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—daidingdaiding

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—daidingdaiding

目录

输出结果

代码设计

输出结果

代码设计

[code]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 = 10batch_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 = 256n_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])),}#定义encoderdef 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#定义decoderdef 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 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:<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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