prototorch_models/examples/warm_starting.py

104 lines
2.6 KiB
Python

"""Warm-starting GLVQ with prototypes from Growing Neural Gas."""
import argparse
import prototorch as pt
import pytorch_lightning as pl
import torch
from torch.optim.lr_scheduler import ExponentialLR
if __name__ == "__main__":
# Command-line arguments
parser = argparse.ArgumentParser()
parser = pl.Trainer.add_argparse_args(parser)
args = parser.parse_args()
# Prepare the data
train_ds = pt.datasets.Iris(dims=[0, 2])
train_loader = torch.utils.data.DataLoader(train_ds, batch_size=64)
# Initialize the gng
gng = pt.models.GrowingNeuralGas(
hparams=dict(num_prototypes=5, insert_freq=2, lr=0.1),
prototypes_initializer=pt.initializers.ZCI(2),
lr_scheduler=ExponentialLR,
lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
)
# Callbacks
es = pl.callbacks.EarlyStopping(
monitor="loss",
min_delta=0.001,
patience=20,
mode="min",
verbose=False,
check_on_train_epoch_end=True,
)
# Setup trainer for GNG
trainer = pl.Trainer(
max_epochs=100,
callbacks=[es],
weights_summary=None,
)
# Training loop
trainer.fit(gng, train_loader)
# Hyperparameters
hparams = dict(
distribution=[],
lr=0.01,
)
# Warm-start prototypes
knn = pt.models.KNN(dict(k=1), data=train_ds)
prototypes = gng.prototypes
plabels = knn.predict(prototypes)
# Initialize the model
model = pt.models.GLVQ(
hparams,
optimizer=torch.optim.Adam,
prototypes_initializer=pt.initializers.LCI(prototypes),
labels_initializer=pt.initializers.LLI(plabels),
lr_scheduler=ExponentialLR,
lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
)
# Compute intermediate input and output sizes
model.example_input_array = torch.zeros(4, 2)
# Callbacks
vis = pt.models.VisGLVQ2D(data=train_ds)
pruning = pt.models.PruneLoserPrototypes(
threshold=0.02,
idle_epochs=2,
prune_quota_per_epoch=5,
frequency=1,
verbose=True,
)
es = pl.callbacks.EarlyStopping(
monitor="train_loss",
min_delta=0.001,
patience=10,
mode="min",
verbose=True,
check_on_train_epoch_end=True,
)
# Setup trainer
trainer = pl.Trainer.from_argparse_args(
args,
callbacks=[
vis,
pruning,
es,
],
weights_summary="full",
accelerator="ddp",
)
# Training loop
trainer.fit(model, train_loader)