tensorboard的使用

# -*- coding: utf-8 -*-
"""
Created on: 2017/10/29
@author   : Shawn
function  :
"""
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

# 入口函数
if __name__ == '__main__':

    # 载入数据
    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

    # 命名空间
    with tf.name_scope('input'):
        # 定义两个placeholder
        x = tf.placeholder(tf.float32, [None, 784], name='x-input')  # 输入层784个神经元
        y = tf.placeholder(tf.float32, [None, 10], name='y-input')  # 输出层10个神经元，10类

    W = tf.Variable(tf.zeros([784, 10]))
    b = tf.Variable(tf.zeros([10]))
    prediction = tf.nn.softmax(tf.matmul(x, W)+b)

    # 二次代价函数
    # loss = tf.reduce_mean(tf.square(y-prediction))
    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))

    # 使用梯度下降法
    # train_step= tf.train.GradientDescentOptimizer(0.2).minimize(loss) # 0.2为学习率
    train_step = tf.train.AdamOptimizer(1e-1).minimize(loss)

    # 初始化变量
    init = tf.global_variables_initializer()

    # 结果存在一个bool类型的列表中
    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(prediction, 1)) # agmax返回一维张量中最大值所在的位置

    # 求准确率
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    with tf.Session() as sess:
        sess.run(init)
        writer = tf.summary.FileWriter('logs/', sess.graph)

        # 把所有图片训练21次
        for epoch in range(1):

            # 训练n_batch批次
            for batch in range(n_batch):
                batch_xs, batch_ys = mnist.train.next_batch(batch_size)
                sess.run(train_step, feed_dict={x:batch_xs, y:batch_ys})

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

    pass

进入cmd:

tensorboard --logdir=F:PycharmProjectsTFlearnsrclogs

输出一个网址：

用google浏览器或者火狐打开