python实现FNN【tensorflow2.0】

糖逗

论文：https://arxiv.org/pdf/1601.02376.pdf

参考链接：https://github.com/DSXiangLi/CTR

说明：待验证

1. class FNN(keras.Model):
   
2.     def __init__(self, w_init, v_init):
   
3.         super(FNN, self).__init__()
   
4.         self.w_init = w_init
   
5.         self.v_init = v_init
   
6.         
   
7.     def build(self, input_shape):
   
8.         #[None, k=30] -> [None, 24] -> [None, 8] -> [None, 2]
   
9.         self.fc1 = layers.Dense(24, activation = tf.tanh)
   
10.         self.bn1 = layers.BatchNormalization(axis = 1)
    
11.         self.drop1 = layers.Dropout(0.5)
    
12.         self.fc2 = layers.Dense(8, activation = tf.tanh)
    
13.         self.bn2 = layers.BatchNormalization(axis = 1)
    
14.         self.drop2 = layers.Dropout(0.5)
    
15.         self.fc3 = layers.Dense(2, activation = None)
    
16.         super(FNN, self).build(input_shape)
    
17.         
    
18.     def call(self, x, training = None):
    
19.         #[None, dim]@[dim, 1]+ [None, dim]@[dim, k] = [None, k]
    
20.         out = x @ self.w_init + x @ self.v_init
    
21.         out = self.drop1(self.bn1(self.fc1(out), training), training)
    
22.         out = self.drop2(self.bn2(self.fc2(out), training), training)
    
23.         out = self.fc3(out)
    
24.         return out
    
25.    
    
26. test = FNN(model.trainable_variables[0], model.trainable_variables[2])
    
27. test.build((None, 30))
    
28. test.summary()

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注意事项：
1.前提是要实现FM：https://fishc.com.cn/thread-187546-1-1.html
2.model.trainable_variables[0], model.trainable_variables[2] 是注意事项1中FM模型参数的输出

糖逗

@不二如是 您好，可以审核一下这个帖子吗(*≧ｍ≦*)