prototorch_models/examples/probabilistic.py
2021-05-25 20:26:15 +02:00

54 lines
1.4 KiB
Python

"""GLVQ example using the Iris dataset."""
import argparse
import prototorch as pt
import pytorch_lightning as pl
import torch
from sklearn.datasets import load_iris
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Dataset
x_train, y_train = load_iris(return_X_y=True)
x_train = x_train[:, [0, 2]]
train_ds = pt.datasets.NumpyDataset(x_train, y_train)
# Dataloaders
train_loader = torch.utils.data.DataLoader(train_ds,
num_workers=0,
batch_size=150)
# Hyperparameters
num_classes = 3
prototypes_per_class = 2
hparams = dict(
distribution=(num_classes, prototypes_per_class),
lr=0.05,
variance=1,
)
# Initialize the model
model = pt.models.probabilistic.RSLVQ(
hparams,
optimizer=torch.optim.Adam,
prototype_initializer=pt.components.SSI(train_ds, noise=2),
#prototype_initializer=pt.components.UniformInitializer(2),
)
# Callbacks
vis = pt.models.VisGLVQ2D(data=(x_train, y_train), block=False)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[vis],
)
# Training loop
trainer.fit(model, train_loader)