Tensorboard案例

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tensorboard 图

步骤：

1、首先需要定义命名空间,如下所示（tf.name_scope('XXXX')）：

#命名空间
with tf.name_scope('input_hupo'):
    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('wights'):
        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
    with tf.name_scope('softmax'):
        prediction=tf.nn.softmax(wx_plus_b)

2、然后在会话中生成图（tf.summary.FileWriter('路径'，sess.graph)）：

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

3、打开CMD,然后定位到自己设置的路径上，输出tensorboard --logdir=路径，然后按下回车，复制生成的地址到谷歌或者火狐浏览器中，就可以看到tensorboard生成的内容了。

附上全部代码：

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

mnist = input_data.read_data_sets('MNIST_data', one_hot=True)

batch_size = 100
n_batch = mnist.train.num_examples // batch_size

# 命名空间
with tf.name_scope('input_hupo'):
    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('wights'):
        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
    with tf.name_scope('softmax'):
        prediction = tf.nn.softmax(wx_plus_b)
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y, logits=prediction))
train_step = tf.train.GradientDescentOptimizer(0.2).minimize(loss)
init = tf.global_variables_initializer()
correct_prediction = tf.equal(tf.argmax(y), tf.argmax(prediction))
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))