prototorch_models/examples/siamese_gtlvq_iris.py

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"""Siamese GTLVQ example using all four dimensions of 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 SiameseGTLVQ, VisSiameseGLVQ2D
from pytorch_lightning.utilities.warnings import PossibleUserWarning
from torch.utils.data import DataLoader
warnings.filterwarnings("ignore", category=PossibleUserWarning)
warnings.filterwarnings("ignore", category=UserWarning)
class Backbone(torch.nn.Module):
def __init__(self, input_size=4, hidden_size=10, latent_size=2):
super().__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.latent_size = latent_size
self.dense1 = torch.nn.Linear(self.input_size, self.hidden_size)
self.dense2 = torch.nn.Linear(self.hidden_size, self.latent_size)
self.activation = torch.nn.Sigmoid()
def forward(self, x):
x = self.activation(self.dense1(x))
out = self.activation(self.dense2(x))
return out
if __name__ == "__main__":
# 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()
# Reproducibility
seed_everything(seed=2)
# Dataloaders
train_loader = DataLoader(train_ds, batch_size=150)
# Hyperparameters
hparams = dict(
distribution=[1, 2, 3],
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lr=0.01,
input_dim=2,
latent_dim=1,
)
# Initialize the backbone
backbone = Backbone(latent_size=hparams["input_dim"])
# Initialize the model
model = SiameseGTLVQ(
hparams,
prototypes_initializer=pt.initializers.SMCI(train_ds),
backbone=backbone,
both_path_gradients=False,
)
# Callbacks
vis = VisSiameseGLVQ2D(data=train_ds, border=0.1)
# 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=1000,
log_every_n_steps=1,
detect_anomaly=True,
)
# Training loop
trainer.fit(model, train_loader)