prototorch_models/examples/median_lvq_iris.py
2022-05-17 12:03:43 +02:00

69 lines
1.7 KiB
Python

"""Median-LVQ example using the Iris dataset."""
import argparse
import warnings
import prototorch as pt
import pytorch_lightning as pl
import torch
from prototorch.models import MedianLVQ, 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)
if __name__ == "__main__":
# Reproducibility
seed_everything(seed=4)
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Dataset
train_ds = pt.datasets.Iris(dims=[0, 2])
# Dataloaders
train_loader = DataLoader(
train_ds,
batch_size=len(train_ds), # MedianLVQ cannot handle mini-batches
)
# Initialize the model
model = MedianLVQ(
hparams=dict(distribution=(3, 2), lr=0.01),
prototypes_initializer=pt.initializers.SSCI(train_ds),
)
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
# Callbacks
vis = VisGLVQ2D(data=train_ds)
es = EarlyStopping(
monitor="train_acc",
min_delta=0.01,
patience=5,
mode="max",
verbose=True,
check_on_train_epoch_end=True,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
es,
],
max_epochs=1000,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)