ca39aa00d5
Pass the component initializer as an hparam slows down the script very much. The API has now been changed to pass it as a kwarg to the models instead. The example scripts have also been updated to reflect the new changes. Also, ImageGMLVQ and an example script `gmlvq_mnist.py` that uses it have also been added.
69 lines
1.7 KiB
Python
69 lines
1.7 KiB
Python
"""GMLVQ example using the MNIST dataset."""
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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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# 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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nclasses = 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=(nclasses, prototypes_per_class),
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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(data=train_ds,
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nrow=5,
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show=False,
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tensorboard=True)
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# Setup trainer
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trainer = pl.Trainer(
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max_epochs=50,
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callbacks=[vis],
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# overfit_batches=1,
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# fast_dev_run=3,
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)
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# Training loop
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trainer.fit(model, train_loader)
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