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model.py
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# -*- coding: utf-8 -*-
# @Time : 2019/7/2 8:43
# @Author : skydm
# @Email : [email protected]
# @File : model.py
# @Software: PyCharm
import random
import numpy as np
import tensorflow as tf
import tensorflow.python.keras as keras
import tensorflow.python.keras.backend as K
# 设置随机种子，方便复现
seed = 1234
random.seed(seed)
np.random.seed(seed)
tf.set_random_seed(seed)
class Attention(keras.layers.Layer):
def __init__(self, attention_hidden_units=(80, 40, 1), attention_activation="sigmoid", supports_masking=True):
super(Attention, self).__init__()
self.attention_hidden_units = attention_hidden_units
self.attention_activation = attention_activation
self.supports_masking = supports_masking
def build(self, input_shape):
super(Attention, self).build(input_shape)
def call(self, x, mask=None):
'''
i_emb: [Batch_size, Hidden_units]
hist_emb: [Batch_size, max_len, Hidden_units]
hist_len: [Batch_size]
'''
assert len(x) == 3
i_emb, hist_emb, hist_len = x[0], x[1], x[2]
hidden_units = K.int_shape(hist_emb)[-1]
max_len = tf.shape(hist_emb)[1]
i_emb = tf.tile(i_emb, [1, max_len]) # (batch_size, max_len * hidden_units)
i_emb = tf.reshape(i_emb, [-1, max_len, hidden_units]) # (batch_size, max_len, hidden_units)
concat = K.concatenate([i_emb, hist_emb, i_emb - hist_emb, i_emb * hist_emb],
axis=2) # (batch_size, max_len, hidden_units * 4)
for i in range(len(self.attention_hidden_units)):
activation = None if i == 2 else self.attention_activation
outputs = keras.layers.Dense(self.attention_hidden_units[i], activation=activation)(concat)
concat = outputs
outputs = tf.reshape(outputs, [-1, 1, max_len]) # (batch_size, 1, max_len)
if self.supports_masking:
mask = tf.sequence_mask(hist_len, max_len) # (batch_size, 1, max_len)
padding = tf.ones_like(outputs) * (-1e12)
outputs = tf.where(mask, outputs, padding)
# 对outputs进行scale
outputs = outputs / (hidden_units ** 0.5)
outputs = K.softmax(outputs)
outputs = tf.matmul(outputs, hist_emb) # batch_size, 1, hidden_units)
outputs = tf.squeeze(outputs) # (batch_size, hidden_units)
return outputs
def compute_output_shape(self, input_shape):
return (input_shape[0][0], input_shape[0][-1])
def share_weights(hidden_units=63930):
'''
reuse a group of keras layers(封装多层，同时可以共享)
'''
layers_units = (80, 40, 1)
share_input = keras.layers.Input(shape=(hidden_units, ))
share_layer = share_input
for i in range(len(layers_units)):
activation = None if i == 2 else "sigmoid"
share_layer = keras.layers.Dense(layers_units[i], activation=activation)(share_layer)
out_layer = share_layer
model = keras.models.Model(share_input, out_layer)
return model
def din(item_count, cate_count, hidden_units=128):
'''
:param item_count: 商品数
:param cate_count: 类别数
:param hidden_units: 隐藏单元数
:return: model
'''
target_item = keras.layers.Input(shape=(1,), name='target_item', dtype="int32") # 点击的item
target_cate = keras.layers.Input(shape=(1,), name='target_cate', dtype="int32") # 点击的item对应的所属类别
label = keras.layers.Input(shape=(1,), name='label', dtype="float32") # 是否点击
hist_item_seq = keras.layers.Input(shape=(None,), name="hist_item_seq", dtype="int32") # 点击序列
hist_cate_seq = keras.layers.Input(shape=(None,), name="hist_cate_seq", dtype="int32") # 点击序列对应的类别序列
hist_len = keras.layers.Input(shape=(1,), name='hist_len', dtype="int32") # 序列本来的长度
item_emb = keras.layers.Embedding(input_dim=item_count,
output_dim=hidden_units // 2,
embeddings_initializer=keras.initializers.RandomNormal(mean=0.0, stddev=1e-4,
seed=seed))
cate_emb = keras.layers.Embedding(input_dim=cate_count,
output_dim=hidden_units // 2,
embeddings_initializer=keras.initializers.RandomNormal(mean=0.0, stddev=1e-4,
seed=seed))
item_b = keras.layers.Embedding(input_dim=item_count, output_dim=1,
embeddings_initializer=keras.initializers.Constant(0.0))
# get target bias embedding
target_item_bias_emb = item_b(target_item) # (batch_size, 1, 1)
#
target_item_bias_emb = keras.layers.Lambda(lambda x: K.squeeze(x, axis=1))(target_item_bias_emb)
# get target embedding
target_item_emb = item_emb(target_item) # (batch_size, 1, hidden_units//2)
target_cate_emb = cate_emb(target_cate) # (batch_size, 1, hidden_units//2)
i_emb = keras.layers.Lambda(lambda x: K.concatenate([x[0], x[1]], axis=-1))(
[target_item_emb, target_cate_emb]) # (batch_size, 1, hidden_units)
i_emb = keras.layers.Lambda(lambda x: K.squeeze(x, axis=1))(i_emb) # (batch_size, hidden_units)
# get history item embedding
hist_item_emb = item_emb(hist_item_seq) # (batch_size, max_len, hidden_units//2)
hist_cate_emb = cate_emb(hist_cate_seq) # (batch_size, max_len, hidden_units//2)
hist_emb = keras.layers.Lambda(lambda x: K.concatenate([x[0], x[1]], axis=-1))(
[hist_item_emb, hist_cate_emb]) # (batch_size, max_len, hidden_units)
# 构建点击序列与候选的attention关系
din_attention = Attention()([i_emb, hist_emb, hist_len]) # (batch_size, hidden_units)
din_attention = keras.layers.Lambda(lambda x: tf.reshape(x, [-1, hidden_units]))(din_attention)
# keras.layers.BatchNormalization实现暂时有坑，借用paddle相关代码实现
din_attention_fc = keras.layers.Dense(63802)(din_attention) # (batch_size, item_count + cate_count)
# item_count: 63001 cate_count: 801 hidden_units: 128 (batch_size, item_count + cate_count + hidden_units)
din_item = keras.layers.Lambda(lambda x: K.concatenate([x[0], x[1]], axis=1))([i_emb, din_attention_fc])
din_item = share_weights()(din_item) # (batch_size, 1)
print("logits:", din_item, target_item_bias_emb)
logits = keras.layers.Add()([din_item, target_item_bias_emb])
label_model = keras.models.Model(inputs=[hist_item_seq, hist_cate_seq, target_item, target_cate, hist_len], outputs=[logits])
train_model = keras.models.Model(inputs=[hist_item_seq, hist_cate_seq, target_item, target_cate, hist_len, label],
outputs=logits)
# 计算损失函数
loss = K.binary_crossentropy(target=label, output=logits, from_logits=True)
train_model.add_loss(loss)
train_model.compile(optimizer=keras.optimizers.SGD(1e-3), metrics=["accuracy"])
return train_model, label_model