Use Components instead of Prototypes and refactor old examples
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@ -35,7 +35,7 @@ workon pt
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git clone git@github.com:si-cim/prototorch_models.git
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git clone git@github.com:si-cim/prototorch_models.git
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cd prototorch_models
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cd prototorch_models
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git checkout dev
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git checkout dev
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pip install -e .[all] # \[all\] if you are using zsh
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pip install -e .[all] # \[all\] if you are using zsh or MacOS
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```
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```
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To assist in the development process, you may also find it useful to install
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To assist in the development process, you may also find it useful to install
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@ -1,63 +1,14 @@
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"""GLVQ example using the Iris dataset."""
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"""GLVQ example using the Iris dataset."""
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import numpy as np
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import pytorch_lightning as pl
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import pytorch_lightning as pl
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import torch
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import torch
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from matplotlib import pyplot as plt
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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 VisGLVQ2D
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from prototorch.models.glvq import GLVQ
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from sklearn.datasets import load_iris
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from sklearn.datasets import load_iris
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from torch.utils.data import DataLoader
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from torch.utils.data import DataLoader
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from prototorch.datasets.abstract import NumpyDataset
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from prototorch.models.glvq import GLVQ
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class VisualizationCallback(pl.Callback):
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def __init__(self,
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x_train,
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y_train,
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title="Prototype Visualization",
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cmap="viridis"):
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super().__init__()
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self.x_train = x_train
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self.y_train = y_train
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self.title = title
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self.fig = plt.figure(self.title)
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self.cmap = cmap
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def on_epoch_end(self, trainer, pl_module):
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protos = pl_module.prototypes
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plabels = pl_module.prototype_labels
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x_train, y_train = self.x_train, self.y_train
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ax = self.fig.gca()
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ax.cla()
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ax.set_title(self.title)
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ax.set_xlabel("Data dimension 1")
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ax.set_ylabel("Data dimension 2")
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ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c=plabels,
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cmap=self.cmap,
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edgecolor="k",
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marker="D",
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s=50,
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)
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x = np.vstack((x_train, protos))
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x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
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y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
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xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
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np.arange(y_min, y_max, 1 / 50))
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mesh_input = np.c_[xx.ravel(), yy.ravel()]
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y_pred = pl_module.predict(torch.Tensor(mesh_input))
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y_pred = y_pred.reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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ax.set_xlim(left=x_min + 0, right=x_max - 0)
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ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
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plt.pause(0.1)
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if __name__ == "__main__":
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if __name__ == "__main__":
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# Dataset
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# Dataset
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x_train, y_train = load_iris(return_X_y=True)
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x_train, y_train = load_iris(return_X_y=True)
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@ -69,24 +20,21 @@ if __name__ == "__main__":
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# Hyperparameters
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# Hyperparameters
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hparams = dict(
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hparams = dict(
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input_dim=x_train.shape[1],
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nclasses=3,
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nclasses=3,
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prototypes_per_class=3,
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prototypes_per_class=2,
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prototype_initializer="stratified_mean",
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prototype_initializer=cinit.StratifiedMeanInitializer(
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lr=0.1,
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torch.Tensor(x_train), torch.Tensor(y_train)),
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lr=0.01,
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)
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)
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# Initialize the model
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# Initialize the model
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model = GLVQ(hparams, data=[x_train, y_train])
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model = GLVQ(hparams, data=[x_train, y_train])
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# Model summary
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print(model)
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# Callbacks
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vis = VisualizationCallback(x_train, y_train)
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# Setup trainer
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# Setup trainer
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trainer = pl.Trainer(max_epochs=50, callbacks=[vis])
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trainer = pl.Trainer(
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max_epochs=50,
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callbacks=[VisGLVQ2D(x_train, y_train)],
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)
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# Training loop
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# Training loop
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trainer.fit(model, train_loader)
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trainer.fit(model, train_loader)
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@ -3,63 +3,13 @@
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import numpy as np
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import numpy as np
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import pytorch_lightning as pl
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import pytorch_lightning as pl
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from matplotlib import pyplot as plt
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from matplotlib import pyplot as plt
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from prototorch.datasets.abstract import NumpyDataset
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from prototorch.models.callbacks.visualization import VisNG2D
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from prototorch.models.neural_gas import NeuralGas
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from sklearn.datasets import load_iris
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from sklearn.datasets import load_iris
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from sklearn.preprocessing import StandardScaler
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from sklearn.preprocessing import StandardScaler
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from torch.utils.data import DataLoader
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from torch.utils.data import DataLoader
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from prototorch.datasets.abstract import NumpyDataset
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from prototorch.models.neural_gas import NeuralGas
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class VisualizationCallback(pl.Callback):
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def __init__(self,
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x_train,
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y_train,
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title="Neural Gas Visualization",
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cmap="viridis"):
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super().__init__()
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self.x_train = x_train
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self.y_train = y_train
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self.title = title
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self.fig = plt.figure(self.title)
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self.cmap = cmap
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def on_epoch_end(self, trainer, pl_module: NeuralGas):
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protos = pl_module.proto_layer.prototypes.detach().cpu().numpy()
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cmat = pl_module.topology_layer.cmat.cpu().numpy()
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# Visualize the data and the prototypes
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ax = self.fig.gca()
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ax.cla()
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ax.set_title(self.title)
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ax.set_xlabel("Data dimension 1")
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ax.set_ylabel("Data dimension 2")
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ax.scatter(self.x_train[:, 0],
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self.x_train[:, 1],
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c=self.y_train,
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edgecolor="k")
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c="k",
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edgecolor="k",
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marker="D",
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s=50,
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)
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# Draw connections
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for i in range(len(protos)):
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for j in range(len(protos)):
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if cmat[i][j]:
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ax.plot(
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[protos[i, 0], protos[j, 0]],
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[protos[i, 1], protos[j, 1]],
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"k-",
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)
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plt.pause(0.01)
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if __name__ == "__main__":
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if __name__ == "__main__":
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# Dataset
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# Dataset
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x_train, y_train = load_iris(return_X_y=True)
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x_train, y_train = load_iris(return_X_y=True)
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@ -68,7 +18,6 @@ if __name__ == "__main__":
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scaler.fit(x_train)
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scaler.fit(x_train)
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x_train = scaler.transform(x_train)
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x_train = scaler.transform(x_train)
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y_single_class = np.zeros_like(y_train)
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train_ds = NumpyDataset(x_train, y_train)
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train_ds = NumpyDataset(x_train, y_train)
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# Dataloaders
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# Dataloaders
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# Hyperparameters
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# Hyperparameters
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hparams = dict(
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hparams = dict(
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input_dim=x_train.shape[1],
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input_dim=x_train.shape[1],
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nclasses=1,
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num_prototypes=30,
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prototypes_per_class=30,
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lr=0.01,
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prototype_initializer="rand",
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lr=0.1,
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)
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)
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# Initialize the model
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# Initialize the model
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model = NeuralGas(hparams, data=[x_train, y_single_class])
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model = NeuralGas(hparams)
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# Model summary
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# Model summary
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print(model)
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print(model)
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# Callbacks
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# Callbacks
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vis = VisualizationCallback(x_train, y_train)
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vis = VisNG2D(x_train, y_train)
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# Setup trainer
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# Setup trainer
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trainer = pl.Trainer(
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trainer = pl.Trainer(
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@ -1,70 +1,15 @@
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"""Siamese GLVQ example using all four dimensions of the Iris dataset."""
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"""Siamese GLVQ example using all four dimensions of the Iris dataset."""
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import numpy as np
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import pytorch_lightning as pl
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import pytorch_lightning as pl
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import torch
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import torch
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from matplotlib import pyplot as plt
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from prototorch.components import (StratifiedMeanInitializer,
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StratifiedSelectionInitializer)
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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 SiameseGLVQ
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from sklearn.datasets import load_iris
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from sklearn.datasets import load_iris
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from torch.utils.data import DataLoader
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from torch.utils.data import DataLoader
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from prototorch.datasets.abstract import NumpyDataset
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from prototorch.models.glvq import SiameseGLVQ
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class VisualizationCallback(pl.Callback):
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def __init__(self,
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x_train,
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y_train,
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title="Prototype Visualization",
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cmap="viridis"):
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super().__init__()
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self.x_train = x_train
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self.y_train = y_train
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self.title = title
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self.fig = plt.figure(self.title)
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self.cmap = cmap
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def on_epoch_end(self, trainer, pl_module):
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protos = pl_module.prototypes
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plabels = pl_module.prototype_labels
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x_train, y_train = self.x_train, self.y_train
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x_train = pl_module.backbone(torch.Tensor(x_train)).detach()
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protos = pl_module.backbone(torch.Tensor(protos)).detach()
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ax = self.fig.gca()
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ax.cla()
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ax.set_title(self.title)
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ax.axis("off")
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ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
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ax.scatter(
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protos[:, 0],
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protos[:, 1],
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c=plabels,
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cmap=self.cmap,
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edgecolor="k",
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marker="D",
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s=50,
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)
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x = np.vstack((x_train, protos))
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x_min, x_max = x[:, 0].min() - 0.2, x[:, 0].max() + 0.2
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y_min, y_max = x[:, 1].min() - 0.2, x[:, 1].max() + 0.2
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xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
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np.arange(y_min, y_max, 1 / 50))
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mesh_input = np.c_[xx.ravel(), yy.ravel()]
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y_pred = pl_module.predict_latent(torch.Tensor(mesh_input))
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y_pred = y_pred.reshape(xx.shape)
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ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
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ax.set_xlim(left=x_min + 0, right=x_max - 0)
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ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
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tb = pl_module.logger.experiment
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tb.add_figure(
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tag=f"{self.title}",
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figure=self.fig,
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global_step=trainer.current_epoch,
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close=False,
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)
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plt.pause(0.1)
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class Backbone(torch.nn.Module):
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class Backbone(torch.nn.Module):
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def __init__(self, input_size=4, hidden_size=10, latent_size=2):
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def __init__(self, input_size=4, hidden_size=10, latent_size=2):
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# Hyperparameters
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# Hyperparameters
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hparams = dict(
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hparams = dict(
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input_dim=x_train.shape[1],
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nclasses=3,
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nclasses=3,
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prototypes_per_class=1,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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prototype_initializer=StratifiedMeanInitializer(
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torch.Tensor(x_train), torch.Tensor(y_train)),
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lr=0.01,
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lr=0.01,
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)
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)
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# Initialize the model
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# Initialize the model
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model = SiameseGLVQ(hparams,
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model = SiameseGLVQ(
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hparams,
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backbone_module=Backbone,
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backbone_module=Backbone,
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data=[x_train, y_train])
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)
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# Model summary
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# Model summary
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print(model)
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print(model)
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# Callbacks
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# Callbacks
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vis = VisualizationCallback(x_train, y_train)
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vis = VisSiameseGLVQ2D(x_train, y_train)
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# Setup trainer
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# Setup trainer
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trainer = pl.Trainer(max_epochs=100, callbacks=[vis])
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trainer = pl.Trainer(max_epochs=100, callbacks=[vis])
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import os
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import os
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import numpy as np
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import numpy as np
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import pytorch_lightning as pl
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import torch
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import torch
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from matplotlib import pyplot as plt
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from matplotlib import pyplot as plt
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from matplotlib.offsetbox import AnchoredText
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from matplotlib.offsetbox import AnchoredText
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from prototorch.utils.celluloid import Camera
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from prototorch.utils.celluloid import Camera
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from prototorch.utils.colors import color_scheme
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from prototorch.utils.colors import color_scheme
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from prototorch.utils.utils import gif_from_dir, make_directory, prettify_string
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from prototorch.utils.utils import (gif_from_dir, make_directory,
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prettify_string)
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class VisWeights(Callback):
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class VisWeights(pl.Callback):
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"""Abstract weight visualization callback."""
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"""Abstract weight visualization callback."""
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def __init__(
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def __init__(
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self,
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self,
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@ -258,3 +259,155 @@ class VisPointProtos(VisWeights):
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epoch = trainer.current_epoch
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epoch = trainer.current_epoch
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self._display_logs(self.ax, epoch, logs)
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self._display_logs(self.ax, epoch, logs)
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self._show_and_save(epoch)
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self._show_and_save(epoch)
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class VisGLVQ2D(pl.Callback):
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def __init__(self,
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x_train,
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y_train,
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title="Prototype Visualization",
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cmap="viridis"):
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super().__init__()
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self.x_train = x_train
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|
self.y_train = y_train
|
||||||
|
self.title = title
|
||||||
|
self.fig = plt.figure(self.title)
|
||||||
|
self.cmap = cmap
|
||||||
|
|
||||||
|
def on_epoch_end(self, trainer, pl_module):
|
||||||
|
protos = pl_module.prototypes
|
||||||
|
plabels = pl_module.prototype_labels
|
||||||
|
x_train, y_train = self.x_train, self.y_train
|
||||||
|
ax = self.fig.gca()
|
||||||
|
ax.cla()
|
||||||
|
ax.set_title(self.title)
|
||||||
|
ax.axis("off")
|
||||||
|
ax.set_xlabel("Data dimension 1")
|
||||||
|
ax.set_ylabel("Data dimension 2")
|
||||||
|
ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
|
||||||
|
ax.scatter(
|
||||||
|
protos[:, 0],
|
||||||
|
protos[:, 1],
|
||||||
|
c=plabels,
|
||||||
|
cmap=self.cmap,
|
||||||
|
edgecolor="k",
|
||||||
|
marker="D",
|
||||||
|
s=50,
|
||||||
|
)
|
||||||
|
x = np.vstack((x_train, protos))
|
||||||
|
x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
|
||||||
|
y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
|
||||||
|
xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
|
||||||
|
np.arange(y_min, y_max, 1 / 50))
|
||||||
|
mesh_input = np.c_[xx.ravel(), yy.ravel()]
|
||||||
|
y_pred = pl_module.predict(torch.Tensor(mesh_input))
|
||||||
|
y_pred = y_pred.reshape(xx.shape)
|
||||||
|
|
||||||
|
ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
|
||||||
|
ax.set_xlim(left=x_min + 0, right=x_max - 0)
|
||||||
|
ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
|
||||||
|
plt.pause(0.1)
|
||||||
|
|
||||||
|
|
||||||
|
class VisSiameseGLVQ2D(pl.Callback):
|
||||||
|
def __init__(self,
|
||||||
|
x_train,
|
||||||
|
y_train,
|
||||||
|
title="Prototype Visualization",
|
||||||
|
cmap="viridis"):
|
||||||
|
super().__init__()
|
||||||
|
self.x_train = x_train
|
||||||
|
self.y_train = y_train
|
||||||
|
self.title = title
|
||||||
|
self.fig = plt.figure(self.title)
|
||||||
|
self.cmap = cmap
|
||||||
|
|
||||||
|
def on_epoch_end(self, trainer, pl_module):
|
||||||
|
protos = pl_module.prototypes
|
||||||
|
plabels = pl_module.prototype_labels
|
||||||
|
x_train, y_train = self.x_train, self.y_train
|
||||||
|
x_train = pl_module.backbone(torch.Tensor(x_train)).detach()
|
||||||
|
protos = pl_module.backbone(torch.Tensor(protos)).detach()
|
||||||
|
ax = self.fig.gca()
|
||||||
|
ax.cla()
|
||||||
|
ax.set_title(self.title)
|
||||||
|
ax.axis("off")
|
||||||
|
ax.scatter(x_train[:, 0], x_train[:, 1], c=y_train, edgecolor="k")
|
||||||
|
ax.scatter(
|
||||||
|
protos[:, 0],
|
||||||
|
protos[:, 1],
|
||||||
|
c=plabels,
|
||||||
|
cmap=self.cmap,
|
||||||
|
edgecolor="k",
|
||||||
|
marker="D",
|
||||||
|
s=50,
|
||||||
|
)
|
||||||
|
x = np.vstack((x_train, protos))
|
||||||
|
x_min, x_max = x[:, 0].min() - 1, x[:, 0].max() + 1
|
||||||
|
y_min, y_max = x[:, 1].min() - 1, x[:, 1].max() + 1
|
||||||
|
xx, yy = np.meshgrid(np.arange(x_min, x_max, 1 / 50),
|
||||||
|
np.arange(y_min, y_max, 1 / 50))
|
||||||
|
mesh_input = np.c_[xx.ravel(), yy.ravel()]
|
||||||
|
y_pred = pl_module.predict_latent(torch.Tensor(mesh_input))
|
||||||
|
y_pred = y_pred.reshape(xx.shape)
|
||||||
|
|
||||||
|
ax.contourf(xx, yy, y_pred, cmap=self.cmap, alpha=0.35)
|
||||||
|
ax.set_xlim(left=x_min + 0, right=x_max - 0)
|
||||||
|
ax.set_ylim(bottom=y_min + 0, top=y_max - 0)
|
||||||
|
tb = pl_module.logger.experiment
|
||||||
|
tb.add_figure(
|
||||||
|
tag=f"{self.title}",
|
||||||
|
figure=self.fig,
|
||||||
|
global_step=trainer.current_epoch,
|
||||||
|
close=False,
|
||||||
|
)
|
||||||
|
plt.pause(0.1)
|
||||||
|
|
||||||
|
|
||||||
|
class VisNG2D(pl.Callback):
|
||||||
|
def __init__(self,
|
||||||
|
x_train,
|
||||||
|
y_train,
|
||||||
|
title="Neural Gas Visualization",
|
||||||
|
cmap="viridis"):
|
||||||
|
super().__init__()
|
||||||
|
self.x_train = x_train
|
||||||
|
self.y_train = y_train
|
||||||
|
self.title = title
|
||||||
|
self.fig = plt.figure(self.title)
|
||||||
|
self.cmap = cmap
|
||||||
|
|
||||||
|
def on_epoch_end(self, trainer, pl_module):
|
||||||
|
protos = pl_module.prototypes
|
||||||
|
cmat = pl_module.topology_layer.cmat.cpu().numpy()
|
||||||
|
|
||||||
|
# Visualize the data and the prototypes
|
||||||
|
ax = self.fig.gca()
|
||||||
|
ax.cla()
|
||||||
|
ax.set_title(self.title)
|
||||||
|
ax.set_xlabel("Data dimension 1")
|
||||||
|
ax.set_ylabel("Data dimension 2")
|
||||||
|
ax.scatter(self.x_train[:, 0],
|
||||||
|
self.x_train[:, 1],
|
||||||
|
c=self.y_train,
|
||||||
|
edgecolor="k")
|
||||||
|
ax.scatter(
|
||||||
|
protos[:, 0],
|
||||||
|
protos[:, 1],
|
||||||
|
c="k",
|
||||||
|
edgecolor="k",
|
||||||
|
marker="D",
|
||||||
|
s=50,
|
||||||
|
)
|
||||||
|
|
||||||
|
# Draw connections
|
||||||
|
for i in range(len(protos)):
|
||||||
|
for j in range(len(protos)):
|
||||||
|
if cmat[i][j]:
|
||||||
|
ax.plot(
|
||||||
|
[protos[i, 0], protos[j, 0]],
|
||||||
|
[protos[i, 1], protos[j, 1]],
|
||||||
|
"k-",
|
||||||
|
)
|
||||||
|
|
||||||
|
plt.pause(0.01)
|
||||||
|
@ -1,14 +1,16 @@
|
|||||||
import pytorch_lightning as pl
|
import pytorch_lightning as pl
|
||||||
import torch
|
import torch
|
||||||
import torchmetrics
|
import torchmetrics
|
||||||
|
from prototorch.components import LabeledComponents
|
||||||
from prototorch.functions.competitions import wtac
|
from prototorch.functions.competitions import wtac
|
||||||
from prototorch.functions.distances import euclidean_distance
|
from prototorch.functions.distances import euclidean_distance
|
||||||
from prototorch.functions.losses import glvq_loss
|
from prototorch.functions.losses import glvq_loss
|
||||||
from prototorch.modules.prototypes import Prototypes1D
|
from prototorch.modules.prototypes import Prototypes1D
|
||||||
|
|
||||||
|
from .abstract import AbstractPrototypeModel
|
||||||
|
|
||||||
class GLVQ(pl.LightningModule):
|
|
||||||
|
class GLVQ(AbstractPrototypeModel):
|
||||||
"""Generalized Learning Vector Quantization."""
|
"""Generalized Learning Vector Quantization."""
|
||||||
def __init__(self, hparams, **kwargs):
|
def __init__(self, hparams, **kwargs):
|
||||||
super().__init__()
|
super().__init__()
|
||||||
@ -18,29 +20,18 @@ class GLVQ(pl.LightningModule):
|
|||||||
# Default Values
|
# Default Values
|
||||||
self.hparams.setdefault("distance", euclidean_distance)
|
self.hparams.setdefault("distance", euclidean_distance)
|
||||||
|
|
||||||
self.proto_layer = Prototypes1D(
|
self.proto_layer = LabeledComponents(
|
||||||
input_dim=self.hparams.input_dim,
|
labels=(self.hparams.nclasses, self.hparams.prototypes_per_class),
|
||||||
nclasses=self.hparams.nclasses,
|
initializer=self.hparams.prototype_initializer)
|
||||||
prototypes_per_class=self.hparams.prototypes_per_class,
|
|
||||||
prototype_initializer=self.hparams.prototype_initializer,
|
|
||||||
**kwargs)
|
|
||||||
|
|
||||||
self.train_acc = torchmetrics.Accuracy()
|
self.train_acc = torchmetrics.Accuracy()
|
||||||
|
|
||||||
@property
|
|
||||||
def prototypes(self):
|
|
||||||
return self.proto_layer.prototypes.detach().numpy()
|
|
||||||
|
|
||||||
@property
|
@property
|
||||||
def prototype_labels(self):
|
def prototype_labels(self):
|
||||||
return self.proto_layer.prototype_labels.detach().numpy()
|
return self.proto_layer.component_labels.detach().numpy()
|
||||||
|
|
||||||
def configure_optimizers(self):
|
|
||||||
optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
|
|
||||||
return optimizer
|
|
||||||
|
|
||||||
def forward(self, x):
|
def forward(self, x):
|
||||||
protos = self.proto_layer.prototypes
|
protos, _ = self.proto_layer()
|
||||||
dis = self.hparams.distance(x, protos)
|
dis = self.hparams.distance(x, protos)
|
||||||
return dis
|
return dis
|
||||||
|
|
||||||
@ -48,7 +39,7 @@ class GLVQ(pl.LightningModule):
|
|||||||
x, y = train_batch
|
x, y = train_batch
|
||||||
x = x.view(x.size(0), -1)
|
x = x.view(x.size(0), -1)
|
||||||
dis = self(x)
|
dis = self(x)
|
||||||
plabels = self.proto_layer.prototype_labels
|
plabels = self.proto_layer.component_labels
|
||||||
mu = glvq_loss(dis, y, prototype_labels=plabels)
|
mu = glvq_loss(dis, y, prototype_labels=plabels)
|
||||||
loss = mu.sum(dim=0)
|
loss = mu.sum(dim=0)
|
||||||
self.log("train_loss", loss)
|
self.log("train_loss", loss)
|
||||||
@ -77,7 +68,7 @@ class GLVQ(pl.LightningModule):
|
|||||||
# model.eval() # ?!
|
# model.eval() # ?!
|
||||||
with torch.no_grad():
|
with torch.no_grad():
|
||||||
d = self(x)
|
d = self(x)
|
||||||
plabels = self.proto_layer.prototype_labels
|
plabels = self.proto_layer.component_labels
|
||||||
y_pred = wtac(d, plabels)
|
y_pred = wtac(d, plabels)
|
||||||
return y_pred.numpy()
|
return y_pred.numpy()
|
||||||
|
|
||||||
@ -89,7 +80,7 @@ class ImageGLVQ(GLVQ):
|
|||||||
clamping after updates.
|
clamping after updates.
|
||||||
"""
|
"""
|
||||||
def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
|
def on_train_batch_end(self, outputs, batch, batch_idx, dataloader_idx):
|
||||||
self.proto_layer.prototypes.data.clamp_(0.0, 1.0)
|
self.proto_layer.components.data.clamp_(0.0, 1.0)
|
||||||
|
|
||||||
|
|
||||||
class SiameseGLVQ(GLVQ):
|
class SiameseGLVQ(GLVQ):
|
||||||
@ -115,7 +106,7 @@ class SiameseGLVQ(GLVQ):
|
|||||||
|
|
||||||
def forward(self, x):
|
def forward(self, x):
|
||||||
self.sync_backbones()
|
self.sync_backbones()
|
||||||
protos = self.proto_layer.prototypes
|
protos, _ = self.proto_layer()
|
||||||
|
|
||||||
latent_x = self.backbone(x)
|
latent_x = self.backbone(x)
|
||||||
latent_protos = self.backbone_dependent(protos)
|
latent_protos = self.backbone_dependent(protos)
|
||||||
@ -126,9 +117,8 @@ class SiameseGLVQ(GLVQ):
|
|||||||
def predict_latent(self, x):
|
def predict_latent(self, x):
|
||||||
# model.eval() # ?!
|
# model.eval() # ?!
|
||||||
with torch.no_grad():
|
with torch.no_grad():
|
||||||
protos = self.proto_layer.prototypes
|
protos, plabels = self.proto_layer()
|
||||||
latent_protos = self.backbone_dependent(protos)
|
latent_protos = self.backbone_dependent(protos)
|
||||||
d = euclidean_distance(x, latent_protos)
|
d = euclidean_distance(x, latent_protos)
|
||||||
plabels = self.proto_layer.prototype_labels
|
|
||||||
y_pred = wtac(d, plabels)
|
y_pred = wtac(d, plabels)
|
||||||
return y_pred.numpy()
|
return y_pred.numpy()
|
||||||
|
@ -1,10 +1,13 @@
|
|||||||
import pytorch_lightning as pl
|
import pytorch_lightning as pl
|
||||||
import torch
|
import torch
|
||||||
|
from prototorch.components import Components
|
||||||
|
from prototorch.components import initializers as cinit
|
||||||
from prototorch.functions.distances import euclidean_distance
|
from prototorch.functions.distances import euclidean_distance
|
||||||
from prototorch.modules import Prototypes1D
|
from prototorch.modules import Prototypes1D
|
||||||
from prototorch.modules.losses import NeuralGasEnergy
|
from prototorch.modules.losses import NeuralGasEnergy
|
||||||
|
|
||||||
|
from .abstract import AbstractPrototypeModel
|
||||||
|
|
||||||
|
|
||||||
class EuclideanDistance(torch.nn.Module):
|
class EuclideanDistance(torch.nn.Module):
|
||||||
def forward(self, x, y):
|
def forward(self, x, y):
|
||||||
@ -34,41 +37,35 @@ class ConnectionTopology(torch.nn.Module):
|
|||||||
return f"agelimit: {self.agelimit}"
|
return f"agelimit: {self.agelimit}"
|
||||||
|
|
||||||
|
|
||||||
class NeuralGas(pl.LightningModule):
|
class NeuralGas(AbstractPrototypeModel):
|
||||||
def __init__(self, hparams, **kwargs):
|
def __init__(self, hparams, **kwargs):
|
||||||
super().__init__()
|
super().__init__()
|
||||||
|
|
||||||
self.save_hyperparameters(hparams)
|
self.save_hyperparameters(hparams)
|
||||||
|
|
||||||
# Default Values
|
# Default Values
|
||||||
|
self.hparams.setdefault("input_dim", 2)
|
||||||
self.hparams.setdefault("agelimit", 10)
|
self.hparams.setdefault("agelimit", 10)
|
||||||
self.hparams.setdefault("lm", 1)
|
self.hparams.setdefault("lm", 1)
|
||||||
self.hparams.setdefault("prototype_initializer", "zeros")
|
self.hparams.setdefault("prototype_initializer",
|
||||||
|
cinit.ZerosInitializer(self.hparams.input_dim))
|
||||||
|
|
||||||
self.proto_layer = Prototypes1D(
|
self.proto_layer = Components(
|
||||||
input_dim=self.hparams.input_dim,
|
self.hparams.num_prototypes,
|
||||||
nclasses=self.hparams.nclasses,
|
initializer=self.hparams.prototype_initializer)
|
||||||
prototypes_per_class=self.hparams.prototypes_per_class,
|
|
||||||
prototype_initializer=self.hparams.prototype_initializer,
|
|
||||||
**kwargs,
|
|
||||||
)
|
|
||||||
|
|
||||||
self.distance_layer = EuclideanDistance()
|
self.distance_layer = EuclideanDistance()
|
||||||
self.energy_layer = NeuralGasEnergy(lm=self.hparams.lm)
|
self.energy_layer = NeuralGasEnergy(lm=self.hparams.lm)
|
||||||
self.topology_layer = ConnectionTopology(
|
self.topology_layer = ConnectionTopology(
|
||||||
agelimit=self.hparams.agelimit,
|
agelimit=self.hparams.agelimit,
|
||||||
num_prototypes=len(self.proto_layer.prototypes),
|
num_prototypes=self.hparams.num_prototypes,
|
||||||
)
|
)
|
||||||
|
|
||||||
def training_step(self, train_batch, batch_idx):
|
def training_step(self, train_batch, batch_idx):
|
||||||
x, _ = train_batch
|
x = train_batch[0]
|
||||||
protos, _ = self.proto_layer()
|
protos = self.proto_layer()
|
||||||
d = self.distance_layer(x, protos)
|
d = self.distance_layer(x, protos)
|
||||||
cost, order = self.energy_layer(d)
|
cost, order = self.energy_layer(d)
|
||||||
|
|
||||||
self.topology_layer(d)
|
self.topology_layer(d)
|
||||||
return cost
|
return cost
|
||||||
|
|
||||||
def configure_optimizers(self):
|
|
||||||
optimizer = torch.optim.Adam(self.parameters(), lr=self.hparams.lr)
|
|
||||||
return optimizer
|
|
||||||
|
Loading…
Reference in New Issue
Block a user