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

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])),
    }
 
#定义encoder
def 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decoder
def 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 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 error
 
cost = 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 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()

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