Reformatted files

deepof/hypermodels.py

@@ -92,7 +92,7 @@ class SEQ_2_SEQ_AE(HyperModel):
             ENCODING,
             activation="relu",
             kernel_constraint=UnitNorm(axis=1),
-            activity_regularizer=UncorrelatedFeaturesConstraint(3, weightage=1.0),
+            activity_regularizer=uncorrelated_features_constraint(3, weightage=1.0),
             kernel_initializer=Orthogonal(),
         )
 
@@ -340,7 +340,7 @@ class SEQ_2_SEQ_GMVAE(HyperModel):
                     for k in range(self.number_of_components)
                 ],
             ),
-            activity_regularizer=UncorrelatedFeaturesConstraint(3, weightage=1.0),
+            activity_regularizer=uncorrelated_features_constraint(3, weightage=1.0),
         )([z_cat, z_gauss])
 
         if "ELBO" in self.loss:

deepof/model_utils.py

@@ -172,7 +172,14 @@ class one_cycle_scheduler(tf.keras.callbacks.Callback):
         K.set_value(self.model.optimizer.lr, rate)
 
 
-class UncorrelatedFeaturesConstraint(Constraint):
+class uncorrelated_features_constraint(Constraint):
+    """
+
+    Tensorflow Constraint subclass that forces a layer to have uncorrelated features.
+    Useful, among others, for auto encoder bottleneck layers
+
+    """
+
     def __init__(self, encoding_dim, weightage=1.0):
         self.encoding_dim = encoding_dim
         self.weightage = weightage

deepof/models.py

@@ -92,7 +92,7 @@ class SEQ_2_SEQ_AE:
             self.ENCODING,
             activation="elu",
             kernel_constraint=UnitNorm(axis=1),
-            activity_regularizer=UncorrelatedFeaturesConstraint(2, weightage=1.0),
+            activity_regularizer=uncorrelated_features_constraint(2, weightage=1.0),
             kernel_initializer=Orthogonal(),
         )