Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Minor changes #26

Open
wants to merge 3 commits into
base: master
Choose a base branch
from
Open

Minor changes #26

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
872c065
Changes to accomodate newer pytorch version
ArkAung Jul 26, 2020
c5b5ab6
Fix shape issue in labels for discriminator
ArkAung Jul 26, 2020
8993aa7
Add learning rates as arguments
ArkAung Jul 26, 2020
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
3 changes: 3 additions & 0 deletions arguments.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -7,6 +7,9 @@ def get_args():
parser.add_argument('--dataset', type=str, default='cifar10', help='Name of the dataset used.')
parser.add_argument('--batch_size', type=int, default=128, help='Batch size used for training and testing')
parser.add_argument('--train_epochs', type=int, default=100, help='Number of training epochs')
parser.add_argument('--lr_vae', type=float, default=5e-4, help='Learning rate for VAE')
parser.add_argument('--lr_dis', type=float, default=5e-4, help='Learning rate for Discriminator')
parser.add_argument('--lr_task', type=float, default=5e-4, help='Learning rate for Task Module')
parser.add_argument('--latent_dim', type=int, default=32, help='The dimensionality of the VAE latent dimension')
parser.add_argument('--data_path', type=str, default='./data', help='Path to where the data is')
parser.add_argument('--beta', type=float, default=1, help='Hyperparameter for training. The parameter for VAE')
Expand Down
2 changes: 1 addition & 1 deletion model.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -110,7 +110,7 @@ def forward(self, z):

def kaiming_init(m):
if isinstance(m, (nn.Linear, nn.Conv2d)):
init.kaiming_normal(m.weight)
init.kaiming_normal_(m.weight)
if m.bias is not None:
m.bias.data.fill_(0)
elif isinstance(m, (nn.BatchNorm1d, nn.BatchNorm2d)):
Expand Down
14 changes: 7 additions & 7 deletions solver.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -42,9 +42,9 @@ def train(self, querry_dataloader, val_dataloader, task_model, vae, discriminato
labeled_data = self.read_data(querry_dataloader)
unlabeled_data = self.read_data(unlabeled_dataloader, labels=False)

optim_vae = optim.Adam(vae.parameters(), lr=5e-4)
optim_task_model = optim.SGD(task_model.parameters(), lr=0.01, weight_decay=5e-4, momentum=0.9)
optim_discriminator = optim.Adam(discriminator.parameters(), lr=5e-4)
optim_vae = optim.Adam(vae.parameters(), lr=self.args.lr_vae)
optim_task_model = optim.SGD(task_model.parameters(), lr=self.args.lr_task, weight_decay=5e-4, momentum=0.9)
optim_discriminator = optim.Adam(discriminator.parameters(), lr=self.args.lr_dis)


vae.train()
Expand Down Expand Up @@ -87,8 +87,8 @@ def train(self, querry_dataloader, val_dataloader, task_model, vae, discriminato
labeled_preds = discriminator(mu)
unlabeled_preds = discriminator(unlab_mu)

lab_real_preds = torch.ones(labeled_imgs.size(0))
unlab_real_preds = torch.ones(unlabeled_imgs.size(0))
lab_real_preds = torch.ones(labeled_imgs.size(0), 1)
unlab_real_preds = torch.ones(unlabeled_imgs.size(0), 1)

if self.args.cuda:
lab_real_preds = lab_real_preds.cuda()
Expand Down Expand Up @@ -120,8 +120,8 @@ def train(self, querry_dataloader, val_dataloader, task_model, vae, discriminato
labeled_preds = discriminator(mu)
unlabeled_preds = discriminator(unlab_mu)

lab_real_preds = torch.ones(labeled_imgs.size(0))
unlab_fake_preds = torch.zeros(unlabeled_imgs.size(0))
lab_real_preds = torch.ones(labeled_imgs.size(0), 1)
unlab_fake_preds = torch.zeros(unlabeled_imgs.size(0), 1)

if self.args.cuda:
lab_real_preds = lab_real_preds.cuda()
Expand Down