Remove examples/dynamic_components.py

examples/dynamic_components.py

@@ -1,71 +0,0 @@
-"""Dynamically update the number of prototypes in GLVQ."""
-
-import argparse
-
-import prototorch as pt
-import pytorch_lightning as pl
-import torch
-from pytorch_lightning.callbacks import Callback
-
-
-class PrototypeScheduler(Callback):
-    def __init__(self, train_ds, freq=20):
-        self.train_ds = train_ds
-        self.freq = freq
-
-    def on_epoch_end(self, trainer, pl_module):
-        if (trainer.current_epoch + 1) % self.freq == 0:
-            pl_module.increase_prototypes(
-                pt.components.SMI(self.train_ds),
-                distribution=[1, 1, 1],
-            )
-
-
-if __name__ == "__main__":
-    # Command-line arguments
-    parser = argparse.ArgumentParser()
-    parser = pl.Trainer.add_argparse_args(parser)
-    args = parser.parse_args()
-
-    # Dataset
-    train_ds = pt.datasets.Iris(dims=[0, 2])
-
-    # Dataloaders
-    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
-
-    # Hyperparameters
-    hparams = dict(
-        distribution=[1, 1, 1],
-        transfer_function="sigmoid_beta",
-        transfer_beta=10.0,
-        lr=0.01,
-    )
-
-    # Initialize the model
-    model = pt.models.GLVQ(
-        hparams,
-        prototype_initializer=pt.components.SMI(train_ds),
-    )
-
-    # Summary
-    print(model)
-
-    # Callbacks
-    vis = pt.models.VisGLVQ2D(train_ds)
-    proto_scheduler = PrototypeScheduler(train_ds, 10)
-
-    # Setup trainer
-    trainer = pl.Trainer.from_argparse_args(
-        args,
-        max_epochs=100,
-        callbacks=[
-            vis,
-            proto_scheduler,
-        ],
-        terminate_on_nan=True,
-        weights_summary=None,
-        accelerator="ddp",
-    )
-
-    # Training loop
-    trainer.fit(model, train_loader)