64 lines
1.5 KiB
Python
64 lines
1.5 KiB
Python
"""Neural Gas example using the Iris dataset."""
|
|
|
|
import argparse
|
|
|
|
import pytorch_lightning as pl
|
|
import torch
|
|
from sklearn.datasets import load_iris
|
|
from sklearn.preprocessing import StandardScaler
|
|
from torch.optim.lr_scheduler import ExponentialLR
|
|
|
|
import prototorch as pt
|
|
|
|
if __name__ == "__main__":
|
|
# Command-line arguments
|
|
parser = argparse.ArgumentParser()
|
|
parser = pl.Trainer.add_argparse_args(parser)
|
|
args = parser.parse_args()
|
|
|
|
# Prepare and pre-process the dataset
|
|
x_train, y_train = load_iris(return_X_y=True)
|
|
x_train = x_train[:, [0, 2]]
|
|
scaler = StandardScaler()
|
|
scaler.fit(x_train)
|
|
x_train = scaler.transform(x_train)
|
|
|
|
train_ds = pt.datasets.NumpyDataset(x_train, y_train)
|
|
|
|
# Dataloaders
|
|
train_loader = torch.utils.data.DataLoader(train_ds, batch_size=150)
|
|
|
|
# Hyperparameters
|
|
hparams = dict(
|
|
num_prototypes=30,
|
|
input_dim=2,
|
|
lr=0.03,
|
|
)
|
|
|
|
# Initialize the model
|
|
model = pt.models.NeuralGas(
|
|
hparams,
|
|
prototype_initializer=pt.components.Zeros(2),
|
|
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)
|
|
|
|
# Model summary
|
|
print(model)
|
|
|
|
# Callbacks
|
|
vis = pt.models.VisNG2D(data=train_ds)
|
|
|
|
# Setup trainer
|
|
trainer = pl.Trainer.from_argparse_args(
|
|
args,
|
|
callbacks=[vis],
|
|
weights_summary="full",
|
|
)
|
|
|
|
# Training loop
|
|
trainer.fit(model, train_loader)
|