modified: src/nf/nf_layer.f90

src/nf/nf_conv2d_layer.f90

@@ -1,103 +1,112 @@
 module nf_conv2d_layer
 
-  !! This modules provides a 2-d convolutional `conv2d_layer` type.
-
-  use nf_activation_3d, only: activation_function
-  use nf_base_layer, only: base_layer
-  implicit none
-
-  private
-  public :: conv2d_layer
-
-  type, extends(base_layer) :: conv2d_layer
-
-    integer :: width
-    integer :: height
-    integer :: channels
-    integer :: kernel_size
-    integer :: filters
-
-    real, allocatable :: biases(:) ! size(filters)
-    real, allocatable :: kernel(:,:,:,:) ! filters x channels x window x window
-    real, allocatable :: output(:,:,:) ! filters x output_width * output_height
-    real, allocatable :: z(:,:,:) ! kernel .dot. input + bias
-
-    real, allocatable :: dw(:,:,:,:) ! weight (kernel) gradients
-    real, allocatable :: db(:) ! bias gradients
-    real, allocatable :: gradient(:,:,:)
-
-    procedure(activation_function), pointer, nopass :: &
-      activation => null()
-    procedure(activation_function), pointer, nopass :: &
-      activation_prime => null()
-
-  contains
-
-    procedure :: init
-    procedure :: forward
-    procedure :: backward
-    procedure :: set_activation
-    procedure :: update
-
-  end type conv2d_layer
-
-  interface conv2d_layer
-    pure module function conv2d_layer_cons(filters, kernel_size, activation) &
-      result(res)
-      !! `conv2d_layer` constructor function
-      integer, intent(in) :: filters
-      integer, intent(in) :: kernel_size
-      character(*), intent(in) :: activation
-      type(conv2d_layer) :: res
-    end function conv2d_layer_cons
-  end interface conv2d_layer
-
-  interface
-
-    module subroutine init(self, input_shape)
-      !! Initialize the layer data structures.
-      !!
-      !! This is a deferred procedure from the `base_layer` abstract type.
-      class(conv2d_layer), intent(in out) :: self
-        !! A `conv2d_layer` instance
-      integer, intent(in) :: input_shape(:)
-        !! Input layer dimensions
-    end subroutine init
-
-    pure module subroutine forward(self, input)
-      !! Apply a forward pass on the `conv2d` layer.
-      class(conv2d_layer), intent(in out) :: self
-        !! A `conv2d_layer` instance
-      real, intent(in) :: input(:,:,:)
-        !! Input data
-    end subroutine forward
-
-    pure module subroutine backward(self, input, gradient)
-      !! Apply a backward pass on the `conv2d` layer.
-      class(conv2d_layer), intent(in out) :: self
-        !! A `conv2d_layer` instance
-      real, intent(in) :: input(:,:,:)
-        !! Input data (previous layer)
-      real, intent(in) :: gradient(:,:,:)
-        !! Gradient (next layer)
-    end subroutine backward
-
-    elemental module subroutine set_activation(self, activation)
-    !! Set the activation functions.
-    class(conv2d_layer), intent(in out) :: self
-      !! Layer instance
-    character(*), intent(in) :: activation
-      !! String with the activation function name
-    end subroutine set_activation
-
-    module subroutine update(self, learning_rate)
-      !! Update the weights and biases.
-      class(conv2d_layer), intent(in out) :: self
-        !! Dense layer instance
-      real, intent(in) :: learning_rate
-        !! Learning rate (must be > 0)
-    end subroutine update
-
-  end interface
+   !! This modules provides a 2-d convolutional `conv2d_layer` type.
+
+   use nf_activation_3d, only: activation_function
+   use nf_base_layer, only: base_layer
+   implicit none
+
+   private
+   public :: conv2d_layer
+
+   type, extends(base_layer) :: conv2d_layer
+
+      integer :: width
+      integer :: height
+      integer :: channels
+      integer :: kernel_size
+      integer :: filters
+
+      real, allocatable :: biases(:) ! size(filters)
+      real, allocatable :: kernel(:,:,:,:) ! filters x channels x window x window
+      real, allocatable :: output(:,:,:) ! filters x output_width * output_height
+      real, allocatable :: z(:,:,:) ! kernel .dot. input + bias
+
+      real, allocatable :: dw(:,:,:,:) ! weight (kernel) gradients
+      real, allocatable :: db(:) ! bias gradients
+      real, allocatable :: gradient(:,:,:)
+
+      procedure(activation_function), pointer, nopass :: &
+         activation => null()
+      procedure(activation_function), pointer, nopass :: &
+         activation_prime => null()
+
+   contains
+
+      procedure :: init
+      procedure :: forward
+      procedure :: get_num_params
+      procedure :: backward
+      procedure :: set_activation
+      procedure :: update
+
+   end type conv2d_layer
+
+   interface conv2d_layer
+      pure module function conv2d_layer_cons(filters, kernel_size, activation) &
+         result(res)
+         !! `conv2d_layer` constructor function
+         integer, intent(in) :: filters
+         integer, intent(in) :: kernel_size
+         character(*), intent(in) :: activation
+         type(conv2d_layer) :: res
+      end function conv2d_layer_cons
+   end interface conv2d_layer
+
+   interface
+
+      module subroutine init(self, input_shape)
+         !! Initialize the layer data structures.
+         !!
+         !! This is a deferred procedure from the `base_layer` abstract type.
+         class(conv2d_layer), intent(in out) :: self
+         !! A `conv2d_layer` instance
+         integer, intent(in) :: input_shape(:)
+         !! Input layer dimensions
+      end subroutine init
+
+      pure module subroutine forward(self, input)
+         !! Apply a forward pass on the `conv2d` layer.
+         class(conv2d_layer), intent(in out) :: self
+         !! A `conv2d_layer` instance
+         real, intent(in) :: input(:,:,:)
+         !! Input data
+      end subroutine forward
+
+      pure module subroutine backward(self, input, gradient)
+         !! Apply a backward pass on the `conv2d` layer.
+         class(conv2d_layer), intent(in out) :: self
+         !! A `conv2d_layer` instance
+         real, intent(in) :: input(:,:,:)
+         !! Input data (previous layer)
+         real, intent(in) :: gradient(:,:,:)
+         !! Gradient (next layer)
+      end subroutine backward
+
+      pure module function get_num_params(self) result(res)
+         !! Get the number of parameters in the layer.
+         class(conv2d_layer), intent(in) :: self
+         !! A `conv2d_layer` instance
+         integer :: res
+         !! Number of parameters
+      end function get_num_params
+
+      elemental module subroutine set_activation(self, activation)
+         !! Set the activation functions.
+         class(conv2d_layer), intent(in out) :: self
+         !! Layer instance
+         character(*), intent(in) :: activation
+         !! String with the activation function name
+      end subroutine set_activation
+
+      module subroutine update(self, learning_rate)
+         !! Update the weights and biases.
+         class(conv2d_layer), intent(in out) :: self
+         !! Dense layer instance
+         real, intent(in) :: learning_rate
+         !! Learning rate (must be > 0)
+      end subroutine update
+
+   end interface
 
 end module nf_conv2d_layer