写一个深度学习DSIN模型 python 代码
时间: 2023-02-12 20:37:44 浏览: 77
以下是一个示例代码:
```
import tensorflow as tf
import numpy as np
# 定义DSIN模型
class DSIN(tf.keras.Model):
def __init__(self, num_fields, embed_size, deep_layers, dropout):
super(DSIN, self).__init__()
self.num_fields = num_fields
self.embed_size = embed_size
self.deep_layers = deep_layers
self.dropout = dropout
self.embeddings = tf.keras.layers.Embedding(input_dim=num_fields, output_dim=embed_size)
self.flatten = tf.keras.layers.Flatten()
self.fc_deep = tf.keras.Sequential()
for i in range(len(deep_layers)):
self.fc_deep.add(tf.keras.layers.Dense(units=deep_layers[i], activation='relu'))
self.fc_deep.add(tf.keras.layers.Dropout(dropout))
self.out = tf.keras.layers.Dense(1)
def call(self, inputs, training=None, mask=None):
embed = self.embeddings(inputs)
embed = tf.reduce_sum(embed, axis=1)
embed = tf.nn.l2_normalize(embed, axis=1)
embed = self.flatten(embed)
deep_inputs = self.fc_deep(embed)
out = self.out(deep_inputs)
return out
# 实例化DSIN模型
model = DSIN(num_fields=100, embed_size=8, deep_layers=[32, 16], dropout=0.5)
# 训练DSIN模型
model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])
model.fit(train_inputs, train_labels, batch_size=32, epochs=10, validation_data=(val_inputs, val_labels))
```
请注意,这是一个仅供参考的代码,需要根据实际任务的不同而进行修改。