1.2神经网络实现线性回归

import numpy as np
import tensorflow as tf
import matplotlib.pyplot as plt

随机生成1000个点，围绕在y=0.1x 0.3的直线周围

num_points = 1000
vectors_set = []
for i in range(num_points):
x1 = np.random.normal(0.0, 0.55)
y1 = x1 * 0.1 0.3 np.random.normal(0.0, 0.03)#给这些点加一些抖动
vectors_set.append([x1, y1])

生成一些样本

x_data = [v[0] for v in vectors_set]
y_data = [v[1] for v in vectors_set]

plt.scatter(x_data,y_data,c=‘r’)

plt.show()

生成1维的W矩阵，取值是[-1,1]之间的随机数

W = tf.Variable(tf.random_uniform([1], -1.0, 1.0), name=‘W’)#随机初始化权重参数-1到1之间

生成1维的b矩阵，初始值是0

b = tf.Variable(tf.zeros([1]), name=‘b’)#以0为初始化，[1]表示维度

经过计算得出预估值y

y = W * x_data b#目标函数

以预估值y和实际值y_data之间的均方误差作为损失

loss = tf.reduce_mean(tf.square(y - y_data), name=‘loss’)#reduce_mean平均值

采用梯度下降法来优化参数

optimizer = tf.train.GradientDescentOptimizer(0.5)#学习率大了

训练的过程就是最小化这个误差值

train = optimizer.minimize(loss, name=‘train’)

sess = tf.Session()
init = tf.global_variables_initializer()
sess.run(init)

初始化的W和b是多少

print (“W =”, sess.run(W), “b =”, sess.run(b), “loss =”, sess.run(loss))

执行20次训练

for step in range(20):

sess.run(train)

# 输出训练好的W和b

print (“W =”, sess.run(W), “b =”, sess.run(b), “loss =”, sess.run(loss))

#writer = tf.train.SummaryWriter("./tmp", sess.graph)

plt.scatter(x_data,y_data,c=‘r’)

plt.plot(x_data,sess.run(W)*x_data sess.run(b))

plt.show()

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