实现一个简单的神经网络

import tensorflow as tf

import numpy as np

import matplotlib.pyplot as plt

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

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

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

#定义神经网络中间层

Weight_L1 = tf.Variable(tf.random_normal([1,10]))

baises_L1 = tf.Variable(tf.zeros([1,10]))

Wx_plus_b_L1 = tf.matmul(x,Weight_L1)+baises_L1

L1 = tf.nn.tanh(Wx_plus_b_L1)

#定义输出层

Weights_L2 = tf.Variable(tf.random_normal([10,1]))

baises_L2 = tf.Variable(tf.zeros([1,1]))

Wx_plus_b_L2 = tf.matmul(L1,Weights_L2)+baises_L2

prediction = tf.nn.tanh(Wx_plus_b_L2)

#二次代价函数

loss = tf.reduce_mean(tf.square(y-prediction))

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

with tf.Session() as sess:

init = tf.global_variables_initializer()

sess.run(init)

for _ 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})

plt.figure()

plt.scatter(x_data,y_data)

plt.plot(x_data,prediction_value,'r-',lw=5)

plt.show()