VAE及其代码解读

版权声明：本文为博主原创文章，未经博主允许不得转载。

这几天在看GAN模型的时候，顺便关注了另外一种生成模型——VAE。其实这种生成模型在早几年就有了，而且有了一些应用。著名黑客George Hotz在其开源的自主驾驶项目中就应用到了VAE模型。这其中的具体应用在我上一篇转载的博客comma.ai中有详细介绍。在对VAE基本原理有一个认识后，就开始啃代码了。在代码中最能直观的体现其思想，结合理论来看，有利于理解。理论介绍，网上资料很多，就不赘述了。

          基本网络框架：

                 

          

下面是基于keras的VAE代码解读：（自己加了一层，发现收敛的快一点了，附实验结果）

 <span style="font-size:14px;">from __future__ import division
 from __future__ import print_function
 import os.path

 import numpy as np
 np.random.seed(1337)  # for reproducibility

 import tensorflow as tf
 from tensorflow.examples.tutorials.mnist import input_data

 mnist = input_data.read_data_sets('MNIST')

 input_dim = 784
 hidden_encoder_dim_1 = 1000
 hidden_encoder_dim_2 = 400
 hidden_decoder_dim = 400
 latent_dim = 20#（latent Variable）
 lam = 0


 def weight_variable(shape):
   initial = tf.truncated_normal(shape, stddev=0.001)
   return tf.Variable(initial)

 def bias_variable(shape):
   initial = tf.constant(0., shape=shape)
   return tf.Variable(initial)

 x = tf.placeholder("float", shape=[None, input_dim])##input x
 l2_loss = tf.constant(0.0)

 #encoder1 W b
 W_encoder_input_hidden_1 = weight_variable([input_dim,hidden_encoder_dim_1])##784*1000
 b_encoder_input_hidden_1 = bias_variable([hidden_encoder_dim_1])#1000
 l2_loss += tf.nn.l2_loss(W_encoder_input_hidden_1)

 # Hidden layer1 encoder
 hidden_encoder_1 = tf.nn.relu(tf.matmul(x, W_encoder_input_hidden_1) + b_encoder_input_hidden_1)##w*x+b

 #encoder2 W b
 W_encoder_input_hidden_2 = weight_variable([hidden_encoder_dim_1,hidden_encoder_dim_2])##1000*400
 b_encoder_input_hidden_2 = bias_variable([hidden_encoder_dim_2])#400
 l2_loss += tf.nn.l2_loss(W_encoder_input_hidden_2)

 # Hidden layer2 encoder
 hidden_encoder_2 = tf.nn.relu(tf.matmul(hidden_encoder_1, W_encoder_input_hidden_2) + b_encoder_input_hidden_2)##w*x+b

 W_encoder_hidden_mu = weight_variable([hidden_encoder_dim_2,latent_dim])##400*20
 b_encoder_hidden_mu = bias_variable([latent_dim])##20
 l2_loss += tf.nn.l2_loss(W_encoder_hidden_mu)

 # Mu encoder=+
 mu_encoder = tf.matmul(hidden_encoder_2, W_encoder_hidden_mu) + b_encoder_hidden_mu##mu_encoder:1*20(1*400 400*20)

 W_encoder_hidden_logvar = weight_variable([hidden_encoder_dim_2,latent_dim])##W_encoder_hidden_logvar:400*20
 b_encoder_hidden_logvar = bias_variable([latent_dim])#20
 l2_loss += tf.nn.l2_loss(W_encoder_hidden_logvar)

 # Sigma encoder
 logvar_encoder = tf.matmul(hidden_encoder_2, W_encoder_hidden_logvar) + b_encoder_hidden_logvar#logvar_encoder:1*20(1*400 400*20)

 # Sample epsilon
 epsilon = tf.random_normal(tf.shape(logvar_encoder), name='epsilon')

 # Sample latent variable
 std_encoder = tf.exp(0.5 * logvar_encoder)
 z = mu_encoder + tf.mul(std_encoder, epsilon)##z_mu+epsilon*z_std=z,as decoder's input;z:1*20

 W_decoder_z_hidden = weight_variable([latent_dim,hidden_decoder_dim])#W_decoder_z_hidden:20*400
 b_decoder_z_hidden = bias_variable([hidden_decoder_dim])##400
 l2_loss += tf.nn.l2_loss(W_decoder_z_hidden)

 # Hidden layer decoder
 hidden_decoder = tf.nn.relu(tf.matmul(z, W_decoder_z_hidden) + b_decoder_z_hidden)##hidden_decoder:1*400(1*20 20*400)

 W_decoder_hidden_reconstruction = weight_variable([hidden_decoder_dim, input_dim])##400*784
 b_decoder_hidden_reconstruction = bias_variable([input_dim])
 l2_loss += tf.nn.l2_loss(W_decoder_hidden_reconstruction)

 KLD = -0.5 * tf.reduce_sum(1 + logvar_encoder - tf.pow(mu_encoder, 2) - tf.exp(logvar_encoder), reduction_indices=1)##KLD

 x_hat = tf.matmul(hidden_decoder, W_decoder_hidden_reconstruction) + b_decoder_hidden_reconstruction##x_hat:1*784(reconstruction x)

 BCE = tf.reduce_sum(tf.nn.sigmoid_cross_entropy_with_logits(x_hat, x), reduction_indices=1)##sum cross_entropy

 loss = tf.reduce_mean(BCE + KLD)##average value

 regularized_loss = loss + lam * l2_loss

 loss_summ = tf.scalar_summary("lowerbound", loss)##Record the stored value of loss
 train_step = tf.train.AdamOptimizer(0.01).minimize(regularized_loss)##Optimization Strategy

 # add op for merging summary
 summary_op = tf.merge_all_summaries()

 # add Saver ops
 saver = tf.train.Saver()

 n_steps = int(1e5+1)##step:1000000
 batch_size = 100

 with tf.Session() as sess:
   summary_writer = tf.train.SummaryWriter('experiment',
                                           graph=sess.graph)##draw graph in tensorboard
   #if os.path.isfile("save/model.ckpt"):
    # print("Restoring saved parameters")
     #saver.restore(sess, "save/model.ckpt")
   #else:
    # print("Initializing parameters")
   sess.run(tf.initialize_all_variables())

   for step in range(1, n_steps):
     batch = mnist.train.next_batch(batch_size)
     feed_dict = {x: batch[0]}
     _, cur_loss, summary_str = sess.run([train_step, loss, summary_op], feed_dict=feed_dict)
     summary_writer.add_summary(summary_str, step)

     if step % 50 == 0:
       save_path = saver.save(sess, "save/model.ckpt")
       print("Step {0} | Loss: {1}".format(step, cur_loss))

 # save weights every epoch
     #if step % 100==0 :
       # generator.save_weights(
       #          'mlp_generator_epoch_{0:03d}.hdf5'.format(epoch), True)
        #critic.save_weights(
         #        'mlp_critic_epoch_{0:03d}.hdf5'.format(epoch), True)

 ##Step 999900 | Loss: 114.41309356689453
 ##Step 999950 | Loss: 115.09370422363281
 ##Step 100000 | Loss: 124.32205200195312 ##Step 99700 | Loss: 116.05304718017578

 #1000 encode hidden layer=1 Step 950 | Loss: 159.3329620361328
 #1000 encode hidden layer=2 Step 950 | Loss: 128.81312561035156
 span>

变分自编码器参考资料：

1.变分自编码器（Variational Autoencoder, VAE）通俗教程 – 邓范鑫——致力于变革未来的智能技术 http://www.dengfanxin.cn/?p=334

2.VAE variation inference变分推理 清爽介绍 http://mp.weixin.qq.com/s/9lNWkEEOk5vEkJ1f840zxA

3.深度学习变分贝叶斯自编码器（上下） https://zhuanlan.zhihu.com/p/25429082