python import torch import torch.nn as nn import torch.optim as optim import os

class SimpleCNN: def init: super.init self.conv1 = nn.Conv2d self.conv2 = nn.Conv2d self.conv2_drop = nn.Dropout2d self.fc1 = nn.Linear self.fc2 = nn.Linear

def forward:
    x = nn.functional.relu, 2))
    x = nn.functional.relu), 2))
    x = x.view
    x = nn.functional.relu)
    x = nn.functional.dropout
    x = self.fc2
    return nn.functional.log_softmax

model = SimpleCNN optimizer = optim.SGD, lr=0.01, momentum=0.5) criterion = nn.CrossEntropyLoss

train_loader = ...

modeldir = './model/' if not os.path.exists: os.makedirs modelpath = os.path.join

for epoch in range: # 虚假设总共训练2个epoch model.train for i, in enumerate: optimizer.zero_grad output = model loss = criterion loss.backward optimizer.step

    # 每隔5个batch保存一次模型
    if i % 5 == 0:
        checkpoint = {
            'epoch': epoch,
            'model_state_dict': model.state_dict,
            'optimizer_state_dict': optimizer.state_dict,
            'loss': loss.item
        }
        torch.save

checkpoint = torch.load model.loadstatedict optimizer.loadstatedict