prototorch_models/examples/median_lvq_iris.py

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2021-07-06 15:12:51 +00:00
"""Median-LVQ example using the Iris dataset."""
import argparse
import prototorch as pt
import pytorch_lightning as pl
import torch
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.Iris(dims=[0, 2])
# Dataloaders
train_loader = torch.utils.data.DataLoader(
train_ds,
batch_size=len(train_ds), # MedianLVQ cannot handle mini-batches
)
# Initialize the model
model = pt.models.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 = pt.models.VisGLVQ2D(data=train_ds)
es = pl.callbacks.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],
weights_summary="full",
)
# Training loop
trainer.fit(model, train_loader)