PyTorch MNIST example：examplesmnistmain.py at main · pytorchexamples

import torch.nn.functional as F

import torch.optim as optim

from torchvision import datasets, transforms

from torch.optim.lr_scheduler import StepLR

super(Net, self).__init__()

self.conv1 = nn.Conv2d(1, 32, 3, 1)

self.conv2 = nn.Conv2d(32, 64, 3, 1)

self.dropout1 = nn.Dropout(0.25)

self.dropout2 = nn.Dropout(0.5)

self.fc1 = nn.Linear(9216, 128)

self.fc2 = nn.Linear(128, 10)

output = F.log_softmax(x, dim=1)

def train(args, model, device, train_loader, optimizer, epoch):

for batch_idx, (data, target) in enumerate(train_loader):

data, target = data.to(device), target.to(device)

loss = F.nll_loss(output, target)

if batch_idx % args.log_interval == 0:

print(Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}.format(

epoch, batch_idx * len(data), len(train_loader.datas...