FCN activation name now works with relu

mlair/model_modules/fully_connected_networks.py

@@ -88,7 +88,9 @@ class FCN(AbstractModelClass):
 
         # settings
         self.activation = self._set_activation(activation)
+        self.activation_name = activation
         self.activation_output = self._set_activation(activation_output)
+        self.activation_output_name = activation_output
         self.optimizer = self._set_optimizer(optimizer, **kwargs)
         self.layer_configuration = (n_layer, n_hidden) if layer_configuration is None else layer_configuration
         self._update_model_name()
@@ -164,7 +166,7 @@ class FCN(AbstractModelClass):
             for layer in range(n_layer):
                 x_in = keras.layers.Dense(n_hidden, kernel_initializer=self.kernel_initializer,
                                           kernel_regularizer=self.kernel_regularizer)(x_in)
-                x_in = self.activation(name=f"{self.activation.args[0]}_{layer + 1}")(x_in)
+                x_in = self.activation(name=f"{self.activation_name}_{layer + 1}")(x_in)
                 if self.dropout is not None:
                     x_in = keras.layers.Dropout(self.dropout)(x_in)
         else:
@@ -172,11 +174,11 @@ class FCN(AbstractModelClass):
             for layer, n_hidden in enumerate(self.layer_configuration):
                 x_in = keras.layers.Dense(n_hidden, kernel_initializer=self.kernel_initializer,
                                           kernel_regularizer=self.kernel_regularizer)(x_in)
-                x_in = self.activation(name=f"{self.activation.args[0]}_{layer + 1}")(x_in)
+                x_in = self.activation(name=f"{self.activation_name}_{layer + 1}")(x_in)
                 if self.dropout is not None:
                     x_in = keras.layers.Dropout(self.dropout)(x_in)
         x_in = keras.layers.Dense(self._output_shape)(x_in)
-        out = self.activation_output(name=f"{self.activation_output.args[0]}_output")(x_in)
+        out = self.activation_output(name=f"{self.activation_output_name}_output")(x_in)
         self.model = keras.Model(inputs=x_input, outputs=[out])
 
     def set_compile_options(self):