restructured code

.gitignore

@@ -1,5 +1,8 @@
-scripts/wandb/*
-scripts/images/*
-plr.egg-info/*
+2025_lecture_examples/scripts/wandb/*
+2025_lecture_examples/scripts/images/*
+2025_lecture_examples/plr.egg-info/*
+2025_lecture_examples/.venv/*
+2024_home_work/.data/*
 2024_home_work/.venv/*
-.venv/*
+2024_home_work/plr_exercise.egg-info/*
+2024_home_work/data/MNIST/raw/train-images-idx3-ubyte

2024_home_work/train.py

@@ -66,15 +66,22 @@ def test(model, device, test_loader, epoch):
         for data, target in test_loader:
             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
+            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()
 
     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),
         )
     )
 
@@ -83,25 +90,65 @@ def main():
     # Training settings
     parser = argparse.ArgumentParser(description="PyTorch MNIST Example")
     parser.add_argument(
-        "--batch-size", type=int, default=64, metavar="N", help="input batch size for training (default: 64)"
+        "--batch-size",
+        type=int,
+        default=64,
+        metavar="N",
+        help="input batch size for training (default: 64)",
+    )
+    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(
-        "--test-batch-size", type=int, default=1000, metavar="N", help="input batch size for testing (default: 1000)"
+        "--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,
         default=10,
         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()
 
@@ -119,9 +166,11 @@ 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)
-    dataset2 = datasets.MNIST("../data", train=False, 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)
 
@@ -140,7 +189,3 @@ def main():
 
 if __name__ == "__main__":
     main()
-
-    test = 12
-
-    test = 13