104 lines
2.6 KiB
Python
104 lines
2.6 KiB
Python
"""Warm-starting GLVQ with prototypes from Growing Neural Gas."""
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import argparse
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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 torch.optim.lr_scheduler import ExponentialLR
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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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# Prepare the data
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train_ds = pt.datasets.Iris(dims=[0, 2])
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train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64)
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# Initialize the gng
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gng = pt.models.GrowingNeuralGas(
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hparams=dict(num_prototypes=5, insert_freq=2, lr=0.1),
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prototypes_initializer=pt.initializers.ZCI(2),
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lr_scheduler=ExponentialLR,
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lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
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)
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# Callbacks
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es = pl.callbacks.EarlyStopping(
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monitor="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=False,
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check_on_train_epoch_end=True,
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)
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# Setup trainer for GNG
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trainer = pl.Trainer(
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max_epochs=100,
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callbacks=[es],
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weights_summary=None,
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)
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# Training loop
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trainer.fit(gng, train_loader)
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# Hyperparameters
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hparams = dict(
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distribution=[],
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lr=0.01,
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)
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# Warm-start prototypes
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knn = pt.models.KNN(dict(k=1), data=train_ds)
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prototypes = gng.prototypes
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plabels = knn.predict(prototypes)
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# Initialize the model
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model = pt.models.GLVQ(
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hparams,
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optimizer=torch.optim.Adam,
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prototypes_initializer=pt.initializers.LCI(prototypes),
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labels_initializer=pt.initializers.LLI(plabels),
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lr_scheduler=ExponentialLR,
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lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
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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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# Callbacks
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vis = pt.models.VisGLVQ2D(data=train_ds)
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pruning = pt.models.PruneLoserPrototypes(
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threshold=0.02,
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idle_epochs=2,
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prune_quota_per_epoch=5,
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frequency=1,
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verbose=True,
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)
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es = pl.callbacks.EarlyStopping(
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monitor="train_loss",
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min_delta=0.001,
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patience=10,
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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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weights_summary="full",
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accelerator="ddp",
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)
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# Training loop
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trainer.fit(model, train_loader)
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