prototorch_models/examples/siamese_glvq_iris.py
Jensun Ravichandran d812bb0620 Update examples
2021-05-17 17:03:37 +02:00

65 lines
1.7 KiB
Python

"""Siamese GLVQ example using all four dimensions of the Iris dataset."""
import prototorch as pt
import pytorch_lightning as pl
import torch
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__":
# Dataset
train_ds = pt.datasets.Iris()
# Reproducibility
pl.utilities.seed.seed_everything(seed=2)
# Dataloaders
train_loader = torch.utils.data.DataLoader(train_ds,
num_workers=0,
batch_size=150)
# Hyperparameters
hparams = dict(
distribution=[1, 2, 3],
proto_lr=0.01,
bb_lr=0.01,
)
# Initialize the backbone
backbone = Backbone()
# Initialize the model
model = pt.models.SiameseGLVQ(
hparams,
prototype_initializer=pt.components.SMI(train_ds),
backbone=backbone,
both_path_gradients=True,
)
# Model summary
print(model)
# Callbacks
vis = pt.models.VisSiameseGLVQ2D(data=train_ds, border=0.1)
# Setup trainer
trainer = pl.Trainer(max_epochs=100, callbacks=[vis], gpus=0)
# Training loop
trainer.fit(model, train_loader)