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}))