100 lines
2.4 KiB
Python
100 lines
2.4 KiB
Python
"""Dynamically prune 'loser' prototypes in GLVQ-type models."""
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import argparse
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import logging
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import warnings
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import prototorch as pt
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import pytorch_lightning as pl
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import torch
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from prototorch.models import (
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CELVQ,
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PruneLoserPrototypes,
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VisGLVQ2D,
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)
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from pytorch_lightning.callbacks import EarlyStopping
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from pytorch_lightning.utilities.seed import seed_everything
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from pytorch_lightning.utilities.warnings import PossibleUserWarning
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from torch.utils.data import DataLoader
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warnings.filterwarnings("ignore", category=PossibleUserWarning)
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warnings.filterwarnings("ignore", category=UserWarning)
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if __name__ == "__main__":
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# Reproducibility
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seed_everything(seed=4)
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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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num_classes = 4
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num_features = 2
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num_clusters = 1
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train_ds = pt.datasets.Random(
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num_samples=500,
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num_classes=num_classes,
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num_features=num_features,
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num_clusters=num_clusters,
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separation=3.0,
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seed=42,
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)
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# Dataloaders
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train_loader = DataLoader(train_ds, batch_size=256)
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# Hyperparameters
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prototypes_per_class = num_clusters * 5
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hparams = dict(
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distribution=(num_classes, prototypes_per_class),
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lr=0.2,
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)
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# Initialize the model
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model = CELVQ(
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hparams,
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prototypes_initializer=pt.initializers.FVCI(2, 3.0),
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)
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# Compute intermediate input and output sizes
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model.example_input_array = torch.zeros(4, 2)
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# Summary
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logging.info(model)
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# Callbacks
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vis = VisGLVQ2D(train_ds)
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pruning = PruneLoserPrototypes(
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threshold=0.01, # prune prototype if it wins less than 1%
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idle_epochs=20, # pruning too early may cause problems
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prune_quota_per_epoch=2, # prune at most 2 prototypes per epoch
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frequency=1, # prune every epoch
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verbose=True,
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)
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es = EarlyStopping(
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monitor="train_loss",
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min_delta=0.001,
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patience=20,
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mode="min",
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verbose=True,
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check_on_train_epoch_end=True,
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)
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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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callbacks=[
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vis,
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pruning,
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es,
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],
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detect_anomaly=True,
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log_every_n_steps=1,
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max_epochs=1000,
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)
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# Training loop
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trainer.fit(model, train_loader)
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