Tensorboard可视化（关于TensorFlow不同版本引起的错误）

# -*- coding: utf-8 -*-
"""
Created on Sun Nov 5 15:28:50 2017

@author: Administrator
"""

import tensorflow as tf
import numpy as np

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')

# tf.histogram_summary(layer_name + '/weights', Weights)
tf.summary.histogram(layer_name + '/weights', Weights)

with tf.name_scope('biases'):
biases = tf.Variable(tf.zeros([1, out_size]) + 0.1, name='b')
# tf.histogram_summary(layer_name + '/biases', biases)
tf.summary.histogram(layer_name + '/biases', biases)
with tf.name_scope('Wx_plus_b'):
Wx_plus_b = tf.add(tf.matmul(inputs, Weights),biases)
if activation_function is None:
outputs = Wx_plus_b
else:
outputs = activation_function(Wx_plus_b)
# tf.histogram_summary(layer_name + '/output', outputs)
tf.summary.histogram(layer_name + '/output', outputs)
return outputs

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

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')

l1 = add_layer(xs, 1, 10, n_layer=1, activation_function=tf.nn.relu)
prediction = add_layer(l1, 10, 1, n_layer=2, activation_function=None)

with tf.name_scope('loss'):
loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - prediction), reduction_indices=[1]))

# tf.scalar_summary('loss', loss) # 纯量的变化 EVENTS显示
tf.summary.scalar('loss', loss)

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

sess = tf.Session()
# merged = tf.merge_all_summaries() #把所有的summary合并在一起，打包
merged = tf.summary.merge_all()
# writer = tf.train.SummaryWriter("D://logs",sess.graph)
writer = tf.summary.FileWriter("D://logs",sess.graph)

# sess.run(tf.initialize_all_variable())
sess.run(tf.global_variables_initializer())

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, feed_dict={xs:x_data, ys:y_data})
writer.add_summary(result, i)

1.   tf.histogram_summary()  >>  tf.summary.histogram()

2.   tf.scalar_summary()  >>  tf.summary.scalar()

3.   tf.merge_all_summaries()  >>  tf.summary.merge_all()

4.   tf.train.SummaryWriter()  >>  tf.summary.FileWriter()

5.   tf.initialize_all_variable()  >>  tf.global_variables_initializer()

6.   重复运行报错： D://logs文件夹下只能有一个events.out.tfevents......事件