prototorch_models/examples/liramlvq_tecator.py
2021-05-15 12:43:00 +02:00

50 lines
1.3 KiB
Python

"""Limited Rank Matrix LVQ example using the Tecator dataset."""
import prototorch as pt
import pytorch_lightning as pl
import torch
if __name__ == "__main__":
# Dataset
train_ds = pt.datasets.Tecator(root="~/datasets/", train=True)
# Reproducibility
pl.utilities.seed.seed_everything(seed=42)
# Dataloaders
train_loader = torch.utils.data.DataLoader(train_ds,
num_workers=0,
batch_size=32)
# Hyperparameters
nclasses = 2
prototypes_per_class = 2
hparams = dict(
distribution=(nclasses, prototypes_per_class),
input_dim=100,
latent_dim=2,
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)
# Setup trainer
trainer = pl.Trainer(max_epochs=200, callbacks=[vis], gpus=0)
# Training loop
trainer.fit(model, train_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()