prototorch_models/examples/siamese_glvq_iris.py
Alexander Engelsberger 1a17193b35
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2022-01-11 18:28:50 +01:00

74 lines
1.8 KiB
Python

"""Siamese GLVQ example using all four dimensions of the Iris dataset."""
import argparse
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__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Dataset
train_ds = pt.datasets.Iris()
# Reproducibility
pl.utilities.seed.seed_everything(seed=2)
# Dataloaders
train_loader = torch.utils.data.DataLoader(train_ds, 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,
prototypes_initializer=pt.initializers.SMCI(train_ds),
backbone=backbone,
both_path_gradients=False,
)
# Model summary
print(model)
# Callbacks
vis = pt.models.VisSiameseGLVQ2D(data=train_ds, border=0.1)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[vis],
)
# Training loop
trainer.fit(model, train_loader)