import torch from torch import nn from torch.utils.data import DataLoader from torchvision import datasets from torchvision.transforms import ToTensor # Download training data from open datasets training_data = datasets.FashionMNIST( root="data", train=True, download=True, transform=ToTensor(), ) # Download test data from open datasets test_data = datasets.FashionMNIST( root="data", train=False, download=True, transform=ToTensor(), ) batch_size = 64 # Create data loaders. train_dataloader = DataLoader(training_data, batch_size=batch_size) test_dataloader = DataLoader(test_data, batch_size=batch_size) for X, y in test_dataloader: print(f"Shape of X [N, C, H, W]: {X.shape}") print(f"Shape of y: {y.shape} {y.dtype}") break # Get cpu or gpu device for training device = "cuda" if torch.cuda.is_available() else "mps" if torch.backends.mps.is_available() else "cpu" print(f"Using {device} device") # Define model class NeuralNetwork(nn.Module): def __init__(self): super().__init__() self.flatten = nn.Flatten() self.linear_relu_stack = nn.Sequential( nn.Linear(28*28, 512), nn.ReLU(), nn.Linear(512, 512), nn.ReLU(), nn.Linear(512,10) ) def forward(self, x): x = self.flatten(x) logits = self.linear_relu_stack(x) return logits model = NeuralNetwork().to(device=device) print(model) loss_fn = nn.CrossEntropyLoss() optimizer = torch.optim.SGD(model.parameters(), lr=1e-3) def train(dataloader, model, loss_fn, optimizer): size = len(dataloader.dataset) model.train() for batch, (X, y) in enumerate(dataloader): X, y = X.to(device), y.to(device) # Compute prediction error pred = model(X) loss = loss_fn(pred, y) # Backpropegation optimizer.zero_grad() loss.backward() optimizer.step() if batch % 100 == 0: loss, current = loss.item(), (batch + 1) * len(X) print(f"loss: {loss:>7f} [{current:>5d}|{size:>5d}]") def test(dataloader, model, loss_fn): size = len(dataloader.dataset) num_batches = len(dataloader) model.eval() test_loss, correct = 0,0 with torch.no_grad(): for X, y in dataloader: X, y = X.to(device), y.to(device) pred = model(X) test_loss += loss_fn(pred, y).item() correct += (pred.argmax(1)== y).type(torch.float).sum().item() test_loss /= num_batches correct /= size print(f"Test Error: \n Accuracy: {(100*correct):>0.1f}%, Avg loss: {test_loss:>8f}\n") epochs = 40 for t in range(epochs): print(f"Epoch {t+1}\n-------------------------------") train(train_dataloader, model, loss_fn=loss_fn, optimizer=optimizer) test(test_dataloader, model, loss_fn=loss_fn) print("Done!") torch.save(model.state_dict(), "models/model.pth") print("Saved PyTorch Model State to model.pth")