78 lines
2.1 KiB
Python
78 lines
2.1 KiB
Python
"""Neural Gas example using 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 NeuralGas, VisNG2D
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from pytorch_lightning.utilities.warnings import PossibleUserWarning
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from sklearn.datasets import load_iris
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from sklearn.preprocessing import StandardScaler
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from torch.optim.lr_scheduler import ExponentialLR
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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.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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# Prepare and pre-process the dataset
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x_train, y_train = load_iris(return_X_y=True)
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x_train = x_train[:, 0:3:2]
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scaler = StandardScaler()
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scaler.fit(x_train)
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x_train = scaler.transform(x_train)
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train_ds = pt.datasets.NumpyDataset(x_train, y_train)
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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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num_prototypes=30,
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input_dim=2,
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lr=0.03,
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)
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# Initialize the model
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model = NeuralGas(
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hparams,
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prototypes_initializer=pt.core.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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# 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 = VisNG2D(data=train_ds)
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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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],
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max_epochs=1000,
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log_every_n_steps=1,
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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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