Add option to use batch normalization

mlpp_lib/models.py

@@ -32,6 +32,7 @@ def call(self, inputs):
 def _build_fcn_block(
     inputs,
     hidden_layers,
+    batchnorm,
     activations,
     dropout,
     mc_dropout,
@@ -40,7 +41,10 @@ def _build_fcn_block(
 ):
     x = inputs
     for i, units in enumerate(hidden_layers):
-        x = Dense(units, activation=activations[i], name=f"dense_{idx}_{i}")(x)
+        x = Dense(units, name=f"dense_{idx}_{i}")(x)
+        if batchnorm:
+            x = BatchNormalization()(x)
+        x = Activation(activations[i])(x)
         if i < len(dropout) and 0.0 < dropout[i] < 1.0:
             if mc_dropout:
                 x = MonteCarloDropout(dropout[i], name=f"mc_dropout_{idx}_{i}")(x)
@@ -82,6 +86,7 @@ def fully_connected_network(
     input_shape: tuple[int],
     output_size: int,
     hidden_layers: list,
+    batchnorm: bool = False,
     activations: Optional[Union[str, list[str]]] = "relu",
     dropout: Optional[Union[float, list[float]]] = None,
     mc_dropout: bool = False,
@@ -101,6 +106,8 @@ def fully_connected_network(
     hidden_layers: list[int]
         List that is used to define the fully connected block. Each element creates
         a Dense layer with the corresponding units.
+    batchnorm: bool
+        Use batch normalization. Default is False.
     activations: str or list[str]
         (Optional) Activation function(s) for the Dense layer(s). See https://keras.io/api/layers/activations/#relu-function.
         If a string is passed, the same activation is used for all layers. Default is `relu`.
@@ -150,6 +157,7 @@ def fully_connected_network(
     x = _build_fcn_block(
         inputs,
         hidden_layers,
+        batchnorm,
         activations,
         dropout,
         mc_dropout,
@@ -165,6 +173,7 @@ def fully_connected_multibranch_network(
     input_shape: tuple[int],
     output_size: int,
     hidden_layers: list,
+    batchnorm: bool = False,
     activations: Optional[Union[str, list[str]]] = "relu",
     dropout: Optional[Union[float, list[float]]] = None,
     mc_dropout: bool = False,
@@ -184,6 +193,8 @@ def fully_connected_multibranch_network(
     hidden_layers: list[int]
         List that is used to define the fully connected block. Each element creates
         a Dense layer with the corresponding units.
+    batchnorm: bool
+        Use batch normalization. Default is False.
     activations: str or list[str]
         (Optional) Activation function(s) for the Dense layer(s). See https://keras.io/api/layers/activations/#relu-function.
         If a string is passed, the same activation is used for all layers. Default is `relu`.
@@ -242,6 +253,7 @@ def fully_connected_multibranch_network(
         x = _build_fcn_block(
             inputs,
             hidden_layers,
+            batchnorm,
             activations,
             dropout,
             mc_dropout,
@@ -263,6 +275,7 @@ def deep_cross_network(
     input_shape: tuple[int],
     output_size: int,
     hidden_layers: list,
+    batchnorm: bool = True,
     activations: Optional[Union[str, list[str]]] = "relu",
     dropout: Optional[Union[float, list[float]]] = None,
     mc_dropout: bool = False,
@@ -282,6 +295,8 @@ def deep_cross_network(
     hidden_layers: list[int]
         List that is used to define the fully connected block. Each element creates
         a Dense layer with the corresponding units.
+    batchnorm: bool
+        Use batch normalization. Default is True.
     activations: str or list[str]
         (Optional) Activation function(s) for the Dense layer(s). See https://keras.io/api/layers/activations/#relu-function.
         If a string is passed, the same activation is used for all layers. Default is `relu`.
@@ -338,16 +353,15 @@ def deep_cross_network(
 
     # deep part
     deep = inputs
-    for i, u in enumerate(hidden_layers):
-        deep = Dense(u)(deep)
-        deep = BatchNormalization()(deep)
-        deep = Activation(activations[i])(deep)
-        if i < len(dropout) and 0.0 < dropout[i] < 1.0:
-            if mc_dropout:
-                deep = MonteCarloDropout(dropout[i])(deep)
-            else:
-                deep = Dropout(dropout[i])(deep)
-    # deep = tf.keras.Model(inputs=inputs, outputs=deep, name="deepblock")
+    deep = _build_fcn_block(
+        deep,
+        hidden_layers,
+        batchnorm,
+        activations,
+        dropout,
+        mc_dropout,
+        skip_connection=False,
+    )
 
     # merge
     merge = tf.keras.layers.Concatenate()([cross, deep])