python学习笔记之tensorboard

# coding: utf-8

# In[2]:

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

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# 载入数据集
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

#定义一个命名空间
with tf.name_scope('input'):
    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"):
    with tf.name_scope('weights'):
        W = tf.Variable(tf.zeros([784, 10]))
    with tf.name_scope('biases'):
        b = tf.Variable(tf.zeros([10]))
    with tf.name_scope('wx_plus_b'):
        wx_plus_b=tf.matmul(x, W) + b
# 创建一个简单的神经网络
    with tf.name_scope('prediction'):
        prediction = tf.nn.softmax(wx_plus_b)

with tf.name_scope('loss'):
    # 二次代价函数
    loss = tf.reduce_mean(tf.square(y - prediction))
with tf.name_scope("train"):
    # 使用梯度下降法
    train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)

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

with tf.name_scope('accuracy'):
    with tf.name_scope("correct_prediction"):
# 结果存放在一个布尔型列表中
        correct_prediction = tf.equal(tf.argmax(y, 1), tf.argmax(prediction, 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(correct_prediction, tf.float32))
with tf.Session() as sess:
    sess.run(init)
    writer=tf.summary.FileWriter('logs/',sess.graph)
    for epoch in range(1):
        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))

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我的tensorboard生成文件夹为：E:\Py3.6_Proje\Tensorflow\logs

打开cmd  

1：输入 e:    按enter

2：输入tensorboard --logdir=E:\Py3.6_Proje\Tensorflow\logs

3:将cmd生成的本地连接打开，复制到Google浏览器中，即可打开tensorboard模型