Update example scripts

examples/dynamic_pruning.py

@@ -30,7 +30,7 @@ if __name__ == "__main__":
     prototypes_per_class = num_clusters * 5
     hparams = dict(
         distribution=(num_classes, prototypes_per_class),
-        lr=0.1,
+        lr=0.2,
     )
 
     # Initialize the model
@@ -39,6 +39,12 @@ if __name__ == "__main__":
         prototype_initializer=pt.components.Ones(2, scale=3),
     )
 
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 2)
+
+    # Summary
+    print(model)
+
     # Callbacks
     vis = pt.models.VisGLVQ2D(train_ds)
     pruning = pt.models.PruneLoserPrototypes(
@@ -67,7 +73,7 @@ if __name__ == "__main__":
         ],
         progress_bar_refresh_rate=0,
         terminate_on_nan=True,
-        weights_summary=None,
+        weights_summary="full",
         accelerator="ddp",
     )
 

examples/glvq_spiral.py

@@ -2,11 +2,10 @@
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
-import prototorch as pt
-
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
@@ -14,35 +13,63 @@ if __name__ == "__main__":
     args = parser.parse_args()
 
     # Dataset
-    train_ds = pt.datasets.Spiral(num_samples=600, noise=0.6)
+    train_ds = pt.datasets.Spiral(num_samples=500, noise=0.5)
 
     # Dataloaders
-    train_loader = torch.utils.data.DataLoader(train_ds,
+    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=256)
-                                               num_workers=0,
-                                               batch_size=256)
 
     # Hyperparameters
     num_classes = 2
-    prototypes_per_class = 20
+    prototypes_per_class = 10
     hparams = dict(
         distribution=(num_classes, prototypes_per_class),
-        transfer_function="sigmoid_beta",
+        transfer_function="swish_beta",
         transfer_beta=10.0,
-        lr=0.01,
+        # lr=0.1,
+        proto_lr=0.1,
+        bb_lr=0.1,
+        input_dim=2,
+        latent_dim=2,
     )
 
     # Initialize the model
-    model = pt.models.GLVQ(hparams,
+    model = pt.models.GMLVQ(
-                           prototype_initializer=pt.components.SSI(train_ds,
+        hparams,
-                                                                   noise=1e-1))
+        optimizer=torch.optim.Adam,
+        prototype_initializer=pt.components.SSI(train_ds, noise=1e-2),
+    )
 
     # Callbacks
-    vis = pt.models.VisGLVQ2D(train_ds, show_last_only=True, block=True)
+    vis = pt.models.VisGLVQ2D(
+        train_ds,
+        show_last_only=False,
+        block=False,
+    )
+    pruning = pt.models.PruneLoserPrototypes(
+        threshold=0.02,
+        idle_epochs=10,
+        prune_quota_per_epoch=5,
+        frequency=2,
+        replace=True,
+        initializer=pt.components.SSI(train_ds, noise=1e-2),
+        verbose=True,
+    )
+    es = pl.callbacks.EarlyStopping(
+        monitor="train_loss",
+        min_delta=1.0,
+        patience=5,
+        mode="min",
+        check_on_train_epoch_end=True,
+    )
 
     # Setup trainer
     trainer = pl.Trainer.from_argparse_args(
         args,
-        callbacks=[vis],
+        callbacks=[
+            vis,
+            # es,
+            pruning,
+        ],
         terminate_on_nan=True,
     )
 

examples/gmlvq_mnist.py

@@ -43,7 +43,7 @@ if __name__ == "__main__":
 
     # Hyperparameters
     num_classes = 10
-    prototypes_per_class = 2
+    prototypes_per_class = 10
     hparams = dict(
         input_dim=28 * 28,
         latent_dim=28 * 28,
@@ -62,19 +62,40 @@ if __name__ == "__main__":
     # Callbacks
     vis = pt.models.VisImgComp(
         data=train_ds,
-        num_columns=5,
+        num_columns=10,
         show=False,
         tensorboard=True,
-        random_data=20,
+        random_data=100,
         add_embedding=True,
-        embedding_data=100,
+        embedding_data=200,
         flatten_data=False,
     )
+    pruning = pt.models.PruneLoserPrototypes(
+        threshold=0.01,
+        idle_epochs=1,
+        prune_quota_per_epoch=10,
+        frequency=1,
+        verbose=True,
+    )
+    es = pl.callbacks.EarlyStopping(
+        monitor="train_loss",
+        min_delta=0.001,
+        patience=15,
+        mode="min",
+        check_on_train_epoch_end=True,
+    )
 
     # Setup trainer
     trainer = pl.Trainer.from_argparse_args(
         args,
-        callbacks=[vis],
+        callbacks=[
+            vis,
+            pruning,
+            # es,
+        ],
+        terminate_on_nan=True,
+        weights_summary=None,
+        accelerator="ddp",
     )
 
     # Training loop

examples/gng_iris.py

@@ -4,10 +4,7 @@ import argparse
 
 import prototorch as pt
 import pytorch_lightning as pl
-from prototorch.components.initializers import Zeros
+import torch
-from prototorch.datasets import Iris
-from prototorch.models.unsupervised import GrowingNeuralGas
-from torch.utils.data import DataLoader
 
 if __name__ == "__main__":
     # Command-line arguments
@@ -19,8 +16,8 @@ if __name__ == "__main__":
     pl.utilities.seed.seed_everything(seed=42)
 
     # Prepare the data
-    train_ds = Iris(dims=[0, 2])
+    train_ds = pt.datasets.Iris(dims=[0, 2])
-    train_loader = DataLoader(train_ds, batch_size=8)
+    train_loader = torch.utils.data.DataLoader(train_ds, batch_size=8)
 
     # Hyperparameters
     hparams = dict(
@@ -29,11 +26,14 @@ if __name__ == "__main__":
     )
 
     # Initialize the model
-    model = GrowingNeuralGas(
+    model = pt.models.GrowingNeuralGas(
         hparams,
-        prototype_initializer=Zeros(2),
+        prototype_initializer=pt.components.Zeros(2),
     )
 
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 2)
+
     # Model summary
     print(model)
 
@@ -45,6 +45,7 @@ if __name__ == "__main__":
         args,
         max_epochs=100,
         callbacks=[vis],
+        weights_summary="full",
     )
 
     # Training loop

examples/knn_iris.py

@@ -1,13 +1,12 @@
-"""k-NN example using the Iris dataset."""
+"""k-NN example using the Iris dataset from scikit-learn."""
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 from sklearn.datasets import load_iris
 
-import prototorch as pt
-
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
@@ -23,18 +22,30 @@ if __name__ == "__main__":
     train_loader = torch.utils.data.DataLoader(train_ds, batch_size=150)
 
     # Hyperparameters
-    hparams = dict(k=20)
+    hparams = dict(k=5)
 
     # Initialize the model
     model = pt.models.KNN(hparams, data=train_ds)
 
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 2)
+
+    # Summary
+    print(model)
+
     # Callbacks
-    vis = pt.models.VisGLVQ2D(data=(x_train, y_train), resolution=200)
+    vis = pt.models.VisGLVQ2D(
+        data=(x_train, y_train),
+        resolution=200,
+        block=True,
+    )
 
     # Setup trainer
     trainer = pl.Trainer.from_argparse_args(
         args,
+        max_epochs=1,
         callbacks=[vis],
+        weights_summary="full",
     )
 
     # Training loop

examples/ng_iris.py

@@ -7,6 +7,7 @@ import pytorch_lightning as pl
 import torch
 from sklearn.datasets import load_iris
 from sklearn.preprocessing import StandardScaler
+from torch.optim.lr_scheduler import ExponentialLR
 
 if __name__ == "__main__":
     # Command-line arguments
@@ -30,8 +31,15 @@ if __name__ == "__main__":
     hparams = dict(num_prototypes=30, lr=0.03)
 
     # Initialize the model
-    model = pt.models.NeuralGas(hparams,
+    model = pt.models.NeuralGas(
-                                prototype_initializer=pt.components.Zeros(2))
+        hparams,
+        prototype_initializer=pt.components.Zeros(2),
+        lr_scheduler=ExponentialLR,
+        lr_scheduler_kwargs=dict(gamma=0.99, verbose=False),
+    )
+
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 2)
 
     # Model summary
     print(model)
@@ -43,6 +51,7 @@ if __name__ == "__main__":
     trainer = pl.Trainer.from_argparse_args(
         args,
         callbacks=[vis],
+        weights_summary="full",
     )
 
     # Training loop

examples/rslvq_iris.py

@@ -1,12 +1,11 @@
-"""Probabilistic-LVQ example using the Iris dataset."""
+"""RSLVQ example using the Iris dataset."""
 
 import argparse
 
+import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
-import prototorch as pt
-
 if __name__ == "__main__":
     # Command-line arguments
     parser = argparse.ArgumentParser()
@@ -26,16 +25,23 @@ if __name__ == "__main__":
     hparams = dict(
         distribution=[2, 2, 3],
         lr=0.05,
-        variance=0.3,
+        variance=0.1,
     )
 
     # Initialize the model
     model = pt.models.probabilistic.RSLVQ(
         hparams,
         optimizer=torch.optim.Adam,
+        # prototype_initializer=pt.components.SMI(train_ds),
         prototype_initializer=pt.components.SSI(train_ds, noise=0.2),
+        # prototype_initializer=pt.components.Zeros(2),
+        # prototype_initializer=pt.components.Ones(2, scale=2.0),
     )
 
+    # Compute intermediate input and output sizes
+    model.example_input_array = torch.zeros(4, 2)
+
+    # Summary
     print(model)
 
     # Callbacks
@@ -46,8 +52,8 @@ if __name__ == "__main__":
         args,
         callbacks=[vis],
         terminate_on_nan=True,
-        weights_summary=None,
+        weights_summary="full",
-        # accelerator="ddp",
+        accelerator="ddp",
     )
 
     # Training loop