prototorch_models/prototorch/models/lvq.py

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