吴裕雄 PYTHON 神经网络——TENSORFLOW 双隐藏层自编码器设计处理MNIST手写数字数据集并使用TENSORBORD描绘神经网络数据2

import os
import tensorflow as tf

from tensorflow.examples.tutorials.mnist import input_data
 
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
 
batch_size = 128  # batch容量
display_step = 1  # 展示间隔
learning_rate = 0.01  # 学习率
training_epochs = 20  # 训练轮数，1轮等于n_samples/batch_size
example_to_show = 10  # 展示图像数目
 
n_hidden1_units = 256  # 第一隐藏层
n_hidden2_units = 128  # 第二隐藏层
n_input_units = 784
n_output_units = n_input_units
 
def variable_summaries(var): 
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.histogram('mean', mean)
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
            tf.summary.scalar('stddev', stddev)  # 注意，这是标量
            tf.summary.scalar('max', tf.reduce_max(var))
            tf.summary.scalar('min', tf.reduce_min(var))
            tf.summary.histogram('histogram', var)
def WeightsVariable(n_in,n_out,name_str):
    return tf.Variable(tf.random_normal([n_in,n_out]),dtype=tf.float32,name=name_str)
 
def biasesVariable(n_out,name_str):
    return tf.Variable(tf.random_normal([n_out]),dtype=tf.float32,name=name_str)
 
def encoder(x_origin,activate_func=tf.nn.sigmoid):
    with tf.name_scope('Layer1'):
        Weights = WeightsVariable(n_input_units,n_hidden1_units,'Weights')
        biases = biasesVariable(n_hidden1_units,'biases')
        x_code1 = activate_func(tf.add(tf.matmul(x_origin,Weights),biases))
        variable_summaries(Weights) 
        variable_summaries(biases) 
    with tf.name_scope('Layer2'):
        Weights = WeightsVariable(n_hidden1_units,n_hidden2_units,'Weights')
        biases = biasesVariable(n_hidden2_units,'biases')
        x_code2 = activate_func(tf.add(tf.matmul(x_code1,Weights),biases))
        variable_summaries(Weights) 
        variable_summaries(biases) 
    return x_code2
 
def decode(x_code,activate_func=tf.nn.sigmoid):
    with tf.name_scope('Layer1'):
        Weights = WeightsVariable(n_hidden2_units,n_hidden1_units,'Weights')
        biases = biasesVariable(n_hidden1_units,'biases')
        x_decode1 = activate_func(tf.add(tf.matmul(x_code,Weights),biases))
        variable_summaries(Weights) 
        variable_summaries(biases) 
    with tf.name_scope('Layer2'):
        Weights = WeightsVariable(n_hidden1_units,n_output_units,'Weights')
        biases = biasesVariable(n_output_units,'biases')
        x_decode2 = activate_func(tf.add(tf.matmul(x_decode1,Weights),biases))
        variable_summaries(Weights) 
        variable_summaries(biases)
    return x_decode2
 
with tf.Graph().as_default():
    with tf.name_scope('Input'):
        X_input = tf.placeholder(tf.float32,[None,n_input_units])
    with tf.name_scope('Encode'):
        X_code = encoder(X_input)
    with tf.name_scope('decode'):
        X_decode = decode(X_code)
    with tf.name_scope('loss'):
        loss = tf.reduce_mean(tf.pow(X_input - X_decode,2))
    with tf.name_scope('train'):
        Optimizer = tf.train.RMSPropOptimizer(learning_rate)
        train = Optimizer.minimize(loss)
    # 标量汇总
    with tf.name_scope('LossSummary'):
        tf.summary.scalar('loss',loss)
        tf.summary.scalar('learning_rate',learning_rate)
    # 图像展示
    with tf.name_scope('ImageSummary'):
        image_original = tf.reshape(X_input,[-1, 28, 28, 1])
        image_reconstruction = tf.reshape(X_decode, [-1, 28, 28, 1])
        tf.summary.image('image_original', image_original, 9)
        tf.summary.image('image_recinstruction', image_reconstruction, 9)
    # 汇总
    merged_summary = tf.summary.merge_all()
 
    init = tf.global_variables_initializer()
 
    writer = tf.summary.FileWriter(logdir='E:\tensorboard\logsssxx', graph=tf.get_default_graph())
    writer.flush()
 
    mnist = input_data.read_data_sets('E:\MNIST_data\', one_hot=True)
 
    with tf.Session() as sess:
        sess.run(init)
        total_batch = int(mnist.train.num_examples / batch_size)
        for epoch in range(training_epochs):
            for i in range(total_batch):
                batch_xs,batch_ys = mnist.train.next_batch(batch_size)
                _,Loss = sess.run([train,loss],feed_dict={X_input: batch_xs})
                Loss = sess.run(loss,feed_dict={X_input: batch_xs})
            if epoch % display_step == 0:
                print('Epoch: %04d' % (epoch + 1),'loss= ','{:.9f}'.format(Loss))
                summary_str = sess.run(merged_summary,feed_dict={X_input: batch_xs})
                writer.add_summary(summary_str,epoch) 
                writer.flush() 
        writer.close()
        print('训练完毕！')