# 导入模块import numpy as npimport tensorflow as tfimport matplotlib.pyplot as plt# 加载数据from tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets("E:\\MNIST_data\\", one_hot=True)#模型训练# 设置超参数learning_rate = 0.01 # 学习率training_epochs = 20 # 训练轮数batch_size = 256 # 每次训练的数据display_step = 1 # 每隔多少轮显示一次训练结果examples_to_show = 10 # 提示从测试集中选择10张图片取验证自动编码器的结果# 网络参数n_hidden_1 = 256 # 第一个隐藏层神经元个数（特征值格式）n_hidden_2 = 128 # 第二个隐藏层神经元格式n_input = 784 # 输入数据的特征个数  28*28=784# 定义输入数据，无监督不需要标注数据，所以只有输入图片X = tf.placeholder("float", [None, n_input])#初始化每一层的权重和偏置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])),    'decoder_h1': tf.Variable(tf.random_normal([n_hidden_2, n_hidden_1])),    'decoder_h2': 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])),    'decoder_b1': tf.Variable(tf.random_normal([n_hidden_1])),    'decoder_b2': tf.Variable(tf.random_normal([n_input])),}#定义自动编码模型的网络结构，包括压缩和解压的过程# 定义压缩函数def encoder(x):    # Encoder Hidden layer with sigmoid activation #1    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),biases['encoder_b1']))    # Decoder Hidden layer with sigmoid activation #2    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),biases['encoder_b2']))    return layer_2# 定义解压函数def decoder(x):    # Encoder Hidden layer with sigmoid activation #1    layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),biases['decoder_b1']))    # Decoder Hidden layer with sigmoid activation #2    layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),biases['decoder_b2']))    return layer_2# 建立模型encoder_op = encoder(X)decoder_op = decoder(encoder_op)# 得出预测分类值y_pred = decoder_op# 得出真实值，即输入值y_true = X# 定义损失函数和优化器cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)# 初始化变量init = tf.global_variables_initializer()# 3 训练数据及评估模型with tf.Session() as sess:    sess.run(init)    total_batch = int(mnist.train.num_examples/batch_size)    # 开始训练    for epoch in range(training_epochs):        # Loop over all batches        for i in range(total_batch):            batch_xs, batch_ys = mnist.train.next_batch(batch_size)            # Run optimization op (backprop) and cost op (to get loss value)            _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})        # 每一轮，打印一次损失值        if epoch % display_step == 0:            print("Epoch:", '%04d' % (epoch+1),"cost=", "{:.9f}".format(c))    print("Optimization Finished!")    # 对测试集应用训练好的自动编码网络    encode_decode = sess.run(        y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})    # 比较测试集原始图片和自动编码网络的重建结果    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)))    f.show()    plt.draw()    #plt.waitforbuttonpress()