Uniformize naming of layers in NN

NNPDF · Mar 4, 2024 · e441a5a · e441a5a
1 parent 93d2d21
commit e441a5a
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Showing 2 changed files with 7 additions and 6 deletions.
diff --git a/n3fit/src/n3fit/backends/keras_backend/base_layers.py b/n3fit/src/n3fit/backends/keras_backend/base_layers.py
@@ -197,6 +197,8 @@ def base_layer_selector(layer_name, **kwargs):
             value = custom_activations.get(value, value)
         if key in layer_args.keys():
             layer_args[key] = value
+        if key == "name":
+            layer_args[key] = value
 
     return layer_class(**layer_args)
 

diff --git a/n3fit/src/n3fit/model_gen.py b/n3fit/src/n3fit/model_gen.py
@@ -73,7 +73,9 @@ def _generate_loss(self, mask=None):
         if self.invcovmat is not None:
             if self.rotation:
                 # If we have a matrix diagonal only, padd with 0s and hope it's not too heavy on memory
-                invcovmat_matrix = np.eye(self.invcovmat.shape[-1]) * self.invcovmat[..., np.newaxis]
+                invcovmat_matrix = (
+                    np.eye(self.invcovmat.shape[-1]) * self.invcovmat[..., np.newaxis]
+                )
                 if self.covmat is not None:
                     covmat_matrix = np.eye(self.covmat.shape[-1]) * self.covmat[..., np.newaxis]
                 else:
@@ -82,11 +84,7 @@ def _generate_loss(self, mask=None):
                 covmat_matrix = self.covmat
                 invcovmat_matrix = self.invcovmat
             loss = losses.LossInvcovmat(
-                invcovmat_matrix,
-                self.data,
-                mask,
-                covmat=covmat_matrix,
-                name=self.name
+                invcovmat_matrix, self.data, mask, covmat=covmat_matrix, name=self.name
             )
         elif self.positivity:
             loss = losses.LossPositivity(name=self.name, c=self.multiplier)
@@ -759,6 +757,7 @@ def layer_generator(i_layer, nodes_out, activation):
                 activation=activation,
                 is_first_layer=(i_layer == 0),
                 regularizer=reg,
+                name=f"multi_dense_{i_layer}",
             )
 
     else: