64 lines
1.7 KiB
Python
64 lines
1.7 KiB
Python
"""Siamese GLVQ example using all four dimensions of the Iris dataset."""
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import pytorch_lightning as pl
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import torch
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from prototorch.components import (
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StratifiedMeanInitializer
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)
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from prototorch.datasets.abstract import NumpyDataset
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from sklearn.datasets import load_iris
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from torch.utils.data import DataLoader
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from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
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from prototorch.models.glvq import SiameseGLVQ
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class Backbone(torch.nn.Module):
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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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return self.relu(self.dense2(self.relu(self.dense1(x))))
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if __name__ == "__main__":
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# Dataset
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x_train, y_train = load_iris(return_X_y=True)
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train_ds = NumpyDataset(x_train, y_train)
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# Dataloaders
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train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
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# Hyperparameters
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hparams = dict(
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nclasses=3,
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prototypes_per_class=1,
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prototype_initializer=StratifiedMeanInitializer(
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torch.Tensor(x_train), torch.Tensor(y_train)),
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lr=0.01,
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)
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# Initialize the model
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model = SiameseGLVQ(
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hparams,
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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 = VisSiameseGLVQ2D(x_train, y_train)
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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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