diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md index a21b2abc..a99cfe41 100644 --- a/.github/ISSUE_TEMPLATE/bug_report.md +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -19,8 +19,8 @@ Steps to reproduce the behavior: **Operating environment(运行环境):** - python version [e.g. 3.6, 3.7] - - tensorflow version [e.g. 1.4.0, 1.15.0, 2.5.0] - - deepctr version [e.g. 0.9.0,] + - tensorflow version [e.g. 1.4.0, 1.15.0, 2.10.0] + - deepctr version [e.g. 0.9.2,] **Additional context** Add any other context about the problem here. diff --git a/.github/ISSUE_TEMPLATE/question.md b/.github/ISSUE_TEMPLATE/question.md index 8aaf7ee6..8b7f819d 100644 --- a/.github/ISSUE_TEMPLATE/question.md +++ b/.github/ISSUE_TEMPLATE/question.md @@ -16,5 +16,5 @@ Add any other context about the problem here. **Operating environment(运行环境):** - python version [e.g. 3.6] - - tensorflow version [e.g. 1.4.0, 1.15.0, 2.5.0] - - deepctr version [e.g. 0.9.0,] + - tensorflow version [e.g. 1.4.0, 1.15.0, 2.10.0] + - deepctr version [e.g. 0.9.2,] diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml index 44bcc9a5..7ed5bd15 100644 --- a/.github/workflows/ci.yml +++ b/.github/workflows/ci.yml @@ -17,8 +17,8 @@ jobs: timeout-minutes: 180 strategy: matrix: - python-version: [3.6,3.7,3.8] - tf-version: [1.4.0,1.15.0,2.5.0,2.6.0,2.7.0,2.8.0,2.9.0] + python-version: [3.6,3.7,3.8,3.9,3.10.7] + tf-version: [1.4.0,1.15.0,2.6.0,2.7.0,2.8.0,2.9.0,2.10.0] exclude: - python-version: 3.7 @@ -37,12 +37,32 @@ jobs: tf-version: 2.8.0 - python-version: 3.6 tf-version: 2.9.0 + - python-version: 3.6 + tf-version: 2.10.0 - python-version: 3.9 tf-version: 1.4.0 - python-version: 3.9 tf-version: 1.15.0 - python-version: 3.9 tf-version: 2.2.0 + - python-version: 3.9 + tf-version: 2.5.0 + - python-version: 3.9 + tf-version: 2.6.0 + - python-version: 3.9 + tf-version: 2.7.0 + - python-version: 3.10.7 + tf-version: 1.4.0 + - python-version: 3.10.7 + tf-version: 1.15.0 + - python-version: 3.10.7 + tf-version: 2.2.0 + - python-version: 3.10.7 + tf-version: 2.5.0 + - python-version: 3.10.7 + tf-version: 2.6.0 + - python-version: 3.10.7 + tf-version: 2.7.0 steps: - uses: actions/checkout@v3 diff --git a/README.md b/README.md index 70ec4e28..f0d90c13 100644 --- a/README.md +++ b/README.md @@ -18,14 +18,12 @@ -DeepCTR is a **Easy-to-use**,**Modular** and **Extendible** package of deep-learning based CTR models along with lots of +DeepCTR is a **Easy-to-use**, **Modular** and **Extendible** package of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models.You can use any complex model with `model.fit()` ，and `model.predict()` . -- Provide `tf.keras.Model` like interface for **quick experiment** - . [example](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr) -- Provide `tensorflow estimator` interface for **large scale data** and **distributed training** - . [example](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr-estimator-with-tfrecord) +- Provide `tf.keras.Model` like interfaces for **quick experiment**. [example](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr) +- Provide `tensorflow estimator` interface for **large scale data** and **distributed training**. [example](https://deepctr-doc.readthedocs.io/en/latest/Quick-Start.html#getting-started-4-steps-to-deepctr-estimator-with-tfrecord) - It is compatible with both `tf 1.x` and `tf 2.x`. Some related projects: diff --git a/deepctr/__init__.py b/deepctr/__init__.py index d42e620d..3c6d40b5 100644 --- a/deepctr/__init__.py +++ b/deepctr/__init__.py @@ -1,4 +1,4 @@ from .utils import check_version -__version__ = '0.9.1' +__version__ = '0.9.2' check_version(__version__) diff --git a/deepctr/feature_column.py b/deepctr/feature_column.py index 6f277ba1..5cc1930e 100644 --- a/deepctr/feature_column.py +++ b/deepctr/feature_column.py @@ -95,7 +95,7 @@ def __hash__(self): class DenseFeat(namedtuple('DenseFeat', ['name', 'dimension', 'dtype', 'transform_fn'])): """ Dense feature Args: - name: feature name, + name: feature name. dimension: dimension of the feature, default = 1. dtype: dtype of the feature, default="float32". transform_fn: If not `None` , a function that can be used to transform diff --git a/deepctr/layers/sequence.py b/deepctr/layers/sequence.py index 45a65915..93866640 100644 --- a/deepctr/layers/sequence.py +++ b/deepctr/layers/sequence.py @@ -442,7 +442,7 @@ class Transformer(Layer): - **blinding**: bool. Whether or not use blinding. - **seed**: A Python integer to use as random seed. - **supports_masking**:bool. Whether or not support masking. - - **attention_type**: str, Type of attention, the value must be one of { ``'scaled_dot_product'`` , ``'additive'`` }. + - **attention_type**: str, Type of attention, the value must be one of { ``'scaled_dot_product'`` , ``'cos'`` , ``'ln'`` , ``'additive'`` }. - **output_type**: ``'mean'`` , ``'sum'`` or `None`. Whether or not use average/sum pooling for output. References @@ -490,6 +490,9 @@ def build(self, input_shape): initializer=glorot_uniform(seed=self.seed)) self.v = self.add_weight('v', shape=[self.att_embedding_size], dtype=tf.float32, initializer=glorot_uniform(seed=self.seed)) + elif self.attention_type == "ln": + self.att_ln_q = LayerNormalization() + self.att_ln_k = LayerNormalization() # if self.use_res: # self.W_Res = self.add_weight(name='res', shape=[embedding_size, self.att_embedding_size * self.head_num], dtype=tf.float32, # initializer=TruncatedNormal(seed=self.seed)) @@ -529,28 +532,42 @@ def call(self, inputs, mask=None, training=None, **kwargs): queries = self.query_pe(queries) keys = self.key_pe(queries) - querys = tf.tensordot(queries, self.W_Query, - axes=(-1, 0)) # None T_q D*head_num - keys = tf.tensordot(keys, self.W_key, axes=(-1, 0)) - values = tf.tensordot(keys, self.W_Value, axes=(-1, 0)) + Q = tf.tensordot(queries, self.W_Query, + axes=(-1, 0)) # N T_q D*h + K = tf.tensordot(keys, self.W_key, axes=(-1, 0)) + V = tf.tensordot(keys, self.W_Value, axes=(-1, 0)) - # head_num*None T_q D - querys = tf.concat(tf.split(querys, self.head_num, axis=2), axis=0) - keys = tf.concat(tf.split(keys, self.head_num, axis=2), axis=0) - values = tf.concat(tf.split(values, self.head_num, axis=2), axis=0) + # h*N T_q D + Q_ = tf.concat(tf.split(Q, self.head_num, axis=2), axis=0) + K_ = tf.concat(tf.split(K, self.head_num, axis=2), axis=0) + V_ = tf.concat(tf.split(V, self.head_num, axis=2), axis=0) if self.attention_type == "scaled_dot_product": - # head_num*None T_q T_k - outputs = tf.matmul(querys, keys, transpose_b=True) + # h*N T_q T_k + outputs = tf.matmul(Q_, K_, transpose_b=True) - outputs = outputs / (keys.get_shape().as_list()[-1] ** 0.5) + outputs = outputs / (K_.get_shape().as_list()[-1] ** 0.5) + elif self.attention_type == "cos": + Q_cos = tf.nn.l2_normalize(Q_, dim=-1) + K_cos = tf.nn.l2_normalize(K_, dim=-1) + + outputs = tf.matmul(Q_cos, K_cos, transpose_b=True) # h*N T_q T_k + + outputs = outputs * 20 # Scale + elif self.attention_type == 'ln': + Q_ = self.att_ln_q(Q_) + K_ = self.att_ln_k(K_) + + outputs = tf.matmul(Q_, K_, transpose_b=True) # h*N T_q T_k + # Scale + outputs = outputs / (K_.get_shape().as_list()[-1] ** 0.5) elif self.attention_type == "additive": - querys_reshaped = tf.expand_dims(querys, axis=-2) - keys_reshaped = tf.expand_dims(keys, axis=-3) - outputs = tf.tanh(tf.nn.bias_add(querys_reshaped + keys_reshaped, self.b)) + Q_reshaped = tf.expand_dims(Q_, axis=-2) + K_reshaped = tf.expand_dims(K_, axis=-3) + outputs = tf.tanh(tf.nn.bias_add(Q_reshaped + K_reshaped, self.b)) outputs = tf.squeeze(tf.tensordot(outputs, tf.expand_dims(self.v, axis=-1), axes=[-1, 0]), axis=-1) else: - raise ValueError("attention_type must be scaled_dot_product or additive") + raise ValueError("attention_type must be [scaled_dot_product,cos,ln,additive]") key_masks = tf.tile(key_masks, [self.head_num, 1]) @@ -583,7 +600,7 @@ def call(self, inputs, mask=None, training=None, **kwargs): outputs = self.dropout(outputs, training=training) # Weighted sum # ( h*N, T_q, C/h) - result = tf.matmul(outputs, values) + result = tf.matmul(outputs, V_) result = tf.concat(tf.split(result, self.head_num, axis=0), axis=2) if self.use_res: diff --git a/deepctr/models/deepfm.py b/deepctr/models/deepfm.py index 49456f4f..f156e5fb 100644 --- a/deepctr/models/deepfm.py +++ b/deepctr/models/deepfm.py @@ -24,8 +24,8 @@ def DeepFM(linear_feature_columns, dnn_feature_columns, fm_group=(DEFAULT_GROUP_ dnn_activation='relu', dnn_use_bn=False, task='binary'): """Instantiates the DeepFM Network architecture. - :param linear_feature_columns: An iterable containing all the features used by linear part of the model. - :param dnn_feature_columns: An iterable containing all the features used by deep part of the model. + :param linear_feature_columns: An iterable containing all the features used by the linear part of the model. + :param dnn_feature_columns: An iterable containing all the features used by the deep part of the model. :param fm_group: list, group_name of features that will be used to do feature interactions. :param dnn_hidden_units: list,list of positive integer or empty list, the layer number and units in each layer of DNN :param l2_reg_linear: float. L2 regularizer strength applied to linear part diff --git a/docs/source/History.md b/docs/source/History.md index e74dba8d..2e19942a 100644 --- a/docs/source/History.md +++ b/docs/source/History.md @@ -1,4 +1,5 @@ # History +- 10/15/2022 : [v0.9.2](https://github.com/shenweichen/DeepCTR/releases/tag/v0.9.2) released.Support python `3.9`,`3.10`. - 06/11/2022 : [v0.9.1](https://github.com/shenweichen/DeepCTR/releases/tag/v0.9.1) released.Improve compatibility with tensorflow `2.x`. - 09/03/2021 : [v0.9.0](https://github.com/shenweichen/DeepCTR/releases/tag/v0.9.0) released.Add multitask learning models:[SharedBottom](./Features.html#sharedbottom),[ESMM](./Features.html#esmm-entire-space-multi-task-model),[MMOE](./Features.html#mmoe-multi-gate-mixture-of-experts) and [PLE](./Features.html#ple-progressive-layered-extraction). [running example](./Examples.html#multitask-learning-mmoe) - 07/18/2021 : [v0.8.7](https://github.com/shenweichen/DeepCTR/releases/tag/v0.8.7) released.Support pre-defined key-value vocabulary in `Hash` Layer. [example](./Examples.html#hash-layer-with-pre-defined-key-value-vocabulary) diff --git a/docs/source/conf.py b/docs/source/conf.py index 50b0f80e..d0f0df24 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -26,7 +26,7 @@ # The short X.Y version version = '' # The full version, including alpha/beta/rc tags -release = '0.9.1' +release = '0.9.2' # -- General configuration --------------------------------------------------- diff --git a/docs/source/index.rst b/docs/source/index.rst index c64d26e5..0330a10d 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -42,12 +42,12 @@ You can read the latest code and related projects News ----- +10/15/2022 : Support python `3.9`,`3.10`. `Changelog `_ + 06/11/2022 : Improve compatibility with tensorflow `2.x`. `Changelog `_ 09/03/2021 : Add multitask learning models: `SharedBottom <./Features.html#sharedbottom>`_ , `ESMM <./Features.html#esmm-entire-space-multi-task-model>`_ , `MMOE <./Features.html#mmoe-multi-gate-mixture-of-experts>`_ , `PLE <./Features.html#ple-progressive-layered-extraction>`_ . `running example <./Examples.html#multitask-learning-mmoe>`_ `Changelog `_ -07/18/2021 : Support pre-defined key-value vocabulary in `Hash` Layer. `example <./Examples.html#hash-layer-with-pre-defined-key-value-vocabulary>`_ `Changelog `_ - DisscussionGroup ----------------------- diff --git a/setup.py b/setup.py index 62316c0c..43eee556 100644 --- a/setup.py +++ b/setup.py @@ -1,15 +1,21 @@ import setuptools -with open("README.md", "r") as fh: +with open("README.md", "r",encoding='utf-8') as fh: long_description = fh.read() -REQUIRED_PACKAGES = [ +import sys +if sys.version_info < (3, 9): + REQUIRED_PACKAGES = [ 'h5py==2.10.0', 'requests' -] + ] +else: + REQUIRED_PACKAGES = [ + 'h5py==3.7.0', 'requests' + ] setuptools.setup( name="deepctr", - version="0.9.1", + version="0.9.2", author="Weichen Shen", author_email="weichenswc@163.com", description="Easy-to-use,Modular and Extendible package of deep learning based CTR(Click Through Rate) prediction models with tensorflow 1.x and 2.x .", @@ -35,10 +41,11 @@ 'Intended Audience :: Science/Research', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 2.7', - 'Programming Language :: Python :: 3.5', 'Programming Language :: Python :: 3.6', 'Programming Language :: Python :: 3.7', 'Programming Language :: Python :: 3.8', + 'Programming Language :: Python :: 3.9', + 'Programming Language :: Python :: 3.10', 'Topic :: Scientific/Engineering', 'Topic :: Scientific/Engineering :: Artificial Intelligence', 'Topic :: Software Development', diff --git a/tests/layers/sequence_test.py b/tests/layers/sequence_test.py index 1639baca..1e2a89a6 100644 --- a/tests/layers/sequence_test.py +++ b/tests/layers/sequence_test.py @@ -81,11 +81,15 @@ def test_BiLSTM(merge_mode): input_shape=(BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE)) -def test_Transformer(): +@pytest.mark.parametrize( + 'attention_type', + ['scaled_dot_product', 'cos', 'ln', 'additive'] +) +def test_Transformer(attention_type): with CustomObjectScope({'Transformer': sequence.Transformer}): layer_test(sequence.Transformer, kwargs={'att_embedding_size': 1, 'head_num': 8, 'use_layer_norm': True, 'supports_masking': False, - 'attention_type': 'additive', 'dropout_rate': 0.5, 'output_type': 'sum'}, + 'attention_type': attention_type, 'dropout_rate': 0.5, 'output_type': 'sum'}, input_shape=[(BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE), (BATCH_SIZE, SEQ_LENGTH, EMBEDDING_SIZE), (BATCH_SIZE, 1), (BATCH_SIZE, 1)])