Resolve "custom dense layers in rnn"

Closes #361 (closed)

mlair/model_modules/recurrent_networks.py

@@ -139,9 +139,9 @@ class RNN(AbstractModelClass):  # pragma: no cover
                 for layer, n_hidden in enumerate(self.dense_layer_configuration):
                     if n_hidden < self._output_shape:
                         break
-                    x_in = keras.layers.Dense(n_hidden, name=f"Dense_{layer + 1}",
+                    x_in = keras.layers.Dense(n_hidden, name=f"Dense_{len(conf) + layer + 1}",
                                               kernel_initializer=self.kernel_initializer, )(x_in)
-                    x_in = self.activation(name=f"{self.activation_name}_{layer + 1}")(x_in)
+                    x_in = self.activation(name=f"{self.activation_name}_{len(conf) + layer + 1}")(x_in)
                     if self.dropout is not None:
                         x_in = self.dropout(self.dropout_rate)(x_in)