89 lines
2.2 KiB
Python
89 lines
2.2 KiB
Python
"""Limited Rank Matrix LVQ example using the Tecator dataset."""
|
|
|
|
import argparse
|
|
|
|
import matplotlib.pyplot as plt
|
|
import prototorch as pt
|
|
import pytorch_lightning as pl
|
|
import torch
|
|
|
|
|
|
def plot_matrix(matrix):
|
|
title = "Lambda matrix"
|
|
plt.figure(title)
|
|
plt.title(title)
|
|
plt.imshow(matrix, cmap="gray")
|
|
plt.axis("off")
|
|
plt.colorbar()
|
|
plt.show(block=True)
|
|
|
|
|
|
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=10)
|
|
|
|
# Dataloaders
|
|
train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
|
|
test_loader = torch.utils.data.DataLoader(test_ds, batch_size=32)
|
|
|
|
# Hyperparameters
|
|
hparams = dict(
|
|
distribution={
|
|
"num_classes": 2,
|
|
"per_class": 1,
|
|
},
|
|
input_dim=100,
|
|
latent_dim=2,
|
|
proto_lr=0.001,
|
|
bb_lr=0.001,
|
|
)
|
|
|
|
# Initialize the model
|
|
model = pt.models.SiameseGMLVQ(
|
|
hparams,
|
|
optimizer=torch.optim.Adam,
|
|
prototypes_initializer=pt.initializers.SMCI(train_ds),
|
|
)
|
|
|
|
# Summary
|
|
print(model)
|
|
|
|
# Callbacks
|
|
vis = pt.models.VisSiameseGLVQ2D(train_ds, border=0.1)
|
|
es = pl.callbacks.EarlyStopping(monitor="val_loss",
|
|
min_delta=0.001,
|
|
patience=50,
|
|
verbose=False,
|
|
mode="min")
|
|
|
|
# Setup trainer
|
|
trainer = pl.Trainer.from_argparse_args(
|
|
args,
|
|
callbacks=[vis, es],
|
|
weights_summary=None,
|
|
)
|
|
|
|
# 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
|
|
plot_matrix(saved_model.lambda_matrix)
|
|
|
|
# Testing
|
|
trainer.test(model, test_dataloaders=test_loader)
|