prototorch_models/examples/lvqmln_iris.py

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"""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
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):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.latent_size = latent_size
self.dense1 = torch.nn.Linear(self.input_size, self.hidden_size)
self.dense2 = torch.nn.Linear(self.hidden_size, self.latent_size)
self.activation = torch.nn.Sigmoid()
def forward(self, x):
x = self.activation(self.dense1(x))
out = self.activation(self.dense2(x))
return out
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if __name__ == "__main__":
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# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
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# Dataset
train_ds = pt.datasets.Iris()
# Reproducibility
pl.utilities.seed.seed_everything(seed=42)
# Dataloaders
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train_loader = torch.utils.data.DataLoader(train_ds, batch_size=150)
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# Hyperparameters
hparams = dict(
distribution=[3, 4, 5],
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proto_lr=0.001,
bb_lr=0.001,
)
# Initialize the backbone
backbone = Backbone()
# Initialize the model
model = pt.models.LVQMLN(
hparams,
prototypes_initializer=pt.initializers.SSCI(
train_ds,
transform=backbone,
),
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backbone=backbone,
)
# Model summary
print(model)
# Callbacks
vis = pt.models.VisSiameseGLVQ2D(
data=train_ds,
map_protos=False,
border=0.1,
resolution=500,
axis_off=True,
)
pruning = pt.models.PruneLoserPrototypes(
threshold=0.01,
idle_epochs=20,
prune_quota_per_epoch=2,
frequency=10,
verbose=True,
)
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# Setup trainer
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trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
pruning,
],
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)
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# Training loop
trainer.fit(model, train_loader)