PyTorch基本使用——构建基本神经网络

最新推荐文章于 2025-06-13 00:31:25 发布

liang1232015

最新推荐文章于 2025-06-13 00:31:25 发布

阅读量469

点赞数

CC 4.0 BY-SA版权

分类专栏：深度学习

本文链接：https://blog.youkuaiyun.com/liang1232015/article/details/79692209

深度学习专栏收录该内容

2 篇文章

订阅专栏

本文介绍了一个使用PyTorch实现的卷积神经网络(CNN)实例，包括网络结构定义、前向传播过程及张量处理方法。通过这个例子，读者可以了解CNN的基本组件及其工作流程。

摘要生成于 C知道，由 DeepSeek-R1 满血版支持，前往体验 >

一个简单的例子

import torch
from torch.autograd import Variable
import torch.nn as nn
import torch.nn.functional as F


class Net(nn.Module):

    def __init__(self):
        super(Net, self).__init__()
        # 1 input image channel, 6 output channels, 5x5 square convolution
        # kernel
        self.conv1 = nn.Conv2d(1, 6, 5)
        self.conv2 = nn.Conv2d(6, 16, 5)
        # an affine operation: y = Wx + b
        self.fc1 = nn.Linear(16 * 5 * 5, 120)
        self.fc2 = nn.Linear(120, 84)
        self.fc3 = nn.Linear(84, 10)

    def forward(self, x):
        # Max pooling over a (2, 2) window
        x = F.max_pool2d(F.relu(self.conv1(x)), (2, 2))
        # If the size is a square you can only specify a single number
        x = F.max_pool2d(F.relu(self.conv2(x)), 2)
        x = x.view(-1, self.num_flat_features(x))
        x = F.relu(self.fc1(x))
        x = F.relu(self.fc2(x))
        x = self.fc3(x)
        return x

    def num_flat_features(self, x):
        size = x.size()[1:]  # all dimensions except the batch dimension
        num_features = 1
        for s in size:
            num_features *= s
        return num_features


net = Net()
print(net)