Tensorflow神经网络框架（第三课 3-1Tensorflow简单实例 非线性回归 梯度下降法）

3-1非线性回归Last Checkpoint: 上星期五16:01(unsaved changes) Logout

Python 3 

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In [1]:

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

In [2]:

#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)#生成随机噪声

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) #中间层的输出

​

#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) #预测的结果

​

#二次代价函数

loss = tf.reduce_mean(tf.square(y - prediction)) #真实值 - 预测值  平方

#使用梯度下降法

train_step = tf.train.GradientDescentOptimizer(0.1).minimize(loss)

​

with tf.Session() as sess:

    sess.run(tf.global_variables_initializer())

    for _ in range(2000):

        sess.run(train_step,feed_dict={x:x_data,y:y_data})

    #获取预测值

    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()

​