莫烦TensorFlow_07 tensorboard可视化

import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt  

def add_layer(inputs, in_size, out_size, activation_function = None):  

  with tf.name_scope('layer'):

    with tf.name_scope('Weights'):
      Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')  # hang lie  

    with tf.name_scope('biases'):
      biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name = 'b')  

    with tf.name_scope('Wx_plus_b'):
      Wx_plus_b = tf.matmul(inputs, Weights) + biases  

    if activation_function is None:
      outputs = Wx_plus_b
    else:
      outputs = activation_function(Wx_plus_b)
    return outputs  

#define placeholder
with tf.name_scope('inputs'):
  xs = tf.placeholder(tf.float32, [None, 1], name = 'x_input')
  ys = tf.placeholder(tf.float32, [None, 1], name = 'y_input')  

#add hidden layer
l1 = add_layer(xs, 1, 10, activation_function = tf.nn.relu)
#add output layer
prediction = add_layer(l1, 10, 1, activation_function = None)  

#the error between prediction and real data
with tf.name_scope('loss'):
  loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction),
				    reduction_indices=[1]  ))
with tf.name_scope('train'):
  train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)  

sess = tf.Session()
writer = tf.summary.FileWriter("logs/", sess.graph)

#import step
sess.run(tf.global_variables_initializer() )

　　

注意：有些浏览器可能支持的不好，推荐使用最新的Chrome

命令行输入：

tensorboard --logdir=logs/