Add more experimental changes
The code gets very messy very quickly as soon as serialization features are needed.
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@ -25,6 +25,8 @@ class GLVQIris(GLVQ):
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parser.add_argument("--epochs", type=int, default=1)
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parser.add_argument("--lr", type=float, default=1e-1)
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parser.add_argument("--batch_size", type=int, default=150)
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parser.add_argument("--input_dim", type=int, default=2)
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parser.add_argument("--nclasses", type=int, default=3)
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parser.add_argument("--prototypes_per_class", type=int, default=3)
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parser.add_argument("--prototype_initializer",
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type=str,
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@ -101,6 +103,7 @@ if __name__ == "__main__":
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# Setup trainer
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trainer = pl.Trainer.from_argparse_args(
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parser,
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max_epochs=10,
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callbacks=[
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vis, # comment this line out to disable the visualization
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],
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@ -109,12 +112,7 @@ if __name__ == "__main__":
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# Initialize the model
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args = parser.parse_args()
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model = GLVQIris(
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args,
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input_dim=x_train.shape[1],
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nclasses=3,
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data=[x_train, y_train],
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)
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model = GLVQIris(args, data=[x_train, y_train])
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# Model summary
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print(model)
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@ -12,20 +12,14 @@ from prototorch.modules.prototypes import Prototypes1D
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class GLVQ(pl.LightningModule):
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"""Generalized Learning Vector Quantization."""
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def __init__(self, hparams, input_dim, nclasses, **kwargs):
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def __init__(self, hparams, **kwargs):
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super().__init__()
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self.lr = hparams.lr
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self.hparams = hparams
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# self.save_hyperparameters(
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# "lr",
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# "prototypes_per_class",
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# "prototype_initializer",
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# )
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self.save_hyperparameters(hparams)
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self.proto_layer = Prototypes1D(
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input_dim=input_dim,
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nclasses=nclasses,
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prototypes_per_class=hparams.prototypes_per_class,
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prototype_initializer=hparams.prototype_initializer,
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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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self.train_acc = torchmetrics.Accuracy()
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@ -38,22 +32,9 @@ class GLVQ(pl.LightningModule):
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return self.proto_layer.prototype_labels.detach().numpy()
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def configure_optimizers(self):
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optimizer = torch.optim.Adam(self.parameters(), lr=self.lr)
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optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
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return optimizer
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@staticmethod
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def add_model_specific_args(parent_parser):
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parser = argparse.ArgumentParser(parents=[parent_parser],
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add_help=False)
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parser.add_argument("--epochs", type=int, default=1)
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parser.add_argument("--lr", type=float, default=1e-2)
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parser.add_argument("--batch_size", type=int, default=32)
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parser.add_argument("--prototypes_per_class", type=int, default=1)
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parser.add_argument("--prototype_initializer",
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type=str,
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default="zeros")
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return parser
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def forward(self, x):
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protos = self.proto_layer.prototypes
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dis = euclidean_distance(x, protos)
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