Merge pull request #56 from aertslab/model_output_activations

add output activation option to all models

src/crested/tl/zoo/_chrombpnet.py

@@ -16,6 +16,7 @@ def chrombpnet(
     num_filters: int = 512,
     filter_size: int = 3,
     activation: str = "relu",
+    output_activation: str = "softplus",
     l2: float = 0.00001,
     dropout: float = 0.1,
     batch_norm: bool = True,
@@ -50,6 +51,8 @@ def chrombpnet(
         Size of the kernel in the dilated convolutional layers.
     activation
         Activation function in the dilated convolutional layers.
+    output_activation
+        Activation function for the output layer.
     l2
         L2 regularization for the dilated convolutional layers.
     dropout
@@ -126,7 +129,10 @@ def chrombpnet(
 
     x = keras.layers.GlobalAveragePooling1D()(x)
     outputs = keras.layers.Dense(
-        units=num_classes, activation="softplus", use_bias=dense_bias, name="dense_out"
+        units=num_classes,
+        activation=output_activation,
+        use_bias=dense_bias,
+        name="dense_out",
     )(x)
 
     model = keras.Model(inputs=inputs, outputs=outputs)

src/crested/tl/zoo/_deeptopic_cnn.py

@@ -14,6 +14,7 @@ def deeptopic_cnn(
     dense_out: int = 1024,
     first_activation: str = "gelu",
     activation: str = "relu",
+    output_activation: str = "sigmoid",
     conv_do: float = 0.15,
     normalization: str = "batch",
     dense_do: float = 0.5,
@@ -43,6 +44,8 @@ def deeptopic_cnn(
         Activation function for the first conv block.
     activation
         Activation function for subsequent blocks.
+    output_activation
+        Activation function for the output layer.
     conv_do
         Dropout rate for the convolutional layers.
     normalization
@@ -135,6 +138,7 @@ def deeptopic_cnn(
         name_prefix="denseblock",
         use_bias=False,
     )
-    logits = keras.layers.Dense(num_classes, activation="linear", use_bias=True)(x)
-    outputs = keras.layers.Activation("sigmoid")(logits)
+    outputs = keras.layers.Dense(
+        num_classes, activation=output_activation, use_bias=True
+    )(x)
     return keras.Model(inputs=inputs, outputs=outputs)

src/crested/tl/zoo/_deeptopic_lstm.py

@@ -18,6 +18,7 @@ def deeptopic_lstm(
     lstm_out: int = 128,
     first_activation: str = "relu",
     activation: str = "relu",
+    output_activation: str = "sigmoid",
     lstm_do: float = 0.1,
     dense_do: float = 0.4,
     pre_dense_do: float = 0.2,
@@ -49,6 +50,8 @@ def deeptopic_lstm(
         Activation function for the first conv block.
     activation
         Activation function for subsequent blocks.
+    output_activation
+        Activation function for the output layer.
     lstm_do
         Dropout rate for the lstm layer.
     dense_do
@@ -91,7 +94,7 @@ def deeptopic_lstm(
         keras.layers.Flatten(),
         keras.layers.Dense(dense_out, activation=activation),
         keras.layers.Dropout(dense_do),
-        keras.layers.Dense(num_classes, activation="sigmoid"),
+        keras.layers.Dense(num_classes, activation=output_activation),
     ]
 
     outputs = get_output(inputs, hidden_layers)