add EDCN model

- add EDCN model
- fix savedmodel error in tf2.4
- fix typo in position encoding layer

.github/workflows/ci.yml

@@ -1,6 +1,6 @@
-name: CI
+name: CI_TF2
 
-on: 
+on:
   push:
     path:
       - 'deepctr/*'
@@ -9,17 +9,17 @@ on:
     path:
       - 'deepctr/*'
       - 'tests/*'
-      
+
 jobs:
   build:
 
     runs-on: ubuntu-latest
     timeout-minutes: 180
     strategy:
       matrix:
-        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]
-        
+        python-version: [ 3.6,3.7,3.8, 3.9,3.10.7 ]
+        tf-version: [ 2.6.0,2.7.0,2.8.0,2.9.0,2.10.0 ]
+
         exclude:
           - python-version: 3.7
             tf-version: 1.4.0
@@ -64,31 +64,31 @@ jobs:
           - python-version: 3.10.7
             tf-version: 2.7.0
     steps:
-
-    - uses: actions/checkout@v3
-
-    - name: Setup python environment
-      uses: actions/setup-python@v4
-      with:
-        python-version: ${{ matrix.python-version }}
 
-    - name: Install dependencies
-      run: |
-        pip3 install -q tensorflow==${{ matrix.tf-version }}
-        pip install -q protobuf==3.19.0
-        pip install -q requests
-        pip install -e .
-    - name: Test with pytest
-      timeout-minutes: 180
-      run: |
-        pip install -q pytest
-        pip install -q pytest-cov
-        pip install -q python-coveralls
-        pytest --cov=deepctr --cov-report=xml
-    - name: Upload coverage to Codecov  
-      uses: codecov/codecov-action@v3.1.0
-      with:
-        token: ${{secrets.CODECOV_TOKEN}}
-        file: ./coverage.xml
-        flags: pytest
-        name: py${{ matrix.python-version }}-tf${{ matrix.tf-version }}
+      - uses: actions/checkout@v3
+
+      - name: Setup python environment
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          pip3 install -q tensorflow==${{ matrix.tf-version }}
+          pip install -q protobuf==3.19.0
+          pip install -q requests
+          pip install -e .
+      - name: Test with pytest
+        timeout-minutes: 180
+        run: |
+          pip install -q pytest
+          pip install -q pytest-cov
+          pip install -q python-coveralls
+          pytest --cov=deepctr --cov-report=xml
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v3.1.0
+        with:
+          token: ${{secrets.CODECOV_TOKEN}}
+          file: ./coverage.xml
+          flags: pytest
+          name: py${{ matrix.python-version }}-tf${{ matrix.tf-version }}

.github/workflows/ci2.yml

@@ -0,0 +1,96 @@
+name: CI_TF1
+
+on:
+  push:
+    path:
+      - 'deepctr/*'
+      - 'tests/*'
+  pull_request:
+    path:
+      - 'deepctr/*'
+      - 'tests/*'
+
+jobs:
+  build:
+
+    runs-on: ubuntu-latest
+    timeout-minutes: 180
+    strategy:
+      matrix:
+        python-version: [ 3.6,3.7 ]
+        tf-version: [ 1.15.0 ]
+
+        exclude:
+          - python-version: 3.7
+            tf-version: 1.4.0
+          - python-version: 3.7
+            tf-version: 1.12.0
+          - python-version: 3.7
+            tf-version: 1.15.0
+          - python-version: 3.8
+            tf-version: 1.4.0
+          - python-version: 3.8
+            tf-version: 1.14.0
+          - python-version: 3.8
+            tf-version: 1.15.0
+          - python-version: 3.6
+            tf-version: 2.7.0
+          - python-version: 3.6
+            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
+
+      - name: Setup python environment
+        uses: actions/setup-python@v4
+        with:
+          python-version: ${{ matrix.python-version }}
+
+      - name: Install dependencies
+        run: |
+          pip3 install -q tensorflow==${{ matrix.tf-version }}
+          pip install -q protobuf==3.19.0
+          pip install -q requests
+          pip install -e .
+      - name: Test with pytest
+        timeout-minutes: 180
+        run: |
+          pip install -q pytest
+          pip install -q pytest-cov
+          pip install -q python-coveralls
+          pytest --cov=deepctr --cov-report=xml
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v3.1.0
+        with:
+          token: ${{secrets.CODECOV_TOKEN}}
+          file: ./coverage.xml
+          flags: pytest
+          name: py${{ matrix.python-version }}-tf${{ matrix.tf-version }}

README.md

@@ -66,6 +66,7 @@ Introduction](https://zhuanlan.zhihu.com/p/53231955)) and [welcome to join us!](
 |   ESMM                    | [SIGIR 2018][Entire Space Multi-Task Model: An Effective Approach for Estimating Post-Click Conversion Rate](https://arxiv.org/abs/1804.07931)                       |
 |   MMOE                    | [KDD 2018][Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts](https://dl.acm.org/doi/abs/10.1145/3219819.3220007)                   |
 |   PLE                    | [RecSys 2020][Progressive Layered Extraction (PLE): A Novel Multi-Task Learning (MTL) Model for Personalized Recommendations](https://dl.acm.org/doi/10.1145/3383313.3412236)                   |
+|   EDCN                   | [KDD 2021][Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models](https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_12.pdf)                   |
 
 ## Citation
 

deepctr/__init__.py

@@ -1,4 +1,4 @@
 from .utils import check_version
 
-__version__ = '0.9.2'
+__version__ = '0.9.3'
 check_version(__version__)

deepctr/layers/__init__.py

@@ -1,17 +1,16 @@
 import tensorflow as tf
 
 from .activation import Dice
-from .core import DNN, LocalActivationUnit, PredictionLayer
+from .core import DNN, LocalActivationUnit, PredictionLayer, RegulationModule
 from .interaction import (CIN, FM, AFMLayer, BiInteractionPooling, CrossNet, CrossNetMix,
                           InnerProductLayer, InteractingLayer,
                           OutterProductLayer, FGCNNLayer, SENETLayer, BilinearInteraction,
-                          FieldWiseBiInteraction, FwFMLayer, FEFMLayer)
+                          FieldWiseBiInteraction, FwFMLayer, FEFMLayer, BridgeModule)
 from .normalization import LayerNormalization
 from .sequence import (AttentionSequencePoolingLayer, BiasEncoding, BiLSTM,
                        KMaxPooling, SequencePoolingLayer, WeightedSequenceLayer,
-                       Transformer, DynamicGRU,PositionEncoding)
-
-from .utils import NoMask, Hash, Linear, _Add, combined_dnn_input, softmax, reduce_sum
+                       Transformer, DynamicGRU, PositionEncoding)
+from .utils import NoMask, Hash, Linear, _Add, combined_dnn_input, softmax, reduce_sum, Concat
 
 custom_objects = {'tf': tf,
                   'InnerProductLayer': InnerProductLayer,
@@ -38,6 +37,7 @@
                   'FGCNNLayer': FGCNNLayer,
                   'Hash': Hash,
                   'Linear': Linear,
+                  'Concat': Concat,
                   'DynamicGRU': DynamicGRU,
                   'SENETLayer': SENETLayer,
                   'BilinearInteraction': BilinearInteraction,
@@ -48,5 +48,7 @@
                   'softmax': softmax,
                   'FEFMLayer': FEFMLayer,
                   'reduce_sum': reduce_sum,
-                  'PositionEncoding':PositionEncoding
+                  'PositionEncoding': PositionEncoding,
+                  'RegulationModule': RegulationModule,
+                  'BridgeModule': BridgeModule
                   }

deepctr/layers/core.py

@@ -10,9 +10,9 @@
 from tensorflow.python.keras import backend as K
 
 try:
-    from tensorflow.python.ops.init_ops_v2 import Zeros, glorot_normal
+    from tensorflow.python.ops.init_ops_v2 import Zeros, Ones, glorot_normal
 except ImportError:
-    from tensorflow.python.ops.init_ops import Zeros, glorot_normal_initializer as glorot_normal
+    from tensorflow.python.ops.init_ops import Zeros, Ones, glorot_normal_initializer as glorot_normal
 
 from tensorflow.python.keras.layers import Layer, Dropout
 
@@ -265,3 +265,57 @@ def get_config(self, ):
         config = {'task': self.task, 'use_bias': self.use_bias}
         base_config = super(PredictionLayer, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
+
+
+class RegulationModule(Layer):
+    """Regulation module used in EDCN.
+
+      Input shape
+        - 3D tensor with shape: ``(batch_size,field_size,embedding_size)``.
+
+      Output shape
+        - 2D tensor with shape: ``(batch_size,field_size * embedding_size)``.
+
+      Arguments
+        - **tau** : Positive float, the temperature coefficient to control
+        distribution of field-wise gating unit.
+
+      References
+        - [Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models.](https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_12.pdf)
+    """
+
+    def __init__(self, tau=1.0, **kwargs):
+        if tau == 0:
+            raise ValueError("RegulationModule tau can not be zero.")
+        self.tau = 1.0 / tau
+        super(RegulationModule, self).__init__(**kwargs)
+
+    def build(self, input_shape):
+        self.field_size = int(input_shape[1])
+        self.embedding_size = int(input_shape[2])
+        self.g = self.add_weight(
+            shape=(1, self.field_size, 1),
+            initializer=Ones(),
+            name=self.name + '_field_weight')
+
+        # Be sure to call this somewhere!
+        super(RegulationModule, self).build(input_shape)
+
+    def call(self, inputs, **kwargs):
+
+        if K.ndim(inputs) != 3:
+            raise ValueError(
+                "Unexpected inputs dimensions %d, expect to be 3 dimensions" % (K.ndim(inputs)))
+
+        feild_gating_score = tf.nn.softmax(self.g * self.tau, 1)
+        E = inputs * feild_gating_score
+        return tf.reshape(E, [-1, self.field_size * self.embedding_size])
+
+    def compute_output_shape(self, input_shape):
+        return (None, self.field_size * self.embedding_size)
+
+    def get_config(self):
+        config = {'tau': self.tau}
+        base_config = super(RegulationModule, self).get_config()
+        base_config.update(config)
+        return base_config

deepctr/layers/interaction.py

@@ -3,7 +3,8 @@
 
 Authors:
     Weichen Shen,weichenswc@163.com,
-    Harshit Pande
+    Harshit Pande,
+    Yi He, heyi_jack@163.com
 
 """
 
@@ -26,6 +27,7 @@
 
 from .activation import activation_layer
 from .utils import concat_func, reduce_sum, softmax, reduce_mean
+from .core import DNN
 
 
 class AFMLayer(Layer):
@@ -1489,3 +1491,69 @@ def get_config(self):
             'regularizer': self.regularizer,
         })
         return config
+
+
+class BridgeModule(Layer):
+    """Bridge Module used in EDCN
+
+      Input shape
+        - A list of two 2D tensor with shape: ``(batch_size, units)``.
+
+      Output shape
+        - 2D tensor with shape: ``(batch_size, units)``.
+
+    Arguments
+        - **bridge_type**: The type of bridge interaction, one of 'pointwise_addition', 'hadamard_product', 'concatenation', 'attention_pooling'
+
+        - **activation**: Activation function to use.
+
+      References
+        - [Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models.](https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_12.pdf)
+
+    """
+
+    def __init__(self, bridge_type='hadamard_product', activation='relu', **kwargs):
+        self.bridge_type = bridge_type
+        self.activation = activation
+
+        super(BridgeModule, self).__init__(**kwargs)
+
+    def build(self, input_shape):
+        if not isinstance(input_shape, list) or len(input_shape) < 2:
+            raise ValueError(
+                'A `BridgeModule` layer should be called '
+                'on a list of 2 inputs')
+
+        self.dnn_dim = int(input_shape[0][-1])
+        if self.bridge_type == "concatenation":
+            self.dense = Dense(self.dnn_dim, self.activation)
+        elif self.bridge_type == "attention_pooling":
+            self.dense_x = DNN([self.dnn_dim, self.dnn_dim], self.activation, output_activation='softmax')
+            self.dense_h = DNN([self.dnn_dim, self.dnn_dim], self.activation, output_activation='softmax')
+
+        super(BridgeModule, self).build(input_shape)  # Be sure to call this somewhere!
+
+    def call(self, inputs, **kwargs):
+        x, h = inputs
+        if self.bridge_type == "pointwise_addition":
+            return x + h
+        elif self.bridge_type == "hadamard_product":
+            return x * h
+        elif self.bridge_type == "concatenation":
+            return self.dense(tf.concat([x, h], axis=-1))
+        elif self.bridge_type == "attention_pooling":
+            a_x = self.dense_x(x)
+            a_h = self.dense_h(h)
+            return a_x * x + a_h * h
+
+    def compute_output_shape(self, input_shape):
+        return (None, self.dnn_dim)
+
+    def get_config(self):
+        base_config = super(BridgeModule, self).get_config().copy()
+        config = {
+            'bridge_type': self.bridge_type,
+            'activation': self.activation
+        }
+        config.update(base_config)
+        return config