77 lines
2.2 KiB
Python
77 lines
2.2 KiB
Python
"""ProtoTorch CBC example using 2D Iris data."""
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import torch
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import prototorch as pt
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class GMLVQ(torch.nn.Module):
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"""
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Implementation of Generalized Matrix Learning Vector Quantization.
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"""
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def __init__(self, data, **kwargs):
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super().__init__(**kwargs)
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self.components_layer = pt.components.LabeledComponents(
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distribution=[1, 1, 1],
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components_initializer=pt.initializers.SMCI(data, noise=0.1),
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)
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self.backbone = pt.transforms.Omega(
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len(data[0][0]),
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len(data[0][0]),
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pt.initializers.RandomLinearTransformInitializer(),
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)
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def forward(self, data):
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"""
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Forward function that returns a tuple of dissimilarities and label information.
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Feed into GLVQLoss to get a complete GMLVQ model.
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"""
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components, label = self.components_layer()
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latent_x = self.backbone(data)
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latent_components = self.backbone(components)
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distance = pt.distances.squared_euclidean_distance(
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latent_x, latent_components)
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return distance, label
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def predict(self, data):
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"""
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The GMLVQ has a modified prediction step, where a competition layer is applied.
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"""
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components, label = self.components_layer()
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distance = pt.distances.squared_euclidean_distance(data, components)
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winning_label = pt.competitions.wtac(distance, label)
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return winning_label
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if __name__ == "__main__":
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train_ds = pt.datasets.Iris()
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train_loader = torch.utils.data.DataLoader(train_ds, batch_size=32)
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model = GMLVQ(train_ds)
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optimizer = torch.optim.Adam(model.parameters(), lr=0.05)
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criterion = pt.losses.GLVQLoss()
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for epoch in range(200):
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correct = 0.0
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for x, y in train_loader:
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d, labels = model(x)
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loss = criterion(d, y, labels).mean(0)
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optimizer.zero_grad()
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loss.backward()
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optimizer.step()
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with torch.no_grad():
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y_pred = model.predict(x)
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correct += (y_pred == y).float().sum(0)
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acc = 100 * correct / len(train_ds)
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print(f"Epoch: {epoch} Accuracy: {acc:05.02f}%")
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