TensorFlow學(xué)習(xí)之建立一個(gè)神經(jīng)網(wǎng)絡(luò)添加層

1.建立一個(gè)神經(jīng)網(wǎng)絡(luò)添加層

輸入值、輸入的大小、輸出的大小和激勵(lì)函數(shù)

學(xué)過(guò)神經(jīng)網(wǎng)絡(luò)的人看下面這個(gè)圖就明白了，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]))#隨機(jī)初始化

baises = tf.Variable(tf.zeros([1,out_size])+0.1)#可以隨機(jī)但是不要初始化為0，都為固定值比隨機(jī)好點(diǎn)

y = tf.matmul(inputs, Weights) + baises #matmul:矩陣乘法，multipy:一般是數(shù)量的乘法

if activate:

y = activate(y)

return y

2.訓(xùn)練一個(gè)二次函數(shù)

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]))#隨機(jī)初始化

baises = tf.Variable(tf.zeros([1,out_size])+0.1)#可以隨機(jī)但是不要初始化為0，都為固定值比隨機(jī)好點(diǎn)

y = tf.matmul(inputs, Weights) + baises #matmul:矩陣乘法，multipy:一般是數(shù)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]#創(chuàng)建-1,1的300個(gè)數(shù)，此時(shí)為一維矩陣，后面轉(zhuǎn)化為二維矩陣===[1,2,3]-->>[[1,2,3]]

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

y_data = np.square(x_data) - 0.5 + noise #帶有噪聲的拋物線

xs = tf.placeholder(tf.float32,[None,1]) #外界輸入數(shù)據(jù)

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]))#誤差

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)#對(duì)誤差進(jìn)行梯度優(yōu)化，步伐為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})#訓(xùn)練

if i%50 == 0:

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

3.動(dòng)態(tài)顯示訓(xùn)練過(guò)程

顯示的步驟程序之中部分進(jìn)行說(shuō)明，其它說(shuō)明請(qǐng)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]))#隨機(jī)初始化

baises = tf.Variable(tf.zeros([1,out_size])+0.1)#可以隨機(jī)但是不要初始化為0，都為固定值比隨機(jī)好點(diǎn)

y = tf.matmul(inputs, Weights) + baises #matmul:矩陣乘法，multipy:一般是數(shù)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]#創(chuàng)建-1,1的300個(gè)數(shù)，此時(shí)為一維矩陣，后面轉(zhuǎn)化為二維矩陣===[1,2,3]-->>[[1,2,3]]

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

y_data = np.square(x_data) - 0.5 + noise #帶有噪聲的拋物線

fig = plt.figure('show_data')# figure("data")指定圖表名稱

ax = fig.add_subplot(111)

ax.scatter(x_data,y_data)

plt.ion()

plt.show()

xs = tf.placeholder(tf.float32,[None,1]) #外界輸入數(shù)據(jù)

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]))#誤差

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)#對(duì)誤差進(jìn)行梯度優(yōu)化，步伐為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})#訓(xùn)練

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}))#查看誤差

plt.pause(2)

while True:

plt.pause(0.01)

4.TensorBoard整體結(jié)構(gòu)化顯示

A.利用with tf.name_scope("name")創(chuàng)建大結(jié)構(gòu)、利用函數(shù)的name="name"去創(chuàng)建小結(jié)構(gòu)：tf.placeholder(tf.float32,[None,1],name="x_data")

B.利用writer = tf.summary.FileWriter("G:/test/",graph=sess.graph)創(chuàng)建一個(gè)graph文件

C.利用TessorBoard去執(zhí)行這個(gè)文件

這里得注意--->>>首先到你存放文件的上一個(gè)目錄--->>然后再去運(yùn)行這個(gè)文件

tensorboard --logdir=test

(被屏蔽的GIF動(dòng)圖，具體安裝操作歡迎戳“原文鏈接”哈！)

5.TensorBoard局部結(jié)構(gòu)化顯示

A. tf.summary.histogram(layer_name+"Weight",Weights)：直方圖顯示

B. tf.summary.scalar("Loss",loss):折線圖顯示，loss的走向決定你的網(wǎng)絡(luò)訓(xùn)練的好壞，至關(guān)重要一點(diǎn)

C.初始化與運(yùn)行設(shè)定的圖表

merge = tf.summary.merge_all()#合并圖表2 writer = tf.summary.FileWriter("G:/test/",graph=sess.graph)#寫(xiě)進(jìn)文件3 result = sess.run(merge,feed_dict={xs:x_data,ys:y_data})#運(yùn)行打包的圖表merge4 writer.add_summary(result,i)#寫(xiě)入文件，并且單步長(zhǎng)50

完整代碼及顯示效果：

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")#隨機(jī)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")#可以隨機(jī)但是不要初始化為0，都為固定值比隨機(jī)好點(diǎn)

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

y = tf.matmul(inputs, Weights) + baises #matmul:矩陣乘法，multipy:一般是數(shù)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]#創(chuàng)建-1,1的300個(gè)數(shù)，此時(shí)為一維矩陣，后面轉(zhuǎn)化為二維矩陣===[1,2,3]-->>[[1,2,3]]

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

y_data = np.square(x_data) - 0.5 + noise #帶有噪聲的拋物線

fig = plt.figure('show_data')# figure("data")指定圖表名稱

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") #外界輸入數(shù)據(jù)

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]))#誤差

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

with tf.name_scope("train_step"):

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)#對(duì)誤差進(jìn)行梯度優(yōu)化，步伐為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})#訓(xùn)練

if i%100 == 0:

result = sess.run(merge,feed_dict={xs:x_data,ys:y_data})#運(yùn)行打包的圖表merge

writer.add_summary(result,i)#寫(xiě)入文件，并且單步長(zhǎng)50