5.3 tensorflow学习与应用——Tensorboard网络运行

import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

mnist = input_data.read_data_sets("MNIST_data", one_hot=True)
#每个批次的大小
batch_size = 100
#计算一共有多少个批次
n_batch = mnist.train.num_examples // batch_size

def variable_summaries(var):
    with tf.name_scope('summaries'):
        mean = tf.reduce_mean(var)
        tf.summary.scalar('mean', mean)
        with tf.name_scope('stddev'):
            stddev = tf.sqrt(tf.reduce_mean(tf.square(var - mean)))
        tf.summary.scalar('stddev', stddev)
        tf.summary.scalar('max', tf.reduce_max(var))
        tf.summary.scalar('min', tf.reduce_min(var))
        tf.summary.histogram('histogram', var)
#命名空间
with tf.name_scope("input"):
    #定义输入输出
    x = tf.placeholder(tf.float32, [None, 784], name='x-input')
    y = tf.placeholder(tf.float32, [None, 10], name='y-input')
with tf.name_scope("layer"):
    #创建一个简单的神经网络
    with tf.name_scope('weights'):
        W = tf.Variable(tf.zeros([784, 10]))
        variable_summaries(W)
    with tf.name_scope('bias'):
        b = tf.Variable(tf.zeros([1, 10]))
        variable_summaries(b)
    with tf.name_scope('predictions'):
        predictions_1 = tf.matmul(x, W) + b
#定义二次代价函数
with tf.name_scope('loss'):
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=predictions_1))
    tf.summary.scalar('loss', loss)
#使用梯度下降
with tf.name_scope('train'):
    train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)
#初始化变量
init = tf.global_variables_initializer()

#结果存放在一个bool型列表中
with tf.name_scope('acc'):
    with tf.name_scope('correction'):
        correction = tf.equal(tf.argmax(y, 1), tf.argmax(predictions_1, 1))#argmax返回一维张量中最大的值所在的位置
    #求准确率
    with tf.name_scope('accuracy'):
        accuracy = tf.reduce_mean(tf.cast(correction, tf.float32))
        tf.summary.scalar('accuracy', accuracy)

#合并所有的summary
merged = tf.summary.merge_all()

with tf.Session() as sess:
    sess.run(init)
    writer = tf.summary.FileWriter('logs/', sess.graph)
    for epoch in range(51):
        for batch in range(n_batch):
            batch_xs, batch_ys = mnist.train.next_batch(batch_size)
            summary, _ = sess.run([merged, train_step], feed_dict={x: batch_xs, y: batch_ys})

        writer.add_summary(summary, epoch)
        acc = sess.run(accuracy, feed_dict={x: mnist.test.images, y: mnist.test.labels})
        print("epoch " + str(epoch) + ", accuracy " + str(acc))