| import torch | |
| import torch.nn as nn | |
| import torch.optim as optim | |
| from torchvision import datasets, transforms | |
| from torch.utils.data import DataLoader | |
| device = torch.device("cuda" if torch.cuda.is_available() else "cpu") | |
| transform = transforms.ToTensor() | |
| train_set = datasets.MNIST(root='./data', | |
| train=True, | |
| download=True, | |
| transform=transform) | |
| test_set = datasets.MNIST(root='./data', | |
| train=False, | |
| download=True, | |
| transform=transform) | |
| train_loader = DataLoader(train_set, batch_size=64, shuffle=True) | |
| test_loader = DataLoader(test_set, batch_size=64, shuffle=False) | |
| class MNISTNet(nn.Module): | |
| def __init__(self): | |
| super().__init__() | |
| self.flatten = nn.Flatten() | |
| self.fc1 = nn.Linear(28*28,128) | |
| self.relu = nn.ReLU() | |
| self.fc2 = nn.Linear(128,10) | |
| def forward(self, x): | |
| print("input:" , x.shape) | |
| x = self.flatten(x) | |
| print("after flatten:" , x.shape) | |
| x = self.fc1(x) | |
| print("after fc1:" , x.shape) | |
| x = self.relu(x) | |
| x = self.fc2(x) | |
| print("after fc2:" , x.shape) | |
| return x | |
| model = MNISTNet().to(device) | |
| criterion = nn.CrossEntropyLoss() | |
| optimizer = optim.Adam(model.parameters(), lr=0.001) | |
| def train(): | |
| model.train() | |
| for images, labels in train_loader: | |
| images, labels = images.to(device), labels.to(device) | |
| outputs = model(images) | |
| loss = criterion(outputs, labels) | |
| optimizer.zero_grad() | |
| loss.backward() | |
| optimizer.step() | |
| def test(): | |
| model.eval() | |
| correct = 0 | |
| total = 0 | |
| with torch.no_grad(): | |
| for images, labels in test_loader: | |
| images, labels = images.to(device), labels.to(device) | |
| outputs = model(images) | |
| pred = torch.argmax(outputs, dim=1) | |
| correct += (pred == labels).sum().item() | |
| total += labels.size(0) | |
| acc = correct / total | |
| print(f"Accuracy: {acc:.4f}") | |
| for epoch in range(5): | |
| train() | |
| print(f"Epoch {epoch+1} finished") | |
| test() |