# coding: utf-8

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

# 导入MNIST数据

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("F:\\PythonPro\\testTensorFlow\\src\\MNIST_data", one_hot=False)

learning_rate = 0.01

training_epochs = 10

batch_size = 256

display_step = 1

examples_to_show = 10

n_input = 784

# tf Graph input (only pictures)

X = tf.placeholder("float", [None, n_input])

# 用字典的方式存储各隐藏层的参数

n_hidden_1 = 256 # 第一编码层神经元个数

n_hidden_2 = 128 # 第二编码层神经元个数

n_hidden_3 = 64

n_hidden_4 = 32

# 权重和偏置的变化在编码层和解码层顺序是相逆的

# 权重参数矩阵维度是每层的 输入*输出，偏置参数维度取决于输出层的单元数

# Map()

weights = {

'encoder_h1': tf.Variable(tf.random_normal([n_input, n_hidden_1])),

'encoder_h2': tf.Variable(tf.random_normal([n_hidden_1, n_hidden_2])),

'encoder_h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_3])),

'encoder_h4': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_4])),

'decoder_h1': tf.Variable(tf.random_normal([n_hidden_4, n_hidden_3])),

'decoder_h2': tf.Variable(tf.random_normal([n_hidden_3, n_hidden_2])),

'decoder_h3': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),

'decoder_h4': tf.Variable(tf.random_normal([n_hidden_1, n_input])),

}

biases = {

'encoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),

'encoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),

'encoder_b3': tf.Variable(tf.random_normal([n_hidden_3])),

'encoder_b4': tf.Variable(tf.random_normal([n_hidden_4])),

'decoder_b1': tf.Variable(tf.random_normal([n_hidden_3])),

'decoder_b2': tf.Variable(tf.random_normal([n_hidden_2])),

'decoder_b3': tf.Variable(tf.random_normal([n_hidden_1])),

'decoder_b4': tf.Variable(tf.random_normal([n_input])),

}

# 每一层结构都是 xW + b

# 构建编码器

def encoder(x):

layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),

biases['encoder_b1']))

layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),

biases['encoder_b2']))

layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['encoder_h3']),

biases['encoder_b3']))

# 为了便于编码层的输出，编码层随后一层不使用激活函数

layer_4 = tf.add(tf.matmul(layer_3, weights['encoder_h4']),

biases['encoder_b4'])

return layer_4

# 构建解码器

def decoder(x):

layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),

biases['decoder_b1']))

layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),

biases['decoder_b2']))

layer_3 = tf.nn.sigmoid(tf.add(tf.matmul(layer_2, weights['decoder_h3']),

biases['decoder_b3']))

layer_4 = tf.nn.sigmoid(tf.add(tf.matmul(layer_3, weights['decoder_h4']),

biases['decoder_b4']))

return layer_4

# 构建模型

encoder_op = encoder(X)

decoder_op = decoder(encoder_op)

# 预测

y_pred = decoder_op

y_true = X

# 定义代价函数和优化器

cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2)) #最小二乘法

optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)

with tf.Session() as sess:

# tf.initialize_all_variables() no long valid from

# 2017-03-02 if using tensorflow >= 0.12

#if int((tf.__version__).split('.')[1]) < 12 and int((tf.__version__).split('.')[0]) < 1:

# init = tf.initialize_all_variables()

#else:

init = tf.global_variables_initializer()

sess.run(init)

# 首先计算总批数，保证每次循环训练集中的每个样本都参与训练，不同于批量训练

#总批数

total_batch = int(mnist.train.num_examples/batch_size)

for epoch in range(training_epochs):

for i in range(total_batch):

batch_xs, batch_ys = mnist.train.next_batch(batch_size) # max(x) = 1, min(x) = 0

# Run optimization op (backprop) and cost op (to get loss value)

_, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})

if epoch % display_step == 0:

print("Epoch:", '%04d' % (epoch+1), "cost=", "{:.9f}".format(c))

print("Optimization Finished!")

encode_decode = sess.run(

y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})

f, a = plt.subplots(2, 10, figsize=(10, 2))

for i in range(examples_to_show):

a[0][i].imshow(np.reshape(mnist.test.images[i], (28, 28)))

a[1][i].imshow(np.reshape(encode_decode[i], (28, 28)))

plt.show()