莫烦TensorFlow_08 tensorboard可视化进阶

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

 #
 # add layer
 #
def add_layer(inputs, in_size, out_size,n_layer, activation_function = None):
  layer_name = 'layer%s' % n_layer
  with tf.name_scope(layer_name):
    with tf.name_scope('Weights'):
      Weights = tf.Variable(tf.random_normal([in_size, out_size]), name='W')  # hang lie
      tf.summary.histogram(layer_name + '/weights', Weights)#保存成一个直方图，bin是取值
    with tf.name_scope('biases'):
      biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name = 'b')
      tf.summary.histogram(layer_name + '/biases', biases)#注意histogram的路径
    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)  

    tf.summary.histogram(layer_name + '/outputs', outputs)
    return outputs
#
#make up some data
#
x_data = np.linspace(-1,1,300)[:, np.newaxis]
noise  = np.random.normal(0, 0.05, x_data.shape)
y_data = np.square(x_data) - 0.5 + noise

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

#add hidden layer
l1 = add_layer(xs, 1, 10, n_layer = 1,activation_function = tf.nn.relu)
#add output layer
prediction = add_layer(l1, 10, 1, n_layer = 2, 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]  ))
  tf.summary.scalar('loss', loss)#记录operation，是存储在scaler里的

with tf.name_scope('train'):
  train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)  

sess = tf.Session()
merged = tf.summary.merge_all() #所有的summary在merge以后，在一个run中就可执行
writer = tf.summary.FileWriter("logs/", sess.graph) #定义writer

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

#
# Session
#

for i in range(1000):
  sess.run(train_step, feed_dict={xs:x_data, ys:y_data})
  if i % 50 == 0:
    result = sess.run(merged, # 否则要一个个run summary。
		      feed_dict = {xs:x_data, ys:y_data})

    writer.add_summary(result, i)#按序列写入结果
    print(sess.run(loss, feed_dict={xs:x_data, ys:y_data}))