TF之AE：AE實現TF自帶數據集數字真實值對比AE先encoder后decoder預測數字的精確對比—daidingdaiding

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代碼設計

import tensorflow as tf import numpy as np import matplotlib.pyplot as plt#Import MNIST data from tensorflow.examples.tutorials.mnist import input_data mnist=input_data.read_data_sets("/niu/mnist_data/",one_hot=False)# Parameter learning_rate = 0.01 training_epochs = 10 batch_size = 256 display_step = 1 examples_to_show = 10# Network Parameters n_input = 784#tf Graph input(only pictures) X=tf.placeholder("float", [None,n_input])# hidden layer settings n_hidden_1 = 256 n_hidden_2 = 128 <br> 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])),}#定義encoder def encoder(x):# Encoder Hidden layer with sigmoid activation #1layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['encoder_h1']),biases['encoder_b1']))# Decoder Hidden layer with sigmoid activation #2layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['encoder_h2']),biases['encoder_b2']))return layer_2#定義decoder def decoder(x):# Encoder Hidden layer with sigmoid activation #1layer_1 = tf.nn.sigmoid(tf.add(tf.matmul(x, weights['decoder_h1']),biases['decoder_b1']))# Decoder Hidden layer with sigmoid activation #2layer_2 = tf.nn.sigmoid(tf.add(tf.matmul(layer_1, weights['decoder_h2']),biases['decoder_b2']))return layer_2# Construct model encoder_op = encoder(X) # 128 Features decoder_op = decoder(encoder_op) # 784 Features# Prediction y_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 graph with tf.Session() as sess:<br>sess.run(tf.initialize_all_variables())total_batch = int(mnist.train.num_examples/batch_size)# Training cyclefor epoch in range(training_epochs):# Loop over all batchesfor 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 stepif epoch % display_step == 0:print("Epoch:", '%04d' % (epoch+1),"cost=", "{:.9f}".format(c))print("Optimization Finished!")# # Applying encode and decode over test setencode_decode = sess.run(y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})# Compare original images with their reconstructionsf, 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()

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TF之AE：AE實現TF自帶數據集數字真實值對比AE先encoder后decoder預測數字的精確對比

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