kernel size can be set from outside

mlair/model_modules/convolutional_networks.py

@@ -22,7 +22,7 @@ class CNN(AbstractModelClass):
     _requirements = ["lr", "beta_1", "beta_2", "epsilon", "decay", "amsgrad"]
 
     def __init__(self, input_shape: list, output_shape: list, activation="relu", activation_output="linear",
-                 optimizer="adam", regularizer=None, **kwargs):
+                 optimizer="adam", regularizer=None, kernel_size=1, **kwargs):
 
         assert len(input_shape) == 1
         assert len(output_shape) == 1
@@ -35,6 +35,7 @@ class CNN(AbstractModelClass):
         self.activation_output_name = activation_output
         self.kernel_initializer = self._initializer.get(activation, "glorot_uniform")
         self.kernel_regularizer = self._set_regularizer(regularizer, **kwargs)
+        self.kernel_size = kernel_size
         self.optimizer = self._set_optimizer(optimizer, **kwargs)
 
         # apply to model
@@ -81,7 +82,7 @@ class CNN(AbstractModelClass):
         Build the model.
         """
         x_input = keras.layers.Input(shape=self._input_shape)
-        kernel = (5, 1)
+        kernel = (self.kernel_size, 1)
         pad_size = PadUtils.get_padding_for_same(kernel)
         x_in = Padding2D("SymPad2D")(padding=pad_size, name="SymPad1")(x_input)
         x_in = keras.layers.Conv2D(filters=16, kernel_size=kernel,