Add examples/dynamic_components.py
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examples/dynamic_components.py
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56
examples/dynamic_components.py
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"""Dynamically update the number of prototypes in GLVQ."""
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import argparse
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import pytorch_lightning as pl
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import torch
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import prototorch as pt
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if __name__ == "__main__":
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# Command-line arguments
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parser = argparse.ArgumentParser()
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parser = pl.Trainer.add_argparse_args(parser)
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args = parser.parse_args()
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# Dataset
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train_ds = pt.datasets.Iris(dims=[0, 2])
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# Dataloaders
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train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
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# Hyperparameters
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hparams = dict(
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distribution=[1, 1, 1],
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transfer_function="sigmoid_beta",
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transfer_beta=10.0,
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lr=0.01,
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)
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# Initialize the model
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model = pt.models.GLVQ(
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hparams,
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prototype_initializer=pt.components.SMI(train_ds),
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)
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for _ in range(5):
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# Callbacks
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vis = pt.models.VisGLVQ2D(train_ds)
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# Setup trainer
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trainer = pl.Trainer.from_argparse_args(
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args,
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max_epochs=20,
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callbacks=[vis],
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terminate_on_nan=True,
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weights_summary=None,
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)
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# Training loop
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trainer.fit(model, train_loader)
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# Increase prototypes
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model.increase_prototypes(
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pt.components.SMI(train_ds),
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distribution=[1, 1, 1],
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)
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@ -1,6 +1,5 @@
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import pytorch_lightning as pl
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import pytorch_lightning as pl
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import torch
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import torch
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from prototorch.functions.competitions import wtac
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from torch.optim.lr_scheduler import ExponentialLR
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from torch.optim.lr_scheduler import ExponentialLR
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@ -120,6 +120,9 @@ class GLVQ(AbstractPrototypeModel):
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# def predict_step(self, batch, batch_idx, dataloader_idx=None):
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# def predict_step(self, batch, batch_idx, dataloader_idx=None):
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# pass
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# pass
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def increase_prototypes(self, initializer, distribution):
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self.proto_layer.increase_components(initializer, distribution)
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def __repr__(self):
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def __repr__(self):
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super_repr = super().__repr__()
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super_repr = super().__repr__()
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return f"{super_repr}"
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return f"{super_repr}"
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