diff --git a/.github/workflows/ci.yml b/.github/workflows/ci.yml
index 7ed5bd15..3001b2e0 100644
--- a/.github/workflows/ci.yml
+++ b/.github/workflows/ci.yml
@@ -1,6 +1,6 @@
-name: CI
+name: CI_TF2
 
-on: 
+on:
   push:
     path:
       - 'deepctr/*'
@@ -9,7 +9,7 @@ on:
     path:
       - 'deepctr/*'
       - 'tests/*'
-      
+
 jobs:
   build:
 
@@ -17,9 +17,9 @@ jobs:
     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 }}
diff --git a/.github/workflows/ci2.yml b/.github/workflows/ci2.yml
new file mode 100644
index 00000000..e9901cb1
--- /dev/null
+++ b/.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 }}
diff --git a/README.md b/README.md
index f0d90c13..ec42fd31 100644
--- a/README.md
+++ b/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
 
diff --git a/deepctr/__init__.py b/deepctr/__init__.py
index 3c6d40b5..7eaabe48 100644
--- a/deepctr/__init__.py
+++ b/deepctr/__init__.py
@@ -1,4 +1,4 @@
 from .utils import check_version
 
-__version__ = '0.9.2'
+__version__ = '0.9.3'
 check_version(__version__)
diff --git a/deepctr/layers/__init__.py b/deepctr/layers/__init__.py
index 1bfd40ef..18e45011 100644
--- a/deepctr/layers/__init__.py
+++ b/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
                   }
diff --git a/deepctr/layers/core.py b/deepctr/layers/core.py
index 668348d2..ad249473 100644
--- a/deepctr/layers/core.py
+++ b/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
diff --git a/deepctr/layers/interaction.py b/deepctr/layers/interaction.py
index d26eb2c1..f76eda32 100644
--- a/deepctr/layers/interaction.py
+++ b/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
diff --git a/deepctr/layers/sequence.py b/deepctr/layers/sequence.py
index 93866640..6b8b93b6 100644
--- a/deepctr/layers/sequence.py
+++ b/deepctr/layers/sequence.py
@@ -11,10 +11,9 @@
 from tensorflow.python.keras import backend as K
 
 try:
-    from tensorflow.python.ops.init_ops import TruncatedNormal, glorot_uniform_initializer as glorot_uniform, \
-        identity_initializer as identity
+    from tensorflow.python.ops.init_ops import TruncatedNormal, Constant, glorot_uniform_initializer as glorot_uniform
 except ImportError:
-    from tensorflow.python.ops.init_ops_v2 import TruncatedNormal, glorot_uniform, identity
+    from tensorflow.python.ops.init_ops_v2 import TruncatedNormal, Constant, glorot_uniform
 
 from tensorflow.python.keras.layers import LSTM, Lambda, Layer, Dropout
 
@@ -387,7 +386,7 @@ def call(self, inputs, mask=None, **kwargs):
         elif self.merge_mode == "bw":
             output = output_bw
         elif self.merge_mode == 'concat':
-            output = K.concatenate([output_fw, output_bw])
+            output = tf.concat([output_fw, output_bw], axis=-1)
         elif self.merge_mode == 'sum':
             output = output_fw + output_bw
         elif self.merge_mode == 'ave':
@@ -530,7 +529,7 @@ def call(self, inputs, mask=None, training=None, **kwargs):
 
         if self.use_positional_encoding:
             queries = self.query_pe(queries)
-            keys = self.key_pe(queries)
+            keys = self.key_pe(keys)
 
         Q = tf.tensordot(queries, self.W_Query,
                          axes=(-1, 0))  # N T_q D*h
@@ -665,7 +664,7 @@ def build(self, input_shape):
         if self.zero_pad:
             position_enc[0, :] = np.zeros(num_units)
         self.lookup_table = self.add_weight("lookup_table", (T, num_units),
-                                            initializer=identity(position_enc),
+                                            initializer=Constant(position_enc),
                                             trainable=self.pos_embedding_trainable)
 
         # Be sure to call this somewhere!
@@ -867,52 +866,3 @@ def get_config(self, ):
         config = {'k': self.k, 'axis': self.axis}
         base_config = super(KMaxPooling, self).get_config()
         return dict(list(base_config.items()) + list(config.items()))
-
-# def positional_encoding(inputs,
-#                         pos_embedding_trainable=True,
-#                         zero_pad=False,
-#                         scale=True,
-#                         ):
-#     '''Sinusoidal Positional_Encoding.
-#
-#     Args:
-#
-#       - inputs: A 2d Tensor with shape of (N, T).
-#       - num_units: Output dimensionality
-#       - zero_pad: Boolean. If True, all the values of the first row (id = 0) should be constant zero
-#       - scale: Boolean. If True, the output will be multiplied by sqrt num_units(check details from paper)
-#       - scope: Optional scope for `variable_scope`.
-#       - reuse: Boolean, whether to reuse the weights of a previous layer by the same name.
-#
-#     Returns:
-#
-#       - A 'Tensor' with one more rank than inputs's, with the dimensionality should be 'num_units'
-#     '''
-#
-#     _, T, num_units = inputs.get_shape().as_list()
-#     # with tf.variable_scope(scope, reuse=reuse):
-#     position_ind = tf.expand_dims(tf.range(T), 0)
-#     # First part of the PE function: sin and cos argument
-#     position_enc = np.array([
-#         [pos / np.power(10000, 2. * i / num_units)
-#          for i in range(num_units)]
-#         for pos in range(T)])
-#
-#     # Second part, apply the cosine to even columns and sin to odds.
-#     position_enc[:, 0::2] = np.sin(position_enc[:, 0::2])  # dim 2i
-#     position_enc[:, 1::2] = np.cos(position_enc[:, 1::2])  # dim 2i+1
-#
-#     # Convert to a tensor
-#
-#     if pos_embedding_trainable:
-#         lookup_table = K.variable(position_enc, dtype=tf.float32)
-#
-#     if zero_pad:
-#         lookup_table = tf.concat((tf.zeros(shape=[1, num_units]),
-#                                   lookup_table[1:, :]), 0)
-#
-#     outputs = tf.nn.embedding_lookup(lookup_table, position_ind)
-#
-#     if scale:
-#         outputs = outputs * num_units ** 0.5
-#     return outputs + inputs
diff --git a/deepctr/layers/utils.py b/deepctr/layers/utils.py
index 2be8f3fe..07eec6e0 100644
--- a/deepctr/layers/utils.py
+++ b/deepctr/layers/utils.py
@@ -6,7 +6,8 @@
 
 """
 import tensorflow as tf
-from tensorflow.python.keras.layers import Flatten, Concatenate, Layer, Add
+from tensorflow.python.keras import backend as K
+from tensorflow.python.keras.layers import Flatten, Layer, Add
 from tensorflow.python.ops.lookup_ops import TextFileInitializer
 
 try:
@@ -185,13 +186,60 @@ def get_config(self, ):
         return dict(list(base_config.items()) + list(config.items()))
 
 
+class Concat(Layer):
+    def __init__(self, axis, supports_masking=True, **kwargs):
+        super(Concat, self).__init__(**kwargs)
+        self.axis = axis
+        self.supports_masking = supports_masking
+
+    def call(self, inputs):
+        return tf.concat(inputs, axis=self.axis)
+
+    def compute_mask(self, inputs, mask=None):
+        if not self.supports_masking:
+            return None
+        if mask is None:
+            mask = [inputs_i._keras_mask if hasattr(inputs_i, "_keras_mask") else None for inputs_i in inputs]
+        if mask is None:
+            return None
+        if not isinstance(mask, list):
+            raise ValueError('`mask` should be a list.')
+        if not isinstance(inputs, list):
+            raise ValueError('`inputs` should be a list.')
+        if len(mask) != len(inputs):
+            raise ValueError('The lists `inputs` and `mask` '
+                             'should have the same length.')
+        if all([m is None for m in mask]):
+            return None
+        # Make a list of masks while making sure
+        # the dimensionality of each mask
+        # is the same as the corresponding input.
+        masks = []
+        for input_i, mask_i in zip(inputs, mask):
+            if mask_i is None:
+                # Input is unmasked. Append all 1s to masks,
+                masks.append(tf.ones_like(input_i, dtype='bool'))
+            elif K.ndim(mask_i) < K.ndim(input_i):
+                # Mask is smaller than the input, expand it
+                masks.append(tf.expand_dims(mask_i, axis=-1))
+            else:
+                masks.append(mask_i)
+        concatenated = K.concatenate(masks, axis=self.axis)
+        return K.all(concatenated, axis=-1, keepdims=False)
+
+    def get_config(self, ):
+        config = {'axis': self.axis, 'supports_masking': self.supports_masking}
+        base_config = super(Concat, self).get_config()
+        return dict(list(base_config.items()) + list(config.items()))
+
+
 def concat_func(inputs, axis=-1, mask=False):
-    if not mask:
-        inputs = list(map(NoMask(), inputs))
     if len(inputs) == 1:
-        return inputs[0]
-    else:
-        return Concatenate(axis=axis)(inputs)
+        input = inputs[0]
+        if not mask:
+            input = NoMask()(input)
+        return input
+    return Concat(axis, supports_masking=mask)(inputs)
 
 
 def reduce_mean(input_tensor,
@@ -271,10 +319,6 @@ def build(self, input_shape):
         super(_Add, self).build(input_shape)
 
     def call(self, inputs, **kwargs):
-        # if not isinstance(inputs, list):
-        #     return inputs
-        # if len(inputs) == 1:
-        #     return inputs[0]
         if len(inputs) == 0:
             return tf.constant([[0.0]])
 
diff --git a/deepctr/models/__init__.py b/deepctr/models/__init__.py
index 2d19714b..1d797e78 100644
--- a/deepctr/models/__init__.py
+++ b/deepctr/models/__init__.py
@@ -20,7 +20,8 @@
 from .sequence import DIN, DIEN, DSIN, BST
 from .wdl import WDL
 from .xdeepfm import xDeepFM
+from .edcn import EDCN
 
 __all__ = ["AFM", "CCPM", "DCN", "IFM", "DIFM", "DCNMix", "MLR", "DeepFM", "MLR", "NFM", "DIN", "DIEN", "FNN", "PNN",
            "WDL", "xDeepFM", "AutoInt", "ONN", "FGCNN", "DSIN", "FiBiNET", 'FLEN', "FwFM", "BST", "DeepFEFM",
-           "SharedBottom", "ESMM", "MMOE", "PLE"]
+           "SharedBottom", "ESMM", "MMOE", "PLE", 'EDCN']
diff --git a/deepctr/models/edcn.py b/deepctr/models/edcn.py
new file mode 100644
index 00000000..973d6391
--- /dev/null
+++ b/deepctr/models/edcn.py
@@ -0,0 +1,94 @@
+# -*- coding:utf-8 -*-
+"""
+Author:
+    Yi He, heyi_jack@163.com
+
+Reference:
+    [1] Chen, B., Wang, Y., Liu, et al. Enhancing Explicit and Implicit Feature Interactions via Information Sharing for Parallel Deep CTR Models. CIKM, 2021, October (https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_12.pdf)
+"""
+from tensorflow.python.keras.layers import Dense, Reshape, Concatenate
+from tensorflow.python.keras.models import Model
+
+from ..feature_column import build_input_features, get_linear_logit, input_from_feature_columns
+from ..layers.core import PredictionLayer, DNN, RegulationModule
+from ..layers.interaction import CrossNet, BridgeModule
+from ..layers.utils import add_func, concat_func
+
+
+def EDCN(linear_feature_columns,
+         dnn_feature_columns,
+         cross_num=2,
+         cross_parameterization='vector',
+         bridge_type='concatenation',
+         tau=1.0,
+         l2_reg_linear=1e-5,
+         l2_reg_embedding=1e-5,
+         l2_reg_cross=1e-5,
+         l2_reg_dnn=0,
+         seed=1024,
+         dnn_dropout=0,
+         dnn_use_bn=False,
+         dnn_activation='relu',
+         task='binary'):
+    """Instantiates the Enhanced Deep&Cross 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 cross_num: positive integet,cross layer number
+    :param cross_parameterization: str, ``"vector"`` or ``"matrix"``, how to parameterize the cross network.
+    :param bridge_type: The type of bridge interaction, one of ``"pointwise_addition"``, ``"hadamard_product"``, ``"concatenation"`` , ``"attention_pooling"``
+    :param tau: Positive float, the temperature coefficient to control distribution of field-wise gating unit
+    :param l2_reg_linear: float. L2 regularizer strength applied to linear part
+    :param l2_reg_embedding: float. L2 regularizer strength applied to embedding vector
+    :param l2_reg_cross: float. L2 regularizer strength applied to cross net
+    :param l2_reg_dnn: float. L2 regularizer strength applied to DNN
+    :param seed: integer ,to use as random seed.
+    :param dnn_dropout: float in [0,1), the probability we will drop out a given DNN coordinate.
+    :param dnn_use_bn: bool. Whether use BatchNormalization before activation or not DNN
+    :param dnn_activation: Activation function to use in DNN
+    :param task: str, ``"binary"`` for  binary logloss or  ``"regression"`` for regression loss
+    :return: A Keras model instance.
+
+    """
+    if cross_num == 0:
+        raise ValueError("Cross layer num must > 0")
+
+    print('EDCN brige type: ', bridge_type)
+
+    features = build_input_features(dnn_feature_columns)
+    inputs_list = list(features.values())
+
+    linear_logit = get_linear_logit(features, linear_feature_columns, seed=seed, prefix='linear', l2_reg=l2_reg_linear)
+
+    sparse_embedding_list, _ = input_from_feature_columns(
+        features, dnn_feature_columns, l2_reg_embedding, seed, support_dense=False)
+
+    emb_input = concat_func(sparse_embedding_list, axis=1)
+    deep_in = RegulationModule(tau)(emb_input)
+    cross_in = RegulationModule(tau)(emb_input)
+
+    field_size = len(sparse_embedding_list)
+    embedding_size = int(sparse_embedding_list[0].shape[-1])
+    cross_dim = field_size * embedding_size
+
+    for i in range(cross_num):
+        cross_out = CrossNet(1, parameterization=cross_parameterization,
+                             l2_reg=l2_reg_cross)(cross_in)
+        deep_out = DNN([cross_dim], dnn_activation, l2_reg_dnn,
+                       dnn_dropout, dnn_use_bn, seed=seed)(deep_in)
+        print(cross_out, deep_out)
+        bridge_out = BridgeModule(bridge_type)([cross_out, deep_out])
+        if i + 1 < cross_num:
+            bridge_out_list = Reshape([field_size, embedding_size])(bridge_out)
+            deep_in = RegulationModule(tau)(bridge_out_list)
+            cross_in = RegulationModule(tau)(bridge_out_list)
+
+    stack_out = Concatenate()([cross_out, deep_out, bridge_out])
+    final_logit = Dense(1, use_bias=False)(stack_out)
+
+    final_logit = add_func([final_logit, linear_logit])
+    output = PredictionLayer(task)(final_logit)
+
+    model = Model(inputs=inputs_list, outputs=output)
+
+    return model
diff --git a/deepctr/models/sequence/din.py b/deepctr/models/sequence/din.py
index 14877a7a..84b7b432 100644
--- a/deepctr/models/sequence/din.py
+++ b/deepctr/models/sequence/din.py
@@ -6,15 +6,15 @@
 Reference:
     [1] Zhou G, Zhu X, Song C, et al. Deep interest network for click-through rate prediction[C]//Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. ACM, 2018: 1059-1068. (https://arxiv.org/pdf/1706.06978.pdf)
 """
+from tensorflow.python.keras.layers import Dense, Flatten
 from tensorflow.python.keras.models import Model
-from tensorflow.python.keras.layers import Dense, Concatenate, Flatten
 
 from ...feature_column import SparseFeat, VarLenSparseFeat, DenseFeat, build_input_features
 from ...inputs import create_embedding_matrix, embedding_lookup, get_dense_input, varlen_embedding_lookup, \
     get_varlen_pooling_list
 from ...layers.core import DNN, PredictionLayer
 from ...layers.sequence import AttentionSequencePoolingLayer
-from ...layers.utils import concat_func, NoMask, combined_dnn_input
+from ...layers.utils import concat_func, combined_dnn_input
 
 
 def DIN(dnn_feature_columns, history_feature_list, dnn_use_bn=False,
@@ -84,7 +84,7 @@ def DIN(dnn_feature_columns, history_feature_list, dnn_use_bn=False,
                                          weight_normalization=att_weight_normalization, supports_masking=True)([
         query_emb, keys_emb])
 
-    deep_input_emb = Concatenate()([NoMask()(deep_input_emb), hist])
+    deep_input_emb = concat_func([deep_input_emb, hist])
     deep_input_emb = Flatten()(deep_input_emb)
     dnn_input = combined_dnn_input([deep_input_emb], dense_value_list)
     output = DNN(dnn_hidden_units, dnn_activation, l2_reg_dnn, dnn_dropout, dnn_use_bn, seed=seed)(dnn_input)
diff --git a/deepctr/models/sequence/dsin.py b/deepctr/models/sequence/dsin.py
index c7c2ea1a..f02f89cb 100644
--- a/deepctr/models/sequence/dsin.py
+++ b/deepctr/models/sequence/dsin.py
@@ -24,7 +24,7 @@
 
 
 def DSIN(dnn_feature_columns, sess_feature_list, sess_max_count=5, bias_encoding=False,
-         att_embedding_size=1, att_head_num=8, dnn_hidden_units=(256, 128, 64), dnn_activation='sigmoid', dnn_dropout=0,
+         att_embedding_size=1, att_head_num=8, dnn_hidden_units=(256, 128, 64), dnn_activation='relu', dnn_dropout=0,
          dnn_use_bn=False, l2_reg_dnn=0, l2_reg_embedding=1e-6, seed=1024, task='binary',
          ):
     """Instantiates the Deep Session Interest Network architecture.
diff --git a/docs/pics/EDCN.png b/docs/pics/EDCN.png
new file mode 100644
index 00000000..fea8cd5e
Binary files /dev/null and b/docs/pics/EDCN.png differ
diff --git a/docs/source/FAQ.md b/docs/source/FAQ.md
index 41cbc3b6..5e7dfdf6 100644
--- a/docs/source/FAQ.md
+++ b/docs/source/FAQ.md
@@ -128,7 +128,7 @@ from deepctr.models import DeepFM
 from deepctr.feature_column import SparseFeat,get_feature_names
 
 pretrained_item_weights = np.random.randn(60,4)
-pretrained_weights_initializer = tf.initializers.identity(pretrained_item_weights)
+pretrained_weights_initializer = tf.initializers.constant(pretrained_item_weights)
 
 feature_columns = [SparseFeat('user_id',120,),SparseFeat('item_id',60,embedding_dim=4,embeddings_initializer=pretrained_weights_initializer,trainable=False)]
 fixlen_feature_names = get_feature_names(feature_columns)
diff --git a/docs/source/Features.md b/docs/source/Features.md
index c9a9a550..6acee071 100644
--- a/docs/source/Features.md
+++ b/docs/source/Features.md
@@ -304,6 +304,17 @@ feature. FEFM has significantly lower model complexity than FFM and roughly the
 
 [Pande H. Field-Embedded Factorization Machines for Click-through rate prediction[J]. arXiv preprint arXiv:2009.09931, 2020.](https://arxiv.org/pdf/2009.09931)
 
+### EDCN(Enhancing Explicit and Implicit Feature Interactions DCN)
+
+EDCN introduces two advanced modules, namelybridge moduleandregulation module, which work collaboratively tocapture the layer-wise interactive signals and learn discriminativefeature distributions for each hidden layer of the parallel networks.
+
+[**EDCN Model API**](./deepctr.models.edcn.html)
+
+![EDCN](../pics/EDCN.png)
+
+[Chen B, Wang Y, Liu Z, et al. Enhancing explicit and implicit feature interactions via information sharing for parallel deep ctr models[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management. 2021: 3757-3766.](https://dlp-kdd.github.io/assets/pdf/DLP-KDD_2021_paper_12.pdf)
+
+
 ## Sequence Models
 
 ### DIN (Deep Interest Network)
@@ -413,6 +424,8 @@ information routing across tasks in a general setup.
 
 [Tang H, Liu J, Zhao M, et al. Progressive layered extraction (ple): A novel multi-task learning (mtl) model for personalized recommendations[C]//Fourteenth ACM Conference on Recommender Systems. 2020.](https://dl.acm.org/doi/10.1145/3383313.3412236)
 
+
+
 ## Layers
 
 The models of deepctr are modular, so you can use different modules to build your own models.
diff --git a/docs/source/History.md b/docs/source/History.md
index 2e19942a..8735d457 100644
--- a/docs/source/History.md
+++ b/docs/source/History.md
@@ -1,4 +1,5 @@
 # History
+- 11/10/2022 : [v0.9.3](https://github.com/shenweichen/DeepCTR/releases/tag/v0.9.3) released.Add [EDCN](./Features.html#edcn-enhancing-explicit-and-implicit-feature-interactions-dcn).
 - 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)
@@ -10,8 +11,8 @@
 - 10/11/2020 : [v0.8.2](https://github.com/shenweichen/DeepCTR/releases/tag/v0.8.2) released.Refactor `DNN` Layer.
 - 09/12/2020 : [v0.8.1](https://github.com/shenweichen/DeepCTR/releases/tag/v0.8.1) released.Improve the reproducibility & fix some bugs.
 - 06/27/2020 : [v0.8.0](https://github.com/shenweichen/DeepCTR/releases/tag/v0.8.0) released.
-  - Support `Tensorflow Estimator` for large scale data and distributed training. [example: Estimator with TFRecord](https://deepctr-doc.readthedocs.io/en/latest/Examples.html#estimator-with-tfrecord-classification-criteo) 
-  - Support different initializers for different embedding weights and loading pretrained embeddings. [example](https://deepctr-doc.readthedocs.io/en/latest/FAQ.html#how-to-use-pretrained-weights-to-initialize-embedding-weights-and-frozen-embedding-weights)
+  - Support `Tensorflow Estimator` for large scale data and distributed training. [example: Estimator with TFRecord](./Examples.html#estimator-with-tfrecord-classification-criteo) 
+  - Support different initializers for different embedding weights and loading pretrained embeddings. [example](./FAQ.html#how-to-use-pretrained-weights-to-initialize-embedding-weights-and-frozen-embedding-weights)
   - Add new model `FwFM`.
 - 05/17/2020 : [v0.7.5](https://github.com/shenweichen/DeepCTR/releases/tag/v0.7.5) released.Fix numerical instability in `LayerNormalization`.
 - 03/15/2020 : [v0.7.4](https://github.com/shenweichen/DeepCTR/releases/tag/v0.7.4) released.Add [FLEN](./Features.html#flen-field-leveraged-embedding-network) and `FieldWiseBiInteraction`.
diff --git a/docs/source/Models.rst b/docs/source/Models.rst
index a3f5691e..4f864184 100644
--- a/docs/source/Models.rst
+++ b/docs/source/Models.rst
@@ -30,5 +30,6 @@ DeepCTR Models API
    ESMM<deepctr.models.multitask.esmm>
    MMOE<deepctr.models.multitask.mmoe>
    PLE<deepctr.models.multitask.ple>
+   EDCN<deepctr.models.edcn>
 
    
\ No newline at end of file
diff --git a/docs/source/conf.py b/docs/source/conf.py
index d0f0df24..e0ae9c06 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 = '0.9.2'
+release = '0.9.3'
 
 
 # -- General configuration ---------------------------------------------------
diff --git a/docs/source/deepctr.models.edcn.rst b/docs/source/deepctr.models.edcn.rst
new file mode 100644
index 00000000..3772f3b5
--- /dev/null
+++ b/docs/source/deepctr.models.edcn.rst
@@ -0,0 +1,7 @@
+deepctr.models.edcn module
+=========================
+
+.. automodule:: deepctr.models.edcn
+    :members:
+    :no-undoc-members:
+    :no-show-inheritance:
diff --git a/docs/source/deepctr.models.rst b/docs/source/deepctr.models.rst
index 2b4e9e18..4acf2a12 100644
--- a/docs/source/deepctr.models.rst
+++ b/docs/source/deepctr.models.rst
@@ -11,6 +11,7 @@ Submodules
    deepctr.models.ccpm
    deepctr.models.dcn
    deepctr.models.dcnmix
+   deepctr.models.edcn
    deepctr.models.deepfm
    deepctr.models.dien
    deepctr.models.din
diff --git a/docs/source/index.rst b/docs/source/index.rst
index 0330a10d..93316678 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -42,11 +42,11 @@ You can read the latest code and related projects
 
 News
 -----
-10/15/2022 : Support python `3.9`,`3.10`. `Changelog <https://github.com/shenweichen/DeepCTR/releases/tag/v0.9.2>`_
+11/10/2022 : Add `EDCN` . `Changelog <https://github.com/shenweichen/DeepCTR/releases/tag/v0.9.3>`_
 
-06/11/2022 : Improve compatibility with tensorflow `2.x`. `Changelog <https://github.com/shenweichen/DeepCTR/releases/tag/v0.9.1>`_
+10/15/2022 : Support python `3.9` , `3.10` . `Changelog <https://github.com/shenweichen/DeepCTR/releases/tag/v0.9.2>`_
 
-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 <https://github.com/shenweichen/DeepCTR/releases/tag/v0.9.0>`_
+06/11/2022 : Improve compatibility with tensorflow `2.x`. `Changelog <https://github.com/shenweichen/DeepCTR/releases/tag/v0.9.1>`_
 
 DisscussionGroup
 -----------------------
diff --git a/setup.py b/setup.py
index 43eee556..c389ea41 100644
--- a/setup.py
+++ b/setup.py
@@ -1,21 +1,19 @@
+import sys
+
 import setuptools
 
-with open("README.md", "r",encoding='utf-8') as fh:
+with open("README.md", "r") as fh:
     long_description = fh.read()
 
-import sys
-if sys.version_info < (3, 9):
-    REQUIRED_PACKAGES = [
-    'h5py==2.10.0', 'requests'
-    ]
-else:
-    REQUIRED_PACKAGES = [
-    'h5py==3.7.0', 'requests'
-    ]
+REQUIRED_PACKAGES = [
+    'requests',
+    'h5py==3.7.0; python_version>="3.9"',
+    'h5py==2.10.0; python_version<"3.9"'
+]
 
 setuptools.setup(
     name="deepctr",
-    version="0.9.2",
+    version="0.9.3",
     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 .",
diff --git a/tests/models/EDCN_test.py b/tests/models/EDCN_test.py
new file mode 100644
index 00000000..f01f7fe0
--- /dev/null
+++ b/tests/models/EDCN_test.py
@@ -0,0 +1,28 @@
+import pytest
+
+from deepctr.models import EDCN
+from ..utils import check_model, get_test_data, SAMPLE_SIZE
+
+
+@pytest.mark.parametrize(
+    'bridge_type, cross_num, cross_parameterization, sparse_feature_num',
+    [
+        ('pointwise_addition', 2, 'vector', 3),
+        ('hadamard_product', 2, 'vector', 4),
+        ('concatenation', 1, 'vector', 5),
+        ('attention_pooling', 2, 'matrix', 6),
+    ]
+)
+def test_EDCN(bridge_type, cross_num, cross_parameterization, sparse_feature_num):
+    model_name = "EDCN"
+
+    sample_size = SAMPLE_SIZE
+    x, y, feature_columns = get_test_data(sample_size, sparse_feature_num=sparse_feature_num,
+                                          dense_feature_num=0)
+
+    model = EDCN(feature_columns, feature_columns, cross_num, cross_parameterization, bridge_type)
+    check_model(model, model_name, x, y)
+
+
+if __name__ == "__main__":
+    pass
diff --git a/tests/models/xDeepFM_test.py b/tests/models/xDeepFM_test.py
index 3981e229..b350ad28 100644
--- a/tests/models/xDeepFM_test.py
+++ b/tests/models/xDeepFM_test.py
@@ -61,4 +61,4 @@ def test_xDeepFMEstimator(dnn_hidden_units, cin_layer_size, cin_split_half, cin_
 
 
 if __name__ == "__main__":
-    pass
+    pass
\ No newline at end of file