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.
62 lines
1.6 KiB
Python
62 lines
1.6 KiB
Python
"""Siamese GLVQ example using all four dimensions of the Iris 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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class Backbone(torch.nn.Module):
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"""Two fully connected layers with ReLU activation."""
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def __init__(self, input_size=4, hidden_size=10, latent_size=2):
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super().__init__()
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self.input_size = input_size
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self.hidden_size = hidden_size
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self.latent_size = latent_size
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self.dense1 = torch.nn.Linear(self.input_size, self.hidden_size)
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self.dense2 = torch.nn.Linear(self.hidden_size, self.latent_size)
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self.relu = torch.nn.ReLU()
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def forward(self, x):
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x = self.relu(self.dense1(x))
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out = self.relu(self.dense2(x))
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return out
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if __name__ == "__main__":
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# Dataset
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train_ds = pt.datasets.Iris()
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# Reproducibility
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pl.utilities.seed.seed_everything(seed=2)
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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=150)
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# Hyperparameters
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hparams = dict(
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distribution=[1, 2, 3],
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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.SiameseGLVQ(
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hparams,
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prototype_initializer=pt.components.SMI(train_ds),
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backbone_module=Backbone,
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)
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# Model summary
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print(model)
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# Callbacks
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vis = pt.models.VisSiameseGLVQ2D(data=train_ds, border=0.1)
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# Setup trainer
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trainer = pl.Trainer(max_epochs=100, callbacks=[vis])
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# Training loop
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trainer.fit(model, train_loader)
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