59 lines
1.6 KiB
Python
59 lines
1.6 KiB
Python
"""GLVQ example using the spiral dataset."""
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import pytorch_lightning as pl
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import torch
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from prototorch.components import initializers as cinit
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from prototorch.datasets.abstract import NumpyDataset
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from prototorch.datasets.spiral import make_spiral
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from torch.utils.data import DataLoader
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from prototorch.models.callbacks.visualization import VisGLVQ2D
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from prototorch.models.glvq import GLVQ
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class StopOnNaN(pl.Callback):
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def __init__(self, param):
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super().__init__()
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self.param = param
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def on_epoch_end(self, trainer, pl_module, logs={}):
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if torch.isnan(self.param).any():
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raise ValueError("NaN encountered. Stopping.")
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if __name__ == "__main__":
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# Dataset
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x_train, y_train = make_spiral(n_samples=600, noise=0.6)
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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=256)
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# Hyperparameters
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hparams = dict(
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nclasses=2,
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prototypes_per_class=20,
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prototype_initializer=cinit.SSI(torch.Tensor(x_train),
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torch.Tensor(y_train),
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noise=1e-7),
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transfer_function="sigmoid_beta",
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transfer_beta=10.0,
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lr=0.01,
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)
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# Initialize the model
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model = GLVQ(hparams)
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# Callbacks
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vis = VisGLVQ2D(x_train, y_train, show_last_only=True, block=True)
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snan = StopOnNaN(model.proto_layer.components)
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# Setup trainer
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trainer = pl.Trainer(
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max_epochs=200,
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callbacks=[vis, snan],
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)
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# Training loop
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trainer.fit(model, train_loader)
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