diff --git a/plr_exercise.egg-info/PKG-INFO b/plr_exercise.egg-info/PKG-INFO new file mode 100644 index 0000000..0c29d2b --- /dev/null +++ b/plr_exercise.egg-info/PKG-INFO @@ -0,0 +1,10 @@ +Metadata-Version: 2.1 +Name: plr_exercise +Version: 1.0.0 +Summary: A small example package +Author: Jonas Frey +Author-email: jonfrey@ethz.ch +Requires-Python: >=3.7 +License-File: LICENSE +Requires-Dist: numpy +Requires-Dist: torch>=1.21 diff --git a/plr_exercise.egg-info/SOURCES.txt b/plr_exercise.egg-info/SOURCES.txt new file mode 100644 index 0000000..6531f8c --- /dev/null +++ b/plr_exercise.egg-info/SOURCES.txt @@ -0,0 +1,9 @@ +LICENSE +README.md +setup.py +plr_exercise/__init__.py +plr_exercise.egg-info/PKG-INFO +plr_exercise.egg-info/SOURCES.txt +plr_exercise.egg-info/dependency_links.txt +plr_exercise.egg-info/requires.txt +plr_exercise.egg-info/top_level.txt \ No newline at end of file diff --git a/plr_exercise.egg-info/dependency_links.txt b/plr_exercise.egg-info/dependency_links.txt new file mode 100644 index 0000000..2b90dfa --- /dev/null +++ b/plr_exercise.egg-info/dependency_links.txt @@ -0,0 +1 @@ +https://download.pytorch.org/whl/torch-2.1.0+cu121-cp38-cp38-linux_x86_64.whl diff --git a/plr_exercise.egg-info/requires.txt b/plr_exercise.egg-info/requires.txt new file mode 100644 index 0000000..f50dca9 --- /dev/null +++ b/plr_exercise.egg-info/requires.txt @@ -0,0 +1,2 @@ +numpy +torch>=1.21 diff --git a/plr_exercise.egg-info/top_level.txt b/plr_exercise.egg-info/top_level.txt new file mode 100644 index 0000000..1279457 --- /dev/null +++ b/plr_exercise.egg-info/top_level.txt @@ -0,0 +1 @@ +plr_exercise diff --git a/train.py b/train.py index 2cfae71..ed42c38 100644 --- a/train.py +++ b/train.py @@ -6,15 +6,25 @@ import torch.optim as optim from torchvision import datasets, transforms from torch.optim.lr_scheduler import StepLR +import wandb + + +run = wandb.init( + project="mon projet", + config={ + "learning_rate": 0.01, + "epochs": 10, + + }, +) class Net(nn.Module): def __init__(self): - super(Net, self).__init__() self.conv1 = nn.Conv2d(1, 32, 3, 1) - self.conv2 = nn.Conv2d( 32, 64, 3, 1) + self.conv2 = nn.Conv2d(32, 64, 3, 1) self.dropout1 = nn.Dropout(0.25) self.dropout2 = nn.Dropout(0.5) self.fc1 = nn.Linear(9216, 128) @@ -43,7 +53,7 @@ def train(args, model, device, train_loader, optimizer, epoch): data, target = data.to(device), target.to(device) optimizer.zero_grad() output = model(data) - loss = F.nll_loss( output, target) + loss = F.nll_loss(output, target) loss.backward() optimizer.step() if batch_idx % args.log_interval == 0: @@ -52,7 +62,7 @@ def train(args, model, device, train_loader, optimizer, epoch): epoch, batch_idx * len(data), len(train_loader.dataset), - 100.0 * batch_idx / len(train_loader), + 100.0 * batch_idx / len(train_loader), loss.item(), ) ) @@ -70,17 +80,21 @@ def test(model, device, test_loader, epoch): data, target = data.to(device), target.to(device) output = model(data) - test_loss += F.nll_loss( output, target, reduction="sum").item() # sum up batch loss - pred = output.argmax(dim=1, keepdim=True) # get the index of the max log-probability - correct += pred.eq(target.view_as(pred) ).sum().item() + # sum up batch loss + test_loss += F.nll_loss(output, target, reduction="sum").item() + # get the index of the max log-probability + pred = output.argmax(dim=1, keepdim=True) + correct += pred.eq(target.view_as(pred)).sum().item() - test_loss /= len(test_loader.dataset ) + test_loss /= len(test_loader.dataset) print( "\nTest set: Average loss: {:.4f}, Accuracy: {}/{} ({:.0f}%)\n".format( - test_loss, correct, len(test_loader.dataset), 100.0 * correct / len(test_loader.dataset) + test_loss, correct, len( + test_loader.dataset), 100.0 * correct / len(test_loader.dataset) ) ) + wandb.log({"Loss": test_loss, "epoch": epoch}) def main(): @@ -92,12 +106,18 @@ def main(): parser.add_argument( "--test-batch-size", type=int, default=1000, metavar="N", help="input batch size for testing (default: 1000)" ) - parser.add_argument("--epochs", type=int, default=2, metavar="N", help="number of epochs to train (default: 14)") - parser.add_argument("--lr", type=float, default=1.0, metavar="LR", help="learning rate (default: 1.0)") - parser.add_argument("--gamma", type=float, default=0.7, metavar="M", help="Learning rate step gamma (default: 0.7)") - parser.add_argument("--no-cuda", action="store_true", default=False, help="disables CUDA training") - parser.add_argument("--dry-run", action="store_true", default=False, help="quickly check a single pass") - parser.add_argument("--seed", type=int, default=1, metavar="S", help="random seed (default: 1)") + parser.add_argument("--epochs", type=int, default=2, metavar="N", + help="number of epochs to train (default: 14)") + parser.add_argument("--lr", type=float, default=1.0, + metavar="LR", help="learning rate (default: 1.0)") + parser.add_argument("--gamma", type=float, default=0.7, + metavar="M", help="Learning rate step gamma (default: 0.7)") + parser.add_argument("--no-cuda", action="store_true", + default=False, help="disables CUDA training") + parser.add_argument("--dry-run", action="store_true", + default=False, help="quickly check a single pass") + parser.add_argument("--seed", type=int, default=1, + metavar="S", help="random seed (default: 1)") parser.add_argument( "--log-interval", type=int, @@ -105,7 +125,8 @@ def main(): metavar="N", help="how many batches to wait before logging training status", ) - parser.add_argument("--save-model", action="store_true", default=False, help="For Saving the current Model") + parser.add_argument("--save-model", action="store_true", + default=False, help="For Saving the current Model") args = parser.parse_args() use_cuda = not args.no_cuda and torch.cuda.is_available() @@ -123,8 +144,10 @@ def main(): train_kwargs.update(cuda_kwargs) test_kwargs.update(cuda_kwargs) - transform = transforms.Compose([transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) - dataset1 = datasets.MNIST("../data", train=True, download=True, transform=transform) + transform = transforms.Compose( + [transforms.ToTensor(), transforms.Normalize((0.1307,), (0.3081,))]) + dataset1 = datasets.MNIST("../data", train=True, + download=True, transform=transform) dataset2 = datasets.MNIST("../data", train=False, transform=transform) train_loader = torch.utils.data.DataLoader(dataset1, **train_kwargs) test_loader = torch.utils.data.DataLoader(dataset2, **test_kwargs) diff --git a/wandb/debug-internal.log b/wandb/debug-internal.log new file mode 120000 index 0000000..2dd7b87 --- /dev/null +++ b/wandb/debug-internal.log @@ -0,0 +1 @@ +run-20240306_104616-pufbvux4/logs/debug-internal.log \ No newline at end of file diff --git a/wandb/debug.log b/wandb/debug.log new file mode 120000 index 0000000..d96adce --- /dev/null +++ b/wandb/debug.log @@ -0,0 +1 @@ +run-20240306_104616-pufbvux4/logs/debug.log \ No newline at end of file diff --git a/wandb/latest-run b/wandb/latest-run new file mode 120000 index 0000000..0c6c897 --- /dev/null +++ b/wandb/latest-run @@ -0,0 +1 @@ +run-20240306_104616-pufbvux4 \ No newline at end of file diff --git a/wandb/run-20240306_104616-pufbvux4/files/config.yaml b/wandb/run-20240306_104616-pufbvux4/files/config.yaml new file mode 100644 index 0000000..0e8e9a0 --- /dev/null +++ b/wandb/run-20240306_104616-pufbvux4/files/config.yaml @@ -0,0 +1,35 @@ +wandb_version: 1 + +learning_rate: + desc: null + value: 0.01 +epochs: + desc: null + value: 10 +_wandb: + desc: null + value: + python_version: 3.11.5 + cli_version: 0.16.4 + framework: torch + is_jupyter_run: false + is_kaggle_kernel: false + start_time: 1709718376.0 + t: + 1: + - 1 + - 41 + - 55 + 2: + - 1 + - 41 + - 55 + 3: + - 16 + - 23 + 4: 3.11.5 + 5: 0.16.4 + 8: + - 4 + - 5 + 13: darwin-arm64 diff --git a/wandb/run-20240306_104616-pufbvux4/files/output.log b/wandb/run-20240306_104616-pufbvux4/files/output.log new file mode 100644 index 0000000..8ee4957 --- /dev/null +++ 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