先将输入数据从784维降维到256，再降到128。解码阶段经过128再经256，最终还原到原来的图像，其过程如下图所示。
import tensorflow as tf import numpy as np import matplotlib.pyplot as plt # 导入 MINST 数据集 from tensorflow.examples.tutorials.mnist import input_data mnist = input_data.read_data_sets("/data/", one_hot=True) # 网络模型参数 learning_rate = 0.01 n_hidden_1 = 256 # 第一层256 n_hidden_2 = 128 # 第二层128 n_input = 784 x = tf.placeholder("float", [None, n_input]) y = x # 参数 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.zeros([n_hidden_1])), 'encoder_b2': tf.Variable(tf.zeros([n_hidden_2])), 'decoder_b1': tf.Variable(tf.zeros([n_hidden_1])), 'decoder_b2': tf.Variable(tf.zeros([n_input])), } # 编码 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"])) return layer_2 # 解码 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"])) return layer_2 # 输出的节点 encoder_out = encoder(x) pred = decoder(encoder_out) # 使用平方差为cost cost = tf.reduce_mean(tf.pow(y - pred, 2)) optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost) # 训练参数 training_epochs = 20 batch_size = 256 display_step = 5 with tf.Session() as sess: sess.run(tf.global_variables_initializer()) total_batch = int(mnist.train.num_examples/batch_size) # 开始训练 for epoch in range(training_epochs): # 迭代 for i in range(total_batch): batch_xs, batch_ys = mnist.train.next_batch(batch_size) _, c = sess.run([optimizer, cost], feed_dict={x: batch_xs}) # 训练模型 if epoch % display_step == 0: print("Epoch:", "d"%(epoch+1), "cost=", "{:.9f}".format(c)) print("Done") # test correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # 错误率 accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) print("Accuracy: ", 1-accuracy.eval({x: mnist.test.images, y: mnist.test.images})) # 可视化结果 show_num = 10 reconstruction = sess.run(pred, feed_dict={x: mnist.test.images[:show_num]}) f, a = plt.subplots(2, 10, figsize=(10, 2)) for i in range(show_num): a[0][i].imshow(np.reshape(mnist.test.images[i], (28,28))) a[1][i].imshow(np.reshape(reconstruction[i], (28,28))) plt.draw()可以看出输出与真实图像基本一样
