79 lines
1.9 KiB
Python
79 lines
1.9 KiB
Python
"""LVQMLN example using all four dimensions of 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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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 = pl.Trainer.add_argparse_args(parser)
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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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pl.utilities.seed.seed_everything(seed=42)
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# Dataloaders
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train_loader = torch.utils.data.DataLoader(train_ds,
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num_workers=0,
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batch_size=150)
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# Hyperparameters
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hparams = dict(
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distribution=[1, 2, 2],
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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 = pt.models.LVQMLN(
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hparams,
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prototype_initializer=pt.components.SSI(train_ds, transform=backbone),
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backbone=backbone,
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)
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# Model summary
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print(model)
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# Callbacks
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vis = pt.models.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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# 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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)
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# Training loop
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trainer.fit(model, train_loader)
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