81 lines
1.9 KiB
Python
81 lines
1.9 KiB
Python
"""GMLVQ example using the MNIST dataset."""
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import argparse
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import prototorch as pt
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import pytorch_lightning as pl
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import torch
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from torchvision import transforms
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from torchvision.datasets import MNIST
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if __name__ == "__main__":
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# Command-line arguments
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parser = argparse.ArgumentParser()
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parser = pl.Trainer.add_argparse_args(parser)
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args = parser.parse_args()
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# Dataset
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train_ds = MNIST(
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"~/datasets",
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train=True,
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download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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]),
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)
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test_ds = MNIST(
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"~/datasets",
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train=False,
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download=True,
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transform=transforms.Compose([
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transforms.ToTensor(),
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]),
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)
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# Dataloaders
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train_loader = torch.utils.data.DataLoader(train_ds,
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num_workers=0,
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batch_size=256)
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test_loader = torch.utils.data.DataLoader(test_ds,
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num_workers=0,
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batch_size=256)
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# Hyperparameters
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num_classes = 10
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prototypes_per_class = 2
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hparams = dict(
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input_dim=28 * 28,
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latent_dim=28 * 28,
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distribution=(num_classes, prototypes_per_class),
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proto_lr=0.01,
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bb_lr=0.01,
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)
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# Initialize the model
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model = pt.models.ImageGMLVQ(
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hparams,
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optimizer=torch.optim.Adam,
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prototype_initializer=pt.components.SMI(train_ds),
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)
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# Callbacks
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vis = pt.models.VisImgComp(
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data=train_ds,
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num_columns=5,
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show=False,
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tensorboard=True,
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random_data=20,
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add_embedding=True,
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embedding_data=100,
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flatten_data=False,
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)
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# Setup trainer
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trainer = pl.Trainer.from_argparse_args(
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args,
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callbacks=[vis],
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)
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# Training loop
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trainer.fit(model, train_loader)
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