prototorch_models/prototorch/models/lvq.py

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"""LVQ models that are optimized using non-gradient methods."""
from ..core.losses import _get_dp_dm
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from ..nn.activations import get_activation
from ..nn.wrappers import LambdaLayer
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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, plables = self.proto_layer()
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)
print(f"dis={dis}")
print(f"y={y}")
# 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, plabels = self.proto_layer()
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):
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"""Median LVQ
# TODO Avoid computing distances over and over
"""
def __init__(self, hparams, verbose=True, **kwargs):
self.verbose = verbose
super().__init__(hparams, **kwargs)
self.transfer_layer = LambdaLayer(
get_activation(self.hparams.transfer_fn))
def _f(self, x, y, protos, plabels):
d = self.distance_layer(x, protos)
dp, dm = _get_dp_dm(d, y, plabels)
mu = (dp - dm) / (dp + dm)
invmu = -1.0 * mu
f = self.transfer_layer(invmu, beta=self.hparams.transfer_beta) + 1.0
return f
def expectation(self, x, y, protos, plabels):
f = self._f(x, y, protos, plabels)
gamma = f / f.sum()
return gamma
def lower_bound(self, x, y, protos, plabels, gamma):
f = self._f(x, y, protos, plabels)
lower_bound = (gamma * f.log()).sum()
return lower_bound
def training_step(self, train_batch, batch_idx, optimizer_idx=None):
protos, plabels = self.proto_layer()
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x, y = train_batch
dis = self.compute_distances(x)
for i, _ in enumerate(protos):
# Expectation step
gamma = self.expectation(x, y, protos, plabels)
lower_bound = self.lower_bound(x, y, protos, plabels, gamma)
# Maximization step
_protos = protos + 0
for k, xk in enumerate(x):
_protos[i] = xk
_lower_bound = self.lower_bound(x, y, _protos, plabels, gamma)
if _lower_bound > lower_bound:
if self.verbose:
print(f"Updating prototype {i} to data {k}...")
self.proto_layer.load_state_dict({"_components": _protos},
strict=False)
break
# Logging
self.log_acc(dis, y, tag="train_acc")
return None