Tensorflow教程_练习代码

#6.session 指定到想运算的那部分
import tensorflow as tf
import numpy as np

matrix1=tf.constant([[3,3]])#一行两列的向量
matrix2=tf.constant([[2],#两行一列
                     [2]])
product=tf.matmul(matrix1,matrix2)#相当np.dot()，矩阵相乘=12
sess=tf.Session()
result=sess.run(product)#Session指定到输出product这步
print(result)
sess.close()

#或用以下方法替换“sess=tf.Session()、sess.close()”：
#with tf.Session()as sess:
  #  result = sess.run(product)
   # print(result)————[[12]]

#7.variable
import tensorflow as tf
state=tf.Variable(0,name='counter')
one=tf.constant(1)

new_value=tf.add(state,one)
update=tf.assign(state,new_value)

init=tf.initialize_all_variables()#用变量，此步必须有，初始化
with tf.Session()as sess:
    sess.run(init)#run and activate
    for _ in range(3):
        sess.run(update)
        print(sess.run(state))————1 2 3

#8.feeds 传入值（从外部）
import tensorflow as tf
input1=tf.placeholder(tf.float32) #后面run某个东西时就会从外部索取数据，将外部数据放到placeholder里才能用
input2=tf.placeholder(tf.float32)
output=tf.multiply(input1,input2)#矩阵相乘，注意不是tf,mul而是tf.multiply

with tf.Session()as sess:
    print(sess.run(output,feed_dict={input1:[7.],input2:[2.]}))#因为output涉及到两个laceholder，所以feed传入——————[ 14.]

#10、11：添加层def add_layer()—建造神经网络
import tensorflow as tf
import numpy as np

def add_layer(inputs,in_size,out_size,activation_function=None):
    Weights=tf.Variable(tf.random_normal([in_size,out_size]))#in_size行out_size列的矩阵
    biases=tf.Variable(tf.zeros([1,out_size])+0.1)#列表，不为0，故+0.1
    Wx_plus_b=tf.matmul(inputs,Weights)+biases
    if activation_function is None:#即线性关系
        outputs=Wx_plus_b
    else:
        outputs=activation_function(Wx_plus_b)
    return outputs

x_data=np.linspace(-1,1,300)[:,np.newaxis]
noise=np.random.normal(0,0.05,x_data.shape)#加噪声，更像真实数据
y_data=np.square(x_data)-0.5+noise
#10个隐层，1个输出层
xs=tf.placeholder(tf.float32,[None,1])#x_data的1
ys=tf.placeholder(tf.float32,[None,1])
l1=add_layer(xs,1,10,activation_function=tf.nn.relu)#定义隐层：x_data输入1个参数，隐层10个神经元
prediction=add_layer(l1,10,1,activation_function=None)#定义输出层：l1_size=10,y_data_size=1，None即线性

loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))#求平均误差
train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)#给定学习效率0.1(<1)，每次都更正误差
init=tf.initialize_all_variables()#初始所有变量
sess=tf.Session()
sess.run(init)

for i in range (1000):#学习1000次
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i % 50==0:
        print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))#若误差是不断减小的 √ loss也通过placeholder

#结果： （嗯，误差在不断减小） 0.625046 0.0103892 0.00880997 0.00816188 0.0077936 0.0075142 0.00726819 0.00701968 0.00677203 0.00653569 0.00632172 0.00609444 0.00587196 0.00567781 0.00551067 0.00533964 0.00513579 0.00497731 0.00483084 0.00470275

#12：结果可视化（在上例基础上）
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt


def add_layer(inputs,in_size,out_size,activation_function=None):
    Weights=tf.Variable(tf.random_normal([in_size,out_size]))#in_size行out_size列的矩阵
    biases=tf.Variable(tf.zeros([1,out_size])+0.1)#列表，不为0，故+0.1
    Wx_plus_b=tf.matmul(inputs,Weights)+biases
    if activation_function is None:#即线性关系
        outputs=Wx_plus_b
    else:
        outputs=activation_function(Wx_plus_b)
    return outputs

x_data=np.linspace(-1,1,300)[:,np.newaxis]
noise=np.random.normal(0,0.05,x_data.shape)#加噪声，更像真实数据
y_data=np.square(x_data)-0.5+noise
#10个隐层，1个输出层
xs=tf.placeholder(tf.float32,[None,1])#x_data的1
ys=tf.placeholder(tf.float32,[None,1])
l1=add_layer(xs,1,10,activation_function=tf.nn.relu)#定义隐层：x_data输入1个参数，隐层10个神经元
prediction=add_layer(l1,10,1,activation_function=None)#定义输出层：l1_size=10,y_data_size=1，None即线性

loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))#求平均误差
train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)#给定学习效率0.1(<1)，每次都更正误差
init=tf.initialize_all_variables()#初始所有变量
sess=tf.Session()
sess.run(init)

fig=plt.figure()#先生成一个图片框
ax=fig.add_subplot(1,1,1)#编号为1，1，1
ax.scatter(x_data,y_data)
plt.ion()#使plt.show()后不停继续往下走
plt.show()
for i in range (1000):#学习1000次
    sess.run(train_step,feed_dict={xs:x_data,ys:y_data})
    if i % 50==0:
        #print(sess.run(loss,feed_dict={xs:x_data,ys:y_data}))#若误差是不断减小的 √ loss也通过placeholder
        try:
         ax.lines.remove(lines[0])  # 抹除lines的第一条线段（防止太多线看不出）
        except Exception:
         pass
         prediction_value=sess.run(prediction,feed_dict={xs:x_data})
         lines=ax.plot(x_data,prediction_value,"r-",lw=5)#线宽为5
         plt.pause(0.1)#停0.1s

#14.Tensorboard 可视化好帮手
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt


def add_layer(inputs,in_size,out_size,activation_function=None):
    with tf.name_scope("layer"):
        with tf.name_scope("Weights"):
            Weights=tf.Variable(tf.random_normal([in_size,out_size]),name='W')#in_size行out_size列的矩阵
        with tf.name_scope("biases"):
            biases=tf.Variable(tf.zeros([1,out_size])+0.1,name="b")#列表，不为0，故+0.1
        with tf.name_scope("Wx_plus_b"):
            Wx_plus_b=tf.add(tf.matmul(inputs,Weights),biases)
        if activation_function is None:#即线性关系
            outputs=Wx_plus_b
        else:
            outputs=activation_function(Wx_plus_b)
        return outputs

x_data=np.linspace(-1,1,300)[:,np.newaxis]
noise=np.random.normal(0,0.05,x_data.shape)#加噪声，更像真实数据
y_data=np.square(x_data)-0.5+noise
#10个隐层，1个输出层
with tf.name_scope("inputs"):
    xs=tf.placeholder(tf.float32,[None,1],name="x_input")#x_data的1
    ys=tf.placeholder(tf.float32,[None,1],name="y_input")
l1=add_layer(xs,1,10,activation_function=tf.nn.relu)#定义隐层：x_data输入1个参数，隐层10个神经元
prediction=add_layer(l1,10,1,activation_function=None)#定义输出层：l1_size=10,y_data_size=1，None即线性

with tf.name_scope("loss"):
    loss=tf.reduce_mean(tf.reduce_sum(tf.square(ys-prediction),reduction_indices=[1]))#求平均误差
with tf.name_scope("train"):
    train_step=tf.train.GradientDescentOptimizer(0.1).minimize(loss)#给定学习效率0.1(<1)，每次都更正误差
init=tf.initialize_all_variables()#初始所有变量
sess=tf.Session()
writer=tf.summary.FileWriter("D:/",sess.graph)#存文件中，在浏览器中查看（terminal中看
sess.run(init)

参考：http://blog.csdn.net/flying_sfeng/article/details/69943260