_unit'] = vocab_unit
 
-        vocab_size = len(vocab_unit.state['term_index']) + 1
+        vocab_size = len(vocab_unit.state['term_index'])
         self._context['vocab_size'] = vocab_size
         self._context['embedding_input_dim'] = vocab_size
         self._context['input_shapes'] = [(self._fixed_length_left,),
diff --git a/matchzoo/preprocessors/cdssm_preprocessor.py b/matchzoo/preprocessors/cdssm_preprocessor.py
index edeac4e9..d7f16754 100644
--- a/matchzoo/preprocessors/cdssm_preprocessor.py
+++ b/matchzoo/preprocessors/cdssm_preprocessor.py
@@ -73,7 +73,7 @@ def fit(self, data_pack: DataPack, verbose: int = 1):
         vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
 
         self._context['vocab_unit'] = vocab_unit
-        vocab_size = len(vocab_unit.state['term_index']) + 1
+        vocab_size = len(vocab_unit.state['term_index'])
         self._context['input_shapes'] = [
             (self._fixed_length_left, vocab_size),
             (self._fixed_length_right, vocab_size)
diff --git a/matchzoo/preprocessors/dssm_preprocessor.py b/matchzoo/preprocessors/dssm_preprocessor.py
index 561cb2c2..2c0212a4 100644
--- a/matchzoo/preprocessors/dssm_preprocessor.py
+++ b/matchzoo/preprocessors/dssm_preprocessor.py
@@ -58,7 +58,7 @@ def fit(self, data_pack: DataPack, verbose: int = 1):
         vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
 
         self._context['vocab_unit'] = vocab_unit
-        vocab_size = len(vocab_unit.state['term_index']) + 1
+        vocab_size = len(vocab_unit.state['term_index'])
         self._context['vocab_size'] = vocab_size
         self._context['embedding_input_dim'] = vocab_size
         self._context['input_shapes'] = [(vocab_size,), (vocab_size,)]
diff --git a/matchzoo/preprocessors/units/vocabulary.py b/matchzoo/preprocessors/units/vocabulary.py
index c725c469..dbc6323c 100644
--- a/matchzoo/preprocessors/units/vocabulary.py
+++ b/matchzoo/preprocessors/units/vocabulary.py
@@ -5,20 +5,23 @@ class Vocabulary(StatefulUnit):
     """
     Vocabulary class.
 
+    :param pad_value: The string value for the padding position.
+    :param oov_value: The string value for the out-of-vocabulary terms.
+
     Examples:
-        >>> vocab = Vocabulary()
+        >>> vocab = Vocabulary(pad_value='[PAD]', oov_value='[OOV]')
         >>> vocab.fit(['A', 'B', 'C', 'D', 'E'])
         >>> term_index = vocab.state['term_index']
         >>> term_index  # doctest: +SKIP
-        {'E': 1, 'C': 2, 'D': 3, 'A': 4, 'B': 5}
+        {'[PAD]': 0, '[OOV]': 1, 'D': 2, 'A': 3, 'B': 4, 'C': 5, 'E': 6}
         >>> index_term = vocab.state['index_term']
         >>> index_term  # doctest: +SKIP
-        {1: 'C', 2: 'A', 3: 'E', 4: 'B', 5: 'D'}
+        {0: '[PAD]', 1: '[OOV]', 2: 'D', 3: 'A', 4: 'B', 5: 'C', 6: 'E'}
 
         >>> term_index['out-of-vocabulary-term']
-        0
+        1
         >>> index_term[0]
-        ''
+        '[PAD]'
         >>> index_term[42]
         Traceback (most recent call last):
             ...
@@ -27,39 +30,39 @@ class Vocabulary(StatefulUnit):
         >>> c_index = term_index['C']
         >>> vocab.transform(['C', 'A', 'C']) == [c_index, a_index, c_index]
         True
-        >>> vocab.transform(['C', 'A', 'OOV']) == [c_index, a_index, 0]
+        >>> vocab.transform(['C', 'A', '[OOV]']) == [c_index, a_index, 1]
         True
         >>> indices = vocab.transform(list('ABCDDZZZ'))
-        >>> ''.join(vocab.state['index_term'][i] for i in indices)
-        'ABCDD'
+        >>> ' '.join(vocab.state['index_term'][i] for i in indices)
+        'A B C D D [OOV] [OOV] [OOV]'
 
     """
 
-    class IndexTerm(dict):
-        """Map index to term."""
-
-        def __missing__(self, key):
-            """Map out-of-vocabulary indices to empty string."""
-            if key == 0:
-                return ''
-            else:
-                raise KeyError(key)
+    def __init__(self, pad_value: str = '<PAD>', oov_value: str = '<OOV>'):
+        """Vocabulary unit initializer."""
+        self._pad = pad_value
+        self._oov = oov_value
+        self._state = {}
+        self._state['term_index'] = self.TermIndex()
+        self._state['index_term'] = dict()
 
     class TermIndex(dict):
         """Map term to index."""
 
         def __missing__(self, key):
-            """Map out-of-vocabulary terms to index 0."""
-            return 0
+            """Map out-of-vocabulary terms to index 1."""
+            return 1
 
     def fit(self, tokens: list):
         """Build a :class:`TermIndex` and a :class:`IndexTerm`."""
-        self._state['term_index'] = self.TermIndex()
-        self._state['index_term'] = self.IndexTerm()
+        self._state['term_index'][self._pad] = 0
+        self._state['term_index'][self._oov] = 1
+        self._state['index_term'][0] = self._pad
+        self._state['index_term'][1] = self._oov
         terms = set(tokens)
         for index, term in enumerate(terms):
-            self._state['term_index'][term] = index + 1
-            self._state['index_term'][index + 1] = term
+            self._state['term_index'][term] = index + 2
+            self._state['index_term'][index + 2] = term
 
     def transform(self, input_: list) -> list:
         """Transform a list of tokens to corresponding indices."""
diff --git a/matchzoo/preprocessors/units/word_hashing.py b/matchzoo/preprocessors/units/word_hashing.py
index a17a1677..805c1ba3 100644
--- a/matchzoo/preprocessors/units/word_hashing.py
+++ b/matchzoo/preprocessors/units/word_hashing.py
@@ -19,12 +19,14 @@ class WordHashing(Unit):
     Examples:
        >>> letters = [['#te', 'tes','est', 'st#'], ['oov']]
        >>> word_hashing = WordHashing(
-       ...     term_index={'': 0,'st#': 1, '#te': 2, 'est': 3, 'tes': 4})
+       ...     term_index={
+       ...      '_PAD': 0, 'OOV': 1, 'st#': 2, '#te': 3, 'est': 4, 'tes': 5
+       ...      })
        >>> hashing = word_hashing.transform(letters)
        >>> hashing[0]
-       [0.0, 1.0, 1.0, 1.0, 1.0, 0.0]
+       [0.0, 0.0, 1.0, 1.0, 1.0, 1.0]
        >>> hashing[1]
-       [1.0, 0.0, 0.0, 0.0, 0.0, 0.0]
+       [0.0, 1.0, 0.0, 0.0, 0.0, 0.0]
 
     """
 
@@ -52,18 +54,18 @@ def transform(self, input_: list) -> list:
         if any([isinstance(elem, list) for elem in input_]):
             # The input shape for CDSSM is
             # [[word1 ngram, ngram], [word2, ngram, ngram], ...].
-            hashing = np.zeros((len(input_), len(self._term_index) + 1))
+            hashing = np.zeros((len(input_), len(self._term_index)))
             for idx, word in enumerate(input_):
                 counted_letters = collections.Counter(word)
                 for key, value in counted_letters.items():
-                    letter_id = self._term_index.get(key, 0)
+                    letter_id = self._term_index.get(key, 1)
                     hashing[idx, letter_id] = value
         else:
             # The input shape for DSSM model [ngram, ngram, ...].
-            hashing = np.zeros((len(self._term_index) + 1))
+            hashing = np.zeros(len(self._term_index))
             counted_letters = collections.Counter(input_)
             for key, value in counted_letters.items():
-                letter_id = self._term_index.get(key, 0)
+                letter_id = self._term_index.get(key, 1)
                 hashing[letter_id] = value
 
         return hashing.tolist()
diff --git a/tests/unit_test/test_embedding.py b/tests/unit_test/test_embedding.py
index 444e9daa..81a9c9e6 100644
--- a/tests/unit_test/test_embedding.py
+++ b/tests/unit_test/test_embedding.py
@@ -5,15 +5,15 @@
 
 @pytest.fixture
 def term_index():
-    return {'G': 1, 'C': 2, 'D': 3, 'A': 4, '[PAD]': 0}
+    return {'G': 1, 'C': 2, 'D': 3, 'A': 4, '_PAD': 0}
 
 
 def test_embedding(term_index):
     embed = mz.embedding.load_from_file(mz.datasets.embeddings.EMBED_RANK)
     matrix = embed.build_matrix(term_index)
-    assert matrix.shape == (len(term_index) + 1, 50)
+    assert matrix.shape == (len(term_index), 50)
     embed = mz.embedding.load_from_file(mz.datasets.embeddings.EMBED_10_GLOVE,
                                         mode='glove')
     matrix = embed.build_matrix(term_index)
-    assert matrix.shape == (len(term_index) + 1, 10)
+    assert matrix.shape == (len(term_index), 10)
     assert embed.input_dim == 5

From bf4abce32cfc9e7b1746796c39aaa234b7fb71fb Mon Sep 17 00:00:00 2001
From: ZizhenWang <948280670@qq.com>
Date: Wed, 10 Apr 2019 19:58:01 +0800
Subject: [PATCH 028/100] refactor cdssm tutorial for branch 2.2-dev

---
 tutorials/wikiqa/cdssm.ipynb | 220 ++++++++++++++++++-----------------
 1 file changed, 115 insertions(+), 105 deletions(-)

diff --git a/tutorials/wikiqa/cdssm.ipynb b/tutorials/wikiqa/cdssm.ipynb
index eaf75547..296935cd 100644
--- a/tutorials/wikiqa/cdssm.ipynb
+++ b/tutorials/wikiqa/cdssm.ipynb
@@ -14,23 +14,24 @@
     }
    ],
    "source": [
+    "import os\n",
     "import keras\n",
     "import pandas as pd\n",
     "import numpy as np\n",
-    "import matchzoo as mz"
+    "import matchzoo as mz\n",
+    "\n",
+    "os.environ['CUDA_VISIBLE_DEVICES'] = ''"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 2,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "train_pack = mz.datasets.wiki_qa.load_data('train', task='ranking')\n",
-    "valid_pack = mz.datasets.wiki_qa.load_data('dev', task='ranking', filter=True)\n",
-    "predict_pack = mz.datasets.wiki_qa.load_data('test', task='ranking', filter=True)"
+    "valid_pack = mz.datasets.wiki_qa.load_data('dev', task='ranking', filtered=True)\n",
+    "predict_pack = mz.datasets.wiki_qa.load_data('test', task='ranking', filtered=True)"
    ]
   },
   {
@@ -42,22 +43,22 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Processing text_left with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit => NgramLetterUnit: 100%|██████████| 2118/2118 [00:00<00:00, 6751.07it/s]\n",
-      "Processing text_right with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit => NgramLetterUnit: 100%|██████████| 18841/18841 [00:05<00:00, 3730.90it/s]\n",
-      "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 478072.11it/s]\n",
-      "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 279964.01it/s]\n",
-      "Building VocabularyUnit from a datapack.: 100%|██████████| 1614998/1614998 [00:00<00:00, 3407400.32it/s]\n",
-      "Processing text_left with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit: 100%|██████████| 2118/2118 [00:00<00:00, 8756.29it/s]\n",
-      "Processing text_right with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit: 100%|██████████| 18841/18841 [00:04<00:00, 4251.64it/s]\n",
-      "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 122800.84it/s]\n",
-      "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 59226.17it/s]\n",
-      "Processing text_left with chain_transform of NgramLetterUnit => WordHashingUnit: 100%|██████████| 2118/2118 [00:00<00:00, 5882.98it/s]\n",
-      "Processing text_right with chain_transform of NgramLetterUnit => WordHashingUnit: 100%|██████████| 18841/18841 [00:08<00:00, 2111.86it/s]\n"
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 2118/2118 [00:00<00:00, 6542.53it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 18841/18841 [00:05<00:00, 3325.99it/s]\n",
+      "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 584789.41it/s]\n",
+      "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 438625.05it/s]\n",
+      "Building Vocabulary from a datapack.: 100%|██████████| 1614998/1614998 [00:00<00:00, 3082231.57it/s]\n",
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 7934.07it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 18841/18841 [00:04<00:00, 3811.25it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 115942.78it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 87315.29it/s]\n",
+      "Processing text_left with chain_transform of NgramLetter => WordHashing: 100%|██████████| 2118/2118 [00:08<00:00, 261.04it/s]\n",
+      "Processing text_right with chain_transform of NgramLetter => WordHashing: 100%|██████████| 18841/18841 [01:24<00:00, 220.85it/s]\n"
      ]
     }
    ],
    "source": [
-    "preprocessor = mz.preprocessors.CDSSMPreprocessor()\n",
+    "preprocessor = mz.preprocessors.CDSSMPreprocessor(fixed_length_left=10, fixed_length_right=10)\n",
     "train_pack_processed = preprocessor.fit_transform(train_pack)"
    ]
   },
@@ -72,18 +73,18 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Processing text_left with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit: 100%|██████████| 122/122 [00:00<00:00, 8118.95it/s]\n",
-      "Processing text_right with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit: 100%|██████████| 1115/1115 [00:00<00:00, 4421.77it/s]\n",
-      "Processing text_left with transform: 100%|██████████| 122/122 [00:00<00:00, 82961.27it/s]\n",
-      "Processing text_right with transform: 100%|██████████| 1115/1115 [00:00<00:00, 63304.89it/s]\n",
-      "Processing text_left with chain_transform of NgramLetterUnit => WordHashingUnit: 100%|██████████| 122/122 [00:00<00:00, 6042.02it/s]\n",
-      "Processing text_right with chain_transform of NgramLetterUnit => WordHashingUnit: 100%|██████████| 1115/1115 [00:00<00:00, 2165.76it/s]\n",
-      "Processing text_left with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit: 100%|██████████| 237/237 [00:00<00:00, 4295.23it/s]\n",
-      "Processing text_right with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit: 100%|██████████| 2300/2300 [00:00<00:00, 4193.45it/s]\n",
-      "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 94026.68it/s]\n",
-      "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 70195.00it/s]\n",
-      "Processing text_left with chain_transform of NgramLetterUnit => WordHashingUnit: 100%|██████████| 237/237 [00:00<00:00, 6278.78it/s]\n",
-      "Processing text_right with chain_transform of NgramLetterUnit => WordHashingUnit: 100%|██████████| 2300/2300 [00:01<00:00, 2156.54it/s]\n"
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 122/122 [00:00<00:00, 7332.17it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 1115/1115 [00:00<00:00, 4039.28it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 122/122 [00:00<00:00, 71677.42it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 1115/1115 [00:00<00:00, 86562.93it/s]\n",
+      "Processing text_left with chain_transform of NgramLetter => WordHashing: 100%|██████████| 122/122 [00:00<00:00, 249.16it/s]\n",
+      "Processing text_right with chain_transform of NgramLetter => WordHashing: 100%|██████████| 1115/1115 [00:18<00:00, 60.13it/s] \n",
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 237/237 [00:00<00:00, 8283.96it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 2300/2300 [00:01<00:00, 1951.14it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 92016.11it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 89923.46it/s]\n",
+      "Processing text_left with chain_transform of NgramLetter => WordHashing: 100%|██████████| 237/237 [00:00<00:00, 249.05it/s]\n",
+      "Processing text_right with chain_transform of NgramLetter => WordHashing: 100%|██████████| 2300/2300 [00:11<00:00, 239.25it/s]\n"
      ]
     }
    ],
@@ -95,9 +96,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": [
     "ranking_task = mz.tasks.Ranking(loss=mz.losses.RankHingeLoss())\n",
@@ -110,7 +109,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 21,
+   "execution_count": 6,
    "metadata": {
     "scrolled": false
    },
@@ -119,51 +118,50 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Parameter \"name\" set to CDSSMModel.\n",
       "__________________________________________________________________________________________________\n",
       "Layer (type)                    Output Shape         Param #     Connected to                     \n",
       "==================================================================================================\n",
       "text_left (InputLayer)          (None, 10, 9644)     0                                            \n",
       "__________________________________________________________________________________________________\n",
-      "text_right (InputLayer)         (None, 40, 9644)     0                                            \n",
+      "text_right (InputLayer)         (None, 10, 9644)     0                                            \n",
       "__________________________________________________________________________________________________\n",
-      "conv1d_11 (Conv1D)              (None, 10, 300)      8679900     text_left[0][0]                  \n",
+      "conv1d_1 (Conv1D)               (None, 10, 64)       1851712     text_left[0][0]                  \n",
       "__________________________________________________________________________________________________\n",
-      "conv1d_12 (Conv1D)              (None, 40, 300)      8679900     text_right[0][0]                 \n",
+      "conv1d_2 (Conv1D)               (None, 10, 64)       1851712     text_right[0][0]                 \n",
       "__________________________________________________________________________________________________\n",
-      "dropout_11 (Dropout)            (None, 10, 300)      0           conv1d_11[0][0]                  \n",
+      "dropout_1 (Dropout)             (None, 10, 64)       0           conv1d_1[0][0]                   \n",
       "__________________________________________________________________________________________________\n",
-      "dropout_12 (Dropout)            (None, 40, 300)      0           conv1d_12[0][0]                  \n",
+      "dropout_2 (Dropout)             (None, 10, 64)       0           conv1d_2[0][0]                   \n",
       "__________________________________________________________________________________________________\n",
-      "global_max_pooling1d_11 (Global (None, 300)          0           dropout_11[0][0]                 \n",
+      "global_max_pooling1d_1 (GlobalM (None, 64)           0           dropout_1[0][0]                  \n",
       "__________________________________________________________________________________________________\n",
-      "global_max_pooling1d_12 (Global (None, 300)          0           dropout_12[0][0]                 \n",
+      "global_max_pooling1d_2 (GlobalM (None, 64)           0           dropout_2[0][0]                  \n",
       "__________________________________________________________________________________________________\n",
-      "dense_22 (Dense)                (None, 300)          90300       global_max_pooling1d_11[0][0]    \n",
+      "dense_1 (Dense)                 (None, 64)           4160        global_max_pooling1d_1[0][0]     \n",
       "__________________________________________________________________________________________________\n",
-      "dense_24 (Dense)                (None, 300)          90300       global_max_pooling1d_12[0][0]    \n",
+      "dense_3 (Dense)                 (None, 64)           4160        global_max_pooling1d_2[0][0]     \n",
       "__________________________________________________________________________________________________\n",
-      "dense_23 (Dense)                (None, 128)          38528       dense_22[0][0]                   \n",
+      "dense_2 (Dense)                 (None, 64)           4160        dense_1[0][0]                    \n",
       "__________________________________________________________________________________________________\n",
-      "dense_25 (Dense)                (None, 128)          38528       dense_24[0][0]                   \n",
+      "dense_4 (Dense)                 (None, 64)           4160        dense_3[0][0]                    \n",
       "__________________________________________________________________________________________________\n",
-      "dot_6 (Dot)                     (None, 1)            0           dense_23[0][0]                   \n",
-      "                                                                 dense_25[0][0]                   \n",
+      "dot_1 (Dot)                     (None, 1)            0           dense_2[0][0]                    \n",
+      "                                                                 dense_4[0][0]                    \n",
       "__________________________________________________________________________________________________\n",
-      "dense_26 (Dense)                (None, 1)            2           dot_6[0][0]                      \n",
+      "dense_5 (Dense)                 (None, 1)            2           dot_1[0][0]                      \n",
       "==================================================================================================\n",
-      "Total params: 17,617,458\n",
-      "Trainable params: 17,617,458\n",
+      "Total params: 3,720,066\n",
+      "Trainable params: 3,720,066\n",
       "Non-trainable params: 0\n",
       "__________________________________________________________________________________________________\n"
      ]
     }
    ],
    "source": [
-    "model = mz.models.CDSSMModel()\n",
+    "model = mz.models.CDSSM()\n",
     "model.params['input_shapes'] = preprocessor.context['input_shapes']\n",
     "model.params['task'] = ranking_task\n",
-    "model.params['filters'] = 300\n",
+    "model.params['filters'] = 64\n",
     "model.params['kernel_size'] = 3\n",
     "model.params['strides'] = 1\n",
     "model.params['padding'] = 'same'\n",
@@ -171,8 +169,8 @@
     "model.params['w_initializer'] = 'glorot_normal'\n",
     "model.params['b_initializer'] = 'zeros'\n",
     "model.params['mlp_num_layers'] = 1\n",
-    "model.params['mlp_num_units'] = 300\n",
-    "model.params['mlp_num_fan_out'] = 128\n",
+    "model.params['mlp_num_units'] = 64\n",
+    "model.params['mlp_num_fan_out'] = 64\n",
     "model.params['mlp_activation_func'] = 'tanh'\n",
     "model.params['dropout_rate'] = 0.8\n",
     "model.params['optimizer'] = 'adadelta'\n",
@@ -184,20 +182,34 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 22,
-   "metadata": {
-    "collapsed": true
-   },
+   "execution_count": 7,
+   "metadata": {},
    "outputs": [],
    "source": [
-    "pred_x, pred_y = predict_pack_processed[:].unpack()\n",
+    "pred_x, pred_y = predict_pack_processed[:].unpack()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: PairDataGenerator will be deprecated in MatchZoo v2.2. Use `DataGenerator` with callbacks instead.\n"
+     ]
+    }
+   ],
+   "source": [
     "evaluate = mz.callbacks.EvaluateAllMetrics(model, x=pred_x, y=pred_y, batch_size=len(pred_x))\n",
     "train_generator = mz.PairDataGenerator(train_pack_processed, num_dup=2, num_neg=1, batch_size=64, shuffle=True)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 9,
    "metadata": {
     "scrolled": false
    },
@@ -207,65 +219,65 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
-      "32/32 [==============================] - 24s 748ms/step - loss: 0.7844\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.483600 - normalized_discounted_cumulative_gain@5(0):0.553078 - mean_average_precision(0):0.512808\n",
+      "32/32 [==============================] - 72s 2s/step - loss: 0.9808\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.42488215100974375 - normalized_discounted_cumulative_gain@5(0.0): 0.4909840291447614 - mean_average_precision(0.0): 0.45215174584733625\n",
       "Epoch 2/20\n",
-      "32/32 [==============================] - 29s 898ms/step - loss: 0.5235\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.488520 - normalized_discounted_cumulative_gain@5(0):0.561723 - mean_average_precision(0):0.510881\n",
+      "32/32 [==============================] - 39s 1s/step - loss: 0.7890\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4523924010888256 - normalized_discounted_cumulative_gain@5(0.0): 0.526580105002082 - mean_average_precision(0.0): 0.4843661871232366\n",
       "Epoch 3/20\n",
-      "32/32 [==============================] - 23s 706ms/step - loss: 0.2984\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.484800 - normalized_discounted_cumulative_gain@5(0):0.555596 - mean_average_precision(0):0.512422\n",
+      "32/32 [==============================] - 39s 1s/step - loss: 0.6269\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.42912083006483787 - normalized_discounted_cumulative_gain@5(0.0): 0.5059856809765926 - mean_average_precision(0.0): 0.46958607676474695\n",
       "Epoch 4/20\n",
-      "32/32 [==============================] - 25s 775ms/step - loss: 0.1954\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.475072 - normalized_discounted_cumulative_gain@5(0):0.562940 - mean_average_precision(0):0.512383\n",
+      "32/32 [==============================] - 40s 1s/step - loss: 0.5425\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45161347891396936 - normalized_discounted_cumulative_gain@5(0.0): 0.5246607686333176 - mean_average_precision(0.0): 0.4894374228928154\n",
       "Epoch 5/20\n",
-      "32/32 [==============================] - 23s 721ms/step - loss: 0.1056\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.487088 - normalized_discounted_cumulative_gain@5(0):0.566688 - mean_average_precision(0):0.519216\n",
+      "32/32 [==============================] - 39s 1s/step - loss: 0.4786\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.44182189699245444 - normalized_discounted_cumulative_gain@5(0.0): 0.5185925913281365 - mean_average_precision(0.0): 0.4785410987945083\n",
       "Epoch 6/20\n",
-      "32/32 [==============================] - 23s 706ms/step - loss: 0.0727\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.478561 - normalized_discounted_cumulative_gain@5(0):0.562530 - mean_average_precision(0):0.512396\n",
+      "32/32 [==============================] - 40s 1s/step - loss: 0.4312\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4353736449145257 - normalized_discounted_cumulative_gain@5(0.0): 0.5193283626946119 - mean_average_precision(0.0): 0.47581070046166846\n",
       "Epoch 7/20\n",
-      "32/32 [==============================] - 28s 884ms/step - loss: 0.0505\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.486433 - normalized_discounted_cumulative_gain@5(0):0.567996 - mean_average_precision(0):0.522548\n",
+      "32/32 [==============================] - 40s 1s/step - loss: 0.3918\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.42860677523860297 - normalized_discounted_cumulative_gain@5(0.0): 0.511305679951288 - mean_average_precision(0.0): 0.465896748198743\n",
       "Epoch 8/20\n",
-      "32/32 [==============================] - 26s 822ms/step - loss: 0.0544\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.498047 - normalized_discounted_cumulative_gain@5(0):0.575753 - mean_average_precision(0):0.526914\n",
+      "32/32 [==============================] - 40s 1s/step - loss: 0.3399\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4483104574863611 - normalized_discounted_cumulative_gain@5(0.0): 0.514850172629585 - mean_average_precision(0.0): 0.47729829884300434\n",
       "Epoch 9/20\n",
-      "32/32 [==============================] - 22s 700ms/step - loss: 0.0386\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.495465 - normalized_discounted_cumulative_gain@5(0):0.574323 - mean_average_precision(0):0.523699\n",
+      "32/32 [==============================] - 39s 1s/step - loss: 0.3133\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.44683839351327287 - normalized_discounted_cumulative_gain@5(0.0): 0.5179511840387869 - mean_average_precision(0.0): 0.47938829437070807\n",
       "Epoch 10/20\n",
-      "32/32 [==============================] - 24s 748ms/step - loss: 0.0290\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.502232 - normalized_discounted_cumulative_gain@5(0):0.581065 - mean_average_precision(0):0.527918\n",
+      "32/32 [==============================] - 39s 1s/step - loss: 0.2852\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45147495790907527 - normalized_discounted_cumulative_gain@5(0.0): 0.5115591547752104 - mean_average_precision(0.0): 0.4816185898296844\n",
       "Epoch 11/20\n",
-      "32/32 [==============================] - 28s 867ms/step - loss: 0.0297\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.496569 - normalized_discounted_cumulative_gain@5(0):0.576849 - mean_average_precision(0):0.521037\n",
+      "32/32 [==============================] - 39s 1s/step - loss: 0.2503\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45606725359003863 - normalized_discounted_cumulative_gain@5(0.0): 0.5137853825095071 - mean_average_precision(0.0): 0.4898566499228558\n",
       "Epoch 12/20\n",
-      "32/32 [==============================] - 24s 735ms/step - loss: 0.0212\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.511699 - normalized_discounted_cumulative_gain@5(0):0.589066 - mean_average_precision(0):0.532874\n",
+      "32/32 [==============================] - 40s 1s/step - loss: 0.2114\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4536596944525606 - normalized_discounted_cumulative_gain@5(0.0): 0.5195598827593197 - mean_average_precision(0.0): 0.48509265834715537\n",
       "Epoch 13/20\n",
-      "32/32 [==============================] - 23s 705ms/step - loss: 0.0197\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.491434 - normalized_discounted_cumulative_gain@5(0):0.570174 - mean_average_precision(0):0.513504\n",
+      "32/32 [==============================] - 40s 1s/step - loss: 0.1886\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.46086526212639095 - normalized_discounted_cumulative_gain@5(0.0): 0.5347221469997477 - mean_average_precision(0.0): 0.4972628568563632\n",
       "Epoch 14/20\n",
-      "32/32 [==============================] - 28s 869ms/step - loss: 0.0132\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.504107 - normalized_discounted_cumulative_gain@5(0):0.575971 - mean_average_precision(0):0.519329\n",
+      "32/32 [==============================] - 40s 1s/step - loss: 0.1770\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4524514918879093 - normalized_discounted_cumulative_gain@5(0.0): 0.5259706685008083 - mean_average_precision(0.0): 0.4886127130697417\n",
       "Epoch 15/20\n",
-      "32/32 [==============================] - 26s 826ms/step - loss: 0.0150\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.503686 - normalized_discounted_cumulative_gain@5(0):0.580463 - mean_average_precision(0):0.524712\n",
+      "32/32 [==============================] - 38s 1s/step - loss: 0.1454\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.447793222144205 - normalized_discounted_cumulative_gain@5(0.0): 0.523210520595902 - mean_average_precision(0.0): 0.4842295350811732\n",
       "Epoch 16/20\n",
-      "32/32 [==============================] - 25s 778ms/step - loss: 0.0123\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.499589 - normalized_discounted_cumulative_gain@5(0):0.576160 - mean_average_precision(0):0.516276\n",
+      "32/32 [==============================] - 39s 1s/step - loss: 0.1573\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4617597821704854 - normalized_discounted_cumulative_gain@5(0.0): 0.5233844194501931 - mean_average_precision(0.0): 0.48736454745855345\n",
       "Epoch 17/20\n",
-      "32/32 [==============================] - 22s 699ms/step - loss: 0.0165\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.501098 - normalized_discounted_cumulative_gain@5(0):0.577585 - mean_average_precision(0):0.525730\n",
+      "32/32 [==============================] - 40s 1s/step - loss: 0.1437\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.446051744365399 - normalized_discounted_cumulative_gain@5(0.0): 0.511689458533883 - mean_average_precision(0.0): 0.47390160852977303\n",
       "Epoch 18/20\n",
-      "32/32 [==============================] - 28s 867ms/step - loss: 0.0089\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.502192 - normalized_discounted_cumulative_gain@5(0):0.575193 - mean_average_precision(0):0.527620\n",
+      "32/32 [==============================] - 40s 1s/step - loss: 0.1396\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.44155513060981444 - normalized_discounted_cumulative_gain@5(0.0): 0.508852366919329 - mean_average_precision(0.0): 0.47430841711913996\n",
       "Epoch 19/20\n",
-      "32/32 [==============================] - 23s 722ms/step - loss: 0.0074\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.508068 - normalized_discounted_cumulative_gain@5(0):0.580008 - mean_average_precision(0):0.534251\n",
+      "32/32 [==============================] - 39s 1s/step - loss: 0.1176\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4480183178506102 - normalized_discounted_cumulative_gain@5(0.0): 0.5177348617853279 - mean_average_precision(0.0): 0.4799578376954719\n",
       "Epoch 20/20\n",
-      "32/32 [==============================] - 28s 881ms/step - loss: 0.0098\n",
-      "Validation: loss:nan - normalized_discounted_cumulative_gain@3(0):0.504778 - normalized_discounted_cumulative_gain@5(0):0.572644 - mean_average_precision(0):0.531077\n"
+      "32/32 [==============================] - 37s 1s/step - loss: 0.1094\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.44665417415821596 - normalized_discounted_cumulative_gain@5(0.0): 0.5142203489288135 - mean_average_precision(0.0): 0.47726846390007227\n"
      ]
     }
    ],
@@ -276,9 +288,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
+   "metadata": {},
    "outputs": [],
    "source": []
   }
@@ -299,7 +309,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.3"
+   "version": "3.6.8"
   }
  },
  "nbformat": 4,

From 3294e75f94c1e14e29e6970262369866fd8942a8 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Thu, 11 Apr 2019 11:42:20 +0800
Subject: [PATCH 029/100] Update CI.

---
 .travis.yml | 34 ++++++++++++++++++----------------
 Makefile    | 22 +++++++++++++++-------
 2 files changed, 33 insertions(+), 23 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 025215c8..96c6905f 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -5,22 +5,22 @@ cache: pip
 sudo: true
 
 env:
-    global:
-        - PYTHONPATH=$PYTHONPATH:$TRAVIS_BUILD_DIR/tests:$TRAVIS_BUILD_DIR/matchzoo
+  global:
+    - PYTHONPATH=$PYTHONPATH:$TRAVIS_BUILD_DIR/tests:$TRAVIS_BUILD_DIR/matchzoo
 
 matrix:
-    allow_failures:
-      - os: osx
-    include:
-      - os: linux
-        dist: trusty
-        python: 3.6
-      - os: osx
-        language: generic
-        env: PYTHON_VERSION=3.6
-      - os: osx
-        language: generic
-        env: PYTHON_VERSION=3.7
+  allow_failures:
+    - os: osx
+  include:
+    - os: linux
+      dist: trusty
+      python: 3.6
+    - os: osx
+      language: generic
+      env: PYTHON_VERSION=3.6
+    - os: osx
+      language: generic
+      env: PYTHON_VERSION=3.7
 
 install:
   - pip3 install -r requirements.txt
@@ -31,7 +31,9 @@ install:
 script:
   - stty cols 80
   - export COLUMNS=80
-  - make test
+  - if [ "$TRAVIS_EVENT_TYPE" == "pull_request" ]; then make push; fi
+  - if [ "$TRAVIS_EVENT_TYPE" == "cron" ]; then make cron; fi
+
 
 after_success:
-      - codecov
\ No newline at end of file
+  - codecov
diff --git a/Makefile b/Makefile
index 82957121..b8b28d35 100644
--- a/Makefile
+++ b/Makefile
@@ -14,13 +14,14 @@
 #   - for rapid prototyping
 #   - CI run this for all PRs
 #
-# 2. "slow" tests that run in minutes
+# 2. "slow" tests
 #   - run in minutes
 #   - include all unit tests marked "slow"
 #   - CI run this for all PRs
 #
-# 3. "cron" tests that not only slow, but also undeterministic (all tests marked "cron")
-#   - run in minutes and involves underministic behavoirs (e.g. network connection)
+# 3. "cron" tests
+#   - run in minutes
+#   - involves underministic behavoirs (e.g. network connection)
 #   - include all unit tests marked "cron"
 #   - CI run this on a daily basis
 #
@@ -33,6 +34,12 @@
 # to run crons:
 # $ make cron
 #
+# to run all tests:
+# $ make test
+#
+# to run CI push/PR tests:
+# $ make push
+#
 # to run docstring style check:
 # $ make flake
 
@@ -43,7 +50,11 @@ TEST_ARGS = --doctest-modules --doctest-continue-on-failure --cov matchzoo/ --co
 FLAKE_ARGS = ./matchzoo --exclude=__init__.py,matchzoo/contrib
 
 test:
-	pytest $(TEST_ARGS) ${ARGS}
+	pytest $(TEST_ARGS)
+	flake8 $(FLAKE_ARGS)
+
+push:
+	pytest -m 'not cron' $(TEST_ARGS) ${ARGS}
 	flake8 $(FLAKE_ARGS)
 
 quick:
@@ -57,6 +68,3 @@ cron:
 
 flake:
 	flake8 $(FLAKE_ARGS) ${ARGS}
-
-doc:
-	flake8 $(FLAKE_ARGS) ${ARGS}

From a653e00ecbd1bc9204160307078bc9ace79e5b02 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Thu, 11 Apr 2019 11:44:24 +0800
Subject: [PATCH 030/100] Update CI.

---
 .travis.yml | 1 +
 1 file changed, 1 insertion(+)

diff --git a/.travis.yml b/.travis.yml
index 96c6905f..5599f633 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -32,6 +32,7 @@ script:
   - stty cols 80
   - export COLUMNS=80
   - if [ "$TRAVIS_EVENT_TYPE" == "pull_request" ]; then make push; fi
+  - if [ "$TRAVIS_EVENT_TYPE" == "push" ]; then make push; fi
   - if [ "$TRAVIS_EVENT_TYPE" == "cron" ]; then make cron; fi
 
 

From df698b182e5c6a9644d094a53dd1239d5dc85a50 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Thu, 11 Apr 2019 16:05:13 +0800
Subject: [PATCH 031/100] Exclude contrib for testing.

---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index b8b28d35..c1caf3ab 100644
--- a/Makefile
+++ b/Makefile
@@ -46,7 +46,7 @@
 init:
 	pip install -r requirements.txt
 
-TEST_ARGS = --doctest-modules --doctest-continue-on-failure --cov matchzoo/ --cov-report term-missing --cov-report html --cov-config .coveragerc matchzoo/ tests/ -W ignore::DeprecationWarning
+TEST_ARGS = --doctest-modules --doctest-continue-on-failure --cov matchzoo/ --cov-report term-missing --cov-report html --cov-config .coveragerc matchzoo/ tests/ -W ignore::DeprecationWarning --ignore=matchzoo/contrib
 FLAKE_ARGS = ./matchzoo --exclude=__init__.py,matchzoo/contrib
 
 test:

From 9914265aeb3e125ded944eb17dbd621d5a9f5d82 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Thu, 11 Apr 2019 17:16:53 +0800
Subject: [PATCH 032/100] Rename `unit.state` to `unit.context`.

Backward compatible for now, but `unit.state` will eventually be removed (I guess in 2.5 or something once all tutorials and guides use `context` instead.)
---
 .../preprocessors/units/frequency_filter.py   |  4 +--
 matchzoo/preprocessors/units/stateful_unit.py | 25 +++++++++++++++----
 matchzoo/preprocessors/units/vocabulary.py    | 20 +++++++--------
 3 files changed, 32 insertions(+), 17 deletions(-)

diff --git a/matchzoo/preprocessors/units/frequency_filter.py b/matchzoo/preprocessors/units/frequency_filter.py
index 5de7d69e..89a7523c 100644
--- a/matchzoo/preprocessors/units/frequency_filter.py
+++ b/matchzoo/preprocessors/units/frequency_filter.py
@@ -66,11 +66,11 @@ def fit(self, list_of_tokens: typing.List[typing.List[str]]):
             if self._low <= v < self._high:
                 valid_terms.add(k)
 
-        self._state[self._mode] = valid_terms
+        self._context[self._mode] = valid_terms
 
     def transform(self, input_: list) -> list:
         """Transform a list of tokens by filtering out unwanted words."""
-        valid_terms = self._state[self._mode]
+        valid_terms = self._context[self._mode]
         return list(filter(lambda token: token in valid_terms, input_))
 
     @classmethod
diff --git a/matchzoo/preprocessors/units/stateful_unit.py b/matchzoo/preprocessors/units/stateful_unit.py
index 9f8b3fca..423075dc 100644
--- a/matchzoo/preprocessors/units/stateful_unit.py
+++ b/matchzoo/preprocessors/units/stateful_unit.py
@@ -5,17 +5,32 @@
 
 
 class StatefulUnit(Unit, metaclass=abc.ABCMeta):
-    """Process unit do persive state (i.e. need fit)."""
+    """
+    Unit with inner state.
+
+    Usually need to be fit before transforming. All information gathered in the
+    fit phrase will be stored into its `context`.
+    """
 
     def __init__(self):
         """Initialization."""
-        self._state = {}
+        self._context = {}
 
     @property
     def state(self):
-        """Get current state."""
-        return self._state
+        """
+        Get current context. Same as `unit.context`.
+
+        Deprecated since v2.2.0, and will be removed in the future.
+        Used `unit.context` instead.
+        """
+        return self._context
+
+    @property
+    def context(self):
+        """Get current context. Same as `unit.state`."""
+        return self._context
 
     @abc.abstractmethod
-    def fit(self, input: typing.Any):
+    def fit(self, input_: typing.Any):
         """Abstract base method, need to be implemented in subclass."""
diff --git a/matchzoo/preprocessors/units/vocabulary.py b/matchzoo/preprocessors/units/vocabulary.py
index dbc6323c..783df94f 100644
--- a/matchzoo/preprocessors/units/vocabulary.py
+++ b/matchzoo/preprocessors/units/vocabulary.py
@@ -40,11 +40,11 @@ class Vocabulary(StatefulUnit):
 
     def __init__(self, pad_value: str = '<PAD>', oov_value: str = '<OOV>'):
         """Vocabulary unit initializer."""
+        super().__init__()
         self._pad = pad_value
         self._oov = oov_value
-        self._state = {}
-        self._state['term_index'] = self.TermIndex()
-        self._state['index_term'] = dict()
+        self._context['term_index'] = self.TermIndex()
+        self._context['index_term'] = dict()
 
     class TermIndex(dict):
         """Map term to index."""
@@ -55,15 +55,15 @@ def __missing__(self, key):
 
     def fit(self, tokens: list):
         """Build a :class:`TermIndex` and a :class:`IndexTerm`."""
-        self._state['term_index'][self._pad] = 0
-        self._state['term_index'][self._oov] = 1
-        self._state['index_term'][0] = self._pad
-        self._state['index_term'][1] = self._oov
+        self._context['term_index'][self._pad] = 0
+        self._context['term_index'][self._oov] = 1
+        self._context['index_term'][0] = self._pad
+        self._context['index_term'][1] = self._oov
         terms = set(tokens)
         for index, term in enumerate(terms):
-            self._state['term_index'][term] = index + 2
-            self._state['index_term'][index + 2] = term
+            self._context['term_index'][term] = index + 2
+            self._context['index_term'][index + 2] = term
 
     def transform(self, input_: list) -> list:
         """Transform a list of tokens to corresponding indices."""
-        return [self._state['term_index'][token] for token in input_]
+        return [self._context['term_index'][token] for token in input_]

From a5ecb6a063d338a592a35f98937f09a05505b4d2 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Thu, 11 Apr 2019 17:06:29 +0800
Subject: [PATCH 033/100] Update doc test for better test coverage and
 documenation.

---
 .../auto/tuner/callbacks/lambda_callback.py   | 37 ++++++++++++++++---
 .../tuner/callbacks/load_embedding_matrix.py  | 28 +++++++++++++-
 matchzoo/data_pack/data_pack.py               | 15 +++-----
 3 files changed, 65 insertions(+), 15 deletions(-)

diff --git a/matchzoo/auto/tuner/callbacks/lambda_callback.py b/matchzoo/auto/tuner/callbacks/lambda_callback.py
index 47ee3102..205d5626 100644
--- a/matchzoo/auto/tuner/callbacks/lambda_callback.py
+++ b/matchzoo/auto/tuner/callbacks/lambda_callback.py
@@ -1,5 +1,5 @@
 from matchzoo.engine.base_model import BaseModel
-from .callback import Callback
+from matchzoo.auto.tuner.callbacks.callback import Callback
 
 
 class LambdaCallback(Callback):
@@ -7,18 +7,45 @@ class LambdaCallback(Callback):
     LambdaCallback. Just a shorthand for creating a callback class.
 
     See :class:`matchzoo.tuner.callbacks.Callback` for more details.
+
+    Example:
+
+        >>> import matchzoo as mz
+        >>> model = mz.models.Naive()
+        >>> model.guess_and_fill_missing_params(verbose=0)
+        >>> data = mz.datasets.toy.load_data()
+        >>> data = model.get_default_preprocessor().fit_transform(
+        ...     data, verbose=0)
+        >>> show_inputs = lambda *args: print(*map(type, args))
+        >>> callback = mz.auto.tuner.callbacks.LambdaCallback(
+        ...     on_run_start=show_inputs,
+        ...     on_build_end=show_inputs,
+        ...     on_run_end=show_inputs
+        ... )
+        >>> _ = mz.auto.tune(
+        ...     params=model.params,
+        ...     train_data=data,
+        ...     test_data=data,
+        ...     num_runs=1,
+        ...     callbacks=[callback],
+        ...     verbose=0,
+        ... ) # noqa: E501
+        <class 'matchzoo.auto.tuner.tuner.Tuner'> <class 'dict'>
+        <class 'matchzoo.auto.tuner.tuner.Tuner'> <class 'matchzoo.models.naive.Naive'>
+        <class 'matchzoo.auto.tuner.tuner.Tuner'> <class 'matchzoo.models.naive.Naive'> <class 'dict'>
+        >>> print('hello')
     """
 
     def __init__(
         self,
         on_run_start=None,
         on_build_end=None,
-        on_result_end=None
+        on_run_end=None
     ):
         """Init."""
         self._on_run_start = on_run_start
         self._on_build_end = on_build_end
-        self._on_result_end = on_result_end
+        self._on_run_end = on_run_end
 
     def on_run_start(self, tuner, sample: dict):
         """`on_run_start`."""
@@ -32,5 +59,5 @@ def on_build_end(self, tuner, model: BaseModel):
 
     def on_run_end(self, tuner, model: BaseModel, result: dict):
         """`on_run_end`."""
-        if self._on_result_end:
-            self._on_result_end(tuner, model, result)
+        if self._on_run_end:
+            self._on_run_end(tuner, model, result)
diff --git a/matchzoo/auto/tuner/callbacks/load_embedding_matrix.py b/matchzoo/auto/tuner/callbacks/load_embedding_matrix.py
index 0b59582f..931d38b2 100644
--- a/matchzoo/auto/tuner/callbacks/load_embedding_matrix.py
+++ b/matchzoo/auto/tuner/callbacks/load_embedding_matrix.py
@@ -1,13 +1,39 @@
 from matchzoo.engine.base_model import BaseModel
-from .callback import Callback
+from matchzoo.auto.tuner.callbacks.callback import Callback
 
 
 class LoadEmbeddingMatrix(Callback):
     """
     Load a pre-trained embedding after the model is built.
 
+    Used with tuner to load a pre-trained embedding matrix for each newly built
+    model instance.
+
     :param embedding_matrix: Embedding matrix to load.
 
+    Example:
+
+        >>> import matchzoo as mz
+        >>> model = mz.models.ArcI()
+        >>> prpr = model.get_default_preprocessor()
+        >>> data = mz.datasets.toy.load_data()
+        >>> data = prpr.fit_transform(data, verbose=0)
+        >>> embed = mz.datasets.toy.load_embedding()
+        >>> term_index = prpr.context['vocab_unit'].state['term_index']
+        >>> matrix = embed.build_matrix(term_index)
+        >>> callback = mz.auto.tuner.callbacks.LoadEmbeddingMatrix(matrix)
+        >>> model.params.update(prpr.context)
+        >>> model.params['task'] = mz.tasks.Ranking()
+        >>> model.params['embedding_output_dim'] = embed.output_dim
+        >>> result = mz.auto.tune(
+        ...     params=model.params,
+        ...     train_data=data,
+        ...     test_data=data,
+        ...     num_runs=1,
+        ...     callbacks=[callback],
+        ...     verbose=0
+        ... )
+
     """
 
     def __init__(self, embedding_matrix):
diff --git a/matchzoo/data_pack/data_pack.py b/matchzoo/data_pack/data_pack.py
index e8a94015..df2f1746 100644
--- a/matchzoo/data_pack/data_pack.py
+++ b/matchzoo/data_pack/data_pack.py
@@ -350,27 +350,24 @@ def apply_on_text(
             ...                         rename='length_left',
             ...                         inplace=True,
             ...                         verbose=0)
-            >>> list(frame[0].columns)
-            ['id_left', 'text_left', 'length_left', 'id_right', 'text_right', \
-'label']
+            >>> list(frame[0].columns) # noqa: E501
+            ['id_left', 'text_left', 'length_left', 'id_right', 'text_right', 'label']
 
         To do the same to the right text:
             >>> data_pack.apply_on_text(len, mode='right',
             ...                         rename='length_right',
             ...                         inplace=True,
             ...                         verbose=0)
-            >>> list(frame[0].columns)
-            ['id_left', 'text_left', 'length_left', 'id_right', 'text_right', \
-'length_right', 'label']
+            >>> list(frame[0].columns) # noqa: E501
+            ['id_left', 'text_left', 'length_left', 'id_right', 'text_right', 'length_right', 'label']
 
         To do the same to the both texts at the same time:
             >>> data_pack.apply_on_text(len, mode='both',
             ...                         rename=('extra_left', 'extra_right'),
             ...                         inplace=True,
             ...                         verbose=0)
-            >>> list(frame[0].columns)
-            ['id_left', 'text_left', 'length_left', 'extra_left', 'id_right', \
-'text_right', 'length_right', 'extra_right', 'label']
+            >>> list(frame[0].columns) # noqa: E501
+            ['id_left', 'text_left', 'length_left', 'extra_left', 'id_right', 'text_right', 'length_right', 'extra_right', 'label']
 
         To suppress outputs:
             >>> data_pack.apply_on_text(len, mode='both', verbose=0,

From 50de721410dce728f553f026a54dfdde4fef5bdb Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Thu, 11 Apr 2019 18:02:28 +0800
Subject: [PATCH 034/100] Update.

---
 matchzoo/auto/tuner/callbacks/lambda_callback.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/matchzoo/auto/tuner/callbacks/lambda_callback.py b/matchzoo/auto/tuner/callbacks/lambda_callback.py
index 205d5626..72490eeb 100644
--- a/matchzoo/auto/tuner/callbacks/lambda_callback.py
+++ b/matchzoo/auto/tuner/callbacks/lambda_callback.py
@@ -33,7 +33,7 @@ class LambdaCallback(Callback):
         <class 'matchzoo.auto.tuner.tuner.Tuner'> <class 'dict'>
         <class 'matchzoo.auto.tuner.tuner.Tuner'> <class 'matchzoo.models.naive.Naive'>
         <class 'matchzoo.auto.tuner.tuner.Tuner'> <class 'matchzoo.models.naive.Naive'> <class 'dict'>
-        >>> print('hello')
+
     """
 
     def __init__(

From 69b5b1d96018adca0aaf7b5761814251081b8162 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Thu, 11 Apr 2019 18:42:47 +0800
Subject: [PATCH 035/100] Fix print problem.

---
 matchzoo/auto/tuner/callbacks/lambda_callback.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/matchzoo/auto/tuner/callbacks/lambda_callback.py b/matchzoo/auto/tuner/callbacks/lambda_callback.py
index 72490eeb..c64090de 100644
--- a/matchzoo/auto/tuner/callbacks/lambda_callback.py
+++ b/matchzoo/auto/tuner/callbacks/lambda_callback.py
@@ -16,7 +16,8 @@ class LambdaCallback(Callback):
         >>> data = mz.datasets.toy.load_data()
         >>> data = model.get_default_preprocessor().fit_transform(
         ...     data, verbose=0)
-        >>> show_inputs = lambda *args: print(*map(type, args))
+        >>> def show_inputs(*args):
+        ...     print(' '.join(map(str, map(type, args))))
         >>> callback = mz.auto.tuner.callbacks.LambdaCallback(
         ...     on_run_start=show_inputs,
         ...     on_build_end=show_inputs,

From 8b65bfbc77036beeee1a84c2fb703de422b4ef47 Mon Sep 17 00:00:00 2001
From: Crystina <xzhangbx@ust.hk>
Date: Thu, 11 Apr 2019 22:08:40 -0400
Subject: [PATCH 036/100] undo the unconscious change on base_model, local make
 test passed, yet seems over 50 mins

---
 matchzoo/engine/base_model.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/matchzoo/engine/base_model.py b/matchzoo/engine/base_model.py
index 9fc07dcc..c134bbb2 100644
--- a/matchzoo/engine/base_model.py
+++ b/matchzoo/engine/base_model.py
@@ -528,7 +528,7 @@ def _make_embedding_layer(
             name=name,
             **kwargs
         )
-    
+
     def _make_multi_layer_perceptron_layer(self) -> keras.layers.Layer:
         # TODO: do not create new layers for a second call
         if not self._params['with_multi_layer_perceptron']:

From 529a60ed2bbf333391be4d34fa74ba84f02ca57b Mon Sep 17 00:00:00 2001
From: Wang Bo <kingbolanda@live.com>
Date: Fri, 12 Apr 2019 06:38:28 +0200
Subject: [PATCH 037/100] refactor dssm tutorial (#702)

---
 tutorials/wikiqa/dssm.ipynb | 195 +++++++++++++++++++-----------------
 1 file changed, 102 insertions(+), 93 deletions(-)

diff --git a/tutorials/wikiqa/dssm.ipynb b/tutorials/wikiqa/dssm.ipynb
index 8d99ebc7..8eb6e11d 100644
--- a/tutorials/wikiqa/dssm.ipynb
+++ b/tutorials/wikiqa/dssm.ipynb
@@ -9,30 +9,25 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Using TensorFlow backend.\n",
-      "/data/users/fyx/.local/python3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
-      "  return f(*args, **kwds)\n",
-      "/data/users/fyx/.local/python3/lib/python3.6/importlib/_bootstrap.py:219: RuntimeWarning: numpy.dtype size changed, may indicate binary incompatibility. Expected 96, got 88\n",
-      "  return f(*args, **kwds)\n"
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "matchzoo version 2.1.0\n",
+      "\n",
+      "data loading ...\n",
+      "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n",
+      "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n",
+      "loading embedding ...\n",
+      "embedding loaded as `glove_embedding`\n"
      ]
     }
    ],
    "source": [
-    "import keras\n",
-    "import numpy as np\n",
-    "import pandas as pd\n",
-    "import matchzoo as mz"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_pack = mz.datasets.wiki_qa.load_data('train', task='ranking')\n",
-    "valid_pack = mz.datasets.wiki_qa.load_data('dev', task='ranking', filter=True)\n",
-    "predict_pack = mz.datasets.wiki_qa.load_data('test', task='ranking', filter=True)"
+    "%run init.ipynb"
    ]
   },
   {
@@ -44,19 +39,25 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Processing text_left with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit => NgramLetterUnit: 100%|██████████| 2118/2118 [00:00<00:00, 6893.66it/s]\n",
-      "Processing text_right with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit => NgramLetterUnit: 100%|██████████| 18841/18841 [00:05<00:00, 3323.25it/s]\n",
-      "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 514034.02it/s]\n",
-      "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 430982.12it/s]\n",
-      "Building VocabularyUnit from a datapack.: 100%|██████████| 1614976/1614976 [00:00<00:00, 2964446.21it/s]\n",
-      "Processing text_left with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit => NgramLetterUnit => WordHashingUnit: 100%|██████████| 2118/2118 [00:00<00:00, 4878.11it/s]\n",
-      "Processing text_right with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit => NgramLetterUnit => WordHashingUnit: 100%|██████████| 18841/18841 [00:07<00:00, 2572.20it/s]\n"
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 2118/2118 [00:00<00:00, 3802.39it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 18841/18841 [00:04<00:00, 3959.06it/s]\n",
+      "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 822625.79it/s]\n",
+      "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 597166.86it/s]\n",
+      "Building Vocabulary from a datapack.: 100%|██████████| 1614998/1614998 [00:00<00:00, 4642343.92it/s]\n",
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 2118/2118 [00:00<00:00, 2853.90it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 18841/18841 [00:12<00:00, 1456.96it/s]\n",
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 122/122 [00:00<00:00, 2308.40it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 1115/1115 [00:00<00:00, 2025.86it/s]\n",
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 237/237 [00:00<00:00, 2678.58it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 2300/2300 [00:01<00:00, 1345.18it/s]\n"
      ]
     }
    ],
    "source": [
     "preprocessor = mz.preprocessors.DSSMPreprocessor()\n",
-    "train_pack_processed = preprocessor.fit_transform(train_pack)"
+    "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n",
+    "valid_pack_processed = preprocessor.transform(dev_pack_raw)\n",
+    "test_pack_processed = preprocessor.transform(test_pack_raw)"
    ]
   },
   {
@@ -65,19 +66,21 @@
    "metadata": {},
    "outputs": [
     {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "Processing text_left with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit => NgramLetterUnit => WordHashingUnit: 100%|██████████| 122/122 [00:00<00:00, 4624.45it/s]\n",
-      "Processing text_right with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit => NgramLetterUnit => WordHashingUnit: 100%|██████████| 1115/1115 [00:00<00:00, 2609.60it/s]\n",
-      "Processing text_left with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit => NgramLetterUnit => WordHashingUnit: 100%|██████████| 237/237 [00:00<00:00, 5193.33it/s]\n",
-      "Processing text_right with chain_transform of TokenizeUnit => LowercaseUnit => PuncRemovalUnit => StopRemovalUnit => NgramLetterUnit => WordHashingUnit: 100%|██████████| 2300/2300 [00:00<00:00, 2579.14it/s]\n"
-     ]
+     "data": {
+      "text/plain": [
+       "{'vocab_unit': <matchzoo.preprocessors.units.vocabulary.Vocabulary at 0x7ff6ede81828>,\n",
+       " 'vocab_size': 9645,\n",
+       " 'embedding_input_dim': 9645,\n",
+       " 'input_shapes': [(9645,), (9645,)]}"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
     }
    ],
    "source": [
-    "valid_pack_processed = preprocessor.transform(valid_pack)\n",
-    "predict_pack_processed = preprocessor.transform(predict_pack)"
+    "preprocessor.context"
    ]
   },
   {
@@ -103,17 +106,16 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Parameter \"name\" set to DSSM.\n",
       "__________________________________________________________________________________________________\n",
       "Layer (type)                    Output Shape         Param #     Connected to                     \n",
       "==================================================================================================\n",
-      "text_left (InputLayer)          (None, 9644)         0                                            \n",
+      "text_left (InputLayer)          (None, 9645)         0                                            \n",
       "__________________________________________________________________________________________________\n",
-      "text_right (InputLayer)         (None, 9644)         0                                            \n",
+      "text_right (InputLayer)         (None, 9645)         0                                            \n",
       "__________________________________________________________________________________________________\n",
-      "dense_1 (Dense)                 (None, 300)          2893500     text_left[0][0]                  \n",
+      "dense_1 (Dense)                 (None, 300)          2893800     text_left[0][0]                  \n",
       "__________________________________________________________________________________________________\n",
-      "dense_5 (Dense)                 (None, 300)          2893500     text_right[0][0]                 \n",
+      "dense_5 (Dense)                 (None, 300)          2893800     text_right[0][0]                 \n",
       "__________________________________________________________________________________________________\n",
       "dense_2 (Dense)                 (None, 300)          90300       dense_1[0][0]                    \n",
       "__________________________________________________________________________________________________\n",
@@ -132,8 +134,8 @@
       "__________________________________________________________________________________________________\n",
       "dense_9 (Dense)                 (None, 1)            2           dot_1[0][0]                      \n",
       "==================================================================================================\n",
-      "Total params: 6,225,258\n",
-      "Trainable params: 6,225,258\n",
+      "Total params: 6,225,858\n",
+      "Trainable params: 6,225,858\n",
       "Non-trainable params: 0\n",
       "__________________________________________________________________________________________________\n"
      ]
@@ -155,38 +157,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [],
    "source": [
-    "pred_x, pred_y = predict_pack_processed[:].unpack()\n",
+    "pred_x, pred_y = test_pack_processed[:].unpack()\n",
     "evaluate = mz.callbacks.EvaluateAllMetrics(model, x=pred_x, y=pred_y, batch_size=len(pred_x))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 11,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "WARNING: PairDataGenerator will be deprecated in MatchZoo v2.2. Use `DataGenerator` with callbacks instead.\n"
+     ]
+    },
     {
      "data": {
       "text/plain": [
-       "16"
+       "32"
       ]
      },
-     "execution_count": 8,
+     "execution_count": 11,
      "metadata": {},
      "output_type": "execute_result"
     }
    ],
    "source": [
-    "train_generator = mz.PairDataGenerator(train_pack_processed, num_dup=1, num_neg=4, batch_size=64, shuffle=True)\n",
+    "train_generator = mz.PairDataGenerator(train_pack_processed, num_dup=1, num_neg=4, batch_size=32, shuffle=True)\n",
     "len(train_generator)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 12,
    "metadata": {
     "scrolled": false
    },
@@ -196,65 +205,65 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
-      "16/16 [==============================] - 3s 175ms/step - loss: 1.5564\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.45901904458281206 - normalized_discounted_cumulative_gain@5(0): 0.5475429093192175 - mean_average_precision(0): 0.4911167747235369\n",
+      "32/32 [==============================] - 7s 215ms/step - loss: 1.3325\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4431849853601904 - normalized_discounted_cumulative_gain@5(0.0): 0.5295386323998266 - mean_average_precision(0.0): 0.48303488812718776\n",
       "Epoch 2/20\n",
-      "16/16 [==============================] - 2s 96ms/step - loss: 1.2718\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.4654009025837788 - normalized_discounted_cumulative_gain@5(0): 0.5392725183775516 - mean_average_precision(0): 0.48941174032387735\n",
+      "32/32 [==============================] - 6s 176ms/step - loss: 1.3159\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4353814849661657 - normalized_discounted_cumulative_gain@5(0.0): 0.5032525911610362 - mean_average_precision(0.0): 0.4776049822282439\n",
       "Epoch 3/20\n",
-      "16/16 [==============================] - 2s 100ms/step - loss: 1.1539\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.4791185363644891 - normalized_discounted_cumulative_gain@5(0): 0.5538418263100713 - mean_average_precision(0): 0.5071942064030672\n",
+      "32/32 [==============================] - 5s 171ms/step - loss: 1.2955\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4088637099689691 - normalized_discounted_cumulative_gain@5(0.0): 0.48351010067595823 - mean_average_precision(0.0): 0.4432379861560312\n",
       "Epoch 4/20\n",
-      "16/16 [==============================] - 2s 99ms/step - loss: 1.0995\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.4886745351048811 - normalized_discounted_cumulative_gain@5(0): 0.5562701289500901 - mean_average_precision(0): 0.5133703768780384\n",
+      "32/32 [==============================] - 6s 173ms/step - loss: 1.2726\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.46569627211992487 - normalized_discounted_cumulative_gain@5(0.0): 0.5305277638291452 - mean_average_precision(0.0): 0.4903964896023526\n",
       "Epoch 5/20\n",
-      "16/16 [==============================] - 2s 95ms/step - loss: 1.0674\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.4853827809349306 - normalized_discounted_cumulative_gain@5(0): 0.5682223793780434 - mean_average_precision(0): 0.5169149799106053\n",
+      "32/32 [==============================] - 6s 172ms/step - loss: 1.2439\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.44778538209256513 - normalized_discounted_cumulative_gain@5(0.0): 0.5104380434420628 - mean_average_precision(0.0): 0.47615129143046664\n",
       "Epoch 6/20\n",
-      "16/16 [==============================] - 2s 99ms/step - loss: 1.0479\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.46480280740048857 - normalized_discounted_cumulative_gain@5(0): 0.5281305738905067 - mean_average_precision(0): 0.492201293611383\n",
+      "32/32 [==============================] - 6s 172ms/step - loss: 1.2202\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4452573045503587 - normalized_discounted_cumulative_gain@5(0.0): 0.5137975378931312 - mean_average_precision(0.0): 0.4742872412051932\n",
       "Epoch 7/20\n",
-      "16/16 [==============================] - 2s 94ms/step - loss: 1.0274\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.4898804500151732 - normalized_discounted_cumulative_gain@5(0): 0.560890920302221 - mean_average_precision(0): 0.5058987750652866\n",
+      "32/32 [==============================] - 5s 170ms/step - loss: 1.2038\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.41264292792428936 - normalized_discounted_cumulative_gain@5(0.0): 0.4740615140630128 - mean_average_precision(0.0): 0.45294026408574084\n",
       "Epoch 8/20\n",
-      "16/16 [==============================] - 2s 97ms/step - loss: 1.0142\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.4941487381639577 - normalized_discounted_cumulative_gain@5(0): 0.5665778188888335 - mean_average_precision(0): 0.5140344974996873\n",
+      "32/32 [==============================] - 6s 172ms/step - loss: 1.1848\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45527149721829696 - normalized_discounted_cumulative_gain@5(0.0): 0.5229678873030444 - mean_average_precision(0.0): 0.48490323375232625\n",
       "Epoch 9/20\n",
-      "16/16 [==============================] - 2s 98ms/step - loss: 1.0008\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.5069173712527351 - normalized_discounted_cumulative_gain@5(0): 0.5866287176354987 - mean_average_precision(0): 0.5269820047509921\n",
+      "32/32 [==============================] - 5s 171ms/step - loss: 1.1504 3\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4401749964298954 - normalized_discounted_cumulative_gain@5(0.0): 0.5202410581724496 - mean_average_precision(0.0): 0.47967943778482564\n",
       "Epoch 10/20\n",
-      "16/16 [==============================] - 2s 97ms/step - loss: 0.9873\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.49700543338958786 - normalized_discounted_cumulative_gain@5(0): 0.5805879493729443 - mean_average_precision(0): 0.5150557804829956\n",
+      "32/32 [==============================] - 5s 172ms/step - loss: 1.1314\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.44883790476151675 - normalized_discounted_cumulative_gain@5(0.0): 0.5215788412779597 - mean_average_precision(0.0): 0.48274548802838624\n",
       "Epoch 11/20\n",
-      "16/16 [==============================] - 2s 109ms/step - loss: 0.9750\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.5025024116631714 - normalized_discounted_cumulative_gain@5(0): 0.5923504552250592 - mean_average_precision(0): 0.5225994206215725\n",
+      "32/32 [==============================] - 6s 173ms/step - loss: 1.1109\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45835958548802597 - normalized_discounted_cumulative_gain@5(0.0): 0.5254562351939174 - mean_average_precision(0.0): 0.48819163523037407\n",
       "Epoch 12/20\n",
-      "16/16 [==============================] - 2s 98ms/step - loss: 0.9644\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.5103579714392016 - normalized_discounted_cumulative_gain@5(0): 0.5903011924569881 - mean_average_precision(0): 0.5302960840144384\n",
+      "32/32 [==============================] - 6s 174ms/step - loss: 1.0915\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4540812972538116 - normalized_discounted_cumulative_gain@5(0.0): 0.502728792326375 - mean_average_precision(0.0): 0.48229166522394096\n",
       "Epoch 13/20\n",
-      "16/16 [==============================] - 2s 95ms/step - loss: 0.9534\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.5160164432378087 - normalized_discounted_cumulative_gain@5(0): 0.5993189848710705 - mean_average_precision(0): 0.5396924014803761\n",
+      "32/32 [==============================] - 6s 173ms/step - loss: 1.0805\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4462255256302118 - normalized_discounted_cumulative_gain@5(0.0): 0.5097488218798687 - mean_average_precision(0.0): 0.4751972950775518\n",
       "Epoch 14/20\n",
-      "16/16 [==============================] - 2s 97ms/step - loss: 0.9442\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.5032196943848134 - normalized_discounted_cumulative_gain@5(0): 0.5852110589773137 - mean_average_precision(0): 0.5268703682283354\n",
+      "32/32 [==============================] - 6s 174ms/step - loss: 1.0575\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4263585495923587 - normalized_discounted_cumulative_gain@5(0.0): 0.5014903707963352 - mean_average_precision(0.0): 0.46364289738480496\n",
       "Epoch 15/20\n",
-      "16/16 [==============================] - 2s 95ms/step - loss: 0.9336\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.5317052606549753 - normalized_discounted_cumulative_gain@5(0): 0.6023052111746768 - mean_average_precision(0): 0.5478608387627374\n",
+      "32/32 [==============================] - 6s 179ms/step - loss: 1.0396\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.43936705108731194 - normalized_discounted_cumulative_gain@5(0.0): 0.5218713927469146 - mean_average_precision(0.0): 0.47233172236473137\n",
       "Epoch 16/20\n",
-      "16/16 [==============================] - 2s 97ms/step - loss: 0.9239\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.508831525587222 - normalized_discounted_cumulative_gain@5(0): 0.5817387639362221 - mean_average_precision(0): 0.5274733502791022\n",
+      "32/32 [==============================] - 6s 182ms/step - loss: 1.0156\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45080782122574514 - normalized_discounted_cumulative_gain@5(0.0): 0.5181271382497495 - mean_average_precision(0.0): 0.4832342072703635\n",
       "Epoch 17/20\n",
-      "16/16 [==============================] - 2s 97ms/step - loss: 0.9144\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.5203610304609562 - normalized_discounted_cumulative_gain@5(0): 0.5991831842478874 - mean_average_precision(0): 0.5431669157143841\n",
+      "32/32 [==============================] - 6s 175ms/step - loss: 0.9932\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.423108628561739 - normalized_discounted_cumulative_gain@5(0.0): 0.49596605935842625 - mean_average_precision(0.0): 0.4667294180948952\n",
       "Epoch 18/20\n",
-      "16/16 [==============================] - 2s 98ms/step - loss: 0.9071\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.5138966254324063 - normalized_discounted_cumulative_gain@5(0): 0.5919938110840292 - mean_average_precision(0): 0.5360874679703793\n",
+      "32/32 [==============================] - 5s 172ms/step - loss: 0.9800\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4378084124127128 - normalized_discounted_cumulative_gain@5(0.0): 0.5098753091251295 - mean_average_precision(0.0): 0.4734416114488085\n",
       "Epoch 19/20\n",
-      "16/16 [==============================] - 2s 97ms/step - loss: 0.8990\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.4872557976860261 - normalized_discounted_cumulative_gain@5(0): 0.5698167643783474 - mean_average_precision(0): 0.5167562193088068\n",
+      "32/32 [==============================] - 6s 172ms/step - loss: 0.9662\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4504450479915345 - normalized_discounted_cumulative_gain@5(0.0): 0.519107636100811 - mean_average_precision(0.0): 0.48712867088141415\n",
       "Epoch 20/20\n",
-      "16/16 [==============================] - 2s 98ms/step - loss: 0.8915\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0): 0.5492809107350253 - normalized_discounted_cumulative_gain@5(0): 0.6194080901274281 - mean_average_precision(0): 0.5626976311311754\n"
+      "32/32 [==============================] - 6s 172ms/step - loss: 0.9512\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45663442312293695 - normalized_discounted_cumulative_gain@5(0.0): 0.5363645153841258 - mean_average_precision(0.0): 0.4956098197015037\n"
      ]
     }
    ],
@@ -286,7 +295,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.5"
+   "version": "3.6.7"
   }
  },
  "nbformat": 4,

From c59179d609532270921b6adf1055633aea64b66b Mon Sep 17 00:00:00 2001
From: zhen <ZizhenWang@users.noreply.github.com>
Date: Fri, 12 Apr 2019 16:17:42 +0800
Subject: [PATCH 038/100] refactor cdssm tutorial for branch 2.2-dev (#709)

---
 tutorials/wikiqa/cdssm.ipynb | 220 +++++++++++++++--------------------
 1 file changed, 92 insertions(+), 128 deletions(-)

diff --git a/tutorials/wikiqa/cdssm.ipynb b/tutorials/wikiqa/cdssm.ipynb
index 296935cd..84f17bb1 100644
--- a/tutorials/wikiqa/cdssm.ipynb
+++ b/tutorials/wikiqa/cdssm.ipynb
@@ -11,105 +11,70 @@
      "text": [
       "Using TensorFlow backend.\n"
      ]
-    }
-   ],
-   "source": [
-    "import os\n",
-    "import keras\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
-    "import matchzoo as mz\n",
-    "\n",
-    "os.environ['CUDA_VISIBLE_DEVICES'] = ''"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "train_pack = mz.datasets.wiki_qa.load_data('train', task='ranking')\n",
-    "valid_pack = mz.datasets.wiki_qa.load_data('dev', task='ranking', filtered=True)\n",
-    "predict_pack = mz.datasets.wiki_qa.load_data('test', task='ranking', filtered=True)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [
+    },
     {
-     "name": "stderr",
+     "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 2118/2118 [00:00<00:00, 6542.53it/s]\n",
-      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 18841/18841 [00:05<00:00, 3325.99it/s]\n",
-      "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 584789.41it/s]\n",
-      "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 438625.05it/s]\n",
-      "Building Vocabulary from a datapack.: 100%|██████████| 1614998/1614998 [00:00<00:00, 3082231.57it/s]\n",
-      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 7934.07it/s]\n",
-      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 18841/18841 [00:04<00:00, 3811.25it/s]\n",
-      "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 115942.78it/s]\n",
-      "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 87315.29it/s]\n",
-      "Processing text_left with chain_transform of NgramLetter => WordHashing: 100%|██████████| 2118/2118 [00:08<00:00, 261.04it/s]\n",
-      "Processing text_right with chain_transform of NgramLetter => WordHashing: 100%|██████████| 18841/18841 [01:24<00:00, 220.85it/s]\n"
+      "matchzoo version 2.1.0\n",
+      "\n",
+      "data loading ...\n",
+      "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n",
+      "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n",
+      "loading embedding ...\n",
+      "embedding loaded as `glove_embedding`\n"
      ]
     }
    ],
    "source": [
-    "preprocessor = mz.preprocessors.CDSSMPreprocessor(fixed_length_left=10, fixed_length_right=10)\n",
-    "train_pack_processed = preprocessor.fit_transform(train_pack)"
+    "%run init.ipynb"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "scrolled": false
-   },
+   "execution_count": 2,
+   "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 122/122 [00:00<00:00, 7332.17it/s]\n",
-      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 1115/1115 [00:00<00:00, 4039.28it/s]\n",
-      "Processing text_left with transform: 100%|██████████| 122/122 [00:00<00:00, 71677.42it/s]\n",
-      "Processing text_right with transform: 100%|██████████| 1115/1115 [00:00<00:00, 86562.93it/s]\n",
-      "Processing text_left with chain_transform of NgramLetter => WordHashing: 100%|██████████| 122/122 [00:00<00:00, 249.16it/s]\n",
-      "Processing text_right with chain_transform of NgramLetter => WordHashing: 100%|██████████| 1115/1115 [00:18<00:00, 60.13it/s] \n",
-      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 237/237 [00:00<00:00, 8283.96it/s]\n",
-      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 2300/2300 [00:01<00:00, 1951.14it/s]\n",
-      "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 92016.11it/s]\n",
-      "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 89923.46it/s]\n",
-      "Processing text_left with chain_transform of NgramLetter => WordHashing: 100%|██████████| 237/237 [00:00<00:00, 249.05it/s]\n",
-      "Processing text_right with chain_transform of NgramLetter => WordHashing: 100%|██████████| 2300/2300 [00:11<00:00, 239.25it/s]\n"
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 2118/2118 [00:00<00:00, 5365.33it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 18841/18841 [00:05<00:00, 3205.80it/s]\n",
+      "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 310569.71it/s]\n",
+      "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 349915.35it/s]\n",
+      "Building Vocabulary from a datapack.: 100%|██████████| 1614998/1614998 [00:00<00:00, 3031577.24it/s]\n",
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 8384.04it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 18841/18841 [00:04<00:00, 3939.48it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 125562.34it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 96621.02it/s]\n",
+      "Processing text_left with chain_transform of NgramLetter => WordHashing: 100%|██████████| 2118/2118 [00:07<00:00, 269.84it/s]\n",
+      "Processing text_right with chain_transform of NgramLetter => WordHashing: 100%|██████████| 18841/18841 [01:27<00:00, 216.37it/s]\n",
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 122/122 [00:00<00:00, 7746.89it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 1115/1115 [00:14<00:00, 77.28it/s] \n",
+      "Processing text_left with transform: 100%|██████████| 122/122 [00:00<00:00, 63447.62it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 1115/1115 [00:00<00:00, 88946.88it/s]\n",
+      "Processing text_left with chain_transform of NgramLetter => WordHashing: 100%|██████████| 122/122 [00:00<00:00, 273.77it/s]\n",
+      "Processing text_right with chain_transform of NgramLetter => WordHashing: 100%|██████████| 1115/1115 [00:04<00:00, 226.13it/s]\n",
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 237/237 [00:00<00:00, 8707.90it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval: 100%|██████████| 2300/2300 [00:01<00:00, 2237.22it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 237/237 [00:00<00:00, 101299.30it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 2300/2300 [00:00<00:00, 97484.78it/s]\n",
+      "Processing text_left with chain_transform of NgramLetter => WordHashing: 100%|██████████| 237/237 [00:00<00:00, 269.12it/s]\n",
+      "Processing text_right with chain_transform of NgramLetter => WordHashing: 100%|██████████| 2300/2300 [00:10<00:00, 212.35it/s]\n"
      ]
     }
    ],
    "source": [
-    "valid_pack_processed = preprocessor.transform(valid_pack)\n",
-    "predict_pack_processed = preprocessor.transform(predict_pack)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "ranking_task = mz.tasks.Ranking(loss=mz.losses.RankHingeLoss())\n",
-    "ranking_task.metrics = [\n",
-    "    mz.metrics.NormalizedDiscountedCumulativeGain(k=3),\n",
-    "    mz.metrics.NormalizedDiscountedCumulativeGain(k=5),\n",
-    "    mz.metrics.MeanAveragePrecision()\n",
-    "]"
+    "preprocessor = mz.preprocessors.CDSSMPreprocessor(fixed_length_left=10, fixed_length_right=10)\n",
+    "train_pack_processed = preprocessor.fit_transform(train_pack_raw)\n",
+    "valid_pack_processed = preprocessor.transform(dev_pack_raw)\n",
+    "test_pack_processed = preprocessor.transform(test_pack_raw)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 3,
    "metadata": {
     "scrolled": false
    },
@@ -182,34 +147,33 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "pred_x, pred_y = predict_pack_processed[:].unpack()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "WARNING: PairDataGenerator will be deprecated in MatchZoo v2.2. Use `DataGenerator` with callbacks instead.\n"
+      "num batches: 102\n"
      ]
     }
    ],
    "source": [
+    "pred_x, pred_y = test_pack_processed[:].unpack()\n",
     "evaluate = mz.callbacks.EvaluateAllMetrics(model, x=pred_x, y=pred_y, batch_size=len(pred_x))\n",
-    "train_generator = mz.PairDataGenerator(train_pack_processed, num_dup=2, num_neg=1, batch_size=64, shuffle=True)"
+    "train_generator = mz.DataGenerator(\n",
+    "    train_pack_processed,\n",
+    "    mode='pair',\n",
+    "    num_dup=2,\n",
+    "    num_neg=1,\n",
+    "    batch_size=20\n",
+    ")\n",
+    "print('num batches:', len(train_generator))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 5,
    "metadata": {
     "scrolled": false
    },
@@ -219,65 +183,65 @@
      "output_type": "stream",
      "text": [
       "Epoch 1/20\n",
-      "32/32 [==============================] - 72s 2s/step - loss: 0.9808\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.42488215100974375 - normalized_discounted_cumulative_gain@5(0.0): 0.4909840291447614 - mean_average_precision(0.0): 0.45215174584733625\n",
+      "102/102 [==============================] - 65s 635ms/step - loss: 0.8021\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.42304382290227854 - normalized_discounted_cumulative_gain@5(0.0): 0.49915948768338086 - mean_average_precision(0.0): 0.46037758752542035\n",
       "Epoch 2/20\n",
-      "32/32 [==============================] - 39s 1s/step - loss: 0.7890\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4523924010888256 - normalized_discounted_cumulative_gain@5(0.0): 0.526580105002082 - mean_average_precision(0.0): 0.4843661871232366\n",
+      "102/102 [==============================] - 45s 445ms/step - loss: 0.5966\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.43781763271520285 - normalized_discounted_cumulative_gain@5(0.0): 0.520097599085372 - mean_average_precision(0.0): 0.4762598411822459\n",
       "Epoch 3/20\n",
-      "32/32 [==============================] - 39s 1s/step - loss: 0.6269\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.42912083006483787 - normalized_discounted_cumulative_gain@5(0.0): 0.5059856809765926 - mean_average_precision(0.0): 0.46958607676474695\n",
+      "102/102 [==============================] - 46s 447ms/step - loss: 0.4992\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.44923101748788413 - normalized_discounted_cumulative_gain@5(0.0): 0.5136672947113214 - mean_average_precision(0.0): 0.4803110559647868\n",
       "Epoch 4/20\n",
-      "32/32 [==============================] - 40s 1s/step - loss: 0.5425\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45161347891396936 - normalized_discounted_cumulative_gain@5(0.0): 0.5246607686333176 - mean_average_precision(0.0): 0.4894374228928154\n",
+      "102/102 [==============================] - 46s 446ms/step - loss: 0.4143\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.467714954371615 - normalized_discounted_cumulative_gain@5(0.0): 0.5353130653753986 - mean_average_precision(0.0): 0.5017560318255102\n",
       "Epoch 5/20\n",
-      "32/32 [==============================] - 39s 1s/step - loss: 0.4786\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.44182189699245444 - normalized_discounted_cumulative_gain@5(0.0): 0.5185925913281365 - mean_average_precision(0.0): 0.4785410987945083\n",
+      "102/102 [==============================] - 46s 451ms/step - loss: 0.3489\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4706511291875292 - normalized_discounted_cumulative_gain@5(0.0): 0.5275832328072992 - mean_average_precision(0.0): 0.5026243479583462\n",
       "Epoch 6/20\n",
-      "32/32 [==============================] - 40s 1s/step - loss: 0.4312\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4353736449145257 - normalized_discounted_cumulative_gain@5(0.0): 0.5193283626946119 - mean_average_precision(0.0): 0.47581070046166846\n",
+      "102/102 [==============================] - 45s 443ms/step - loss: 0.3231\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4641151831570107 - normalized_discounted_cumulative_gain@5(0.0): 0.5219564667466021 - mean_average_precision(0.0): 0.4934049132027672\n",
       "Epoch 7/20\n",
-      "32/32 [==============================] - 40s 1s/step - loss: 0.3918\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.42860677523860297 - normalized_discounted_cumulative_gain@5(0.0): 0.511305679951288 - mean_average_precision(0.0): 0.465896748198743\n",
+      "102/102 [==============================] - 44s 433ms/step - loss: 0.2695\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4760514687477512 - normalized_discounted_cumulative_gain@5(0.0): 0.5285019348702702 - mean_average_precision(0.0): 0.49994736585416333\n",
       "Epoch 8/20\n",
-      "32/32 [==============================] - 40s 1s/step - loss: 0.3399\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4483104574863611 - normalized_discounted_cumulative_gain@5(0.0): 0.514850172629585 - mean_average_precision(0.0): 0.47729829884300434\n",
+      "102/102 [==============================] - 47s 457ms/step - loss: 0.2331\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4661781341235399 - normalized_discounted_cumulative_gain@5(0.0): 0.5260071867435453 - mean_average_precision(0.0): 0.4922605321622356\n",
       "Epoch 9/20\n",
-      "32/32 [==============================] - 39s 1s/step - loss: 0.3133\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.44683839351327287 - normalized_discounted_cumulative_gain@5(0.0): 0.5179511840387869 - mean_average_precision(0.0): 0.47938829437070807\n",
+      "102/102 [==============================] - 46s 453ms/step - loss: 0.1942\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4645719968337067 - normalized_discounted_cumulative_gain@5(0.0): 0.5238558790194195 - mean_average_precision(0.0): 0.48851468090847294\n",
       "Epoch 10/20\n",
-      "32/32 [==============================] - 39s 1s/step - loss: 0.2852\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45147495790907527 - normalized_discounted_cumulative_gain@5(0.0): 0.5115591547752104 - mean_average_precision(0.0): 0.4816185898296844\n",
+      "102/102 [==============================] - 45s 444ms/step - loss: 0.1734\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4600910137969285 - normalized_discounted_cumulative_gain@5(0.0): 0.5320923473092672 - mean_average_precision(0.0): 0.48703092961044614\n",
       "Epoch 11/20\n",
-      "32/32 [==============================] - 39s 1s/step - loss: 0.2503\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45606725359003863 - normalized_discounted_cumulative_gain@5(0.0): 0.5137853825095071 - mean_average_precision(0.0): 0.4898566499228558\n",
+      "102/102 [==============================] - 47s 464ms/step - loss: 0.1644\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.45786306386326225 - normalized_discounted_cumulative_gain@5(0.0): 0.5246949873542252 - mean_average_precision(0.0): 0.48502089087514016\n",
       "Epoch 12/20\n",
-      "32/32 [==============================] - 40s 1s/step - loss: 0.2114\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4536596944525606 - normalized_discounted_cumulative_gain@5(0.0): 0.5195598827593197 - mean_average_precision(0.0): 0.48509265834715537\n",
+      "102/102 [==============================] - 45s 443ms/step - loss: 0.1560\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4567332855642369 - normalized_discounted_cumulative_gain@5(0.0): 0.528074374789356 - mean_average_precision(0.0): 0.4905494464640722\n",
       "Epoch 13/20\n",
-      "32/32 [==============================] - 40s 1s/step - loss: 0.1886\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.46086526212639095 - normalized_discounted_cumulative_gain@5(0.0): 0.5347221469997477 - mean_average_precision(0.0): 0.4972628568563632\n",
+      "102/102 [==============================] - 45s 440ms/step - loss: 0.1365\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4698836510431016 - normalized_discounted_cumulative_gain@5(0.0): 0.5317255666034969 - mean_average_precision(0.0): 0.49152222966181813\n",
       "Epoch 14/20\n",
-      "32/32 [==============================] - 40s 1s/step - loss: 0.1770\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4524514918879093 - normalized_discounted_cumulative_gain@5(0.0): 0.5259706685008083 - mean_average_precision(0.0): 0.4886127130697417\n",
+      "102/102 [==============================] - 45s 437ms/step - loss: 0.1263\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.46841048236088156 - normalized_discounted_cumulative_gain@5(0.0): 0.5197949838102164 - mean_average_precision(0.0): 0.4887341126171474\n",
       "Epoch 15/20\n",
-      "32/32 [==============================] - 38s 1s/step - loss: 0.1454\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.447793222144205 - normalized_discounted_cumulative_gain@5(0.0): 0.523210520595902 - mean_average_precision(0.0): 0.4842295350811732\n",
+      "102/102 [==============================] - 46s 449ms/step - loss: 0.1208\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4591952265806063 - normalized_discounted_cumulative_gain@5(0.0): 0.5306329604843507 - mean_average_precision(0.0): 0.4956899590808506\n",
       "Epoch 16/20\n",
-      "32/32 [==============================] - 39s 1s/step - loss: 0.1573\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4617597821704854 - normalized_discounted_cumulative_gain@5(0.0): 0.5233844194501931 - mean_average_precision(0.0): 0.48736454745855345\n",
+      "102/102 [==============================] - 44s 430ms/step - loss: 0.0977\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4698790408918565 - normalized_discounted_cumulative_gain@5(0.0): 0.5355447042513717 - mean_average_precision(0.0): 0.5005823464725863\n",
       "Epoch 17/20\n",
-      "32/32 [==============================] - 40s 1s/step - loss: 0.1437\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.446051744365399 - normalized_discounted_cumulative_gain@5(0.0): 0.511689458533883 - mean_average_precision(0.0): 0.47390160852977303\n",
+      "102/102 [==============================] - 44s 436ms/step - loss: 0.0975\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4699380823064665 - normalized_discounted_cumulative_gain@5(0.0): 0.5335843828018585 - mean_average_precision(0.0): 0.4945873841691485\n",
       "Epoch 18/20\n",
-      "32/32 [==============================] - 40s 1s/step - loss: 0.1396\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.44155513060981444 - normalized_discounted_cumulative_gain@5(0.0): 0.508852366919329 - mean_average_precision(0.0): 0.47430841711913996\n",
+      "102/102 [==============================] - 44s 431ms/step - loss: 0.1070\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4558123512520484 - normalized_discounted_cumulative_gain@5(0.0): 0.5280824209271964 - mean_average_precision(0.0): 0.49009730599920476\n",
       "Epoch 19/20\n",
-      "32/32 [==============================] - 39s 1s/step - loss: 0.1176\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4480183178506102 - normalized_discounted_cumulative_gain@5(0.0): 0.5177348617853279 - mean_average_precision(0.0): 0.4799578376954719\n",
+      "102/102 [==============================] - 45s 446ms/step - loss: 0.0846\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.47468488947984894 - normalized_discounted_cumulative_gain@5(0.0): 0.5462940161373581 - mean_average_precision(0.0): 0.5050693971440435\n",
       "Epoch 20/20\n",
-      "32/32 [==============================] - 37s 1s/step - loss: 0.1094\n",
-      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.44665417415821596 - normalized_discounted_cumulative_gain@5(0.0): 0.5142203489288135 - mean_average_precision(0.0): 0.47726846390007227\n"
+      "102/102 [==============================] - 43s 425ms/step - loss: 0.0833\n",
+      "Validation: normalized_discounted_cumulative_gain@3(0.0): 0.4571219999869289 - normalized_discounted_cumulative_gain@5(0.0): 0.527098973778668 - mean_average_precision(0.0): 0.4884211445807594\n"
      ]
     }
    ],

From bc85e1ad7b2346a33ae02fd8180f22e5800ee034 Mon Sep 17 00:00:00 2001
From: bo <kingbolanda@live.com>
Date: Fri, 12 Apr 2019 15:51:00 +0200
Subject: [PATCH 039/100] fix variable error in readme

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 6e4e0571..bb49d2dd 100644
--- a/README.md
+++ b/README.md
@@ -110,7 +110,7 @@ Generate pair-wise training data on-the-fly, evaluate model performance using cu
 train_generator = mz.PairDataGenerator(train_processed, num_dup=1, num_neg=4, batch_size=64, shuffle=True)
 
 valid_x, valid_y = valid_processed.unpack()
-evaluate = mz.callbacks.EvaluateAllMetrics(model, x=valid_x, y=valid_y, batch_size=len(pred_x))
+evaluate = mz.callbacks.EvaluateAllMetrics(model, x=valid_x, y=valid_y, batch_size=len(valid_x))
 
 history = model.fit_generator(train_generator, epochs=20, callbacks=[evaluate], workers=5, use_multiprocessing=False)
 ```

From dc950a7ee2a392b6e4b419fa5004338537d80e71 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Sat, 13 Apr 2019 13:53:08 +0800
Subject: [PATCH 040/100] Increase CI information on failure.

'-v'  prints out all test names
'--full-trace' prints out entire stack trace on errors
'-l' prints out local vars on errors
---
 Makefile | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/Makefile b/Makefile
index c1caf3ab..df5408cd 100644
--- a/Makefile
+++ b/Makefile
@@ -46,7 +46,7 @@
 init:
 	pip install -r requirements.txt
 
-TEST_ARGS = --doctest-modules --doctest-continue-on-failure --cov matchzoo/ --cov-report term-missing --cov-report html --cov-config .coveragerc matchzoo/ tests/ -W ignore::DeprecationWarning --ignore=matchzoo/contrib
+TEST_ARGS = -v --full-trace -l --doctest-modules --doctest-continue-on-failure --cov matchzoo/ --cov-report term-missing --cov-report html --cov-config .coveragerc matchzoo/ tests/ -W ignore::DeprecationWarning --ignore=matchzoo/contrib
 FLAKE_ARGS = ./matchzoo --exclude=__init__.py,matchzoo/contrib
 
 test:

From b93a9856f3ca2ac0358fb4d544265c5bd588cf27 Mon Sep 17 00:00:00 2001
From: Wang Bo <kingbolanda@live.com>
Date: Sat, 13 Apr 2019 08:21:27 +0200
Subject: [PATCH 041/100] Feature/dssm tutorial (#711)

* refactor dssm tutorial

* add parametetrs to readme
---
 tutorials/wikiqa/README.rst | 17 +++++++++++++++++
 tutorials/wikiqa/dssm.ipynb | 38 +++++++++++++++++++------------------
 2 files changed, 37 insertions(+), 18 deletions(-)

diff --git a/tutorials/wikiqa/README.rst b/tutorials/wikiqa/README.rst
index 58da266f..a5521c9a 100644
--- a/tutorials/wikiqa/README.rst
+++ b/tutorials/wikiqa/README.rst
@@ -92,3 +92,20 @@ MatchLSTM
   10  dropout_rate          0.5
 ====  ====================  ======================================================
 
+DSSM
+####
+
+====  ===========================  ===================================
+  ..  Name                         Value
+====  ===========================  ===================================
+   0  model_class                  <class 'matchzoo.models.dssm.DSSM'>
+   1  input_shapes                 [(9645,), (9645,)]
+   2  task                         Ranking Task
+   3  optimizer                    adam
+   4  with_multi_layer_perceptron  True
+   5  mlp_num_units                300
+   6  mlp_num_layers               3
+   7  mlp_num_fan_out              128
+   8  mlp_activation_func          relu
+====  ===========================  ===================================
+
diff --git a/tutorials/wikiqa/dssm.ipynb b/tutorials/wikiqa/dssm.ipynb
index 8eb6e11d..a7545b85 100644
--- a/tutorials/wikiqa/dssm.ipynb
+++ b/tutorials/wikiqa/dssm.ipynb
@@ -32,24 +32,24 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 2118/2118 [00:00<00:00, 3802.39it/s]\n",
-      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 18841/18841 [00:04<00:00, 3959.06it/s]\n",
-      "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 822625.79it/s]\n",
-      "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 597166.86it/s]\n",
-      "Building Vocabulary from a datapack.: 100%|██████████| 1614998/1614998 [00:00<00:00, 4642343.92it/s]\n",
-      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 2118/2118 [00:00<00:00, 2853.90it/s]\n",
-      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 18841/18841 [00:12<00:00, 1456.96it/s]\n",
-      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 122/122 [00:00<00:00, 2308.40it/s]\n",
-      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 1115/1115 [00:00<00:00, 2025.86it/s]\n",
-      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 237/237 [00:00<00:00, 2678.58it/s]\n",
-      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 2300/2300 [00:01<00:00, 1345.18it/s]\n"
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 2118/2118 [00:00<00:00, 3587.72it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter: 100%|██████████| 18841/18841 [00:04<00:00, 4528.13it/s]\n",
+      "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 592156.77it/s]\n",
+      "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 432217.30it/s]\n",
+      "Building Vocabulary from a datapack.: 100%|██████████| 1614998/1614998 [00:00<00:00, 4239505.32it/s]\n",
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 2118/2118 [00:00<00:00, 2709.71it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 18841/18841 [00:11<00:00, 1656.57it/s]\n",
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 122/122 [00:00<00:00, 1120.91it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 1115/1115 [00:00<00:00, 1895.34it/s]\n",
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 237/237 [00:00<00:00, 1910.44it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval => StopRemoval => NgramLetter => WordHashing: 100%|██████████| 2300/2300 [00:01<00:00, 1630.79it/s]\n"
      ]
     }
    ],
@@ -62,19 +62,19 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'vocab_unit': <matchzoo.preprocessors.units.vocabulary.Vocabulary at 0x7ff6ede81828>,\n",
+       "{'vocab_unit': <matchzoo.preprocessors.units.vocabulary.Vocabulary at 0x7f0d7fe29fd0>,\n",
        " 'vocab_size': 9645,\n",
        " 'embedding_input_dim': 9645,\n",
        " 'input_shapes': [(9645,), (9645,)]}"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -152,7 +152,9 @@
     "model.guess_and_fill_missing_params()\n",
     "model.build()\n",
     "model.compile()\n",
-    "model.backend.summary()"
+    "model.backend.summary()\n",
+    "\n",
+    "append_params_to_readme(model)"
    ]
   },
   {

From d5789ac415a419680b3a1e0ff8c599d142468ebb Mon Sep 17 00:00:00 2001
From: rgtjf <rgtjf1@163.com>
Date: Sun, 14 Apr 2019 22:16:25 +0800
Subject: [PATCH 042/100] hotfix: typo

---
 matchzoo/contrib/models/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/matchzoo/contrib/models/__init__.py b/matchzoo/contrib/models/__init__.py
index 59aa4b78..7b48145f 100644
--- a/matchzoo/contrib/models/__init__.py
+++ b/matchzoo/contrib/models/__init__.py
@@ -1,3 +1,3 @@
 from .match_lstm import MatchLSTM
 from .esim import ESIM
-from .bimpm_model import BiMpM
+from .bimpm import BiMPM

From 49cfea5a578b3f1bad74f09988467bc453791514 Mon Sep 17 00:00:00 2001
From: rgtjf <rgtjf1@163.com>
Date: Sun, 14 Apr 2019 22:46:06 +0800
Subject: [PATCH 043/100] hotfix: reduce hidden_size from 128 to 32

---
 matchzoo/contrib/models/bimpm.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/matchzoo/contrib/models/bimpm.py b/matchzoo/contrib/models/bimpm.py
index d795889e..e49e0de8 100644
--- a/matchzoo/contrib/models/bimpm.py
+++ b/matchzoo/contrib/models/bimpm.py
@@ -45,7 +45,7 @@ def get_default_params(cls) -> ParamTable:
                                          'max-attentive': True}))
         params.add(Param('mp_dim', 20))
         params.add(Param('att_dim', 20))
-        params.add(Param('hidden_size', 128))
+        params.add(Param('hidden_size', 32))
         params.add(Param('dropout_rate', 0.0))
         params.add(Param('w_initializer', 'glorot_uniform'))
         params.add(Param('b_initializer', 'zeros'))

From 42b4fcf274615480bc6a3afa891ce2ae34d3bcea Mon Sep 17 00:00:00 2001
From: rgtjf <rgtjf1@163.com>
Date: Sun, 14 Apr 2019 23:14:23 +0800
Subject: [PATCH 044/100] hotfix: remove unused code

---
 matchzoo/contrib/layers/attention_layer.py | 12 +++---------
 1 file changed, 3 insertions(+), 9 deletions(-)

diff --git a/matchzoo/contrib/layers/attention_layer.py b/matchzoo/contrib/layers/attention_layer.py
index 74bb1331..3ec88114 100644
--- a/matchzoo/contrib/layers/attention_layer.py
+++ b/matchzoo/contrib/layers/attention_layer.py
@@ -23,7 +23,7 @@ class AttentionLayer(Layer):
     def __init__(self,
                  att_dim: int,
                  att_type: str = 'default',
-                 remove_diagonal: bool = False,
+                 # remove_diagonal: bool = False,
                  dropout_rate: float = 0.0):
         """
         class: `AttentionLayer` constructor.
@@ -35,7 +35,7 @@ def __init__(self,
         super(AttentionLayer, self).__init__()
         self._att_dim = att_dim
         self._att_type = att_type
-        self._remove_diagonal = remove_diagonal
+        # self._remove_diagonal = remove_diagonal
         self._dropout_rate = dropout_rate
 
     @property
@@ -128,16 +128,10 @@ def call(self, x: list, **kwargs):
         #     diagonal = tf.expand_dims(diagonal, axis=0)  # ['x', len1, len1]
         #     attn_value = attn_value * diagonal
 
-        if len(x) == 4:
-            mask_lt = x[2]
-            mask_rt = x[3]
-            attn_value = attn_value * K.expand_dims(mask_lt, axis=2)
-            attn_value = attn_value * K.expand_dims(mask_rt, axis=1)
-
         # softmax
         attn_prob = K.softmax(attn_value)  # [batch_size, len_1, len_2]
-
         # if remove_diagonal: attn_value = attn_value * diagonal
+
         if len(x) == 4:
             mask_lt = x[2]
             mask_rt = x[3]

From 62acc732d9c8cf5f1088e154aa606f42b8473773 Mon Sep 17 00:00:00 2001
From: bo <kingbolanda@live.com>
Date: Mon, 15 Apr 2019 10:20:07 +0200
Subject: [PATCH 045/100] simplify quick start

---
 README.md | 8 --------
 1 file changed, 8 deletions(-)

diff --git a/README.md b/README.md
index bb49d2dd..ad5c345c 100644
--- a/README.md
+++ b/README.md
@@ -67,7 +67,6 @@ import matchzoo as mz
 
 train_pack = mz.datasets.wiki_qa.load_data('train', task='ranking')
 valid_pack = mz.datasets.wiki_qa.load_data('dev', task='ranking')
-predict_pack = mz.datasets.wiki_qa.load_data('test', task='ranking')
 ```
 
 Preprocess your input data in three lines of code, keep track parameters to be passed into the model.
@@ -84,7 +83,6 @@ Make use of MatchZoo customized loss functions and evaluation metrics:
 ranking_task = mz.tasks.Ranking(loss=mz.losses.RankCrossEntropyLoss(num_neg=4))
 ranking_task.metrics = [
     mz.metrics.NormalizedDiscountedCumulativeGain(k=3),
-    mz.metrics.NormalizedDiscountedCumulativeGain(k=5),
     mz.metrics.MeanAveragePrecision()
 ]
 ```
@@ -95,10 +93,6 @@ Initialize the model, fine-tune the hyper-parameters.
 model = mz.models.DSSM()
 model.params['input_shapes'] = preprocessor.context['input_shapes']
 model.params['task'] = ranking_task
-model.params['mlp_num_layers'] = 3
-model.params['mlp_num_units'] = 300
-model.params['mlp_num_fan_out'] = 128
-model.params['mlp_activation_func'] = 'relu'
 model.guess_and_fill_missing_params()
 model.build()
 model.compile()
@@ -108,10 +102,8 @@ Generate pair-wise training data on-the-fly, evaluate model performance using cu
 
 ```python
 train_generator = mz.PairDataGenerator(train_processed, num_dup=1, num_neg=4, batch_size=64, shuffle=True)
-
 valid_x, valid_y = valid_processed.unpack()
 evaluate = mz.callbacks.EvaluateAllMetrics(model, x=valid_x, y=valid_y, batch_size=len(valid_x))
-
 history = model.fit_generator(train_generator, epochs=20, callbacks=[evaluate], workers=5, use_multiprocessing=False)
 ```
 

From 9137e0180e67b96f8963aa3a93521c0f1e6aceae Mon Sep 17 00:00:00 2001
From: caiyinqiong <1198593462@qq.com>
Date: Mon, 15 Apr 2019 19:43:35 +0800
Subject: [PATCH 046/100] add HBMP model (#663)

* add HBMP model

* makefile modify

* remove changes to Makefile

* rename 'alpha' param; move _output_layer to build()

* Modify the model name; use Bidirectional Wrappers

* modify variable nameing issue, line formatting and first letter upper case
---
 matchzoo/contrib/models/__init__.py |   1 +
 matchzoo/contrib/models/hbmp.py     | 154 ++++++++++++++++++++++++++++
 2 files changed, 155 insertions(+)
 create mode 100644 matchzoo/contrib/models/hbmp.py

diff --git a/matchzoo/contrib/models/__init__.py b/matchzoo/contrib/models/__init__.py
index 6a81f8f3..d6d85e33 100644
--- a/matchzoo/contrib/models/__init__.py
+++ b/matchzoo/contrib/models/__init__.py
@@ -1,2 +1,3 @@
 from .match_lstm import MatchLSTM
+from .hbmp import HBMP
 from .esim import ESIM
\ No newline at end of file
diff --git a/matchzoo/contrib/models/hbmp.py b/matchzoo/contrib/models/hbmp.py
new file mode 100644
index 00000000..342ec53b
--- /dev/null
+++ b/matchzoo/contrib/models/hbmp.py
@@ -0,0 +1,154 @@
+"""HBMP model."""
+import keras
+import typing
+
+from matchzoo.engine import hyper_spaces
+from matchzoo.engine.param_table import ParamTable
+from matchzoo.engine.param import Param
+from matchzoo.engine.base_model import BaseModel
+
+
+class HBMP(BaseModel):
+    """
+    HBMP model.
+
+    Examples:
+        >>> model = HBMP()
+        >>> model.guess_and_fill_missing_params(verbose=0)
+        >>> model.params['embedding_input_dim'] = 10000
+        >>> model.params['embedding_output_dim'] = 100
+        >>> model.params['embedding_trainable'] = True
+        >>> model.params['alpha'] = 0.1
+        >>> model.params['mlp_num_layers'] = 3
+        >>> model.params['mlp_num_units'] = [600, 600]
+        >>> model.params['lstm_num_units'] = 600
+        >>> model.params['dropout_rate'] = 0.1
+        >>> model.build()
+    """
+
+    @classmethod
+    def get_default_params(cls) -> ParamTable:
+        """:return: model default parameters."""
+        params = super().get_default_params(with_embedding=True)
+        params['optimizer'] = 'adam'
+        params.add(Param(name='alpha', value=0.1,
+                         desc="Negative slope coefficient of LeakyReLU "
+                              "function."))
+        params.add(Param(name='mlp_num_layers', value=3,
+                         desc="The number of layers of mlp."))
+        params.add(Param(name='mlp_num_units', value=[600, 600],
+                         desc="The hidden size of the FC layers, but not "
+                              "include the final layer."))
+        params.add(Param(name='lstm_num_units', value=600,
+                         desc="The hidden size of the LSTM layer."))
+        params.add(Param(
+            name='dropout_rate', value=0.1,
+            hyper_space=hyper_spaces.quniform(
+                low=0.0, high=0.8, q=0.01),
+            desc="The dropout rate."
+        ))
+        return params
+
+    def build(self):
+        """Build model structure."""
+        input_left, input_right = self._make_inputs()
+
+        embedding = self._make_embedding_layer()
+        embed_left = embedding(input_left)
+        embed_right = embedding(input_right)
+
+        # Get sentence embedding
+        embed_sen_left = self._sentence_encoder(
+            embed_left,
+            lstm_num_units=self._params['lstm_num_units'],
+            drop_rate=self._params['dropout_rate'])
+        embed_sen_right = self._sentence_encoder(
+            embed_right,
+            lstm_num_units=self._params['lstm_num_units'],
+            drop_rate=self._params['dropout_rate'])
+
+        # Concatenate two sentence embedding: [embed_sen_left, embed_sen_right,
+        # |embed_sen_left-embed_sen_right|, embed_sen_left*embed_sen_right]
+        embed_minus = keras.layers.Subtract()(
+            [embed_sen_left, embed_sen_right])
+        embed_minus_abs = keras.layers.Lambda(lambda x: abs(x))(embed_minus)
+        embed_multiply = keras.layers.Multiply()(
+            [embed_sen_left, embed_sen_right])
+        concat = keras.layers.Concatenate(axis=1)(
+            [embed_sen_left, embed_sen_right, embed_minus_abs, embed_multiply])
+
+        # Multiply perception layers to classify
+        mlp_out = self._classifier(
+            concat,
+            mlp_num_layers=self._params['mlp_num_layers'],
+            mlp_num_units=self._params['mlp_num_units'],
+            drop_rate=self._params['dropout_rate'],
+            leaky_relu_alpah=self._params['alpha'])
+        out = self._make_output_layer()(mlp_out)
+
+        self._backend = keras.Model(
+            inputs=[input_left, input_right], outputs=out)
+
+    def _classifier(
+        self,
+        input_: typing.Any,
+        mlp_num_layers: int,
+        mlp_num_units: list,
+        drop_rate: float,
+        leaky_relu_alpah: float
+    ) -> typing.Any:
+        for i in range(mlp_num_layers - 1):
+            input_ = keras.layers.Dropout(rate=drop_rate)(input_)
+            input_ = keras.layers.Dense(mlp_num_units[i])(input_)
+            input_ = keras.layers.LeakyReLU(alpha=leaky_relu_alpah)(input_)
+
+        return input_
+
+    def _sentence_encoder(
+        self,
+        input_: typing.Any,
+        lstm_num_units: int,
+        drop_rate: float
+    ) -> typing.Any:
+        """
+        Stack three BiLSTM MaxPooling blocks as a hierarchical structure.
+        Concatenate the output of three blocs as the input sentence embedding.
+        Each BiLSTM layer reads the input sentence as the input.
+        Each BiLSTM layer except the first one is initialized(the initial
+        hidden state and the cell state) with the final state of the previous
+        layer.
+        """
+        emb1 = keras.layers.Bidirectional(
+            keras.layers.LSTM(
+                units=lstm_num_units,
+                return_sequences=True,
+                return_state=True,
+                dropout=drop_rate,
+                recurrent_dropout=drop_rate),
+            merge_mode='concat')(input_)
+        emb1_maxpooling = keras.layers.GlobalMaxPooling1D()(emb1[0])
+
+        emb2 = keras.layers.Bidirectional(
+            keras.layers.LSTM(
+                units=lstm_num_units,
+                return_sequences=True,
+                return_state=True,
+                dropout=drop_rate,
+                recurrent_dropout=drop_rate),
+            merge_mode='concat')(input_, initial_state=emb1[1:5])
+        emb2_maxpooling = keras.layers.GlobalMaxPooling1D()(emb2[0])
+
+        emb3 = keras.layers.Bidirectional(
+            keras.layers.LSTM(
+                units=lstm_num_units,
+                return_sequences=True,
+                return_state=True,
+                dropout=drop_rate,
+                recurrent_dropout=drop_rate),
+            merge_mode='concat')(input_, initial_state=emb2[1:5])
+        emb3_maxpooling = keras.layers.GlobalMaxPooling1D()(emb3[0])
+
+        emb = keras.layers.Concatenate(axis=1)(
+            [emb1_maxpooling, emb2_maxpooling, emb3_maxpooling])
+
+        return emb

From a3d97fa002033ac975cd00487499c2b2daab0754 Mon Sep 17 00:00:00 2001
From: rgtjf <rgtjf1@163.com>
Date: Thu, 18 Apr 2019 14:02:58 +0800
Subject: [PATCH 047/100] feature/bimpm: clean code 1. add layer examples 2.
 remove comment codes 3. resolve conficts

---
 matchzoo/contrib/layers/attention_layer.py    |  59 ++++----
 .../contrib/layers/multi_perspective_layer.py | 138 +++++++++++++-----
 matchzoo/contrib/models/__init__.py           |   1 +
 matchzoo/contrib/models/bimpm.py              |  44 ------
 4 files changed, 126 insertions(+), 116 deletions(-)

diff --git a/matchzoo/contrib/layers/attention_layer.py b/matchzoo/contrib/layers/attention_layer.py
index 3ec88114..53714f06 100644
--- a/matchzoo/contrib/layers/attention_layer.py
+++ b/matchzoo/contrib/layers/attention_layer.py
@@ -6,7 +6,7 @@
 
 class AttentionLayer(Layer):
     """
-    Layer that compute attention for Bimpm model.
+    Layer that compute attention for BiMPM model.
 
     For detailed information, see Bilateral Multi-Perspective Matching for
     Natural Language Sentences, section 3.2.
@@ -17,25 +17,24 @@ class AttentionLayer(Layer):
     Examples:
         >>> import matchzoo as mz
         >>> layer = mz.contrib.layers.AttentionLayer(att_dim=50)
+        >>> layer.compute_output_shape([(32, 10, 100), (32, 40, 100)])
+        (32, 10, 40)
 
     """
 
     def __init__(self,
                  att_dim: int,
                  att_type: str = 'default',
-                 # remove_diagonal: bool = False,
                  dropout_rate: float = 0.0):
         """
         class: `AttentionLayer` constructor.
 
         :param att_dim: int
         :param att_type: int
-        :param remove_diagonal: bool
         """
         super(AttentionLayer, self).__init__()
         self._att_dim = att_dim
         self._att_type = att_type
-        # self._remove_diagonal = remove_diagonal
         self._dropout_rate = dropout_rate
 
     @property
@@ -52,14 +51,15 @@ def build(self, input_shapes):
         """
         Build the layer.
 
-        :param input_shapes: input_shapes
+        :param input_shapes: input_shape_lt, input_shape_rt
         """
         if not isinstance(input_shapes, list):
             raise ValueError('A attention layer should be called '
                              'on a list of inputs.')
-        # input_shapes[0]: batch, time_steps, d
+
         hidden_dim_lt = input_shapes[0][2]
         hidden_dim_rt = input_shapes[1][2]
+
         self.attn_w1 = self.add_weight(name='attn_w1',
                                        shape=(hidden_dim_lt,
                                               self._att_dim),
@@ -73,7 +73,7 @@ def build(self, input_shapes):
                                                   self._att_dim),
                                            initializer='uniform',
                                            trainable=True)
-
+        # diagonal_W: (1, 1, a)
         self.diagonal_W = self.add_weight(name='diagonal_W',
                                           shape=(1,
                                                  1,
@@ -87,7 +87,7 @@ def call(self, x: list, **kwargs):
         Calculate attention.
 
         :param x: [reps_lt, reps_rt]
-        :return attn_prob: [batch_size, len_1, len_2]
+        :return attn_prob: [b, s_lt, s_rt]
         """
 
         if not isinstance(x, list):
@@ -96,47 +96,41 @@ def call(self, x: list, **kwargs):
 
         reps_lt, reps_rt = x
 
-        # [1, 1, d, 20]
         attn_w1 = self.attn_w1
         attn_w1 = K.expand_dims(K.expand_dims(attn_w1, axis=0), axis=0)
-        # [b, s, d, -1]
+        # => [1, 1, d, a]
+
         reps_lt = K.expand_dims(reps_lt, axis=-1)
         attn_reps_lt = K.sum(reps_lt * attn_w1, axis=2)
+        # => [b, s_lt, d, -1]
 
-        # [1, 1, d, 20]
         attn_w2 = self.attn_w2
         attn_w2 = K.expand_dims(K.expand_dims(attn_w2, axis=0), axis=0)
-        # [b, s, d, -1]
+        # => [1, 1, d, a]
+
         reps_rt = K.expand_dims(reps_rt, axis=-1)
-        attn_reps_rt = K.sum(reps_rt * attn_w2, axis=2)
+        attn_reps_rt = K.sum(reps_rt * attn_w2, axis=2)  # [b, s_rt, d, -1]
 
-        # Tanh
-        attn_reps_lt = K.tanh(attn_reps_lt)  # [b, s, 20]
-        attn_reps_rt = K.tanh(attn_reps_rt)
+        attn_reps_lt = K.tanh(attn_reps_lt)  # [b, s_lt, a]
+        attn_reps_rt = K.tanh(attn_reps_rt)  # [b, s_rt, a]
 
         # diagonal_W
-        attn_reps_lt = attn_reps_lt * self.diagonal_W  # [b, s, 20]
+        attn_reps_lt = attn_reps_lt * self.diagonal_W  # [b, s_lt, a]
         attn_reps_rt = K.permute_dimensions(attn_reps_rt, (0, 2, 1))
+        # => [b, a, s_rt]
 
-        # [batch_size, s, s]
-        attn_value = K.batch_dot(attn_reps_lt, attn_reps_rt)
+        attn_value = K.batch_dot(attn_reps_lt, attn_reps_rt)  # [b, s_lt, s_rt]
 
-        # TODO(tjf) or remove: normalize
-        # if self.remove_diagonal:
-        #     diagonal = K.ones([len_1], tf.float32)  # [len1]
-        #     diagonal = 1.0 - tf.diag(diagonal)  # [len1, len1]
-        #     diagonal = tf.expand_dims(diagonal, axis=0)  # ['x', len1, len1]
-        #     attn_value = attn_value * diagonal
+        # Softmax operation
+        attn_prob = K.softmax(attn_value)  # [b, s_lt, s_rt]
 
-        # softmax
-        attn_prob = K.softmax(attn_value)  # [batch_size, len_1, len_2]
+        # TODO(tjf) remove diagonal or not for normalization
         # if remove_diagonal: attn_value = attn_value * diagonal
 
         if len(x) == 4:
-            mask_lt = x[2]
-            mask_rt = x[3]
-            attn_prob = attn_prob * K.expand_dims(mask_lt, axis=2)
-            attn_prob = attn_prob * K.expand_dims(mask_rt, axis=1)
+            mask_lt, mask_rt = x[2], x[3]
+            attn_prob *= K.expand_dims(mask_lt, axis=2)
+            attn_prob *= K.expand_dims(mask_rt, axis=1)
 
         return attn_prob
 
@@ -145,4 +139,5 @@ def compute_output_shape(self, input_shapes):
         if not isinstance(input_shapes, list):
             raise ValueError('A attention layer should be called '
                              'on a list of inputs.')
-        return input_shapes[0][0], input_shapes[0][1], input_shapes[1][1]
+        input_shape_lt, input_shape_rt = input_shapes[0], input_shapes[1]
+        return input_shape_lt[0], input_shape_lt[1], input_shape_rt[1]
diff --git a/matchzoo/contrib/layers/multi_perspective_layer.py b/matchzoo/contrib/layers/multi_perspective_layer.py
index 212da13c..e801df3a 100644
--- a/matchzoo/contrib/layers/multi_perspective_layer.py
+++ b/matchzoo/contrib/layers/multi_perspective_layer.py
@@ -8,14 +8,21 @@
 
 class MultiPerspectiveLayer(Layer):
     """
-    A keras implementation of Bimpm multi-perspective layer.
+    A keras implementation of multi-perspective layer of BiMPM.
 
     For detailed information, see Bilateral Multi-Perspective
     Matching for Natural Language Sentences, section 3.2.
 
     Examples:
         >>> import matchzoo as mz
-        >>> layer = mz.contrib.layers.MultiPerspectiveLayer(50, 20, None)
+        >>> perspective={'full': True, 'max-pooling': True,
+        ...     'attentive': True, 'max-attentive': True}
+        >>> layer = mz.contrib.layers.MultiPerspectiveLayer(
+        ...     att_dim=50, mp_dim=20, perspective=perspective)
+        >>> layer.compute_output_shape(
+        ...     [(32, 10, 100), (32, 50), None, (32, 50), None,
+        ...     [(32, 40, 100), (32, 50), None, (32, 50), None]])
+        (32, 10, 83)
 
     """
 
@@ -54,7 +61,6 @@ def build(self, input_shape: list):
 
         if self._perspective.get('max-attentive'):
             self.max_attentive_match = MpMaxAttentiveMatch(self._att_dim)
-
         self.built = True
 
     def call(self, x: list, **kwargs):
@@ -105,14 +111,23 @@ def call(self, x: list, **kwargs):
             match_dim += self._mp_dim + 1
 
         mp_tensor = K.concatenate(match_tensor_list, axis=-1)
-        self.output_dim = match_dim
-
         return mp_tensor
 
     def compute_output_shape(self, input_shape: list):
         """Compute output shape."""
         shape_a = input_shape[0]
-        return shape_a[0], shape_a[1], self.output_dim
+
+        match_dim = 0
+        if self._perspective.get('full'):
+            match_dim += self._mp_dim + 1
+        if self._perspective.get('max-pooling'):
+            match_dim += self._mp_dim
+        if self._perspective.get('attentive'):
+            match_dim += self._mp_dim + 1
+        if self._perspective.get('max-attentive'):
+            match_dim += self._mp_dim + 1
+
+        return shape_a[0], shape_a[1], match_dim
 
 
 class MpFullMatch(Layer):
@@ -191,6 +206,13 @@ class MpAttentiveMatch(Layer):
     Reference:
     https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L188-L193
 
+    Examples:
+        >>> import matchzoo as mz
+        >>> layer = mz.contrib.layers.multi_perspective_layer.MpAttentiveMatch(
+        ...     att_dim=30, mp_dim=20)
+        >>> layer.compute_output_shape([(32, 10, 100), (32, 40, 100)])
+        (32, 10, 20)
+
     """
 
     def __init__(self, att_dim, mp_dim):
@@ -265,8 +287,8 @@ def collect_representation(representation, positions):
     Collect_representation.
 
     :param representation: [batch_size, node_num, feature_dim]
-    :param positions: [batch_size, neigh_num]
-    :return:
+    :param positions: [batch_size, neighbour_num]
+    :return: [batch_size, neighbour_num]?
     """
     return collect_probs(representation, positions)
 
@@ -275,7 +297,7 @@ def collect_final_step_of_lstm(lstm_representation, lengths):
     """
     Collect final step of lstm.
 
-    :param lstm_representation: [batch_size, passsage_length, dim]
+    :param lstm_representation: [batch_size, len_rt, dim]
     :param lengths: [batch_size]
     :return: [batch_size, dim]
     """
@@ -294,7 +316,7 @@ def collect_final_step_of_lstm(lstm_representation, lengths):
 
 def collect_probs(probs, positions):
     """
-    Collect Probs.
+    Collect Probabilities.
 
     Reference:
     https://github.com/zhiguowang/BiMPM/blob/master/src/layer_utils.py#L128-L140
@@ -312,7 +334,7 @@ def collect_probs(probs, positions):
     batch_nums = K.tile(batch_nums, [1, pair_size])
 
     # shape (batch_size, pair_size, 2)
-    # alert: to solve error message
+    # Alert: to solve error message
     positions = K.tf.to_int32(positions)
     indices = K.stack([batch_nums, positions], axis=2)
 
@@ -329,28 +351,31 @@ def _multi_perspective_match(mp_dim, reps_lt, reps_rt,
     reference:
     https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L207-L223
     :param mp_dim: about 20
-    :param reps_lt: [batch, len, dim]
-    :param reps_rt: [batch, len, dim]
+    :param reps_lt: [batch, len_lt, dim]
+    :param reps_rt: [batch, len_rt, dim]
     :param with_cosine: True
     :param with_mp_cosine: True
     :return: [batch, len, feature_dim*2]
     """
-    input_shape = K.shape(reps_lt)
-    batch_size = input_shape[0]
-    seq_length = input_shape[1]
+    shape_lt = K.shape(reps_lt)
+    batch_size = shape_lt[0]
+    len_lt = shape_lt[1]
 
     match_dim = 0
     match_result_list = []
     if with_cosine:
-        cosine_value = _cosine_distance(reps_lt, reps_rt, False)
-        cosine_value = K.reshape(cosine_value, [batch_size, seq_length, 1])
-        match_result_list.append(cosine_value)
+        cosine_tensor = _safe_cosine_distance(reps_lt, reps_rt, False)
+        cosine_tensor = K.reshape(cosine_tensor,
+                                  [batch_size, len_lt, 1])
+        match_result_list.append(cosine_tensor)
         match_dim += 1
 
     if with_mp_cosine:
         mp_cosine_layer = MpCosineLayer(mp_dim)
-        match_tensor = mp_cosine_layer([reps_lt, reps_rt])
-        match_result_list.append(match_tensor)
+        mp_cosine_tensor = mp_cosine_layer([reps_lt, reps_rt])
+        mp_cosine_tensor = K.reshape(mp_cosine_tensor,
+                                     [batch_size, len_lt, mp_dim])
+        match_result_list.append(mp_cosine_tensor)
         match_dim += mp_cosine_layer.mp_dim
 
     match_result = K.concatenate(match_result_list, 2)
@@ -363,6 +388,14 @@ class MpCosineLayer(Layer):
 
     Reference:
     https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L121-L129
+
+    Examples:
+        >>> import matchzoo as mz
+        >>> layer = mz.contrib.layers.multi_perspective_layer.MpCosineLayer(
+        ...     mp_dim=50)
+        >>> layer.compute_output_shape([(32, 10, 100), (32, 40, 100)])
+        (32, 10, 40, 50)
+
     """
 
     def __init__(self, mp_dim, **kwargs):
@@ -382,44 +415,69 @@ def build(self, input_shape):
     def call(self, x, **kwargs):
         """Call."""
         v1, v2 = x
-        v1 = K.expand_dims(v1, 2) * self.kernel
-        v2 = K.expand_dims(v2, 2)
-        return _cosine_distance(v1, v2, False)
+        v1 = K.expand_dims(v1, 2) * self.kernel  # [b, s_lt, m, d]
+        v2 = K.expand_dims(v2, 2)  # [b, s_rt, 1, d]
+        return _safe_cosine_distance(v1, v2, False)
 
     def compute_output_shape(self, input_shape):
         """Compute output shape."""
-        return input_shape[0][0], input_shape[0][1], self.mp_dim
+        return input_shape[0][0], input_shape[0][1], input_shape[1][1], \
+            self.mp_dim
 
 
 def _calc_relevancy_matrix(reps_lt, reps_rt):
-    # -> [batch_size, 1, len_lt, dim]
-    reps_lt = K.expand_dims(reps_lt, 1)
-    reps_rt = K.expand_dims(reps_rt, 2)
-    # -> [batch_size, len_rt, 1, dim]
-    # in_passage_repres_tmp = K.expand_dims(reps_rt, 2)
+    reps_lt = K.expand_dims(reps_lt, 1)  # [b, 1, len_lt, d]
+    reps_rt = K.expand_dims(reps_rt, 2)  # [b, len_rt, 1, d]
     relevancy_matrix = _cosine_distance(reps_lt, reps_rt)
     return relevancy_matrix
 
 
-def _mask_relevancy_matrix(relevancy_matrix, question_mask, passage_mask):
-    # relevancy_matrix: [batch_size, passage_len, question_len]
-    # question_mask: [batch_size, question_len]
-    # passage_mask: [batch_size, passsage_len]
-    if question_mask is not None:
-        relevancy_matrix = relevancy_matrix * K.expand_dims(question_mask, 1)
-    relevancy_matrix = relevancy_matrix * K.expand_dims(passage_mask, 2)
+def _mask_relevancy_matrix(relevancy_matrix, mask_lt, mask_rt):
+    """
+    Mask relevancy matrix.
+
+    :param relevancy_matrix: [b, len_rt, len_lt]
+    :param mask_lt: [b, len_lt]
+    :param mask_rt: [b, len_rt]
+    :return: masked_matrix: [b, len_rt, len_lt]
+    """
+    if mask_lt is not None:
+        relevancy_matrix = relevancy_matrix * K.expand_dims(mask_lt, 1)
+    relevancy_matrix = relevancy_matrix * K.expand_dims(mask_rt, 2)
     return relevancy_matrix
 
 
+def _safe_cosine_distance(v1, v2, cosine_norm=True, eps=1e-6):
+    """
+    Only requires `K.sum(v1 * v2, axis=-1)`.
+
+    :param v1: [batch, time_steps(v1), 1, m, d]
+    :param v2: [batch, 1, time_steps(v2), m, d]
+    :param cosine_norm: True
+    :param eps: 1e-6
+    :return: [batch, time_steps(v1), time_steps(v2), m]
+    """
+    shape1, shape2 = K.shape(v1), K.shape(v2)
+    if shape1[1] != shape2[1]:
+        v1 = K.expand_dims(v1, 2)  # [b, s_lt, 1, m, d]
+        v2 = K.expand_dims(v2, 1)  # [b, 1, s_rt, m, d]
+    cosine_numerator = K.sum(v1 * v2, axis=-1)
+    if not cosine_norm:
+        return K.tanh(cosine_numerator)
+    v1_norm = K.sqrt(K.maximum(K.sum(K.square(v1), axis=-1), eps))
+    v2_norm = K.sqrt(K.maximum(K.sum(K.square(v2), axis=-1), eps))
+    return cosine_numerator / v1_norm / v2_norm
+
+
 def _cosine_distance(v1, v2, cosine_norm=True, eps=1e-6):
     """
     Only requires `K.sum(v1 * v2, axis=-1)`.
 
-    :param v1: [batch, time_steps(v1), 1, l, d]
-    :param v2: [batch, 1, time_steps(v2), l, d]
+    :param v1: [batch, time_steps(v1), 1, m, d]
+    :param v2: [batch, 1, time_steps(v2), m, d]
     :param cosine_norm: True
     :param eps: 1e-6
-    :return: [batch, time_steps(v1), time_steps(v2), l]
+    :return: [batch, time_steps(v1), time_steps(v2), m]
     """
     cosine_numerator = K.sum(v1 * v2, axis=-1)
     if not cosine_norm:
diff --git a/matchzoo/contrib/models/__init__.py b/matchzoo/contrib/models/__init__.py
index 7b48145f..7c51acbb 100644
--- a/matchzoo/contrib/models/__init__.py
+++ b/matchzoo/contrib/models/__init__.py
@@ -1,3 +1,4 @@
 from .match_lstm import MatchLSTM
+
 from .esim import ESIM
 from .bimpm import BiMPM
diff --git a/matchzoo/contrib/models/bimpm.py b/matchzoo/contrib/models/bimpm.py
index e49e0de8..b5ee162f 100644
--- a/matchzoo/contrib/models/bimpm.py
+++ b/matchzoo/contrib/models/bimpm.py
@@ -56,50 +56,6 @@ def get_default_params(cls) -> ParamTable:
 
         return params
 
-    # @classmethod
-    # def get_default_preprocessor(cls):
-    #     """:return: Instance of :class:`NaivePreprocessor`."""
-    #     return preprocessors.NaivePreprocessor()
-    #
-    # @property
-    # def word_embedding_mat(self) -> np.ndarray:
-    #     """Get pretrained embedding for ArcI model."""
-    #     # Check if provided embedding matrix
-    #     if self._params['word_embedding_mat'] is None:
-    #         raise TypeError('No pre-trained word embeddings provided.')
-    #
-    # @word_embedding_mat.setter
-    # def word_embedding_mat(self, embedding_mat: np.ndarray):
-    #     """
-    #     Set pretrained embedding for ArcI model.
-    #
-    #     :param embedding_mat: pretrained embedding in numpy format.
-    #     """
-    #     self._params['word_embedding_mat'] = embedding_mat
-    #     self._params['vocab_size'], self._params['dim_word_embedding'] = \
-    #         embedding_mat.shape
-    #
-    # @property
-    # def char_embedding_mat(self) -> np.ndarray:
-    #     """Initialize character level embedding."""
-    #     s = self._params['embedding_random_scale']
-    #     self._params['char_embedding_mat'] = \
-    #         np.random.uniform(-s, s, (self._params['vocab_size'],
-    #                                   self._params['dim_char_embedding']))
-    #     return self._params['char_embedding_mat']
-    #
-    # def char_embedding(self, char_input_shape, char_vocab_size):
-    #     """Create a character level embedding model."""
-    #     input_char = Input(shape=char_input_shape)
-    #     embed_char = LSTM(self._params['dim_char_embedding'],
-    #                       kernel_initializer=self._params['w_initializer'],
-    #                       bias_initializer=self._params['b_initializer'])(
-    #                           input_char)
-    #     embed_char = Dense(char_vocab_size,
-    #                        activation=self._params['activation_embedding'])(
-    #                            embed_char)
-    #     return Model(input_char, embed_char)
-
     def build(self):
         """Build model structure."""
         # ~ Input Layer

From e3bb68bb772a39121d9ab9393a1529ca07e3f6aa Mon Sep 17 00:00:00 2001
From: rgtjf <rgtjf1@163.com>
Date: Thu, 18 Apr 2019 14:41:42 +0800
Subject: [PATCH 048/100] feature/bimpm: fix att matching

---
 .../contrib/layers/multi_perspective_layer.py | 55 ++++++-------------
 1 file changed, 16 insertions(+), 39 deletions(-)

diff --git a/matchzoo/contrib/layers/multi_perspective_layer.py b/matchzoo/contrib/layers/multi_perspective_layer.py
index e801df3a..dccd8bd9 100644
--- a/matchzoo/contrib/layers/multi_perspective_layer.py
+++ b/matchzoo/contrib/layers/multi_perspective_layer.py
@@ -233,11 +233,11 @@ def call(self, x, **kwargs):
         attention_layer = AttentionLayer(self.att_dim)
         attn_prob = attention_layer([reps_rt, reps_lt])
         # attention reps
-        reps_lt = K.batch_dot(attn_prob, reps_lt)
+        att_lt = K.batch_dot(attn_prob, reps_lt)
         # mp match
         attn_match_tensor, match_dim = _multi_perspective_match(self.mp_dim,
-                                                                reps_lt,
-                                                                reps_rt)
+                                                                reps_rt,
+                                                                att_lt)
         return attn_match_tensor
 
     def compute_output_shape(self, input_shape):
@@ -343,7 +343,7 @@ def collect_probs(probs, positions):
     return pair_probs
 
 
-def _multi_perspective_match(mp_dim, reps_lt, reps_rt,
+def _multi_perspective_match(mp_dim, reps_rt, att_lt,
                              with_cosine=True, with_mp_cosine=True):
     """
     The core function of zhiguowang's implementation.
@@ -351,20 +351,20 @@ def _multi_perspective_match(mp_dim, reps_lt, reps_rt,
     reference:
     https://github.com/zhiguowang/BiMPM/blob/master/src/match_utils.py#L207-L223
     :param mp_dim: about 20
-    :param reps_lt: [batch, len_lt, dim]
     :param reps_rt: [batch, len_rt, dim]
+    :param att_lt: [batch, len_rt, dim]
     :param with_cosine: True
     :param with_mp_cosine: True
-    :return: [batch, len, feature_dim*2]
+    :return: [batch, len, 1 + mp_dim]
     """
-    shape_lt = K.shape(reps_lt)
-    batch_size = shape_lt[0]
-    len_lt = shape_lt[1]
+    shape_rt = K.shape(reps_rt)
+    batch_size = shape_rt[0]
+    len_lt = shape_rt[1]
 
     match_dim = 0
     match_result_list = []
     if with_cosine:
-        cosine_tensor = _safe_cosine_distance(reps_lt, reps_rt, False)
+        cosine_tensor = _cosine_distance(reps_rt, att_lt, False)
         cosine_tensor = K.reshape(cosine_tensor,
                                   [batch_size, len_lt, 1])
         match_result_list.append(cosine_tensor)
@@ -372,7 +372,7 @@ def _multi_perspective_match(mp_dim, reps_lt, reps_rt,
 
     if with_mp_cosine:
         mp_cosine_layer = MpCosineLayer(mp_dim)
-        mp_cosine_tensor = mp_cosine_layer([reps_lt, reps_rt])
+        mp_cosine_tensor = mp_cosine_layer([reps_rt, att_lt])
         mp_cosine_tensor = K.reshape(mp_cosine_tensor,
                                      [batch_size, len_lt, mp_dim])
         match_result_list.append(mp_cosine_tensor)
@@ -393,8 +393,8 @@ class MpCosineLayer(Layer):
         >>> import matchzoo as mz
         >>> layer = mz.contrib.layers.multi_perspective_layer.MpCosineLayer(
         ...     mp_dim=50)
-        >>> layer.compute_output_shape([(32, 10, 100), (32, 40, 100)])
-        (32, 10, 40, 50)
+        >>> layer.compute_output_shape([(32, 10, 100), (32, 10, 100)])
+        (32, 10, 50)
 
     """
 
@@ -416,13 +416,12 @@ def call(self, x, **kwargs):
         """Call."""
         v1, v2 = x
         v1 = K.expand_dims(v1, 2) * self.kernel  # [b, s_lt, m, d]
-        v2 = K.expand_dims(v2, 2)  # [b, s_rt, 1, d]
-        return _safe_cosine_distance(v1, v2, False)
+        v2 = K.expand_dims(v2, 2)  # [b, s_lt, 1, d]
+        return _cosine_distance(v1, v2, False)
 
     def compute_output_shape(self, input_shape):
         """Compute output shape."""
-        return input_shape[0][0], input_shape[0][1], input_shape[1][1], \
-            self.mp_dim
+        return input_shape[0][0], input_shape[0][1], self.mp_dim
 
 
 def _calc_relevancy_matrix(reps_lt, reps_rt):
@@ -447,28 +446,6 @@ def _mask_relevancy_matrix(relevancy_matrix, mask_lt, mask_rt):
     return relevancy_matrix
 
 
-def _safe_cosine_distance(v1, v2, cosine_norm=True, eps=1e-6):
-    """
-    Only requires `K.sum(v1 * v2, axis=-1)`.
-
-    :param v1: [batch, time_steps(v1), 1, m, d]
-    :param v2: [batch, 1, time_steps(v2), m, d]
-    :param cosine_norm: True
-    :param eps: 1e-6
-    :return: [batch, time_steps(v1), time_steps(v2), m]
-    """
-    shape1, shape2 = K.shape(v1), K.shape(v2)
-    if shape1[1] != shape2[1]:
-        v1 = K.expand_dims(v1, 2)  # [b, s_lt, 1, m, d]
-        v2 = K.expand_dims(v2, 1)  # [b, 1, s_rt, m, d]
-    cosine_numerator = K.sum(v1 * v2, axis=-1)
-    if not cosine_norm:
-        return K.tanh(cosine_numerator)
-    v1_norm = K.sqrt(K.maximum(K.sum(K.square(v1), axis=-1), eps))
-    v2_norm = K.sqrt(K.maximum(K.sum(K.square(v2), axis=-1), eps))
-    return cosine_numerator / v1_norm / v2_norm
-
-
 def _cosine_distance(v1, v2, cosine_norm=True, eps=1e-6):
     """
     Only requires `K.sum(v1 * v2, axis=-1)`.

From d7de730d9d1c20915524088ca4d1aeb4bfca0534 Mon Sep 17 00:00:00 2001
From: Chriskuei <chenjiangui@outlook.com>
Date: Thu, 18 Apr 2019 14:47:56 +0800
Subject: [PATCH 049/100] Add test for SpatialGRU and MatchingTensorLayer

---
 .../contrib/layers/matching_tensor_layer.py   |  4 +--
 matchzoo/contrib/layers/spatial_gru.py        | 15 ++++-----
 tests/unit_test/test_layers.py                | 33 +++++++++++++++++++
 3 files changed, 41 insertions(+), 11 deletions(-)

diff --git a/matchzoo/contrib/layers/matching_tensor_layer.py b/matchzoo/contrib/layers/matching_tensor_layer.py
index 44de9f2f..1d494a6c 100644
--- a/matchzoo/contrib/layers/matching_tensor_layer.py
+++ b/matchzoo/contrib/layers/matching_tensor_layer.py
@@ -23,7 +23,7 @@ class MatchingTensorLayer(Layer):
 
     Examples:
         >>> import matchzoo as mz
-        >>> layer = mz.contrib.layers.MatchingTensorLayer(channels=10,
+        >>> layer = mz.contrib.layers.MatchingTensorLayer(channels=4,
         ...                                               normalize=True,
         ...                                               init_diag=True)
         >>> num_batch, left_len, right_len, num_dim = 5, 3, 2, 10
@@ -32,7 +32,7 @@ class MatchingTensorLayer(Layer):
 
     """
 
-    def __init__(self, channels: int = 10, normalize: bool = False,
+    def __init__(self, channels: int = 4, normalize: bool = True,
                  init_diag: bool = True, **kwargs):
         """:class:`MatchingTensorLayer` constructor."""
         super(MatchingTensorLayer, self).__init__(**kwargs)
diff --git a/matchzoo/contrib/layers/spatial_gru.py b/matchzoo/contrib/layers/spatial_gru.py
index 7676f744..656daacc 100644
--- a/matchzoo/contrib/layers/spatial_gru.py
+++ b/matchzoo/contrib/layers/spatial_gru.py
@@ -45,15 +45,15 @@ class SpatialGRU(Layer):
 
     Examples:
         >>> import matchzoo as mz
-        >>> layer = mz.contrib.layers.SpatialGRU(units=50,
+        >>> layer = mz.contrib.layers.SpatialGRU(units=10,
         ...                                      direction='lt')
-        >>> num_batch, channel, left_len, right_len = 5, 10, 3, 2
+        >>> num_batch, channel, left_len, right_len = 5, 5, 3, 2
         >>> layer.build([num_batch, channel, left_len, right_len])
 
     """
 
     def __init__(self,
-                 units: int = 50,
+                 units: int = 10,
                  normalize: bool = False,
                  init_diag: bool = False,
                  activation: str = 'tanh',
@@ -166,9 +166,9 @@ def calculate_recurrent_unit(self,
         z = self._time_distributed_dense(self._wz, q, self._bz)
         zi, zl, zt, zd = self.softmax_by_row(z)
 
-        hij_1 = self._time_distributed_dense(self._w_ij, s_ij, self._b_ij)
-        hij_2 = K.dot(r * (K.tf.concat([h_left, h_top, h_diag], 1)), self._U)
-        hij_ = self._activation(hij_1 + hij_2)
+        hij_l = self._time_distributed_dense(self._w_ij, s_ij, self._b_ij)
+        hij_r = K.dot(r * (K.tf.concat([h_left, h_top, h_diag], 1)), self._U)
+        hij_ = self._activation(hij_l + hij_r)
         hij = zl * h_left + zt * h_top + zd * h_diag + zi * hij_
         states = states.write(((i + 1) * (self._text2_maxlen + 1) + j + 1),
                               hij)
@@ -261,7 +261,4 @@ def _time_distributed_dense(cls, w, x, b):
         if K.backend() == 'tensorflow':
             x = K.dot(x, w)
             x = K.bias_add(x, b)
-        else:
-            raise Exception("time_distributed_dense doesn't "
-                            "backend tensorflow")
         return x
diff --git a/tests/unit_test/test_layers.py b/tests/unit_test/test_layers.py
index 88c9c1ab..8d77816a 100644
--- a/tests/unit_test/test_layers.py
+++ b/tests/unit_test/test_layers.py
@@ -3,6 +3,8 @@
 from keras import backend as K
 
 from matchzoo import layers
+from matchzoo.contrib.layers import SpatialGRU
+from matchzoo.contrib.layers import MatchingTensorLayer
 
 
 def test_matching_layers():
@@ -26,3 +28,34 @@ def test_matching_layers():
         layers.MatchingLayer(matching_type='error')
     with pytest.raises(ValueError):
         layers.MatchingLayer()([s1_tensor, s3_tensor])
+
+
+def test_spatial_gru():
+    s_value = K.variable(np.array([[[[1, 2], [2, 3], [3, 4]],
+                                    [[4, 5], [5, 6], [6, 7]]],
+                                   [[[0.1, 0.2], [0.2, 0.3], [0.3, 0.4]],
+                                    [[0.4, 0.5], [0.5, 0.6], [0.6, 0.7]]]]))
+    for direction in ['lt', 'rb']:
+        model = SpatialGRU(direction=direction)(s_value)
+        _ = K.eval(model)
+    with pytest.raises(ValueError):
+        SpatialGRU(direction='lr')(s_value)
+
+
+def test_matching_tensor_layer():
+    s1_value = np.array([[[1, 2], [2, 3], [3, 4]],
+                         [[0.1, 0.2], [0.2, 0.3], [0.3, 0.4]]])
+    s2_value = np.array([[[1, 2], [2, 3]],
+                         [[0.1, 0.2], [0.2, 0.3]]])
+    s3_value = np.array([[[1, 2], [2, 3]],
+                         [[0.1, 0.2], [0.2, 0.3]],
+                         [[0.1, 0.2], [0.2, 0.3]]])
+    s1_tensor = K.variable(s1_value)
+    s2_tensor = K.variable(s2_value)
+    s3_tensor = K.variable(s3_value)
+    for init_diag in [True, False]:
+        model = MatchingTensorLayer(
+            init_diag=init_diag)([s1_tensor, s2_tensor])
+        _ = K.eval(model)
+    with pytest.raises(ValueError):
+        layers.MatchingLayer()([s1_tensor, s3_tensor])

From b95b76b5b3b8cc18d60d09541e521bffead1a999 Mon Sep 17 00:00:00 2001
From: Chriskuei <chenjiangui@outlook.com>
Date: Thu, 18 Apr 2019 15:07:05 +0800
Subject: [PATCH 050/100] Update test for SpatialGRU and MatchingTensorLayer

---
 tests/unit_test/test_layers.py | 13 +++++++------
 1 file changed, 7 insertions(+), 6 deletions(-)

diff --git a/tests/unit_test/test_layers.py b/tests/unit_test/test_layers.py
index 8d77816a..abfdebf4 100644
--- a/tests/unit_test/test_layers.py
+++ b/tests/unit_test/test_layers.py
@@ -36,8 +36,9 @@ def test_spatial_gru():
                                    [[[0.1, 0.2], [0.2, 0.3], [0.3, 0.4]],
                                     [[0.4, 0.5], [0.5, 0.6], [0.6, 0.7]]]]))
     for direction in ['lt', 'rb']:
-        model = SpatialGRU(direction=direction)(s_value)
-        _ = K.eval(model)
+        model = SpatialGRU(direction=direction)
+        _ = K.eval(model(s_value))
+        _ = model.get_config()
     with pytest.raises(ValueError):
         SpatialGRU(direction='lr')(s_value)
 
@@ -54,8 +55,8 @@ def test_matching_tensor_layer():
     s2_tensor = K.variable(s2_value)
     s3_tensor = K.variable(s3_value)
     for init_diag in [True, False]:
-        model = MatchingTensorLayer(
-            init_diag=init_diag)([s1_tensor, s2_tensor])
-        _ = K.eval(model)
+        model = MatchingTensorLayer(init_diag=init_diag)
+        _ = K.eval(model([s1_tensor, s2_tensor]))
+        _ = model.get_config()
     with pytest.raises(ValueError):
-        layers.MatchingLayer()([s1_tensor, s3_tensor])
+        MatchingTensorLayer()([s1_tensor, s3_tensor])

From e9cb468d6aaffee9059646dc25af423f8bb5fbe8 Mon Sep 17 00:00:00 2001
From: rgtjf <rgtjf1@163.com>
Date: Thu, 18 Apr 2019 15:07:12 +0800
Subject: [PATCH 051/100] feature/bimpm: fix full matching

---
 .../contrib/layers/multi_perspective_layer.py | 39 ++++++++++---------
 1 file changed, 21 insertions(+), 18 deletions(-)

diff --git a/matchzoo/contrib/layers/multi_perspective_layer.py b/matchzoo/contrib/layers/multi_perspective_layer.py
index dccd8bd9..f8dd3566 100644
--- a/matchzoo/contrib/layers/multi_perspective_layer.py
+++ b/matchzoo/contrib/layers/multi_perspective_layer.py
@@ -78,8 +78,8 @@ def call(self, x: list, **kwargs):
         if self._perspective.get('full'):
             # Each forward & backward contextual embedding compare
             # with the last step of the last time step of the other sentence.
-            h_rt = K.concatenate([forward_h_rt, backward_h_rt], axis=-1)
-            full_match_tensor = self.full_match([lstm_reps_lt, h_rt])
+            h_lt = K.concatenate([forward_h_lt, backward_h_lt], axis=-1)
+            full_match_tensor = self.full_match([h_lt, lstm_reps_rt])
             match_tensor_list.append(full_match_tensor)
             match_dim += self._mp_dim + 1
 
@@ -144,18 +144,20 @@ def build(self, input_shapes):
         self.built = True
 
     def call(self, x, **kwargs):
-        """Call."""
-        reps_lt, rep_rt = x
-        reps_rt = K.expand_dims(rep_rt, 1)
-        # match_tensor: [batch, steps_lt, mp_dim+1]
+        """Call.
+        """
+        rep_lt, reps_rt = x
+        att_lt = K.expand_dims(rep_lt, 1)
+
         match_tensor, match_dim = _multi_perspective_match(self.mp_dim,
-                                                           reps_lt,
-                                                           reps_rt)
+                                                           reps_rt,
+                                                           att_lt)
+        # match_tensor => [b, len_rt, mp_dim+1]
         return match_tensor
 
     def compute_output_shape(self, input_shape):
         """Compute output shape."""
-        return input_shape[0][0], input_shape[0][1], self.mp_dim + 1
+        return input_shape[1][0], input_shape[1][1], self.mp_dim + 1
 
 
 class MpMaxPoolingMatch(Layer):
@@ -168,9 +170,9 @@ def __init__(self, mp_dim):
 
     def build(self, input_shapes):
         """Build."""
+        d = input_shapes[0][-1]
         self.kernel = self.add_weight(name='kernel',
-                                      shape=(1, 1, 1,
-                                             self.mp_dim, input_shapes[0][-1]),
+                                      shape=(1, 1, 1, self.mp_dim, d),
                                       initializer='uniform',
                                       trainable=True)
         self.built = True
@@ -180,23 +182,23 @@ def call(self, x, **kwargs):
         reps_lt, reps_rt = x
 
         # kernel: [1, 1, 1, mp_dim, d]
-        # lstm_lt -> [batch, steps_lt, 1, 1, d]
+        # lstm_lt => [b, len_lt, 1, 1, d]
         reps_lt = K.expand_dims(reps_lt, axis=2)
         reps_lt = K.expand_dims(reps_lt, axis=2)
         reps_lt = reps_lt * self.kernel
 
-        # lstm_rt -> [batch, 1, steps_rt, 1, d]
+        # lstm_rt -> [b, 1, len_rt, 1, d]
         reps_rt = K.expand_dims(reps_rt, axis=2)
         reps_rt = K.expand_dims(reps_rt, axis=1)
 
-        # match_tensor -> [batch, steps_lt, steps_rt, mp_dim]
         match_tensor = _cosine_distance(reps_lt, reps_rt, cosine_norm=False)
-        max_match_tensor = K.max(match_tensor, axis=2)
+        max_match_tensor = K.max(match_tensor, axis=1)
+        # match_tensor => [b, len_rt, m]
         return max_match_tensor
 
     def compute_output_shape(self, input_shape):
         """Compute output shape."""
-        return input_shape[0][0], input_shape[0][1], self.mp_dim
+        return input_shape[1][0], input_shape[1][1], self.mp_dim
 
 
 class MpAttentiveMatch(Layer):
@@ -211,7 +213,7 @@ class MpAttentiveMatch(Layer):
         >>> layer = mz.contrib.layers.multi_perspective_layer.MpAttentiveMatch(
         ...     att_dim=30, mp_dim=20)
         >>> layer.compute_output_shape([(32, 10, 100), (32, 40, 100)])
-        (32, 10, 20)
+        (32, 40, 20)
 
     """
 
@@ -242,7 +244,7 @@ def call(self, x, **kwargs):
 
     def compute_output_shape(self, input_shape):
         """Compute output shape."""
-        return input_shape[0][0], input_shape[0][1], self.mp_dim
+        return input_shape[1][0], input_shape[1][1], self.mp_dim
 
 
 class MpMaxAttentiveMatch(Layer):
@@ -428,6 +430,7 @@ def _calc_relevancy_matrix(reps_lt, reps_rt):
     reps_lt = K.expand_dims(reps_lt, 1)  # [b, 1, len_lt, d]
     reps_rt = K.expand_dims(reps_rt, 2)  # [b, len_rt, 1, d]
     relevancy_matrix = _cosine_distance(reps_lt, reps_rt)
+    # => [b, len_rt, len_lt, d]
     return relevancy_matrix
 
 

From c31c8439f5f263c1f8f928785b364540c82d87ac Mon Sep 17 00:00:00 2001
From: Chriskuei <chenjiangui@outlook.com>
Date: Thu, 18 Apr 2019 17:04:26 +0800
Subject: [PATCH 052/100] Remove redundant code

---
 .../contrib/layers/matching_tensor_layer.py   | 31 ++++++-------------
 matchzoo/contrib/layers/spatial_gru.py        | 21 -------------
 tests/unit_test/test_layers.py                |  2 --
 3 files changed, 9 insertions(+), 45 deletions(-)

diff --git a/matchzoo/contrib/layers/matching_tensor_layer.py b/matchzoo/contrib/layers/matching_tensor_layer.py
index 1d494a6c..2afc23d6 100644
--- a/matchzoo/contrib/layers/matching_tensor_layer.py
+++ b/matchzoo/contrib/layers/matching_tensor_layer.py
@@ -64,26 +64,23 @@ def build(self, input_shape: list):
                 )
 
         if self._init_diag:
-            if self._shape1[2] != self._shape2[2]:
-                raise ValueError('Use init_diag need same embedding shape.')
-            M_diag = np.float32(
+            interaction_matrix = np.float32(
                 np.random.uniform(
                     -0.05, 0.05,
                     [self._channels, self._shape1[2], self._shape2[2]]
                 )
             )
-            for i in range(self._channels):
-                for j in range(self._shape1[2]):
-                    M_diag[i][j][j] = 0.1
-            self.M = self.add_weight(
-                name='M',
+            for channel_index in range(self._channels):
+                np.fill_diagonal(interaction_matrix[channel_index], 0.1)
+            self.interaction_matrix = self.add_weight(
+                name='interaction_matrix',
                 shape=(self._channels, self._shape1[2], self._shape2[2]),
-                initializer=constant(M_diag),
+                initializer=constant(interaction_matrix),
                 trainable=True
             )
         else:
-            self.M = self.add_weight(
-                name='M',
+            self.interaction_matrix = self.add_weight(
+                name='interaction_matrix',
                 shape=(self._channels, self._shape1[2], self._shape2[2]),
                 initializer='uniform',
                 trainable=True
@@ -101,7 +98,7 @@ def call(self, inputs: list, **kwargs) -> typing.Any:
         if self._normalize:
             x1 = K.l2_normalize(x1, axis=2)
             x2 = K.l2_normalize(x2, axis=2)
-        output = K.tf.einsum('abd,fde,ace->afbc', x1, self.M, x2)
+        output = K.tf.einsum('abd,fde,ace->afbc', x1, self.interaction_matrix, x2)
         return output
 
     def compute_output_shape(self, input_shape: list) -> tuple:
@@ -125,13 +122,3 @@ def compute_output_shape(self, input_shape: list) -> tuple:
 
         output_shape = [shape1[0], self._channels, shape1[1], shape2[1]]
         return tuple(output_shape)
-
-    def get_config(self) -> dict:
-        """Get the config dict of MatchingTensorLayer."""
-        config = {
-            'channels': self._channels,
-            'normalize': self._normalize,
-            'init_diag': self._init_diag,
-        }
-        base_config = super(MatchingTensorLayer, self).get_config()
-        return dict(list(base_config.items()) + list(config.items()))
diff --git a/matchzoo/contrib/layers/spatial_gru.py b/matchzoo/contrib/layers/spatial_gru.py
index 656daacc..9a9361b9 100644
--- a/matchzoo/contrib/layers/spatial_gru.py
+++ b/matchzoo/contrib/layers/spatial_gru.py
@@ -235,27 +235,6 @@ def compute_output_shape(self, input_shape: typing.Any) -> tuple:
         output_shape = [input_shape[0], self._units]
         return tuple(output_shape)
 
-    def get_config(self) -> dict:
-        """Get the config dict of SpatialGRU."""
-        config = {
-            'units': self._units,
-            'normalize': self._normalize,
-            'init_diag': self._init_diag,
-            'activation': activations.serialize(self._activation),
-            'recurrent_activation':
-                activations.serialize(self._recurrent_activation),
-            'use_bias': self._use_bias,
-            'kernel_initializer':
-                initializers.serialize(self._kernel_initializer),
-            'recurrent_initializer':
-                initializers.serialize(self._recurrent_initializer),
-            'bias_initializer':
-                initializers.serialize(self._bias_initializer),
-            'direction': self._direction
-        }
-        base_config = super(SpatialGRU, self).get_config()
-        return dict(list(base_config.items()) + list(config.items()))
-
     @classmethod
     def _time_distributed_dense(cls, w, x, b):
         if K.backend() == 'tensorflow':
diff --git a/tests/unit_test/test_layers.py b/tests/unit_test/test_layers.py
index abfdebf4..cda16191 100644
--- a/tests/unit_test/test_layers.py
+++ b/tests/unit_test/test_layers.py
@@ -38,7 +38,6 @@ def test_spatial_gru():
     for direction in ['lt', 'rb']:
         model = SpatialGRU(direction=direction)
         _ = K.eval(model(s_value))
-        _ = model.get_config()
     with pytest.raises(ValueError):
         SpatialGRU(direction='lr')(s_value)
 
@@ -57,6 +56,5 @@ def test_matching_tensor_layer():
     for init_diag in [True, False]:
         model = MatchingTensorLayer(init_diag=init_diag)
         _ = K.eval(model([s1_tensor, s2_tensor]))
-        _ = model.get_config()
     with pytest.raises(ValueError):
         MatchingTensorLayer()([s1_tensor, s3_tensor])

From be9cb7cbe38b7a7fab2840580044cc7ec12bb565 Mon Sep 17 00:00:00 2001
From: bo <kingbolanda@live.com>
Date: Thu, 18 Apr 2019 11:09:48 +0200
Subject: [PATCH 053/100] ignore notebook from code percentage calculate

---
 .github/.gitattributes | 1 +
 1 file changed, 1 insertion(+)
 create mode 100644 .github/.gitattributes

diff --git a/.github/.gitattributes b/.github/.gitattributes
new file mode 100644
index 00000000..037a5516
--- /dev/null
+++ b/.github/.gitattributes
@@ -0,0 +1 @@
+tutorials/* linguist-vendored
\ No newline at end of file

From 4b6da1b4c174ed743c248a454abc1fcbf5cb137e Mon Sep 17 00:00:00 2001
From: Chriskuei <chenjiangui@outlook.com>
Date: Thu, 18 Apr 2019 17:55:52 +0800
Subject: [PATCH 054/100] Remove unused code

---
 matchzoo/contrib/layers/matching_tensor_layer.py | 3 +--
 matchzoo/contrib/layers/spatial_gru.py           | 2 +-
 2 files changed, 2 insertions(+), 3 deletions(-)

diff --git a/matchzoo/contrib/layers/matching_tensor_layer.py b/matchzoo/contrib/layers/matching_tensor_layer.py
index 2afc23d6..112b3955 100644
--- a/matchzoo/contrib/layers/matching_tensor_layer.py
+++ b/matchzoo/contrib/layers/matching_tensor_layer.py
@@ -2,7 +2,6 @@
 import typing
 
 import numpy as np
-
 from keras import backend as K
 from keras.engine import Layer
 from keras.initializers import constant
@@ -35,7 +34,7 @@ class MatchingTensorLayer(Layer):
     def __init__(self, channels: int = 4, normalize: bool = True,
                  init_diag: bool = True, **kwargs):
         """:class:`MatchingTensorLayer` constructor."""
-        super(MatchingTensorLayer, self).__init__(**kwargs)
+        super().__init__(**kwargs)
         self._channels = channels
         self._normalize = normalize
         self._init_diag = init_diag
diff --git a/matchzoo/contrib/layers/spatial_gru.py b/matchzoo/contrib/layers/spatial_gru.py
index 9a9361b9..fb9f5e66 100644
--- a/matchzoo/contrib/layers/spatial_gru.py
+++ b/matchzoo/contrib/layers/spatial_gru.py
@@ -65,7 +65,7 @@ def __init__(self,
                  direction: str = 'lr',
                  **kwargs):
         """:class:`SpatialGRU` constructor."""
-        super(SpatialGRU, self).__init__(**kwargs)
+        super().__init__(**kwargs)
         self._units = units
         self._normalize = normalize
         self._init_diag = init_diag

From b5ccdef6fd93705aa8f52c6ddde6bf4395464ef5 Mon Sep 17 00:00:00 2001
From: bo <kingbolanda@live.com>
Date: Thu, 18 Apr 2019 13:42:51 +0200
Subject: [PATCH 055/100] take out git attributes

---
 .github/.gitattributes => .gitattributes | 0
 1 file changed, 0 insertions(+), 0 deletions(-)
 rename .github/.gitattributes => .gitattributes (100%)

diff --git a/.github/.gitattributes b/.gitattributes
similarity index 100%
rename from .github/.gitattributes
rename to .gitattributes

From de3a0b66d0fc1df42295b82e956afc4b5457b46e Mon Sep 17 00:00:00 2001
From: jellying <469413628@qq.com>
Date: Sat, 20 Apr 2019 11:00:03 +0800
Subject: [PATCH 056/100] add berttokenize unit

---
 matchzoo/preprocessors/units/__init__.py      |   1 +
 .../preprocessors/units/bert_tokenization.py  | 399 ++++++++++++++++++
 matchzoo/preprocessors/units/bert_tokenize.py |  23 +
 .../processor_units/test_processor_units.py   |  26 ++
 4 files changed, 449 insertions(+)
 create mode 100644 matchzoo/preprocessors/units/bert_tokenization.py
 create mode 100644 matchzoo/preprocessors/units/bert_tokenize.py

diff --git a/matchzoo/preprocessors/units/__init__.py b/matchzoo/preprocessors/units/__init__.py
index d9e88953..926b24cc 100644
--- a/matchzoo/preprocessors/units/__init__.py
+++ b/matchzoo/preprocessors/units/__init__.py
@@ -13,6 +13,7 @@
 from .tokenize import Tokenize
 from .vocabulary import Vocabulary
 from .word_hashing import WordHashing
+from .bert_tokenize import BertTokenize
 
 
 def list_available() -> list:
diff --git a/matchzoo/preprocessors/units/bert_tokenization.py b/matchzoo/preprocessors/units/bert_tokenization.py
new file mode 100644
index 00000000..0ee13595
--- /dev/null
+++ b/matchzoo/preprocessors/units/bert_tokenization.py
@@ -0,0 +1,399 @@
+# coding=utf-8
+# Copyright 2018 The Google AI Language Team Authors.
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+"""Tokenization classes."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import collections
+import re
+import unicodedata
+import six
+import tensorflow as tf
+
+
+def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
+  """Checks whether the casing config is consistent with the checkpoint name."""
+
+  # The casing has to be passed in by the user and there is no explicit check
+  # as to whether it matches the checkpoint. The casing information probably
+  # should have been stored in the bert_config.json file, but it's not, so
+  # we have to heuristically detect it to validate.
+
+  if not init_checkpoint:
+    return
+
+  m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
+  if m is None:
+    return
+
+  model_name = m.group(1)
+
+  lower_models = [
+      "uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
+      "multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
+  ]
+
+  cased_models = [
+      "cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
+      "multi_cased_L-12_H-768_A-12"
+  ]
+
+  is_bad_config = False
+  if model_name in lower_models and not do_lower_case:
+    is_bad_config = True
+    actual_flag = "False"
+    case_name = "lowercased"
+    opposite_flag = "True"
+
+  if model_name in cased_models and do_lower_case:
+    is_bad_config = True
+    actual_flag = "True"
+    case_name = "cased"
+    opposite_flag = "False"
+
+  if is_bad_config:
+    raise ValueError(
+        "You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
+        "However, `%s` seems to be a %s model, so you "
+        "should pass in `--do_lower_case=%s` so that the fine-tuning matches "
+        "how the model was pre-training. If this error is wrong, please "
+        "just comment out this check." % (actual_flag, init_checkpoint,
+                                          model_name, case_name, opposite_flag))
+
+
+def convert_to_unicode(text):
+  """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
+  if six.PY3:
+    if isinstance(text, str):
+      return text
+    elif isinstance(text, bytes):
+      return text.decode("utf-8", "ignore")
+    else:
+      raise ValueError("Unsupported string type: %s" % (type(text)))
+  elif six.PY2:
+    if isinstance(text, str):
+      return text.decode("utf-8", "ignore")
+    elif isinstance(text, unicode):
+      return text
+    else:
+      raise ValueError("Unsupported string type: %s" % (type(text)))
+  else:
+    raise ValueError("Not running on Python2 or Python 3?")
+
+
+def printable_text(text):
+  """Returns text encoded in a way suitable for print or `tf.logging`."""
+
+  # These functions want `str` for both Python2 and Python3, but in one case
+  # it's a Unicode string and in the other it's a byte string.
+  if six.PY3:
+    if isinstance(text, str):
+      return text
+    elif isinstance(text, bytes):
+      return text.decode("utf-8", "ignore")
+    else:
+      raise ValueError("Unsupported string type: %s" % (type(text)))
+  elif six.PY2:
+    if isinstance(text, str):
+      return text
+    elif isinstance(text, unicode):
+      return text.encode("utf-8")
+    else:
+      raise ValueError("Unsupported string type: %s" % (type(text)))
+  else:
+    raise ValueError("Not running on Python2 or Python 3?")
+
+
+def load_vocab(vocab_file):
+  """Loads a vocabulary file into a dictionary."""
+  vocab = collections.OrderedDict()
+  index = 0
+  with tf.gfile.GFile(vocab_file, "r") as reader:
+    while True:
+      token = convert_to_unicode(reader.readline())
+      if not token:
+        break
+      token = token.strip()
+      vocab[token] = index
+      index += 1
+  return vocab
+
+
+def convert_by_vocab(vocab, items):
+  """Converts a sequence of [tokens|ids] using the vocab."""
+  output = []
+  for item in items:
+    output.append(vocab[item])
+  return output
+
+
+def convert_tokens_to_ids(vocab, tokens):
+  return convert_by_vocab(vocab, tokens)
+
+
+def convert_ids_to_tokens(inv_vocab, ids):
+  return convert_by_vocab(inv_vocab, ids)
+
+
+def whitespace_tokenize(text):
+  """Runs basic whitespace cleaning and splitting on a piece of text."""
+  text = text.strip()
+  if not text:
+    return []
+  tokens = text.split()
+  return tokens
+
+
+class FullTokenizer(object):
+  """Runs end-to-end tokenziation."""
+
+  def __init__(self, vocab_file, do_lower_case=True):
+    self.vocab = load_vocab(vocab_file)
+    self.inv_vocab = {v: k for k, v in self.vocab.items()}
+    self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
+    self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
+
+  def tokenize(self, text):
+    split_tokens = []
+    for token in self.basic_tokenizer.tokenize(text):
+      for sub_token in self.wordpiece_tokenizer.tokenize(token):
+        split_tokens.append(sub_token)
+
+    return split_tokens
+
+  def convert_tokens_to_ids(self, tokens):
+    return convert_by_vocab(self.vocab, tokens)
+
+  def convert_ids_to_tokens(self, ids):
+    return convert_by_vocab(self.inv_vocab, ids)
+
+
+class BasicTokenizer(object):
+  """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
+
+  def __init__(self, do_lower_case=True):
+    """Constructs a BasicTokenizer.
+
+    Args:
+      do_lower_case: Whether to lower case the input.
+    """
+    self.do_lower_case = do_lower_case
+
+  def tokenize(self, text):
+    """Tokenizes a piece of text."""
+    text = convert_to_unicode(text)
+    text = self._clean_text(text)
+
+    # This was added on November 1st, 2018 for the multilingual and Chinese
+    # models. This is also applied to the English models now, but it doesn't
+    # matter since the English models were not trained on any Chinese data
+    # and generally don't have any Chinese data in them (there are Chinese
+    # characters in the vocabulary because Wikipedia does have some Chinese
+    # words in the English Wikipedia.).
+    text = self._tokenize_chinese_chars(text)
+
+    orig_tokens = whitespace_tokenize(text)
+    split_tokens = []
+    for token in orig_tokens:
+      if self.do_lower_case:
+        token = token.lower()
+        token = self._run_strip_accents(token)
+      split_tokens.extend(self._run_split_on_punc(token))
+
+    output_tokens = whitespace_tokenize(" ".join(split_tokens))
+    return output_tokens
+
+  def _run_strip_accents(self, text):
+    """Strips accents from a piece of text."""
+    text = unicodedata.normalize("NFD", text)
+    output = []
+    for char in text:
+      cat = unicodedata.category(char)
+      if cat == "Mn":
+        continue
+      output.append(char)
+    return "".join(output)
+
+  def _run_split_on_punc(self, text):
+    """Splits punctuation on a piece of text."""
+    chars = list(text)
+    i = 0
+    start_new_word = True
+    output = []
+    while i < len(chars):
+      char = chars[i]
+      if _is_punctuation(char):
+        output.append([char])
+        start_new_word = True
+      else:
+        if start_new_word:
+          output.append([])
+        start_new_word = False
+        output[-1].append(char)
+      i += 1
+
+    return ["".join(x) for x in output]
+
+  def _tokenize_chinese_chars(self, text):
+    """Adds whitespace around any CJK character."""
+    output = []
+    for char in text:
+      cp = ord(char)
+      if self._is_chinese_char(cp):
+        output.append(" ")
+        output.append(char)
+        output.append(" ")
+      else:
+        output.append(char)
+    return "".join(output)
+
+  def _is_chinese_char(self, cp):
+    """Checks whether CP is the codepoint of a CJK character."""
+    # This defines a "chinese character" as anything in the CJK Unicode block:
+    #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+    #
+    # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+    # despite its name. The modern Korean Hangul alphabet is a different block,
+    # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+    # space-separated words, so they are not treated specially and handled
+    # like the all of the other languages.
+    if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
+        (cp >= 0x3400 and cp <= 0x4DBF) or  #
+        (cp >= 0x20000 and cp <= 0x2A6DF) or  #
+        (cp >= 0x2A700 and cp <= 0x2B73F) or  #
+        (cp >= 0x2B740 and cp <= 0x2B81F) or  #
+        (cp >= 0x2B820 and cp <= 0x2CEAF) or
+        (cp >= 0xF900 and cp <= 0xFAFF) or  #
+        (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
+      return True
+
+    return False
+
+  def _clean_text(self, text):
+    """Performs invalid character removal and whitespace cleanup on text."""
+    output = []
+    for char in text:
+      cp = ord(char)
+      if cp == 0 or cp == 0xfffd or _is_control(char):
+        continue
+      if _is_whitespace(char):
+        output.append(" ")
+      else:
+        output.append(char)
+    return "".join(output)
+
+
+class WordpieceTokenizer(object):
+  """Runs WordPiece tokenziation."""
+
+  def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200):
+    self.vocab = vocab
+    self.unk_token = unk_token
+    self.max_input_chars_per_word = max_input_chars_per_word
+
+  def tokenize(self, text):
+    """Tokenizes a piece of text into its word pieces.
+
+    This uses a greedy longest-match-first algorithm to perform tokenization
+    using the given vocabulary.
+
+    For example:
+      input = "unaffable"
+      output = ["un", "##aff", "##able"]
+
+    Args:
+      text: A single token or whitespace separated tokens. This should have
+        already been passed through `BasicTokenizer.
+
+    Returns:
+      A list of wordpiece tokens.
+    """
+
+    text = convert_to_unicode(text)
+
+    output_tokens = []
+    for token in whitespace_tokenize(text):
+      chars = list(token)
+      if len(chars) > self.max_input_chars_per_word:
+        output_tokens.append(self.unk_token)
+        continue
+
+      is_bad = False
+      start = 0
+      sub_tokens = []
+      while start < len(chars):
+        end = len(chars)
+        cur_substr = None
+        while start < end:
+          substr = "".join(chars[start:end])
+          if start > 0:
+            substr = "##" + substr
+          if substr in self.vocab:
+            cur_substr = substr
+            break
+          end -= 1
+        if cur_substr is None:
+          is_bad = True
+          break
+        sub_tokens.append(cur_substr)
+        start = end
+
+      if is_bad:
+        output_tokens.append(self.unk_token)
+      else:
+        output_tokens.extend(sub_tokens)
+    return output_tokens
+
+
+def _is_whitespace(char):
+  """Checks whether `chars` is a whitespace character."""
+  # \t, \n, and \r are technically contorl characters but we treat them
+  # as whitespace since they are generally considered as such.
+  if char == " " or char == "\t" or char == "\n" or char == "\r":
+    return True
+  cat = unicodedata.category(char)
+  if cat == "Zs":
+    return True
+  return False
+
+
+def _is_control(char):
+  """Checks whether `chars` is a control character."""
+  # These are technically control characters but we count them as whitespace
+  # characters.
+  if char == "\t" or char == "\n" or char == "\r":
+    return False
+  cat = unicodedata.category(char)
+  if cat in ("Cc", "Cf"):
+    return True
+  return False
+
+
+def _is_punctuation(char):
+  """Checks whether `chars` is a punctuation character."""
+  cp = ord(char)
+  # We treat all non-letter/number ASCII as punctuation.
+  # Characters such as "^", "$", and "`" are not in the Unicode
+  # Punctuation class but we treat them as punctuation anyways, for
+  # consistency.
+  if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
+      (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
+    return True
+  cat = unicodedata.category(char)
+  if cat.startswith("P"):
+    return True
+  return False
diff --git a/matchzoo/preprocessors/units/bert_tokenize.py b/matchzoo/preprocessors/units/bert_tokenize.py
new file mode 100644
index 00000000..de4e546c
--- /dev/null
+++ b/matchzoo/preprocessors/units/bert_tokenize.py
@@ -0,0 +1,23 @@
+
+from .unit import Unit
+from .bert_tokenization import FullTokenizer
+
+class BertTokenize(Unit):
+    """Process unit for text tokenization by bert tokenizer.
+
+        :param vocab_path: vocab file path in bert pretrained model.
+        """
+
+    def __init__(self, vocab_path, do_lower_case=False):
+        """Initialization."""
+        self.tokenizer = FullTokenizer(vocab_path, do_lower_case=do_lower_case)
+
+    def transform(self, input_: str) -> list:
+        """
+        Process input data from raw terms to list of tokens.
+
+        :param input_: raw textual input.
+
+        :return tokens: tokenized tokens as a list.
+        """
+        return self.tokenizer.tokenize(input_)
diff --git a/tests/unit_test/processor_units/test_processor_units.py b/tests/unit_test/processor_units/test_processor_units.py
index 45afb0ad..4812bece 100644
--- a/tests/unit_test/processor_units/test_processor_units.py
+++ b/tests/unit_test/processor_units/test_processor_units.py
@@ -136,3 +136,29 @@ def test_this():
     type(train_data_processed)
     test_data_transformed = dssm_preprocessor.transform(test_data)
     type(test_data_transformed)
+
+
+import tempfile
+import os
+
+
+def test_bert_tokenizer_unit():
+    vocab_tokens = [
+        "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
+        "##ing", ","
+    ]
+    with tempfile.NamedTemporaryFile(delete=False) as vocab_writer:
+        vocab_writer.write("".join(
+            [x + "\n" for x in vocab_tokens]).encode("utf-8"))
+
+    vocab_file = vocab_writer.name
+
+    tokenizer_unit = units.BertTokenize(vocab_file, do_lower_case=True)
+    os.unlink(vocab_file)
+
+    tokens = tokenizer_unit.transform(u"UNwant\u00E9d,running")
+    assert tokens == ["un", "##want", "##ed", ",", "runn", "##ing"]
+
+    assert tokenizer_unit.tokenizer.convert_tokens_to_ids(tokens) == [7, 4, 5, 10, 8, 9]
+
+    assert tokenizer_unit.tokenizer.basic_tokenizer.tokenize(u"ah\u535A\u63A8zz") == [u"ah", u"\u535A", u"\u63A8", u"zz"]

From 154a4f6a092be950a682dde5bfceec398462ed20 Mon Sep 17 00:00:00 2001
From: Chriskuei <chenjiangui@outlook.com>
Date: Sat, 20 Apr 2019 14:00:06 +0800
Subject: [PATCH 057/100] Improve code readability

---
 .../contrib/layers/matching_tensor_layer.py   |  13 +-
 matchzoo/contrib/layers/spatial_gru.py        | 241 +++++++++++-------
 matchzoo/contrib/models/match_srnn.py         |  25 +-
 3 files changed, 170 insertions(+), 109 deletions(-)

diff --git a/matchzoo/contrib/layers/matching_tensor_layer.py b/matchzoo/contrib/layers/matching_tensor_layer.py
index 112b3955..3c2aabb4 100644
--- a/matchzoo/contrib/layers/matching_tensor_layer.py
+++ b/matchzoo/contrib/layers/matching_tensor_layer.py
@@ -94,10 +94,21 @@ def call(self, inputs: list, **kwargs) -> typing.Any:
         """
         x1 = inputs[0]
         x2 = inputs[1]
+        # Normalize x1 and x2
         if self._normalize:
             x1 = K.l2_normalize(x1, axis=2)
             x2 = K.l2_normalize(x2, axis=2)
-        output = K.tf.einsum('abd,fde,ace->afbc', x1, self.interaction_matrix, x2)
+
+        # b = batch size
+        # l = length of `x1`
+        # r = length of `x2`
+        # d, e = embedding size
+        # c = number of channels
+        # output = [b, c, l, r]
+        output = K.tf.einsum(
+            'bld,cde,bre->bclr',
+            x1, self.interaction_matrix, x2
+        )
         return output
 
     def compute_output_shape(self, input_shape: list) -> tuple:
diff --git a/matchzoo/contrib/layers/spatial_gru.py b/matchzoo/contrib/layers/spatial_gru.py
index fb9f5e66..76f9df9f 100644
--- a/matchzoo/contrib/layers/spatial_gru.py
+++ b/matchzoo/contrib/layers/spatial_gru.py
@@ -8,21 +8,12 @@
 from keras.layers import activations
 from keras.layers import initializers
 
-from tensorflow.python.ops import control_flow_ops
-from tensorflow.python.ops import tensor_array_ops
-
 
 class SpatialGRU(Layer):
     """
     Spatial GRU layer.
 
     :param units: Number of SpatialGRU units.
-    :param normalize: Whether to L2-normalize samples along the
-        dot product axis before taking the dot product.
-        If set to True, then the output of the dot product
-        is the cosine proximity between the two samples.
-    :param init_diag: Whether to initialize the diagonal elements
-        of the matrix.
     :param activation: Activation function to use. Default:
         hyperbolic tangent (`tanh`). If you pass `None`, no
         activation is applied (ie. "linear" activation: `a(x) = x`).
@@ -30,13 +21,11 @@ class SpatialGRU(Layer):
         the recurrent step. Default: sigmoid (`sigmoid`).
         If you pass `None`, no activation is applied (ie. "linear"
         activation: `a(x) = x`).
-    :param use_bias: Boolean, whether the layer uses a bias vector.
     :param kernel_initializer: Initializer for the `kernel` weights
         matrix, used for the linear transformation of the inputs.
     :param recurrent_initializer: Initializer for the `recurrent_kernel`
         weights matrix, used for the linear transformation of the
         recurrent state.
-    :param bias_initializer: Initializer for the bias vector.
     :param direction: Scanning direction. `lt` (i.e., left top)
         indicates the scanning from left top to right bottom, and
         `rb` (i.e., right bottom) indicates the scanning from
@@ -52,30 +41,24 @@ class SpatialGRU(Layer):
 
     """
 
-    def __init__(self,
-                 units: int = 10,
-                 normalize: bool = False,
-                 init_diag: bool = False,
-                 activation: str = 'tanh',
-                 recurrent_activation: str = 'sigmoid',
-                 use_bias: bool = True,
-                 kernel_initializer: str = 'glorot_uniform',
-                 recurrent_initializer: str = 'orthogonal',
-                 bias_initializer: str = 'zeros',
-                 direction: str = 'lr',
-                 **kwargs):
+    def __init__(
+        self,
+        units: int = 10,
+        activation: str = 'tanh',
+        recurrent_activation: str = 'sigmoid',
+        kernel_initializer: str = 'glorot_uniform',
+        recurrent_initializer: str = 'orthogonal',
+        direction: str = 'lt',
+        **kwargs
+    ):
         """:class:`SpatialGRU` constructor."""
         super().__init__(**kwargs)
         self._units = units
-        self._normalize = normalize
-        self._init_diag = init_diag
         self._activation = activations.get(activation)
         self._recurrent_activation = activations.get(recurrent_activation)
-        self._use_bias = use_bias
 
         self._kernel_initializer = initializers.get(kernel_initializer)
         self._recurrent_initializer = initializers.get(recurrent_initializer)
-        self._bias_initializer = initializers.get(bias_initializer)
         self._direction = direction
 
     def build(self, input_shape: typing.Any):
@@ -84,7 +67,14 @@ def build(self, input_shape: typing.Any):
 
         :param input_shape: the shapes of the input tensors.
         """
-        # input_shape: (batch_size, channel, text1_maxlen, text2_maxlen)
+        # Scalar dimensions referenced here:
+        #   B = batch size (number of sequences)
+        #   L = `input_left` sequence length
+        #   R = `input_right` sequence length
+        #   C = number of channels
+        #   U = number of units
+
+        # input_shape = [B, C, L, R]
         self._batch_size = input_shape[0]
         self._channel = input_shape[1]
         self._input_dim = self._channel + 3 * self._units
@@ -92,88 +82,131 @@ def build(self, input_shape: typing.Any):
         self._text1_maxlen = input_shape[2]
         self._text2_maxlen = input_shape[3]
         self._recurrent_step = self._text1_maxlen * self._text2_maxlen
+        # W = [3*U+C, 7*U]
+        self._W = self.add_weight(
+            name='W',
+            shape=(self._input_dim, self._units * 7),
+            initializer=self._kernel_initializer,
+            trainable=True
+        )
+        # U = [3*U, U]
+        self._U = self.add_weight(
+            name='U',
+            shape=(self._units * 3, self._units),
+            initializer=self._recurrent_initializer,
+            trainable=True
+        )
+        # bias = [8*U,]
+        self._bias = self.add_weight(
+            name='bias',
+            shape=(self._units * 8,),
+            initializer='zeros',
+            trainable=True
+        )
 
-        self._W = self.add_weight(name='W',
-                                  shape=(self._input_dim, self._units * 7),
-                                  initializer=self._kernel_initializer,
-                                  trainable=True)
-
-        self._U = self.add_weight(name='U',
-                                  shape=(self._units * 3, self._units),
-                                  initializer=self._recurrent_initializer,
-                                  trainable=True)
-
-        self._bias = self.add_weight(name='bias',
-                                     shape=(self._units * 8,),
-                                     initializer='zeros',
-                                     trainable=True)
-
-        # w_rl, w_rt, w_rd
+        # w_rl, w_rt, w_rd = [B, 3*U]
         self._wr = self._W[:, :self._units * 3]
-        # b_rl, b_rt, b_rd
+        # b_rl, b_rt, b_rd = [B, 3*U]
         self._br = self._bias[:self._units * 3]
-        # w_zi, w_zl, w_zt, w_zd
+        # w_zi, w_zl, w_zt, w_zd = [B, 4*U]
         self._wz = self._W[:, self._units * 3: self._units * 7]
-        # b_zi, b_zl, b_zt, b_zd
+        # b_zi, b_zl, b_zt, b_zd = [B, 4*U]
         self._bz = self._bias[self._units * 3: self._units * 7]
-        self._w_ij = self.add_weight(name='Wij',
-                                     shape=(self._channel, self._units),
-                                     initializer=self._recurrent_initializer,
-                                     trainable=True)
+        # w_ij = [C, U]
+        self._w_ij = self.add_weight(
+            name='W_ij',
+            shape=(self._channel, self._units),
+            initializer=self._recurrent_initializer,
+            trainable=True
+        )
+        # b_ij = [7*U]
         self._b_ij = self._bias[self._units * 7:]
         super(SpatialGRU, self).build(input_shape)
 
     def softmax_by_row(self, z: typing.Any) -> tuple:
         """Conduct softmax on each dimension across the four gates."""
 
+        # z_transform: [B, U, 4]
         z_transform = Permute((2, 1))(Reshape((4, self._units))(z))
+        size = [-1, 1, -1]
+        # Perform softmax on each slice
         for i in range(0, self._units):
-            begin1 = [0, i, 0]
-            size = [-1, 1, -1]
+            begin = [0, i, 0]
+            # z_slice: [B, 1, 4]
+            z_slice = K.tf.slice(z_transform, begin, size)
             if i == 0:
-                z_s = K.tf.nn.softmax(K.tf.slice(z_transform, begin1, size))
+                z_s = K.tf.nn.softmax(z_slice)
             else:
-                z_s = K.tf.concat(
-                    [z_s, K.tf.nn.softmax(
-                        K.tf.slice(z_transform, begin1, size))], 1)
-
+                z_s = K.tf.concat([z_s, K.tf.nn.softmax(z_slice)], 1)
+        # zi, zl, zt, zd: [B, U]
         zi, zl, zt, zd = K.tf.unstack(z_s, axis=2)
         return zi, zl, zt, zd
 
-    def calculate_recurrent_unit(self,
-                                 inputs_ta: typing.Any,
-                                 states: typing.Any,
-                                 step: int,
-                                 h: typing.Any,
-                                 h0: typing.Any) -> tuple:
+    def calculate_recurrent_unit(
+        self,
+        inputs: typing.Any,
+        states: typing.Any,
+        step: int,
+        h: typing.Any,
+    ) -> tuple:
         """
         Calculate recurrent unit.
 
-        :param inputs: input tensors.
+        :param inputs: A TensorArray which contains interaction
+            between left text and right text.
+        :param states: A TensorArray which stores the hidden state
+            of every step.
+        :param step: Recurrent step.
+        :param h: Hidden state from last operation.
         """
-        i = K.tf.div(step, K.tf.constant(self._text2_maxlen))
+        # Get index i, j
+        i = K.tf.floordiv(step, K.tf.constant(self._text2_maxlen))
         j = K.tf.mod(step, K.tf.constant(self._text2_maxlen))
 
+        # Get hidden state h_diag, h_top, h_left
+        # h_diag, h_top, h_left = [B, U]
         h_diag = states.read(i * (self._text2_maxlen + 1) + j)
         h_top = states.read(i * (self._text2_maxlen + 1) + j + 1)
         h_left = states.read((i + 1) * (self._text2_maxlen + 1) + j)
 
-        s_ij = inputs_ta.read(step)
+        # Get interaction between word i, j: s_ij
+        # s_ij = [B, C]
+        s_ij = inputs.read(step)
+
+        # Concatenate h_top, h_left, h_diag, s_ij
+        # q = [B, 3*U+C]
         q = K.tf.concat([K.tf.concat([h_top, h_left], 1),
                         K.tf.concat([h_diag, s_ij], 1)], 1)
+
+        # Calculate reset gate
+        # r = [B, 3*U]
         r = self._recurrent_activation(
             self._time_distributed_dense(self._wr, q, self._br))
+
+        # Calculate updating gate
+        # z: [B, 4*U]
         z = self._time_distributed_dense(self._wz, q, self._bz)
+
+        # Perform softmax
+        # zi, zl, zt, zd: [B, U]
         zi, zl, zt, zd = self.softmax_by_row(z)
 
-        hij_l = self._time_distributed_dense(self._w_ij, s_ij, self._b_ij)
-        hij_r = K.dot(r * (K.tf.concat([h_left, h_top, h_diag], 1)), self._U)
-        hij_ = self._activation(hij_l + hij_r)
-        hij = zl * h_left + zt * h_top + zd * h_diag + zi * hij_
+        # Get h_ij_
+        # h_ij_ = [B, U]
+        h_ij_l = self._time_distributed_dense(self._w_ij, s_ij, self._b_ij)
+        h_ij_r = K.dot(r * (K.tf.concat([h_left, h_top, h_diag], 1)), self._U)
+        h_ij_ = self._activation(h_ij_l + h_ij_r)
+
+        # Calculate h_ij
+        # h_ij = [B, U]
+        h_ij = zl * h_left + zt * h_top + zd * h_diag + zi * h_ij_
+
+        # Write h_ij to states
         states = states.write(((i + 1) * (self._text2_maxlen + 1) + j + 1),
-                              hij)
-        hij.set_shape(h_top.get_shape())
-        return inputs_ta, states, step + 1, hij, h0
+                              h_ij)
+        h_ij.set_shape(h_top.get_shape())
+
+        return inputs, states, step + 1, h_ij
 
     def call(self, inputs: list, **kwargs) -> typing.Any:
         """
@@ -182,49 +215,64 @@ def call(self, inputs: list, **kwargs) -> typing.Any:
         :param inputs: input tensors.
         """
         batch_size = K.tf.shape(inputs)[0]
+        # h0 = [B, U]
         self._bounder_state_h0 = K.tf.zeros([batch_size, self._units])
 
-        # input_x: (text1_maxlen, text2_maxlen, batch_size, channel)
+        # input_x = [L, R, B, C]
         input_x = K.tf.transpose(inputs, [2, 3, 0, 1])
         if self._direction == 'rb':
+            # input_x: [R, L, B, C]
             input_x = K.tf.reverse(input_x, [0, 1])
         elif self._direction != 'lt':
             raise ValueError(f"Invalid direction. "
                              f"`{self._direction}` received. "
                              f"Must be in `lt`, `rb`.")
+        # input_x = [L*R*B, C]
         input_x = K.tf.reshape(input_x, [-1, self._channel])
+        # input_x = L*R * [B, C]
         input_x = K.tf.split(
             axis=0,
             num_or_size_splits=self._text1_maxlen * self._text2_maxlen,
-            value=input_x)
-        inputs_ta = K.tf.TensorArray(
+            value=input_x
+        )
+
+        # inputs = L*R * [B, C]
+        inputs = K.tf.TensorArray(
             dtype=K.tf.float32,
             size=self._text1_maxlen * self._text2_maxlen,
-            name='input_ta')
-        states_ta = K.tf.TensorArray(
+            name='inputs'
+        )
+        inputs = inputs.unstack(input_x)
+
+        # states = (L+1)*(R+1) * [B, U]
+        states = K.tf.TensorArray(
             dtype=K.tf.float32,
             size=(self._text1_maxlen + 1) * (self._text2_maxlen + 1),
-            name='state_ta', clear_after_read=False)
-
+            name='states',
+            clear_after_read=False
+        )
+        # Initialize states
         for i in range(self._text2_maxlen + 1):
-            states_ta = states_ta.write(i, self._bounder_state_h0)
-        for i in range(self._text1_maxlen):
-            states_ta = states_ta.write((i + 1) * (self._text2_maxlen + 1),
-                                        self._bounder_state_h0)
-        inputs_ta = inputs_ta.unstack(input_x)
-        _, _, _, hij, _ = control_flow_ops.while_loop(
-            cond=lambda _0, _1, i, _3, _4: i < self._recurrent_step,
+            states = states.write(i, self._bounder_state_h0)
+        for i in range(1, self._text1_maxlen + 1):
+            states = states.write(i * (self._text2_maxlen + 1),
+                                  self._bounder_state_h0)
+
+        # Calculate h_ij
+        # h_ij = [B, U]
+        _, _, _, h_ij = K.tf.while_loop(
+            cond=lambda _0, _1, i, _3: K.tf.less(i, self._recurrent_step),
             body=self.calculate_recurrent_unit,
             loop_vars=(
-                inputs_ta,
-                states_ta,
-                K.tf.Variable(0, dtype=K.tf.int32),
-                self._bounder_state_h0,
-                self._bounder_state_h0),
+                inputs,
+                states,
+                K.tf.constant(0, dtype=K.tf.int32),
+                self._bounder_state_h0
+            ),
             parallel_iterations=1,
             swap_memory=True
         )
-        return hij
+        return h_ij
 
     def compute_output_shape(self, input_shape: typing.Any) -> tuple:
         """
@@ -237,7 +285,6 @@ def compute_output_shape(self, input_shape: typing.Any) -> tuple:
 
     @classmethod
     def _time_distributed_dense(cls, w, x, b):
-        if K.backend() == 'tensorflow':
-            x = K.dot(x, w)
-            x = K.bias_add(x, b)
+        x = K.dot(x, w)
+        x = K.bias_add(x, b)
         return x
diff --git a/matchzoo/contrib/models/match_srnn.py b/matchzoo/contrib/models/match_srnn.py
index a6251d39..833581b2 100644
--- a/matchzoo/contrib/models/match_srnn.py
+++ b/matchzoo/contrib/models/match_srnn.py
@@ -9,6 +9,8 @@
 from matchzoo.engine.param import Param
 from matchzoo.engine.param_table import ParamTable
 from matchzoo.engine import hyper_spaces
+from matchzoo.contrib.layers import SpatialGRU
+from matchzoo.contrib.layers import MatchingTensorLayer
 
 
 class MatchSRNN(BaseModel):
@@ -60,34 +62,35 @@ def build(self):
         #   C = number of channels
 
         # Left input and right input.
-        # shape = [B, L]
-        # shape = [B, R]
+        # query = [B, L]
+        # doc = [B, R]
         query, doc = self._make_inputs()
 
-        embedding = self._make_embedding_layer()
         # Process left and right input.
-        # shape = [B, L, D]
+        # embed_query = [B, L, D]
+        # embed_doc = [B, R, D]
+        embedding = self._make_embedding_layer()
         embed_query = embedding(query)
-        # shape = [B, R, D]
         embed_doc = embedding(doc)
 
         # Get matching tensor
-        # shape = [B, C, L, R]
-        matching_tensor_layer = matchzoo.contrib.layers.MatchingTensorLayer(
+        # matching_tensor = [B, C, L, R]
+        matching_tensor_layer = MatchingTensorLayer(
             channels=self._params['channels'])
         matching_tensor = matching_tensor_layer([embed_query, embed_doc])
-        # shape = [B, L, R, C]
-        matching_tensor = keras.layers.Permute((2, 3, 1))(matching_tensor)
 
-        spatial_gru = matchzoo.contrib.layers.SpatialGRU(
+        # Apply spatial GRU to the word level interaction tensor
+        # h_ij = [B, U]
+        spatial_gru = SpatialGRU(
             units=self._params['units'],
             direction=self._params['direction'])
-
         h_ij = spatial_gru(matching_tensor)
 
+        # Apply Dropout
         x = keras.layers.Dropout(
             rate=self._params['dropout_rate'])(h_ij)
 
+        # Make output layer
         x_out = self._make_output_layer()(x)
 
         self._backend = keras.Model(inputs=[query, doc], outputs=x_out)

From 18c8975c675cab71df7877c918257207a0d935e2 Mon Sep 17 00:00:00 2001
From: caiyinqiong <1198593462@qq.com>
Date: Sat, 27 Apr 2019 23:58:27 +0800
Subject: [PATCH 058/100] add DIIN model

---
 matchzoo/contrib/layers/__init__.py           |   6 +-
 .../contrib/layers/decaying_dropout_layer.py  |  98 ++++++
 matchzoo/contrib/layers/encoding_layer.py     | 122 +++++++
 matchzoo/contrib/models/__init__.py           |   1 +
 matchzoo/contrib/models/diin.py               | 310 ++++++++++++++++++
 matchzoo/preprocessors/__init__.py            |   1 +
 matchzoo/preprocessors/diin_preprocessor.py   | 159 +++++++++
 matchzoo/preprocessors/units/__init__.py      |   2 +
 .../preprocessors/units/character_index.py    |  61 ++++
 .../preprocessors/units/word_exact_match.py   |  66 ++++
 10 files changed, 825 insertions(+), 1 deletion(-)
 create mode 100644 matchzoo/contrib/layers/decaying_dropout_layer.py
 create mode 100644 matchzoo/contrib/layers/encoding_layer.py
 create mode 100644 matchzoo/contrib/models/diin.py
 create mode 100644 matchzoo/preprocessors/diin_preprocessor.py
 create mode 100644 matchzoo/preprocessors/units/character_index.py
 create mode 100644 matchzoo/preprocessors/units/word_exact_match.py

diff --git a/matchzoo/contrib/layers/__init__.py b/matchzoo/contrib/layers/__init__.py
index a66fe1ab..ea5c99b7 100644
--- a/matchzoo/contrib/layers/__init__.py
+++ b/matchzoo/contrib/layers/__init__.py
@@ -2,8 +2,12 @@
 from .multi_perspective_layer import MultiPerspectiveLayer
 from .matching_tensor_layer import MatchingTensorLayer
 from .spatial_gru import SpatialGRU
+from .decaying_dropout_layer import DecayingDropoutLayer
+from .encoding_layer import EncodingLayer
 
 layer_dict = {
     "MatchingTensorLayer": MatchingTensorLayer,
-    "SpatialGRU": SpatialGRU
+    "SpatialGRU": SpatialGRU,
+	"DecayingDropoutLayer": DecayingDropoutLayer,
+    "EncodingLayer": EncodingLayer
 }
diff --git a/matchzoo/contrib/layers/decaying_dropout_layer.py b/matchzoo/contrib/layers/decaying_dropout_layer.py
new file mode 100644
index 00000000..fda87212
--- /dev/null
+++ b/matchzoo/contrib/layers/decaying_dropout_layer.py
@@ -0,0 +1,98 @@
+"""An implementation of Decaying Dropout Layer."""
+import typing
+
+import keras.backend as K
+from keras.engine import Layer
+
+
+class DecayingDropoutLayer(Layer):
+    """
+    Layer that processes dropout with exponential decayed keep rate during
+    training.
+
+    :param initial_keep_rate: the initial keep rate of decaying dropout.
+    :param decay_interval: the decay interval of decaying dropout.
+    :param decay_rate: the decay rate of decaying dropout.
+    :param noise_shape: a 1D integer tensor representing the shape of the
+        binary dropout mask that will be multiplied with the input.
+    :param seed: a python integer to use as random seed.
+    :param kwargs: standard layer keyword arguments.
+
+    Examples:
+        >>> import matchzoo as mz
+        >>> layer = mz.contrib.layers.DecayingDropoutLayer(
+        ...     initial_keep_rate=1.0,
+        ...     decay_interval=10000,
+        ...     decay_rate=0.977,
+        ... )
+        >>> num_batch, num_dim =5, 10
+        >>> layer.build([num_batch, num_dim])
+    """
+
+    def __init__(self,
+                 initial_keep_rate: float = 1.0,
+                 decay_interval: int = 10000,
+                 decay_rate: float = 0.977,
+                 noise_shape=None,
+                 seed=None,
+                 **kwargs):
+        """:class: 'DecayingDropoutLayer' constructor."""
+        super(DecayingDropoutLayer, self).__init__(**kwargs)
+        self._iterations = None
+        self._initial_keep_rate = initial_keep_rate
+        self._decay_interval = decay_interval
+        self._decay_rate = min(1.0, max(0.0, decay_rate))
+        self._noise_shape = noise_shape
+        self._seed = seed
+
+    def _get_noise_shape(self, inputs):
+        if self._noise_shape is None:
+            return self._noise_shape
+
+        symbolic_shape = K.shape(inputs)
+        noise_shape = [symbolic_shape[axis] if shape is None else shape
+                       for axis, shape in enumerate(self._noise_shape)]
+        return tuple(noise_shape)
+
+    def build(self, input_shape):
+        """
+        Build the layer.
+
+        :param input_shape: the shape of the input tensor,
+            for DecayingDropoutLayer we need one input tensor.
+        """
+        self._iterations = self.add_weight(name='iterations',
+                                           shape=(1,),
+                                           dtype=K.floatx(),
+                                           initializer='zeros',
+                                           trainable=False)
+        super(DecayingDropoutLayer, self).build(input_shape)
+
+    def call(self, inputs, training=None):
+        """
+        The computation logic of DecayingDropoutLayer.
+
+        :param inputs: an input tensor.
+        """
+        noise_shape = self._get_noise_shape(inputs)
+        t = K.cast(self._iterations, K.floatx()) + 1
+        p = t / float(self._decay_interval)
+
+        keep_rate = self._initial_keep_rate * K.pow(self._decay_rate, p)
+
+        def dropped_inputs():
+            self.add_update([K.update_add(self._iterations, [1])], inputs)
+            return K.dropout(inputs, 1 - keep_rate[0], noise_shape,
+                             seed=self._seed)
+
+        return K.in_train_phase(dropped_inputs, inputs, training=training)
+
+    def get_config(self):
+        """Get the config dict of DecayingDropoutLayer."""
+        config = {'initial_keep_rate': self._initial_keep_rate,
+                  'decay_interval': self._decay_interval,
+                  'decay_rate': self._decay_rate,
+                  'noise_shape': self._noise_shape,
+                  'seed': self._seed}
+        base_config = super(DecayingDropoutLayer, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
diff --git a/matchzoo/contrib/layers/encoding_layer.py b/matchzoo/contrib/layers/encoding_layer.py
new file mode 100644
index 00000000..13bf6f29
--- /dev/null
+++ b/matchzoo/contrib/layers/encoding_layer.py
@@ -0,0 +1,122 @@
+"""An implementation of EncodingModule for DIIN model."""
+import typing
+
+import keras
+import keras.backend as K
+from keras.engine import Layer
+
+from matchzoo.contrib.layers import DecayingDropoutLayer
+
+
+class EncodingLayer(Layer):
+    """
+    Apply a self-attention layer and a semantic composite fuse gate
+    to compute the encoding result of one tensor.
+
+    :param initial_keep_rate: the initial_keep_rate parameter of
+        DecayingDropoutLayer.
+    :param decay_interval: the decay_interval parameter of
+        DecayingDropoutLayer.
+    :param decay_rate: the decay_rate parameter of DecayingDropoutLayer.
+    :param kwargs: standard layer keyword arguments.
+
+    Example:
+        >>> import matchzoo as mz
+        >>> layer = mz.contrib.layers.EncodingLayer(1.0, 10000, 0.977)
+        >>> num_batch, left_len, num_dim = 5, 32, 10
+        >>> layer.build([num_batch, left_len, num_dim])
+    """
+
+    def __init__(self,
+                 initial_keep_rate: float,
+                 decay_interval: int,
+                 decay_rate: float,
+                 **kwargs):
+        """:class: 'EncodingLayer' constructor."""
+        super(EncodingLayer, self).__init__(**kwargs)
+        self._initial_keep_rate = initial_keep_rate
+        self._decay_interval = decay_interval
+        self._decay_rate = decay_rate
+        self._w_itr_att = None
+        self._w1 = None
+        self._w2 = None
+        self._w3 = None
+        self._b1 = None
+        self._b2 = None
+        self._b3 = None
+
+    def build(self, input_shape):
+        """
+        Build the layer.
+
+        :param input_shape: the shape of the input tensor,
+            for EncodingLayer we need one input tensor.
+        """
+        d = input_shape[-1]
+
+        self._w_itr_att = self.add_weight(
+            name='w_itr_att', shape=(3 * d,), initializer='glorot_uniform')
+        self._w1 = self.add_weight(
+            name='w1', shape=(2 * d, d,), initializer='glorot_uniform')
+        self._w2 = self.add_weight(
+            name='w2', shape=(2 * d, d,), initializer='glorot_uniform')
+        self._w3 = self.add_weight(
+            name='w3', shape=(2 * d, d,), initializer='glorot_uniform')
+        self._b1 = self.add_weight(
+            name='b1', shape=(d,), initializer='zeros')
+        self._b2 = self.add_weight(
+            name='b2', shape=(d,), initializer='zeros')
+        self._b3 = self.add_weight(
+            name='b3', shape=(d,), initializer='zeros')
+
+        super(EncodingLayer, self).build(input_shape)
+
+    def call(self, inputs, **kwargs):
+        """
+        The computation logic of EncodingLayer.
+
+        :param inputs: an input tensor.
+        """
+        # Scalar dimensions referenced here:
+        #   b = batch size
+        #   p = inputs.shape()[1]
+        #   d = inputs.shape()[2]
+
+        # The input shape is [b, p, d]
+        # shape = [b, 1, p, d]
+        x = K.expand_dims(inputs, 1) * 0
+        # shape = [b, 1, d, p]
+        x = K.permute_dimensions(x, (0, 1, 3, 2))
+        # shape = [b, p, d, p]
+        mid = x + K.expand_dims(inputs)
+        # shape = [b, p, d, p]
+        up = K.permute_dimensions(mid, pattern=(0, 3, 2, 1))
+        # shape = [b, p, 3d, p]
+        inputs_concat = K.concatenate([up, mid, up * mid], axis=2)
+
+        # Self-attention layer.
+        # shape = [b, p, p]
+        A = K.dot(self._w_itr_att, inputs_concat)
+        # shape = [b, p, p]
+        SA = keras.activations.softmax(A, axis=2)
+        # shape = [b, p, d]
+        itr_attn = K.batch_dot(SA, inputs)
+
+        # Semantic composite fuse gate.
+        # shape = [b, p, 2d]
+        inputs_attn_concat = K.concatenate([inputs, itr_attn], axis=2)
+        concat_dropout = DecayingDropoutLayer(
+            initial_keep_rate=self._initial_keep_rate,
+            decay_interval=self._decay_interval,
+            decay_rate=self._decay_rate
+        )(inputs_attn_concat)
+        # shape = [b, p, d]
+        z = K.tanh(K.dot(concat_dropout, self._w1) + self._b1)
+        # shape = [b, p, d]
+        r = K.sigmoid(K.dot(concat_dropout, self._w2) + self._b2)
+        # shape = [b, p, d]
+        f = K.sigmoid(K.dot(concat_dropout, self._w3) + self._b3)
+        # shape = [b, p, d]
+        encoding = r * inputs + f * z
+
+        return encoding
diff --git a/matchzoo/contrib/models/__init__.py b/matchzoo/contrib/models/__init__.py
index aa8c7f66..cefd02a0 100644
--- a/matchzoo/contrib/models/__init__.py
+++ b/matchzoo/contrib/models/__init__.py
@@ -3,3 +3,4 @@
 from .hbmp import HBMP
 from .esim import ESIM
 from .bimpm import BiMPM
+from .diin import DIIN
diff --git a/matchzoo/contrib/models/diin.py b/matchzoo/contrib/models/diin.py
new file mode 100644
index 00000000..d7f8fcd1
--- /dev/null
+++ b/matchzoo/contrib/models/diin.py
@@ -0,0 +1,310 @@
+"""DIIN model."""
+import typing
+
+import keras
+import keras.backend as K
+
+from matchzoo.engine import hyper_spaces
+from matchzoo.engine.param_table import ParamTable
+from matchzoo.engine.param import Param
+from matchzoo.engine.base_model import BaseModel
+from matchzoo.contrib.layers import DecayingDropoutLayer
+from matchzoo.contrib.layers import EncodingLayer
+from matchzoo import preprocessors
+
+
+class DIIN(BaseModel):
+    """
+    DIIN model.
+
+    Examples:
+        >>> model = DIIN()
+        >>> model.guess_and_fill_missing_params()
+        >>> model.params['embedding_input_dim'] = 10000
+        >>> model.params['embedding_output_dim'] = 300
+        >>> model.params['embedding_trainable'] = True
+        >>> model.params['optimizer'] = 'adam'
+        >>> model.params['dropout_initial_keep_rate'] = 1.0
+        >>> model.params['dropout_decay_interval'] = 10000
+        >>> model.params['dropout_decay_rate'] = 0.977
+        >>> model.params['char_embedding_input_dim'] = 100
+        >>> model.params['char_embedding_output_dim'] = 8
+        >>> model.params['char_conv_filters'] = 100
+        >>> model.params['char_conv_kernel_size'] = 5
+        >>> model.params['first_scale_down_ratio'] = 0.3
+        >>> model.params['nb_dense_blocks'] = 3
+        >>> model.params['layers_per_dense_block'] = 8
+        >>> model.params['growth_rate'] = 20
+        >>> model.params['transition_scale_down_ratio'] = 0.5
+        >>> model.build()
+    """
+
+    @classmethod
+    def get_default_params(cls) -> ParamTable:
+        """:return: model default parameters."""
+        params = super().get_default_params(with_embedding=True)
+        params['optimizer'] = 'adam'
+        params.add(Param(name='dropout_initial_keep_rate', value=1.0,
+                         desc="The initial keep rate of decaying_dropout."))
+        params.add(Param(name='dropout_decay_interval', value=10000,
+                         desc="The decay interval of decaying_dropout."))
+        params.add(Param(name='dropout_decay_rate', value=0.977,
+                         desc="The decay rate of decaying_dropout."))
+        params.add(Param(name='char_embedding_input_dim', value=100,
+                         desc="The input dimension of character embedding "
+                              "layer."))
+        params.add(Param(name='char_embedding_output_dim', value=8,
+                         desc="The output dimension of character embedding "
+                              "layer."))
+        params.add(Param(name='char_conv_filters', value=100,
+                         desc="The filter size of character convolution "
+                              "layer."))
+        params.add(Param(name='char_conv_kernel_size', value=5,
+                         desc="The kernel size of character convolution "
+                              "layer."))
+        params.add(Param(name='first_scale_down_ratio', value=0.3,
+                         desc="The channel scale down ratio of the "
+                              "convolution layer before densenet."))
+        params.add(Param(name='nb_dense_blocks', value=3,
+                         desc="The number of blocks in densenet."))
+        params.add(Param(name='layers_per_dense_block', value=8,
+                         desc="The number of convolution layers in dense "
+                              "block."))
+        params.add(Param(name='growth_rate', value=20,
+                         desc="The filter size of each convolution layer in "
+                              "dense block."))
+        params.add(Param(name='transition_scale_down_ratio', value=0.5,
+                         desc="The channel scale down ratio of the "
+                              "convolution layer in transition block."))
+        return params
+
+    @classmethod
+    def get_default_preprocessor(cls):
+        """:return: Default preprocessor."""
+        return preprocessors.DIINPreprocessor()
+
+    def guess_and_fill_missing_params(self, verbose: int = 1):
+        """
+        Guess and fill missing parameters in :attr:'params'.
+
+        Use this method to automatically fill-in hyper parameters.
+        This involves some guessing so the parameter it fills could be
+        wrong. For example, the default task is 'Ranking', and if we do not
+        set it to 'Classification' manually for data packs prepared for
+        classification, then the shape of the model output and the data will
+        mismatch.
+
+        :param verbose: Verbosity.
+        """
+        self._params.get('input_shapes').set_default([(32,),
+                                                      (32,),
+                                                      (32, 16),
+                                                      (32, 16),
+                                                      (32,),
+                                                      (32,)], verbose)
+        super().guess_and_fill_missing_params(verbose)
+
+    def _make_inputs(self) -> list:
+        text_left = keras.layers.Input(
+            name='text_left',
+            shape=self._params['input_shapes'][0]
+        )
+        text_right = keras.layers.Input(
+            name='text_right',
+            shape=self._params['input_shapes'][1]
+        )
+        char_left = keras.layers.Input(
+            name='char_left',
+            shape=self._params['input_shapes'][2]
+        )
+        char_right = keras.layers.Input(
+            name='char_right',
+            shape=self._params['input_shapes'][3]
+        )
+        match_left = keras.layers.Input(
+            name='match_left',
+            shape=self._params['input_shapes'][4]
+        )
+        match_right = keras.layers.Input(
+            name='match_right',
+            shape=self._params['input_shapes'][5]
+        )
+        return [text_left, text_right,
+                char_left, char_right,
+                match_left, match_right]
+
+    def build(self):
+        """Build model structure."""
+
+        # Scalar dimensions referenced here:
+        #   B = batch size (number of sequences)
+        #   D = word embedding size
+        #   L = 'input_left' sequence length
+        #   R = 'input_right' sequence length
+        #   C = fixed word length
+
+        # Left text and right text.
+        # shape = [B, L]
+        # shape = [B, R]
+        text_left, text_right = self._make_inputs()[0:2]
+        # Left character and right character.
+        # shape = [B, L, C]
+        # shape = [B, R, C]
+        char_left, char_right = self._make_inputs()[2:4]
+        # Left exact match and right exact match.
+        # shape = [B, L]
+        # shape = [B, R]
+        match_left, match_right = self._make_inputs()[4:6]
+
+        # Embedding module
+        inputs = []
+        left_embeddings = []
+        right_embeddings = []
+
+        # Word embedding feature
+        word_embedding = self._make_embedding_layer()
+        # shape = [B, L, D]
+        left_word_embedding = word_embedding(text_left)
+        # shape = [B, R, D]
+        right_word_embedding = word_embedding(text_right)
+        left_word_embedding = DecayingDropoutLayer(
+            initial_keep_rate=self._params['dropout_initial_keep_rate'],
+            decay_interval=self._params['dropout_decay_interval'],
+            decay_rate=self._params['dropout_decay_rate']
+        )(left_word_embedding)
+        right_word_embedding = DecayingDropoutLayer(
+            initial_keep_rate=self._params['dropout_initial_keep_rate'],
+            decay_interval=self._params['dropout_decay_interval'],
+            decay_rate=self._params['dropout_decay_rate']
+        )(right_word_embedding)
+        left_embeddings.append(left_word_embedding)
+        right_embeddings.append(right_word_embedding)
+        inputs.append(text_left)
+        inputs.append(text_right)
+
+        # Exact match feature
+        # shape = [B, L, 1]
+        left_exact_match = keras.layers.Reshape(
+            target_shape=(K.int_shape(match_left)[1], 1,)
+        )(match_left)
+        # shape = [B, R, 1]
+        right_exact_match = keras.layers.Reshape(
+            target_shape=(K.int_shape(match_left)[1], 1,)
+        )(match_right)
+        left_embeddings.append(left_exact_match)
+        right_embeddings.append(right_exact_match)
+        inputs.append(match_left)
+        inputs.append(match_right)
+
+        # Char embedding feature
+        char_embedding = self._make_char_embedding_layer()
+        char_embedding.build(
+            input_shape=(None, None, K.int_shape(char_left)[-1]))
+        left_char_embedding = char_embedding(char_left)
+        right_char_embedding = char_embedding(char_right)
+        left_embeddings.append(left_char_embedding)
+        right_embeddings.append(right_char_embedding)
+        inputs.append(char_left)
+        inputs.append(char_right)
+
+        # Concatenate
+        left_embedding = keras.layers.Concatenate()(left_embeddings)
+        right_embedding = keras.layers.Concatenate()(right_embeddings)
+        d = K.int_shape(left_embedding)[-1]
+
+        # Encoding module
+        left_encoding = EncodingLayer(
+            initial_keep_rate=self._params['dropout_initial_keep_rate'],
+            decay_interval=self._params['dropout_decay_interval'],
+            decay_rate=self._params['dropout_decay_rate']
+        )(left_embedding)
+        right_encoding = EncodingLayer(
+            initial_keep_rate=self._params['dropout_initial_keep_rate'],
+            decay_interval=self._params['dropout_decay_interval'],
+            decay_rate=self._params['dropout_decay_rate']
+        )(right_embedding)
+
+        # Interaction module
+        interaction = keras.layers.Lambda(self._make_interaction)(
+            [left_encoding, right_encoding])
+
+        # Feature extraction module
+        feature_extractor_input = keras.layers.Conv2D(
+            filters=int(d * self._params['first_scale_down_ratio']),
+            kernel_size=(1, 1),
+            activation=None)(interaction)
+        feature_extractor = self._create_densenet()
+        features = feature_extractor(feature_extractor_input)
+
+        # Output module
+        features = DecayingDropoutLayer(
+            initial_keep_rate=self._params['dropout_initial_keep_rate'],
+            decay_interval=self._params['dropout_decay_interval'],
+            decay_rate=self._params['dropout_decay_rate'])(features)
+        out = self._make_output_layer()(features)
+
+        self._backend = keras.Model(inputs=inputs, outputs=out)
+
+    def _make_char_embedding_layer(self) -> keras.layers.Layer:
+        """
+        Apply embedding, conv and maxpooling operation over time dimension
+        for each token to obtain a vector.
+
+        :return: Wrapper Keras 'Layer' as character embedding feature
+            extractor.
+        """
+
+        return keras.layers.TimeDistributed(keras.Sequential([
+            keras.layers.Embedding(
+                input_dim=self._params['char_embedding_input_dim'],
+                output_dim=self._params['char_embedding_output_dim'],
+                input_length=self._params['input_shapes'][2][-1]),
+            keras.layers.Conv1D(
+                filters=self._params['char_conv_filters'],
+                kernel_size=self._params['char_conv_kernel_size']),
+            keras.layers.GlobalMaxPooling1D()]))
+
+    def _make_interaction(self, inputs_) -> typing.Any:
+        left_encoding = inputs_[0]
+        right_encoding = inputs_[1]
+
+        left_encoding = K.expand_dims(left_encoding, axis=2)
+        right_encoding = K.expand_dims(right_encoding, axis=1)
+
+        interaction = left_encoding * right_encoding
+        return interaction
+
+    def _create_densenet(self) -> typing.Callable:
+        """
+        DenseNet is consisted of 'nb_dense_blocks' sets of Dense block
+        and Transition block pair.
+
+        :return: Wrapper Keras 'Layer' as DenseNet, tensor in tensor out.
+        """
+        def _wrapper(x):
+            for _ in range(self._params['nb_dense_blocks']):
+                # Dense block
+                # Apply 'layers_per_dense_block' convolution layers.
+                for _ in range(self._params['layers_per_dense_block']):
+                    out_conv = keras.layers.Conv2D(
+                        filters=self._params['growth_rate'],
+                        kernel_size=(3, 3),
+                        padding='same',
+                        activation='relu')(x)
+                    x = keras.layers.Concatenate(axis=-1)([x, out_conv])
+
+                # Transition block
+                # Apply a convolution layer and a maxpooling layer.
+                scale_down_ratio = self._params['transition_scale_down_ratio']
+                nb_filter = int(K.int_shape(x)[-1] * scale_down_ratio)
+                x = keras.layers.Conv2D(
+                    filters=nb_filter,
+                    kernel_size=(1, 1),
+                    padding='same',
+                    activation=None)(x)
+                x = keras.layers.MaxPool2D(strides=(2, 2))(x)
+
+            out_densenet = keras.layers.Flatten()(x)
+            return out_densenet
+
+        return _wrapper
diff --git a/matchzoo/preprocessors/__init__.py b/matchzoo/preprocessors/__init__.py
index 3042e574..656c1586 100644
--- a/matchzoo/preprocessors/__init__.py
+++ b/matchzoo/preprocessors/__init__.py
@@ -3,6 +3,7 @@
 from .naive_preprocessor import NaivePreprocessor
 from .basic_preprocessor import BasicPreprocessor
 from .cdssm_preprocessor import CDSSMPreprocessor
+from .diin_preprocessor import DIINPreprocessor
 
 
 def list_available() -> list:
diff --git a/matchzoo/preprocessors/diin_preprocessor.py b/matchzoo/preprocessors/diin_preprocessor.py
new file mode 100644
index 00000000..14b0a78e
--- /dev/null
+++ b/matchzoo/preprocessors/diin_preprocessor.py
@@ -0,0 +1,159 @@
+"""DIIN Preprocessor."""
+
+from tqdm import tqdm
+import pandas as pd
+
+from matchzoo.engine.base_preprocessor import BasePreprocessor
+from matchzoo import DataPack
+from .build_vocab_unit import build_vocab_unit
+from .chain_transform import chain_transform
+from . import units
+
+tqdm.pandas()
+
+
+class DIINPreprocessor(BasePreprocessor):
+    """DIIN Model preprocessor."""
+
+    def __init__(self,
+                 fixed_length_left: int = 32,
+                 fixed_length_right: int = 32,
+                 fixed_length_word: int = 16):
+        """
+        DIIN Model preprocessor.
+
+        :param fixed_length_left: Integer, maximize length of :attr:'left' in
+            the data_pack.
+        :param fixed_length_right: Integer, maximize length of :attr:'right' in
+            the data_pack.
+        :param fixed_length_word: Integer, maximize length of each word.
+
+        Example:
+            >>> import matchzoo as mz
+            >>> train_data = mz.datasets.snli.load_data('train')
+            >>> test_data = mz.datasets.snli.load_data('test')
+            >>> diin_preprocessor = mz.preprocessors.DIINPreprocessor(
+            ...     fixed_length_left=32,
+            ...     fixed_length_right=32,
+            ...     fixed_length_word=16,
+            ... )
+            >>> diin_preprocessor = diin_preprocessor.fit(
+            ...     train_data, verbose=0)
+            >>> diin_preprocessor.context['input_shapes']
+            [(32,), (32,), (32,16,), (32,16,), (32,), (32,)]
+            >>> diin_preprocessor.context['vocab_size']
+            33061
+            >>> train_data_processed = diin_preprocessor.transform(
+            ...     train_data, verbose=0)
+            >>> type(train_data_processed)
+            <class 'matchzoo.data_pack.data_pack.DataPack'>
+            >>> test_data_processed = diin_preprocessor.transform(
+            ...     test_data, verbose=0)
+            >>> type(test_data_transformed)
+            <class 'matchzoo.data_pack.data_pack.DataPack'>
+        """
+        super().__init__()
+        self._fixed_length_left = fixed_length_left
+        self._fixed_length_right = fixed_length_right
+        self._fixed_length_word = fixed_length_word
+        self._left_fixedlength_unit = units.FixedLength(
+            self._fixed_length_left,
+            pad_value='0',
+            pad_mode='post'
+        )
+        self._right_fixedlength_unit = units.FixedLength(
+            self._fixed_length_right,
+            pad_value='0',
+            pad_mode='post'
+        )
+        self._units = self._default_units()
+
+    def fit(self, data_pack: DataPack, verbose: int = 1):
+        """
+        Fit pre-processing context for transformation.
+
+        :param data_pack: data_pack to be preprocessed.
+        :param verbose: Verbosity.
+        :return: class:'DIINPreprocessor' instance.
+        """
+        func = chain_transform(self._units)
+        data_pack = data_pack.apply_on_text(func, mode='both', verbose=verbose)
+
+        vocab_unit = build_vocab_unit(data_pack, verbose=verbose)
+        vocab_size = len(vocab_unit.state['term_index'])
+        self._context['vocab_unit'] = vocab_unit
+        self._context['vocab_size'] = vocab_size
+        self._context['embedding_input_dim'] = vocab_size
+
+        data_pack = data_pack.apply_on_text(
+            units.NgramLetter(ngram=1, reduce_dim=True).transform,
+            mode='both', verbose=verbose)
+        char_unit = build_vocab_unit(data_pack, verbose=verbose)
+        self._context['char_unit'] = char_unit
+
+        self._context['input_shapes'] = [
+            (self._fixed_length_left,),
+            (self._fixed_length_right,),
+            (self._fixed_length_left, self._fixed_length_word,),
+            (self._fixed_length_right, self._fixed_length_word,),
+            (self._fixed_length_left,),
+            (self._fixed_length_right,)
+        ]
+        return self
+
+    def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack:
+        """
+        Apply transformation on data, create fixed length word representation,
+        character representation and exact match representation.
+
+        :param data_pack: Inputs to be preprocessed.
+        :param verbose: Verbosity.
+
+        :return: Transformed data as :class:'DataPack' object.
+        """
+        data_pack = data_pack.copy()
+        data_pack.apply_on_text(
+            chain_transform(self._units),
+            mode='both', inplace=True, verbose=verbose)
+
+        # Process character representation
+        data_pack.apply_on_text(
+            units.NgramLetter(ngram=1, reduce_dim=False).transform,
+            rename=('char_left', 'char_right'),
+            mode='both', inplace=True, verbose=verbose)
+        char_index_dict = self._context['char_unit'].state['term_index']
+        left_charindex_unit = units.CharacterIndex(
+            char_index_dict, self._fixed_length_left, self._fixed_length_word)
+        right_charindex_unit = units.CharacterIndex(
+            char_index_dict, self._fixed_length_right, self._fixed_length_word)
+        data_pack.left['char_left'] = data_pack.left['char_left'].apply(
+            left_charindex_unit.transform)
+        data_pack.right['char_right'] = data_pack.right['char_right'].apply(
+            right_charindex_unit.transform)
+
+        # Process word representation
+        data_pack.apply_on_text(
+            self._context['vocab_unit'].transform,
+            mode='both', inplace=True, verbose=verbose)
+
+        # Process exact match representation
+        frame = data_pack.relation.join(
+            data_pack.left, on='id_left', how='left'
+        ).join(data_pack.right, on='id_right', how='left')
+        left_exactmatch_unit = units.WordExactMatch(
+            self._fixed_length_left, match='text_left', to_match='text_right')
+        right_exactmatch_unit = units.WordExactMatch(
+            self._fixed_length_right, match='text_right', to_match='text_left')
+        data_pack.relation['match_left'] = frame.apply(
+            left_exactmatch_unit.transform, axis=1)
+        data_pack.relation['match_right'] = frame.apply(
+            right_exactmatch_unit.transform, axis=1)
+
+        data_pack.apply_on_text(
+            self._left_fixedlength_unit.transform,
+            mode='left', inplace=True, verbose=verbose)
+        data_pack.apply_on_text(
+            self._right_fixedlength_unit.transform,
+            mode='right', inplace=True, verbose=verbose)
+
+        return data_pack
diff --git a/matchzoo/preprocessors/units/__init__.py b/matchzoo/preprocessors/units/__init__.py
index d9e88953..96844953 100644
--- a/matchzoo/preprocessors/units/__init__.py
+++ b/matchzoo/preprocessors/units/__init__.py
@@ -13,6 +13,8 @@
 from .tokenize import Tokenize
 from .vocabulary import Vocabulary
 from .word_hashing import WordHashing
+from .character_index import CharacterIndex
+from .word_exact_match import WordExactMatch
 
 
 def list_available() -> list:
diff --git a/matchzoo/preprocessors/units/character_index.py b/matchzoo/preprocessors/units/character_index.py
new file mode 100644
index 00000000..ae2a46cd
--- /dev/null
+++ b/matchzoo/preprocessors/units/character_index.py
@@ -0,0 +1,61 @@
+import numpy as np
+
+from .unit import Unit
+
+
+class CharacterIndex(Unit):
+    """
+    CharacterIndexUnit for DIIN model.
+
+    The input of :class:'CharacterIndexUnit' should be a list of word
+    character list extracted from a text. The output is the character
+    index representation of this text.
+
+    :class:`NgramLetterUnit` and :class:`VocabularyUnit` are two
+    essential prerequisite of :class:`CharacterIndexUnit`.
+
+    Examples:
+        >>> input_ = [['#', 'a', '#'],['#', 'o', 'n', 'e', '#']]
+        >>> character_index = CharacterIndex(
+        ...     char_index={
+                 '<PAD>': 0, '<OOV>': 1, 'a': 2, 'n': 3, 'e':4, '#':5},
+        ...     fixed_length_text=3,
+        ...     fixed_length_word=5)
+        >>> index = character_index.transform(input_)
+        >>> index
+        [[5, 2, 5, 0, 0], [5, 1, 3, 4, 5], [0, 0, 0, 0, 0]]
+    """
+
+    def __init__(
+        self,
+        char_index: dict,
+        fixed_length_text: int,
+        fixed_length_word: int
+    ):
+        """
+        Class initialization.
+
+        :param char_index: character-index mapping generated by
+            :class:'VocabularyUnit'.
+        :param fixed_length_text: maximize length of a text.
+        :param fixed_length_word: maximize length of a word.
+        """
+        self._char_index = char_index
+        self._fixed_length_text = fixed_length_text
+        self._fixed_length_word = fixed_length_word
+
+    def transform(self, input_: list) -> list:
+        """
+        Transform list of characters to corresponding indices.
+
+        :param input_: list of characters generated by
+            :class:'NgramLetterUnit'.
+
+        :return: character index representation of a text.
+        """
+        idx = np.zeros((self._fixed_length_text, self._fixed_length_word))
+        for i in range(min(len(input_), self._fixed_length_text)):
+            for j in range(min(len(input_[i]), self._fixed_length_word)):
+                idx[i, j] = self._char_index.get(input_[i][j], 1)
+
+        return idx.tolist()
diff --git a/matchzoo/preprocessors/units/word_exact_match.py b/matchzoo/preprocessors/units/word_exact_match.py
new file mode 100644
index 00000000..68a7834b
--- /dev/null
+++ b/matchzoo/preprocessors/units/word_exact_match.py
@@ -0,0 +1,66 @@
+import numpy as np
+
+from .unit import Unit
+
+
+class WordExactMatch(Unit):
+    """
+    WordExactUnit Class.
+
+    Process unit to get a binary match list of two word index lists. The
+    word index list is the word representation of a text.
+
+    Examples:
+        >>> input_ = pandas.DataFrame({
+        ...  'text_left':[[1, 2, 3],[4, 5, 7, 9]],
+        ...  'text_right':[[5, 3, 2, 7],[2, 3, 5]]}
+        ... )
+        >>> left_word_exact_match = WordExactMatch(
+        ...     fixed_length_text=5,
+        ...     match='text_left', to_match='text_right'
+        ... )
+        >>> left_out = input_.apply(left_word_exact_match.transform, axis=1)
+        >>> left_out
+        [[0, 1, 1, 0, 0],[0, 1, 0, 0, 0]]
+        >>> right_word_exact_match = WordExactMatch(
+        ...     fixed_length_text=5,
+        ...     match='text_right', to_match='text_left'
+        ... )
+        >>> right_out = input_.apply(right_word_exact_match.transform, axis=1)
+        >>> right_out
+        [[0, 1, 1, 0, 0], [0, 0, 1, 0, 0]]
+    """
+
+    def __init__(
+        self,
+        fixed_length_text: int,
+        match: str,
+        to_match: str
+    ):
+        """
+        Class initialization.
+
+        :param fixed_length_text: fixed length of the text.
+        :param match: the 'match' column name.
+        :param to_match: the 'to_match' column name.
+        """
+        self._fixed_length_text = fixed_length_text
+        self._match = match
+        self._to_match = to_match
+
+    def transform(self, input_) -> list:
+        """
+        Transform two word index lists into a binary match list.
+
+        :param input_: a dataframe include 'match' column and
+            'to_match' column.
+
+        :return: a binary match result list of two word index lists.
+        """
+        match_length = len(input_[self._match])
+        match_binary = np.zeros((self._fixed_length_text))
+        for i in range(min(self._fixed_length_text, match_length)):
+            if input_[self._match][i] in set(input_[self._to_match]):
+                match_binary[i] = 1
+
+        return match_binary.tolist()

From e11fb8c94a63232127f3cba77792e40413989c7e Mon Sep 17 00:00:00 2001
From: caiyinqiong <1198593462@qq.com>
Date: Sun, 28 Apr 2019 00:09:04 +0800
Subject: [PATCH 059/100] fix contrib/layers/__init__.py

---
 matchzoo/contrib/layers/__init__.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/matchzoo/contrib/layers/__init__.py b/matchzoo/contrib/layers/__init__.py
index ea5c99b7..34623887 100644
--- a/matchzoo/contrib/layers/__init__.py
+++ b/matchzoo/contrib/layers/__init__.py
@@ -8,6 +8,6 @@
 layer_dict = {
     "MatchingTensorLayer": MatchingTensorLayer,
     "SpatialGRU": SpatialGRU,
-	"DecayingDropoutLayer": DecayingDropoutLayer,
+    "DecayingDropoutLayer": DecayingDropoutLayer,
     "EncodingLayer": EncodingLayer
 }

From d459a97a4f2284697649a2bed2d31da801ffd7b6 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Sun, 5 May 2019 17:05:10 +0800
Subject: [PATCH 060/100] Add make_keras_optimizer_picklable.py and its test.

---
 matchzoo/__init__.py                          |  2 +-
 matchzoo/utils/__init__.py                    |  1 +
 .../utils/make_keras_optimizer_picklable.py   | 19 +++++++++++++++++++
 tests/unit_test/test_utils.py                 | 17 +++++++++++++++++
 4 files changed, 38 insertions(+), 1 deletion(-)
 create mode 100644 matchzoo/utils/make_keras_optimizer_picklable.py
 create mode 100644 tests/unit_test/test_utils.py

diff --git a/matchzoo/__init__.py b/matchzoo/__init__.py
index 36484a2e..6205cb23 100644
--- a/matchzoo/__init__.py
+++ b/matchzoo/__init__.py
@@ -43,7 +43,7 @@
 
 from .embedding.embedding import Embedding
 
-from .utils import one_hot
+from .utils import one_hot, make_keras_optimizer_picklable
 from .preprocessors.build_unit_from_data_pack import build_unit_from_data_pack
 from .preprocessors.build_vocab_unit import build_vocab_unit
 
diff --git a/matchzoo/utils/__init__.py b/matchzoo/utils/__init__.py
index 8c76f5fa..63a840db 100644
--- a/matchzoo/utils/__init__.py
+++ b/matchzoo/utils/__init__.py
@@ -1,3 +1,4 @@
 from .one_hot import one_hot
 from .tensor_type import TensorType
 from .list_recursive_subclasses import list_recursive_concrete_subclasses
+from .make_keras_optimizer_picklable import make_keras_optimizer_picklable
diff --git a/matchzoo/utils/make_keras_optimizer_picklable.py b/matchzoo/utils/make_keras_optimizer_picklable.py
new file mode 100644
index 00000000..c45edba4
--- /dev/null
+++ b/matchzoo/utils/make_keras_optimizer_picklable.py
@@ -0,0 +1,19 @@
+import keras
+
+
+def make_keras_optimizer_picklable():
+    """
+    Fix https://github.com/NTMC-Community/MatchZoo/issues/726.
+
+    This function changes how keras behaves, use with caution.
+    """
+    def __getstate__(self):
+        return keras.optimizers.serialize(self)
+
+    def __setstate__(self, state):
+        optimizer = keras.optimizers.deserialize(state)
+        self.__dict__ = optimizer.__dict__
+
+    cls = keras.optimizers.Optimizer
+    cls.__getstate__ = __getstate__
+    cls.__setstate__ = __setstate__
diff --git a/tests/unit_test/test_utils.py b/tests/unit_test/test_utils.py
new file mode 100644
index 00000000..d293ef66
--- /dev/null
+++ b/tests/unit_test/test_utils.py
@@ -0,0 +1,17 @@
+import dill
+import pytest
+import keras
+
+import matchzoo
+
+
+def test_make_keras_optimizer_picklable():
+    adam = keras.optimizers.Adam(lr=0.1)
+    with pytest.raises(Exception):
+        s = dill.dumps(adam)
+        assert dill.loads(s)
+
+    matchzoo.utils.make_keras_optimizer_picklable()
+
+    s = dill.dumps(adam)
+    assert dill.loads(s)

From a33d93a07acaba77e63f6c69779bd14ba3217224 Mon Sep 17 00:00:00 2001
From: bo <kingbolanda@live.com>
Date: Fri, 10 May 2019 11:59:53 +0200
Subject: [PATCH 061/100] take out gitter

---
 README.md | 1 -
 1 file changed, 1 deletion(-)

diff --git a/README.md b/README.md
index ad5c345c..97ef5ad0 100644
--- a/README.md
+++ b/README.md
@@ -8,7 +8,6 @@
 > MatchZoo 是一个通用的文本匹配工具包，它旨在方便大家快速的实现、比较、以及分享最新的深度文本匹配模型。
 
 [![Python 3.6](https://img.shields.io/badge/python-3.6%20%7C%203.7-blue.svg)](https://www.python.org/downloads/release/python-360/)
-[![Gitter chat](https://badges.gitter.im/gitterHQ/gitter.png)](https://gitter.im/NTMC-Community/community)
 [![Pypi Downloads](https://img.shields.io/pypi/dm/matchzoo.svg?label=pypi)](https://pypi.org/project/MatchZoo/)
 [![Documentation Status](https://readthedocs.org/projects/matchzoo/badge/?version=master)](https://matchzoo.readthedocs.io/en/master/?badge=master)
 [![Build Status](https://travis-ci.org/NTMC-Community/MatchZoo.svg?branch=master)](https://travis-ci.org/NTMC-Community/MatchZoo/)

From ac8d91ee2d29c6ef8d1724bfe70618b7cacec173 Mon Sep 17 00:00:00 2001
From: Crystina <xzhangbx@ust.hk>
Date: Sat, 11 May 2019 06:28:56 -0400
Subject: [PATCH 062/100] recommit esim tutorial

---
 tutorials/wikiqa/esim.ipynb | 524 ++++++++++++++++++++++++++++++++++++
 1 file changed, 524 insertions(+)
 create mode 100644 tutorials/wikiqa/esim.ipynb

diff --git a/tutorials/wikiqa/esim.ipynb b/tutorials/wikiqa/esim.ipynb
new file mode 100644
index 00000000..042910bc
--- /dev/null
+++ b/tutorials/wikiqa/esim.ipynb
@@ -0,0 +1,524 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "matchzoo version 2.1.0\n",
+      "\n",
+      "data loading ...\n",
+      "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n",
+      "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n",
+      "loading embedding ...\n",
+      "embedding loaded as `glove_embedding`\n"
+     ]
+    }
+   ],
+   "source": [
+    "%run ./tutorials/wikiqa/init.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "from keras.backend.tensorflow_backend import set_session\n",
+    "config = tf.ConfigProto()\n",
+    "config.gpu_options.visible_device_list=\"1\"\n",
+    "config.gpu_options.allow_growth = True  # dynamically grow the memory used on the GPU\n",
+    "sess = tf.Session(config=config)\n",
+    "set_session(sess)  # set this TensorFlow session as the default session for Keras"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_filtered_data(preprocessor, data_type):\n",
+    "    assert ( data_type in ['train', 'dev', 'test'])\n",
+    "    data_pack = mz.datasets.wiki_qa.load_data(data_type, task='ranking')\n",
+    "\n",
+    "    if data_type == 'train':\n",
+    "        X, Y = preprocessor.fit_transform(data_pack).unpack()\n",
+    "    else:\n",
+    "        X, Y = preprocessor.transform(data_pack).unpack()\n",
+    "\n",
+    "    new_idx = []\n",
+    "    for i in range(Y.shape[0]):\n",
+    "        if X[\"length_left\"][i] == 0 or X[\"length_right\"][i] == 0:\n",
+    "            continue\n",
+    "        new_idx.append(i)\n",
+    "    new_idx = np.array(new_idx)\n",
+    "    print(\"Removed empty data. Found \", (Y.shape[0] - new_idx.shape[0]))\n",
+    "\n",
+    "    for k in X.keys():\n",
+    "        X[k] = X[k][new_idx]\n",
+    "    Y = Y[new_idx]\n",
+    "\n",
+    "    pos_idx = (Y == 1)[:, 0]\n",
+    "    pos_qid = X[\"id_left\"][pos_idx]\n",
+    "    keep_idx_bool = np.array([ qid in pos_qid for qid in X[\"id_left\"]])\n",
+    "    keep_idx = np.arange(keep_idx_bool.shape[0])\n",
+    "    keep_idx = keep_idx[keep_idx_bool]\n",
+    "    print(\"Removed questions with no pos label. Found \", (keep_idx_bool == 0).sum())\n",
+    "\n",
+    "    print(\"shuffling...\")\n",
+    "    np.random.shuffle(keep_idx)\n",
+    "    for k in X.keys():\n",
+    "        X[k] = X[k][keep_idx]\n",
+    "    Y = Y[keep_idx]\n",
+    "\n",
+    "    return X, Y, preprocessor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 12754.26it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:02<00:00, 6500.31it/s]\n",
+      "Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 1215206.55it/s]\n",
+      "Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 185258.28it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 184455.70it/s]\n",
+      "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 922581.36it/s]\n",
+      "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 1082236.12it/s]\n",
+      "Building Vocabulary from a datapack.: 100%|██████████| 404432/404432 [00:00<00:00, 3795031.47it/s]\n",
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 13650.60it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:02<00:00, 6764.51it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 171037.31it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 288623.28it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 90725.37it/s]\n",
+      "Processing length_left with len: 100%|██████████| 2118/2118 [00:00<00:00, 583636.81it/s]\n",
+      "Processing length_right with len: 100%|██████████| 18841/18841 [00:00<00:00, 1203693.44it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 193145.54it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 134549.60it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed empty data. Found  38\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 296/296 [00:00<00:00, 14135.26it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval:   0%|          | 0/2708 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed questions with no pos label. Found  11672\n",
+      "shuffling...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2708/2708 [00:00<00:00, 6731.87it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 168473.93it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 296/296 [00:00<00:00, 204701.40it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 159066.95it/s]\n",
+      "Processing length_left with len: 100%|██████████| 296/296 [00:00<00:00, 442607.48it/s]\n",
+      "Processing length_right with len: 100%|██████████| 2708/2708 [00:00<00:00, 1038699.15it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 296/296 [00:00<00:00, 149130.81it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 140864.36it/s]\n",
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 633/633 [00:00<00:00, 12189.39it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed empty data. Found  2\n",
+      "Removed questions with no pos label. Found  1601\n",
+      "shuffling...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 5961/5961 [00:00<00:00, 7064.16it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 5961/5961 [00:00<00:00, 187399.25it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 633/633 [00:00<00:00, 259733.36it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 5961/5961 [00:00<00:00, 160878.23it/s]\n",
+      "Processing length_left with len: 100%|██████████| 633/633 [00:00<00:00, 688714.51it/s]\n",
+      "Processing length_right with len: 100%|██████████| 5961/5961 [00:00<00:00, 1166965.98it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 633/633 [00:00<00:00, 158526.06it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 5961/5961 [00:00<00:00, 137558.64it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed empty data. Found  18\n",
+      "Removed questions with no pos label. Found  3805\n",
+      "shuffling...\n"
+     ]
+    }
+   ],
+   "source": [
+    "preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=20,\n",
+    "                                                  fixed_length_right=40,\n",
+    "                                                  remove_stop_words=False)\n",
+    "train_X, train_Y, preprocessor = load_filtered_data(preprocessor, 'train')\n",
+    "val_X, val_Y, _ = load_filtered_data(preprocessor, 'dev')\n",
+    "pred_X, pred_Y, _ = load_filtered_data(preprocessor, 'test')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "text_left (InputLayer)          (None, 20)           0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "text_right (InputLayer)         (None, 40)           0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "embedding (Embedding)           multiple             5002500     text_left[0][0]                  \n",
+      "                                                                 text_right[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_1 (Dropout)             multiple             0           embedding[0][0]                  \n",
+      "                                                                 embedding[1][0]                  \n",
+      "                                                                 dense_1[0][0]                    \n",
+      "                                                                 dense_1[1][0]                    \n",
+      "                                                                 dense_2[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_1 (Lambda)               multiple             0           text_left[0][0]                  \n",
+      "                                                                 text_right[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "bidirectional_1 (Bidirectional) multiple             1442400     dropout_1[0][0]                  \n",
+      "                                                                 dropout_1[1][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_2 (Lambda)               (None, 20, 1)        0           lambda_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_3 (Lambda)               (None, 40, 1)        0           lambda_1[1][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_1 (Multiply)           (None, 20, 600)      0           bidirectional_1[0][0]            \n",
+      "                                                                 lambda_2[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_2 (Multiply)           (None, 40, 600)      0           bidirectional_1[1][0]            \n",
+      "                                                                 lambda_3[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_4 (Lambda)               (None, 20, 1)        0           lambda_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_5 (Lambda)               (None, 1, 40)        0           lambda_1[1][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "dot_1 (Dot)                     (None, 20, 40)       0           multiply_1[0][0]                 \n",
+      "                                                                 multiply_2[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_3 (Multiply)           (None, 20, 40)       0           lambda_4[0][0]                   \n",
+      "                                                                 lambda_5[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "permute_1 (Permute)             (None, 40, 20)       0           dot_1[0][0]                      \n",
+      "                                                                 multiply_3[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "atten_mask (Lambda)             multiple             0           dot_1[0][0]                      \n",
+      "                                                                 multiply_3[0][0]                 \n",
+      "                                                                 permute_1[0][0]                  \n",
+      "                                                                 permute_1[1][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "softmax_1 (Softmax)             multiple             0           atten_mask[0][0]                 \n",
+      "                                                                 atten_mask[1][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "dot_2 (Dot)                     (None, 20, 600)      0           softmax_1[0][0]                  \n",
+      "                                                                 multiply_2[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "dot_3 (Dot)                     (None, 40, 600)      0           softmax_1[1][0]                  \n",
+      "                                                                 multiply_1[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "subtract_1 (Subtract)           (None, 20, 600)      0           multiply_1[0][0]                 \n",
+      "                                                                 dot_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_4 (Multiply)           (None, 20, 600)      0           multiply_1[0][0]                 \n",
+      "                                                                 dot_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "subtract_2 (Subtract)           (None, 40, 600)      0           multiply_2[0][0]                 \n",
+      "                                                                 dot_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_5 (Multiply)           (None, 40, 600)      0           multiply_2[0][0]                 \n",
+      "                                                                 dot_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_1 (Concatenate)     (None, 20, 2400)     0           multiply_1[0][0]                 \n",
+      "                                                                 dot_2[0][0]                      \n",
+      "                                                                 subtract_1[0][0]                 \n",
+      "                                                                 multiply_4[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_2 (Concatenate)     (None, 40, 2400)     0           multiply_2[0][0]                 \n",
+      "                                                                 dot_3[0][0]                      \n",
+      "                                                                 subtract_2[0][0]                 \n",
+      "                                                                 multiply_5[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_1 (Dense)                 multiple             720300      concatenate_1[0][0]              \n",
+      "                                                                 concatenate_2[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "bidirectional_2 (Bidirectional) multiple             1442400     dropout_1[2][0]                  \n",
+      "                                                                 dropout_1[3][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_6 (Lambda)               (None, 20, 1)        0           lambda_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_8 (Lambda)               (None, 20, 1)        0           lambda_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_10 (Lambda)              (None, 40, 1)        0           lambda_1[1][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_12 (Lambda)              (None, 40, 1)        0           lambda_1[1][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_6 (Multiply)           (None, 20, 600)      0           bidirectional_2[0][0]            \n",
+      "                                                                 lambda_6[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_7 (Multiply)           (None, 20, 600)      0           bidirectional_2[0][0]            \n",
+      "                                                                 lambda_8[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_8 (Multiply)           (None, 40, 600)      0           bidirectional_2[1][0]            \n",
+      "                                                                 lambda_10[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_9 (Multiply)           (None, 40, 600)      0           bidirectional_2[1][0]            \n",
+      "                                                                 lambda_12[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_7 (Lambda)               (None, 600)          0           multiply_6[0][0]                 \n",
+      "                                                                 lambda_6[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_9 (Lambda)               (None, 600)          0           multiply_7[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_11 (Lambda)              (None, 600)          0           multiply_8[0][0]                 \n",
+      "                                                                 lambda_10[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_13 (Lambda)              (None, 600)          0           multiply_9[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_3 (Concatenate)     (None, 1200)         0           lambda_7[0][0]                   \n",
+      "                                                                 lambda_9[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_4 (Concatenate)     (None, 1200)         0           lambda_11[0][0]                  \n",
+      "                                                                 lambda_13[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_5 (Concatenate)     (None, 2400)         0           concatenate_3[0][0]              \n",
+      "                                                                 concatenate_4[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_2 (Dense)                 (None, 300)          720300      concatenate_5[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_3 (Dense)                 (None, 2)            602         dropout_1[4][0]                  \n",
+      "==================================================================================================\n",
+      "Total params: 9,328,502\n",
+      "Trainable params: 4,326,002\n",
+      "Non-trainable params: 5,002,500\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "from keras.optimizers import Adam\n",
+    "import matchzoo\n",
+    "\n",
+    "model = matchzoo.contrib.models.ESIM()\n",
+    "\n",
+    "# update `input_shapes` and `embedding_input_dim`\n",
+    "# model.params['task'] = mz.tasks.Ranking() \n",
+    "# or \n",
+    "model.params['task'] = mz.tasks.Classification(num_classes=2)\n",
+    "model.params.update(preprocessor.context)\n",
+    "\n",
+    "model.params['mask_value'] = 0\n",
+    "model.params['lstm_dim'] = 300\n",
+    "model.params['embedding_output_dim'] = 300\n",
+    "model.params['embedding_trainable'] = False\n",
+    "model.params['dropout_rate'] = 0.5\n",
+    "\n",
+    "model.params['mlp_num_units'] = 300\n",
+    "model.params['mlp_num_layers'] = 0\n",
+    "model.params['mlp_num_fan_out'] = 300\n",
+    "model.params['mlp_activation_func'] = 'tanh'\n",
+    "model.params['optimizer'] = Adam(lr=1e-4)\n",
+    "model.guess_and_fill_missing_params()\n",
+    "model.build()\n",
+    "model.compile()\n",
+    "model.backend.summary()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "embedding_matrix = glove_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'], initializer=lambda: 0)\n",
+    "model.load_embedding_matrix(embedding_matrix)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 8650 samples, validate on 1130 samples\n",
+      "Epoch 1/10\n",
+      "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0985 - val_loss: 0.0977\n",
+      "Validation: mean_average_precision(0.0): 0.6377925262180991\n",
+      "Epoch 2/10\n",
+      "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0947 - val_loss: 0.0939\n",
+      "Validation: mean_average_precision(0.0): 0.6323746460063332\n",
+      "Epoch 3/10\n",
+      "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0923 - val_loss: 0.0896\n",
+      "Validation: mean_average_precision(0.0): 0.6447892278707743\n",
+      "Epoch 4/10\n",
+      "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0895 - val_loss: 0.0904\n",
+      "Validation: mean_average_precision(0.0): 0.6645210508066117\n",
+      "Epoch 5/10\n",
+      "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0883 - val_loss: 0.0900\n",
+      "Validation: mean_average_precision(0.0): 0.6622282952529867\n",
+      "Epoch 6/10\n",
+      "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0839 - val_loss: 0.0900\n",
+      "Validation: mean_average_precision(0.0): 0.6654279587941297\n",
+      "Epoch 7/10\n",
+      "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0821 - val_loss: 0.0896\n",
+      "Validation: mean_average_precision(0.0): 0.6668269018575894\n",
+      "Epoch 8/10\n",
+      "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0792 - val_loss: 0.0885\n",
+      "Validation: mean_average_precision(0.0): 0.6723704781393599\n",
+      "Epoch 9/10\n",
+      "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0754 - val_loss: 0.0895\n",
+      "Validation: mean_average_precision(0.0): 0.6552521148587158\n",
+      "Epoch 10/10\n",
+      "8650/8650 [==============================] - 52s 6ms/step - loss: 0.0731 - val_loss: 0.0910\n",
+      "Validation: mean_average_precision(0.0): 0.6695447388956829\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train as ranking task\n",
+    "model.params['task'] = mz.tasks.Ranking()\n",
+    "evaluate = mz.callbacks.EvaluateAllMetrics(model,\n",
+    "                                           x=pred_X,\n",
+    "                                           y=pred_Y,\n",
+    "                                           once_every=1,\n",
+    "                                           batch_size=len(pred_Y))\n",
+    "history = model.fit(x = [train_X['text_left'],\n",
+    "                         train_X['text_right']],                  # (20360, 1000)\n",
+    "                    y = train_Y,                                  # (20360, 2)\n",
+    "                    validation_data = (val_X, val_Y),\n",
+    "                    callbacks=[evaluate],\n",
+    "                    batch_size = 32,\n",
+    "                    epochs = 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 8650 samples, validate on 1130 samples\n",
+      "Epoch 1/10\n",
+      "8650/8650 [==============================] - 68s 8ms/step - loss: 0.3628 - val_loss: 0.3552\n",
+      "Epoch 2/10\n",
+      "8650/8650 [==============================] - 63s 7ms/step - loss: 0.3285 - val_loss: 0.3591\n",
+      "Epoch 3/10\n",
+      "8650/8650 [==============================] - 63s 7ms/step - loss: 0.3105 - val_loss: 0.3681\n",
+      "Epoch 4/10\n",
+      "8650/8650 [==============================] - 64s 7ms/step - loss: 0.3012 - val_loss: 0.3166\n",
+      "Epoch 5/10\n",
+      "8650/8650 [==============================] - 64s 7ms/step - loss: 0.2888 - val_loss: 0.2961\n",
+      "Epoch 6/10\n",
+      "8650/8650 [==============================] - 64s 7ms/step - loss: 0.2801 - val_loss: 0.3362\n",
+      "Epoch 7/10\n",
+      "8650/8650 [==============================] - 64s 7ms/step - loss: 0.2692 - val_loss: 0.3324\n",
+      "Epoch 8/10\n",
+      "8650/8650 [==============================] - 64s 7ms/step - loss: 0.2609 - val_loss: 0.3172\n",
+      "Epoch 9/10\n",
+      "8650/8650 [==============================] - 58s 7ms/step - loss: 0.2542 - val_loss: 0.3296\n",
+      "Epoch 10/10\n",
+      "8650/8650 [==============================] - 53s 6ms/step - loss: 0.2365 - val_loss: 0.3058\n"
+     ]
+    }
+   ],
+   "source": [
+    "# train as classification task \n",
+    "\n",
+    "from keras.utils import to_categorical\n",
+    "train_Y = to_categorical(train_Y)\n",
+    "val_Y = to_categorical(val_Y)\n",
+    "\n",
+    "model.params['task'] = mz.tasks.Classification(num_classes=2)\n",
+    "\n",
+    "history = model.fit(x = [train_X['text_left'],\n",
+    "                         train_X['text_right']],                  # (20360, 1000)\n",
+    "                    y = train_Y,                                  # (20360, 2)\n",
+    "                    validation_data = (val_X, val_Y),\n",
+    "                    batch_size = 32,\n",
+    "                    epochs = 10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mz_play",
+   "language": "python",
+   "name": "mz_play"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From cea292769af4ac688649573a11b20f4d69024e3d Mon Sep 17 00:00:00 2001
From: jellying <469413628@qq.com>
Date: Wed, 15 May 2019 19:13:35 +0800
Subject: [PATCH 063/100] add bert_preprocessor with some new units

---
 matchzoo/preprocessors/__init__.py            |   1 +
 matchzoo/preprocessors/bert_preprocessor.py   | 145 +++++++
 matchzoo/preprocessors/build_vocab_unit.py    |  14 +-
 matchzoo/preprocessors/units/__init__.py      |   7 +-
 matchzoo/preprocessors/units/bert_clean.py    |  41 ++
 .../preprocessors/units/bert_tokenization.py  | 399 ------------------
 matchzoo/preprocessors/units/bert_tokenize.py |  23 -
 matchzoo/preprocessors/units/tokenize.py      | 101 +++++
 matchzoo/preprocessors/units/vocabulary.py    |  42 ++
 matchzoo/utils/bert_utils.py                  | 106 +++++
 .../processor_units/test_processor_units.py   |  30 +-
 11 files changed, 472 insertions(+), 437 deletions(-)
 create mode 100644 matchzoo/preprocessors/bert_preprocessor.py
 create mode 100644 matchzoo/preprocessors/units/bert_clean.py
 delete mode 100644 matchzoo/preprocessors/units/bert_tokenization.py
 delete mode 100644 matchzoo/preprocessors/units/bert_tokenize.py
 create mode 100644 matchzoo/utils/bert_utils.py

diff --git a/matchzoo/preprocessors/__init__.py b/matchzoo/preprocessors/__init__.py
index 3042e574..3e941f23 100644
--- a/matchzoo/preprocessors/__init__.py
+++ b/matchzoo/preprocessors/__init__.py
@@ -3,6 +3,7 @@
 from .naive_preprocessor import NaivePreprocessor
 from .basic_preprocessor import BasicPreprocessor
 from .cdssm_preprocessor import CDSSMPreprocessor
+from .bert_preprocessor import BertPreprocessor
 
 
 def list_available() -> list:
diff --git a/matchzoo/preprocessors/bert_preprocessor.py b/matchzoo/preprocessors/bert_preprocessor.py
new file mode 100644
index 00000000..2e2e132b
--- /dev/null
+++ b/matchzoo/preprocessors/bert_preprocessor.py
@@ -0,0 +1,145 @@
+"""Bert Preprocessor."""
+
+from tqdm import tqdm
+
+from . import units
+from .chain_transform import chain_transform
+from matchzoo import DataPack
+from matchzoo.engine.base_preprocessor import BasePreprocessor
+from .build_vocab_unit import built_bert_vocab_unit
+from .build_unit_from_data_pack import build_unit_from_data_pack
+
+tqdm.pandas()
+
+
+class BertPreprocessor(BasePreprocessor):
+    """Bert-base Model preprocessor."""
+
+    def __init__(self, fixed_length_left: int = 30,
+                 fixed_length_right: int = 30,
+                 filter_mode: str = 'df',
+                 filter_low_freq: float = 2,
+                 filter_high_freq: float = float('inf'),
+                 remove_stop_words: bool = False,
+                 lower_case: bool=True,
+                 chinese_version: bool=True,
+                 bert_vocab_path: str="bert_dir/vocab.txt"):
+        """
+        Bert-base Model preprocessor.
+
+        TODO: doc here.
+
+
+        Example:
+            >>> import matchzoo as mz
+            >>> train_data = mz.datasets.toy.load_data()
+            >>> test_data = mz.datasets.toy.load_data(stage='test')
+            >>> bert_preprocessor = mz.preprocessors.BertPreprocessor()
+            >>> train_data_processed = bert_preprocessor.fit_transform(
+            ...     train_data
+            ... )
+            >>> type(train_data_processed)
+            <class 'matchzoo.data_pack.data_pack.DataPack'>
+            >>> test_data_transformed = bert_preprocessor.transform(test_data)
+            >>> type(test_data_transformed)
+            <class 'matchzoo.data_pack.data_pack.DataPack'>
+
+        """
+        super().__init__()
+        self._fixed_length_left = fixed_length_left
+        self._fixed_length_right = fixed_length_right
+        self._bert_vocab_path = bert_vocab_path
+        self._left_fixedlength_unit = units.FixedLength(
+            self._fixed_length_left,
+            pad_mode='post'
+        )
+        self._right_fixedlength_unit = units.FixedLength(
+            self._fixed_length_right,
+            pad_mode='post'
+        )
+        self._filter_unit = units.FrequencyFilter(
+            low=filter_low_freq,
+            high=filter_high_freq,
+            mode=filter_mode
+        )
+        self._units = self._default_units()
+        self._vocab_unit = built_bert_vocab_unit(self._bert_vocab_path)
+
+        if chinese_version:
+            self._units.insert(1, units.ChineseTokenize())
+        if lower_case:
+            self._units.append(units.Lowercase())
+            self._units.append(units.StripAccent())
+        self._units.append(units.WordPieceTokenize(self._vocab_unit.state['term_index']))
+        if remove_stop_words:
+            self._units.append(units.StopRemoval())
+
+    def fit(self, data_pack: DataPack, verbose: int = 1):
+        """
+        Fit pre-processing context for transformation.
+
+        :param verbose: Verbosity.
+        :param data_pack: Data_pack to be preprocessed.
+        :return: class:`BertPreprocessor` instance.
+        """
+        data_pack = data_pack.apply_on_text(chain_transform(self._units),
+                                            verbose=verbose)
+        fitted_filter_unit = build_unit_from_data_pack(self._filter_unit,
+                                                       data_pack,
+                                                       flatten=False,
+                                                       mode='right',
+                                                       verbose=verbose)
+        self._context['filter_unit'] = fitted_filter_unit
+        self._context['vocab_unit'] = self._vocab_unit
+        vocab_size = len(self._vocab_unit.state['term_index'])
+        self._context['vocab_size'] = vocab_size
+        self._context['embedding_input_dim'] = vocab_size
+        self._context['input_shapes'] = [(self._fixed_length_left,),
+                                         (self._fixed_length_right,)]
+        return self
+
+    def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack:
+        """
+        Apply transformation on data, create fixed length representation.
+
+        :param data_pack: Inputs to be preprocessed.
+        :param verbose: Verbosity.
+
+        :return: Transformed data as :class:`DataPack` object.
+        """
+        data_pack = data_pack.copy()
+        data_pack.apply_on_text(chain_transform(self._units), inplace=True,
+                                verbose=verbose)
+
+        data_pack.apply_on_text(self._context['filter_unit'].transform,
+                                mode='right', inplace=True, verbose=verbose)
+        data_pack.apply_on_text(self._context['vocab_unit'].transform,
+                                mode='both', inplace=True, verbose=verbose)
+        data_pack.append_text_length(inplace=True, verbose=verbose)
+        data_pack.apply_on_text(self._left_fixedlength_unit.transform,
+                                mode='left', inplace=True, verbose=verbose)
+        data_pack.apply_on_text(self._right_fixedlength_unit.transform,
+                                mode='right', inplace=True, verbose=verbose)
+
+        max_len_left = self._fixed_length_left
+        max_len_right = self._fixed_length_right
+
+        data_pack.left['length_left'] = \
+            data_pack.left['length_left'].apply(
+                lambda val: min(val, max_len_left))
+
+        data_pack.right['length_right'] = \
+            data_pack.right['length_right'].apply(
+                lambda val: min(val, max_len_right))
+        return data_pack
+
+    @classmethod
+    def _default_units(cls) -> list:
+        """Prepare needed process units."""
+        return [
+            units.BertClean(),
+            units.BasicTokenize()
+        ]
+
+
+
diff --git a/matchzoo/preprocessors/build_vocab_unit.py b/matchzoo/preprocessors/build_vocab_unit.py
index 3d9442de..ac1af5d6 100644
--- a/matchzoo/preprocessors/build_vocab_unit.py
+++ b/matchzoo/preprocessors/build_vocab_unit.py
@@ -1,7 +1,7 @@
 from matchzoo.data_pack import DataPack
 from .units import Vocabulary
 from .build_unit_from_data_pack import build_unit_from_data_pack
-
+from .units import BertVocabulary
 
 def build_vocab_unit(
     data_pack: DataPack,
@@ -28,3 +28,15 @@ def build_vocab_unit(
         mode=mode,
         flatten=True, verbose=verbose
     )
+
+
+def built_bert_vocab_unit(vocab_path: str) -> BertVocabulary:
+    """
+    Build a :class:`preprocessor.units.BertVocabulary` given `vocab_path`.
+    :param vocab_path: bert vocabulary path.
+    :return: A built vocabulary unit.
+
+    """
+    vocab_unit = BertVocabulary(pad_value='[PAD]', oov_value='[UNK]')
+    vocab_unit.fit(vocab_path)
+    return vocab_unit
diff --git a/matchzoo/preprocessors/units/__init__.py b/matchzoo/preprocessors/units/__init__.py
index 926b24cc..e6ba360a 100644
--- a/matchzoo/preprocessors/units/__init__.py
+++ b/matchzoo/preprocessors/units/__init__.py
@@ -13,7 +13,12 @@
 from .tokenize import Tokenize
 from .vocabulary import Vocabulary
 from .word_hashing import WordHashing
-from .bert_tokenize import BertTokenize
+from .bert_clean import BertClean
+from .bert_clean import StripAccent
+from.tokenize import ChineseTokenize
+from .tokenize import BasicTokenize
+from .tokenize import WordPieceTokenize
+from .vocabulary import BertVocabulary
 
 
 def list_available() -> list:
diff --git a/matchzoo/preprocessors/units/bert_clean.py b/matchzoo/preprocessors/units/bert_clean.py
new file mode 100644
index 00000000..74f7b5fc
--- /dev/null
+++ b/matchzoo/preprocessors/units/bert_clean.py
@@ -0,0 +1,41 @@
+from .unit import Unit
+from matchzoo.utils.bert_utils import is_whitespace, is_control, run_strip_accents
+
+
+class BertClean(Unit):
+    """Clean unit for raw text."""
+
+    def transform(self, input_: str) -> str:
+        """
+        Process input data from raw terms to cleaned text.
+
+        :param input_: raw textual input.
+
+        :return cleaned_text: cleaned text.
+        """
+        output = []
+        for char in input_:
+            cp = ord(char)
+            if cp == 0 or cp == 0xfffd or is_control(char):
+                continue
+            if is_whitespace(char):
+                output.append(" ")
+            else:
+                output.append(char)
+        cleaned_text = "".join(output)
+        return cleaned_text
+
+
+class StripAccent(Unit):
+    """Process unit for text lower case."""
+
+    def transform(self, input_: list) -> list:
+        """
+        Strips accents from each token.
+
+        :param input_: list of tokens.
+
+        :return tokens: Accent-stripped list of tokens.
+        """
+
+        return [run_strip_accents(token) for token in input_]
diff --git a/matchzoo/preprocessors/units/bert_tokenization.py b/matchzoo/preprocessors/units/bert_tokenization.py
deleted file mode 100644
index 0ee13595..00000000
--- a/matchzoo/preprocessors/units/bert_tokenization.py
+++ /dev/null
@@ -1,399 +0,0 @@
-# coding=utf-8
-# Copyright 2018 The Google AI Language Team Authors.
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-#     http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tokenization classes."""
-
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import collections
-import re
-import unicodedata
-import six
-import tensorflow as tf
-
-
-def validate_case_matches_checkpoint(do_lower_case, init_checkpoint):
-  """Checks whether the casing config is consistent with the checkpoint name."""
-
-  # The casing has to be passed in by the user and there is no explicit check
-  # as to whether it matches the checkpoint. The casing information probably
-  # should have been stored in the bert_config.json file, but it's not, so
-  # we have to heuristically detect it to validate.
-
-  if not init_checkpoint:
-    return
-
-  m = re.match("^.*?([A-Za-z0-9_-]+)/bert_model.ckpt", init_checkpoint)
-  if m is None:
-    return
-
-  model_name = m.group(1)
-
-  lower_models = [
-      "uncased_L-24_H-1024_A-16", "uncased_L-12_H-768_A-12",
-      "multilingual_L-12_H-768_A-12", "chinese_L-12_H-768_A-12"
-  ]
-
-  cased_models = [
-      "cased_L-12_H-768_A-12", "cased_L-24_H-1024_A-16",
-      "multi_cased_L-12_H-768_A-12"
-  ]
-
-  is_bad_config = False
-  if model_name in lower_models and not do_lower_case:
-    is_bad_config = True
-    actual_flag = "False"
-    case_name = "lowercased"
-    opposite_flag = "True"
-
-  if model_name in cased_models and do_lower_case:
-    is_bad_config = True
-    actual_flag = "True"
-    case_name = "cased"
-    opposite_flag = "False"
-
-  if is_bad_config:
-    raise ValueError(
-        "You passed in `--do_lower_case=%s` with `--init_checkpoint=%s`. "
-        "However, `%s` seems to be a %s model, so you "
-        "should pass in `--do_lower_case=%s` so that the fine-tuning matches "
-        "how the model was pre-training. If this error is wrong, please "
-        "just comment out this check." % (actual_flag, init_checkpoint,
-                                          model_name, case_name, opposite_flag))
-
-
-def convert_to_unicode(text):
-  """Converts `text` to Unicode (if it's not already), assuming utf-8 input."""
-  if six.PY3:
-    if isinstance(text, str):
-      return text
-    elif isinstance(text, bytes):
-      return text.decode("utf-8", "ignore")
-    else:
-      raise ValueError("Unsupported string type: %s" % (type(text)))
-  elif six.PY2:
-    if isinstance(text, str):
-      return text.decode("utf-8", "ignore")
-    elif isinstance(text, unicode):
-      return text
-    else:
-      raise ValueError("Unsupported string type: %s" % (type(text)))
-  else:
-    raise ValueError("Not running on Python2 or Python 3?")
-
-
-def printable_text(text):
-  """Returns text encoded in a way suitable for print or `tf.logging`."""
-
-  # These functions want `str` for both Python2 and Python3, but in one case
-  # it's a Unicode string and in the other it's a byte string.
-  if six.PY3:
-    if isinstance(text, str):
-      return text
-    elif isinstance(text, bytes):
-      return text.decode("utf-8", "ignore")
-    else:
-      raise ValueError("Unsupported string type: %s" % (type(text)))
-  elif six.PY2:
-    if isinstance(text, str):
-      return text
-    elif isinstance(text, unicode):
-      return text.encode("utf-8")
-    else:
-      raise ValueError("Unsupported string type: %s" % (type(text)))
-  else:
-    raise ValueError("Not running on Python2 or Python 3?")
-
-
-def load_vocab(vocab_file):
-  """Loads a vocabulary file into a dictionary."""
-  vocab = collections.OrderedDict()
-  index = 0
-  with tf.gfile.GFile(vocab_file, "r") as reader:
-    while True:
-      token = convert_to_unicode(reader.readline())
-      if not token:
-        break
-      token = token.strip()
-      vocab[token] = index
-      index += 1
-  return vocab
-
-
-def convert_by_vocab(vocab, items):
-  """Converts a sequence of [tokens|ids] using the vocab."""
-  output = []
-  for item in items:
-    output.append(vocab[item])
-  return output
-
-
-def convert_tokens_to_ids(vocab, tokens):
-  return convert_by_vocab(vocab, tokens)
-
-
-def convert_ids_to_tokens(inv_vocab, ids):
-  return convert_by_vocab(inv_vocab, ids)
-
-
-def whitespace_tokenize(text):
-  """Runs basic whitespace cleaning and splitting on a piece of text."""
-  text = text.strip()
-  if not text:
-    return []
-  tokens = text.split()
-  return tokens
-
-
-class FullTokenizer(object):
-  """Runs end-to-end tokenziation."""
-
-  def __init__(self, vocab_file, do_lower_case=True):
-    self.vocab = load_vocab(vocab_file)
-    self.inv_vocab = {v: k for k, v in self.vocab.items()}
-    self.basic_tokenizer = BasicTokenizer(do_lower_case=do_lower_case)
-    self.wordpiece_tokenizer = WordpieceTokenizer(vocab=self.vocab)
-
-  def tokenize(self, text):
-    split_tokens = []
-    for token in self.basic_tokenizer.tokenize(text):
-      for sub_token in self.wordpiece_tokenizer.tokenize(token):
-        split_tokens.append(sub_token)
-
-    return split_tokens
-
-  def convert_tokens_to_ids(self, tokens):
-    return convert_by_vocab(self.vocab, tokens)
-
-  def convert_ids_to_tokens(self, ids):
-    return convert_by_vocab(self.inv_vocab, ids)
-
-
-class BasicTokenizer(object):
-  """Runs basic tokenization (punctuation splitting, lower casing, etc.)."""
-
-  def __init__(self, do_lower_case=True):
-    """Constructs a BasicTokenizer.
-
-    Args:
-      do_lower_case: Whether to lower case the input.
-    """
-    self.do_lower_case = do_lower_case
-
-  def tokenize(self, text):
-    """Tokenizes a piece of text."""
-    text = convert_to_unicode(text)
-    text = self._clean_text(text)
-
-    # This was added on November 1st, 2018 for the multilingual and Chinese
-    # models. This is also applied to the English models now, but it doesn't
-    # matter since the English models were not trained on any Chinese data
-    # and generally don't have any Chinese data in them (there are Chinese
-    # characters in the vocabulary because Wikipedia does have some Chinese
-    # words in the English Wikipedia.).
-    text = self._tokenize_chinese_chars(text)
-
-    orig_tokens = whitespace_tokenize(text)
-    split_tokens = []
-    for token in orig_tokens:
-      if self.do_lower_case:
-        token = token.lower()
-        token = self._run_strip_accents(token)
-      split_tokens.extend(self._run_split_on_punc(token))
-
-    output_tokens = whitespace_tokenize(" ".join(split_tokens))
-    return output_tokens
-
-  def _run_strip_accents(self, text):
-    """Strips accents from a piece of text."""
-    text = unicodedata.normalize("NFD", text)
-    output = []
-    for char in text:
-      cat = unicodedata.category(char)
-      if cat == "Mn":
-        continue
-      output.append(char)
-    return "".join(output)
-
-  def _run_split_on_punc(self, text):
-    """Splits punctuation on a piece of text."""
-    chars = list(text)
-    i = 0
-    start_new_word = True
-    output = []
-    while i < len(chars):
-      char = chars[i]
-      if _is_punctuation(char):
-        output.append([char])
-        start_new_word = True
-      else:
-        if start_new_word:
-          output.append([])
-        start_new_word = False
-        output[-1].append(char)
-      i += 1
-
-    return ["".join(x) for x in output]
-
-  def _tokenize_chinese_chars(self, text):
-    """Adds whitespace around any CJK character."""
-    output = []
-    for char in text:
-      cp = ord(char)
-      if self._is_chinese_char(cp):
-        output.append(" ")
-        output.append(char)
-        output.append(" ")
-      else:
-        output.append(char)
-    return "".join(output)
-
-  def _is_chinese_char(self, cp):
-    """Checks whether CP is the codepoint of a CJK character."""
-    # This defines a "chinese character" as anything in the CJK Unicode block:
-    #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
-    #
-    # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
-    # despite its name. The modern Korean Hangul alphabet is a different block,
-    # as is Japanese Hiragana and Katakana. Those alphabets are used to write
-    # space-separated words, so they are not treated specially and handled
-    # like the all of the other languages.
-    if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
-        (cp >= 0x3400 and cp <= 0x4DBF) or  #
-        (cp >= 0x20000 and cp <= 0x2A6DF) or  #
-        (cp >= 0x2A700 and cp <= 0x2B73F) or  #
-        (cp >= 0x2B740 and cp <= 0x2B81F) or  #
-        (cp >= 0x2B820 and cp <= 0x2CEAF) or
-        (cp >= 0xF900 and cp <= 0xFAFF) or  #
-        (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
-      return True
-
-    return False
-
-  def _clean_text(self, text):
-    """Performs invalid character removal and whitespace cleanup on text."""
-    output = []
-    for char in text:
-      cp = ord(char)
-      if cp == 0 or cp == 0xfffd or _is_control(char):
-        continue
-      if _is_whitespace(char):
-        output.append(" ")
-      else:
-        output.append(char)
-    return "".join(output)
-
-
-class WordpieceTokenizer(object):
-  """Runs WordPiece tokenziation."""
-
-  def __init__(self, vocab, unk_token="[UNK]", max_input_chars_per_word=200):
-    self.vocab = vocab
-    self.unk_token = unk_token
-    self.max_input_chars_per_word = max_input_chars_per_word
-
-  def tokenize(self, text):
-    """Tokenizes a piece of text into its word pieces.
-
-    This uses a greedy longest-match-first algorithm to perform tokenization
-    using the given vocabulary.
-
-    For example:
-      input = "unaffable"
-      output = ["un", "##aff", "##able"]
-
-    Args:
-      text: A single token or whitespace separated tokens. This should have
-        already been passed through `BasicTokenizer.
-
-    Returns:
-      A list of wordpiece tokens.
-    """
-
-    text = convert_to_unicode(text)
-
-    output_tokens = []
-    for token in whitespace_tokenize(text):
-      chars = list(token)
-      if len(chars) > self.max_input_chars_per_word:
-        output_tokens.append(self.unk_token)
-        continue
-
-      is_bad = False
-      start = 0
-      sub_tokens = []
-      while start < len(chars):
-        end = len(chars)
-        cur_substr = None
-        while start < end:
-          substr = "".join(chars[start:end])
-          if start > 0:
-            substr = "##" + substr
-          if substr in self.vocab:
-            cur_substr = substr
-            break
-          end -= 1
-        if cur_substr is None:
-          is_bad = True
-          break
-        sub_tokens.append(cur_substr)
-        start = end
-
-      if is_bad:
-        output_tokens.append(self.unk_token)
-      else:
-        output_tokens.extend(sub_tokens)
-    return output_tokens
-
-
-def _is_whitespace(char):
-  """Checks whether `chars` is a whitespace character."""
-  # \t, \n, and \r are technically contorl characters but we treat them
-  # as whitespace since they are generally considered as such.
-  if char == " " or char == "\t" or char == "\n" or char == "\r":
-    return True
-  cat = unicodedata.category(char)
-  if cat == "Zs":
-    return True
-  return False
-
-
-def _is_control(char):
-  """Checks whether `chars` is a control character."""
-  # These are technically control characters but we count them as whitespace
-  # characters.
-  if char == "\t" or char == "\n" or char == "\r":
-    return False
-  cat = unicodedata.category(char)
-  if cat in ("Cc", "Cf"):
-    return True
-  return False
-
-
-def _is_punctuation(char):
-  """Checks whether `chars` is a punctuation character."""
-  cp = ord(char)
-  # We treat all non-letter/number ASCII as punctuation.
-  # Characters such as "^", "$", and "`" are not in the Unicode
-  # Punctuation class but we treat them as punctuation anyways, for
-  # consistency.
-  if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
-      (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
-    return True
-  cat = unicodedata.category(char)
-  if cat.startswith("P"):
-    return True
-  return False
diff --git a/matchzoo/preprocessors/units/bert_tokenize.py b/matchzoo/preprocessors/units/bert_tokenize.py
deleted file mode 100644
index de4e546c..00000000
--- a/matchzoo/preprocessors/units/bert_tokenize.py
+++ /dev/null
@@ -1,23 +0,0 @@
-
-from .unit import Unit
-from .bert_tokenization import FullTokenizer
-
-class BertTokenize(Unit):
-    """Process unit for text tokenization by bert tokenizer.
-
-        :param vocab_path: vocab file path in bert pretrained model.
-        """
-
-    def __init__(self, vocab_path, do_lower_case=False):
-        """Initialization."""
-        self.tokenizer = FullTokenizer(vocab_path, do_lower_case=do_lower_case)
-
-    def transform(self, input_: str) -> list:
-        """
-        Process input data from raw terms to list of tokens.
-
-        :param input_: raw textual input.
-
-        :return tokens: tokenized tokens as a list.
-        """
-        return self.tokenizer.tokenize(input_)
diff --git a/matchzoo/preprocessors/units/tokenize.py b/matchzoo/preprocessors/units/tokenize.py
index 1aeb2e62..a7747f8a 100644
--- a/matchzoo/preprocessors/units/tokenize.py
+++ b/matchzoo/preprocessors/units/tokenize.py
@@ -1,4 +1,5 @@
 import nltk
+from matchzoo.utils.bert_utils import is_chinese_char, whitespace_tokenize, run_split_on_punc
 
 from .unit import Unit
 
@@ -15,3 +16,103 @@ def transform(self, input_: str) -> list:
         :return tokens: tokenized tokens as a list.
         """
         return nltk.word_tokenize(input_)
+
+
+class ChineseTokenize(Unit):
+    """Process unit for text containing Chinese tokens."""
+
+    def transform(self, input_: str) -> str:
+        """
+        Process input data from raw terms to processed text.
+
+        :param input_: raw textual input.
+
+        :return output: text with a blank between adjacent Chinese tokens.
+        """
+        output = []
+        for char in input_:
+            cp = ord(char)
+            if is_chinese_char(cp):
+                output.append(" ")
+                output.append(char)
+                output.append(" ")
+            else:
+                output.append(char)
+        return "".join(output)
+
+
+class BasicTokenize(Unit):
+    """Process unit for text tokenization."""
+
+    def transform(self, input_: str) -> list:
+        """
+        Process input data from raw terms to list of tokens.
+
+        :param input_: raw textual input.
+
+        :return tokens: tokenized tokens as a list.
+        """
+        orig_tokens = whitespace_tokenize(input_)
+        split_tokens = []
+        for token in orig_tokens:
+            split_tokens.extend(run_split_on_punc(token))
+
+        output_tokens = whitespace_tokenize(" ".join(split_tokens))
+        return output_tokens
+
+
+class WordPieceTokenize(Unit):
+    """Process unit for text tokenization."""
+
+    def __init__(self, vocab: dict, max_input_chars_per_word: int=200):
+        """Initialization. """
+        self.vocab = vocab
+        self.unk_token = '[UNK]'
+        self.max_input_chars_per_word = max_input_chars_per_word
+
+    def transform(self, input_: list) -> list:
+        """
+        Tokenizes a piece of text into its word pieces.
+        This uses a greedy longest-match-first algorithm to perform tokenization
+        using the given vocabulary.
+
+        For example:
+          input = "unaffable"
+          output = ["un", "##aff", "##able"]
+
+        :param input_: token list.
+
+        :return tokens: A list of wordpiece tokens.
+        """
+        output_tokens = []
+        for token in input_:
+            chars = list(token)
+            if len(chars) > self.max_input_chars_per_word:
+                output_tokens.append(self.unk_token)
+                continue
+
+            is_bad = False
+            start = 0
+            sub_tokens = []
+            while start < len(chars):
+                end = len(chars)
+                cur_substr = None
+                while start < end:
+                    substr = "".join(chars[start:end])
+                    if start > 0:
+                        substr = "##" + substr
+                    if substr in self.vocab:
+                        cur_substr = substr
+                        break
+                    end -= 1
+                if cur_substr is None:
+                    is_bad = True
+                    break
+                sub_tokens.append(cur_substr)
+                start = end
+
+            if is_bad:
+                output_tokens.append(self.unk_token)
+            else:
+                output_tokens.extend(sub_tokens)
+        return output_tokens
diff --git a/matchzoo/preprocessors/units/vocabulary.py b/matchzoo/preprocessors/units/vocabulary.py
index 783df94f..7504b4cc 100644
--- a/matchzoo/preprocessors/units/vocabulary.py
+++ b/matchzoo/preprocessors/units/vocabulary.py
@@ -67,3 +67,45 @@ def fit(self, tokens: list):
     def transform(self, input_: list) -> list:
         """Transform a list of tokens to corresponding indices."""
         return [self._context['term_index'][token] for token in input_]
+
+
+class BertVocabulary(StatefulUnit):
+    """
+    Vocabulary class.
+
+    :param pad_value: The string value for the padding position.
+    :param oov_value: The string value for the out-of-vocabulary terms.
+
+    Examples:
+        >>> vocab = BertVocabulary(pad_value='[PAD]', oov_value='[UNK]')
+        >>> indices = vocab.transform(list('ABCDDZZZ'))
+
+    """
+
+    def __init__(self, pad_value: str = '[PAD]', oov_value: str = '[UNK]'):
+        """Vocabulary unit initializer."""
+        super().__init__()
+        self._pad = pad_value
+        self._oov = oov_value
+        self._context['term_index'] = self.TermIndex()
+        self._context['index_term'] = dict()
+
+    class TermIndex(dict):
+        """Map term to index."""
+
+        def __missing__(self, key):
+            """Map out-of-vocabulary terms to index 100 ."""
+            return 100
+
+    def fit(self, vocab_path: str):
+        """Build a :class:`TermIndex` and a :class:`IndexTerm`."""
+        with open(vocab_path, 'r', encoding='utf-8') as vocab_file:
+            for idx, line in enumerate(vocab_file):
+                term = line.strip()
+                self._context['term_index'][term] = idx
+                self._context['index_term'][idx] = term
+
+    def transform(self, input_: list) -> list:
+        """Transform a list of tokens to corresponding indices."""
+        return [self._context['term_index'][token] for token in input_]
+
diff --git a/matchzoo/utils/bert_utils.py b/matchzoo/utils/bert_utils.py
new file mode 100644
index 00000000..9ed234f3
--- /dev/null
+++ b/matchzoo/utils/bert_utils.py
@@ -0,0 +1,106 @@
+import unicodedata
+
+
+def is_whitespace(char):
+    """Checks whether `chars` is a whitespace character."""
+    # \t, \n, and \r are technically contorl characters but we treat them
+    # as whitespace since they are generally considered as such.
+    if char == " " or char == "\t" or char == "\n" or char == "\r":
+        return True
+    cat = unicodedata.category(char)
+    if cat == "Zs":
+        return True
+    return False
+
+
+def is_control(char):
+    """Checks whether `chars` is a control character."""
+    # These are technically control characters but we count them as whitespace
+    # characters.
+    if char == "\t" or char == "\n" or char == "\r":
+        return False
+    cat = unicodedata.category(char)
+    if cat in ("Cc", "Cf"):
+        return True
+    return False
+
+
+def is_punctuation(char):
+    """Checks whether `chars` is a punctuation character."""
+    cp = ord(char)
+    # We treat all non-letter/number ASCII as punctuation.
+    # Characters such as "^", "$", and "`" are not in the Unicode
+    # Punctuation class but we treat them as punctuation anyways, for
+    # consistency.
+    if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
+            (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
+        return True
+    cat = unicodedata.category(char)
+    if cat.startswith("P"):
+        return True
+    return False
+
+
+def is_chinese_char(cp):
+    """Checks whether CP is the codepoint of a CJK character."""
+    # This defines a "chinese character" as anything in the CJK Unicode block:
+    #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
+    #
+    # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
+    # despite its name. The modern Korean Hangul alphabet is a different block,
+    # as is Japanese Hiragana and Katakana. Those alphabets are used to write
+    # space-separated words, so they are not treated specially and handled
+    # like the all of the other languages.
+    if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
+            (cp >= 0x3400 and cp <= 0x4DBF) or  #
+            (cp >= 0x20000 and cp <= 0x2A6DF) or  #
+            (cp >= 0x2A700 and cp <= 0x2B73F) or  #
+            (cp >= 0x2B740 and cp <= 0x2B81F) or  #
+            (cp >= 0x2B820 and cp <= 0x2CEAF) or
+            (cp >= 0xF900 and cp <= 0xFAFF) or  #
+            (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
+        return True
+
+    return False
+
+
+def run_strip_accents(text):
+    """Strips accents from a piece of text."""
+    text = unicodedata.normalize("NFD", text)
+    output = []
+    for char in text:
+        cat = unicodedata.category(char)
+        if cat == "Mn":
+            continue
+        output.append(char)
+    return "".join(output)
+
+
+def run_split_on_punc(text):
+    """Splits punctuation on a piece of text."""
+    chars = list(text)
+    i = 0
+    start_new_word = True
+    output = []
+    while i < len(chars):
+        char = chars[i]
+        if is_punctuation(char):
+            output.append([char])
+            start_new_word = True
+        else:
+            if start_new_word:
+                output.append([])
+            start_new_word = False
+            output[-1].append(char)
+        i += 1
+
+    return ["".join(x) for x in output]
+
+
+def whitespace_tokenize(text):
+    """Runs basic whitespace cleaning and splitting on a piece of text."""
+    text = text.strip()
+    if not text:
+        return []
+    tokens = text.split()
+    return tokens
diff --git a/tests/unit_test/processor_units/test_processor_units.py b/tests/unit_test/processor_units/test_processor_units.py
index 4812bece..fccd87e0 100644
--- a/tests/unit_test/processor_units/test_processor_units.py
+++ b/tests/unit_test/processor_units/test_processor_units.py
@@ -144,21 +144,25 @@ def test_this():
 
 def test_bert_tokenizer_unit():
     vocab_tokens = [
-        "[UNK]", "[CLS]", "[SEP]", "want", "##want", "##ed", "wa", "un", "runn",
-        "##ing", ","
+        "[PAD]", "further", "##more", ",", "under", "the", "micro", "##scope", "neither",
+        "entity", "contains", "glands", ".", "此", "外", "在", "显", "微", "镜", "下"
     ]
-    with tempfile.NamedTemporaryFile(delete=False) as vocab_writer:
-        vocab_writer.write("".join(
-            [x + "\n" for x in vocab_tokens]).encode("utf-8"))
+    raw_text = "furthermore, \r under the microscope \t neither entity  \n contains sebaceous glands. 此外, 在显微镜下"
 
-    vocab_file = vocab_writer.name
+    golden_tokens = ['further', '##more', ',', 'under', 'the', 'micro', '##scope', 'neither', 'entity', 'contains',
+                     '[UNK]', 'glands', '.', '此', '外', ',', '在', '显', '微', '镜', '下']
 
-    tokenizer_unit = units.BertTokenize(vocab_file, do_lower_case=True)
-    os.unlink(vocab_file)
+    vocab_dict = {}
+    for idx, token in enumerate(vocab_tokens):
+        vocab_dict[token] = idx
 
-    tokens = tokenizer_unit.transform(u"UNwant\u00E9d,running")
-    assert tokens == ["un", "##want", "##ed", ",", "runn", "##ing"]
+    clean_unit = units.BertClean()
+    cleaned_text = clean_unit.transform(raw_text)
+    chinese_tokenize_unit = units.ChineseTokenize()
+    chinese_tokenized_text = chinese_tokenize_unit.transform(cleaned_text)
+    basic_tokenize_unit = units.BasicTokenize()
+    basic_tokens = basic_tokenize_unit.transform(chinese_tokenized_text)
+    wordpiece_unit = units.WordPieceTokenize(vocab_dict)
+    wordpiece_tokens = wordpiece_unit.transform(basic_tokens)
 
-    assert tokenizer_unit.tokenizer.convert_tokens_to_ids(tokens) == [7, 4, 5, 10, 8, 9]
-
-    assert tokenizer_unit.tokenizer.basic_tokenizer.tokenize(u"ah\u535A\u63A8zz") == [u"ah", u"\u535A", u"\u63A8", u"zz"]
+    assert wordpiece_tokens == golden_tokens

From 768ea0e85810e5189c0b26650b9f2aeb5fe052e4 Mon Sep 17 00:00:00 2001
From: jellying <469413628@qq.com>
Date: Wed, 15 May 2019 21:20:56 +0800
Subject: [PATCH 064/100] comment the bert_preprocessor examples to pass test

---
 matchzoo/preprocessors/bert_preprocessor.py | 21 ++++++++-------------
 1 file changed, 8 insertions(+), 13 deletions(-)

diff --git a/matchzoo/preprocessors/bert_preprocessor.py b/matchzoo/preprocessors/bert_preprocessor.py
index 2e2e132b..a7ee4493 100644
--- a/matchzoo/preprocessors/bert_preprocessor.py
+++ b/matchzoo/preprocessors/bert_preprocessor.py
@@ -23,7 +23,7 @@ def __init__(self, fixed_length_left: int = 30,
                  remove_stop_words: bool = False,
                  lower_case: bool=True,
                  chinese_version: bool=True,
-                 bert_vocab_path: str="bert_dir/vocab.txt"):
+                 bert_vocab_path: str="bert_resources/vocab.txt"):
         """
         Bert-base Model preprocessor.
 
@@ -31,18 +31,13 @@ def __init__(self, fixed_length_left: int = 30,
 
 
         Example:
-            >>> import matchzoo as mz
-            >>> train_data = mz.datasets.toy.load_data()
-            >>> test_data = mz.datasets.toy.load_data(stage='test')
-            >>> bert_preprocessor = mz.preprocessors.BertPreprocessor()
-            >>> train_data_processed = bert_preprocessor.fit_transform(
-            ...     train_data
-            ... )
-            >>> type(train_data_processed)
-            <class 'matchzoo.data_pack.data_pack.DataPack'>
-            >>> test_data_transformed = bert_preprocessor.transform(test_data)
-            >>> type(test_data_transformed)
-            <class 'matchzoo.data_pack.data_pack.DataPack'>
+            import matchzoo as mz
+            train_data = mz.datasets.toy.load_data()
+            test_data = mz.datasets.toy.load_data(stage='test')
+            # the argument 'bert_vocab_path' must feed the bert vocab path
+            bert_preprocessor = mz.preprocessors.BertPreprocessor(bert_vocab_path='vocab.txt')
+            train_data_processed = bert_preprocessor.fit_transform(train_data)
+            test_data_processed = bert_preprocessor.transform(test_data)
 
         """
         super().__init__()

From 675d4b321f64cc43d7108be2393d587e7c96cfbc Mon Sep 17 00:00:00 2001
From: caiyinqiong <1198593462@qq.com>
Date: Thu, 16 May 2019 16:57:47 +0800
Subject: [PATCH 065/100] rename encoding_layer, fix diin.py

---
 matchzoo/contrib/layers/__init__.py           |  2 +-
 ...g_layer.py => semantic_composite_layer.py} |  0
 matchzoo/contrib/models/diin.py               | 30 ++++++++++---------
 matchzoo/preprocessors/diin_preprocessor.py   |  6 ++--
 4 files changed, 20 insertions(+), 18 deletions(-)
 rename matchzoo/contrib/layers/{encoding_layer.py => semantic_composite_layer.py} (100%)

diff --git a/matchzoo/contrib/layers/__init__.py b/matchzoo/contrib/layers/__init__.py
index 34623887..09ef7e7c 100644
--- a/matchzoo/contrib/layers/__init__.py
+++ b/matchzoo/contrib/layers/__init__.py
@@ -3,7 +3,7 @@
 from .matching_tensor_layer import MatchingTensorLayer
 from .spatial_gru import SpatialGRU
 from .decaying_dropout_layer import DecayingDropoutLayer
-from .encoding_layer import EncodingLayer
+from .semantic_composite_layer import EncodingLayer
 
 layer_dict = {
     "MatchingTensorLayer": MatchingTensorLayer,
diff --git a/matchzoo/contrib/layers/encoding_layer.py b/matchzoo/contrib/layers/semantic_composite_layer.py
similarity index 100%
rename from matchzoo/contrib/layers/encoding_layer.py
rename to matchzoo/contrib/layers/semantic_composite_layer.py
diff --git a/matchzoo/contrib/models/diin.py b/matchzoo/contrib/models/diin.py
index d7f8fcd1..52045b59 100644
--- a/matchzoo/contrib/models/diin.py
+++ b/matchzoo/contrib/models/diin.py
@@ -44,12 +44,8 @@ def get_default_params(cls) -> ParamTable:
         """:return: model default parameters."""
         params = super().get_default_params(with_embedding=True)
         params['optimizer'] = 'adam'
-        params.add(Param(name='dropout_initial_keep_rate', value=1.0,
-                         desc="The initial keep rate of decaying_dropout."))
         params.add(Param(name='dropout_decay_interval', value=10000,
                          desc="The decay interval of decaying_dropout."))
-        params.add(Param(name='dropout_decay_rate', value=0.977,
-                         desc="The decay rate of decaying_dropout."))
         params.add(Param(name='char_embedding_input_dim', value=100,
                          desc="The input dimension of character embedding "
                               "layer."))
@@ -76,6 +72,18 @@ def get_default_params(cls) -> ParamTable:
         params.add(Param(name='transition_scale_down_ratio', value=0.5,
                          desc="The channel scale down ratio of the "
                               "convolution layer in transition block."))
+        params.add(Param(
+            name='dropout_initial_keep_rate', value=1.0,
+            hyper_space=hyper_spaces.quniform(
+                low=0.8, high=1.0, q=0.02),
+            desc="The initial keep rate of decaying_dropout."
+        ))
+        params.add(Param(
+            name='dropout_decay_rate', value=0.97,
+            hyper_space=hyper_spaces.quniform(
+                low=0.90, high=0.99, q=0.01),
+            desc="The decay rate of decaying_dropout."
+        ))
         return params
 
     @classmethod
@@ -143,21 +151,21 @@ def build(self):
         #   R = 'input_right' sequence length
         #   C = fixed word length
 
+        inputs = self._make_inputs()
         # Left text and right text.
         # shape = [B, L]
         # shape = [B, R]
-        text_left, text_right = self._make_inputs()[0:2]
+        text_left, text_right = inputs[0:2]
         # Left character and right character.
         # shape = [B, L, C]
         # shape = [B, R, C]
-        char_left, char_right = self._make_inputs()[2:4]
+        char_left, char_right = inputs[2:4]
         # Left exact match and right exact match.
         # shape = [B, L]
         # shape = [B, R]
-        match_left, match_right = self._make_inputs()[4:6]
+        match_left, match_right = inputs[4:6]
 
         # Embedding module
-        inputs = []
         left_embeddings = []
         right_embeddings = []
 
@@ -179,8 +187,6 @@ def build(self):
         )(right_word_embedding)
         left_embeddings.append(left_word_embedding)
         right_embeddings.append(right_word_embedding)
-        inputs.append(text_left)
-        inputs.append(text_right)
 
         # Exact match feature
         # shape = [B, L, 1]
@@ -193,8 +199,6 @@ def build(self):
         )(match_right)
         left_embeddings.append(left_exact_match)
         right_embeddings.append(right_exact_match)
-        inputs.append(match_left)
-        inputs.append(match_right)
 
         # Char embedding feature
         char_embedding = self._make_char_embedding_layer()
@@ -204,8 +208,6 @@ def build(self):
         right_char_embedding = char_embedding(char_right)
         left_embeddings.append(left_char_embedding)
         right_embeddings.append(right_char_embedding)
-        inputs.append(char_left)
-        inputs.append(char_right)
 
         # Concatenate
         left_embedding = keras.layers.Concatenate()(left_embeddings)
diff --git a/matchzoo/preprocessors/diin_preprocessor.py b/matchzoo/preprocessors/diin_preprocessor.py
index 14b0a78e..b8c5a4af 100644
--- a/matchzoo/preprocessors/diin_preprocessor.py
+++ b/matchzoo/preprocessors/diin_preprocessor.py
@@ -16,9 +16,9 @@ class DIINPreprocessor(BasePreprocessor):
     """DIIN Model preprocessor."""
 
     def __init__(self,
-                 fixed_length_left: int = 32,
-                 fixed_length_right: int = 32,
-                 fixed_length_word: int = 16):
+                 fixed_length_left: int = 10,
+                 fixed_length_right: int = 10,
+                 fixed_length_word: int = 5):
         """
         DIIN Model preprocessor.
 

From e0ccda6edb19a9a0616cf96d1455163ea56d9d51 Mon Sep 17 00:00:00 2001
From: jellying <469413628@qq.com>
Date: Fri, 17 May 2019 14:58:23 +0800
Subject: [PATCH 066/100] add bert uncased_vocab for examples

---
 .../datasets/bert_resources/uncased_vocab.txt | 30522 ++++++++++++++++
 matchzoo/preprocessors/bert_preprocessor.py   |    14 +-
 2 files changed, 30529 insertions(+), 7 deletions(-)
 create mode 100644 matchzoo/datasets/bert_resources/uncased_vocab.txt

diff --git a/matchzoo/datasets/bert_resources/uncased_vocab.txt b/matchzoo/datasets/bert_resources/uncased_vocab.txt
new file mode 100644
index 00000000..fb140275
--- /dev/null
+++ b/matchzoo/datasets/bert_resources/uncased_vocab.txt
@@ -0,0 +1,30522 @@
+[PAD]
+[unused0]
+[unused1]
+[unused2]
+[unused3]
+[unused4]
+[unused5]
+[unused6]
+[unused7]
+[unused8]
+[unused9]
+[unused10]
+[unused11]
+[unused12]
+[unused13]
+[unused14]
+[unused15]
+[unused16]
+[unused17]
+[unused18]
+[unused19]
+[unused20]
+[unused21]
+[unused22]
+[unused23]
+[unused24]
+[unused25]
+[unused26]
+[unused27]
+[unused28]
+[unused29]
+[unused30]
+[unused31]
+[unused32]
+[unused33]
+[unused34]
+[unused35]
+[unused36]
+[unused37]
+[unused38]
+[unused39]
+[unused40]
+[unused41]
+[unused42]
+[unused43]
+[unused44]
+[unused45]
+[unused46]
+[unused47]
+[unused48]
+[unused49]
+[unused50]
+[unused51]
+[unused52]
+[unused53]
+[unused54]
+[unused55]
+[unused56]
+[unused57]
+[unused58]
+[unused59]
+[unused60]
+[unused61]
+[unused62]
+[unused63]
+[unused64]
+[unused65]
+[unused66]
+[unused67]
+[unused68]
+[unused69]
+[unused70]
+[unused71]
+[unused72]
+[unused73]
+[unused74]
+[unused75]
+[unused76]
+[unused77]
+[unused78]
+[unused79]
+[unused80]
+[unused81]
+[unused82]
+[unused83]
+[unused84]
+[unused85]
+[unused86]
+[unused87]
+[unused88]
+[unused89]
+[unused90]
+[unused91]
+[unused92]
+[unused93]
+[unused94]
+[unused95]
+[unused96]
+[unused97]
+[unused98]
+[UNK]
+[CLS]
+[SEP]
+[MASK]
+[unused99]
+[unused100]
+[unused101]
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diff --git a/matchzoo/preprocessors/bert_preprocessor.py b/matchzoo/preprocessors/bert_preprocessor.py
index a7ee4493..ac6f26ba 100644
--- a/matchzoo/preprocessors/bert_preprocessor.py
+++ b/matchzoo/preprocessors/bert_preprocessor.py
@@ -31,13 +31,13 @@ def __init__(self, fixed_length_left: int = 30,
 
 
         Example:
-            import matchzoo as mz
-            train_data = mz.datasets.toy.load_data()
-            test_data = mz.datasets.toy.load_data(stage='test')
-            # the argument 'bert_vocab_path' must feed the bert vocab path
-            bert_preprocessor = mz.preprocessors.BertPreprocessor(bert_vocab_path='vocab.txt')
-            train_data_processed = bert_preprocessor.fit_transform(train_data)
-            test_data_processed = bert_preprocessor.transform(test_data)
+            >>> import matchzoo as mz
+            >>> train_data = mz.datasets.toy.load_data()
+            >>> test_data = mz.datasets.toy.load_data(stage='test')
+            >>> # the argument 'bert_vocab_path' must feed the bert vocab path
+            >>> bert_preprocessor = mz.preprocessors.BertPreprocessor(bert_vocab_path='../datasets/bert_resources/uncased_vocab.txt')
+            >>> train_data_processed = bert_preprocessor.fit_transform(train_data)
+            >>> test_data_processed = bert_preprocessor.transform(test_data)
 
         """
         super().__init__()

From 872be4bf7bcf5d9f541490143f399aa28a537ae6 Mon Sep 17 00:00:00 2001
From: jellying <469413628@qq.com>
Date: Fri, 17 May 2019 14:59:20 +0800
Subject: [PATCH 067/100] fix the vocab_path and pass the quick test

---
 matchzoo/preprocessors/bert_preprocessor.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/matchzoo/preprocessors/bert_preprocessor.py b/matchzoo/preprocessors/bert_preprocessor.py
index ac6f26ba..83840d95 100644
--- a/matchzoo/preprocessors/bert_preprocessor.py
+++ b/matchzoo/preprocessors/bert_preprocessor.py
@@ -35,7 +35,7 @@ def __init__(self, fixed_length_left: int = 30,
             >>> train_data = mz.datasets.toy.load_data()
             >>> test_data = mz.datasets.toy.load_data(stage='test')
             >>> # the argument 'bert_vocab_path' must feed the bert vocab path
-            >>> bert_preprocessor = mz.preprocessors.BertPreprocessor(bert_vocab_path='../datasets/bert_resources/uncased_vocab.txt')
+            >>> bert_preprocessor = mz.preprocessors.BertPreprocessor(bert_vocab_path='matchzoo/datasets/bert_resources/uncased_vocab.txt')
             >>> train_data_processed = bert_preprocessor.fit_transform(train_data)
             >>> test_data_processed = bert_preprocessor.transform(test_data)
 

From 8cdfe8b9eaed04397783845a5ec9a6cdbaabf706 Mon Sep 17 00:00:00 2001
From: jellying <469413628@qq.com>
Date: Fri, 17 May 2019 15:17:48 +0800
Subject: [PATCH 068/100] cut the bert vocab size for test

---
 .../datasets/bert_resources/uncased_vocab.txt | 30522 ----------------
 .../bert_resources/uncased_vocab_100.txt      |   101 +
 matchzoo/preprocessors/bert_preprocessor.py   |     2 +-
 3 files changed, 102 insertions(+), 30523 deletions(-)
 delete mode 100644 matchzoo/datasets/bert_resources/uncased_vocab.txt
 create mode 100644 matchzoo/datasets/bert_resources/uncased_vocab_100.txt

diff --git a/matchzoo/datasets/bert_resources/uncased_vocab.txt b/matchzoo/datasets/bert_resources/uncased_vocab.txt
deleted file mode 100644
index fb140275..00000000
--- a/matchzoo/datasets/bert_resources/uncased_vocab.txt
+++ /dev/null
@@ -1,30522 +0,0 @@
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-forced
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-1980
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-1983
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-1982
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-1979
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-direction
-##ion
-paper
-mission
-1981
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-1976
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-1974
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-1975
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-1970
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-1971
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-35
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-1960
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-1944
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-1963
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-1962
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-1942
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-1961
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-1943
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-1956
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-1958
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-1959
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-1980s
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-1941
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-1940
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-1948
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-1957
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-1939
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-1946
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-1950
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-33
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-1970s
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-##吉
-##同
-##名
-##和
-##囗
-##四
-##国
-##國
-##土
-##地
-##坂
-##城
-##堂
-##場
-##士
-##夏
-##外
-##大
-##天
-##太
-##夫
-##奈
-##女
-##子
-##学
-##宀
-##宇
-##安
-##宗
-##定
-##宣
-##宮
-##家
-##宿
-##寺
-##將
-##小
-##尚
-##山
-##岡
-##島
-##崎
-##川
-##州
-##巿
-##帝
-##平
-##年
-##幸
-##广
-##弘
-##張
-##彳
-##後
-##御
-##德
-##心
-##忄
-##志
-##忠
-##愛
-##成
-##我
-##戦
-##戸
-##手
-##扌
-##政
-##文
-##新
-##方
-##日
-##明
-##星
-##春
-##昭
-##智
-##曲
-##書
-##月
-##有
-##朝
-##木
-##本
-##李
-##村
-##東
-##松
-##林
-##森
-##楊
-##樹
-##橋
-##歌
-##止
-##正
-##武
-##比
-##氏
-##民
-##水
-##氵
-##氷
-##永
-##江
-##沢
-##河
-##治
-##法
-##海
-##清
-##漢
-##瀬
-##火
-##版
-##犬
-##王
-##生
-##田
-##男
-##疒
-##発
-##白
-##的
-##皇
-##目
-##相
-##省
-##真
-##石
-##示
-##社
-##神
-##福
-##禾
-##秀
-##秋
-##空
-##立
-##章
-##竹
-##糹
-##美
-##義
-##耳
-##良
-##艹
-##花
-##英
-##華
-##葉
-##藤
-##行
-##街
-##西
-##見
-##訁
-##語
-##谷
-##貝
-##貴
-##車
-##軍
-##辶
-##道
-##郎
-##郡
-##部
-##都
-##里
-##野
-##金
-##鈴
-##镇
-##長
-##門
-##間
-##阝
-##阿
-##陳
-##陽
-##雄
-##青
-##面
-##風
-##食
-##香
-##馬
-##高
-##龍
-##龸
-##fi
-##fl
-##！
-##（
-##）
-##，
-##－
-##．
-##／
-##：
-##？
-##～
diff --git a/matchzoo/datasets/bert_resources/uncased_vocab_100.txt b/matchzoo/datasets/bert_resources/uncased_vocab_100.txt
new file mode 100644
index 00000000..bbdb8526
--- /dev/null
+++ b/matchzoo/datasets/bert_resources/uncased_vocab_100.txt
@@ -0,0 +1,101 @@
+[PAD]
+##ness
+episode
+bed
+added
+table
+indian
+private
+charles
+route
+available
+idea
+throughout
+centre
+addition
+appointed
+style
+1994
+books
+eight
+construction
+press
+mean
+wall
+friends
+remained
+schools
+study
+##ch
+##um
+institute
+oh
+chinese
+sometimes
+events
+possible
+1992
+australian
+type
+brown
+forward
+talk
+process
+food
+debut
+seat
+performance
+committee
+features
+character
+arts
+herself
+else
+lot
+strong
+russian
+range
+hours
+peter
+arm
+##da
+morning
+dr
+sold
+##ry
+quickly
+directed
+1993
+guitar
+china
+##w
+31
+list
+##ma
+performed
+media
+uk
+players
+smile
+##rs
+myself
+40
+placed
+coach
+province
+##gawa
+typed
+##dry
+favors
+allegheny
+glaciers
+##rly
+recalling
+aziz
+##log
+parasite
+requiem
+auf
+##berto
+##llin
+[UNK]
\ No newline at end of file
diff --git a/matchzoo/preprocessors/bert_preprocessor.py b/matchzoo/preprocessors/bert_preprocessor.py
index 83840d95..0df73732 100644
--- a/matchzoo/preprocessors/bert_preprocessor.py
+++ b/matchzoo/preprocessors/bert_preprocessor.py
@@ -35,7 +35,7 @@ def __init__(self, fixed_length_left: int = 30,
             >>> train_data = mz.datasets.toy.load_data()
             >>> test_data = mz.datasets.toy.load_data(stage='test')
             >>> # the argument 'bert_vocab_path' must feed the bert vocab path
-            >>> bert_preprocessor = mz.preprocessors.BertPreprocessor(bert_vocab_path='matchzoo/datasets/bert_resources/uncased_vocab.txt')
+            >>> bert_preprocessor = mz.preprocessors.BertPreprocessor(bert_vocab_path='matchzoo/datasets/bert_resources/uncased_vocab_100.txt')
             >>> train_data_processed = bert_preprocessor.fit_transform(train_data)
             >>> test_data_processed = bert_preprocessor.transform(test_data)
 

From ac1d8ab27f8fa0951a02a10abf31fd28b37fa843 Mon Sep 17 00:00:00 2001
From: wqh17101 <597935261@qq.com>
Date: Sun, 19 May 2019 17:39:17 +0800
Subject: [PATCH 069/100] update rst

---
 docs/source/conf.py                           |   7 +-
 docs/source/matchzoo.auto.preparer.rst        |  30 ++++
 docs/source/matchzoo.auto.rst                 |  22 +--
 docs/source/matchzoo.auto.tuner.callbacks.rst |  46 ++++++
 docs/source/matchzoo.auto.tuner.rst           |  37 +++++
 docs/source/matchzoo.datasets.quora_qp.rst    |  22 +++
 docs/source/matchzoo.datasets.rst             |   1 +
 docs/source/matchzoo.embedding.rst            |  22 +++
 docs/source/matchzoo.preprocessors.units.rst  | 134 ++++++++++++++++++
 docs/source/matchzoo.rst                      |  12 +-
 docs/source/matchzoo.utils.rst                |   8 ++
 11 files changed, 310 insertions(+), 31 deletions(-)
 create mode 100644 docs/source/matchzoo.auto.preparer.rst
 create mode 100644 docs/source/matchzoo.auto.tuner.callbacks.rst
 create mode 100644 docs/source/matchzoo.auto.tuner.rst
 create mode 100644 docs/source/matchzoo.datasets.quora_qp.rst
 create mode 100644 docs/source/matchzoo.embedding.rst
 create mode 100644 docs/source/matchzoo.preprocessors.units.rst

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 8bc624db..32d02f5a 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -20,15 +20,16 @@
 sys.path.insert(0, os.path.abspath('../../matchzoo/data_generator'))
 sys.path.insert(0, os.path.abspath('../../matchzoo/data_pack'))
 sys.path.insert(0, os.path.abspath('../../matchzoo/datasets'))
+sys.path.insert(0, os.path.abspath('../../matchzoo/embedding'))
 sys.path.insert(0, os.path.abspath('../../matchzoo/engine'))
 sys.path.insert(0, os.path.abspath('../../matchzoo/layers'))
 sys.path.insert(0, os.path.abspath('../../matchzoo/losses'))
-sys.path.insert(0, os.path.abspath('../../matchzoo/models'))
 sys.path.insert(0, os.path.abspath('../../matchzoo/metrics'))
+sys.path.insert(0, os.path.abspath('../../matchzoo/models'))
 sys.path.insert(0, os.path.abspath('../../matchzoo/preprocessors'))
-sys.path.insert(0, os.path.abspath('../../matchzoo/processor_units'))
-sys.path.insert(0, os.path.abspath('../../matchzoo/utils'))
 sys.path.insert(0, os.path.abspath('../../matchzoo/tasks'))
+sys.path.insert(0, os.path.abspath('../../matchzoo/utils'))
+
 
 # -- Project information -----------------------------------------------------
 
diff --git a/docs/source/matchzoo.auto.preparer.rst b/docs/source/matchzoo.auto.preparer.rst
new file mode 100644
index 00000000..3291f9fe
--- /dev/null
+++ b/docs/source/matchzoo.auto.preparer.rst
@@ -0,0 +1,30 @@
+matchzoo.auto.preparer package
+==============================
+
+Submodules
+----------
+
+matchzoo.auto.preparer.prepare module
+-------------------------------------
+
+.. automodule:: matchzoo.auto.preparer.prepare
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.auto.preparer.preparer module
+--------------------------------------
+
+.. automodule:: matchzoo.auto.preparer.preparer
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: matchzoo.auto.preparer
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/docs/source/matchzoo.auto.rst b/docs/source/matchzoo.auto.rst
index bb974511..1272de0a 100644
--- a/docs/source/matchzoo.auto.rst
+++ b/docs/source/matchzoo.auto.rst
@@ -1,25 +1,13 @@
 matchzoo.auto package
 =====================
 
-Submodules
-----------
+Subpackages
+-----------
 
-matchzoo.auto.prepare module
-----------------------------
-
-.. automodule:: matchzoo.auto.prepare
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
-matchzoo.auto.tune module
--------------------------
-
-.. automodule:: matchzoo.auto.tune
-    :members:
-    :undoc-members:
-    :show-inheritance:
+.. toctree::
 
+    matchzoo.auto.preparer
+    matchzoo.auto.tuner
 
 Module contents
 ---------------
diff --git a/docs/source/matchzoo.auto.tuner.callbacks.rst b/docs/source/matchzoo.auto.tuner.callbacks.rst
new file mode 100644
index 00000000..1671dc48
--- /dev/null
+++ b/docs/source/matchzoo.auto.tuner.callbacks.rst
@@ -0,0 +1,46 @@
+matchzoo.auto.tuner.callbacks package
+=====================================
+
+Submodules
+----------
+
+matchzoo.auto.tuner.callbacks.callback module
+---------------------------------------------
+
+.. automodule:: matchzoo.auto.tuner.callbacks.callback
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.auto.tuner.callbacks.lambda\_callback module
+-----------------------------------------------------
+
+.. automodule:: matchzoo.auto.tuner.callbacks.lambda_callback
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.auto.tuner.callbacks.load\_embedding\_matrix module
+------------------------------------------------------------
+
+.. automodule:: matchzoo.auto.tuner.callbacks.load_embedding_matrix
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.auto.tuner.callbacks.save\_model module
+------------------------------------------------
+
+.. automodule:: matchzoo.auto.tuner.callbacks.save_model
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: matchzoo.auto.tuner.callbacks
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/docs/source/matchzoo.auto.tuner.rst b/docs/source/matchzoo.auto.tuner.rst
new file mode 100644
index 00000000..024d1e83
--- /dev/null
+++ b/docs/source/matchzoo.auto.tuner.rst
@@ -0,0 +1,37 @@
+matchzoo.auto.tuner package
+===========================
+
+Subpackages
+-----------
+
+.. toctree::
+
+    matchzoo.auto.tuner.callbacks
+
+Submodules
+----------
+
+matchzoo.auto.tuner.tune module
+-------------------------------
+
+.. automodule:: matchzoo.auto.tuner.tune
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.auto.tuner.tuner module
+--------------------------------
+
+.. automodule:: matchzoo.auto.tuner.tuner
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: matchzoo.auto.tuner
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/docs/source/matchzoo.datasets.quora_qp.rst b/docs/source/matchzoo.datasets.quora_qp.rst
new file mode 100644
index 00000000..e0eb3d83
--- /dev/null
+++ b/docs/source/matchzoo.datasets.quora_qp.rst
@@ -0,0 +1,22 @@
+matchzoo.datasets.quora\_qp package
+===================================
+
+Submodules
+----------
+
+matchzoo.datasets.quora\_qp.load\_data module
+---------------------------------------------
+
+.. automodule:: matchzoo.datasets.quora_qp.load_data
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: matchzoo.datasets.quora_qp
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/docs/source/matchzoo.datasets.rst b/docs/source/matchzoo.datasets.rst
index 8a77b0fc..d559538d 100644
--- a/docs/source/matchzoo.datasets.rst
+++ b/docs/source/matchzoo.datasets.rst
@@ -7,6 +7,7 @@ Subpackages
 .. toctree::
 
     matchzoo.datasets.embeddings
+    matchzoo.datasets.quora_qp
     matchzoo.datasets.snli
     matchzoo.datasets.toy
     matchzoo.datasets.wiki_qa
diff --git a/docs/source/matchzoo.embedding.rst b/docs/source/matchzoo.embedding.rst
new file mode 100644
index 00000000..f1b0265b
--- /dev/null
+++ b/docs/source/matchzoo.embedding.rst
@@ -0,0 +1,22 @@
+matchzoo.embedding package
+==========================
+
+Submodules
+----------
+
+matchzoo.embedding.embedding module
+-----------------------------------
+
+.. automodule:: matchzoo.embedding.embedding
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: matchzoo.embedding
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/docs/source/matchzoo.preprocessors.units.rst b/docs/source/matchzoo.preprocessors.units.rst
new file mode 100644
index 00000000..dc501f4a
--- /dev/null
+++ b/docs/source/matchzoo.preprocessors.units.rst
@@ -0,0 +1,134 @@
+matchzoo.preprocessors.units package
+====================================
+
+Submodules
+----------
+
+matchzoo.preprocessors.units.digit\_removal module
+--------------------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.digit_removal
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.preprocessors.units.fixed\_length module
+-------------------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.fixed_length
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.preprocessors.units.frequency\_filter module
+-----------------------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.frequency_filter
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.preprocessors.units.lemmatization module
+-------------------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.lemmatization
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.preprocessors.units.lowercase module
+---------------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.lowercase
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.preprocessors.units.matching\_histogram module
+-------------------------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.matching_histogram
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.preprocessors.units.ngram\_letter module
+-------------------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.ngram_letter
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.preprocessors.units.punc\_removal module
+-------------------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.punc_removal
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.preprocessors.units.stateful\_unit module
+--------------------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.stateful_unit
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.preprocessors.units.stemming module
+--------------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.stemming
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.preprocessors.units.stop\_removal module
+-------------------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.stop_removal
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.preprocessors.units.tokenize module
+--------------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.tokenize
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.preprocessors.units.unit module
+----------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.unit
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.preprocessors.units.vocabulary module
+----------------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.vocabulary
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+matchzoo.preprocessors.units.word\_hashing module
+-------------------------------------------------
+
+.. automodule:: matchzoo.preprocessors.units.word_hashing
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+
+Module contents
+---------------
+
+.. automodule:: matchzoo.preprocessors.units
+    :members:
+    :undoc-members:
+    :show-inheritance:
diff --git a/docs/source/matchzoo.rst b/docs/source/matchzoo.rst
index ef27bcbb..56ee8b77 100644
--- a/docs/source/matchzoo.rst
+++ b/docs/source/matchzoo.rst
@@ -6,7 +6,7 @@ Subpackages
 
 .. toctree::
 
-    matchzoo.contrib
+    matchzoo.auto
     matchzoo.data_generator
     matchzoo.data_pack
     matchzoo.datasets
@@ -16,23 +16,13 @@ Subpackages
     matchzoo.losses
     matchzoo.metrics
     matchzoo.models
-    matchzoo.prepare
     matchzoo.preprocessors
     matchzoo.tasks
-    matchzoo.tune
     matchzoo.utils
 
 Submodules
 ----------
 
-matchzoo.logger module
-----------------------
-
-.. automodule:: matchzoo.logger
-    :members:
-    :undoc-members:
-    :show-inheritance:
-
 matchzoo.version module
 -----------------------
 
diff --git a/docs/source/matchzoo.utils.rst b/docs/source/matchzoo.utils.rst
index d19d2a78..c679d9fd 100644
--- a/docs/source/matchzoo.utils.rst
+++ b/docs/source/matchzoo.utils.rst
@@ -12,6 +12,14 @@ matchzoo.utils.list\_recursive\_subclasses module
     :undoc-members:
     :show-inheritance:
 
+matchzoo.utils.make\_keras\_optimizer\_picklable module
+-------------------------------------------------------
+
+.. automodule:: matchzoo.utils.make_keras_optimizer_picklable
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 matchzoo.utils.one\_hot module
 ------------------------------
 

From 02750ef0e7c98adbd69eefe1da592210487b7938 Mon Sep 17 00:00:00 2001
From: wqh17101 <597935261@qq.com>
Date: Sun, 19 May 2019 17:40:55 +0800
Subject: [PATCH 070/100] update readme

---
 docs/Readme.md | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/docs/Readme.md b/docs/Readme.md
index e5f392ce..7b1b43c7 100644
--- a/docs/Readme.md
+++ b/docs/Readme.md
@@ -16,7 +16,8 @@ pip install -r requirements.txt
 # Enter docs folder.
 cd docs
 # Use sphinx autodoc to generate rst.
-sphinx-apidoc -o source/ ../matchzoo/
+# usage: sphinx-apidoc [OPTIONS] -o <OUTPUT_PATH> <MODULE_PATH> [EXCLUDE_PATTERN,...]
+sphinx-apidoc -o source/ ../matchzoo/ ../matchzoo/contrib
 # Generate html from rst
 make clean
 make html

From 8968ebba27370376db4b8604f183f8db3401dd07 Mon Sep 17 00:00:00 2001
From: wqh17101 <597935261@qq.com>
Date: Sun, 19 May 2019 17:47:08 +0800
Subject: [PATCH 071/100] update release_ver

---
 docs/source/conf.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/docs/source/conf.py b/docs/source/conf.py
index 32d02f5a..55981b7d 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -40,7 +40,7 @@
 # The short X.Y version
 version = ''
 # The full version, including alpha/beta/rc tags
-release = '2.0'
+release = '2.1'
 
 
 # -- General configuration ---------------------------------------------------

From 8804cdb85be16decd1162829fedaf9d020396b1e Mon Sep 17 00:00:00 2001
From: jellying <469413628@qq.com>
Date: Wed, 22 May 2019 16:55:50 +0800
Subject: [PATCH 072/100] fix the code style warning

---
 matchzoo/preprocessors/bert_preprocessor.py | 19 ++++++-------
 matchzoo/preprocessors/build_vocab_unit.py  |  8 +++---
 matchzoo/preprocessors/units/bert_clean.py  |  3 ++-
 matchzoo/preprocessors/units/tokenize.py    | 10 ++++---
 matchzoo/preprocessors/units/vocabulary.py  |  1 -
 matchzoo/utils/bert_utils.py                | 30 ++++++++++-----------
 6 files changed, 38 insertions(+), 33 deletions(-)

diff --git a/matchzoo/preprocessors/bert_preprocessor.py b/matchzoo/preprocessors/bert_preprocessor.py
index 0df73732..ccdcdaee 100644
--- a/matchzoo/preprocessors/bert_preprocessor.py
+++ b/matchzoo/preprocessors/bert_preprocessor.py
@@ -21,9 +21,9 @@ def __init__(self, fixed_length_left: int = 30,
                  filter_low_freq: float = 2,
                  filter_high_freq: float = float('inf'),
                  remove_stop_words: bool = False,
-                 lower_case: bool=True,
-                 chinese_version: bool=True,
-                 bert_vocab_path: str="bert_resources/vocab.txt"):
+                 lower_case: bool = True,
+                 chinese_version: bool = True,
+                 bert_vocab_path: str = "bert_resources/vocab.txt"):
         """
         Bert-base Model preprocessor.
 
@@ -35,8 +35,11 @@ def __init__(self, fixed_length_left: int = 30,
             >>> train_data = mz.datasets.toy.load_data()
             >>> test_data = mz.datasets.toy.load_data(stage='test')
             >>> # the argument 'bert_vocab_path' must feed the bert vocab path
-            >>> bert_preprocessor = mz.preprocessors.BertPreprocessor(bert_vocab_path='matchzoo/datasets/bert_resources/uncased_vocab_100.txt')
-            >>> train_data_processed = bert_preprocessor.fit_transform(train_data)
+            >>> bert_preprocessor = mz.preprocessors.BertPreprocessor(
+            ...     bert_vocab_path=
+            ...     'matchzoo/datasets/bert_resources/uncased_vocab_100.txt')
+            >>> train_data_processed = bert_preprocessor.fit_transform(
+            ...     train_data)
             >>> test_data_processed = bert_preprocessor.transform(test_data)
 
         """
@@ -65,7 +68,8 @@ def __init__(self, fixed_length_left: int = 30,
         if lower_case:
             self._units.append(units.Lowercase())
             self._units.append(units.StripAccent())
-        self._units.append(units.WordPieceTokenize(self._vocab_unit.state['term_index']))
+        self._units.append(units.WordPieceTokenize(
+            self._vocab_unit.state['term_index']))
         if remove_stop_words:
             self._units.append(units.StopRemoval())
 
@@ -135,6 +139,3 @@ def _default_units(cls) -> list:
             units.BertClean(),
             units.BasicTokenize()
         ]
-
-
-
diff --git a/matchzoo/preprocessors/build_vocab_unit.py b/matchzoo/preprocessors/build_vocab_unit.py
index ac1af5d6..77dc54a8 100644
--- a/matchzoo/preprocessors/build_vocab_unit.py
+++ b/matchzoo/preprocessors/build_vocab_unit.py
@@ -3,10 +3,11 @@
 from .build_unit_from_data_pack import build_unit_from_data_pack
 from .units import BertVocabulary
 
+
 def build_vocab_unit(
-    data_pack: DataPack,
-    mode: str = 'both',
-    verbose: int = 1
+        data_pack: DataPack,
+        mode: str = 'both',
+        verbose: int = 1
 ) -> Vocabulary:
     """
     Build a :class:`preprocessor.units.Vocabulary` given `data_pack`.
@@ -33,6 +34,7 @@ def build_vocab_unit(
 def built_bert_vocab_unit(vocab_path: str) -> BertVocabulary:
     """
     Build a :class:`preprocessor.units.BertVocabulary` given `vocab_path`.
+
     :param vocab_path: bert vocabulary path.
     :return: A built vocabulary unit.
 
diff --git a/matchzoo/preprocessors/units/bert_clean.py b/matchzoo/preprocessors/units/bert_clean.py
index 74f7b5fc..e6747a78 100644
--- a/matchzoo/preprocessors/units/bert_clean.py
+++ b/matchzoo/preprocessors/units/bert_clean.py
@@ -1,5 +1,6 @@
 from .unit import Unit
-from matchzoo.utils.bert_utils import is_whitespace, is_control, run_strip_accents
+from matchzoo.utils.bert_utils import \
+    is_whitespace, is_control, run_strip_accents
 
 
 class BertClean(Unit):
diff --git a/matchzoo/preprocessors/units/tokenize.py b/matchzoo/preprocessors/units/tokenize.py
index a7747f8a..c72e65d9 100644
--- a/matchzoo/preprocessors/units/tokenize.py
+++ b/matchzoo/preprocessors/units/tokenize.py
@@ -1,5 +1,6 @@
 import nltk
-from matchzoo.utils.bert_utils import is_chinese_char, whitespace_tokenize, run_split_on_punc
+from matchzoo.utils.bert_utils import is_chinese_char, \
+    whitespace_tokenize, run_split_on_punc
 
 from .unit import Unit
 
@@ -64,7 +65,7 @@ def transform(self, input_: str) -> list:
 class WordPieceTokenize(Unit):
     """Process unit for text tokenization."""
 
-    def __init__(self, vocab: dict, max_input_chars_per_word: int=200):
+    def __init__(self, vocab: dict, max_input_chars_per_word: int = 200):
         """Initialization. """
         self.vocab = vocab
         self.unk_token = '[UNK]'
@@ -73,8 +74,9 @@ def __init__(self, vocab: dict, max_input_chars_per_word: int=200):
     def transform(self, input_: list) -> list:
         """
         Tokenizes a piece of text into its word pieces.
-        This uses a greedy longest-match-first algorithm to perform tokenization
-        using the given vocabulary.
+
+        This uses a greedy longest-match-first algorithm to perform
+         tokenization using the given vocabulary.
 
         For example:
           input = "unaffable"
diff --git a/matchzoo/preprocessors/units/vocabulary.py b/matchzoo/preprocessors/units/vocabulary.py
index 7504b4cc..1ed62bc5 100644
--- a/matchzoo/preprocessors/units/vocabulary.py
+++ b/matchzoo/preprocessors/units/vocabulary.py
@@ -108,4 +108,3 @@ def fit(self, vocab_path: str):
     def transform(self, input_: list) -> list:
         """Transform a list of tokens to corresponding indices."""
         return [self._context['term_index'][token] for token in input_]
-
diff --git a/matchzoo/utils/bert_utils.py b/matchzoo/utils/bert_utils.py
index 9ed234f3..f4b2b8c7 100644
--- a/matchzoo/utils/bert_utils.py
+++ b/matchzoo/utils/bert_utils.py
@@ -32,8 +32,8 @@ def is_punctuation(char):
     # Characters such as "^", "$", and "`" are not in the Unicode
     # Punctuation class but we treat them as punctuation anyways, for
     # consistency.
-    if ((cp >= 33 and cp <= 47) or (cp >= 58 and cp <= 64) or
-            (cp >= 91 and cp <= 96) or (cp >= 123 and cp <= 126)):
+    if ((33 <= cp <= 47) or (58 <= cp <= 64) or
+            (91 <= cp <= 96) or (123 <= cp <= 126)):
         return True
     cat = unicodedata.category(char)
     if cat.startswith("P"):
@@ -46,19 +46,19 @@ def is_chinese_char(cp):
     # This defines a "chinese character" as anything in the CJK Unicode block:
     #   https://en.wikipedia.org/wiki/CJK_Unified_Ideographs_(Unicode_block)
     #
-    # Note that the CJK Unicode block is NOT all Japanese and Korean characters,
-    # despite its name. The modern Korean Hangul alphabet is a different block,
-    # as is Japanese Hiragana and Katakana. Those alphabets are used to write
-    # space-separated words, so they are not treated specially and handled
-    # like the all of the other languages.
-    if ((cp >= 0x4E00 and cp <= 0x9FFF) or  #
-            (cp >= 0x3400 and cp <= 0x4DBF) or  #
-            (cp >= 0x20000 and cp <= 0x2A6DF) or  #
-            (cp >= 0x2A700 and cp <= 0x2B73F) or  #
-            (cp >= 0x2B740 and cp <= 0x2B81F) or  #
-            (cp >= 0x2B820 and cp <= 0x2CEAF) or
-            (cp >= 0xF900 and cp <= 0xFAFF) or  #
-            (cp >= 0x2F800 and cp <= 0x2FA1F)):  #
+    # Note that the CJK Unicode block is NOT all Japanese and Korean
+    # characters, despite its name. The modern Korean Hangul alphabet is a
+    # different block, as is Japanese Hiragana and Katakana. Those alphabets
+    # are used to write space-separated words, so they are not treated
+    # specially and handled like the all of the other languages.
+    if ((0x4E00 <= cp <= 0x9FFF) or  #
+            (0x3400 <= cp <= 0x4DBF) or  #
+            (0x20000 <= cp <= 0x2A6DF) or  #
+            (0x2A700 <= cp <= 0x2B73F) or  #
+            (0x2B740 <= cp <= 0x2B81F) or  #
+            (0x2B820 <= cp <= 0x2CEAF) or
+            (0xF900 <= cp <= 0xFAFF) or  #
+            (0x2F800 <= cp <= 0x2FA1F)):  #
         return True
 
     return False

From 3e08df2d1f8d6c8eeb39354684b5b27bd077e036 Mon Sep 17 00:00:00 2001
From: jellying <469413628@qq.com>
Date: Wed, 22 May 2019 20:03:08 +0800
Subject: [PATCH 073/100] fix the line break warning

---
 matchzoo/preprocessors/units/tokenize.py |  2 +-
 matchzoo/utils/bert_utils.py             | 24 +++++++++++-------------
 2 files changed, 12 insertions(+), 14 deletions(-)

diff --git a/matchzoo/preprocessors/units/tokenize.py b/matchzoo/preprocessors/units/tokenize.py
index c72e65d9..8456f1b2 100644
--- a/matchzoo/preprocessors/units/tokenize.py
+++ b/matchzoo/preprocessors/units/tokenize.py
@@ -66,7 +66,7 @@ class WordPieceTokenize(Unit):
     """Process unit for text tokenization."""
 
     def __init__(self, vocab: dict, max_input_chars_per_word: int = 200):
-        """Initialization. """
+        """Initialization."""
         self.vocab = vocab
         self.unk_token = '[UNK]'
         self.max_input_chars_per_word = max_input_chars_per_word
diff --git a/matchzoo/utils/bert_utils.py b/matchzoo/utils/bert_utils.py
index f4b2b8c7..0814a30d 100644
--- a/matchzoo/utils/bert_utils.py
+++ b/matchzoo/utils/bert_utils.py
@@ -32,8 +32,9 @@ def is_punctuation(char):
     # Characters such as "^", "$", and "`" are not in the Unicode
     # Punctuation class but we treat them as punctuation anyways, for
     # consistency.
-    if ((33 <= cp <= 47) or (58 <= cp <= 64) or
-            (91 <= cp <= 96) or (123 <= cp <= 126)):
+    condition = (33 <= cp <= 47) or (58 <= cp <= 64) or \
+                (91 <= cp <= 96) or (123 <= cp <= 126)
+    if condition:
         return True
     cat = unicodedata.category(char)
     if cat.startswith("P"):
@@ -51,17 +52,14 @@ def is_chinese_char(cp):
     # different block, as is Japanese Hiragana and Katakana. Those alphabets
     # are used to write space-separated words, so they are not treated
     # specially and handled like the all of the other languages.
-    if ((0x4E00 <= cp <= 0x9FFF) or  #
-            (0x3400 <= cp <= 0x4DBF) or  #
-            (0x20000 <= cp <= 0x2A6DF) or  #
-            (0x2A700 <= cp <= 0x2B73F) or  #
-            (0x2B740 <= cp <= 0x2B81F) or  #
-            (0x2B820 <= cp <= 0x2CEAF) or
-            (0xF900 <= cp <= 0xFAFF) or  #
-            (0x2F800 <= cp <= 0x2FA1F)):  #
-        return True
-
-    return False
+    return (0x4E00 <= cp <= 0x9FFF) or \
+           (0x3400 <= cp <= 0x4DBF) or \
+           (0x20000 <= cp <= 0x2A6DF) or \
+           (0x2A700 <= cp <= 0x2B73F) or \
+           (0x2B740 <= cp <= 0x2B81F) or \
+           (0x2B820 <= cp <= 0x2CEAF) or \
+           (0xF900 <= cp <= 0xFAFF) or \
+           (0x2F800 <= cp <= 0x2FA1F)
 
 
 def run_strip_accents(text):

From 40f9a181499b21c80f3a56c021e0a580ee35d279 Mon Sep 17 00:00:00 2001
From: jellying <469413628@qq.com>
Date: Tue, 28 May 2019 14:43:17 +0800
Subject: [PATCH 074/100] modified according to reviews

---
 matchzoo/preprocessors/bert_preprocessor.py | 12 +++++-------
 matchzoo/preprocessors/units/tokenize.py    | 16 +++++++++-------
 matchzoo/utils/bert_utils.py                | 17 ++++++-----------
 3 files changed, 20 insertions(+), 25 deletions(-)

diff --git a/matchzoo/preprocessors/bert_preprocessor.py b/matchzoo/preprocessors/bert_preprocessor.py
index ccdcdaee..2c6b64ce 100644
--- a/matchzoo/preprocessors/bert_preprocessor.py
+++ b/matchzoo/preprocessors/bert_preprocessor.py
@@ -15,26 +15,24 @@
 class BertPreprocessor(BasePreprocessor):
     """Bert-base Model preprocessor."""
 
-    def __init__(self, fixed_length_left: int = 30,
+    def __init__(self, bert_vocab_path: str,
+                 fixed_length_left: int = 30,
                  fixed_length_right: int = 30,
                  filter_mode: str = 'df',
                  filter_low_freq: float = 2,
                  filter_high_freq: float = float('inf'),
                  remove_stop_words: bool = False,
                  lower_case: bool = True,
-                 chinese_version: bool = True,
-                 bert_vocab_path: str = "bert_resources/vocab.txt"):
+                 chinese_version: bool = False,
+                 ):
         """
         Bert-base Model preprocessor.
 
-        TODO: doc here.
-
-
         Example:
             >>> import matchzoo as mz
             >>> train_data = mz.datasets.toy.load_data()
             >>> test_data = mz.datasets.toy.load_data(stage='test')
-            >>> # the argument 'bert_vocab_path' must feed the bert vocab path
+            >>> # The argument 'bert_vocab_path' must feed the bert vocab path
             >>> bert_preprocessor = mz.preprocessors.BertPreprocessor(
             ...     bert_vocab_path=
             ...     'matchzoo/datasets/bert_resources/uncased_vocab_100.txt')
diff --git a/matchzoo/preprocessors/units/tokenize.py b/matchzoo/preprocessors/units/tokenize.py
index 8456f1b2..51d97de6 100644
--- a/matchzoo/preprocessors/units/tokenize.py
+++ b/matchzoo/preprocessors/units/tokenize.py
@@ -28,7 +28,8 @@ def transform(self, input_: str) -> str:
 
         :param input_: raw textual input.
 
-        :return output: text with a blank between adjacent Chinese tokens.
+        :return output: text with at least one blank between adjacent
+                        Chinese tokens.
         """
         output = []
         for char in input_:
@@ -89,15 +90,16 @@ def transform(self, input_: list) -> list:
         output_tokens = []
         for token in input_:
             chars = list(token)
-            if len(chars) > self.max_input_chars_per_word:
+            token_length = len(chars)
+            if token_length > self.max_input_chars_per_word:
                 output_tokens.append(self.unk_token)
                 continue
 
-            is_bad = False
+            unknown_suffix = False
             start = 0
             sub_tokens = []
-            while start < len(chars):
-                end = len(chars)
+            while start < token_length:
+                end = token_length
                 cur_substr = None
                 while start < end:
                     substr = "".join(chars[start:end])
@@ -108,12 +110,12 @@ def transform(self, input_: list) -> list:
                         break
                     end -= 1
                 if cur_substr is None:
-                    is_bad = True
+                    unknown_suffix = True
                     break
                 sub_tokens.append(cur_substr)
                 start = end
 
-            if is_bad:
+            if unknown_suffix:
                 output_tokens.append(self.unk_token)
             else:
                 output_tokens.extend(sub_tokens)
diff --git a/matchzoo/utils/bert_utils.py b/matchzoo/utils/bert_utils.py
index 0814a30d..44908278 100644
--- a/matchzoo/utils/bert_utils.py
+++ b/matchzoo/utils/bert_utils.py
@@ -5,12 +5,11 @@ def is_whitespace(char):
     """Checks whether `chars` is a whitespace character."""
     # \t, \n, and \r are technically contorl characters but we treat them
     # as whitespace since they are generally considered as such.
-    if char == " " or char == "\t" or char == "\n" or char == "\r":
-        return True
-    cat = unicodedata.category(char)
-    if cat == "Zs":
-        return True
-    return False
+    return (char == " ") or \
+           (char == "\t") or \
+           (char == "\n") or \
+           (char == "\r") or \
+           (unicodedata.category(char) == "Zs")
 
 
 def is_control(char):
@@ -34,10 +33,8 @@ def is_punctuation(char):
     # consistency.
     condition = (33 <= cp <= 47) or (58 <= cp <= 64) or \
                 (91 <= cp <= 96) or (123 <= cp <= 126)
-    if condition:
-        return True
     cat = unicodedata.category(char)
-    if cat.startswith("P"):
+    if condition or cat.startswith("P"):
         return True
     return False
 
@@ -98,7 +95,5 @@ def run_split_on_punc(text):
 def whitespace_tokenize(text):
     """Runs basic whitespace cleaning and splitting on a piece of text."""
     text = text.strip()
-    if not text:
-        return []
     tokens = text.split()
     return tokens

From 458a29dbd25a137372374ba4e039a24fa383f6aa Mon Sep 17 00:00:00 2001
From: jellying <469413628@qq.com>
Date: Thu, 30 May 2019 11:35:57 +0800
Subject: [PATCH 075/100] fix some style problems

---
 matchzoo/preprocessors/units/__init__.py   | 2 +-
 matchzoo/preprocessors/units/tokenize.py   | 8 ++++++--
 matchzoo/preprocessors/units/vocabulary.py | 2 +-
 matchzoo/utils/bert_utils.py               | 9 ++-------
 4 files changed, 10 insertions(+), 11 deletions(-)

diff --git a/matchzoo/preprocessors/units/__init__.py b/matchzoo/preprocessors/units/__init__.py
index e6ba360a..71bd7468 100644
--- a/matchzoo/preprocessors/units/__init__.py
+++ b/matchzoo/preprocessors/units/__init__.py
@@ -15,7 +15,7 @@
 from .word_hashing import WordHashing
 from .bert_clean import BertClean
 from .bert_clean import StripAccent
-from.tokenize import ChineseTokenize
+from .tokenize import ChineseTokenize
 from .tokenize import BasicTokenize
 from .tokenize import WordPieceTokenize
 from .vocabulary import BertVocabulary
diff --git a/matchzoo/preprocessors/units/tokenize.py b/matchzoo/preprocessors/units/tokenize.py
index 51d97de6..befdcc56 100644
--- a/matchzoo/preprocessors/units/tokenize.py
+++ b/matchzoo/preprocessors/units/tokenize.py
@@ -80,8 +80,12 @@ def transform(self, input_: list) -> list:
          tokenization using the given vocabulary.
 
         For example:
-          input = "unaffable"
-          output = ["un", "##aff", "##able"]
+        >>> input_list = ["unaffable"]
+        >>> vocab = {"un": 0, "##aff": 1, "##able":2}
+        >>> wordpiece_unit = WordPieceTokenize(vocab)
+        >>> output = wordpiece_unit.transform(input_list)
+        >>> golden_output = ["un", "##aff", "##able"]
+        >>> assert output == golden_output
 
         :param input_: token list.
 
diff --git a/matchzoo/preprocessors/units/vocabulary.py b/matchzoo/preprocessors/units/vocabulary.py
index 1ed62bc5..711e4d50 100644
--- a/matchzoo/preprocessors/units/vocabulary.py
+++ b/matchzoo/preprocessors/units/vocabulary.py
@@ -88,7 +88,7 @@ def __init__(self, pad_value: str = '[PAD]', oov_value: str = '[UNK]'):
         self._pad = pad_value
         self._oov = oov_value
         self._context['term_index'] = self.TermIndex()
-        self._context['index_term'] = dict()
+        self._context['index_term'] = {}
 
     class TermIndex(dict):
         """Map term to index."""
diff --git a/matchzoo/utils/bert_utils.py b/matchzoo/utils/bert_utils.py
index 44908278..8490ddbb 100644
--- a/matchzoo/utils/bert_utils.py
+++ b/matchzoo/utils/bert_utils.py
@@ -19,7 +19,7 @@ def is_control(char):
     if char == "\t" or char == "\n" or char == "\r":
         return False
     cat = unicodedata.category(char)
-    if cat in ("Cc", "Cf"):
+    if cat in ["Cc", "Cf"]:
         return True
     return False
 
@@ -62,12 +62,7 @@ def is_chinese_char(cp):
 def run_strip_accents(text):
     """Strips accents from a piece of text."""
     text = unicodedata.normalize("NFD", text)
-    output = []
-    for char in text:
-        cat = unicodedata.category(char)
-        if cat == "Mn":
-            continue
-        output.append(char)
+    output = [char for char in text if not unicodedata.category(char) == 'Mn']
     return "".join(output)
 
 

From b0c356602777be7cd642d02971b69d106b771afe Mon Sep 17 00:00:00 2001
From: caiyinqiong <1198593462@qq.com>
Date: Sun, 2 Jun 2019 11:39:39 +0800
Subject: [PATCH 076/100] reduce test time

---
 matchzoo/preprocessors/diin_preprocessor.py | 6 +++---
 1 file changed, 3 insertions(+), 3 deletions(-)

diff --git a/matchzoo/preprocessors/diin_preprocessor.py b/matchzoo/preprocessors/diin_preprocessor.py
index b8c5a4af..6fdb7eaf 100644
--- a/matchzoo/preprocessors/diin_preprocessor.py
+++ b/matchzoo/preprocessors/diin_preprocessor.py
@@ -16,9 +16,9 @@ class DIINPreprocessor(BasePreprocessor):
     """DIIN Model preprocessor."""
 
     def __init__(self,
-                 fixed_length_left: int = 10,
-                 fixed_length_right: int = 10,
-                 fixed_length_word: int = 5):
+                 fixed_length_left: int = 5,
+                 fixed_length_right: int = 5,
+                 fixed_length_word: int = 3):
         """
         DIIN Model preprocessor.
 

From e37ff1715d35f152bd4351a2565f75c272195504 Mon Sep 17 00:00:00 2001
From: caiyinqiong <1198593462@qq.com>
Date: Wed, 5 Jun 2019 18:54:14 +0800
Subject: [PATCH 077/100] fix doctest

---
 matchzoo/preprocessors/diin_preprocessor.py   | 22 +++++++++----------
 .../preprocessors/units/character_index.py    |  6 ++---
 .../preprocessors/units/word_exact_match.py   | 13 +++++++----
 3 files changed, 23 insertions(+), 18 deletions(-)

diff --git a/matchzoo/preprocessors/diin_preprocessor.py b/matchzoo/preprocessors/diin_preprocessor.py
index 6fdb7eaf..51c8ecf1 100644
--- a/matchzoo/preprocessors/diin_preprocessor.py
+++ b/matchzoo/preprocessors/diin_preprocessor.py
@@ -16,9 +16,9 @@ class DIINPreprocessor(BasePreprocessor):
     """DIIN Model preprocessor."""
 
     def __init__(self,
-                 fixed_length_left: int = 5,
-                 fixed_length_right: int = 5,
-                 fixed_length_word: int = 3):
+                 fixed_length_left: int = 10,
+                 fixed_length_right: int = 10,
+                 fixed_length_word: int = 5):
         """
         DIIN Model preprocessor.
 
@@ -30,26 +30,26 @@ def __init__(self,
 
         Example:
             >>> import matchzoo as mz
-            >>> train_data = mz.datasets.snli.load_data('train')
-            >>> test_data = mz.datasets.snli.load_data('test')
+            >>> train_data = mz.datasets.toy.load_data()
+            >>> test_data = mz.datasets.toy.load_data(stage='test')
             >>> diin_preprocessor = mz.preprocessors.DIINPreprocessor(
-            ...     fixed_length_left=32,
-            ...     fixed_length_right=32,
-            ...     fixed_length_word=16,
+            ...     fixed_length_left=5,
+            ...     fixed_length_right=5,
+            ...     fixed_length_word=3,
             ... )
             >>> diin_preprocessor = diin_preprocessor.fit(
             ...     train_data, verbose=0)
             >>> diin_preprocessor.context['input_shapes']
-            [(32,), (32,), (32,16,), (32,16,), (32,), (32,)]
+            [(5,), (5,), (5, 3), (5, 3), (5,), (5,)]
             >>> diin_preprocessor.context['vocab_size']
-            33061
+            859
             >>> train_data_processed = diin_preprocessor.transform(
             ...     train_data, verbose=0)
             >>> type(train_data_processed)
             <class 'matchzoo.data_pack.data_pack.DataPack'>
             >>> test_data_processed = diin_preprocessor.transform(
             ...     test_data, verbose=0)
-            >>> type(test_data_transformed)
+            >>> type(test_data_processed)
             <class 'matchzoo.data_pack.data_pack.DataPack'>
         """
         super().__init__()
diff --git a/matchzoo/preprocessors/units/character_index.py b/matchzoo/preprocessors/units/character_index.py
index ae2a46cd..52718c9e 100644
--- a/matchzoo/preprocessors/units/character_index.py
+++ b/matchzoo/preprocessors/units/character_index.py
@@ -18,12 +18,12 @@ class CharacterIndex(Unit):
         >>> input_ = [['#', 'a', '#'],['#', 'o', 'n', 'e', '#']]
         >>> character_index = CharacterIndex(
         ...     char_index={
-                 '<PAD>': 0, '<OOV>': 1, 'a': 2, 'n': 3, 'e':4, '#':5},
-        ...     fixed_length_text=3,
+        ...      '<PAD>': 0, '<OOV>': 1, 'a': 2, 'n': 3, 'e':4, '#':5},
+        ...     fixed_length_text=2,
         ...     fixed_length_word=5)
         >>> index = character_index.transform(input_)
         >>> index
-        [[5, 2, 5, 0, 0], [5, 1, 3, 4, 5], [0, 0, 0, 0, 0]]
+        [[5.0, 2.0, 5.0, 0.0, 0.0], [5.0, 1.0, 3.0, 4.0, 5.0]]
     """
 
     def __init__(
diff --git a/matchzoo/preprocessors/units/word_exact_match.py b/matchzoo/preprocessors/units/word_exact_match.py
index 68a7834b..d08562ab 100644
--- a/matchzoo/preprocessors/units/word_exact_match.py
+++ b/matchzoo/preprocessors/units/word_exact_match.py
@@ -1,4 +1,5 @@
 import numpy as np
+import pandas
 
 from .unit import Unit
 
@@ -20,15 +21,19 @@ class WordExactMatch(Unit):
         ...     match='text_left', to_match='text_right'
         ... )
         >>> left_out = input_.apply(left_word_exact_match.transform, axis=1)
-        >>> left_out
-        [[0, 1, 1, 0, 0],[0, 1, 0, 0, 0]]
+        >>> left_out[0]
+        [0.0, 1.0, 1.0, 0.0, 0.0]
+        >>> left_out[1]
+        [0.0, 1.0, 0.0, 0.0, 0.0]
         >>> right_word_exact_match = WordExactMatch(
         ...     fixed_length_text=5,
         ...     match='text_right', to_match='text_left'
         ... )
         >>> right_out = input_.apply(right_word_exact_match.transform, axis=1)
-        >>> right_out
-        [[0, 1, 1, 0, 0], [0, 0, 1, 0, 0]]
+        >>> right_out[0]
+        [0.0, 1.0, 1.0, 0.0, 0.0]
+        >>> right_out[1]
+        [0.0, 0.0, 1.0, 0.0, 0.0]
     """
 
     def __init__(

From 093e3124acdc903be1eea5ead252d4cf3b862190 Mon Sep 17 00:00:00 2001
From: caiyinqiong <1198593462@qq.com>
Date: Wed, 5 Jun 2019 19:56:16 +0800
Subject: [PATCH 078/100] fix flake8 error

---
 matchzoo/preprocessors/diin_preprocessor.py      | 4 ++--
 matchzoo/preprocessors/units/character_index.py  | 1 +
 matchzoo/preprocessors/units/word_exact_match.py | 1 +
 3 files changed, 4 insertions(+), 2 deletions(-)

diff --git a/matchzoo/preprocessors/diin_preprocessor.py b/matchzoo/preprocessors/diin_preprocessor.py
index 51c8ecf1..4985e3a2 100644
--- a/matchzoo/preprocessors/diin_preprocessor.py
+++ b/matchzoo/preprocessors/diin_preprocessor.py
@@ -51,6 +51,7 @@ def __init__(self,
             ...     test_data, verbose=0)
             >>> type(test_data_processed)
             <class 'matchzoo.data_pack.data_pack.DataPack'>
+
         """
         super().__init__()
         self._fixed_length_left = fixed_length_left
@@ -103,8 +104,7 @@ def fit(self, data_pack: DataPack, verbose: int = 1):
 
     def transform(self, data_pack: DataPack, verbose: int = 1) -> DataPack:
         """
-        Apply transformation on data, create fixed length word representation,
-        character representation and exact match representation.
+        Apply transformation on data.
 
         :param data_pack: Inputs to be preprocessed.
         :param verbose: Verbosity.
diff --git a/matchzoo/preprocessors/units/character_index.py b/matchzoo/preprocessors/units/character_index.py
index 52718c9e..17126765 100644
--- a/matchzoo/preprocessors/units/character_index.py
+++ b/matchzoo/preprocessors/units/character_index.py
@@ -24,6 +24,7 @@ class CharacterIndex(Unit):
         >>> index = character_index.transform(input_)
         >>> index
         [[5.0, 2.0, 5.0, 0.0, 0.0], [5.0, 1.0, 3.0, 4.0, 5.0]]
+
     """
 
     def __init__(
diff --git a/matchzoo/preprocessors/units/word_exact_match.py b/matchzoo/preprocessors/units/word_exact_match.py
index d08562ab..717b196d 100644
--- a/matchzoo/preprocessors/units/word_exact_match.py
+++ b/matchzoo/preprocessors/units/word_exact_match.py
@@ -34,6 +34,7 @@ class WordExactMatch(Unit):
         [0.0, 1.0, 1.0, 0.0, 0.0]
         >>> right_out[1]
         [0.0, 0.0, 1.0, 0.0, 0.0]
+
     """
 
     def __init__(

From 61414aa76252683d24441059f995ff16b4031c35 Mon Sep 17 00:00:00 2001
From: Chris <chenjiangui@outlook.com>
Date: Wed, 26 Jun 2019 15:15:32 +0800
Subject: [PATCH 079/100] Fix macOS CI (#759)

---
 .travis.yml | 7 ++-----
 1 file changed, 2 insertions(+), 5 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index 5599f633..aa960a88 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -16,11 +16,8 @@ matrix:
       dist: trusty
       python: 3.6
     - os: osx
-      language: generic
-      env: PYTHON_VERSION=3.6
-    - os: osx
-      language: generic
-      env: PYTHON_VERSION=3.7
+      osx_image: xcode10.2
+      language: shell
 
 install:
   - pip3 install -r requirements.txt

From 88d949c5a9be1cbf838e8b833e2f6f5e0be1d539 Mon Sep 17 00:00:00 2001
From: Chris <chenjiangui@outlook.com>
Date: Mon, 1 Jul 2019 16:59:12 +0800
Subject: [PATCH 080/100] Fix cron job (#761)

* Fix cron job

* Reduce parameters
---
 matchzoo/contrib/models/bimpm.py      |  6 +++---
 matchzoo/contrib/models/diin.py       | 12 ++++++------
 matchzoo/contrib/models/esim.py       |  2 +-
 matchzoo/contrib/models/hbmp.py       | 10 +++++-----
 tests/unit_test/models/test_models.py |  4 ++--
 5 files changed, 17 insertions(+), 17 deletions(-)

diff --git a/matchzoo/contrib/models/bimpm.py b/matchzoo/contrib/models/bimpm.py
index b5ee162f..112967ea 100644
--- a/matchzoo/contrib/models/bimpm.py
+++ b/matchzoo/contrib/models/bimpm.py
@@ -43,9 +43,9 @@ def get_default_params(cls) -> ParamTable:
                                          'max-pooling': True,
                                          'attentive': True,
                                          'max-attentive': True}))
-        params.add(Param('mp_dim', 20))
-        params.add(Param('att_dim', 20))
-        params.add(Param('hidden_size', 32))
+        params.add(Param('mp_dim', 3))
+        params.add(Param('att_dim', 3))
+        params.add(Param('hidden_size', 4))
         params.add(Param('dropout_rate', 0.0))
         params.add(Param('w_initializer', 'glorot_uniform'))
         params.add(Param('b_initializer', 'zeros'))
diff --git a/matchzoo/contrib/models/diin.py b/matchzoo/contrib/models/diin.py
index 52045b59..3ab3632b 100644
--- a/matchzoo/contrib/models/diin.py
+++ b/matchzoo/contrib/models/diin.py
@@ -49,24 +49,24 @@ def get_default_params(cls) -> ParamTable:
         params.add(Param(name='char_embedding_input_dim', value=100,
                          desc="The input dimension of character embedding "
                               "layer."))
-        params.add(Param(name='char_embedding_output_dim', value=8,
+        params.add(Param(name='char_embedding_output_dim', value=2,
                          desc="The output dimension of character embedding "
                               "layer."))
-        params.add(Param(name='char_conv_filters', value=100,
+        params.add(Param(name='char_conv_filters', value=8,
                          desc="The filter size of character convolution "
                               "layer."))
-        params.add(Param(name='char_conv_kernel_size', value=5,
+        params.add(Param(name='char_conv_kernel_size', value=2,
                          desc="The kernel size of character convolution "
                               "layer."))
         params.add(Param(name='first_scale_down_ratio', value=0.3,
                          desc="The channel scale down ratio of the "
                               "convolution layer before densenet."))
-        params.add(Param(name='nb_dense_blocks', value=3,
+        params.add(Param(name='nb_dense_blocks', value=1,
                          desc="The number of blocks in densenet."))
-        params.add(Param(name='layers_per_dense_block', value=8,
+        params.add(Param(name='layers_per_dense_block', value=2,
                          desc="The number of convolution layers in dense "
                               "block."))
-        params.add(Param(name='growth_rate', value=20,
+        params.add(Param(name='growth_rate', value=2,
                          desc="The filter size of each convolution layer in "
                               "dense block."))
         params.add(Param(name='transition_scale_down_ratio', value=0.5,
diff --git a/matchzoo/contrib/models/esim.py b/matchzoo/contrib/models/esim.py
index 4af0371c..e5e5f851 100644
--- a/matchzoo/contrib/models/esim.py
+++ b/matchzoo/contrib/models/esim.py
@@ -47,7 +47,7 @@ def get_default_params(cls) -> ParamTable:
 
         params.add(Param(
             name='lstm_dim',
-            value=300,
+            value=8,
             desc="The dimension of LSTM layer."
         ))
 
diff --git a/matchzoo/contrib/models/hbmp.py b/matchzoo/contrib/models/hbmp.py
index 342ec53b..bc16605d 100644
--- a/matchzoo/contrib/models/hbmp.py
+++ b/matchzoo/contrib/models/hbmp.py
@@ -15,13 +15,13 @@ class HBMP(BaseModel):
     Examples:
         >>> model = HBMP()
         >>> model.guess_and_fill_missing_params(verbose=0)
-        >>> model.params['embedding_input_dim'] = 10000
+        >>> model.params['embedding_input_dim'] = 200
         >>> model.params['embedding_output_dim'] = 100
         >>> model.params['embedding_trainable'] = True
         >>> model.params['alpha'] = 0.1
         >>> model.params['mlp_num_layers'] = 3
-        >>> model.params['mlp_num_units'] = [600, 600]
-        >>> model.params['lstm_num_units'] = 600
+        >>> model.params['mlp_num_units'] = [10, 10]
+        >>> model.params['lstm_num_units'] = 5
         >>> model.params['dropout_rate'] = 0.1
         >>> model.build()
     """
@@ -36,10 +36,10 @@ def get_default_params(cls) -> ParamTable:
                               "function."))
         params.add(Param(name='mlp_num_layers', value=3,
                          desc="The number of layers of mlp."))
-        params.add(Param(name='mlp_num_units', value=[600, 600],
+        params.add(Param(name='mlp_num_units', value=[10, 10],
                          desc="The hidden size of the FC layers, but not "
                               "include the final layer."))
-        params.add(Param(name='lstm_num_units', value=600,
+        params.add(Param(name='lstm_num_units', value=5,
                          desc="The hidden size of the LSTM layer."))
         params.add(Param(
             name='dropout_rate', value=0.1,
diff --git a/tests/unit_test/models/test_models.py b/tests/unit_test/models/test_models.py
index 3d6c994c..828a8b62 100644
--- a/tests/unit_test/models/test_models.py
+++ b/tests/unit_test/models/test_models.py
@@ -21,7 +21,7 @@ def task(request):
 
 @pytest.fixture(scope='module')
 def train_raw(task):
-    return mz.datasets.toy.load_data('train', task)
+    return mz.datasets.toy.load_data('train', task)[:5]
 
 
 @pytest.fixture(scope='module', params=mz.models.list_available())
@@ -97,7 +97,7 @@ def test_save_load_model(model):
 
 @pytest.mark.cron
 def test_hyper_space(model):
-    for _ in range(8):
+    for _ in range(2):
         new_params = copy.deepcopy(model.params)
         sample = mz.hyper_spaces.sample(new_params.hyper_space)
         for key, value in sample.items():

From 80537130be0e5ab87859af87104649f857ad986f Mon Sep 17 00:00:00 2001
From: faneshion <fanyixing111@hotmail.com>
Date: Mon, 15 Jul 2019 11:14:37 +0800
Subject: [PATCH 081/100] add drop_invlid (#765)

---
 matchzoo/data_pack/data_pack.py | 26 ++++++++++++++++++++++++++
 requirements.txt                |  1 +
 2 files changed, 27 insertions(+)

diff --git a/matchzoo/data_pack/data_pack.py b/matchzoo/data_pack/data_pack.py
index df2f1746..5b3e862d 100644
--- a/matchzoo/data_pack/data_pack.py
+++ b/matchzoo/data_pack/data_pack.py
@@ -294,6 +294,32 @@ def drop_label(self):
         """
         self._relation = self._relation.drop(columns='label')
 
+    @_optional_inplace
+    def drop_invalid(self):
+        """
+        Remove rows from the data pack where the length is zero.
+
+        :param inplace: `True` to modify inplace, `False` to return a modified
+            copy. (default: `False`)
+
+        Example:
+            >>> import matchzoo as mz
+            >>> data_pack = mz.datasets.toy.load_data()
+            >>> data_pack.append_text_length(inplace=True, verbose=0)
+            >>> data_pack.drop_invalid(inplace=True)
+        """
+        if not ('length_left' in self._left and 'length_right' in self._right):
+            raise ValueError(f"`lenght_left` or `length_right` is missing. "
+                             f"Please call `append_text_length` in advance.")
+        valid_left = self._left.loc[self._left.length_left != 0]
+        valid_right = self._right.loc[self._right.length_right != 0]
+        self._left = self._left[self._left.index.isin(valid_left.index)]
+        self._right = self._right[self._right.index.isin(valid_right.index)]
+        self._relation = self._relation[self._relation.id_left.isin(
+            valid_left.index) & self._relation.id_right.isin(
+                valid_right.index)]
+        self._relation.reset_index(drop=True, inplace=True)
+
     @_optional_inplace
     def append_text_length(self, verbose=1):
         """
diff --git a/requirements.txt b/requirements.txt
index 9b04fcd7..7582150e 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -15,3 +15,4 @@ pytest >= 3.7.4
 pytest-cov >= 2.4.0
 flake8 >= 3.6.0
 flake8_docstrings >= 1.3.0
+pydocstyle == 2.1

From 4b86b0891acf665cf27ad565785910036d0337a8 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Wed, 17 Jul 2019 12:58:11 +0800
Subject: [PATCH 082/100] Replace operator `/` with `//`. Extract a validation
 method to get a `dpool_size` suggestion.

---
 matchzoo/layers/dynamic_pooling_layer.py | 35 ++++++++++++++++--------
 1 file changed, 23 insertions(+), 12 deletions(-)

diff --git a/matchzoo/layers/dynamic_pooling_layer.py b/matchzoo/layers/dynamic_pooling_layer.py
index 40b989f7..47b46bd6 100644
--- a/matchzoo/layers/dynamic_pooling_layer.py
+++ b/matchzoo/layers/dynamic_pooling_layer.py
@@ -49,6 +49,7 @@ def call(self, inputs: list, **kwargs) -> typing.Any:
 
         :param inputs: two input tensors.
         """
+        self.validate_dpool_size()
         x, dpool_index = inputs
         dpool_shape = K.tf.shape(dpool_index)
         batch_index_one = K.tf.expand_dims(
@@ -60,18 +61,8 @@ def call(self, inputs: list, **kwargs) -> typing.Any:
             axis=-1)
         dpool_index_ex = K.tf.concat([batch_index, dpool_index], axis=3)
         x_expand = K.tf.gather_nd(x, dpool_index_ex)
-        stride1 = self._msize1 / self._psize1
-        stride2 = self._msize2 / self._psize2
-
-        suggestion1 = self._msize1 / stride1
-        suggestion2 = self._msize2 / stride2
-
-        if suggestion1 != self._psize1 or suggestion2 != self._psize2:
-            raise ValueError("DynamicPooling Layer can not "
-                             "generate ({} x {}) output feature map, "
-                             "please use ({} x {} instead.)"
-                             .format(self._psize1, self._psize2,
-                                     suggestion1, suggestion2))
+        stride1 = self._msize1 // self._psize1
+        stride2 = self._msize2 // self._psize2
 
         x_pool = K.tf.nn.max_pool(x_expand,
                                   [1, stride1, stride2, 1],
@@ -97,3 +88,23 @@ def get_config(self) -> dict:
         }
         base_config = super(DynamicPoolingLayer, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
+
+    def validate_dpool_size(self):
+        suggestion = self.get_size_suggestion(
+            self._msize1, self._msize2, self._psize1, self._psize2
+        )
+        if suggestion != (self._psize1, self._psize2):
+            raise ValueError(
+                "DynamicPooling Layer can not "
+                f"generate ({self._psize1} x {self._psize2}) output feature map, "
+                f"please use ({suggestion[0]} x {suggestion[1]}) instead. "
+                f" `model.params['dpool_size'] = {suggestion}` "
+            )
+
+    @classmethod
+    def get_size_suggestion(cls, msize1, msize2, psize1, psize2):
+        stride1 = msize1 // psize1
+        stride2 = msize2 // psize2
+        suggestion1 = msize1 // stride1
+        suggestion2 = msize2 // stride2
+        return (suggestion1, suggestion2)

From 8cd3df713e23c8e9e8e72829737106c0eca96ec5 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Wed, 17 Jul 2019 12:58:56 +0800
Subject: [PATCH 083/100] Update `auto.prepare` to select the nearest legal
 `dpool_size` based on the given lengths.

---
 matchzoo/auto/preparer/preparer.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/matchzoo/auto/preparer/preparer.py b/matchzoo/auto/preparer/preparer.py
index 1e5b5fb0..eef74f91 100644
--- a/matchzoo/auto/preparer/preparer.py
+++ b/matchzoo/auto/preparer/preparer.py
@@ -137,6 +137,7 @@ def _build_model(
         else:
             embedding_matrix = None
 
+        self._handle_match_pyramid_dpool_size(model)
         self._handle_drmm_input_shapes(model)
 
         assert model.params.completed()
@@ -148,6 +149,16 @@ def _build_model(
 
         return model, embedding_matrix
 
+    def _handle_match_pyramid_dpool_size(self, model):
+        if isinstance(model, mz.models.MatchPyramid):
+            suggestion = mz.layers.DynamicPoolingLayer.get_size_suggestion(
+                msize1=model.params['input_shapes'][0][0],
+                msize2=model.params['input_shapes'][1][0],
+                psize1=model.params['dpool_size'][0],
+                psize2=model.params['dpool_size'][1],
+            )
+            model.params['dpool_size'] = suggestion
+
     def _handle_drmm_input_shapes(self, model):
         if isinstance(model, mz.models.DRMM):
             left = model.params['input_shapes'][0]

From d8292c8caa6b2bd8245ba51e00411a6c44d5f775 Mon Sep 17 00:00:00 2001
From: uduse <i@uduse.com>
Date: Wed, 17 Jul 2019 14:38:52 +0800
Subject: [PATCH 084/100] Update doc.

---
 matchzoo/layers/dynamic_pooling_layer.py | 30 +++++++++++++++++++-----
 1 file changed, 24 insertions(+), 6 deletions(-)

diff --git a/matchzoo/layers/dynamic_pooling_layer.py b/matchzoo/layers/dynamic_pooling_layer.py
index 47b46bd6..27b37888 100644
--- a/matchzoo/layers/dynamic_pooling_layer.py
+++ b/matchzoo/layers/dynamic_pooling_layer.py
@@ -49,7 +49,7 @@ def call(self, inputs: list, **kwargs) -> typing.Any:
 
         :param inputs: two input tensors.
         """
-        self.validate_dpool_size()
+        self._validate_dpool_size()
         x, dpool_index = inputs
         dpool_shape = K.tf.shape(dpool_index)
         batch_index_one = K.tf.expand_dims(
@@ -89,20 +89,38 @@ def get_config(self) -> dict:
         base_config = super(DynamicPoolingLayer, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
 
-    def validate_dpool_size(self):
+    def _validate_dpool_size(self):
         suggestion = self.get_size_suggestion(
             self._msize1, self._msize2, self._psize1, self._psize2
         )
         if suggestion != (self._psize1, self._psize2):
             raise ValueError(
                 "DynamicPooling Layer can not "
-                f"generate ({self._psize1} x {self._psize2}) output feature map, "
-                f"please use ({suggestion[0]} x {suggestion[1]}) instead. "
-                f" `model.params['dpool_size'] = {suggestion}` "
+                f"generate ({self._psize1} x {self._psize2}) output "
+                f"feature map, please use ({suggestion[0]} x {suggestion[1]})"
+                f" instead. `model.params['dpool_size'] = {suggestion}` "
             )
 
     @classmethod
-    def get_size_suggestion(cls, msize1, msize2, psize1, psize2):
+    def get_size_suggestion(
+        cls,
+        msize1: int,
+        msize2: int,
+        psize1: int,
+        psize2: int
+    ) -> typing.Tuple[int, int]:
+        """
+        Get `dpool_size` suggestion for a given shape.
+
+        Returns the nearest legal `dpool_size` for the given combination of
+        `(psize1, psize2)`.
+
+        :param msize1: size of the left text.
+        :param msize2: size of the right text.
+        :param psize1: base size of the pool.
+        :param psize2: base size of the pool.
+        :return:
+        """
         stride1 = msize1 // psize1
         stride2 = msize2 // psize2
         suggestion1 = msize1 // stride1

From 8d4dffd879eec26ad8098039b1aa84d149cd3799 Mon Sep 17 00:00:00 2001
From: bo <kingbolanda@live.com>
Date: Tue, 23 Jul 2019 16:31:39 +0200
Subject: [PATCH 085/100] replace regex with string.translate

---
 matchzoo/preprocessors/units/punc_removal.py | 8 +++-----
 1 file changed, 3 insertions(+), 5 deletions(-)

diff --git a/matchzoo/preprocessors/units/punc_removal.py b/matchzoo/preprocessors/units/punc_removal.py
index 302a4702..af55d582 100644
--- a/matchzoo/preprocessors/units/punc_removal.py
+++ b/matchzoo/preprocessors/units/punc_removal.py
@@ -1,4 +1,4 @@
-import re
+import string
 
 from .unit import Unit
 
@@ -6,8 +6,6 @@
 class PuncRemoval(Unit):
     """Process unit for remove punctuations."""
 
-    _MATCH_PUNC = re.compile(r'[^\w\s]')
-
     def transform(self, input_: list) -> list:
         """
         Remove punctuations from list of tokens.
@@ -16,5 +14,5 @@ def transform(self, input_: list) -> list:
 
         :return rv: tokens  without punctuation.
         """
-        return [token for token in input_ if
-                not self._MATCH_PUNC.search(token)]
+        table = str.maketrans({key: None for key in string.punctuation})
+        return [item.translate(table) for item in input_]

From b08390c30351e164928ad3d417642e4940d44dd0 Mon Sep 17 00:00:00 2001
From: bo <kingbolanda@live.com>
Date: Tue, 23 Jul 2019 16:31:59 +0200
Subject: [PATCH 086/100] update doc test for punc remove unit

---
 matchzoo/preprocessors/basic_preprocessor.py | 2 +-
 matchzoo/preprocessors/diin_preprocessor.py  | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/matchzoo/preprocessors/basic_preprocessor.py b/matchzoo/preprocessors/basic_preprocessor.py
index d14af9f2..0fd82d37 100644
--- a/matchzoo/preprocessors/basic_preprocessor.py
+++ b/matchzoo/preprocessors/basic_preprocessor.py
@@ -44,7 +44,7 @@ class BasicPreprocessor(BasePreprocessor):
         >>> preprocessor.context['input_shapes']
         [(10,), (20,)]
         >>> preprocessor.context['vocab_size']
-        226
+        228
         >>> processed_train_data = preprocessor.transform(train_data,
         ...                                               verbose=0)
         >>> type(processed_train_data)
diff --git a/matchzoo/preprocessors/diin_preprocessor.py b/matchzoo/preprocessors/diin_preprocessor.py
index 4985e3a2..7d64ed16 100644
--- a/matchzoo/preprocessors/diin_preprocessor.py
+++ b/matchzoo/preprocessors/diin_preprocessor.py
@@ -42,7 +42,7 @@ def __init__(self,
             >>> diin_preprocessor.context['input_shapes']
             [(5,), (5,), (5, 3), (5, 3), (5,), (5,)]
             >>> diin_preprocessor.context['vocab_size']
-            859
+            893
             >>> train_data_processed = diin_preprocessor.transform(
             ...     train_data, verbose=0)
             >>> type(train_data_processed)

From 000820326910e70e4076258d7527e1c3f5f9996b Mon Sep 17 00:00:00 2001
From: bo <kingbolanda@live.com>
Date: Tue, 23 Jul 2019 16:44:03 +0200
Subject: [PATCH 087/100] resolve ci for pandas 0.2.5 conflict with tqdm

---
 requirements.txt | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/requirements.txt b/requirements.txt
index 7582150e..772b9465 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -3,10 +3,10 @@ tabulate >= 0.8.2
 tensorflow >= 1.8.0
 nltk >= 3.2.3
 numpy >= 1.14
-tqdm >= 4.19.4
+tqdm >= 4.23.4
 dill >= 0.2.7.1
 hyperopt >= 0.1.1
-pandas >= 0.23.1
+pandas == 0.24.2
 networkx >= 2.1
 h5py >= 2.8.0
 coverage >= 4.3.4

From 4a39fbe345ae69e76994d67b89fd96a315f1d0ee Mon Sep 17 00:00:00 2001
From: jibrilfrej <jibrilfrej@yahoo.fr>
Date: Wed, 7 Aug 2019 12:23:46 +0200
Subject: [PATCH 088/100] Update rank_cross_entropy_loss.py

---
 matchzoo/losses/rank_cross_entropy_loss.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/matchzoo/losses/rank_cross_entropy_loss.py b/matchzoo/losses/rank_cross_entropy_loss.py
index 2be64023..9bf56f43 100644
--- a/matchzoo/losses/rank_cross_entropy_loss.py
+++ b/matchzoo/losses/rank_cross_entropy_loss.py
@@ -48,7 +48,7 @@ def __call__(self, y_true: np.array, y_pred: np.array) -> np.array:
             labels.append(neg_labels)
         logits = K.concatenate(logits, axis=-1)
         labels = K.concatenate(labels, axis=-1)
-        return -K.mean(K.sum(labels * K.log(K.softmax(logits)), axis=-1))
+        return -K.mean(K.sum(labels * K.log(K.softmax(logits) + np.finfo(float).eps), axis=-1))
 
     @property
     def num_neg(self):

From 2bfbc89449e05c7b0a7473707e053b7faae7b1c1 Mon Sep 17 00:00:00 2001
From: Crystina <xzhangbx@ust.hk>
Date: Sun, 11 Aug 2019 21:25:34 +0800
Subject: [PATCH 089/100] Feature/esim quora tutorial (#766)

* refactor cdssm tutorial (#694)

* refactor dssm tutorial (#705)

* refactor cdssm tutorial for branch master (#708)

* fix cron CI

* add esim tutorial for quora dataset

* add acc callback to categorical task

* undo the requirement changes
---
 tutorials/quora/esim.ipynb | 675 +++++++++++++++++++++++++++++++++++++
 1 file changed, 675 insertions(+)
 create mode 100644 tutorials/quora/esim.ipynb

diff --git a/tutorials/quora/esim.ipynb b/tutorials/quora/esim.ipynb
new file mode 100644
index 00000000..4bdf927a
--- /dev/null
+++ b/tutorials/quora/esim.ipynb
@@ -0,0 +1,675 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Using TensorFlow backend.\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "matchzoo version 2.1.0\n",
+      "\n",
+      "data loading ...\n",
+      "data loaded as `train_pack_raw` `dev_pack_raw` `test_pack_raw`\n",
+      "`ranking_task` initialized with metrics [normalized_discounted_cumulative_gain@3(0.0), normalized_discounted_cumulative_gain@5(0.0), mean_average_precision(0.0)]\n",
+      "loading embedding ...\n",
+      "embedding loaded as `glove_embedding`\n"
+     ]
+    }
+   ],
+   "source": [
+    "%run ./tutorials/wikiqa/init.ipynb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "from keras.optimizers import Adam\n",
+    "from keras.utils import to_categorical\n",
+    "\n",
+    "import matchzoo as mz\n",
+    "from matchzoo.contrib.models.esim import ESIM"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_filtered_data(preprocessor, data_type):\n",
+    "    assert ( data_type in ['train', 'dev', 'test'])\n",
+    "    data_pack = mz.datasets.wiki_qa.load_data(data_type, task='ranking')\n",
+    "\n",
+    "    if data_type == 'train':\n",
+    "        X, Y = preprocessor.fit_transform(data_pack).unpack()\n",
+    "    else:\n",
+    "        X, Y = preprocessor.transform(data_pack).unpack()\n",
+    "\n",
+    "    new_idx = []\n",
+    "    for i in range(Y.shape[0]):\n",
+    "        if X[\"length_left\"][i] == 0 or X[\"length_right\"][i] == 0:\n",
+    "            continue\n",
+    "        new_idx.append(i)\n",
+    "    new_idx = np.array(new_idx)\n",
+    "    print(\"Removed empty data. Found \", (Y.shape[0] - new_idx.shape[0]))\n",
+    "\n",
+    "    for k in X.keys():\n",
+    "        X[k] = X[k][new_idx]\n",
+    "    Y = Y[new_idx]\n",
+    "\n",
+    "    pos_idx = (Y == 1)[:, 0]\n",
+    "    pos_qid = X[\"id_left\"][pos_idx]\n",
+    "    keep_idx_bool = np.array([ qid in pos_qid for qid in X[\"id_left\"]])\n",
+    "    keep_idx = np.arange(keep_idx_bool.shape[0])\n",
+    "    keep_idx = keep_idx[keep_idx_bool]\n",
+    "    print(\"Removed questions with no pos label. Found \", (keep_idx_bool == 0).sum())\n",
+    "\n",
+    "    print(\"shuffling...\")\n",
+    "    np.random.shuffle(keep_idx)\n",
+    "    for k in X.keys():\n",
+    "        X[k] = X[k][keep_idx]\n",
+    "    Y = Y[keep_idx]\n",
+    "\n",
+    "    return X, Y, preprocessor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "fixed_length_left = 10\n",
+    "fixed_length_right = 40\n",
+    "batch_size = 32\n",
+    "epochs = 5"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 10798.93it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:02<00:00, 8019.65it/s]\n",
+      "Processing text_right with append: 100%|██████████| 18841/18841 [00:00<00:00, 1415354.12it/s]\n",
+      "Building FrequencyFilter from a datapack.: 100%|██████████| 18841/18841 [00:00<00:00, 226166.63it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 233892.08it/s]\n",
+      "Processing text_left with extend: 100%|██████████| 2118/2118 [00:00<00:00, 782897.32it/s]\n",
+      "Processing text_right with extend: 100%|██████████| 18841/18841 [00:00<00:00, 1175423.27it/s]\n",
+      "Building Vocabulary from a datapack.: 100%|██████████| 358408/358408 [00:00<00:00, 4845654.07it/s]\n",
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2118/2118 [00:00<00:00, 15108.05it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 18841/18841 [00:02<00:00, 8129.15it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 222548.25it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 324738.11it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 122413.67it/s]\n",
+      "Processing length_left with len: 100%|██████████| 2118/2118 [00:00<00:00, 821484.73it/s]\n",
+      "Processing length_right with len: 100%|██████████| 18841/18841 [00:00<00:00, 1319786.92it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 2118/2118 [00:00<00:00, 200871.36it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 18841/18841 [00:00<00:00, 180842.83it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed empty data. Found  91\n",
+      "Removed questions with no pos label. Found  11642\n",
+      "shuffling...\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "Processing text_left with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 296/296 [00:00<00:00, 15853.43it/s]\n",
+      "Processing text_right with chain_transform of Tokenize => Lowercase => PuncRemoval: 100%|██████████| 2708/2708 [00:00<00:00, 8318.22it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 232964.32it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 296/296 [00:00<00:00, 200892.23it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 231808.96it/s]\n",
+      "Processing length_left with len: 100%|██████████| 296/296 [00:00<00:00, 562279.88it/s]\n",
+      "Processing length_right with len: 100%|██████████| 2708/2708 [00:00<00:00, 1159470.73it/s]\n",
+      "Processing text_left with transform: 100%|██████████| 296/296 [00:00<00:00, 183357.55it/s]\n",
+      "Processing text_right with transform: 100%|██████████| 2708/2708 [00:00<00:00, 178815.40it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Removed empty data. Found  8\n",
+      "Removed questions with no pos label. Found  1595\n",
+      "shuffling...\n"
+     ]
+    }
+   ],
+   "source": [
+    "# prepare data\n",
+    "preprocessor = mz.preprocessors.BasicPreprocessor(fixed_length_left=fixed_length_left,\n",
+    "                                                  fixed_length_right=fixed_length_right,\n",
+    "                                                  remove_stop_words=False,\n",
+    "                                                  filter_low_freq=10)\n",
+    "\n",
+    "train_X, train_Y, preprocessor = load_filtered_data(preprocessor, 'train')\n",
+    "val_X, val_Y, _ = load_filtered_data(preprocessor, 'dev')\n",
+    "pred_X, pred_Y = val_X, val_Y\n",
+    "# pred_X, pred_Y, _ = load_filtered_data(preprocessor, 'test') # no prediction label for quora dataset\n",
+    "\n",
+    "embedding_matrix = glove_embedding.build_matrix(preprocessor.context['vocab_unit'].state['term_index'], initializer=lambda: 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "text_left (InputLayer)          (None, 10)           0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "text_right (InputLayer)         (None, 40)           0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "embedding (Embedding)           multiple             1930500     text_left[0][0]                  \n",
+      "                                                                 text_right[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_1 (Dropout)             multiple             0           embedding[0][0]                  \n",
+      "                                                                 embedding[1][0]                  \n",
+      "                                                                 dense_1[0][0]                    \n",
+      "                                                                 dense_1[1][0]                    \n",
+      "                                                                 dense_2[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_1 (Lambda)               multiple             0           text_left[0][0]                  \n",
+      "                                                                 text_right[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "bidirectional_1 (Bidirectional) multiple             1442400     dropout_1[0][0]                  \n",
+      "                                                                 dropout_1[1][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_2 (Lambda)               (None, 10, 1)        0           lambda_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_3 (Lambda)               (None, 40, 1)        0           lambda_1[1][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_1 (Multiply)           (None, 10, 600)      0           bidirectional_1[0][0]            \n",
+      "                                                                 lambda_2[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_2 (Multiply)           (None, 40, 600)      0           bidirectional_1[1][0]            \n",
+      "                                                                 lambda_3[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_4 (Lambda)               (None, 10, 1)        0           lambda_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_5 (Lambda)               (None, 1, 40)        0           lambda_1[1][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "dot_1 (Dot)                     (None, 10, 40)       0           multiply_1[0][0]                 \n",
+      "                                                                 multiply_2[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_3 (Multiply)           (None, 10, 40)       0           lambda_4[0][0]                   \n",
+      "                                                                 lambda_5[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "permute_1 (Permute)             (None, 40, 10)       0           dot_1[0][0]                      \n",
+      "                                                                 multiply_3[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "atten_mask (Lambda)             multiple             0           dot_1[0][0]                      \n",
+      "                                                                 multiply_3[0][0]                 \n",
+      "                                                                 permute_1[0][0]                  \n",
+      "                                                                 permute_1[1][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "softmax_1 (Softmax)             multiple             0           atten_mask[0][0]                 \n",
+      "                                                                 atten_mask[1][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "dot_2 (Dot)                     (None, 10, 600)      0           softmax_1[0][0]                  \n",
+      "                                                                 multiply_2[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "dot_3 (Dot)                     (None, 40, 600)      0           softmax_1[1][0]                  \n",
+      "                                                                 multiply_1[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "subtract_1 (Subtract)           (None, 10, 600)      0           multiply_1[0][0]                 \n",
+      "                                                                 dot_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_4 (Multiply)           (None, 10, 600)      0           multiply_1[0][0]                 \n",
+      "                                                                 dot_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "subtract_2 (Subtract)           (None, 40, 600)      0           multiply_2[0][0]                 \n",
+      "                                                                 dot_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_5 (Multiply)           (None, 40, 600)      0           multiply_2[0][0]                 \n",
+      "                                                                 dot_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_1 (Concatenate)     (None, 10, 2400)     0           multiply_1[0][0]                 \n",
+      "                                                                 dot_2[0][0]                      \n",
+      "                                                                 subtract_1[0][0]                 \n",
+      "                                                                 multiply_4[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_2 (Concatenate)     (None, 40, 2400)     0           multiply_2[0][0]                 \n",
+      "                                                                 dot_3[0][0]                      \n",
+      "                                                                 subtract_2[0][0]                 \n",
+      "                                                                 multiply_5[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_1 (Dense)                 multiple             720300      concatenate_1[0][0]              \n",
+      "                                                                 concatenate_2[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "bidirectional_2 (Bidirectional) multiple             1442400     dropout_1[2][0]                  \n",
+      "                                                                 dropout_1[3][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_6 (Lambda)               (None, 10, 1)        0           lambda_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_8 (Lambda)               (None, 10, 1)        0           lambda_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_10 (Lambda)              (None, 40, 1)        0           lambda_1[1][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_12 (Lambda)              (None, 40, 1)        0           lambda_1[1][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_6 (Multiply)           (None, 10, 600)      0           bidirectional_2[0][0]            \n",
+      "                                                                 lambda_6[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_7 (Multiply)           (None, 10, 600)      0           bidirectional_2[0][0]            \n",
+      "                                                                 lambda_8[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_8 (Multiply)           (None, 40, 600)      0           bidirectional_2[1][0]            \n",
+      "                                                                 lambda_10[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_9 (Multiply)           (None, 40, 600)      0           bidirectional_2[1][0]            \n",
+      "                                                                 lambda_12[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_7 (Lambda)               (None, 600)          0           multiply_6[0][0]                 \n",
+      "                                                                 lambda_6[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_9 (Lambda)               (None, 600)          0           multiply_7[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_11 (Lambda)              (None, 600)          0           multiply_8[0][0]                 \n",
+      "                                                                 lambda_10[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_13 (Lambda)              (None, 600)          0           multiply_9[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_3 (Concatenate)     (None, 1200)         0           lambda_7[0][0]                   \n",
+      "                                                                 lambda_9[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_4 (Concatenate)     (None, 1200)         0           lambda_11[0][0]                  \n",
+      "                                                                 lambda_13[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_5 (Concatenate)     (None, 2400)         0           concatenate_3[0][0]              \n",
+      "                                                                 concatenate_4[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_2 (Dense)                 (None, 300)          720300      concatenate_5[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_3 (Dense)                 (None, 1)            301         dropout_1[4][0]                  \n",
+      "==================================================================================================\n",
+      "Total params: 6,256,201\n",
+      "Trainable params: 4,325,701\n",
+      "Non-trainable params: 1,930,500\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "model = ESIM()\n",
+    "model.params['task'] = mz.tasks.Ranking()\n",
+    "model.params['mask_value'] = 0\n",
+    "model.params['input_shapes'] = [[fixed_length_left, ],\n",
+    "                                [fixed_length_right, ]]\n",
+    "model.params['lstm_dim'] = 300\n",
+    "model.params['embedding_input_dim'] = preprocessor.context['vocab_size']\n",
+    "model.params['embedding_output_dim'] = 300\n",
+    "model.params['embedding_trainable'] = False\n",
+    "model.params['dropout_rate'] = 0.5\n",
+    "\n",
+    "model.params['mlp_num_units'] = 300\n",
+    "model.params['mlp_num_layers'] = 0\n",
+    "model.params['mlp_num_fan_out'] = 300\n",
+    "model.params['mlp_activation_func'] = 'tanh'\n",
+    "model.params['optimizer'] = Adam(lr=4e-4)\n",
+    "\n",
+    "model.guess_and_fill_missing_params()\n",
+    "model.build()\n",
+    "\n",
+    "model.compile()\n",
+    "model.backend.summary() # not visualize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 8627 samples, validate on 1130 samples\n",
+      "Epoch 1/5\n",
+      "8627/8627 [==============================] - 48s 6ms/step - loss: 0.1073 - val_loss: 0.0984\n",
+      "Validation: mean_average_precision(0.0): 0.6222655981584554\n",
+      "Epoch 2/5\n",
+      "8627/8627 [==============================] - 44s 5ms/step - loss: 0.0994 - val_loss: 0.0974\n",
+      "Validation: mean_average_precision(0.0): 0.640342571890191\n",
+      "Epoch 3/5\n",
+      "8627/8627 [==============================] - 44s 5ms/step - loss: 0.0944 - val_loss: 0.0981\n",
+      "Validation: mean_average_precision(0.0): 0.633281742507933\n",
+      "Epoch 4/5\n",
+      "8627/8627 [==============================] - 44s 5ms/step - loss: 0.0915 - val_loss: 0.0898\n",
+      "Validation: mean_average_precision(0.0): 0.6479046351993808\n",
+      "Epoch 5/5\n",
+      "8627/8627 [==============================] - 44s 5ms/step - loss: 0.0893 - val_loss: 0.0931\n",
+      "Validation: mean_average_precision(0.0): 0.6506805763854636\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run as classification task\n",
+    "model.load_embedding_matrix(embedding_matrix)\n",
+    "evaluate = mz.callbacks.EvaluateAllMetrics(model,\n",
+    "                                           x=pred_X,\n",
+    "                                           y=pred_Y,\n",
+    "                                           once_every=1,\n",
+    "                                           batch_size=len(pred_Y))\n",
+    "\n",
+    "history = model.fit(x = [train_X['text_left'],\n",
+    "                         train_X['text_right']],\n",
+    "                    y = train_Y,\n",
+    "                    validation_data = (val_X, val_Y),\n",
+    "                    batch_size = batch_size,\n",
+    "                    epochs = epochs,\n",
+    "                    callbacks=[evaluate]\n",
+    "                    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "__________________________________________________________________________________________________\n",
+      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
+      "==================================================================================================\n",
+      "text_left (InputLayer)          (None, 10)           0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "text_right (InputLayer)         (None, 40)           0                                            \n",
+      "__________________________________________________________________________________________________\n",
+      "embedding (Embedding)           multiple             1930500     text_left[0][0]                  \n",
+      "                                                                 text_right[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "dropout_1 (Dropout)             multiple             0           embedding[0][0]                  \n",
+      "                                                                 embedding[1][0]                  \n",
+      "                                                                 dense_1[0][0]                    \n",
+      "                                                                 dense_1[1][0]                    \n",
+      "                                                                 dense_2[0][0]                    \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_1 (Lambda)               multiple             0           text_left[0][0]                  \n",
+      "                                                                 text_right[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "bidirectional_1 (Bidirectional) multiple             1442400     dropout_1[0][0]                  \n",
+      "                                                                 dropout_1[1][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_2 (Lambda)               (None, 10, 1)        0           lambda_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_3 (Lambda)               (None, 40, 1)        0           lambda_1[1][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_1 (Multiply)           (None, 10, 600)      0           bidirectional_1[0][0]            \n",
+      "                                                                 lambda_2[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_2 (Multiply)           (None, 40, 600)      0           bidirectional_1[1][0]            \n",
+      "                                                                 lambda_3[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_4 (Lambda)               (None, 10, 1)        0           lambda_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_5 (Lambda)               (None, 1, 40)        0           lambda_1[1][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "dot_1 (Dot)                     (None, 10, 40)       0           multiply_1[0][0]                 \n",
+      "                                                                 multiply_2[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_3 (Multiply)           (None, 10, 40)       0           lambda_4[0][0]                   \n",
+      "                                                                 lambda_5[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "permute_1 (Permute)             (None, 40, 10)       0           dot_1[0][0]                      \n",
+      "                                                                 multiply_3[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "atten_mask (Lambda)             multiple             0           dot_1[0][0]                      \n",
+      "                                                                 multiply_3[0][0]                 \n",
+      "                                                                 permute_1[0][0]                  \n",
+      "                                                                 permute_1[1][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "softmax_1 (Softmax)             multiple             0           atten_mask[0][0]                 \n",
+      "                                                                 atten_mask[1][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "dot_2 (Dot)                     (None, 10, 600)      0           softmax_1[0][0]                  \n",
+      "                                                                 multiply_2[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "dot_3 (Dot)                     (None, 40, 600)      0           softmax_1[1][0]                  \n",
+      "                                                                 multiply_1[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "subtract_1 (Subtract)           (None, 10, 600)      0           multiply_1[0][0]                 \n",
+      "                                                                 dot_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_4 (Multiply)           (None, 10, 600)      0           multiply_1[0][0]                 \n",
+      "                                                                 dot_2[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "subtract_2 (Subtract)           (None, 40, 600)      0           multiply_2[0][0]                 \n",
+      "                                                                 dot_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_5 (Multiply)           (None, 40, 600)      0           multiply_2[0][0]                 \n",
+      "                                                                 dot_3[0][0]                      \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_1 (Concatenate)     (None, 10, 2400)     0           multiply_1[0][0]                 \n",
+      "                                                                 dot_2[0][0]                      \n",
+      "                                                                 subtract_1[0][0]                 \n",
+      "                                                                 multiply_4[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_2 (Concatenate)     (None, 40, 2400)     0           multiply_2[0][0]                 \n",
+      "                                                                 dot_3[0][0]                      \n",
+      "                                                                 subtract_2[0][0]                 \n",
+      "                                                                 multiply_5[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_1 (Dense)                 multiple             720300      concatenate_1[0][0]              \n",
+      "                                                                 concatenate_2[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "bidirectional_2 (Bidirectional) multiple             1442400     dropout_1[2][0]                  \n",
+      "                                                                 dropout_1[3][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_6 (Lambda)               (None, 10, 1)        0           lambda_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_8 (Lambda)               (None, 10, 1)        0           lambda_1[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_10 (Lambda)              (None, 40, 1)        0           lambda_1[1][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_12 (Lambda)              (None, 40, 1)        0           lambda_1[1][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_6 (Multiply)           (None, 10, 600)      0           bidirectional_2[0][0]            \n",
+      "                                                                 lambda_6[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_7 (Multiply)           (None, 10, 600)      0           bidirectional_2[0][0]            \n",
+      "                                                                 lambda_8[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_8 (Multiply)           (None, 40, 600)      0           bidirectional_2[1][0]            \n",
+      "                                                                 lambda_10[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "multiply_9 (Multiply)           (None, 40, 600)      0           bidirectional_2[1][0]            \n",
+      "                                                                 lambda_12[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_7 (Lambda)               (None, 600)          0           multiply_6[0][0]                 \n",
+      "                                                                 lambda_6[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_9 (Lambda)               (None, 600)          0           multiply_7[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_11 (Lambda)              (None, 600)          0           multiply_8[0][0]                 \n",
+      "                                                                 lambda_10[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "lambda_13 (Lambda)              (None, 600)          0           multiply_9[0][0]                 \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_3 (Concatenate)     (None, 1200)         0           lambda_7[0][0]                   \n",
+      "                                                                 lambda_9[0][0]                   \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_4 (Concatenate)     (None, 1200)         0           lambda_11[0][0]                  \n",
+      "                                                                 lambda_13[0][0]                  \n",
+      "__________________________________________________________________________________________________\n",
+      "concatenate_5 (Concatenate)     (None, 2400)         0           concatenate_3[0][0]              \n",
+      "                                                                 concatenate_4[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_2 (Dense)                 (None, 300)          720300      concatenate_5[0][0]              \n",
+      "__________________________________________________________________________________________________\n",
+      "dense_3 (Dense)                 (None, 2)            602         dropout_1[4][0]                  \n",
+      "==================================================================================================\n",
+      "Total params: 6,256,502\n",
+      "Trainable params: 4,326,002\n",
+      "Non-trainable params: 1,930,500\n",
+      "__________________________________________________________________________________________________\n"
+     ]
+    }
+   ],
+   "source": [
+    "# run as classification task\n",
+    "classification_task = mz.tasks.Classification(num_classes=2)\n",
+    "classification_task.metrics = 'acc'\n",
+    "\n",
+    "model = ESIM()\n",
+    "model.params['task'] = classification_task\n",
+    "model.params['mask_value'] = 0\n",
+    "model.params['input_shapes'] = [[fixed_length_left, ],\n",
+    "                                [fixed_length_right, ]]\n",
+    "model.params['lstm_dim'] = 300\n",
+    "model.params['embedding_input_dim'] = preprocessor.context['vocab_size']\n",
+    "model.params['embedding_output_dim'] = 300\n",
+    "model.params['embedding_trainable'] = False\n",
+    "model.params['dropout_rate'] = 0.5\n",
+    "\n",
+    "model.params['mlp_num_units'] = 300\n",
+    "model.params['mlp_num_layers'] = 0\n",
+    "model.params['mlp_num_fan_out'] = 300\n",
+    "model.params['mlp_activation_func'] = 'tanh'\n",
+    "model.params['optimizer'] = Adam(lr=4e-4)\n",
+    "\n",
+    "model.guess_and_fill_missing_params()\n",
+    "model.build()\n",
+    "\n",
+    "model.compile()\n",
+    "model.backend.summary() # not visualize"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train on 8627 samples, validate on 1130 samples\n",
+      "Epoch 1/5\n",
+      "8627/8627 [==============================] - 48s 6ms/step - loss: 0.3607 - val_loss: 0.3330\n",
+      "Validation: categorical_accuracy: 1.0\n",
+      "Epoch 2/5\n",
+      "8627/8627 [==============================] - 43s 5ms/step - loss: 0.3273 - val_loss: 0.3490\n",
+      "Validation: categorical_accuracy: 0.9451327323913574\n",
+      "Epoch 3/5\n",
+      "8627/8627 [==============================] - 44s 5ms/step - loss: 0.3096 - val_loss: 0.3498\n",
+      "Validation: categorical_accuracy: 0.9938052892684937\n",
+      "Epoch 4/5\n",
+      "8627/8627 [==============================] - 44s 5ms/step - loss: 0.2970 - val_loss: 0.3170\n",
+      "Validation: categorical_accuracy: 0.969911515712738\n",
+      "Epoch 5/5\n",
+      "8627/8627 [==============================] - 44s 5ms/step - loss: 0.2787 - val_loss: 0.3543\n",
+      "Validation: categorical_accuracy: 0.8778761029243469\n"
+     ]
+    }
+   ],
+   "source": [
+    "evaluate = mz.callbacks.EvaluateAllMetrics(model,\n",
+    "                                           x=pred_X,\n",
+    "                                           y=pred_Y,\n",
+    "                                           once_every=1,\n",
+    "                                           batch_size=len(pred_Y))\n",
+    "\n",
+    "train_Y = to_categorical(train_Y)\n",
+    "val_Y = to_categorical(val_Y)\n",
+    "\n",
+    "model.load_embedding_matrix(embedding_matrix)\n",
+    "history = model.fit(x = [train_X['text_left'],\n",
+    "                         train_X['text_right']],\n",
+    "                    y = train_Y,\n",
+    "                    validation_data = (val_X, val_Y),\n",
+    "                    batch_size = batch_size,\n",
+    "                    epochs = epochs,\n",
+    "                    callbacks=[evaluate]\n",
+    "                    )"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "{'categorical_accuracy': 0.8920354}"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.evaluate(val_X, val_Y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "mz_play",
+   "language": "python",
+   "name": "mz_play"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.6.8"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

From acd533f89b64350667b70ff0f1fd4aacc69939f4 Mon Sep 17 00:00:00 2001
From: faneshion <fanyixing111@hotmail.com>
Date: Mon, 12 Aug 2019 00:23:21 +0800
Subject: [PATCH 090/100] update README

---
 README.md | 25 ++++++++++++++++++-------
 1 file changed, 18 insertions(+), 7 deletions(-)

diff --git a/README.md b/README.md
index 97ef5ad0..0bc891d1 100644
--- a/README.md
+++ b/README.md
@@ -166,18 +166,29 @@ python setup.py install
 If you use MatchZoo in your research, please use the following BibTex entry.
 
 ```
-@article{fan2017matchzoo,
-  title={Matchzoo: A toolkit for deep text matching},
-  author={Fan, Yixing and Pang, Liang and Hou, JianPeng and Guo, Jiafeng and Lan, Yanyan and Cheng, Xueqi},
-  journal={arXiv preprint arXiv:1707.07270},
-  year={2017}
-}
+@inproceedings{Guo:2019:MLP:3331184.3331403,
+ author = {Guo, Jiafeng and Fan, Yixing and Ji, Xiang and Cheng, Xueqi},
+ title = {MatchZoo: A Learning, Practicing, and Developing System for Neural Text Matching},
+ booktitle = {Proceedings of the 42Nd International ACM SIGIR Conference on Research and Development in Information Retrieval},
+ series = {SIGIR'19},
+ year = {2019},
+ isbn = {978-1-4503-6172-9},
+ location = {Paris, France},
+ pages = {1297--1300},
+ numpages = {4},
+ url = {http://doi.acm.org/10.1145/3331184.3331403},
+ doi = {10.1145/3331184.3331403},
+ acmid = {3331403},
+ publisher = {ACM},
+ address = {New York, NY, USA},
+ keywords = {matchzoo, neural network, text matching},
+} 
 ```
 
 
 ## Development Team
 
-​ ​ ​ ​ ​
+ ​ ​ ​ ​
 <table border="0">
   <tbody>
     <tr align="center">

From 7e1e7e300ee3c2da81151a48ccaa80557c72d73a Mon Sep 17 00:00:00 2001
From: "J.Hou" <houjianpeng@software.ict.ac.cn>
Date: Mon, 12 Aug 2019 10:20:50 +0800
Subject: [PATCH 091/100] Remove ":" in the title

---
 README.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 0bc891d1..14900a0e 100644
--- a/README.md
+++ b/README.md
@@ -134,7 +134,7 @@ python setup.py install
 ```
 
 
-## Models:
+## Models
 
 1. [DRMM](https://github.com/NTMC-Community/MatchZoo/tree/master/matchzoo/models/drmm.py): this model is an implementation of <a href="http://www.bigdatalab.ac.cn/~gjf/papers/2016/CIKM2016a_guo.pdf">A Deep Relevance Matching Model for Ad-hoc Retrieval</a>.
 

From 60c0bb6d421e5db5c0e96c0fa1adae8e629e3c9e Mon Sep 17 00:00:00 2001
From: zhen <ZizhenWang@users.noreply.github.com>
Date: Sun, 1 Sep 2019 22:06:49 +0800
Subject: [PATCH 092/100] #714 add SemEval 16 dataset (#721)

* #714 add SemEval 16 dataset

* #714 add SemEval 16 dataset

* #714 rename SemEval 16 to cqa_ql_16 dataset, and add test files

* fix doctest error

* fix style error in `load_data`

* move `xml` to standard package scope
---
 matchzoo/datasets/__init__.py            |   1 +
 matchzoo/datasets/cqa_ql_16/__init__.py  |   1 +
 matchzoo/datasets/cqa_ql_16/load_data.py | 203 +++++++++++++++++++++++
 tests/unit_test/test_datasets.py         |  35 ++++
 4 files changed, 240 insertions(+)
 create mode 100644 matchzoo/datasets/cqa_ql_16/__init__.py
 create mode 100644 matchzoo/datasets/cqa_ql_16/load_data.py

diff --git a/matchzoo/datasets/__init__.py b/matchzoo/datasets/__init__.py
index 45f592ff..44eb4669 100644
--- a/matchzoo/datasets/__init__.py
+++ b/matchzoo/datasets/__init__.py
@@ -3,6 +3,7 @@
 from . import embeddings
 from . import snli
 from . import quora_qp
+from . import cqa_ql_16
 from pathlib import Path
 
 
diff --git a/matchzoo/datasets/cqa_ql_16/__init__.py b/matchzoo/datasets/cqa_ql_16/__init__.py
new file mode 100644
index 00000000..0394d77f
--- /dev/null
+++ b/matchzoo/datasets/cqa_ql_16/__init__.py
@@ -0,0 +1 @@
+from .load_data import load_data
\ No newline at end of file
diff --git a/matchzoo/datasets/cqa_ql_16/load_data.py b/matchzoo/datasets/cqa_ql_16/load_data.py
new file mode 100644
index 00000000..5b6b0a2e
--- /dev/null
+++ b/matchzoo/datasets/cqa_ql_16/load_data.py
@@ -0,0 +1,203 @@
+"""CQA-QL-16 data loader."""
+
+import xml
+import typing
+from pathlib import Path
+
+import keras
+import pandas as pd
+
+import matchzoo
+
+
+_train_dev_url = "http://alt.qcri.org/semeval2016/task3/data/uploads/" \
+                 "semeval2016-task3-cqa-ql-traindev-v3.2.zip"
+_test_url = "http://alt.qcri.org/semeval2016/task3/data/uploads/" \
+            "semeval2016_task3_test.zip"
+
+
+def load_data(
+    stage: str = 'train',
+    task: str = 'classification',
+    target_label: str = 'PerfectMatch',
+    return_classes: bool = False,
+    match_type: str = 'question',
+    mode: str = 'both',
+) -> typing.Union[matchzoo.DataPack, tuple]:
+    """
+    Load CQA-QL-16 data.
+
+    :param stage: One of `train`, `dev`, and `test`.
+        (default: `train`)
+    :param task: Could be one of `ranking`, `classification` or instance
+        of :class:`matchzoo.engine.BaseTask`. (default: `classification`)
+    :param target_label: If `ranking`, choose one of classification
+        label as the positive label. (default: `PerfectMatch`)
+    :param return_classes: `True` to return classes for classification
+        task, `False` otherwise.
+    :param match_type: Matching text types. One of `question`,
+        `answer`, and `external_answer`. (default: `question`)
+    :param mode: Train data use method. One of `part1`, `part2`,
+        and `both`. (default: `both`)
+
+    :return: A DataPack unless `task` is `classification` and `return_classes`
+        is `True`: a tuple of `(DataPack, classes)` in that case.
+    """
+    if stage not in ('train', 'dev', 'test'):
+        raise ValueError(f"{stage} is not a valid stage."
+                         f"Must be one of `train`, `dev`, and `test`.")
+
+    if match_type not in ('question', 'answer', 'external_answer'):
+        raise ValueError(f"{match_type} is not a valid method. Must be one of"
+                         f" `question`, `answer`, `external_answer`.")
+
+    if mode not in ('part1', 'part2', 'both'):
+        raise ValueError(f"{mode} is not a valid method."
+                         f"Must be one of `part1`, `part2`, `both`.")
+
+    data_root = _download_data(stage)
+    data_pack = _read_data(data_root, stage, match_type, mode)
+
+    if task == 'ranking':
+        if match_type in ('anwer', 'external_answer') and target_label not in [
+                'Good', 'PotentiallyUseful', 'Bad']:
+            raise ValueError(f"{target_label} is not a valid target label."
+                             f"Must be one of `Good`, `PotentiallyUseful`,"
+                             f" `Bad`.")
+        elif match_type == 'question' and target_label not in [
+                'PerfectMatch', 'Relevant', 'Irrelevant']:
+            raise ValueError(f"{target_label} is not a valid target label."
+                             f" Must be one of `PerfectMatch`, `Relevant`,"
+                             f" `Irrelevant`.")
+        binary = (data_pack.relation['label'] == target_label).astype(float)
+        data_pack.relation['label'] = binary
+        return data_pack
+    elif task == 'classification':
+        if match_type in ('answer', 'external_answer'):
+            classes = ['Good', 'PotentiallyUseful', 'Bad']
+        else:
+            classes = ['PerfectMatch', 'Relevant', 'Irrelevant']
+        label = data_pack.relation['label'].apply(classes.index)
+        data_pack.relation['label'] = label
+        data_pack.one_hot_encode_label(num_classes=3, inplace=True)
+        if return_classes:
+            return data_pack, classes
+        else:
+            return data_pack
+    else:
+        raise ValueError(f"{task} is not a valid task."
+                         f"Must be one of `Ranking` and `Classification`.")
+
+
+def _download_data(stage):
+    if stage in ['train', 'dev']:
+        return _download_train_dev_data()
+    else:
+        return _download_test_data()
+
+
+def _download_train_dev_data():
+    ref_path = keras.utils.data_utils.get_file(
+        'semeval_train', _train_dev_url, extract=True,
+        cache_dir=matchzoo.USER_DATA_DIR,
+        cache_subdir='semeval_train'
+    )
+    return Path(ref_path).parent.joinpath('v3.2')
+
+
+def _download_test_data():
+    ref_path = keras.utils.data_utils.get_file(
+        'semeval_test', _test_url, extract=True,
+        cache_dir=matchzoo.USER_DATA_DIR,
+        cache_subdir='semeval_test'
+    )
+    return Path(ref_path).parent.joinpath('SemEval2016_task3_test/English')
+
+
+def _read_data(path, stage, match_type, mode='both'):
+    if stage == 'train':
+        if mode == 'part1':
+            path = path.joinpath(
+                'train/SemEval2016-Task3-CQA-QL-train-part1.xml')
+            data = _load_data_by_type(path, match_type)
+        elif mode == 'part2':
+            path = path.joinpath(
+                'train/SemEval2016-Task3-CQA-QL-train-part2.xml')
+            data = _load_data_by_type(path, match_type)
+        else:
+            part1 = path.joinpath(
+                'train/SemEval2016-Task3-CQA-QL-train-part1.xml')
+            p1 = _load_data_by_type(part1, match_type)
+            part2 = path.joinpath(
+                'train/SemEval2016-Task3-CQA-QL-train-part1.xml')
+            p2 = _load_data_by_type(part2, match_type)
+            data = pd.concat([p1, p2], ignore_index=True)
+        return matchzoo.pack(data)
+    elif stage == 'dev':
+        path = path.joinpath('dev/SemEval2016-Task3-CQA-QL-dev.xml')
+        data = _load_data_by_type(path, match_type)
+        return matchzoo.pack(data)
+    else:
+        path = path.joinpath('SemEval2016-Task3-CQA-QL-test.xml')
+        data = _load_data_by_type(path, match_type)
+        return matchzoo.pack(data)
+
+
+def _load_data_by_type(path, match_type):
+    if match_type == 'question':
+        return _load_question(path)
+    elif match_type == 'answer':
+        return _load_answer(path)
+    else:
+        return _load_external_answer(path)
+
+
+def _load_question(path):
+    doc = xml.etree.ElementTree.parse(path)
+    dataset = []
+    for question in doc.iterfind('OrgQuestion'):
+        qid = question.attrib['ORGQ_ID']
+        query = question.findtext('OrgQBody')
+        rel_question = question.find('Thread').find('RelQuestion')
+        question = rel_question.findtext('RelQBody')
+        question_id = rel_question.attrib['RELQ_ID']
+        dataset.append([qid, question_id, query, question,
+                        rel_question.attrib['RELQ_RELEVANCE2ORGQ']])
+    df = pd.DataFrame(dataset, columns=[
+        'id_left', 'id_right', 'text_left', 'text_right', 'label'])
+    return df
+
+
+def _load_answer(path):
+    doc = xml.etree.ElementTree.parse(path)
+    dataset = []
+    for org_q in doc.iterfind('OrgQuestion'):
+        for thread in org_q.iterfind('Thread'):
+            ques = thread.find('RelQuestion')
+            qid = ques.attrib['RELQ_ID']
+            question = ques.findtext('RelQBody')
+            for comment in thread.iterfind('RelComment'):
+                aid = comment.attrib['RELC_ID']
+                answer = comment.findtext('RelCText')
+                dataset.append([qid, aid, question, answer,
+                                comment.attrib['RELC_RELEVANCE2RELQ']])
+    df = pd.DataFrame(dataset, columns=[
+        'id_left', 'id_right', 'text_left', 'text_right', 'label'])
+    return df
+
+
+def _load_external_answer(path):
+    doc = xml.etree.ElementTree.parse(path)
+    dataset = []
+    for question in doc.iterfind('OrgQuestion'):
+        qid = question.attrib['ORGQ_ID']
+        query = question.findtext('OrgQBody')
+        thread = question.find('Thread')
+        for comment in thread.iterfind('RelComment'):
+            answer = comment.findtext('RelCText')
+            aid = comment.attrib['RELC_ID']
+            dataset.append([qid, aid, query, answer,
+                            comment.attrib['RELC_RELEVANCE2ORGQ']])
+    df = pd.DataFrame(dataset, columns=[
+        'id_left', 'id_right', 'text_left', 'text_right', 'label'])
+    return df
diff --git a/tests/unit_test/test_datasets.py b/tests/unit_test/test_datasets.py
index 03b44cc4..f94fb5cd 100644
--- a/tests/unit_test/test_datasets.py
+++ b/tests/unit_test/test_datasets.py
@@ -83,3 +83,38 @@ def test_load_quora_qp():
     x, y = dev_data.unpack()
     assert y.shape == (40371, 1)
 
+
+@pytest.mark.cron
+def test_load_cqa_ql_16():
+    # test load question pairs
+    train_data = mz.datasets.cqa_ql_16.load_data(task='classification')
+    assert len(train_data) == 3998
+    dev_data, tag = mz.datasets.cqa_ql_16.load_data(
+        'dev',
+        task='classification',
+        return_classes=True)
+    assert tag == ['PerfectMatch', 'Relevant', 'Irrelevant']
+    assert len(dev_data) == 500
+    x, y = dev_data.unpack()
+    assert y.shape == (500, 3)
+    test_data = mz.datasets.cqa_ql_16.load_data('test')
+    assert len(test_data) == 700
+
+    # test load answer pairs
+    train_data = mz.datasets.cqa_ql_16.load_data(match_type='answer')
+    assert len(train_data) == 39980
+    test_data = mz.datasets.cqa_ql_16.load_data(stage='test', match_type='answer')
+    assert len(test_data) == 7000
+
+    # test load external answer pairs
+    train_data = mz.datasets.cqa_ql_16.load_data(match_type='external_answer')
+    assert len(train_data) == 39980
+
+    # test load rank data
+    train_data = mz.datasets.cqa_ql_16.load_data(task='ranking')
+    x, y = train_data.unpack()
+    assert y.shape == (3998, 1)
+
+    dev_data = mz.datasets.cqa_ql_16.load_data('dev', task='ranking', match_type='answer', target_label='Good')
+    x, y = dev_data.unpack()
+    assert y.shape == (5000, 1)

From 2096d88830e99719885c5754267f0c94e1362ded Mon Sep 17 00:00:00 2001
From: zhaohao <zhaohao.zeng93@gmail.com>
Date: Thu, 3 Oct 2019 20:25:00 +0900
Subject: [PATCH 093/100] support keras 2.3 and tensorflow 2.0

---
 .travis.yml                                   |  1 +
 .../contrib/layers/matching_tensor_layer.py   |  3 +-
 .../contrib/layers/multi_perspective_layer.py |  9 ++--
 matchzoo/contrib/layers/spatial_gru.py        | 50 +++++++++----------
 matchzoo/layers/dynamic_pooling_layer.py      | 27 +++++-----
 matchzoo/layers/matching_layer.py             |  9 ++--
 matchzoo/losses/rank_cross_entropy_loss.py    | 16 ++++--
 matchzoo/losses/rank_hinge_loss.py            | 16 ++++--
 matchzoo/models/conv_knrm.py                  | 11 ++--
 matchzoo/models/drmm.py                       |  2 +-
 matchzoo/models/drmmtks.py                    | 12 ++---
 matchzoo/models/duet.py                       |  8 +--
 matchzoo/models/knrm.py                       | 11 ++--
 matchzoo/models/mvlstm.py                     |  4 +-
 requirements.txt                              |  4 +-
 15 files changed, 102 insertions(+), 81 deletions(-)

diff --git a/.travis.yml b/.travis.yml
index aa960a88..e3911359 100644
--- a/.travis.yml
+++ b/.travis.yml
@@ -20,6 +20,7 @@ matrix:
       language: shell
 
 install:
+  - pip3 install -U pip
   - pip3 install -r requirements.txt
   - python3 -m nltk.downloader punkt
   - python3 -m nltk.downloader wordnet
diff --git a/matchzoo/contrib/layers/matching_tensor_layer.py b/matchzoo/contrib/layers/matching_tensor_layer.py
index 3c2aabb4..0578ed1c 100644
--- a/matchzoo/contrib/layers/matching_tensor_layer.py
+++ b/matchzoo/contrib/layers/matching_tensor_layer.py
@@ -2,6 +2,7 @@
 import typing
 
 import numpy as np
+import tensorflow as tf
 from keras import backend as K
 from keras.engine import Layer
 from keras.initializers import constant
@@ -105,7 +106,7 @@ def call(self, inputs: list, **kwargs) -> typing.Any:
         # d, e = embedding size
         # c = number of channels
         # output = [b, c, l, r]
-        output = K.tf.einsum(
+        output = tf.einsum(
             'bld,cde,bre->bclr',
             x1, self.interaction_matrix, x2
         )
diff --git a/matchzoo/contrib/layers/multi_perspective_layer.py b/matchzoo/contrib/layers/multi_perspective_layer.py
index f8dd3566..f9f1dcac 100644
--- a/matchzoo/contrib/layers/multi_perspective_layer.py
+++ b/matchzoo/contrib/layers/multi_perspective_layer.py
@@ -1,5 +1,6 @@
 """An implementation of MultiPerspectiveLayer for Bimpm model."""
 
+import tensorflow as tf
 from keras import backend as K
 from keras.engine import Layer
 
@@ -307,10 +308,10 @@ def collect_final_step_of_lstm(lstm_representation, lengths):
 
     batch_size = K.shape(lengths)[0]
     # shape (batch_size)
-    batch_nums = K.tf.range(0, limit=batch_size)
+    batch_nums = tf.range(0, limit=batch_size)
     # shape (batch_size, 2)
     indices = K.stack((batch_nums, lengths), axis=1)
-    result = K.tf.gather_nd(lstm_representation, indices,
+    result = tf.gather_nd(lstm_representation, indices,
                             name='last-forwar-lstm')
     # [batch_size, dim]
     return result
@@ -337,10 +338,10 @@ def collect_probs(probs, positions):
 
     # shape (batch_size, pair_size, 2)
     # Alert: to solve error message
-    positions = K.tf.to_int32(positions)
+    positions = tf.cast(positions, tf.int32)
     indices = K.stack([batch_nums, positions], axis=2)
 
-    pair_probs = K.tf.gather_nd(probs, indices)
+    pair_probs = tf.gather_nd(probs, indices)
     # pair_probs = tf.reshape(pair_probs, shape=[batch_size, pair_size])
     return pair_probs
 
diff --git a/matchzoo/contrib/layers/spatial_gru.py b/matchzoo/contrib/layers/spatial_gru.py
index 76f9df9f..c583c9d6 100644
--- a/matchzoo/contrib/layers/spatial_gru.py
+++ b/matchzoo/contrib/layers/spatial_gru.py
@@ -1,12 +1,12 @@
 """An implementation of Spatial GRU Layer."""
 import typing
-
+import tensorflow as tf
 from keras import backend as K
 from keras.engine import Layer
 from keras.layers import Permute
 from keras.layers import Reshape
-from keras.layers import activations
-from keras.layers import initializers
+from keras import activations
+from keras import initializers
 
 
 class SpatialGRU(Layer):
@@ -133,13 +133,13 @@ def softmax_by_row(self, z: typing.Any) -> tuple:
         for i in range(0, self._units):
             begin = [0, i, 0]
             # z_slice: [B, 1, 4]
-            z_slice = K.tf.slice(z_transform, begin, size)
+            z_slice = tf.slice(z_transform, begin, size)
             if i == 0:
-                z_s = K.tf.nn.softmax(z_slice)
+                z_s = tf.nn.softmax(z_slice)
             else:
-                z_s = K.tf.concat([z_s, K.tf.nn.softmax(z_slice)], 1)
+                z_s = tf.concat([z_s, tf.nn.softmax(z_slice)], 1)
         # zi, zl, zt, zd: [B, U]
-        zi, zl, zt, zd = K.tf.unstack(z_s, axis=2)
+        zi, zl, zt, zd = tf.unstack(z_s, axis=2)
         return zi, zl, zt, zd
 
     def calculate_recurrent_unit(
@@ -160,8 +160,8 @@ def calculate_recurrent_unit(
         :param h: Hidden state from last operation.
         """
         # Get index i, j
-        i = K.tf.floordiv(step, K.tf.constant(self._text2_maxlen))
-        j = K.tf.mod(step, K.tf.constant(self._text2_maxlen))
+        i = tf.math.floordiv(step, tf.constant(self._text2_maxlen))
+        j = tf.math.mod(step, tf.constant(self._text2_maxlen))
 
         # Get hidden state h_diag, h_top, h_left
         # h_diag, h_top, h_left = [B, U]
@@ -175,8 +175,8 @@ def calculate_recurrent_unit(
 
         # Concatenate h_top, h_left, h_diag, s_ij
         # q = [B, 3*U+C]
-        q = K.tf.concat([K.tf.concat([h_top, h_left], 1),
-                        K.tf.concat([h_diag, s_ij], 1)], 1)
+        q = tf.concat([tf.concat([h_top, h_left], 1),
+                        tf.concat([h_diag, s_ij], 1)], 1)
 
         # Calculate reset gate
         # r = [B, 3*U]
@@ -194,7 +194,7 @@ def calculate_recurrent_unit(
         # Get h_ij_
         # h_ij_ = [B, U]
         h_ij_l = self._time_distributed_dense(self._w_ij, s_ij, self._b_ij)
-        h_ij_r = K.dot(r * (K.tf.concat([h_left, h_top, h_diag], 1)), self._U)
+        h_ij_r = K.dot(r * (tf.concat([h_left, h_top, h_diag], 1)), self._U)
         h_ij_ = self._activation(h_ij_l + h_ij_r)
 
         # Calculate h_ij
@@ -214,39 +214,39 @@ def call(self, inputs: list, **kwargs) -> typing.Any:
 
         :param inputs: input tensors.
         """
-        batch_size = K.tf.shape(inputs)[0]
+        batch_size = tf.shape(inputs)[0]
         # h0 = [B, U]
-        self._bounder_state_h0 = K.tf.zeros([batch_size, self._units])
+        self._bounder_state_h0 = tf.zeros([batch_size, self._units])
 
         # input_x = [L, R, B, C]
-        input_x = K.tf.transpose(inputs, [2, 3, 0, 1])
+        input_x = tf.transpose(inputs, [2, 3, 0, 1])
         if self._direction == 'rb':
             # input_x: [R, L, B, C]
-            input_x = K.tf.reverse(input_x, [0, 1])
+            input_x = tf.reverse(input_x, [0, 1])
         elif self._direction != 'lt':
             raise ValueError(f"Invalid direction. "
                              f"`{self._direction}` received. "
                              f"Must be in `lt`, `rb`.")
         # input_x = [L*R*B, C]
-        input_x = K.tf.reshape(input_x, [-1, self._channel])
+        input_x = tf.reshape(input_x, [-1, self._channel])
         # input_x = L*R * [B, C]
-        input_x = K.tf.split(
+        input_x = tf.split(
             axis=0,
             num_or_size_splits=self._text1_maxlen * self._text2_maxlen,
             value=input_x
         )
 
         # inputs = L*R * [B, C]
-        inputs = K.tf.TensorArray(
-            dtype=K.tf.float32,
+        inputs = tf.TensorArray(
+            dtype=tf.float32,
             size=self._text1_maxlen * self._text2_maxlen,
             name='inputs'
         )
         inputs = inputs.unstack(input_x)
 
         # states = (L+1)*(R+1) * [B, U]
-        states = K.tf.TensorArray(
-            dtype=K.tf.float32,
+        states = tf.TensorArray(
+            dtype=tf.float32,
             size=(self._text1_maxlen + 1) * (self._text2_maxlen + 1),
             name='states',
             clear_after_read=False
@@ -260,13 +260,13 @@ def call(self, inputs: list, **kwargs) -> typing.Any:
 
         # Calculate h_ij
         # h_ij = [B, U]
-        _, _, _, h_ij = K.tf.while_loop(
-            cond=lambda _0, _1, i, _3: K.tf.less(i, self._recurrent_step),
+        _, _, _, h_ij = tf.while_loop(
+            cond=lambda _0, _1, i, _3: tf.less(i, self._recurrent_step),
             body=self.calculate_recurrent_unit,
             loop_vars=(
                 inputs,
                 states,
-                K.tf.constant(0, dtype=K.tf.int32),
+                tf.constant(0, dtype=tf.int32),
                 self._bounder_state_h0
             ),
             parallel_iterations=1,
diff --git a/matchzoo/layers/dynamic_pooling_layer.py b/matchzoo/layers/dynamic_pooling_layer.py
index 27b37888..7ae925c4 100644
--- a/matchzoo/layers/dynamic_pooling_layer.py
+++ b/matchzoo/layers/dynamic_pooling_layer.py
@@ -1,7 +1,7 @@
 """An implementation of Dynamic Pooling Layer."""
 import typing
 
-from keras import backend as K
+import tensorflow as tf
 from keras.engine import Layer
 
 
@@ -51,23 +51,24 @@ def call(self, inputs: list, **kwargs) -> typing.Any:
         """
         self._validate_dpool_size()
         x, dpool_index = inputs
-        dpool_shape = K.tf.shape(dpool_index)
-        batch_index_one = K.tf.expand_dims(
-            K.tf.expand_dims(
-                K.tf.range(dpool_shape[0]), axis=-1),
+        dpool_shape = tf.shape(dpool_index)
+        batch_index_one = tf.expand_dims(
+            tf.expand_dims(
+                tf.range(dpool_shape[0]), axis=-1),
             axis=-1)
-        batch_index = K.tf.expand_dims(
-            K.tf.tile(batch_index_one, [1, self._msize1, self._msize2]),
+        batch_index = tf.expand_dims(
+            tf.tile(batch_index_one, [1, self._msize1, self._msize2]),
             axis=-1)
-        dpool_index_ex = K.tf.concat([batch_index, dpool_index], axis=3)
-        x_expand = K.tf.gather_nd(x, dpool_index_ex)
+        dpool_index_ex = tf.concat([batch_index, dpool_index], axis=3)
+        x_expand = tf.gather_nd(x, dpool_index_ex)
         stride1 = self._msize1 // self._psize1
         stride2 = self._msize2 // self._psize2
 
-        x_pool = K.tf.nn.max_pool(x_expand,
-                                  [1, stride1, stride2, 1],
-                                  [1, stride1, stride2, 1],
-                                  "VALID")
+
+        x_pool = tf.nn.max_pool(x_expand,
+                                [1, stride1, stride2, 1],
+                                [1, stride1, stride2, 1],
+                                "VALID")
         return x_pool
 
     def compute_output_shape(self, input_shape: list) -> tuple:
diff --git a/matchzoo/layers/matching_layer.py b/matchzoo/layers/matching_layer.py
index 24b591cf..0f2ce782 100644
--- a/matchzoo/layers/matching_layer.py
+++ b/matchzoo/layers/matching_layer.py
@@ -1,6 +1,7 @@
 """An implementation of Matching Layer."""
 import typing
 
+import tensorflow as tf
 from keras import backend as K
 from keras.engine import Layer
 
@@ -76,7 +77,7 @@ def call(self, inputs: list, **kwargs) -> typing.Any:
             if self._normalize:
                 x1 = K.l2_normalize(x1, axis=2)
                 x2 = K.l2_normalize(x2, axis=2)
-            return K.tf.expand_dims(K.tf.einsum('abd,acd->abc', x1, x2), 3)
+            return K.expand_dims(tf.einsum('abd,acd->abc', x1, x2), 3)
         else:
             if self._matching_type == 'mul':
                 def func(x, y):
@@ -89,14 +90,14 @@ def func(x, y):
                     return x - y
             elif self._matching_type == 'concat':
                 def func(x, y):
-                    return K.tf.concat([x, y], axis=3)
+                    return tf.concat([x, y], axis=3)
             else:
                 raise ValueError(f"Invalid matching type."
                                  f"{self._matching_type} received."
                                  f"Mut be in `dot`, `mul`, `plus`, "
                                  f"`minus` and `concat`.")
-            x1_exp = K.tf.stack([x1] * self._shape2[1], 2)
-            x2_exp = K.tf.stack([x2] * self._shape1[1], 1)
+            x1_exp = K.stack([x1] * self._shape2[1], 2)
+            x2_exp = K.stack([x2] * self._shape1[1], 1)
             return func(x1_exp, x2_exp)
 
     def compute_output_shape(self, input_shape: list) -> tuple:
diff --git a/matchzoo/losses/rank_cross_entropy_loss.py b/matchzoo/losses/rank_cross_entropy_loss.py
index 9bf56f43..ed1ecbe3 100644
--- a/matchzoo/losses/rank_cross_entropy_loss.py
+++ b/matchzoo/losses/rank_cross_entropy_loss.py
@@ -1,10 +1,12 @@
 """The rank cross entropy loss."""
-import numpy as np
 
 from keras import layers, backend as K
+import numpy as np
+from keras.losses import Loss
+from keras.utils import losses_utils
 
 
-class RankCrossEntropyLoss(object):
+class RankCrossEntropyLoss(Loss):
     """
     Rank cross entropy loss.
 
@@ -26,9 +28,12 @@ def __init__(self, num_neg: int = 1):
 
         :param num_neg: number of negative instances in cross entropy loss.
         """
+        super().__init__(reduction=losses_utils.Reduction.SUM_OVER_BATCH_SIZE,
+                         name="rank_crossentropy")
         self._num_neg = num_neg
 
-    def __call__(self, y_true: np.array, y_pred: np.array) -> np.array:
+    def call(self, y_true: np.array, y_pred: np.array,
+             sample_weight=None) -> np.array:
         """
         Calculate rank cross entropy loss.
 
@@ -48,7 +53,10 @@ def __call__(self, y_true: np.array, y_pred: np.array) -> np.array:
             labels.append(neg_labels)
         logits = K.concatenate(logits, axis=-1)
         labels = K.concatenate(labels, axis=-1)
-        return -K.mean(K.sum(labels * K.log(K.softmax(logits) + np.finfo(float).eps), axis=-1))
+
+        loss = -(K.sum(labels * K.log(K.softmax(logits) + np.finfo(float).eps), axis=-1))
+        return losses_utils.compute_weighted_loss(
+            loss, sample_weight, reduction=self.reduction)
 
     @property
     def num_neg(self):
diff --git a/matchzoo/losses/rank_hinge_loss.py b/matchzoo/losses/rank_hinge_loss.py
index 43dccbc7..7b74887d 100644
--- a/matchzoo/losses/rank_hinge_loss.py
+++ b/matchzoo/losses/rank_hinge_loss.py
@@ -1,10 +1,12 @@
 """The rank hinge loss."""
-import numpy as np
 
+import numpy as np
 from keras import layers, backend as K
+from keras.losses import Loss
+from keras.utils import losses_utils
 
 
-class RankHingeLoss(object):
+class RankHingeLoss(Loss):
     """
     Rank hinge loss.
 
@@ -28,13 +30,18 @@ def __init__(self, num_neg: int = 1, margin: float = 1.0):
         :param num_neg: number of negative instances in hinge loss.
         :param margin: the margin between positive and negative scores.
         """
+        super().__init__(reduction=losses_utils.Reduction.SUM_OVER_BATCH_SIZE,
+                         name="rank_hinge")
+
         self._num_neg = num_neg
         self._margin = margin
 
-    def __call__(self, y_true: np.array, y_pred: np.array) -> np.array:
+    def call(self, y_true: np.array, y_pred: np.array,
+             sample_weight=None) -> np.array:
         """
         Calculate rank hinge loss.
 
+        :param **kwargs:
         :param y_true: Label.
         :param y_pred: Predicted result.
         :return: Hinge loss computed by user-defined margin.
@@ -49,7 +56,8 @@ def __call__(self, y_true: np.array, y_pred: np.array) -> np.array:
                     output_shape=(1,))(y_pred))
         y_neg = K.mean(K.concatenate(y_neg, axis=-1), axis=-1, keepdims=True)
         loss = K.maximum(0., self._margin + y_neg - y_pos)
-        return K.mean(loss)
+        return losses_utils.compute_weighted_loss(
+            loss, sample_weight, reduction=self.reduction)
 
     @property
     def num_neg(self):
diff --git a/matchzoo/models/conv_knrm.py b/matchzoo/models/conv_knrm.py
index 47eb64a2..19e07eaa 100644
--- a/matchzoo/models/conv_knrm.py
+++ b/matchzoo/models/conv_knrm.py
@@ -2,11 +2,10 @@
 
 import keras
 import keras.backend as K
+import tensorflow as tf
 
 from .knrm import KNRM
 from matchzoo.engine.param import Param
-from matchzoo.engine.param_table import ParamTable
-from matchzoo.engine import hyper_spaces
 
 
 class ConvKNRM(KNRM):
@@ -87,13 +86,13 @@ def build(self):
                         mu = 1.0
                     mm_exp = self._kernel_layer(mu, sigma)(mm)
                     mm_doc_sum = keras.layers.Lambda(
-                        lambda x: K.tf.reduce_sum(x, 2))(
+                        lambda x: K.sum(x, 2))(
                         mm_exp)
-                    mm_log = keras.layers.Activation(K.tf.log1p)(mm_doc_sum)
+                    mm_log = keras.layers.Activation(tf.math.log1p)(mm_doc_sum)
                     mm_sum = keras.layers.Lambda(
-                        lambda x: K.tf.reduce_sum(x, 1))(mm_log)
+                        lambda x: K.sum(x, 1))(mm_log)
                     KM.append(mm_sum)
 
-        phi = keras.layers.Lambda(lambda x: K.tf.stack(x, 1))(KM)
+        phi = keras.layers.Lambda(lambda x: K.stack(x, 1))(KM)
         out = self._make_output_layer()(phi)
         self._backend = keras.Model(inputs=[query, doc], outputs=[out])
diff --git a/matchzoo/models/drmm.py b/matchzoo/models/drmm.py
index cfaeb4cc..e302062d 100644
--- a/matchzoo/models/drmm.py
+++ b/matchzoo/models/drmm.py
@@ -114,7 +114,7 @@ def attention_layer(cls, attention_input: typing.Any,
             )(dense_input)
         # shape = [B, L, 1]
         attention_probs = keras.layers.Lambda(
-            lambda x: keras.layers.activations.softmax(x, axis=1),
+            lambda x: keras.activations.softmax(x, axis=1),
             output_shape=lambda s: (s[0], s[1], s[2]),
             name="attention_probs"
         )(dense_input)
diff --git a/matchzoo/models/drmmtks.py b/matchzoo/models/drmmtks.py
index b0111b2f..3bfae201 100644
--- a/matchzoo/models/drmmtks.py
+++ b/matchzoo/models/drmmtks.py
@@ -2,7 +2,7 @@
 import typing
 
 import keras
-import keras.backend as K
+import tensorflow as tf
 
 from matchzoo.engine.base_model import BaseModel
 from matchzoo.engine.param import Param
@@ -67,11 +67,11 @@ def build(self):
         # shape = [B, R, D]
         embed_doc = embedding(doc)
         # shape = [B, L]
-        atten_mask = K.not_equal(query, self._params['mask_value'])
+        atten_mask = tf.not_equal(query, self._params['mask_value'])
         # shape = [B, L]
-        atten_mask = K.cast(atten_mask, K.floatx())
+        atten_mask = tf.cast(atten_mask, keras.backend.floatx())
         # shape = [B, L, 1]
-        atten_mask = K.expand_dims(atten_mask, axis=2)
+        atten_mask = tf.expand_dims(atten_mask, axis=2)
         # shape = [B, L, 1]
         attention_probs = self.attention_layer(embed_query, atten_mask)
 
@@ -85,7 +85,7 @@ def build(self):
                               self.params['input_shapes'][0][0],
                               self.params['input_shapes'][1][0])
         matching_topk = keras.layers.Lambda(
-            lambda x: K.tf.nn.top_k(x, k=effective_top_k, sorted=True)[0]
+            lambda x: tf.nn.top_k(x, k=effective_top_k, sorted=True)[0]
         )(matching_matrix)
 
         # Process right input.
@@ -127,7 +127,7 @@ def attention_layer(cls, attention_input: typing.Any,
             )(dense_input)
         # shape = [B, L, 1]
         attention_probs = keras.layers.Lambda(
-            lambda x: keras.layers.activations.softmax(x, axis=1),
+            lambda x: keras.activations.softmax(x, axis=1),
             output_shape=lambda s: (s[0], s[1], s[2]),
             name="attention_probs"
         )(dense_input)
diff --git a/matchzoo/models/duet.py b/matchzoo/models/duet.py
index cb85b602..bce9e9a3 100644
--- a/matchzoo/models/duet.py
+++ b/matchzoo/models/duet.py
@@ -149,10 +149,10 @@ def _xor_match(cls, x):
         t2 = x[1]
         t1_shape = t1.get_shape()
         t2_shape = t2.get_shape()
-        t1_expand = K.tf.stack([t1] * t2_shape[1], 2)
-        t2_expand = K.tf.stack([t2] * t1_shape[1], 1)
-        out_bool = K.tf.equal(t1_expand, t2_expand)
-        out = K.tf.cast(out_bool, K.tf.float32)
+        t1_expand = tf.stack([t1] * t2_shape[1], 2)
+        t2_expand = tf.stack([t2] * t1_shape[1], 1)
+        out_bool = tf.equal(t1_expand, t2_expand)
+        out = tf.cast(out_bool, tf.float32)
         return out
 
     @classmethod
diff --git a/matchzoo/models/knrm.py b/matchzoo/models/knrm.py
index 6e0d3d77..1aefee44 100644
--- a/matchzoo/models/knrm.py
+++ b/matchzoo/models/knrm.py
@@ -1,6 +1,7 @@
 """KNRM model."""
 import keras
 import keras.backend as K
+import tensorflow as tf
 
 from matchzoo.engine.base_model import BaseModel
 from matchzoo.engine.param import Param
@@ -69,13 +70,13 @@ def build(self):
                 mu = 1.0
             mm_exp = self._kernel_layer(mu, sigma)(mm)
             mm_doc_sum = keras.layers.Lambda(
-                lambda x: K.tf.reduce_sum(x, 2))(mm_exp)
-            mm_log = keras.layers.Activation(K.tf.log1p)(mm_doc_sum)
+                lambda x: K.sum(x, 2))(mm_exp)
+            mm_log = keras.layers.Activation(tf.math.log1p)(mm_doc_sum)
             mm_sum = keras.layers.Lambda(
-                lambda x: K.tf.reduce_sum(x, 1))(mm_log)
+                lambda x: K.sum(x, 1))(mm_log)
             KM.append(mm_sum)
 
-        phi = keras.layers.Lambda(lambda x: K.tf.stack(x, 1))(KM)
+        phi = keras.layers.Lambda(lambda x: K.stack(x, 1))(KM)
         out = self._make_output_layer()(phi)
         self._backend = keras.Model(inputs=[query, doc], outputs=[out])
 
@@ -90,6 +91,6 @@ def _kernel_layer(cls, mu: float, sigma: float) -> keras.layers.Layer:
         """
 
         def kernel(x):
-            return K.tf.exp(-0.5 * (x - mu) * (x - mu) / sigma / sigma)
+            return K.exp(-0.5 * (x - mu) * (x - mu) / sigma / sigma)
 
         return keras.layers.Activation(kernel)
diff --git a/matchzoo/models/mvlstm.py b/matchzoo/models/mvlstm.py
index fcd5f648..bb7f8fba 100644
--- a/matchzoo/models/mvlstm.py
+++ b/matchzoo/models/mvlstm.py
@@ -2,7 +2,7 @@
 import typing
 
 import keras
-import keras.backend as K
+import tensorflow as tf
 
 import matchzoo
 from matchzoo.engine.base_model import BaseModel
@@ -73,7 +73,7 @@ def build(self):
             axes=[2, 2], normalize=False)([rep_query, rep_doc])
         matching_signals = keras.layers.Reshape((-1,))(matching_matrix)
         matching_topk = keras.layers.Lambda(
-            lambda x: K.tf.nn.top_k(x, k=self._params['top_k'], sorted=True)[0]
+            lambda x: tf.nn.top_k(x, k=self._params['top_k'], sorted=True)[0]
         )(matching_signals)
 
         # Multilayer perceptron layer.
diff --git a/requirements.txt b/requirements.txt
index 772b9465..995b0417 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,6 +1,6 @@
-keras >= 2.2.4
+keras == 2.3.0
 tabulate >= 0.8.2
-tensorflow >= 1.8.0
+tensorflow >= 2.0.0
 nltk >= 3.2.3
 numpy >= 1.14
 tqdm >= 4.23.4

From ebc4f21f91ffb652b8c0c53eb7e9004ba3336579 Mon Sep 17 00:00:00 2001
From: zhaohao <zhaohao.zeng93@gmail.com>
Date: Thu, 3 Oct 2019 23:00:51 +0900
Subject: [PATCH 094/100] replace keras add_update with assign_add for tf2.0

---
 matchzoo/contrib/layers/decaying_dropout_layer.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/matchzoo/contrib/layers/decaying_dropout_layer.py b/matchzoo/contrib/layers/decaying_dropout_layer.py
index fda87212..21232c6f 100644
--- a/matchzoo/contrib/layers/decaying_dropout_layer.py
+++ b/matchzoo/contrib/layers/decaying_dropout_layer.py
@@ -1,10 +1,9 @@
 """An implementation of Decaying Dropout Layer."""
-import typing
 
 import keras.backend as K
+import tensorflow as tf
 from keras.engine import Layer
 
-
 class DecayingDropoutLayer(Layer):
     """
     Layer that processes dropout with exponential decayed keep rate during
@@ -61,6 +60,7 @@ def build(self, input_shape):
         :param input_shape: the shape of the input tensor,
             for DecayingDropoutLayer we need one input tensor.
         """
+
         self._iterations = self.add_weight(name='iterations',
                                            shape=(1,),
                                            dtype=K.floatx(),
@@ -81,9 +81,9 @@ def call(self, inputs, training=None):
         keep_rate = self._initial_keep_rate * K.pow(self._decay_rate, p)
 
         def dropped_inputs():
-            self.add_update([K.update_add(self._iterations, [1])], inputs)
-            return K.dropout(inputs, 1 - keep_rate[0], noise_shape,
-                             seed=self._seed)
+            with tf.control_dependencies([self._iterations.assign_add([1])]):
+                return K.dropout(inputs, 1 - keep_rate[0], noise_shape,
+                                 seed=self._seed)
 
         return K.in_train_phase(dropped_inputs, inputs, training=training)
 

From e94d888c73769b86ca24115280db2ddc4ff2e136 Mon Sep 17 00:00:00 2001
From: zhaohao <zhaohao.zeng93@gmail.com>
Date: Thu, 3 Oct 2019 23:37:20 +0900
Subject: [PATCH 095/100] remove dump test as keras optimizer is dumpable now

---
 tests/unit_test/test_utils.py | 17 -----------------
 1 file changed, 17 deletions(-)

diff --git a/tests/unit_test/test_utils.py b/tests/unit_test/test_utils.py
index d293ef66..e69de29b 100644
--- a/tests/unit_test/test_utils.py
+++ b/tests/unit_test/test_utils.py
@@ -1,17 +0,0 @@
-import dill
-import pytest
-import keras
-
-import matchzoo
-
-
-def test_make_keras_optimizer_picklable():
-    adam = keras.optimizers.Adam(lr=0.1)
-    with pytest.raises(Exception):
-        s = dill.dumps(adam)
-        assert dill.loads(s)
-
-    matchzoo.utils.make_keras_optimizer_picklable()
-
-    s = dill.dumps(adam)
-    assert dill.loads(s)

From d590adc8ec33413ad9b42139e8e366fbe40020e4 Mon Sep 17 00:00:00 2001
From: zhaohao <zhaohao.zeng93@gmail.com>
Date: Thu, 3 Oct 2019 23:37:31 +0900
Subject: [PATCH 096/100] reformat for flask8

---
 matchzoo/contrib/layers/decaying_dropout_layer.py | 3 ++-
 matchzoo/layers/dynamic_pooling_layer.py          | 1 -
 matchzoo/losses/rank_cross_entropy_loss.py        | 4 ++--
 3 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/matchzoo/contrib/layers/decaying_dropout_layer.py b/matchzoo/contrib/layers/decaying_dropout_layer.py
index 21232c6f..2cb7d961 100644
--- a/matchzoo/contrib/layers/decaying_dropout_layer.py
+++ b/matchzoo/contrib/layers/decaying_dropout_layer.py
@@ -81,7 +81,8 @@ def call(self, inputs, training=None):
         keep_rate = self._initial_keep_rate * K.pow(self._decay_rate, p)
 
         def dropped_inputs():
-            with tf.control_dependencies([self._iterations.assign_add([1])]):
+            update_op = self._iterations.assign_add([1])
+            with tf.control_dependencies([update_op]):
                 return K.dropout(inputs, 1 - keep_rate[0], noise_shape,
                                  seed=self._seed)
 
diff --git a/matchzoo/layers/dynamic_pooling_layer.py b/matchzoo/layers/dynamic_pooling_layer.py
index 7ae925c4..049bbf82 100644
--- a/matchzoo/layers/dynamic_pooling_layer.py
+++ b/matchzoo/layers/dynamic_pooling_layer.py
@@ -64,7 +64,6 @@ def call(self, inputs: list, **kwargs) -> typing.Any:
         stride1 = self._msize1 // self._psize1
         stride2 = self._msize2 // self._psize2
 
-
         x_pool = tf.nn.max_pool(x_expand,
                                 [1, stride1, stride2, 1],
                                 [1, stride1, stride2, 1],
diff --git a/matchzoo/losses/rank_cross_entropy_loss.py b/matchzoo/losses/rank_cross_entropy_loss.py
index ed1ecbe3..32b8d23d 100644
--- a/matchzoo/losses/rank_cross_entropy_loss.py
+++ b/matchzoo/losses/rank_cross_entropy_loss.py
@@ -53,8 +53,8 @@ def call(self, y_true: np.array, y_pred: np.array,
             labels.append(neg_labels)
         logits = K.concatenate(logits, axis=-1)
         labels = K.concatenate(labels, axis=-1)
-
-        loss = -(K.sum(labels * K.log(K.softmax(logits) + np.finfo(float).eps), axis=-1))
+        smoothed_prob = K.softmax(logits) + np.finfo(float).eps
+        loss = -(K.sum(labels * K.log(smoothed_prob), axis=-1))
         return losses_utils.compute_weighted_loss(
             loss, sample_weight, reduction=self.reduction)
 

From d12bfb4bd8c2f26ce2aeb2558f9d43cccfacf8e3 Mon Sep 17 00:00:00 2001
From: zhaohao <zhaohao.zeng93@gmail.com>
Date: Fri, 4 Oct 2019 20:34:07 +0900
Subject: [PATCH 097/100] clear session after each  model test to prevent OOM
 during CI

---
 tests/unit_test/models/test_models.py | 3 ++-
 1 file changed, 2 insertions(+), 1 deletion(-)

diff --git a/tests/unit_test/models/test_models.py b/tests/unit_test/models/test_models.py
index 828a8b62..743fe9c0 100644
--- a/tests/unit_test/models/test_models.py
+++ b/tests/unit_test/models/test_models.py
@@ -9,7 +9,7 @@
 import shutil
 
 import matchzoo as mz
-
+from keras.backend import clear_session
 
 @pytest.fixture(scope='module', params=[
     mz.tasks.Ranking(loss=mz.losses.RankCrossEntropyLoss(num_neg=2)),
@@ -36,6 +36,7 @@ def embedding():
 
 @pytest.fixture(scope='module')
 def setup(task, model_class, train_raw, embedding):
+    clear_session() # prevent OOM during CI tests
     return mz.auto.prepare(
         task=task,
         model_class=model_class,

From 5fae55a1e59a647d138f1a066c331e940c007dd4 Mon Sep 17 00:00:00 2001
From: zhaohao <zhaohao.zeng93@gmail.com>
Date: Fri, 4 Oct 2019 20:35:28 +0900
Subject: [PATCH 098/100] upgrade keras to 2.3.0

---
 setup.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/setup.py b/setup.py
index 40a70fd5..f99f2f42 100644
--- a/setup.py
+++ b/setup.py
@@ -17,7 +17,7 @@
     long_description = f.read()
 
 install_requires = [
-    'keras >= 2.2.4',
+    'keras >= 2.3.0',
     'nltk >= 3.2.3',
     'numpy >= 1.14',
     'tqdm >= 4.19.4',

From e1b38e326acb2c536717c9592de5550e6eaa714c Mon Sep 17 00:00:00 2001
From: zhaohao <zhaohao.zeng93@gmail.com>
Date: Sat, 5 Oct 2019 02:31:14 +0900
Subject: [PATCH 099/100] replace most K.* with tf.*

---
 README.md                                     |  2 +-
 matchzoo/contrib/layers/attention_layer.py    | 25 ++++----
 .../contrib/layers/decaying_dropout_layer.py  |  8 +--
 .../contrib/layers/multi_perspective_layer.py | 60 +++++++++----------
 .../layers/semantic_composite_layer.py        | 23 ++++---
 matchzoo/contrib/models/diin.py               | 15 ++---
 matchzoo/contrib/models/esim.py               |  8 +--
 matchzoo/contrib/models/match_lstm.py         | 15 ++---
 matchzoo/contrib/models/match_srnn.py         |  9 +--
 matchzoo/layers/matching_layer.py             | 11 ++--
 matchzoo/losses/rank_cross_entropy_loss.py    | 11 ++--
 matchzoo/losses/rank_hinge_loss.py            |  7 ++-
 matchzoo/models/conv_knrm.py                  |  7 +--
 matchzoo/models/drmm.py                       | 10 ++--
 matchzoo/models/drmmtks.py                    |  2 +-
 matchzoo/models/duet.py                       |  4 +-
 matchzoo/models/knrm.py                       | 10 ++--
 matchzoo/models/mvlstm.py                     |  4 +-
 18 files changed, 112 insertions(+), 119 deletions(-)

diff --git a/README.md b/README.md
index 14900a0e..26e7171d 100644
--- a/README.md
+++ b/README.md
@@ -117,7 +117,7 @@ If you're interested in the cutting-edge research progress, please take a look a
 
 ## Install
 
-MatchZoo is dependent on [Keras](https://github.com/keras-team/keras), please install one of its backend engines: TensorFlow, Theano, or CNTK. We recommend the TensorFlow backend. Two ways to install MatchZoo:
+MatchZoo is dependent on [Keras](https://github.com/keras-team/keras) and [Tensorflow](https://github.com/tensorflow/tensorflow). Two ways to install MatchZoo:
 
 **Install MatchZoo from Pypi:**
 
diff --git a/matchzoo/contrib/layers/attention_layer.py b/matchzoo/contrib/layers/attention_layer.py
index 53714f06..049d72dc 100644
--- a/matchzoo/contrib/layers/attention_layer.py
+++ b/matchzoo/contrib/layers/attention_layer.py
@@ -1,5 +1,6 @@
 """An implementation of Attention Layer for Bimpm model."""
 
+import tensorflow as tf
 from keras import backend as K
 from keras.engine import Layer
 
@@ -97,40 +98,40 @@ def call(self, x: list, **kwargs):
         reps_lt, reps_rt = x
 
         attn_w1 = self.attn_w1
-        attn_w1 = K.expand_dims(K.expand_dims(attn_w1, axis=0), axis=0)
+        attn_w1 = tf.expand_dims(tf.expand_dims(attn_w1, axis=0), axis=0)
         # => [1, 1, d, a]
 
-        reps_lt = K.expand_dims(reps_lt, axis=-1)
-        attn_reps_lt = K.sum(reps_lt * attn_w1, axis=2)
+        reps_lt = tf.expand_dims(reps_lt, axis=-1)
+        attn_reps_lt = tf.reduce_sum(reps_lt * attn_w1, axis=2)
         # => [b, s_lt, d, -1]
 
         attn_w2 = self.attn_w2
-        attn_w2 = K.expand_dims(K.expand_dims(attn_w2, axis=0), axis=0)
+        attn_w2 = tf.expand_dims(tf.expand_dims(attn_w2, axis=0), axis=0)
         # => [1, 1, d, a]
 
-        reps_rt = K.expand_dims(reps_rt, axis=-1)
-        attn_reps_rt = K.sum(reps_rt * attn_w2, axis=2)  # [b, s_rt, d, -1]
+        reps_rt = tf.expand_dims(reps_rt, axis=-1)
+        attn_reps_rt = tf.reduce_sum(reps_rt * attn_w2, axis=2)  # [b, s_rt, d, -1]
 
-        attn_reps_lt = K.tanh(attn_reps_lt)  # [b, s_lt, a]
-        attn_reps_rt = K.tanh(attn_reps_rt)  # [b, s_rt, a]
+        attn_reps_lt = tf.tanh(attn_reps_lt)  # [b, s_lt, a]
+        attn_reps_rt = tf.tanh(attn_reps_rt)  # [b, s_rt, a]
 
         # diagonal_W
         attn_reps_lt = attn_reps_lt * self.diagonal_W  # [b, s_lt, a]
-        attn_reps_rt = K.permute_dimensions(attn_reps_rt, (0, 2, 1))
+        attn_reps_rt = tf.transpose(attn_reps_rt, (0, 2, 1))
         # => [b, a, s_rt]
 
         attn_value = K.batch_dot(attn_reps_lt, attn_reps_rt)  # [b, s_lt, s_rt]
 
         # Softmax operation
-        attn_prob = K.softmax(attn_value)  # [b, s_lt, s_rt]
+        attn_prob = tf.nn.softmax(attn_value)  # [b, s_lt, s_rt]
 
         # TODO(tjf) remove diagonal or not for normalization
         # if remove_diagonal: attn_value = attn_value * diagonal
 
         if len(x) == 4:
             mask_lt, mask_rt = x[2], x[3]
-            attn_prob *= K.expand_dims(mask_lt, axis=2)
-            attn_prob *= K.expand_dims(mask_rt, axis=1)
+            attn_prob *= tf.expand_dims(mask_lt, axis=2)
+            attn_prob *= tf.expand_dims(mask_rt, axis=1)
 
         return attn_prob
 
diff --git a/matchzoo/contrib/layers/decaying_dropout_layer.py b/matchzoo/contrib/layers/decaying_dropout_layer.py
index 2cb7d961..152b4a31 100644
--- a/matchzoo/contrib/layers/decaying_dropout_layer.py
+++ b/matchzoo/contrib/layers/decaying_dropout_layer.py
@@ -48,7 +48,7 @@ def _get_noise_shape(self, inputs):
         if self._noise_shape is None:
             return self._noise_shape
 
-        symbolic_shape = K.shape(inputs)
+        symbolic_shape = tf.shape(inputs)
         noise_shape = [symbolic_shape[axis] if shape is None else shape
                        for axis, shape in enumerate(self._noise_shape)]
         return tuple(noise_shape)
@@ -75,15 +75,15 @@ def call(self, inputs, training=None):
         :param inputs: an input tensor.
         """
         noise_shape = self._get_noise_shape(inputs)
-        t = K.cast(self._iterations, K.floatx()) + 1
+        t = tf.cast(self._iterations, K.floatx()) + 1
         p = t / float(self._decay_interval)
 
-        keep_rate = self._initial_keep_rate * K.pow(self._decay_rate, p)
+        keep_rate = self._initial_keep_rate * tf.pow(self._decay_rate, p)
 
         def dropped_inputs():
             update_op = self._iterations.assign_add([1])
             with tf.control_dependencies([update_op]):
-                return K.dropout(inputs, 1 - keep_rate[0], noise_shape,
+                return tf.nn.dropout(inputs, 1 - keep_rate[0], noise_shape,
                                  seed=self._seed)
 
         return K.in_train_phase(dropped_inputs, inputs, training=training)
diff --git a/matchzoo/contrib/layers/multi_perspective_layer.py b/matchzoo/contrib/layers/multi_perspective_layer.py
index f9f1dcac..64cfd338 100644
--- a/matchzoo/contrib/layers/multi_perspective_layer.py
+++ b/matchzoo/contrib/layers/multi_perspective_layer.py
@@ -79,7 +79,7 @@ def call(self, x: list, **kwargs):
         if self._perspective.get('full'):
             # Each forward & backward contextual embedding compare
             # with the last step of the last time step of the other sentence.
-            h_lt = K.concatenate([forward_h_lt, backward_h_lt], axis=-1)
+            h_lt = tf.concat([forward_h_lt, backward_h_lt], axis=-1)
             full_match_tensor = self.full_match([h_lt, lstm_reps_rt])
             match_tensor_list.append(full_match_tensor)
             match_dim += self._mp_dim + 1
@@ -111,7 +111,7 @@ def call(self, x: list, **kwargs):
             match_tensor_list.append(max_attentive_tensor)
             match_dim += self._mp_dim + 1
 
-        mp_tensor = K.concatenate(match_tensor_list, axis=-1)
+        mp_tensor = tf.concat(match_tensor_list, axis=-1)
         return mp_tensor
 
     def compute_output_shape(self, input_shape: list):
@@ -148,7 +148,7 @@ def call(self, x, **kwargs):
         """Call.
         """
         rep_lt, reps_rt = x
-        att_lt = K.expand_dims(rep_lt, 1)
+        att_lt = tf.expand_dims(rep_lt, 1)
 
         match_tensor, match_dim = _multi_perspective_match(self.mp_dim,
                                                            reps_rt,
@@ -184,16 +184,16 @@ def call(self, x, **kwargs):
 
         # kernel: [1, 1, 1, mp_dim, d]
         # lstm_lt => [b, len_lt, 1, 1, d]
-        reps_lt = K.expand_dims(reps_lt, axis=2)
-        reps_lt = K.expand_dims(reps_lt, axis=2)
+        reps_lt = tf.expand_dims(reps_lt, axis=2)
+        reps_lt = tf.expand_dims(reps_lt, axis=2)
         reps_lt = reps_lt * self.kernel
 
         # lstm_rt -> [b, 1, len_rt, 1, d]
-        reps_rt = K.expand_dims(reps_rt, axis=2)
-        reps_rt = K.expand_dims(reps_rt, axis=1)
+        reps_rt = tf.expand_dims(reps_rt, axis=2)
+        reps_rt = tf.expand_dims(reps_rt, axis=1)
 
         match_tensor = _cosine_distance(reps_lt, reps_rt, cosine_norm=False)
-        max_match_tensor = K.max(match_tensor, axis=1)
+        max_match_tensor = tf.reduce_max(match_tensor, axis=1)
         # match_tensor => [b, len_rt, m]
         return max_match_tensor
 
@@ -280,7 +280,7 @@ def cal_max_question_representation(reps_lt, attn_scores):
     :param attn_scores: []
     :return: [batch_size, passage_len, hidden_size].
     """
-    attn_positions = K.argmax(attn_scores, axis=2)
+    attn_positions = tf.argmax(attn_scores, axis=2)
     max_reps_lt = collect_representation(reps_lt, attn_positions)
     return max_reps_lt
 
@@ -304,13 +304,13 @@ def collect_final_step_of_lstm(lstm_representation, lengths):
     :param lengths: [batch_size]
     :return: [batch_size, dim]
     """
-    lengths = K.maximum(lengths, K.zeros_like(lengths))
+    lengths = tf.maximum(lengths, K.zeros_like(lengths))
 
-    batch_size = K.shape(lengths)[0]
+    batch_size = tf.shape(lengths)[0]
     # shape (batch_size)
     batch_nums = tf.range(0, limit=batch_size)
     # shape (batch_size, 2)
-    indices = K.stack((batch_nums, lengths), axis=1)
+    indices = tf.stack((batch_nums, lengths), axis=1)
     result = tf.gather_nd(lstm_representation, indices,
                             name='last-forwar-lstm')
     # [batch_size, dim]
@@ -327,19 +327,19 @@ def collect_probs(probs, positions):
     :param positions: [batch_size, pair_size]
     :return: [batch_size, pair_size]
     """
-    batch_size = K.shape(probs)[0]
-    pair_size = K.shape(positions)[1]
+    batch_size = tf.shape(probs)[0]
+    pair_size = tf.shape(positions)[1]
     # shape (batch_size)
     batch_nums = K.arange(0, batch_size)
     # [batch_size, 1]
-    batch_nums = K.reshape(batch_nums, shape=[-1, 1])
+    batch_nums = tf.reshape(batch_nums, shape=[-1, 1])
     # [batch_size, pair_size]
     batch_nums = K.tile(batch_nums, [1, pair_size])
 
     # shape (batch_size, pair_size, 2)
     # Alert: to solve error message
     positions = tf.cast(positions, tf.int32)
-    indices = K.stack([batch_nums, positions], axis=2)
+    indices = tf.stack([batch_nums, positions], axis=2)
 
     pair_probs = tf.gather_nd(probs, indices)
     # pair_probs = tf.reshape(pair_probs, shape=[batch_size, pair_size])
@@ -360,7 +360,7 @@ def _multi_perspective_match(mp_dim, reps_rt, att_lt,
     :param with_mp_cosine: True
     :return: [batch, len, 1 + mp_dim]
     """
-    shape_rt = K.shape(reps_rt)
+    shape_rt = tf.shape(reps_rt)
     batch_size = shape_rt[0]
     len_lt = shape_rt[1]
 
@@ -368,7 +368,7 @@ def _multi_perspective_match(mp_dim, reps_rt, att_lt,
     match_result_list = []
     if with_cosine:
         cosine_tensor = _cosine_distance(reps_rt, att_lt, False)
-        cosine_tensor = K.reshape(cosine_tensor,
+        cosine_tensor = tf.reshape(cosine_tensor,
                                   [batch_size, len_lt, 1])
         match_result_list.append(cosine_tensor)
         match_dim += 1
@@ -376,12 +376,12 @@ def _multi_perspective_match(mp_dim, reps_rt, att_lt,
     if with_mp_cosine:
         mp_cosine_layer = MpCosineLayer(mp_dim)
         mp_cosine_tensor = mp_cosine_layer([reps_rt, att_lt])
-        mp_cosine_tensor = K.reshape(mp_cosine_tensor,
+        mp_cosine_tensor = tf.reshape(mp_cosine_tensor,
                                      [batch_size, len_lt, mp_dim])
         match_result_list.append(mp_cosine_tensor)
         match_dim += mp_cosine_layer.mp_dim
 
-    match_result = K.concatenate(match_result_list, 2)
+    match_result = tf.concat(match_result_list, 2)
     return match_result, match_dim
 
 
@@ -418,8 +418,8 @@ def build(self, input_shape):
     def call(self, x, **kwargs):
         """Call."""
         v1, v2 = x
-        v1 = K.expand_dims(v1, 2) * self.kernel  # [b, s_lt, m, d]
-        v2 = K.expand_dims(v2, 2)  # [b, s_lt, 1, d]
+        v1 = tf.expand_dims(v1, 2) * self.kernel  # [b, s_lt, m, d]
+        v2 = tf.expand_dims(v2, 2)  # [b, s_lt, 1, d]
         return _cosine_distance(v1, v2, False)
 
     def compute_output_shape(self, input_shape):
@@ -428,8 +428,8 @@ def compute_output_shape(self, input_shape):
 
 
 def _calc_relevancy_matrix(reps_lt, reps_rt):
-    reps_lt = K.expand_dims(reps_lt, 1)  # [b, 1, len_lt, d]
-    reps_rt = K.expand_dims(reps_rt, 2)  # [b, len_rt, 1, d]
+    reps_lt = tf.expand_dims(reps_lt, 1)  # [b, 1, len_lt, d]
+    reps_rt = tf.expand_dims(reps_rt, 2)  # [b, len_rt, 1, d]
     relevancy_matrix = _cosine_distance(reps_lt, reps_rt)
     # => [b, len_rt, len_lt, d]
     return relevancy_matrix
@@ -445,14 +445,14 @@ def _mask_relevancy_matrix(relevancy_matrix, mask_lt, mask_rt):
     :return: masked_matrix: [b, len_rt, len_lt]
     """
     if mask_lt is not None:
-        relevancy_matrix = relevancy_matrix * K.expand_dims(mask_lt, 1)
-    relevancy_matrix = relevancy_matrix * K.expand_dims(mask_rt, 2)
+        relevancy_matrix = relevancy_matrix * tf.expand_dims(mask_lt, 1)
+    relevancy_matrix = relevancy_matrix * tf.expand_dims(mask_rt, 2)
     return relevancy_matrix
 
 
 def _cosine_distance(v1, v2, cosine_norm=True, eps=1e-6):
     """
-    Only requires `K.sum(v1 * v2, axis=-1)`.
+    Only requires `tf.reduce_sum(v1 * v2, axis=-1)`.
 
     :param v1: [batch, time_steps(v1), 1, m, d]
     :param v2: [batch, 1, time_steps(v2), m, d]
@@ -460,9 +460,9 @@ def _cosine_distance(v1, v2, cosine_norm=True, eps=1e-6):
     :param eps: 1e-6
     :return: [batch, time_steps(v1), time_steps(v2), m]
     """
-    cosine_numerator = K.sum(v1 * v2, axis=-1)
+    cosine_numerator = tf.reduce_sum(v1 * v2, axis=-1)
     if not cosine_norm:
         return K.tanh(cosine_numerator)
-    v1_norm = K.sqrt(K.maximum(K.sum(K.square(v1), axis=-1), eps))
-    v2_norm = K.sqrt(K.maximum(K.sum(K.square(v2), axis=-1), eps))
+    v1_norm = K.sqrt(tf.maximum(tf.reduce_sum(tf.square(v1), axis=-1), eps))
+    v2_norm = K.sqrt(tf.maximum(tf.reduce_sum(tf.square(v2), axis=-1), eps))
     return cosine_numerator / v1_norm / v2_norm
diff --git a/matchzoo/contrib/layers/semantic_composite_layer.py b/matchzoo/contrib/layers/semantic_composite_layer.py
index 13bf6f29..bda4834b 100644
--- a/matchzoo/contrib/layers/semantic_composite_layer.py
+++ b/matchzoo/contrib/layers/semantic_composite_layer.py
@@ -1,8 +1,7 @@
 """An implementation of EncodingModule for DIIN model."""
-import typing
 
-import keras
 import keras.backend as K
+import tensorflow as tf
 from keras.engine import Layer
 
 from matchzoo.contrib.layers import DecayingDropoutLayer
@@ -84,38 +83,38 @@ def call(self, inputs, **kwargs):
 
         # The input shape is [b, p, d]
         # shape = [b, 1, p, d]
-        x = K.expand_dims(inputs, 1) * 0
+        x = tf.expand_dims(inputs, 1) * 0
         # shape = [b, 1, d, p]
-        x = K.permute_dimensions(x, (0, 1, 3, 2))
+        x = tf.transpose(x, (0, 1, 3, 2))
         # shape = [b, p, d, p]
-        mid = x + K.expand_dims(inputs)
+        mid = x + tf.expand_dims(inputs, -1)
         # shape = [b, p, d, p]
-        up = K.permute_dimensions(mid, pattern=(0, 3, 2, 1))
+        up = tf.transpose(mid, (0, 3, 2, 1))
         # shape = [b, p, 3d, p]
-        inputs_concat = K.concatenate([up, mid, up * mid], axis=2)
+        inputs_concat = tf.concat([up, mid, up * mid], axis=2)
 
         # Self-attention layer.
         # shape = [b, p, p]
         A = K.dot(self._w_itr_att, inputs_concat)
         # shape = [b, p, p]
-        SA = keras.activations.softmax(A, axis=2)
+        SA = tf.nn.softmax(A, axis=2)
         # shape = [b, p, d]
         itr_attn = K.batch_dot(SA, inputs)
 
         # Semantic composite fuse gate.
         # shape = [b, p, 2d]
-        inputs_attn_concat = K.concatenate([inputs, itr_attn], axis=2)
+        inputs_attn_concat = tf.concat([inputs, itr_attn], axis=2)
         concat_dropout = DecayingDropoutLayer(
             initial_keep_rate=self._initial_keep_rate,
             decay_interval=self._decay_interval,
             decay_rate=self._decay_rate
         )(inputs_attn_concat)
         # shape = [b, p, d]
-        z = K.tanh(K.dot(concat_dropout, self._w1) + self._b1)
+        z = tf.tanh(K.dot(concat_dropout, self._w1) + self._b1)
         # shape = [b, p, d]
-        r = K.sigmoid(K.dot(concat_dropout, self._w2) + self._b2)
+        r = tf.sigmoid(K.dot(concat_dropout, self._w2) + self._b2)
         # shape = [b, p, d]
-        f = K.sigmoid(K.dot(concat_dropout, self._w3) + self._b3)
+        f = tf.sigmoid(K.dot(concat_dropout, self._w3) + self._b3)
         # shape = [b, p, d]
         encoding = r * inputs + f * z
 
diff --git a/matchzoo/contrib/models/diin.py b/matchzoo/contrib/models/diin.py
index 3ab3632b..a346c84c 100644
--- a/matchzoo/contrib/models/diin.py
+++ b/matchzoo/contrib/models/diin.py
@@ -3,14 +3,15 @@
 
 import keras
 import keras.backend as K
+import tensorflow as tf
 
-from matchzoo.engine import hyper_spaces
-from matchzoo.engine.param_table import ParamTable
-from matchzoo.engine.param import Param
-from matchzoo.engine.base_model import BaseModel
+from matchzoo import preprocessors
 from matchzoo.contrib.layers import DecayingDropoutLayer
 from matchzoo.contrib.layers import EncodingLayer
-from matchzoo import preprocessors
+from matchzoo.engine import hyper_spaces
+from matchzoo.engine.base_model import BaseModel
+from matchzoo.engine.param import Param
+from matchzoo.engine.param_table import ParamTable
 
 
 class DIIN(BaseModel):
@@ -270,8 +271,8 @@ def _make_interaction(self, inputs_) -> typing.Any:
         left_encoding = inputs_[0]
         right_encoding = inputs_[1]
 
-        left_encoding = K.expand_dims(left_encoding, axis=2)
-        right_encoding = K.expand_dims(right_encoding, axis=1)
+        left_encoding = tf.expand_dims(left_encoding, axis=2)
+        right_encoding = tf.expand_dims(right_encoding, axis=1)
 
         interaction = left_encoding * right_encoding
         return interaction
diff --git a/matchzoo/contrib/models/esim.py b/matchzoo/contrib/models/esim.py
index e5e5f851..d7e8eda5 100644
--- a/matchzoo/contrib/models/esim.py
+++ b/matchzoo/contrib/models/esim.py
@@ -66,7 +66,7 @@ def _expand_dim(self, inp: tf.Tensor, axis: int) -> keras.layers.Layer:
         :param inp: input tensor to expand the dimension
         :param axis: the axis of new dimension
         """
-        return keras.layers.Lambda(lambda x: K.expand_dims(x, axis=axis))(inp)
+        return keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=axis))(inp)
 
     def _make_atten_mask_layer(self) -> keras.layers.Layer:
         """
@@ -100,7 +100,7 @@ def _max(self, texts: tf.Tensor, mask: tf.Tensor) -> tf.Tensor:
         new_texts = keras.layers.Multiply()([texts, mask])
 
         text_max = keras.layers.Lambda(
-            lambda x: K.max(x, axis=1),
+            lambda x: tf.reduce_max(x, axis=1),
         )(new_texts)
 
         return text_max
@@ -119,7 +119,7 @@ def _avg(self, texts: tf.Tensor, mask: tf.Tensor) -> tf.Tensor:
         # timestep-wise division, exclude the PAD number when calc avg
         text_avg = keras.layers.Lambda(
             lambda text_mask:
-                K.sum(text_mask[0], axis=1) / K.sum(text_mask[1], axis=1),
+                tf.reduce_sum(text_mask[0], axis=1) / tf.reduce_sum(text_mask[1], axis=1),
         )([new_texts, mask])
 
         return text_avg
@@ -133,7 +133,7 @@ def build(self):
         # layers
         create_mask = keras.layers.Lambda(
             lambda x:
-                K.cast(K.not_equal(x, self._params['mask_value']), K.floatx())
+                tf.cast(tf.not_equal(x, self._params['mask_value']), K.floatx())
         )
         embedding = self._make_embedding_layer()
         lstm_compare = self._make_bilstm_layer(lstm_dim)
diff --git a/matchzoo/contrib/models/match_lstm.py b/matchzoo/contrib/models/match_lstm.py
index e1150afb..933451fc 100644
--- a/matchzoo/contrib/models/match_lstm.py
+++ b/matchzoo/contrib/models/match_lstm.py
@@ -1,5 +1,6 @@
 """Match LSTM model."""
 import keras
+import tensorflow as tf
 import keras.backend as K
 
 from matchzoo.engine.base_model import BaseModel
@@ -68,19 +69,19 @@ def build(self):
         def attention(tensors):
             """Attention layer."""
             left, right = tensors
-            tensor_left = K.expand_dims(left, axis=2)
-            tensor_right = K.expand_dims(right, axis=1)
+            tensor_left = tf.expand_dims(left, axis=2)
+            tensor_right = tf.expand_dims(right, axis=1)
             tensor_left = K.repeat_elements(tensor_left, len_right, 2)
             tensor_right = K.repeat_elements(tensor_right, len_left, 1)
-            tensor_merged = K.concatenate([tensor_left, tensor_right], axis=-1)
+            tensor_merged = tf.concat([tensor_left, tensor_right], axis=-1)
             middle_output = keras.layers.Dense(self._params['fc_num_units'],
                                                activation='tanh')(
                 tensor_merged)
             attn_scores = keras.layers.Dense(1)(middle_output)
-            attn_scores = K.squeeze(attn_scores, axis=3)
-            exp_attn_scores = K.exp(
-                attn_scores - K.max(attn_scores, axis=-1, keepdims=True))
-            exp_sum = K.sum(exp_attn_scores, axis=-1, keepdims=True)
+            attn_scores = tf.squeeze(attn_scores, axis=3)
+            exp_attn_scores = tf.math.exp(
+                attn_scores - tf.reduce_max(attn_scores, axis=-1, keepdims=True))
+            exp_sum = tf.reduce_sum(exp_attn_scores, axis=-1, keepdims=True)
             attention_weights = exp_attn_scores / exp_sum
             return K.batch_dot(attention_weights, right)
 
diff --git a/matchzoo/contrib/models/match_srnn.py b/matchzoo/contrib/models/match_srnn.py
index 833581b2..66ae800a 100644
--- a/matchzoo/contrib/models/match_srnn.py
+++ b/matchzoo/contrib/models/match_srnn.py
@@ -1,16 +1,13 @@
 """An implementation of Match-SRNN Model."""
-import typing
 
 import keras
-import keras.backend as K
 
-import matchzoo
+from matchzoo.contrib.layers import MatchingTensorLayer
+from matchzoo.contrib.layers import SpatialGRU
+from matchzoo.engine import hyper_spaces
 from matchzoo.engine.base_model import BaseModel
 from matchzoo.engine.param import Param
 from matchzoo.engine.param_table import ParamTable
-from matchzoo.engine import hyper_spaces
-from matchzoo.contrib.layers import SpatialGRU
-from matchzoo.contrib.layers import MatchingTensorLayer
 
 
 class MatchSRNN(BaseModel):
diff --git a/matchzoo/layers/matching_layer.py b/matchzoo/layers/matching_layer.py
index 0f2ce782..54dead2a 100644
--- a/matchzoo/layers/matching_layer.py
+++ b/matchzoo/layers/matching_layer.py
@@ -2,7 +2,6 @@
 import typing
 
 import tensorflow as tf
-from keras import backend as K
 from keras.engine import Layer
 
 
@@ -75,9 +74,9 @@ def call(self, inputs: list, **kwargs) -> typing.Any:
         x2 = inputs[1]
         if self._matching_type == 'dot':
             if self._normalize:
-                x1 = K.l2_normalize(x1, axis=2)
-                x2 = K.l2_normalize(x2, axis=2)
-            return K.expand_dims(tf.einsum('abd,acd->abc', x1, x2), 3)
+                x1 = tf.math.l2_normalize(x1, axis=2)
+                x2 = tf.math.l2_normalize(x2, axis=2)
+            return tf.expand_dims(tf.einsum('abd,acd->abc', x1, x2), 3)
         else:
             if self._matching_type == 'mul':
                 def func(x, y):
@@ -96,8 +95,8 @@ def func(x, y):
                                  f"{self._matching_type} received."
                                  f"Mut be in `dot`, `mul`, `plus`, "
                                  f"`minus` and `concat`.")
-            x1_exp = K.stack([x1] * self._shape2[1], 2)
-            x2_exp = K.stack([x2] * self._shape1[1], 1)
+            x1_exp = tf.stack([x1] * self._shape2[1], 2)
+            x2_exp = tf.stack([x2] * self._shape1[1], 1)
             return func(x1_exp, x2_exp)
 
     def compute_output_shape(self, input_shape: list) -> tuple:
diff --git a/matchzoo/losses/rank_cross_entropy_loss.py b/matchzoo/losses/rank_cross_entropy_loss.py
index 32b8d23d..97e13fe0 100644
--- a/matchzoo/losses/rank_cross_entropy_loss.py
+++ b/matchzoo/losses/rank_cross_entropy_loss.py
@@ -1,7 +1,8 @@
 """The rank cross entropy loss."""
 
-from keras import layers, backend as K
 import numpy as np
+import tensorflow as tf
+from keras import layers, backend as K
 from keras.losses import Loss
 from keras.utils import losses_utils
 
@@ -51,10 +52,10 @@ def call(self, y_true: np.array, y_pred: np.array,
                 lambda a: a[neg_idx + 1::(self._num_neg + 1), :])(y_true)
             logits.append(neg_logits)
             labels.append(neg_labels)
-        logits = K.concatenate(logits, axis=-1)
-        labels = K.concatenate(labels, axis=-1)
-        smoothed_prob = K.softmax(logits) + np.finfo(float).eps
-        loss = -(K.sum(labels * K.log(smoothed_prob), axis=-1))
+        logits = tf.concat(logits, axis=-1)
+        labels = tf.concat(labels, axis=-1)
+        smoothed_prob = tf.nn.softmax(logits) + np.finfo(float).eps
+        loss = -(tf.reduce_sum(labels * tf.math.log(smoothed_prob), axis=-1))
         return losses_utils.compute_weighted_loss(
             loss, sample_weight, reduction=self.reduction)
 
diff --git a/matchzoo/losses/rank_hinge_loss.py b/matchzoo/losses/rank_hinge_loss.py
index 7b74887d..157f8a85 100644
--- a/matchzoo/losses/rank_hinge_loss.py
+++ b/matchzoo/losses/rank_hinge_loss.py
@@ -1,6 +1,7 @@
 """The rank hinge loss."""
 
 import numpy as np
+import tensorflow as tf
 from keras import layers, backend as K
 from keras.losses import Loss
 from keras.utils import losses_utils
@@ -41,7 +42,6 @@ def call(self, y_true: np.array, y_pred: np.array,
         """
         Calculate rank hinge loss.
 
-        :param **kwargs:
         :param y_true: Label.
         :param y_pred: Predicted result.
         :return: Hinge loss computed by user-defined margin.
@@ -54,8 +54,9 @@ def call(self, y_true: np.array, y_pred: np.array,
                 layers.Lambda(
                     lambda a: a[(neg_idx + 1)::(self._num_neg + 1), :],
                     output_shape=(1,))(y_pred))
-        y_neg = K.mean(K.concatenate(y_neg, axis=-1), axis=-1, keepdims=True)
-        loss = K.maximum(0., self._margin + y_neg - y_pos)
+        y_neg = tf.concat(y_neg, axis=-1)
+        y_neg = tf.reduce_mean(y_neg, axis=-1, keepdims=True)
+        loss = tf.maximum(0., self._margin + y_neg - y_pos)
         return losses_utils.compute_weighted_loss(
             loss, sample_weight, reduction=self.reduction)
 
diff --git a/matchzoo/models/conv_knrm.py b/matchzoo/models/conv_knrm.py
index 19e07eaa..c074371e 100644
--- a/matchzoo/models/conv_knrm.py
+++ b/matchzoo/models/conv_knrm.py
@@ -1,7 +1,6 @@
 """ConvKNRM model."""
 
 import keras
-import keras.backend as K
 import tensorflow as tf
 
 from .knrm import KNRM
@@ -86,13 +85,13 @@ def build(self):
                         mu = 1.0
                     mm_exp = self._kernel_layer(mu, sigma)(mm)
                     mm_doc_sum = keras.layers.Lambda(
-                        lambda x: K.sum(x, 2))(
+                        lambda x: tf.reduce_sum(x, 2))(
                         mm_exp)
                     mm_log = keras.layers.Activation(tf.math.log1p)(mm_doc_sum)
                     mm_sum = keras.layers.Lambda(
-                        lambda x: K.sum(x, 1))(mm_log)
+                        lambda x: tf.reduce_sum(x, 1))(mm_log)
                     KM.append(mm_sum)
 
-        phi = keras.layers.Lambda(lambda x: K.stack(x, 1))(KM)
+        phi = keras.layers.Lambda(lambda x: tf.stack(x, 1))(KM)
         out = self._make_output_layer()(phi)
         self._backend = keras.Model(inputs=[query, doc], outputs=[out])
diff --git a/matchzoo/models/drmm.py b/matchzoo/models/drmm.py
index e302062d..f1b19b48 100644
--- a/matchzoo/models/drmm.py
+++ b/matchzoo/models/drmm.py
@@ -3,11 +3,11 @@
 
 import keras
 import keras.backend as K
+import tensorflow as tf
 
 from matchzoo.engine.base_model import BaseModel
 from matchzoo.engine.param import Param
 from matchzoo.engine.param_table import ParamTable
-from matchzoo.engine import hyper_spaces
 
 
 class DRMM(BaseModel):
@@ -67,11 +67,11 @@ def build(self):
         # shape = [B, L, D]
         embed_query = embedding(query)
         # shape = [B, L]
-        atten_mask = K.not_equal(query, self._params['mask_value'])
+        atten_mask = tf.not_equal(query, self._params['mask_value'])
         # shape = [B, L]
-        atten_mask = K.cast(atten_mask, K.floatx())
+        atten_mask = tf.cast(atten_mask, K.floatx())
         # shape = [B, L, D]
-        atten_mask = K.expand_dims(atten_mask, axis=2)
+        atten_mask = tf.expand_dims(atten_mask, axis=2)
         # shape = [B, L, D]
         attention_probs = self.attention_layer(embed_query, atten_mask)
 
@@ -114,7 +114,7 @@ def attention_layer(cls, attention_input: typing.Any,
             )(dense_input)
         # shape = [B, L, 1]
         attention_probs = keras.layers.Lambda(
-            lambda x: keras.activations.softmax(x, axis=1),
+            lambda x: tf.nn.softmax(x, axis=1),
             output_shape=lambda s: (s[0], s[1], s[2]),
             name="attention_probs"
         )(dense_input)
diff --git a/matchzoo/models/drmmtks.py b/matchzoo/models/drmmtks.py
index 3bfae201..4ce5edee 100644
--- a/matchzoo/models/drmmtks.py
+++ b/matchzoo/models/drmmtks.py
@@ -127,7 +127,7 @@ def attention_layer(cls, attention_input: typing.Any,
             )(dense_input)
         # shape = [B, L, 1]
         attention_probs = keras.layers.Lambda(
-            lambda x: keras.activations.softmax(x, axis=1),
+            lambda x: tf.nn.softmax(x, axis=1),
             output_shape=lambda s: (s[0], s[1], s[2]),
             name="attention_probs"
         )(dense_input)
diff --git a/matchzoo/models/duet.py b/matchzoo/models/duet.py
index bce9e9a3..22783fac 100644
--- a/matchzoo/models/duet.py
+++ b/matchzoo/models/duet.py
@@ -1,13 +1,11 @@
 """DUET Model."""
 
 import keras
-import keras.backend as K
 import tensorflow as tf
 
+from matchzoo.engine import hyper_spaces
 from matchzoo.engine.base_model import BaseModel
 from matchzoo.engine.param import Param
-from matchzoo.engine.param_table import ParamTable
-from matchzoo.engine import hyper_spaces
 
 
 class DUET(BaseModel):
diff --git a/matchzoo/models/knrm.py b/matchzoo/models/knrm.py
index 1aefee44..7d1ff915 100644
--- a/matchzoo/models/knrm.py
+++ b/matchzoo/models/knrm.py
@@ -1,11 +1,9 @@
 """KNRM model."""
 import keras
-import keras.backend as K
 import tensorflow as tf
 
 from matchzoo.engine.base_model import BaseModel
 from matchzoo.engine.param import Param
-from matchzoo.engine.param_table import ParamTable
 from matchzoo.engine import hyper_spaces
 
 
@@ -70,13 +68,13 @@ def build(self):
                 mu = 1.0
             mm_exp = self._kernel_layer(mu, sigma)(mm)
             mm_doc_sum = keras.layers.Lambda(
-                lambda x: K.sum(x, 2))(mm_exp)
+                lambda x: tf.reduce_sum(x, 2))(mm_exp)
             mm_log = keras.layers.Activation(tf.math.log1p)(mm_doc_sum)
             mm_sum = keras.layers.Lambda(
-                lambda x: K.sum(x, 1))(mm_log)
+                lambda x: tf.reduce_sum(x, 1))(mm_log)
             KM.append(mm_sum)
 
-        phi = keras.layers.Lambda(lambda x: K.stack(x, 1))(KM)
+        phi = keras.layers.Lambda(lambda x: tf.stack(x, 1))(KM)
         out = self._make_output_layer()(phi)
         self._backend = keras.Model(inputs=[query, doc], outputs=[out])
 
@@ -91,6 +89,6 @@ def _kernel_layer(cls, mu: float, sigma: float) -> keras.layers.Layer:
         """
 
         def kernel(x):
-            return K.exp(-0.5 * (x - mu) * (x - mu) / sigma / sigma)
+            return tf.math.exp(-0.5 * (x - mu) * (x - mu) / sigma / sigma)
 
         return keras.layers.Activation(kernel)
diff --git a/matchzoo/models/mvlstm.py b/matchzoo/models/mvlstm.py
index bb7f8fba..425a2b97 100644
--- a/matchzoo/models/mvlstm.py
+++ b/matchzoo/models/mvlstm.py
@@ -1,14 +1,12 @@
 """An implementation of MVLSTM Model."""
-import typing
 
 import keras
 import tensorflow as tf
 
-import matchzoo
+from matchzoo.engine import hyper_spaces
 from matchzoo.engine.base_model import BaseModel
 from matchzoo.engine.param import Param
 from matchzoo.engine.param_table import ParamTable
-from matchzoo.engine import hyper_spaces
 
 
 class MVLSTM(BaseModel):

From 4ca0ddc56ab5d42b57b03538477f2bddde56caf8 Mon Sep 17 00:00:00 2001
From: zhaohao <zhaohao.zeng93@gmail.com>
Date: Tue, 8 Oct 2019 17:40:43 +0900
Subject: [PATCH 100/100] optimize import

---
 matchzoo/contrib/layers/decaying_dropout_layer.py   | 2 +-
 matchzoo/contrib/layers/semantic_composite_layer.py | 2 +-
 matchzoo/contrib/models/esim.py                     | 5 +++--
 matchzoo/contrib/models/match_lstm.py               | 2 +-
 4 files changed, 6 insertions(+), 5 deletions(-)

diff --git a/matchzoo/contrib/layers/decaying_dropout_layer.py b/matchzoo/contrib/layers/decaying_dropout_layer.py
index 152b4a31..eb3f5949 100644
--- a/matchzoo/contrib/layers/decaying_dropout_layer.py
+++ b/matchzoo/contrib/layers/decaying_dropout_layer.py
@@ -1,7 +1,7 @@
 """An implementation of Decaying Dropout Layer."""
 
-import keras.backend as K
 import tensorflow as tf
+from keras import backend as K
 from keras.engine import Layer
 
 class DecayingDropoutLayer(Layer):
diff --git a/matchzoo/contrib/layers/semantic_composite_layer.py b/matchzoo/contrib/layers/semantic_composite_layer.py
index bda4834b..9f6cb5b4 100644
--- a/matchzoo/contrib/layers/semantic_composite_layer.py
+++ b/matchzoo/contrib/layers/semantic_composite_layer.py
@@ -1,7 +1,7 @@
 """An implementation of EncodingModule for DIIN model."""
 
-import keras.backend as K
 import tensorflow as tf
+from keras import backend as K
 from keras.engine import Layer
 
 from matchzoo.contrib.layers import DecayingDropoutLayer
diff --git a/matchzoo/contrib/models/esim.py b/matchzoo/contrib/models/esim.py
index d7e8eda5..539903ba 100644
--- a/matchzoo/contrib/models/esim.py
+++ b/matchzoo/contrib/models/esim.py
@@ -1,11 +1,12 @@
 """ESIM model."""
+
 import keras
-import tensorflow as tf
 import keras.backend as K
+import tensorflow as tf
 
 import matchzoo as mz
-from matchzoo.engine.param import Param
 from matchzoo.engine.base_model import BaseModel
+from matchzoo.engine.param import Param
 from matchzoo.engine.param_table import ParamTable
 
 
diff --git a/matchzoo/contrib/models/match_lstm.py b/matchzoo/contrib/models/match_lstm.py
index 933451fc..f8c073d3 100644
--- a/matchzoo/contrib/models/match_lstm.py
+++ b/matchzoo/contrib/models/match_lstm.py
@@ -1,7 +1,7 @@
 """Match LSTM model."""
 import keras
-import tensorflow as tf
 import keras.backend as K
+import tensorflow as tf
 
 from matchzoo.engine.base_model import BaseModel
 from matchzoo.engine.param import Param