Add GMLVQ examples
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@ -46,13 +46,13 @@ To assist in the development process, you may also find it useful to install
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- GLVQ
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- Siamese GLVQ
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- Neural Gas
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- GMLVQ
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- Limited-Rank GMLVQ
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## Work in Progress
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- CBC
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- LVQMLN
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- GMLVQ
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- Limited-Rank GMLVQ
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## Planned models
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@ -62,3 +62,4 @@ To assist in the development process, you may also find it useful to install
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- PLVQ
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- SILVQ
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- KNN
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- LVQ1
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examples/gmlvq_iris.py
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examples/gmlvq_iris.py
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"""GMLVQ example using all four dimensions of the Iris dataset."""
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import pytorch_lightning as pl
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import torch
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from prototorch.components import initializers as cinit
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from prototorch.datasets.abstract import NumpyDataset
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from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
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from prototorch.models.glvq import GMLVQ
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from sklearn.datasets import load_iris
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from torch.utils.data import DataLoader
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if __name__ == "__main__":
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# Dataset
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x_train, y_train = load_iris(return_X_y=True)
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train_ds = NumpyDataset(x_train, y_train)
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# Dataloaders
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train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
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# Hyperparameters
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hparams = dict(
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nclasses=3,
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prototypes_per_class=1,
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prototype_initializer=cinit.SMI(torch.Tensor(x_train),
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torch.Tensor(y_train)),
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input_dim=x_train.shape[1],
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latent_dim=2,
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lr=0.01,
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)
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# Initialize the model
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model = GMLVQ(hparams)
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# Model summary
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print(model)
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# Callbacks
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vis = VisSiameseGLVQ2D(x_train, y_train)
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# Namespace hook for the visualization to work
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model.backbone = model.omega_layer
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# Setup trainer
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trainer = pl.Trainer(max_epochs=100, callbacks=[vis])
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# Training loop
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trainer.fit(model, train_loader)
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examples/gmlvq_tecator.py
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examples/gmlvq_tecator.py
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"""GMLVQ example using the Tecator dataset."""
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import pytorch_lightning as pl
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import torch
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from prototorch.components import initializers as cinit
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from prototorch.datasets.tecator import Tecator
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from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
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from prototorch.models.glvq import GMLVQ
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from torch.utils.data import DataLoader
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if __name__ == "__main__":
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# Dataset
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train_ds = Tecator(root="./datasets/", train=True)
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# Dataloaders
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train_loader = DataLoader(train_ds, num_workers=0, batch_size=32)
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# Grab the full dataset to warm-start prototypes
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x, y = next(iter(DataLoader(train_ds, batch_size=len(train_ds))))
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# Hyperparameters
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hparams = dict(
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nclasses=2,
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prototypes_per_class=2,
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prototype_initializer=cinit.SMI(x, y),
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input_dim=x.shape[1],
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latent_dim=2,
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lr=0.01,
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)
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# Initialize the model
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model = GMLVQ(hparams)
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# Model summary
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print(model)
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# Callbacks
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vis = VisSiameseGLVQ2D(x, y)
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# Namespace hook for the visualization to work
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model.backbone = model.omega_layer
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# Setup trainer
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trainer = pl.Trainer(max_epochs=100, callbacks=[vis])
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# Training loop
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trainer.fit(model, train_loader)
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@ -94,11 +94,13 @@ class SiameseGLVQ(GLVQ):
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hparams,
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backbone_module=torch.nn.Identity,
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backbone_params={},
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sync=True,
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**kwargs):
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super().__init__(hparams, **kwargs)
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self.backbone = backbone_module(**backbone_params)
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self.backbone_dependent = backbone_module(
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**backbone_params).requires_grad_(False)
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self.sync = sync
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def sync_backbones(self):
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master_state = self.backbone.state_dict()
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@ -117,7 +119,8 @@ class SiameseGLVQ(GLVQ):
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return proto_opt
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def forward(self, x):
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self.sync_backbones()
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if self.sync:
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self.sync_backbones()
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protos, _ = self.proto_layer()
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latent_x = self.backbone(x)
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latent_protos = self.backbone_dependent(protos)
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@ -145,7 +148,7 @@ class GMLVQ(GLVQ):
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def __init__(self, hparams, **kwargs):
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super().__init__(hparams, **kwargs)
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self.omega_layer = torch.nn.Linear(self.hparams.input_dim,
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self.latent_dim,
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self.hparams.latent_dim,
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bias=False)
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def forward(self, x):
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@ -155,6 +158,21 @@ class GMLVQ(GLVQ):
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dis = squared_euclidean_distance(latent_x, latent_protos)
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return dis
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def predict_latent(self, x):
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"""Predict `x` assuming it is already embedded in the latent space.
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Only the prototypes are embedded in the latent space using the
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backbone.
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"""
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# model.eval() # ?!
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with torch.no_grad():
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protos, plabels = self.proto_layer()
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latent_protos = self.omega_layer(protos)
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d = squared_euclidean_distance(x, latent_protos)
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y_pred = wtac(d, plabels)
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return y_pred.numpy()
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class LVQMLN(GLVQ):
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"""Learning Vector Quantization Multi-Layer Network.
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