add dense layer between rnn and output

mlair/model_modules/recurrent_networks.py

@@ -32,7 +32,7 @@ class RNN(AbstractModelClass):
 
     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, layer_configuration=None,
-                 batch_normalization=False, rnn_type="lstm", **kwargs):
+                 batch_normalization=False, rnn_type="lstm", add_dense_layer=False, **kwargs):
         """
         Sets model and loss depending on the given arguments.
 
@@ -72,6 +72,7 @@ class RNN(AbstractModelClass):
         self.activation_output_name = activation_output
         self.optimizer = self._set_optimizer(optimizer.lower(), **kwargs)
         self.bn = batch_normalization
+        self.add_dense_layer = add_dense_layer
         self.layer_configuration = (n_layer, n_hidden) if layer_configuration is None else layer_configuration
         self.RNN = self._rnn.get(rnn_type.lower())
         self._update_model_name(rnn_type)
@@ -108,6 +109,9 @@ class RNN(AbstractModelClass):
             if self.dropout is not None:
                 x_in = self.dropout(self.dropout_rate)(x_in)
 
+        if self.add_dense_layer is True:
+            x_in = keras.layers.Dense(min(self._output_shape ** 2, conf[-1]), ame=f"Dense_{len(conf) + 1}")(x_in)
+            x_in = self.activation(name=f"{self.activation_name}_{len(conf) + 1}")(x_in)
         x_in = keras.layers.Dense(self._output_shape)(x_in)
         out = self.activation_output(name=f"{self.activation_output_name}_output")(x_in)
         self.model = keras.Model(inputs=x_input, outputs=[out])