107 lines
2.6 KiB
Python
107 lines
2.6 KiB
Python
"""LVQMLN example using all four dimensions of the Iris dataset."""
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import argparse
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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 lightning_fabric.utilities.seed import seed_everything
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from prototorch.models import (
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LVQMLN,
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PruneLoserPrototypes,
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VisSiameseGLVQ2D,
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)
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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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class Backbone(torch.nn.Module):
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def __init__(self, input_size=4, hidden_size=10, latent_size=2):
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super().__init__()
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self.input_size = input_size
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self.hidden_size = hidden_size
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self.latent_size = latent_size
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self.dense1 = torch.nn.Linear(self.input_size, self.hidden_size)
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self.dense2 = torch.nn.Linear(self.hidden_size, self.latent_size)
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self.activation = torch.nn.Sigmoid()
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def forward(self, x):
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x = self.activation(self.dense1(x))
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out = self.activation(self.dense2(x))
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return out
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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.add_argument("--gpus", type=int, default=0)
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parser.add_argument("--fast_dev_run", type=bool, default=False)
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args = parser.parse_args()
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# Dataset
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train_ds = pt.datasets.Iris()
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# Reproducibility
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seed_everything(seed=42)
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# Dataloaders
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train_loader = DataLoader(train_ds, batch_size=150)
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# Hyperparameters
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hparams = dict(
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distribution=[3, 4, 5],
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proto_lr=0.001,
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bb_lr=0.001,
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)
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# Initialize the backbone
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backbone = Backbone()
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# Initialize the model
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model = LVQMLN(
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hparams,
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prototypes_initializer=pt.initializers.SSCI(
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train_ds,
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transform=backbone,
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),
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backbone=backbone,
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)
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# Callbacks
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vis = VisSiameseGLVQ2D(
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data=train_ds,
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map_protos=False,
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border=0.1,
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resolution=500,
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axis_off=True,
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)
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pruning = PruneLoserPrototypes(
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threshold=0.01,
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idle_epochs=20,
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prune_quota_per_epoch=2,
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frequency=10,
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verbose=True,
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)
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# Setup trainer
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trainer = pl.Trainer(
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accelerator="cuda" if args.gpus else "cpu",
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devices=args.gpus if args.gpus else "auto",
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fast_dev_run=args.fast_dev_run,
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callbacks=[
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vis,
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pruning,
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],
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log_every_n_steps=1,
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max_epochs=1000,
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detect_anomaly=True,
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)
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# Training loop
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trainer.fit(model, train_loader)
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