实现过程

搭建两层全连接网络

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class Net(torch.nn.Module):
    def __init__(self, n_feature, n_hidden, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_hidden)
        self.predict = torch.nn.Linear(n_hidden, n_output)

    def forward(self, x):
        x = F.relu(self.hidden(x))
        x = self.predict(x)
        return x

预测的值画成曲线

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# !/usr/bin/env python
# -*-coding:utf-8 -*-
"""
# @File       : 301_regression.py
# @Time       ：
# @Author     ：
# @version    ：python 3.9
# @Software   : PyCharm
# @Description：
"""
# ================【功能：】====================
import torch
import torch.nn.functional as F
import matplotlib.pyplot as plt
import matplotlib.animation as animation
import os
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"

# x data (tensor), shape=(100, 1)
x = torch.unsqueeze(torch.linspace(-1, 1, 100), dim=1)
# noisy y data (tensor), shappe(100, 1)
y = x.pow(2) + 0.2 * torch.rand(x.size())


# torch can only train on Variable, so convert them to Variable


class Net(torch.nn.Module):
    def __init__(self, n_feature, n_hidden, n_output):
        super(Net, self).__init__()
        self.hidden = torch.nn.Linear(n_feature, n_hidden)
        self.predict = torch.nn.Linear(n_hidden, n_output)

    def forward(self, x):
        x = F.relu(self.hidden(x))
        x = self.predict(x)
        return x


net = Net(n_feature=1, n_hidden=10, n_output=1)
print(net)

optimizer = torch.optim.SGD(net.parameters(), lr=0.2)
# 针对回归的均方误差
loss_func = torch.nn.MSELoss()
plt.ion()

for t in range(200):
    prediction = net(x)
    loss = loss_func(prediction, y)
    # clear gradients for next train
    optimizer.zero_grad()
    # backpropagation, comupte gradients
    loss.backward()
    # apply gradients
    optimizer.step()

    if t % 2 == 0:
        plt.cla()
        plt.scatter(x.data.numpy(), y.data.numpy())
        # 绘制预测的值
        plt.plot(x.data.numpy(), prediction.data.numpy(), 'r-', lw=5)
        plt.text(0.5, 0, 'Loss=%.4f' % loss.data.numpy(), fontdict={'size': 20, 'color': 'red'})
        # plt.pause(0.1)
        # plt.ioff()
        # 保存为jpg图像
        plt.savefig(f'./img/regression_{t}.jpg')
        # plt.show()

jgp图像转gif动图

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# !/usr/bin/env python
# -*-coding:utf-8 -*-
"""
# @File       : jpg_2_gif.py
# @Time       ：
# @Author     ：
# @version    ：python 3.9
# @Software   : PyCharm
# @Description：
"""
# ================【功能：】====================
import os

import imageio


def main():
    root_path = '../img'
    image_list = os.listdir(root_path)
    gif_name = './regression.gif'
    # duration between images
    duration = 0.1

    #### read images and write in gif
    images = []
    for image_name in image_list:
        image_name = os.path.join(root_path, image_name)
        images.append(imageio.imread(image_name))
    imageio.mimwrite(gif_name, images, 'GIF', duration=duration)

    print('success')


if __name__ == "__main__":
    main()

结果可视化

image