pytorch线性回归一

线性回归

 

In [1]:

import torch as t
%matplotlib inline
from matplotlib import pyplot as plt
from IPython import display

 

In [2]:

# 设置随机种子，保证在不同计算机上运行时下面的输出一致
t.manual_seed(1000)
def get_fake_data(batch_size=8):
    """
    //产生随机数据：y = x*2+3，加上一些噪声
    """
    x = t.rand(batch_size, 1) * 20
    y = x * 2 + (1 + t.randn(batch_size, 1) )* 3
    return x , y

 

In [3]:

# 查看产生的x-y分布
x , y = get_fake_data()
plt.scatter(x.squeeze().numpy(),y.squeeze().numpy())

 

Out[3]:

<matplotlib.collections.PathCollection at 0x1c54aa25488>

 

 

In [4]:

# 随机初始话参数
w = t.rand(1, 1)
b = t.zeros(1, 1)

 

In [5]:

lr=0.001#学习率
for ii in range(2000):
    x, y = get_fake_data()
    #forward:计算loss
    y_pred = x.mm(w)+b.expand_as(y)
    loss = 0.5 * (y_pred - y) ** 2     #均方误差
    loss = loss.sum()

    #backward:手动计算梯度
    dloss = 1
    dy_pred = dloss * (y_pred - y)

    dw = x.t().mm(dy_pred)
    db = dy_pred.sum()

    #更新参数
    w.sub_(lr * dw)
    b.sub_(lr * db)

    if (ii % 2000) == 0:

        #画图
        display.clear_output (wait=True)
        x = t.arange(0, 20).view(-1,1)

        y = x.mm(w.data.long()) + b.expand_as(x)
        plt.plot(x.numpy(),y.numpy())
        #预测

        x2,y2 = get_fake_data(batch_size=20)
        plt.scatter(x2.numpy (),y2.numpy()) #true data
        plt.xlim(0,20)
        plt.ylim(0,41)
        plt.show()
        plt.pause(0.5)

 

 

 

In [6]:

# 当前w与b
print(w.item(), b.item())

 

 

2.054126262664795 3.014718770980835