prototorch_models/examples/glvq_iris.py

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"""GLVQ example using the Iris dataset."""
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import argparse
import logging
import warnings
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import prototorch as pt
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import pytorch_lightning as pl
import torch
from prototorch.models import GLVQ, VisGLVQ2D
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
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from torch.optim.lr_scheduler import ExponentialLR
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=UserWarning)
warnings.filterwarnings("ignore", category=PossibleUserWarning)
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if __name__ == "__main__":
# Reproducibility
seed_everything(seed=4)
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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
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train_ds = pt.datasets.Iris(dims=[0, 2])
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# Dataloaders
train_loader = DataLoader(train_ds, batch_size=64, num_workers=4)
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# Hyperparameters
hparams = dict(
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distribution={
"num_classes": 3,
"per_class": 4
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},
lr=0.01,
)
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# Initialize the model
model = GLVQ(
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hparams,
optimizer=torch.optim.Adam,
prototypes_initializer=pt.initializers.SMCI(train_ds),
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lr_scheduler=ExponentialLR,
lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
)
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
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# Callbacks
vis = VisGLVQ2D(data=train_ds)
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# Setup trainer
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trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
],
max_epochs=100,
log_every_n_steps=1,
detect_anomaly=True,
)
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# Training loop
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trainer.fit(model, train_loader)
# Manual save
trainer.save_checkpoint("./glvq_iris.ckpt")
# Load saved model
new_model = GLVQ.load_from_checkpoint(
checkpoint_path="./glvq_iris.ckpt",
strict=False,
)
logging.info(new_model)