114 lines
3.6 KiB
Python
114 lines
3.6 KiB
Python
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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 torch
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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.glvq import SiameseGLVQ
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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.tensorboard import SummaryWriter
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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(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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plt.pause(0.1)
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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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super().__init__()
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self.input_size = input_size
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self.hidden_size = hidden_size
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self.latent_size = latent_size
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self.dense1 = torch.nn.Linear(self.input_size, self.hidden_size)
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self.dense2 = torch.nn.Linear(self.hidden_size, self.latent_size)
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self.relu = torch.nn.ReLU()
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def forward(self, x):
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return self.relu(self.dense2(self.relu(self.dense1(x))))
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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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input_dim=x_train.shape[1],
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nclasses=3,
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prototypes_per_class=1,
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prototype_initializer="stratified_mean",
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lr=0.01,
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)
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# Initialize the model
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model = SiameseGLVQ(hparams,
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backbone_module=Backbone,
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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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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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