101 lines
2.3 KiB
Python
101 lines
2.3 KiB
Python
import prototorch as pt
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import pytorch_lightning as pl
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import torchmetrics
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from prototorch.core import SMCI
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from prototorch.y.callbacks import (
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LogTorchmetricCallback,
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PlotLambdaMatrixToTensorboard,
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VisGMLVQ2D,
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)
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from prototorch.y.library.gmlvq import GMLVQ
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from pytorch_lightning.callbacks import EarlyStopping
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from torch.utils.data import DataLoader
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# ##############################################################################
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def main():
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# ------------------------------------------------------------
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# DATA
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# ------------------------------------------------------------
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# Dataset
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train_ds = pt.datasets.Iris()
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# Dataloader
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train_loader = DataLoader(
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train_ds,
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batch_size=32,
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num_workers=0,
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shuffle=True,
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)
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# ------------------------------------------------------------
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# HYPERPARAMETERS
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# ------------------------------------------------------------
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# Select Initializer
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components_initializer = SMCI(train_ds)
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# Define Hyperparameters
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hyperparameters = GMLVQ.HyperParameters(
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lr=dict(components_layer=0.1, _omega=0),
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input_dim=4,
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distribution=dict(
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num_classes=3,
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per_class=1,
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),
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component_initializer=components_initializer,
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)
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# Create Model
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model = GMLVQ(hyperparameters)
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print(model.hparams)
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# ------------------------------------------------------------
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# TRAINING
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# ------------------------------------------------------------
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# Controlling Callbacks
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stopping_criterion = LogTorchmetricCallback(
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'recall',
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torchmetrics.Recall,
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num_classes=3,
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)
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es = EarlyStopping(
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monitor=stopping_criterion.name,
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mode="max",
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patience=10,
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)
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# Visualization Callback
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vis = VisGMLVQ2D(data=train_ds)
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# Define trainer
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trainer = pl.Trainer(callbacks=[
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vis,
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stopping_criterion,
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es,
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PlotLambdaMatrixToTensorboard(),
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], )
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# Train
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trainer.fit(model, train_loader)
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# Manual save
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trainer.save_checkpoint("./y_arch.ckpt")
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# Load saved model
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new_model = GMLVQ.load_from_checkpoint(
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checkpoint_path="./y_arch.ckpt",
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strict=True,
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)
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print(new_model.hparams)
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if __name__ == "__main__":
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main()
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