FCN can use an explicite layer configuration, activation layers are now named...

FCN can use an explicite layer configuration, activation layers are now named according to the act fct

mlair/model_modules/fully_connected_networks.py

@@ -73,7 +73,8 @@ class FCN(AbstractModelClass):
     _requirements = ["lr", "beta_1", "beta_2", "epsilon", "decay", "amsgrad", "momentum", "nesterov", "l1", "l2"]
 
     def __init__(self, input_shape: list, output_shape: list, activation="relu", activation_output="linear",
-                 optimizer="adam", n_layer=1, n_hidden=10, regularizer=None, dropout=None, **kwargs):
+                 optimizer="adam", n_layer=1, n_hidden=10, regularizer=None, dropout=None, explicite_layers=None,
+                 **kwargs):
         """
         Sets model and loss depending on the given arguments.
 
@@ -89,7 +90,7 @@ class FCN(AbstractModelClass):
         self.activation = self._set_activation(activation)
         self.activation_output = self._set_activation(activation_output)
         self.optimizer = self._set_optimizer(optimizer, **kwargs)
-        self.layer_configuration = (n_layer, n_hidden)
+        self.layer_configuration = (n_layer, n_hidden) if explicite_layers is None else explicite_layers
         self._update_model_name()
         self.kernel_initializer = self._initializer.get(activation, "glorot_uniform")
         self.kernel_regularizer = self._set_regularizer(regularizer, **kwargs)
@@ -144,10 +145,13 @@ class FCN(AbstractModelClass):
         return dropout
 
     def _update_model_name(self):
-        n_layer, n_hidden = self.layer_configuration
         n_input = str(reduce(lambda x, y: x * y, self._input_shape))
         n_output = str(self._output_shape)
+        if isinstance(self.layer_configuration, tuple) and len(self.layer_configuration) == 2:
+            n_layer, n_hidden = self.layer_configuration
             self.model_name += "_".join(["", n_input, *[f"{n_hidden}" for _ in range(n_layer)], n_output])
+        else:
+            self.model_name += "_".join(["", n_input, *[f"{n}" for n in self.layer_configuration], n_output])
 
     def set_model(self):
         """
@@ -155,15 +159,24 @@ class FCN(AbstractModelClass):
         """
         x_input = keras.layers.Input(shape=self._input_shape)
         x_in = keras.layers.Flatten()(x_input)
+        if isinstance(self.layer_configuration, tuple) is True:
             n_layer, n_hidden = self.layer_configuration
             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()(x_in)
+                x_in = self.activation(name=f"{self.activation.args[0]}_{layer + 1}")(x_in)
+                if self.dropout is not None:
+                    x_in = keras.layers.Dropout(self.dropout)(x_in)
+        else:
+            assert isinstance(self.layer_configuration, list) is True
+            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)
                 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()(x_in)
+        out = self.activation_output(name=f"{self.activation_output.args[0]}_output")(x_in)
         self.model = keras.Model(inputs=x_input, outputs=[out])
 
     def set_compile_options(self):