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model.py
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model.py
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import tensorflow as tf
import numpy as np
from tensorflow.python.ops.rnn_cell import LSTMCell
from tensorflow.python.ops.rnn import bidirectional_dynamic_rnn
def self_attention(inputs, name, return_alphas=False):
shared = False
# max_len = inputs.shape[1].value
hidden_size = inputs.shape[2].value # D value - hidden size of the RNN layer
if shared:
scope_name = 'self_attn'
else:
scope_name = 'self_attn' + name
with tf.variable_scope(scope_name):
den = True
if den:
x_proj = tf.layers.Dense(hidden_size)(inputs)
x_proj = tf.nn.tanh(x_proj)
else:
x_proj = inputs
u_w = tf.Variable(tf.random_normal([hidden_size, 1], stddev=0.01, seed=1227))
# x_proj = tf.reshape(x_proj, shape=[-1, hidden_size])
# x = tf.reshape(tf.matmul(x_proj, u_w), shape=[-1, max_len, 1])
x = tf.tensordot(x_proj, u_w, axes=1)
alphas = tf.nn.softmax(x, axis=-1)
output = tf.matmul(tf.transpose(inputs, [0, 2, 1]), alphas)
output = tf.squeeze(output, -1)
if not return_alphas:
return output
else:
return output, alphas
def ffn_layer(inputs, hidden_units, output_units, use_bias=True, bias_init=0., activation=tf.nn.tanh,
scope="ffn_layer"):
with tf.variable_scope(scope):
dim = inputs.get_shape().as_list()[-1]
# hidden layer
hidden_weight = tf.get_variable(name="hidden_weight", shape=[dim, hidden_units], dtype=tf.float32)
hidden_output = tf.matmul(inputs, hidden_weight)
if use_bias:
hidden_bias = tf.get_variable(name="hidden_bias", shape=[hidden_units], dtype=tf.float32,
initializer=tf.constant_initializer(bias_init))
hidden_output = tf.nn.bias_add(hidden_output, hidden_bias)
hidden_output = activation(hidden_output)
# output layer
weight = tf.get_variable(name="weight", shape=[hidden_units, output_units], dtype=tf.float32)
output = tf.matmul(hidden_output, weight)
if use_bias:
bias = tf.get_variable(name="bias", shape=[output_units], dtype=tf.float32,
initializer=tf.constant_initializer(bias_init))
output = tf.nn.bias_add(output, bias)
output = activation(output)
return output
class Model:
def __init__(self, cfg, vocab_counts):
# add data placeholders
self.left_context = tf.placeholder(name="left_context", shape=[None, None], dtype=tf.int32)
self.left_seq_len = tf.placeholder(name="left_seq_len", shape=[None], dtype=tf.int32)
self.right_context = tf.placeholder(name="right_context", shape=[None, None], dtype=tf.int32)
self.right_seq_len = tf.placeholder(name="right_seq_len", shape=[None], dtype=tf.int32)
self.verb = tf.placeholder(name="verb", shape=[None], dtype=tf.int32)
# add hyper-parameter placeholders
self.batch_size = tf.placeholder(name="batch_size", dtype=tf.int32)
self.is_train = tf.placeholder(name="is_train", shape=[], dtype=tf.bool)
self.drop_rate = tf.placeholder(name="dropout_rate", dtype=tf.float32)
self.lr = tf.placeholder(name="learning_rate", dtype=tf.float32)
# build embedding lookup table
with tf.device("/gpu:0"):
with tf.variable_scope("context_lookup_table"):
if not cfg.use_pretrained:
self.word_embeddings = tf.get_variable(name="word_embeddings",
shape=[cfg.vocab_size, cfg.word_dim],
dtype=tf.float32,
trainable=True)
else:
self.word_embeddings = tf.Variable(np.load(cfg.pretrained_context)["embeddings"],
name="word_embeddings",
dtype=tf.float32,
trainable=cfg.tune_emb)
self.word_embeddings = tf.concat([tf.zeros([1, cfg.word_dim]), self.word_embeddings[1:, :]], axis=0)
with tf.variable_scope("target_lookup_table"):
if not cfg.use_pretrained:
self.verb_embeddings = tf.get_variable(name="verb_embeddings",
shape=[cfg.verb_size, cfg.word_dim],
dtype=tf.float32,
trainable=True)
else:
self.verb_embeddings = tf.Variable(np.load(cfg.pretrained_target)["embeddings"],
name="verb_embeddings",
dtype=tf.float32,
trainable=cfg.tune_emb)
self.verb_embeddings = tf.concat([tf.zeros([1, cfg.word_dim]), self.verb_embeddings[1:, :]], axis=0)
# negative sampling
self.neg_ids, _, _ = (tf.nn.fixed_unigram_candidate_sampler(
true_classes=tf.cast(tf.expand_dims(self.verb, axis=1), dtype=tf.int64),
num_true=1,
num_sampled=cfg.neg_sample,
unique=True,
range_max=cfg.verb_size - 1,
distortion=0.75,
unigrams=vocab_counts))
# embedding lookup
# with tf.device("/gpu:0"):
with tf.variable_scope("embedding_lookup"):
left_context_emb = tf.nn.embedding_lookup(self.word_embeddings, self.left_context)
right_context_emb = tf.nn.embedding_lookup(self.word_embeddings, self.right_context)
verb_emb = tf.nn.embedding_lookup(self.verb_embeddings, self.verb)
neg_verb_emb = tf.nn.embedding_lookup(self.verb_embeddings, self.neg_ids)
# left context bi-lstm
with tf.device("/gpu:0"):
with tf.variable_scope("right_context_representation"):
cell_fw = LSTMCell(num_units=cfg.num_units)
cell_bw = LSTMCell(num_units=cfg.num_units)
h_rc, _ = bidirectional_dynamic_rnn(cell_fw, cell_bw, right_context_emb,
sequence_length=self.right_seq_len,
dtype=tf.float32,
time_major=False,
scope="bi_lstm")
h_rc = tf.concat(h_rc, axis=-1)
# self-attention
h_rc = self_attention(h_rc, name="self_attn_right")
r_weight = tf.get_variable(name="r_weight",
shape=[2 * cfg.num_units, 2 * cfg.num_units],
dtype=tf.float32)
h_rc = tf.nn.tanh(tf.matmul(h_rc, r_weight))
print("right context shape: {}".format(h_rc.get_shape().as_list()))
with tf.variable_scope("left_context_representation"):
cell_fw = LSTMCell(num_units=cfg.num_units)
cell_bw = LSTMCell(num_units=cfg.num_units)
h_lc, _ = bidirectional_dynamic_rnn(cell_fw, cell_bw, left_context_emb,
sequence_length=self.left_seq_len,
dtype=tf.float32,
time_major=False,
scope="bi_lstm")
h_lc = tf.concat(h_lc, axis=-1) # shape = (batch_size, max_len, 2 * num_units)
# self-attention
h_lc = self_attention(h_lc, name="self_attn_left") # shape = (batch_size, 2 * num_units)
l_weight = tf.get_variable(name="l_weight",
shape=[2 * cfg.num_units, 2 * cfg.num_units],
dtype=tf.float32)
h_lc = tf.nn.tanh(tf.matmul(h_lc, l_weight))
print("left context shape: {}".format(h_lc.get_shape().as_list()))
# with tf.device("/gpu:0"):
with tf.device("/gpu:1"):
with tf.variable_scope("neural_tensor_network"):
T = tf.get_variable(name="T",
shape=[cfg.output_units, 2 * cfg.num_units, 2 * cfg.num_units],
dtype=tf.float32)
W = tf.get_variable(name="W",
shape=[4 * cfg.num_units, cfg.output_units],
dtype=tf.float32)
b = tf.get_variable(name="b",
shape=[cfg.output_units],
dtype=tf.float32)
# compute tensors
ff_product = tf.matmul(tf.concat([h_lc, h_rc], axis=-1), W)
bilinear_list = []
for k in range(cfg.output_units):
cur_res = tf.reduce_sum(tf.matmul(h_lc, T[k]) * h_rc, axis=1)
bilinear_list.append(cur_res)
context = tf.nn.tanh(tf.reshape(tf.concat(bilinear_list, axis=0), shape=[-1, cfg.output_units]) +
ff_product + b) # shape = (batch_size, output_units)
print("context representation shape: {}".format(context.get_shape().as_list()))
# with tf.device("/gpu:1"):
with tf.variable_scope("verb_representation"):
target_verb = ffn_layer(verb_emb, cfg.num_units, cfg.output_units, scope="ffn_layer")
print("verb representation shape: {}".format(target_verb.get_shape().as_list()))
tf.get_variable_scope().reuse_variables()
negative_verbs = ffn_layer(neg_verb_emb, cfg.num_units, cfg.output_units, scope="ffn_layer")
print("negative verb shape: {}".format(negative_verbs.get_shape().as_list()))
with tf.variable_scope("compute_loss"):
true_logits = tf.reduce_sum(context * target_verb, axis=1)
print("true logits shape: {}".format(true_logits.get_shape().as_list()))
neg_logits = tf.matmul(context, tf.transpose(negative_verbs, [1, 0]))
print("negative logits shape: {}".format(neg_logits.get_shape().as_list()))
# with tf.device("/cpu:0"):
with tf.variable_scope("nce_loss"):
# cross-entropy(logits, labels)
true_xent = tf.nn.sigmoid_cross_entropy_with_logits(logits=true_logits,
labels=tf.ones_like(true_logits))
sampled_xent = tf.nn.sigmoid_cross_entropy_with_logits(logits=neg_logits,
labels=tf.zeros_like(neg_logits))
# NCE-loss is the sum of the true and noise (sampled words) contributions, averaged over the batch.
self.loss = (tf.reduce_sum(true_xent) + tf.reduce_sum(sampled_xent)) / tf.cast(self.batch_size,
dtype=tf.float32)
optimizer = tf.train.AdamOptimizer(learning_rate=self.lr)
self.train_op = optimizer.minimize(self.loss)
def get_feed_dict(self, data, is_train=False, drop_rate=0.0, lr=None):
feed_dict = {
self.verb: data["vb"],
self.left_context: data["lc"],
self.left_seq_len: data["ll"],
self.right_context: data["rc"],
self.right_seq_len: data["rl"],
self.batch_size: data["batch_size"]
}
if lr is not None:
feed_dict[self.lr] = lr
feed_dict[self.is_train] = is_train
feed_dict[self.drop_rate] = drop_rate
return feed_dict