#coding=utf-8

import tensorflow as tf

import numpy as np

#创建层

def add_layer(inputs,in_size,out_size,layer_name='Layer_Default',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.summary.scalar('weights',weights)

tf.summary.histogram('weights',weights)

with tf.name_scope('Biases'):

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

#tf.summary.scalar('biases',biases)

tf.summary.histogram('biases',biases)

with tf.name_scope('Wx_plus_b'):

Wx_plus_b = tf.matmul(inputs,weights) + biases

if activation_function == None:

outputs = Wx_plus_b

else:

outputs = activation_function(Wx_plus_b)

tf.summary.histogram('/outputs',outputs)

return outputs

#输入数据

x_data = np.linspace(-1,1,300, dtype=np.float32)[:, np.newaxis]

#添加噪点，是数据更加真实，正态分布随机取值

noise = np.random.normal(0, 0.05, x_data.shape).astype(np.float32)

#标准输出数据

y_data = np.square(x_data) - 0.5 + noise

#利用占位符定义输出与输出，等到需要用到时再feed

with tf.name_scope('inputs'):

xs = tf.placeholder(tf.float32, [None, 1],name='x_in')

ys = tf.placeholder(tf.float32, [None, 1],name='y_in')

hidden_layer = add_layer(xs,1,10,'hidden_layer1',tf.nn.relu)

hidden_layer2 = add_layer(hidden_layer,10,10,'hidden_layer2',tf.nn.relu)

output_layer = add_layer(hidden_layer2,10,1,'output_layer',tf.nn.relu)

#reduction_indices=[1]对行求和，0对列求和，返回tuple数组

with tf.name_scope('Loss'):

loss = tf.reduce_mean(tf.reduce_sum(tf.square(ys - output_layer),reduction_indices=[1]))

tf.summary.scalar('loss',loss)

#训练

with tf.name_scope('train'):

train = tf.train.GradientDescentOptimizer(0.01).minimize(loss)

with tf.Session() as sess:

merged = tf.summary.merge_all()

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

sess.run(tf.global_variables_initializer())

for i in range(1000):

sess.run(train,feed_dict={xs:x_data,ys:y_data})

if i % 50 == 0:

print sess.run(loss, feed_dict={xs: x_data, ys: y_data})

rs = sess.run(merged,feed_dict={xs: x_data, ys: y_data})

writter.add_summary(rs,i)

##测试如何用scalar显示weights和biases的趋势