51 lines
1.2 KiB
Python
51 lines
1.2 KiB
Python
"""Neural Gas example using the Iris dataset."""
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import pytorch_lightning as pl
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from prototorch.datasets.abstract import NumpyDataset
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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.utils.data import DataLoader
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from prototorch.models.callbacks.visualization import VisNG2D
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from prototorch.models.neural_gas import NeuralGas
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if __name__ == "__main__":
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# 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 = NumpyDataset(x_train, y_train)
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# Dataloaders
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train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
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# Hyperparameters
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hparams = dict(
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input_dim=x_train.shape[1],
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num_prototypes=30,
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lr=0.01,
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)
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# Initialize the model
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model = NeuralGas(hparams)
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# Model summary
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print(model)
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# Callbacks
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vis = VisNG2D(x_train, y_train)
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# Setup trainer
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trainer = pl.Trainer(
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max_epochs=100,
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callbacks=[
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vis,
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],
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)
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# Training loop
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trainer.fit(model, train_loader)
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