import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torchvision
import matplotlib.pyplot as plt
import numpy as np

device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
class CNN(nn.Module):
    def __init__(self):
        super(CNN, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(1, 16, 5, 1, 2),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(16, 32, 5, 1, 2),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        self.conv3 = nn.Sequential(
            nn.Conv2d(32, 64, 5, 1, 2),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        self.conv4 = nn.Sequential(
            nn.Conv2d(64, 32, 5, 1, 2),
            nn.ReLU(),
            nn.MaxPool2d(2)
        )
        self.out = nn.Linear(32, 10)  # 确保输出类别数正确

    def forward(self, x):
        x = self.conv1(x).to(device)  # 将数据迁移到GPU
        x = self.conv2(x).to(device)
        x=self.conv3(x).to(device)
        x = self.conv4(x).to(device)
        x = x.view(x.size(0), -1).to(device)
        x = self.out(x).to(device)
        return x