prototorch_models/examples/lgmlvq_moons.py

78 lines
1.8 KiB
Python
Raw Normal View History

2021-06-04 20:58:54 +00:00
"""Localized-GMLVQ example using the Moons dataset."""
import argparse
import logging
import warnings
2021-06-04 20:58:54 +00:00
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import LGMLVQ, VisGLVQ2D
from pytorch_lightning.callbacks import EarlyStopping
from pytorch_lightning.utilities.seed import seed_everything
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
2021-06-04 20:58:54 +00:00
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Reproducibility
seed_everything(seed=2)
2021-06-04 20:58:54 +00:00
# Dataset
train_ds = pt.datasets.Moons(num_samples=300, noise=0.2, seed=42)
2021-06-04 20:58:54 +00:00
# Dataloaders
train_loader = DataLoader(train_ds, batch_size=256, shuffle=True)
2021-06-04 20:58:54 +00:00
# Hyperparameters
hparams = dict(
distribution=[1, 3],
input_dim=2,
latent_dim=2,
)
# Initialize the model
model = LGMLVQ(
hparams,
prototypes_initializer=pt.initializers.SMCI(train_ds),
)
2021-06-04 20:58:54 +00:00
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
# Summary
logging.info(model)
2021-06-04 20:58:54 +00:00
# Callbacks
vis = VisGLVQ2D(data=train_ds)
es = EarlyStopping(
2021-06-04 20:58:54 +00:00
monitor="train_acc",
min_delta=0.001,
patience=20,
mode="max",
verbose=False,
check_on_train_epoch_end=True,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
es,
],
log_every_n_steps=1,
max_epochs=1000,
detect_anomaly=True,
2021-06-04 20:58:54 +00:00
)
# Training loop
trainer.fit(model, train_loader)