All examples use argparse

examples/cbc_iris.py

@@ -1,10 +1,17 @@
 """CBC example using the Iris dataset."""
 
+import argparse
+
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
 if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
     # Dataset
     train_ds = pt.datasets.Iris(dims=[0, 2])
 
@@ -30,18 +37,15 @@ if __name__ == "__main__":
     )
 
     # Callbacks
-    dvis = pt.models.VisCBC2D(data=train_ds,
+    vis = pt.models.VisCBC2D(data=train_ds,
-                              title="CBC Iris Example",
+                             title="CBC Iris Example",
-                              resolution=300,
+                             resolution=300,
-                              axis_off=True)
+                             axis_off=True)
 
     # Setup trainer
-    trainer = pl.Trainer(
+    trainer = pl.Trainer.from_argparse_args(
-        gpus=0,
+        args,
-        max_epochs=200,
+        callbacks=[vis],
-        callbacks=[
-            dvis,
-        ],
     )
 
     # Training loop

examples/glvq_iris.py

@@ -1,12 +1,19 @@
 """GLVQ example using the Iris dataset."""
 
+import argparse
+
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from sklearn.datasets import load_iris
 
 if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
     # Dataset
-    from sklearn.datasets import load_iris
     x_train, y_train = load_iris(return_X_y=True)
     x_train = x_train[:, [0, 2]]
     train_ds = pt.datasets.NumpyDataset(x_train, y_train)
@@ -33,9 +40,8 @@ if __name__ == "__main__":
     vis = pt.models.VisGLVQ2D(data=(x_train, y_train), block=False)
 
     # Setup trainer
-    trainer = pl.Trainer(
+    trainer = pl.Trainer.from_argparse_args(
-        gpus=0,
+        args,
-        max_epochs=50,
         callbacks=[vis],
     )
 

examples/glvq_spiral.py

@@ -1,11 +1,17 @@
 """GLVQ example using the spiral dataset."""
 
+import argparse
+
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
-from prototorch.models.callbacks import StopOnNaN
 
 if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
     # Dataset
     train_ds = pt.datasets.Spiral(n_samples=600, noise=0.6)
 
@@ -31,13 +37,12 @@ if __name__ == "__main__":
 
     # Callbacks
     vis = pt.models.VisGLVQ2D(train_ds, show_last_only=True, block=True)
-    snan = StopOnNaN(model.proto_layer.components)
 
     # Setup trainer
-    trainer = pl.Trainer(
+    trainer = pl.Trainer.from_argparse_args(
-        gpus=0,
+        args,
-        max_epochs=200,
+        callbacks=[vis],
-        callbacks=[vis, snan],
+        terminate_on_nan=True,
     )
 
     # Training loop

examples/gmlvq_iris.py

@@ -1,12 +1,19 @@
 """GMLVQ example using all four dimensions of the Iris dataset."""
 
+import argparse
+
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from sklearn.datasets import load_iris
 
 if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
     # Dataset
-    from sklearn.datasets import load_iris
     x_train, y_train = load_iris(return_X_y=True)
     train_ds = pt.datasets.NumpyDataset(x_train, y_train)
 
@@ -30,7 +37,7 @@ if __name__ == "__main__":
                             prototype_initializer=pt.components.SMI(train_ds))
 
     # Setup trainer
-    trainer = pl.Trainer(max_epochs=100, gpus=0)
+    trainer = pl.Trainer.from_argparse_args(args, )
 
     # Training loop
     trainer.fit(model, train_loader)

examples/gmlvq_mnist.py

@@ -74,10 +74,6 @@ if __name__ == "__main__":
     trainer = pl.Trainer.from_argparse_args(
         args,
         callbacks=[vis],
-        # kwargs override the cli-arguments
-        # max_epochs=50,
-        # overfit_batches=1,
-        # fast_dev_run=1,
     )
 
     # Training loop

examples/knn_iris.py

@@ -1,12 +1,19 @@
 """k-NN example using the Iris dataset."""
 
+import argparse
+
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from sklearn.datasets import load_iris
 
 if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
     # Dataset
-    from sklearn.datasets import load_iris
     x_train, y_train = load_iris(return_X_y=True)
     x_train = x_train[:, [0, 2]]
     train_ds = pt.datasets.NumpyDataset(x_train, y_train)
@@ -26,7 +33,10 @@ if __name__ == "__main__":
     vis = pt.models.VisGLVQ2D(data=(x_train, y_train), resolution=200)
 
     # Setup trainer
-    trainer = pl.Trainer(max_epochs=1, callbacks=[vis], gpus=0)
+    trainer = pl.Trainer.from_argparse_args(
+        args,
+        callbacks=[vis],
+    )
 
     # Training loop
     # This is only for visualization. k-NN has no training phase.

examples/liramlvq_tecator.py

@@ -1,10 +1,17 @@
 """Limited Rank Matrix LVQ example using the Tecator dataset."""
 
+import argparse
+
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
 if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
     # Dataset
     train_ds = pt.datasets.Tecator(root="~/datasets/", train=True)
     test_ds = pt.datasets.Tecator(root="~/datasets/", train=False)
@@ -40,11 +47,9 @@ if __name__ == "__main__":
                                     mode="min")
 
     # Setup trainer
-    trainer = pl.Trainer(
+    trainer = pl.Trainer.from_argparse_args(
-        gpus=0,
+        args,
-        max_epochs=100,
         callbacks=[vis, es],
-        weights_summary=None,
     )
 
     # Training loop

examples/lvqmln_iris.py

@@ -1,12 +1,33 @@
 """LVQMLN example using all four dimensions of the Iris dataset."""
+import argparse
 
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
 
-from siamese_glvq_iris import Backbone
+
+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.activation = torch.nn.Sigmoid()
+
+    def forward(self, x):
+        x = self.activation(self.dense1(x))
+        out = self.activation(self.dense2(x))
+        return out
+
 
 if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
     # Dataset
     train_ds = pt.datasets.Iris()
 
@@ -48,7 +69,10 @@ if __name__ == "__main__":
     )
 
     # Setup trainer
-    trainer = pl.Trainer(max_epochs=100, callbacks=[vis], gpus=0)
+    trainer = pl.Trainer.from_argparse_args(
+        args,
+        callbacks=[vis],
+    )
 
     # Training loop
     trainer.fit(model, train_loader)

examples/ng_iris.py

@@ -1,13 +1,20 @@
 """Neural Gas example using the Iris dataset."""
 
+import argparse
+
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
+from sklearn.datasets import load_iris
+from sklearn.preprocessing import StandardScaler
 
 if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
     # Prepare and pre-process the dataset
-    from sklearn.datasets import load_iris
-    from sklearn.preprocessing import StandardScaler
     x_train, y_train = load_iris(return_X_y=True)
     x_train = x_train[:, [0, 2]]
     scaler = StandardScaler()
@@ -34,7 +41,10 @@ if __name__ == "__main__":
     vis = pt.models.VisNG2D(data=train_ds)
 
     # Setup trainer
-    trainer = pl.Trainer(gpus=0, max_epochs=200, callbacks=[vis])
+    trainer = pl.Trainer.from_argparse_args(
+        args,
+        callbacks=[vis],
+    )
 
     # Training loop
     trainer.fit(model, train_loader)

examples/siamese_glvq_iris.py

@@ -1,5 +1,7 @@
 """Siamese GLVQ example using all four dimensions of the Iris dataset."""
 
+import argparse
+
 import prototorch as pt
 import pytorch_lightning as pl
 import torch
@@ -22,6 +24,11 @@ class Backbone(torch.nn.Module):
 
 
 if __name__ == "__main__":
+    # Command-line arguments
+    parser = argparse.ArgumentParser()
+    parser = pl.Trainer.add_argparse_args(parser)
+    args = parser.parse_args()
+
     # Dataset
     train_ds = pt.datasets.Iris()
 
@@ -58,7 +65,10 @@ if __name__ == "__main__":
     vis = pt.models.VisSiameseGLVQ2D(data=train_ds, border=0.1)
 
     # Setup trainer
-    trainer = pl.Trainer(max_epochs=100, callbacks=[vis], gpus=0)
+    trainer = pl.Trainer.from_argparse_args(
+        args,
+        callbacks=[vis],
+    )
 
     # Training loop
     trainer.fit(model, train_loader)