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5960711 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 | import torch
import torch.nn as nn
import torch.optim as optim
import torchvision
import torchvision.transforms as transforms
# ----------------- TRANSFORMS -----------------
transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.5,), (0.5,))
])
# ----------------- LOAD DATASET -----------------
train_set = torchvision.datasets.CIFAR10(
root="./data", train=True, download=True, transform=transform
)
test_set = torchvision.datasets.CIFAR10(
root="./data", train=False, download=True, transform=transform
)
train_loader = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
test_loader = torch.utils.data.DataLoader(test_set, batch_size=64, shuffle=False)
# ----------------- BUILD CNN MODEL -----------------
class CNN(nn.Module):
def __init__(self):
super().__init__()
self.conv_layer = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2),
nn.Conv2d(32, 64, kernel_size=3, padding=1),
nn.ReLU(),
nn.MaxPool2d(2, 2)
)
self.fc_layer = nn.Sequential(
nn.Linear(64 * 8 * 8, 256),
nn.ReLU(),
nn.Linear(256, 10)
)
def forward(self, x):
x = self.conv_layer(x)
x = x.view(x.size(0), -1)
x = self.fc_layer(x)
return x
model = CNN()
criterion = nn.CrossEntropyLoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)
# ----------------- TRAIN LOOP -----------------
for epoch in range(5):
running_loss = 0.0
for images, labels in train_loader:
optimizer.zero_grad()
outputs = model(images)
loss = criterion(outputs, labels)
loss.backward()
optimizer.step()
running_loss += loss.item()
print(f"Epoch {epoch+1}, Loss: {running_loss/len(train_loader)}")
# ----------------- SAVE MODEL -----------------
torch.save(model.state_dict(), "model.pth")
print("Model saved as model.pth")
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