拟合y=2x+1

import  matplotlib.pyplot as plt

import  numpy as np

import tensorflow as tf

print(tf.__version__)

%matplotlib inline

#载入随机种子

np.random.seed(5)

#生成100个等差序列，每个值在-1 - 1 之间

x_data = np.linspace(-1,1,100)

#y = 2x + 1 + 噪声，噪声的维度和x_Data一致

y_data = 2 * x_data +1.0 +np.random.randn(*x_data.shape) * 0.4 #*表示把元组拆分为一个个单独的实参

plt.scatter(x_data,y_data)

plt.plot(x_data,2*x_data+1,color = 'red' ,linewidth = 3)

#定义模型函数

def model(x,w,b):

    return tf.multiply(x,w)+b

def loss_fun(x,y,w,b):

    err = model(x,w,b)-y

    squared_err = tf.square(err)

    return tf.reduce_mean(squared_err)

def grad(x,y,w,b):

    with tf.GradientTape() as tape:

        loss_ = loss_fun(x,y,w,b)

return tape.gradient(loss_,[w,b])

#构建线性函数的斜率和截距

w = tf.Variable(np.random.randn(),tf.float32)

b = tf.Variable(0.0,tf.float32)

#设置迭代次数和学习率

train_epochs = 10

learning_rate = 0.01

loss = []

count = 0

display_count = 10 #控制显示粒度的参数,每训练10个样本输出一次损失值

#开始训练，轮数为epoch，采用SGD随机梯度下降优化方法

for epoch in range(train_epochs):

    for xs,ys in zip(x_data,y_data):

        #计算损失，并保存本次损失计算结果

        loss_ =loss_fun(xs,ys,w,b)

        loss.append(loss_)

        #计算当前[w,b]的梯度

        delta_w,delta_b = grad(xs,ys,w,b)

        change_w = delta_w * learning_rate

        change_b = delta_b * learning_rate

        w.assign_sub(change_w)

        b.assign_sub(change_b)

        #训练步数加1

        count = count +1

        if count % display_count == 0:

            print('train epoch : ','%02d'%(epoch+1),'step:%03d' % (count),'loss= ','{:.9f}'.format(loss_))

    #完成一轮训练后，画图

plt.plot(x_data,w.numpy() * x_data +b.numpy())

#显示训练结果

print('w: ',w.numpy())

print('b: ',b.numpy())

plt.scatter(x_data,y_data,label = 'original data')

plt.plot(x_data,x_data *2.0 +1.0, label = 'object line',color = 'g',linewidth = 3)

plt.plot(x_data,x_data *w.numpy() + b.numpy(),label = 'fitted line',color = 'r',linewidth = 3)

plt.legend(loc = 2) #通过指定参数loc指定图例的位置

x_test= 3.21

predict = model(x_test,w.numpy(),b.numpy())

print('预测值是：%f' % predict)

target = 2*x_test+1.0

print('目标值是：%f' % target)