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refine code

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Colin 2024-08-18 00:46:36 +08:00
parent d50cb798b6
commit b860d794a6
1 changed files with 14 additions and 10 deletions
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unsuper/minist.py
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@ -15,12 +15,15 @@ learning_rate = 0.001
# Dataset has PILImage images of range [0, 1]. # Dataset has PILImage images of range [0, 1].
# We transform them to Tensors of normalized range [-1, 1] # We transform them to Tensors of normalized range [-1, 1]
transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))]) transform = transforms.Compose([transforms.ToTensor()])
# CIFAR10: 60000 32x32 color images in 10 classes, with 6000 images per class # CIFAR10: 60000 32x32 color images in 10 classes, with 6000 images per class
train_dataset = torchvision.datasets.CIFAR10(root="./data", train=True, download=True, transform=transform) # train_dataset = torchvision.datasets.CIFAR10(root="./data", train=True, download=True, transform=transform)
train_dataset = torchvision.datasets.MNIST(root="./data", train=True, download=True, transform=transform)
test_dataset = torchvision.datasets.CIFAR10(root="./data", train=False, download=True, transform=transform)
# test_dataset = torchvision.datasets.CIFAR10(root="./data", train=False, download=True, transform=transform)
test_dataset = torchvision.datasets.MNIST(root="./data", train=False, download=True, transform=transform)
train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True) train_loader = torch.utils.data.DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
@ -30,22 +33,22 @@ test_loader = torch.utils.data.DataLoader(test_dataset, batch_size=batch_size, s
class ConvNet(nn.Module): class ConvNet(nn.Module):
def __init__(self): def __init__(self):
super(ConvNet, self).__init__() super(ConvNet, self).__init__()
self.conv1 = nn.Conv2d(3, 6, 5) self.conv1 = nn.Conv2d(1, 6, 3, 1, 1)
self.pool = nn.MaxPool2d(2, 2) self.pool = nn.MaxPool2d(2, 2)
self.conv2 = nn.Conv2d(6, 16, 5) self.conv2 = nn.Conv2d(6, 16, 5)
self.fc1 = nn.Linear(16 * 5 * 5, 120) self.fc1 = nn.Linear(16 * 5 * 5, 10)
self.fc2 = nn.Linear(120, 84) # self.fc2 = nn.Linear(120, 84)
self.fc3 = nn.Linear(84, 10) # self.fc3 = nn.Linear(84, 10)
def forward(self, x): def forward(self, x):
x = self.pool(F.relu(self.conv1(x))) x = self.pool(F.relu(self.conv1(x)))
x = self.pool(F.relu(self.conv2(x))) x = self.pool(F.relu(self.conv2(x)))
x = x.view(-1, 16 * 5 * 5) x = x.view(-1, 16 * 5 * 5)
# x = F.relu(self.fc1(x)) # x = F.relu(self.fc1(x))
x = self.fc1(x)
# x = F.relu(self.fc2(x)) # x = F.relu(self.fc2(x))
x = self.fc2(x) # x = self.fc3(x)
x = self.fc3(x)
x = self.fc1(x)
return x return x
@ -54,6 +57,7 @@ model = ConvNet().to(device)
criterion = nn.CrossEntropyLoss() criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate) optimizer = torch.optim.SGD(model.parameters(), lr=learning_rate)
# Train the model # Train the model
n_total_steps = len(train_loader) n_total_steps = len(train_loader)
for epoch in range(num_epochs): for epoch in range(num_epochs):