吴裕雄 PYTHON 神经网络——TENSORFLOW 无监督学习处理MNIST手写数字数据集

# 导入模块

import numpy as np

import tensorflow as tf

import matplotlib.pyplot as plt

# 加载数据

from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("E:\\MNIST_data\\", one_hot=True)

#模型训练

# 设置超参数

learning_rate = 0.01 # 学习率

training_epochs = 20 # 训练轮数

batch_size = 256 # 每次训练的数据

display_step = 1 # 每隔多少轮显示一次训练结果

examples_to_show = 10 # 提示从测试集中选择10张图片取验证自动编码器的结果

# 网络参数

n_hidden_1 = 256 # 第一个隐藏层神经元个数（特征值格式）

n_hidden_2 = 128 # 第二个隐藏层神经元格式

n_input = 784 # 输入数据的特征个数  28*28=784

# 定义输入数据，无监督不需要标注数据，所以只有输入图片

X = tf.placeholder("float", [None, n_input])

#初始化每一层的权重和偏置

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])),

}

#定义自动编码模型的网络结构，包括压缩和解压的过程

# 定义压缩函数

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

# 定义解压函数

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

# 建立模型

encoder_op = encoder(X)

decoder_op = decoder(encoder_op)

# 得出预测分类值

y_pred = decoder_op

# 得出真实值，即输入值

y_true = X

# 定义损失函数和优化器

cost = tf.reduce_mean(tf.pow(y_true - y_pred, 2))

optimizer = tf.train.RMSPropOptimizer(learning_rate).minimize(cost)

# 初始化变量

init = tf.global_variables_initializer()

# 3 训练数据及评估模型

with tf.Session() as sess:

    sess.run(init)

    total_batch = int(mnist.train.num_examples/batch_size)

    # 开始训练

    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)

            # Run optimization op (backprop) and cost op (to get loss value)

            _, c = sess.run([optimizer, cost], feed_dict={X: batch_xs})

        # 每一轮，打印一次损失值

        if epoch % display_step == 0:

            print("Epoch:", '%04d' % (epoch+1),"cost=", "{:.9f}".format(c))

    print("Optimization Finished!")

    # 对测试集应用训练好的自动编码网络

    encode_decode = sess.run(

        y_pred, feed_dict={X: mnist.test.images[:examples_to_show]})

    # 比较测试集原始图片和自动编码网络的重建结果

    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)))

    f.show()

    plt.draw()

    #plt.waitforbuttonpress()