prototorch_models/examples/glvq_spiral.py

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"""GLVQ example using the spiral dataset."""
import pytorch_lightning as pl
import torch
from prototorch.components import initializers as cinit
from prototorch.datasets.abstract import NumpyDataset
from prototorch.datasets.spiral import make_spiral
from prototorch.models.callbacks.visualization import VisGLVQ2D
from prototorch.models.glvq import GLVQ
from torch.utils.data import DataLoader
class StopOnNaN(pl.Callback):
def __init__(self, param):
super().__init__()
self.param = param
def on_epoch_end(self, trainer, pl_module, logs={}):
if torch.isnan(self.param).any():
raise ValueError("NaN encountered. Stopping.")
if __name__ == "__main__":
# Dataset
x_train, y_train = make_spiral(n_samples=600, noise=0.6)
train_ds = NumpyDataset(x_train, y_train)
# Dataloaders
train_loader = DataLoader(train_ds, num_workers=0, batch_size=256)
# Hyperparameters
hparams = dict(
nclasses=2,
prototypes_per_class=20,
# prototype_initializer=cinit.SSI(torch.Tensor(x_train),
prototype_initializer=cinit.SMI(torch.Tensor(x_train),
torch.Tensor(y_train)),
lr=0.01,
)
# Initialize the model
model = GLVQ(hparams)
# Callbacks
vis = VisGLVQ2D(x_train, y_train)
# vis = VisGLVQ2D(x_train, y_train, show_last_only=True, block=True)
snan = StopOnNaN(model.proto_layer.components)
# Setup trainer
trainer = pl.Trainer(
max_epochs=200,
callbacks=[vis, snan],
)
# Training loop
trainer.fit(model, train_loader)