import torch import torch.nn.functional as F from nn_model import Net from torch import nn, optim from torchvision import datasets, transforms n_epochs = 3 batch_size_train = 64 batch_size_test = 1000 model = Net() print("Model loaded.") optimizer = optim.SGD(model.parameters(), lr=0.003, momentum=0.5) criterion = nn.NLLLoss() transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,)), ]) train_set = datasets.MNIST('PATH_TO_STORE_TRAIN_SET', download=True, train=True, transform=transform) test_set = datasets.MNIST('PATH_TO_STORE_TEST_SET', download=True, train=False, transform=transform) train_loader = torch.utils.data.DataLoader(train_set, batch_size=batch_size_train, shuffle=True) test_loader = torch.utils.data.DataLoader(test_set, batch_size=batch_size_test, shuffle=True) print("Data sets loaded.") train_losses = [] train_counter = [] test_losses = [] test_counter = [i * len(train_loader.dataset) for i in range(n_epochs + 1)] def train_model(epoch): print("Training model.") model.train() for batch_idx, (data, target) in enumerate(train_loader): optimizer.zero_grad() output = model(data) loss = criterion(output, target) loss.backward() optimizer.step() if batch_idx % 10 == 0: print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format( epoch, batch_idx * len(data), len(train_loader.dataset), 100. * batch_idx / len(train_loader), loss.item())) train_losses.append(loss.item()) train_counter.append( (batch_idx * 64) + ((epoch - 1) * len(train_loader.dataset))) def test_model(): print("Testing model.") model.eval() test_loss = 0 correct = 0 with torch.no_grad(): for data, target in test_loader: output = model(data) test_loss += F.nll_loss(output, target, size_average=False).item() pred = output.data.max(1, keepdim=True)[1] correct += pred.eq(target.data.view_as(pred)).sum() test_loss /= len(test_loader.dataset) test_losses.append(test_loss) print('\nTest set: Avg. loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n'.format( test_loss, correct, len(test_loader.dataset), 100. * correct / len(test_loader.dataset))) def create_model(): test_model() for epoch in range(1, n_epochs + 1): train_model(epoch) test_model() torch.save(model.state_dict(), './model.pt') create_model()