116 lines
3.6 KiB
Python
116 lines
3.6 KiB
Python
"""Siamese GLVQ example using all four dimensions of the Iris dataset."""
|
|
|
|
import numpy as np
|
|
import pytorch_lightning as pl
|
|
import torch
|
|
from matplotlib import pyplot as plt
|
|
from sklearn.datasets import load_iris
|
|
from torch.utils.data import DataLoader
|
|
|
|
from prototorch.datasets.abstract import NumpyDataset
|
|
from prototorch.models.glvq import SiameseGLVQ
|
|
|
|
|
|
class VisualizationCallback(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() - 0.2, x[:, 0].max() + 0.2
|
|
y_min, y_max = x[:, 1].min() - 0.2, x[:, 1].max() + 0.2
|
|
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 Backbone(torch.nn.Module):
|
|
def __init__(self, input_size=4, hidden_size=10, latent_size=2):
|
|
super().__init__()
|
|
self.input_size = input_size
|
|
self.hidden_size = hidden_size
|
|
self.latent_size = latent_size
|
|
self.dense1 = torch.nn.Linear(self.input_size, self.hidden_size)
|
|
self.dense2 = torch.nn.Linear(self.hidden_size, self.latent_size)
|
|
self.relu = torch.nn.ReLU()
|
|
|
|
def forward(self, x):
|
|
return self.relu(self.dense2(self.relu(self.dense1(x))))
|
|
|
|
|
|
if __name__ == "__main__":
|
|
# Dataset
|
|
x_train, y_train = load_iris(return_X_y=True)
|
|
train_ds = NumpyDataset(x_train, y_train)
|
|
|
|
# Dataloaders
|
|
train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
|
|
|
|
# Hyperparameters
|
|
hparams = dict(
|
|
input_dim=x_train.shape[1],
|
|
nclasses=3,
|
|
prototypes_per_class=1,
|
|
prototype_initializer="stratified_mean",
|
|
lr=0.01,
|
|
)
|
|
|
|
# Initialize the model
|
|
model = SiameseGLVQ(hparams,
|
|
backbone_module=Backbone,
|
|
data=[x_train, y_train])
|
|
|
|
# Model summary
|
|
print(model)
|
|
|
|
# Callbacks
|
|
vis = VisualizationCallback(x_train, y_train)
|
|
|
|
# Setup trainer
|
|
trainer = pl.Trainer(max_epochs=100, callbacks=[vis])
|
|
|
|
# Training loop
|
|
trainer.fit(model, train_loader)
|