Update

deepctr/layers.py

@@ -355,10 +355,9 @@ def call(self, inputs, **kwargs):
         x_0 = tf.expand_dims(inputs, axis=2)
         x_l = x_0
         for i in range(self.layer_num):
-            xl_w = tf.tensordot(tf.transpose(
-                x_l, [0, 2, 1]), self.kernels[i], axes=(-1, 0))
+            xl_w = tf.tensordot(x_l, self.kernels[i], axes=(1, 0))
             dot_ = tf.matmul(x_0, xl_w)
-            x_l = dot_ + x_l + self.bias[i]
+            x_l = dot_ + self.bias[i] + x_l
         x_l = tf.squeeze(x_l, axis=2)
         return x_l
 
@@ -504,7 +503,6 @@ def get_config(self,):
         return dict(list(base_config.items()) + list(config.items()))
 
 
-
 class InteractingLayer(Layer):
     """A Layer used in AutoInt that model the correlations between different feature fields by multi-head self-attention mechanism.
 
@@ -524,6 +522,7 @@ class InteractingLayer(Layer):
       References
             - [Song W, Shi C, Xiao Z, et al. AutoInt: Automatic Feature Interaction Learning via Self-Attentive Neural Networks[J]. arXiv preprint arXiv:1810.11921, 2018.](https://arxiv.org/abs/1810.11921)
     """
+
     def __init__(self, att_embedding_size=8, head_num=2, use_res=True, seed=1024, **kwargs):
         if head_num <= 0:
             raise ValueError('head_num must be a int > 0')
@@ -535,7 +534,8 @@ def __init__(self, att_embedding_size=8, head_num=2, use_res=True, seed=1024, **
 
     def build(self, input_shape):
         if len(input_shape) != 3:
-            raise ValueError("Unexpected inputs dimensions %d, expect to be 3 dimensions" % (len(input_shape)))
+            raise ValueError(
+                "Unexpected inputs dimensions %d, expect to be 3 dimensions" % (len(input_shape)))
         embedding_size = input_shape[-1].value
         self.W_Query = self.add_weight(name='query', shape=[embedding_size, self.att_embedding_size * self.head_num], dtype=tf.float32,
                                        initializer=tf.keras.initializers.glorot_uniform(seed=self.seed))
@@ -547,26 +547,32 @@ def build(self, input_shape):
             self.W_Res = self.add_weight(name='res', shape=[embedding_size, self.att_embedding_size * self.head_num], dtype=tf.float32,
                                          initializer=tf.keras.initializers.glorot_uniform(seed=self.seed))
 
-        super(InteractingLayer, self).build(input_shape)  # Be sure to call this somewhere!
+        # Be sure to call this somewhere!
+        super(InteractingLayer, 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)))
+            raise ValueError(
+                "Unexpected inputs dimensions %d, expect to be 3 dimensions" % (K.ndim(inputs)))
 
-        querys = tf.tensordot(inputs, self.W_Query, axes=(-1, 0))  # None F D*head_num
+        querys = tf.tensordot(inputs, self.W_Query,
+                              axes=(-1, 0))  # None F D*head_num
         keys = tf.tensordot(inputs, self.W_key, axes=(-1, 0))
         values = tf.tensordot(inputs, self.W_Value, axes=(-1, 0))
 
-        querys = tf.stack(tf.split(querys, self.head_num, axis=2))  # head_num None F D
+        # head_num None F D
+        querys = tf.stack(tf.split(querys, self.head_num, axis=2))
         keys = tf.stack(tf.split(keys, self.head_num, axis=2))
         values = tf.stack(tf.split(values, self.head_num, axis=2))
 
-        inner_product = tf.matmul(querys, keys, transpose_b=True)  # head_num None F F
+        inner_product = tf.matmul(
+            querys, keys, transpose_b=True)  # head_num None F F
         self.normalized_att_scores = tf.nn.softmax(inner_product)
 
-        result = tf.matmul(self.normalized_att_scores, values)#head_num None F D
+        result = tf.matmul(self.normalized_att_scores,
+                           values)  # head_num None F D
         result = tf.concat(tf.split(result, self.head_num, ), axis=-1)
-        result = tf.squeeze(result, axis=0)#None F D*head_num
+        result = tf.squeeze(result, axis=0)  # None F D*head_num
 
         if self.use_res:
             result += tf.tensordot(inputs, self.W_Res, axes=(-1, 0))
@@ -901,7 +907,7 @@ class PredictionLayer(Layer):
       Arguments
          - **activation**: Activation function to use.
 
-         - **use_bias**: bool.Whther add bias term.
+         - **use_bias**: bool.Whether add bias term or not.
     """
 
     def __init__(self, activation='sigmoid', use_bias=True, **kwargs):

deepctr/sequence.py

@@ -1,6 +1,7 @@
 from tensorflow.python.keras.layers import Layer
 from .layers import LocalActivationUnit
 import tensorflow as tf
+from tensorflow.python.keras.layers import GRU
 
 
 class SequencePoolingLayer(Layer):
@@ -9,7 +10,7 @@ class SequencePoolingLayer(Layer):
       Input shape
         - A list of two  tensor [seq_value,seq_len]
 
-        - seq_value is a 3D tensor with shape: ``(batch_size, T, embedding_size``
+        - seq_value is a 3D tensor with shape: ``(batch_size, T, embedding_size)``
 
         - seq_len is a 2D tensor with shape : ``(batch_size, 1)``,indicate valid length of each sequence.
 

docs/source/Examples.md

@@ -139,6 +139,7 @@ This example shows how to use ``DeepFM`` with sequence(multi-value) feature. You
 ```python
 import pandas as pd
 import numpy as np
+from sklearn.preprocessing import LabelEncoder
 from tensorflow.python.keras.preprocessing.sequence import pad_sequences
 from deepctr.models import DeepFM
 from deepctr.utils import VarLenFeature

examples/run_multivalue_movielens.py

@@ -1,6 +1,7 @@
 import pandas as pd
 import numpy as np
 from tensorflow.python.keras.preprocessing.sequence import pad_sequences
+from sklearn.preprocessing import LabelEncoder
 from deepctr.models import DeepFM
 from deepctr.utils import VarLenFeature