python实现FNN【tensorflow2.0】
论文：https://arxiv.org/pdf/1601.02376.pdf参考链接：https://github.com/DSXiangLi/CTR
说明：待验证
class FNN(keras.Model):
def __init__(self, w_init, v_init):
super(FNN, self).__init__()
self.w_init = w_init
self.v_init = v_init
def build(self, input_shape):
# -> -> ->
self.fc1 = layers.Dense(24, activation = tf.tanh)
self.bn1 = layers.BatchNormalization(axis = 1)
self.drop1 = layers.Dropout(0.5)
self.fc2 = layers.Dense(8, activation = tf.tanh)
self.bn2 = layers.BatchNormalization(axis = 1)
self.drop2 = layers.Dropout(0.5)
self.fc3 = layers.Dense(2, activation = None)
super(FNN, self).build(input_shape)
def call(self, x, training = None):
#@+ @ =
out = x @ self.w_init + x @ self.v_init
out = self.drop1(self.bn1(self.fc1(out), training), training)
out = self.drop2(self.bn2(self.fc2(out), training), training)
out = self.fc3(out)
return out
test = FNN(model.trainable_variables, model.trainable_variables)
test.build((None, 30))
test.summary()
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, model.trainable_variables 是注意事项1中FM模型参数的输出 @不二如是 您好，可以审核一下这个帖子吗(*≧ｍ≦*)
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