# 导入模块 import numpy as np import tensorflow as tf import matplotlib.pyplot as plt # 加载数据 from tensorflow.examples.tutorials.mnist import input_data mnist = 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()
