pytorch实现不同的神经网络

PyTorch是一个流行的深度学习框架，可以用于实现各种神经网络。以下是一些常见的神经网络的实现示例：

1. 多层感知机（Multilayer Perceptron, MLP）：

import torch
import torch.nn as nn

class MLP(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(MLP, self).__init__()
        self.fc1 = nn.Linear(input_size, hidden_size)
        self.relu = nn.ReLU()
        self.fc2 = nn.Linear(hidden_size, output_size)
    
    def forward(self, x):
        x = self.fc1(x)
        x = self.relu(x)
        x = self.fc2(x)
        return x

2. 卷积神经网络（Convolutional Neural Network, CNN）：

import torch
import torch.nn as nn

class CNN(nn.Module):
    def __init__(self, num_classes):
        super(CNN, self).__init__()
        self.features = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2),
            nn.Conv2d(16, 32, kernel_size=3, stride=1, padding=1),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(kernel_size=2, stride=2)
        )
        self.classifier = nn.Linear(32 * 8 * 8, num_classes)
    
    def forward(self, x):
        x = self.features(x)
        x = torch.flatten(x, 1)
        x = self.classifier(x)
        return x

3. 循环神经网络（Recurrent Neural Network, RNN）：

import torch
import torch.nn as nn

class RNN(nn.Module):
    def __init__(self, input_size, hidden_size, output_size):
        super(RNN, self).__init__()
        self.hidden_size = hidden_size
        self.rnn = nn.RNN(input_size, hidden_size, batch_first=True)
        self.fc = nn.Linear(hidden_size, output_size)
    
    def forward(self, x):
        h0 = torch.zeros(1, x.size(0), self.hidden_size).to(x.device)
        out, _ = self.rnn(x, h0)
        out = self.fc(out[:, -1, :])
        return out

这只是一些示例，PyTorch还提供了各种其他类型的神经网络层和模型，可以根据具体需求进行选择和组合。