69 lines
2.2 KiB
Python
69 lines
2.2 KiB
Python
"""LVQ models that are optimized using non-gradient methods."""
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from prototorch.functions.losses import _get_dp_dm
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from .abstract import NonGradientMixin
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from .glvq import GLVQ
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class LVQ1(NonGradientMixin, GLVQ):
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"""Learning Vector Quantization 1."""
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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protos = self.proto_layer.components
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plabels = self.proto_layer.component_labels
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x, y = train_batch
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dis = self.compute_distances(x)
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# TODO Vectorized implementation
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for xi, yi in zip(x, y):
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d = self.compute_distances(xi.view(1, -1))
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preds = self.competition_layer(d, plabels)
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w = d.argmin(1)
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if yi == preds:
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shift = xi - protos[w]
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else:
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shift = protos[w] - xi
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updated_protos = protos + 0.0
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updated_protos[w] = protos[w] + (self.hparams.lr * shift)
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self.proto_layer.load_state_dict({"_components": updated_protos},
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strict=False)
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# Logging
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self.log_acc(dis, y, tag="train_acc")
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return None
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class LVQ21(NonGradientMixin, GLVQ):
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"""Learning Vector Quantization 2.1."""
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def training_step(self, train_batch, batch_idx, optimizer_idx=None):
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protos = self.proto_layer.components
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plabels = self.proto_layer.component_labels
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x, y = train_batch
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dis = self.compute_distances(x)
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# TODO Vectorized implementation
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for xi, yi in zip(x, y):
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xi = xi.view(1, -1)
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yi = yi.view(1, )
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d = self.compute_distances(xi)
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(_, wp), (_, wn) = _get_dp_dm(d, yi, plabels, with_indices=True)
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shiftp = xi - protos[wp]
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shiftn = protos[wn] - xi
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updated_protos = protos + 0.0
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updated_protos[wp] = protos[wp] + (self.hparams.lr * shiftp)
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updated_protos[wn] = protos[wn] + (self.hparams.lr * shiftn)
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self.proto_layer.load_state_dict({"_components": updated_protos},
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strict=False)
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# Logging
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self.log_acc(dis, y, tag="train_acc")
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return None
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class MedianLVQ(NonGradientMixin, GLVQ):
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"""Median LVQ"""
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