63 lines
1.5 KiB
Python
63 lines
1.5 KiB
Python
"""Neural Gas example using the Iris dataset."""
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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 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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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 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, 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 = torch.utils.data.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 = pt.models.NeuralGas(
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hparams,
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prototype_initializer=pt.components.Zeros(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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# Model summary
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print(model)
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# Callbacks
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vis = pt.models.VisNG2D(data=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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callbacks=[vis],
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weights_summary="full",
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)
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# Training loop
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trainer.fit(model, train_loader)
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