python 学习 Tensorflow 变量 /Fetch and Feed /简单例子与非线性回归DAY2

# -*- coding:utf-8 -*-
#author: Dongyn time:2018/9/18
import tensorflow as tf
x = tf.Variable([1,2])
a = tf.constant([3,3])
#增加一个减法op
sub = tf.subtract(x,a)
#增加一个加法op
add = tf.add(x,sub)

init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    print(sess.run(sub))
    print(sess.run(add))

# -*- coding:utf-8 -*-
#author: Dongyn time:2018/9/19
import tensorflow as tf
#创建一个变量初始化为0
state = tf.Variable(0,name='counter')
#创建一个op,作用是使state加1
new_value = tf.add(state,1)
#赋值op
update = tf.assign(state,new_value)
#变量初始化
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    print(sess.run(state))
    for i in range(5):
        sess.run(update)
        print(sess.run(state))

Fetch and Feed

#Fetch
import tensorflow as tf
input1 = tf.constant(3.0)
input2 = tf.constant(2.0)
input3 = tf.constant(5.0)

add = tf.add(input2,input3)
mul = tf.multiply(input1,add)

with  tf.Session() as sess:
    result = sess.run([mul,add])
    print(result)

#Feed
import tensorflow as tf
#创建占位符
input1 = tf.placeholder(tf.float32)
input2 = tf.placeholder(tf.float32)
output = tf.multiply(input1,input2)
with tf.Session() as sess:
    #feed数据以字典的形式传入
    print(sess.run(output,feed_dict={input1:[7.0],input2:[2.0]}))

#Tensorflow 的简单实例
import tensorflow as tf
import numpy as np
#使用numpy生成100个随机点
x_data = np.random.rand(100)
y_data = x_data*0.1+0.2

#构造一个线性模型
b = tf.Variable(0.)
k = tf.Variable(0.)
y = k*x_data + b

#二次代价函数
loss = tf.reduce_mean(tf.square(y_data-y))
#定义一个梯度下降法来进行训练的优化器
optimizer = tf.train.GradientDescentOptimizer(0.2)
#最小化代价函数
train = optimizer.minimize(loss)

#初始化设置
init = tf.global_variables_initializer()

with tf.Session() as sess:
    sess.run(init)
    for step in range(201):
        sess.run(train)
        if step % 20 == 0:
            print(step,sess.run([k,b]))

#非线性回归
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt

#使用numpy生成200个随机点
x_data = np.linspace(-0.5,0.5,200)[:,np.newaxis]
noise = np.random.normal(0,0.02,x_data.shape)
y_data = np.square(x_data) + noise
#定义两个placeholder
x = tf.placeholder(tf.float32,[None,1])
y = tf.placeholder(tf.float32,[None,1])

#定义神经网络中间层
Weights_L1 = tf.Variable(tf.random_normal([1,10]))
biases_L1 = tf.Variable(tf.zeros([1,10]))
Wx_plus_b_L1 = tf.matmul(x,Weights_L1) + biases_L1
L1 = tf.nn.tanh(Wx_plus_b_L1)

#定义神经网络输出层
Weights_L2 = tf.Variable(tf.random_normal([10,1]))
biases_L2 = tf.Variable(tf.zeros([1,1]))
Wx_plus_b_L2 = tf.matmul(L1,Weights_L2) + biases_L2
prediction = tf.nn.tanh(Wx_plus_b_L2)

#二次代价函数
loss = tf.reduce_mean(tf.square(y-prediction))
#使用梯度下降法训练
train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

with tf.Session() as sess:
    #变量初始化
    sess.run(tf.global_variables_initializer())
    for i in range(2000):
        sess.run(train_step,feed_dict={x:x_data,y:y_data})

    #获得预测值
    prediction_value = sess.run(prediction,feed_dict={x:x_data})
    #画图
    plt.figure()
    plt.scatter(x_data,y_data)
    plt.plot(x_data,prediction_value,'r-',lw=5)
    plt.show()