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