keras Lambda自定义层实现数据的切片方式,Lambda传参数

import numpy as np
from keras.models import Sequential
from keras.layers import Dense, Activation,Reshape
from keras.layers import merge
from keras.utils.visualize_util import plot
from keras.layers import Input, Lambda
from keras.models import Model
 
def slice(x,index):
 return x[:,:,index]
 
a = Input(shape=(4,2))
x1 = Lambda(slice,output_shape=(4,1),arguments={'index':0})(a)
x2 = Lambda(slice,output_shape=(4,1),arguments={'index':1})(a)
x1 = Reshape((4,1,1))(x1)
x2 = Reshape((4,1,1))(x2)
output = merge([x1,x2],mode='concat')
model = Model(a, output)
x_test = np.array([[[1,2],[2,3],[3,4],[4,5]]])
print model.predict(x_test)
plot(model, to_file='lambda.png',show_shapes=True)

def sampling(agrs):
  mean,logvar = agrs[0],agrs[1]
  eps = tf.random.normal(tf.shape(mean))
  return mean + eps*tf.exp(logvar * 0.5)

# 编码阶段
  
x = layers.Input(shape=(784,)) # 输入层
  
h1 = layers.Dense(200,activation='softplus')(x)
h2 = layers.Dense(200,activation='softplus')(h1)
# 均值和方差层不需要激活函数
mean = layers.Dense(latent_dimension)(h2)
log_var = layers.Dense(latent_dimension)(h2)
  
# 将采样过程看成一个Lambda层，这里利用自定义的sampling函数
z = layers.Lambda(sampling,output_shape=(latent_dimension,))([mean,log_var])
  
# 解码阶段
h3 = layers.Dense(200,activation='softplus')
h4 = layers.Dense(200,activation='softplus')
h5 = layers.Dense(200,activation='softplus')
# No activation
end = layers.Dense(784)
z1 = h3(z)
z2 = h4(z1)
z3 = h5(z2)
out = end(z3)
  
# 建立模型
model = tf.keras.Model(x,out)