Make siamese example script reproducible

examples/siamese_glvq_iris.py

@@ -2,18 +2,16 @@
 
 import pytorch_lightning as pl
 import torch
-from prototorch.components import (
-    StratifiedMeanInitializer
-)
+from prototorch.components import initializers as cinit
 from prototorch.datasets.abstract import NumpyDataset
+from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
+from prototorch.models.glvq import SiameseGLVQ
 from sklearn.datasets import load_iris
 from torch.utils.data import DataLoader
 
-from prototorch.models.callbacks.visualization import VisSiameseGLVQ2D
-from prototorch.models.glvq import SiameseGLVQ
-
 
 class Backbone(torch.nn.Module):
+    """Two fully connected layers with ReLU activation."""
     def __init__(self, input_size=4, hidden_size=10, latent_size=2):
         super().__init__()
         self.input_size = input_size
@@ -24,7 +22,9 @@ class Backbone(torch.nn.Module):
         self.relu = torch.nn.ReLU()
 
     def forward(self, x):
-        return self.relu(self.dense2(self.relu(self.dense1(x))))
+        x = self.relu(self.dense1(x))
+        out = self.relu(self.dense2(x))
+        return out
 
 
 if __name__ == "__main__":
@@ -32,16 +32,20 @@ if __name__ == "__main__":
     x_train, y_train = load_iris(return_X_y=True)
     train_ds = NumpyDataset(x_train, y_train)
 
+    # Reproducibility
+    pl.utilities.seed.seed_everything(seed=2)
+
     # Dataloaders
     train_loader = DataLoader(train_ds, num_workers=0, batch_size=150)
 
     # Hyperparameters
     hparams = dict(
         nclasses=3,
-        prototypes_per_class=1,
-        prototype_initializer=StratifiedMeanInitializer(
-            torch.Tensor(x_train), torch.Tensor(y_train)),
-        lr=0.01,
+        prototypes_per_class=2,
+        prototype_initializer=cinit.SMI(torch.Tensor(x_train),
+                                        torch.Tensor(y_train)),
+        proto_lr=0.001,
+        bb_lr=0.001,
     )
 
     # Initialize the model
@@ -54,7 +58,7 @@ if __name__ == "__main__":
     print(model)
 
     # Callbacks
-    vis = VisSiameseGLVQ2D(x_train, y_train)
+    vis = VisSiameseGLVQ2D(x_train, y_train, border=0.1)
 
     # Setup trainer
     trainer = pl.Trainer(max_epochs=100, callbacks=[vis])