TensorFlow学习之建立一个神经网络添加层

1.建立一个神经网络添加层

输入值、输入的大小、输出的大小和激励函数

学过神经网络的人看下面这个图就明白了，不懂的去看看我的另一篇博客（http://www.cnblogs.com/wjy-lulu/p/6547542.html）

def add_layer(inputs , in_size , out_size , activate = None):

Weights = tf.Variable(tf.random_normal([in_size,out_size]))#随机初始化

baises = tf.Variable(tf.zeros([1,out_size])+0.1)#可以随机但是不要初始化为0，都为固定值比随机好点

y = tf.matmul(inputs, Weights) + baises #matmul:矩阵乘法，multipy:一般是数量的乘法

if activate:

y = activate(y)

return y

2.训练一个二次函数

import tensorflow as tf

import numpy as np

def add_layer(inputs , in_size , out_size , activate = None):

Weights = tf.Variable(tf.random_normal([in_size,out_size]))#随机初始化

baises = tf.Variable(tf.zeros([1,out_size])+0.1)#可以随机但是不要初始化为0，都为固定值比随机好点

y = tf.matmul(inputs, Weights) + baises #matmul:矩阵乘法，multipy:一般是数量的乘法

if activate:

y = activate(y)

return y

if __name__ == '__main__':

x_data = np.linspace(-1,1,300,dtype=np.float32)[:,np.newaxis]#创建-1,1的300个数，此时为一维矩阵，后面转化为二维矩阵===[1,2,3]-->>[[1,2,3]]

noise = np.random.normal(0,0.05,x_data.shape).astype(np.float32)#噪声是(1,300)格式,0-0.05大小

y_data = np.square(x_data) - 0.5 + noise #带有噪声的抛物线

xs = tf.placeholder(tf.float32,[None,1]) #外界输入数据

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

l1 = add_layer(xs,1,10,activate=tf.nn.relu)

prediction = add_layer(l1,10,1,activate=None)

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

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)#对误差进行梯度优化，步伐为0.1

sess = tf.Session()

sess.run( tf.global_variables_initializer())

for i in range(1000):

sess.run(train_step, feed_dict={xs: x_data, ys: y_data})#训练

if i%50 == 0:

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

3.动态显示训练过程

显示的步骤程序之中部分进行说明，其它说明请看其它博客（http://www.cnblogs.com/wjy-lulu/p/7735987.html）

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

def add_layer(inputs , in_size , out_size , activate = None):

Weights = tf.Variable(tf.random_normal([in_size,out_size]))#随机初始化

baises = tf.Variable(tf.zeros([1,out_size])+0.1)#可以随机但是不要初始化为0，都为固定值比随机好点

y = tf.matmul(inputs, Weights) + baises #matmul:矩阵乘法，multipy:一般是数量的乘法

if activate:

y = activate(y)

return y

if __name__ == '__main__':

x_data = np.linspace(-1,1,300,dtype=np.float32)[:,np.newaxis]#创建-1,1的300个数，此时为一维矩阵，后面转化为二维矩阵===[1,2,3]-->>[[1,2,3]]

noise = np.random.normal(0,0.05,x_data.shape).astype(np.float32)#噪声是(1,300)格式,0-0.05大小

y_data = np.square(x_data) - 0.5 + noise #带有噪声的抛物线

fig = plt.figure('show_data')# figure("data")指定图表名称

ax = fig.add_subplot(111)

ax.scatter(x_data,y_data)

plt.ion()

plt.show()

xs = tf.placeholder(tf.float32,[None,1]) #外界输入数据

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

l1 = add_layer(xs,1,10,activate=tf.nn.relu)

prediction = add_layer(l1,10,1,activate=None)

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

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)#对误差进行梯度优化，步伐为0.1

sess = tf.Session()

sess.run( tf.global_variables_initializer())

for i in range(1000):

sess.run(train_step, feed_dict={xs: x_data, ys: y_data})#训练

if i%50 == 0:

try:

ax.lines.remove(lines[0])

except Exception:

pass

prediction_value = sess.run(prediction, feed_dict={xs: x_data})

lines = ax.plot(x_data,prediction_value,"r",lw = 3)

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

plt.pause(2)

while True:

plt.pause(0.01)

4.TensorBoard整体结构化显示

A.利用with tf.name_scope("name")创建大结构、利用函数的name="name"去创建小结构：tf.placeholder(tf.float32,[None,1],name="x_data")

B.利用writer = tf.summary.FileWriter("G:/test/",graph=sess.graph)创建一个graph文件

C.利用TessorBoard去执行这个文件

这里得注意--->>>首先到你存放文件的上一个目录--->>然后再去运行这个文件

tensorboard --logdir=test

(被屏蔽的GIF动图，具体安装操作欢迎戳“原文链接”哈！)

5.TensorBoard局部结构化显示

A. tf.summary.histogram(layer_name+"Weight",Weights)：直方图显示

B. tf.summary.scalar("Loss",loss):折线图显示，loss的走向决定你的网络训练的好坏，至关重要一点

C.初始化与运行设定的图表

merge = tf.summary.merge_all()#合并图表2 writer = tf.summary.FileWriter("G:/test/",graph=sess.graph)#写进文件3 result = sess.run(merge,feed_dict={xs:x_data,ys:y_data})#运行打包的图表merge4 writer.add_summary(result,i)#写入文件，并且单步长50

完整代码及显示效果：

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

def add_layer(inputs , in_size , out_size , n_layer = 1 , activate = None):

layer_name = "layer" + str(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")#随机初始化

tf.summary.histogram(layer_name+"Weight",Weights)

with tf.name_scope("Baises"):

baises = tf.Variable(tf.zeros([1,out_size])+0.1,name="B")#可以随机但是不要初始化为0，都为固定值比随机好点

tf.summary.histogram(layer_name+"Baises",baises)

y = tf.matmul(inputs, Weights) + baises #matmul:矩阵乘法，multipy:一般是数量的乘法

if activate:

y = activate(y)

tf.summary.histogram(layer_name+"y_sum",y)

return y

if __name__ == '__main__':

x_data = np.linspace(-1,1,300,dtype=np.float32)[:,np.newaxis]#创建-1,1的300个数，此时为一维矩阵，后面转化为二维矩阵===[1,2,3]-->>[[1,2,3]]

noise = np.random.normal(0,0.05,x_data.shape).astype(np.float32)#噪声是(1,300)格式,0-0.05大小

y_data = np.square(x_data) - 0.5 + noise #带有噪声的抛物线

fig = plt.figure('show_data')# figure("data")指定图表名称

ax = fig.add_subplot(111)

ax.scatter(x_data,y_data)

plt.ion()

plt.show()

with tf.name_scope("inputs"):

xs = tf.placeholder(tf.float32,[None,1],name="x_data") #外界输入数据

ys = tf.placeholder(tf.float32,[None,1],name="y_data")

l1 = add_layer(xs,1,10,n_layer=1,activate=tf.nn.relu)

prediction = add_layer(l1,10,1,n_layer=2,activate=None)

with tf.name_scope("loss"):

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

tf.summary.scalar("Loss",loss)

with tf.name_scope("train_step"):

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)#对误差进行梯度优化，步伐为0.1

sess = tf.Session()

merge = tf.summary.merge_all()#合并

writer = tf.summary.FileWriter("G:/test/",graph=sess.graph)

sess.run( tf.global_variables_initializer())

for i in range(1000):

sess.run(train_step, feed_dict={xs: x_data, ys: y_data})#训练

if i%100 == 0:

result = sess.run(merge,feed_dict={xs:x_data,ys:y_data})#运行打包的图表merge

writer.add_summary(result,i)#写入文件，并且单步长50