01 hotova

hod_1/data/mnist_layers_activations.py

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+#!/usr/bin/env python3
+import argparse
+
+import torch
+import torchmetrics
+
+import npfl138
+npfl138.require_version("2526.1")
+from npfl138.datasets.mnist import MNIST
+
+parser = argparse.ArgumentParser()
+# These arguments will be set appropriately by ReCodEx, even if you change them.
+parser.add_argument("--activation", default="none", choices=["none", "relu", "tanh", "sigmoid"], help="Activation.")
+parser.add_argument("--batch_size", default=50, type=int, help="Batch size.")
+parser.add_argument("--epochs", default=10, type=int, help="Number of epochs.")
+parser.add_argument("--hidden_layer_size", default=100, type=int, help="Size of the hidden layer.")
+parser.add_argument("--hidden_layers", default=1, type=int, help="Number of layers.")
+parser.add_argument("--recodex", default=False, action="store_true", help="Evaluation in ReCodEx.")
+parser.add_argument("--seed", default=42, type=int, help="Random seed.")
+parser.add_argument("--threads", default=1, type=int, help="Maximum number of threads to use.")
+# If you add more arguments, ReCodEx will keep them with your default values.
+
+
+class Dataset(npfl138.TransformedDataset):
+    def transform(self, example):
+        image = example["image"]  # a torch.Tensor with torch.uint8 values in [0, 255] range
+        image = image.to(torch.float32) / 255  # image converted to float32 and rescaled to [0, 1]
+        label = example["label"]  # a torch.Tensor with a single integer representing the label
+        return image, label  # return an (input, target) pair
+
+
+def main(args: argparse.Namespace) -> dict[str, float]:
+    # Set the random seed and the number of threads.
+    npfl138.startup(args.seed, args.threads, args.recodex)
+    npfl138.global_keras_initializers()
+
+    # Load the data and create dataloaders.
+    mnist = MNIST()
+
+    train = torch.utils.data.DataLoader(Dataset(mnist.train), batch_size=args.batch_size, shuffle=True)
+    dev = torch.utils.data.DataLoader(Dataset(mnist.dev), batch_size=args.batch_size)
+
+    # Create the model.
+    model = torch.nn.Sequential()
+
+    # TODO: Finish the model. Namely:
+    # - start by adding the `torch.nn.Flatten()` layer;
+    # - then add `args.hidden_layers` number of fully connected hidden layers
+    #   `torch.nn.Linear()`, each with `args.hidden_layer_size` neurons and followed by
+    #   a specified `args.activation`, allowing "none", "relu", "tanh", "sigmoid";
+    # - finally, add an output fully connected layer with `MNIST.LABELS` units.
+    ...
+
+    # Create the TrainableModule and configure it for training.
+    model = npfl138.TrainableModule(model)
+
+    model.configure(
+        optimizer=torch.optim.Adam(model.parameters()),
+        loss=torch.nn.CrossEntropyLoss(),
+        metrics={"accuracy": torchmetrics.Accuracy("multiclass", num_classes=MNIST.LABELS)},
+        logdir=npfl138.format_logdir("logs/{file-}{timestamp}{-config}", **vars(args)),
+    )
+
+    # Train the model.
+    logs = model.fit(train, dev=dev, epochs=args.epochs)
+
+    # Return development metrics for ReCodEx to validate.
+    return {metric: value for metric, value in logs.items() if metric.startswith("dev:")}
+
+
+if __name__ == "__main__":
+    main_args = parser.parse_args([] if "__file__" not in globals() else None)
+    main(main_args)