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