TF之AE：AE实现TF自带数据集数字真实值对比AE先encoder后decoder预测数字的精确对比—Jason niu

import 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("/niu/mnist_data/",one_hot=False)# Parameterlearning_rate = 0.01training_epochs = 10 batch_size = 256display_step = 1examples_to_show = 10# Network Parametersn_input = 784 #tf Graph input(only pictures)X=tf.placeholder("float", [None,n_input])# hidden layer settingsn_hidden_1 = 256 n_hidden_2 = 128 
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 ???#定义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) ????????????# 128 Featuresdecoder_op = decoder(encoder_op) ???# 784 Features# 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:
 ???sess.run(tf.initialize_all_variables()) ???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)) ???plt.title(‘Matplotlib,AE--Jason Niu‘) ???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()