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