86 lines
2.2 KiB
Python
86 lines
2.2 KiB
Python
"""Siamese GTLVQ example using all four dimensions of 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 prototorch.models import SiameseGTLVQ, VisSiameseGLVQ2D
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from pytorch_lightning.utilities.seed import seed_everything
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from pytorch_lightning.utilities.warnings import PossibleUserWarning
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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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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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seed_everything(seed=2)
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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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distribution=[1, 2, 3],
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proto_lr=0.01,
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bb_lr=0.01,
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input_dim=2,
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latent_dim=1,
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)
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# Initialize the backbone
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backbone = Backbone(latent_size=hparams["input_dim"])
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# Initialize the model
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model = SiameseGTLVQ(
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hparams,
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prototypes_initializer=pt.initializers.SMCI(train_ds),
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backbone=backbone,
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both_path_gradients=False,
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)
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# Callbacks
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vis = VisSiameseGLVQ2D(data=train_ds, border=0.1)
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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=[
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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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