def train_fine_tuning(net, learning_rate, batch_size=128, num_epochs=5):
net = net.to('cuda')
train_imgs.transform = train_augs
train_iter = DataLoader(train_imgs, batch_size, shuffle=True)
test_imgs.transform = test_augs
test_iter = DataLoader(test_imgs, batch_size)
trainer = torch.optim.SGD([{
'params': other,
'lr': learning_rate
}, {
'params': output,
'lr': learning_rate * 10
}],
lr=learning_rate,
weight_decay=0.1)
d2l.train_ch5(net,
train_iter,
test_iter,
trainer,
num_epochs,
device='cuda')
class NLeNet(nn.Module):
def __init__(self, X_shape, in_channels=1):
super().__init__()
X_test = torch.rand(1, in_channels, *X_shape)
self.conv_part = nn.Sequential(
nn.Conv2d(in_channels, 6, kernel_size=5), nn.BatchNorm2d(6),
nn.Sigmoid(), nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(6, 16, kernel_size=5), nn.BatchNorm2d(16), nn.Sigmoid(),
nn.MaxPool2d(kernel_size=2, stride=2))
X_test = self.conv_part(X_test)
self.flatten = X_test.shape[1] * X_test.shape[2] * X_test.shape[3]
self.linear_part = nn.Sequential(nn.Linear(self.flatten, 120),
nn.BatchNorm1d(120), nn.Sigmoid(),
nn.Linear(120, 84),
nn.BatchNorm1d(84), nn.Sigmoid(),
nn.Linear(84, 10))
def forward(self, X):
X = self.conv_part(X)
return self.linear_part(X.view(-1, self.flatten))
if __name__ == '__main__':
lr, num_epochs, batch_size, device = 5.0, 5, 256, torch.device("cuda")
net = NLeNet((28, 28)).to(device)
trainer = torch.optim.SGD(net.parameters(), lr=lr)
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch5(net, train_iter, test_iter, trainer, num_epochs, device)
X_test = torch.rand(1, in_channels, *in_shape)
self.conv_part = nn.Sequential(
nn.Conv2d(in_channels, 6, kernel_size=5), BatchNorm(6, num_dims=4),
nn.Sigmoid(), nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(6, 16, kernel_size=5), BatchNorm(16, num_dims=4),
nn.Sigmoid(), nn.MaxPool2d(kernel_size=2, stride=2))
X_test = self.conv_part(X_test)
self.flatten = X_test.shape[1] * X_test.shape[2] * X_test.shape[3]
self.linear_part = nn.Sequential(nn.Linear(self.flatten, 120),
BatchNorm(120, num_dims=2),
nn.Sigmoid(), nn.Linear(120, 84),
BatchNorm(84, num_dims=2),
nn.Sigmoid(), nn.Linear(84, 10))
def forward(self, X):
X = self.conv_part(X)
return self.linear_part(X.view(-1, self.flatten))
if __name__ == '__main__':
lr, num_epochs, batch_size, device = 1.0, 5, 256, torch.device("cuda")
net = NLeNet((28, 28))
d2l.initial(net)
trainer = torch.optim.SGD(net.parameters(), lr=lr)
train_tier, test_iter = d2l.load_data_fashion_mnist(batch_size)
d2l.train_ch5(net, train_tier, test_iter, trainer, num_epochs)
for layer in net.modules():
if isinstance(layer, BatchNorm):
print(layer.gamma.view(-1, ), layer.beta.view(-1, ), sep='\n')
break