Pytorch学习2

最新推荐文章于 2023-05-28 08:06:45 发布

weixin_39287932

最新推荐文章于 2023-05-28 08:06:45 发布

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文章标签： Pytorch

本文链接：https://blog.youkuaiyun.com/weixin_39287932/article/details/90217499

该博客介绍了如何使用Pytorch实现线性回归和传统的神经网络，详细步骤涵盖从模型构建到训练过程，是Pytorch初学者的实用教程。参考资料链接指向作者的GitHub仓库。

1. 实现一个线性回归

导入模块
import torch
import torch.nn as nn
import numpy as np
import matplotlib.pyplot as plt
# 超参数
input_size = 1
output_size = 1
num_epochs = 100
learning_rate = 0.001
# 模拟数据集
x_train = np.array([[3.3], [4.4], [5.5], [6.71], [6.93], [4.168], 
                    [9.779], [6.182], [7.59], [2.167], [7.042], 
                    [10.791], [5.313], [7.997], [3.1]], dtype=np.float32)
y_train = np.array([[1.7], [2.76], [2.09], [3.19], [1.694], [1.573], 
                    [3.366], [2.596], [2.53], [1.221], [2.827], 
                    [3.465], [1.65], [2.904], [1.3]], dtype=np.float32)
# 线性回归模型
model = nn.Linear(input_size, output_size)
# 损失函数和优化器
criterion = nn.MSELoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)  
# 训练模型
for epoch in range(num_epochs):
    # 将numpy数组转化为Tensor数组
    inputs = torch.from_numpy(x_train)
    targets = torch.from_numpy(y_train)
    # 前向传播
    outputs = model(inputs)
    loss = criterion(ou