prototorch_models/examples/gmlvq_iris.py

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2021-09-01 08:49:57 +00:00
"""GMLVQ example using the Iris dataset."""
import argparse
import prototorch as pt
import pytorch_lightning as pl
import torch
from torch.optim.lr_scheduler import ExponentialLR
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()
# Dataloaders
train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64)
# Hyperparameters
hparams = dict(
input_dim=4,
latent_dim=4,
distribution={
"num_classes": 3,
"per_class": 2
},
proto_lr=0.01,
bb_lr=0.01,
)
# Initialize the model
model = pt.models.GMLVQ(
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, 4)
# Callbacks
vis = pt.models.VisGMLVQ2D(data=train_ds)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[vis],
weights_summary="full",
accelerator="ddp",
)
# Training loop
trainer.fit(model, train_loader)