prototorch_models/examples/liramlvq_tecator.py
2021-05-25 15:41:10 +02:00

69 lines
1.8 KiB
Python

"""Limited Rank Matrix LVQ example using the Tecator dataset."""
import argparse
import prototorch as pt
import pytorch_lightning as pl
import torch
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Dataset
train_ds = pt.datasets.Tecator(root="~/datasets/", train=True)
test_ds = pt.datasets.Tecator(root="~/datasets/", train=False)
# Reproducibility
pl.utilities.seed.seed_everything(seed=42)
# Dataloaders
train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
test_loader = torch.utils.data.DataLoader(test_ds, batch_size=32)
# Hyperparameters
num_classes = 2
prototypes_per_class = 2
hparams = dict(
distribution=(num_classes, prototypes_per_class),
input_dim=100,
latent_dim=2,
proto_lr=0.001,
bb_lr=0.001,
)
# Initialize the model
model = pt.models.GMLVQ(hparams,
prototype_initializer=pt.components.SMI(train_ds))
# Callbacks
vis = pt.models.VisSiameseGLVQ2D(train_ds, border=0.1)
es = pl.callbacks.EarlyStopping(monitor="val_loss",
min_delta=0.001,
patience=3,
verbose=False,
mode="min")
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[vis, es],
)
# Training loop
trainer.fit(model, train_loader, test_loader)
# Save the model
torch.save(model, "liramlvq_tecator.pt")
# Load a saved model
saved_model = torch.load("liramlvq_tecator.pt")
# Display the Lambda matrix
saved_model.show_lambda()
# Testing
trainer.test(model, test_dataloaders=test_loader)