Tensorboard

代码

#!/usr/bin/python2.7
# -*- coding: UTF-8 -*-



import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import matplotlib.pyplot as plt

# 载入数据
mnist = input_data.read_data_sets("MNIST_data",one_hot=True)




# 定义批次大小
batch_size=100

#计算批次个数
n_batch=mnist.train.num_examples // batch_size

#  参数的概要

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)


# 定义命名空空间

with tf.name_scope('input'):
    # 定义placeholder
    x=tf.placeholder(tf.float32,[None,784],name='x-input') # None和batch_size关联
    y=tf.placeholder(tf.float32,[None,10],name='y-input')

with  tf.name_scope('layer'):
    with tf.name_scope('weights'):
        W=tf.Variable(tf.zeros([784,10]),name='W')
        variable_summaries(W)
    with tf.name_scope('biases'):
        b = tf.Variable(tf.zeros([10]), name='b')
        variable_summaries(b)
    with tf.name_scope('wx_plus_b'):
        wx_plus_b=tf.matmul(x,W)+b
    with tf.name_scope('softmax'):
        prediction=tf.nn.softmax(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=prediction))
    tf.summary.scalar('loss',loss)
with tf.name_scope('train'):
    train_step=tf.train.GradientDescentOptimizer(0.2).minimize(loss)

# 初始化变量

init=tf.global_variables_initializer()

# 结果存放在bool列表
with tf.name_scope('accuracy'):
    with tf.name_scope('correct_prediction'):
        correct_prediction=tf.equal(tf.argmax(y,1),tf.argmax(prediction,1))
    with tf.name_scope('accuracy'):
        accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
        tf.summary.scalar('accuracy', accuracy)


# 合并summary

merged=tf.summary.merge_all()


with tf.Session() as sess:
    sess.run(init)
    writer=tf.summary.FileWriter('milogs/',sess.graph)
    for epoch in range(51):  # 所有图片训练21次
        for bacth 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})  # 测试集的数据
        print("iter " + str(epoch) + " ,Testing Accuracy " + str(acc))

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