75 lines
2.3 KiB
Python
75 lines
2.3 KiB
Python
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import pytorch_lightning as pl
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import torch
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from prototorch.functions.distances import euclidean_distance
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from prototorch.modules import Prototypes1D
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from prototorch.modules.losses import NeuralGasEnergy
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class EuclideanDistance(torch.nn.Module):
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def forward(self, x, y):
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return euclidean_distance(x, y)
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class ConnectionTopology(torch.nn.Module):
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def __init__(self, agelimit, num_prototypes):
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super().__init__()
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self.agelimit = agelimit
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self.num_prototypes = num_prototypes
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self.cmat = torch.zeros((self.num_prototypes, self.num_prototypes))
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self.age = torch.zeros_like(self.cmat)
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def forward(self, d):
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order = torch.argsort(d, dim=1)
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for element in order:
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i0, i1 = element[0], element[1]
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self.cmat[i0][i1] = 1
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self.age[i0][i1] = 0
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self.age[i0][self.cmat[i0] == 1] += 1
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self.cmat[i0][self.age[i0] > self.agelimit] = 0
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def extra_repr(self):
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return f"agelimit: {self.agelimit}"
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class NeuralGas(pl.LightningModule):
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def __init__(self, hparams, **kwargs):
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super().__init__()
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self.save_hyperparameters(hparams)
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# Default Values
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self.hparams.setdefault("agelimit", 10)
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self.hparams.setdefault("lm", 1)
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self.hparams.setdefault("prototype_initializer", "zeros")
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self.proto_layer = Prototypes1D(
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input_dim=self.hparams.input_dim,
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nclasses=self.hparams.nclasses,
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prototypes_per_class=self.hparams.prototypes_per_class,
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prototype_initializer=self.hparams.prototype_initializer,
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**kwargs,
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)
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self.distance_layer = EuclideanDistance()
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self.energy_layer = NeuralGasEnergy(lm=self.hparams.lm)
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self.topology_layer = ConnectionTopology(
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agelimit=self.hparams.agelimit,
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num_prototypes=len(self.proto_layer.prototypes),
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)
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def training_step(self, train_batch, batch_idx):
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x, _ = train_batch
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protos, _ = self.proto_layer()
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d = self.distance_layer(x, protos)
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cost, order = self.energy_layer(d)
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self.topology_layer(d)
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return cost
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def configure_optimizers(self):
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optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
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return optimizer
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