TABLE 3.

Parameters for CNNs.

Layer	Parameters (2D-CNN)	Parameters (3D-CNN)
Conv2D (for 2D-CNN) or Conv3D (for 3D-CNN)	Filters = 64	Filters = 64
	Kernel_size = (3,3)	Kernel_size = (3,3,3)
	Padding = “same”	Padding = “same”
	Activation = “relu”	Activation = “relu”
	Input_shape = (5,5,7)	Input_shape = (5,5,5,7)
Conv2D (for 2D-CNN) or Conv3D (for 3D-CNN)	Filters = 128	Filters = 128
	Kernel_size = (2,2)	Kernel_size = (2,2,3)
	Padding = “same”	Padding = “same”
	Activation = “relu”	Activation = “relu”
Conv2D (for 2D-CNN) or Conv3D (for 3D-CNN)	Filters = 256	Filters = 256
	Kernel_size = (1,1)	Kernel_size = (1,1,5)
	Padding = “valid”	Padding = “valid”
	Activation = “relu”	Activation = “relu”
Reshape	Target_shape = (5,5,256)
Flatten	NA
Dense	Units = 256
	Activation = “relu”
Dense	Units = 5,400
Reshape	Target_shape = (5,5,256)
Conv2D	Filters = 128
	Kernel_size = (2,2)
	Padding = “same”
	Activation = “relu”
Conv2D	Filters = 64
	Kernel_size = (3,3)
	Padding = “same”
	Activation = “relu”
Conv2D	Filters = 1
	Kernel_size = (1,1)
	Padding = “same”
	Activation = “linear”

Each Keras layer refers to a building block of the neural network, including convolution layers (Conv2D and Conv3D), reshaping layers (Flatten and Reshape), and fully connected layers (Dense). Besides the last layer, all layers used a rectified linear unit (“relu”) activation function that directly outputs the input, if positive, or zero otherwise.