prototorch_models/examples/gmlvq_iris.py
Alexander Engelsberger 1c3613019b Update Examples to new initializer architecture.
Visualization still borken for some examples.
2021-05-06 14:10:09 +02:00

49 lines
1.3 KiB
Python

"""GMLVQ example using all four dimensions of the Iris dataset."""
import pytorch_lightning as pl
import torch
from prototorch.components import initializers as cinit
from prototorch.datasets.abstract import NumpyDataset
from sklearn.datasets import load_iris
from torch.utils.data import DataLoader
from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
from prototorch.models.glvq import GMLVQ
if __name__ == "__main__":
# Dataset
x_train, y_train = load_iris(return_X_y=True)
train_ds = NumpyDataset(x_train, y_train)
# Dataloaders
train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
# Hyperparameters
hparams = dict(
nclasses=3,
prototypes_per_class=1,
prototype_initializer=cinit.SMI(torch.Tensor(x_train),
torch.Tensor(y_train)),
input_dim=x_train.shape[1],
latent_dim=2,
lr=0.01,
)
# Initialize the model
model = GMLVQ(hparams)
# Model summary
print(model)
# Callbacks
vis = VisSiameseGLVQ2D(x_train, y_train)
# Namespace hook for the visualization to work
model.backbone = model.omega_layer
# Setup trainer
trainer = pl.Trainer(max_epochs=100, callbacks=[vis])
# Training loop
trainer.fit(model, train_loader)