prototorch_models/examples/grlvq_iris.py
2023-06-20 17:30:21 +02:00

78 lines
2.0 KiB
Python

"""GMLVQ example using the Iris dataset."""
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from lightning_fabric.utilities.seed import seed_everything
from prototorch.models import GRLVQ, VisSiameseGLVQ2D
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.optim.lr_scheduler import ExponentialLR
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
if __name__ == "__main__":
# Reproducibility
seed_everything(seed=4)
# Command-line arguments
parser = argparse.ArgumentParser()
parser.add_argument("--gpus", type=int, default=0)
parser.add_argument("--fast_dev_run", type=bool, default=False)
args = parser.parse_args()
# Dataset
train_ds = pt.datasets.Iris([0, 1])
# Dataloaders
train_loader = DataLoader(train_ds, batch_size=64)
# Hyperparameters
hparams = dict(
input_dim=2,
distribution={
"num_classes": 3,
"per_class": 2
},
proto_lr=0.01,
bb_lr=0.01,
)
# Initialize the model
model = GRLVQ(
hparams,
optimizer=torch.optim.Adam,
prototypes_initializer=pt.initializers.SMCI(train_ds),
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)
# Callbacks
vis = VisSiameseGLVQ2D(data=train_ds)
# Setup trainer
trainer = pl.Trainer(
accelerator="cuda" if args.gpus else "cpu",
devices=args.gpus if args.gpus else "auto",
fast_dev_run=args.fast_dev_run,
callbacks=[
vis,
],
max_epochs=5,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)
torch.save(model, "iris.pth")