生成模型-变分自动编码器

https://yq.aliyun.com/articles/68410

network= {

  # encoder
  encoder_x = Input_layer(size=input_size, input=data)
  encoder_h = Dense_layer(size=hidden_size, input= encoder_x)

  # the re-parameterized distributions that are inferred from data 
  z_mean = Dense(size=number_of_distributions, input=encoder_h)
  z_variance = Dense(size=number_of_distributions, input=encoder_h)
  epsilon= random(size=number_of_distributions)

  # decoder network needs a sample from the code distribution
  z_sample= z_mean + exp(z_variance / 2) * epsilon

  #decoder
  decoder_h = Dense_layer(size=hidden_size, input=z_sample)
  decoder_output = Dense_layer(size=input_size, input=decoder_h)
}

cost={
  reconstruction_loss = input_size * crossentropy(data, decoder_output)
  kl_loss = - 0.5 * sum(1 + z_variance - square(z_mean) - exp(z_variance))
  cost_total= reconstruction_loss + kl_loss
}

stochastic_gradient_descent(data, network, cost_total)