本文共 3802 字，大约阅读时间需要 12 分钟。

先放上代码，效果图如下：

from __future__ import division, print_function, absolute_importimport tensorflow as tfimport numpy as npimport matplotlib.pyplot as plt# Import MNIST datafrom tensorflow.examples.tutorials.mnist import input_datamnist = input_data.read_data_sets('MNIST_data', one_hot=False)# Visualize decoder setting# Parameterslearning_rate = 0.01training_epochs = 5batch_size = 256display_step = 1examples_to_show = 10# Network Parametersn_input = 784  # MNIST data input (img shape: 28*28)# tf Graph input (only pictures)X = tf.placeholder("float", [None, n_input])# hidden layer settingsn_hidden_1 = 256 # 1st layer num featuresn_hidden_2 = 128 # 2nd layer num featuresweights = {    '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])),}# Building the encoderdef 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# Building the decoderdef 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# Construct modelencoder_op = encoder(X)decoder_op = decoder(encoder_op)# Predictiony_pred = decoder_op# Targets (Labels) are the input data.y_true = X# Define loss and optimizer, minimize the squared errorcost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)# Launch the graphwith 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)    total_batch = int(mnist.train.num_examples/batch_size)    # Training cycle    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)  # 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})        # Display logs per epoch step        if epoch % display_step == 0:            print("Epoch:", '%04d' % (epoch+1),                  "cost=", "{:.9f}".format(c))    print("Optimization Finished!")    # # Applying encode and decode over test set    encode_decode = sess.run(        y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})    # Compare original images with their reconstructions    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()