PyTorch CNN 模型构建教程：从零开始实现卷积神经网络

import torch.nn as nn

class CNN(nn.Module): def init(self): super(CNN, self).init() self.conv1 = nn.Conv2d(in_channels=1, out_channels=16, kernel_size=3, stride=1, padding=1) self.pool1 = nn.MaxPool2d(kernel_size=2) self.conv2 = nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1) self.pool2 = nn.MaxPool2d(kernel_size=2) self.fc1 = nn.Linear(in_features=3277, out_features=128) self.fc2 = nn.Linear(in_features=128, out_features=10)

def forward(self, x):
    x = nn.functional.relu(self.conv1(x))
    x = self.pool1(x)
    x = nn.functional.relu(self.conv2(x))
    x = self.pool2(x)
    x = x.view(-1, 32*7*7)
    x = nn.functional.relu(self.fc1(x))
    x = self.fc2(x)
    return x

cnn = CNN() print(cnn)