Connected locally_connected_submodule to environment; as for now, it does not still work

Author: Riccardo231

example/cnn_mnist_1d.f90

@@ -20,8 +20,11 @@ program cnn_mnist
 
     net = network([ &
       input(784), &
-      reshape_generalized([28, 28]), &
+      reshape([1,28,28]), &
       locally_connected_1d(filters=8, kernel_size=3, activation=relu()), &
+      maxpool2d(pool_size=2), &
+      locally_connected_1d(filters=16, kernel_size=3, activation=relu()), &
+      maxpool2d(pool_size=2), &
       dense(10, activation=softmax()) &
     ])
 
@@ -34,7 +37,7 @@ program cnn_mnist
         label_digits(training_labels), &
         batch_size=16, &
         epochs=1, &
-        optimizer=sgd(learning_rate=1) &
+        optimizer=sgd(learning_rate=0.1) &
       )
 
       print '(a,i2,a,f5.2,a)', 'Epoch ', n, ' done, Accuracy: ', accuracy( &

src/nf/nf_conv2d_layer_submodule.f90

@@ -243,4 +243,4 @@ module subroutine set_params(self, params)
 
   end subroutine set_params
 
-end submodule nf_conv2d_layer_submodule
+end submodule nf_conv2d_layer_submodule

src/nf/nf_layer_submodule.f90

@@ -45,6 +45,8 @@ pure module subroutine backward_1d(self, previous, gradient)
             call this_layer % backward(prev_layer % output, gradient)
           type is(conv2d_layer)
             call this_layer % backward(prev_layer % output, gradient)
+          type is(locally_connected_1d_layer)
+            call this_layer % backward(prev_layer % output, gradient)
           type is(maxpool2d_layer)
             call this_layer % backward(prev_layer % output, gradient)
         end select
@@ -86,6 +88,8 @@ pure module subroutine backward_3d(self, previous, gradient)
             call this_layer % backward(prev_layer % output, gradient)
           type is(conv2d_layer)
             call this_layer % backward(prev_layer % output, gradient)
+          type is(locally_connected_1d_layer)
+            call this_layer % backward(prev_layer % output, gradient)
           type is(reshape3d_layer)
             call this_layer % backward(prev_layer % output, gradient)
         end select
@@ -100,6 +104,8 @@ pure module subroutine backward_3d(self, previous, gradient)
             call this_layer % backward(prev_layer % output, gradient)
           type is(input3d_layer)
             call this_layer % backward(prev_layer % output, gradient)
+          type is(locally_connected_1d_layer)
+            call this_layer % backward(prev_layer % output, gradient)
           type is(reshape3d_layer)
             call this_layer % backward(prev_layer % output, gradient)
         end select
@@ -148,6 +154,8 @@ pure module subroutine forward(self, input)
             call this_layer % forward(prev_layer % output)
           type is(conv2d_layer)
             call this_layer % forward(prev_layer % output)
+          type is(locally_connected_1d_layer)
+            call this_layer % forward(prev_layer % output)
           type is(maxpool2d_layer)
             call this_layer % forward(prev_layer % output)
           type is(reshape3d_layer)
@@ -162,6 +170,8 @@ pure module subroutine forward(self, input)
             call this_layer % forward(prev_layer % output)
           type is(conv2d_layer)
             call this_layer % forward(prev_layer % output)
+          type is(locally_connected_1d_layer)
+            call this_layer % forward(prev_layer % output)
           type is(maxpool2d_layer)
             call this_layer % forward(prev_layer % output)
           type is(reshape3d_layer)
@@ -176,6 +186,8 @@ pure module subroutine forward(self, input)
             call this_layer % forward(prev_layer % output)
           type is(conv2d_layer)
             call this_layer % forward(prev_layer % output)
+          type is(locally_connected_1d_layer)
+            call this_layer % forward(prev_layer % output)
           type is(maxpool2d_layer)
             call this_layer % forward(prev_layer % output)
           type is(reshape3d_layer)
@@ -248,6 +260,8 @@ pure module subroutine get_output_3d(self, output)
         allocate(output, source=this_layer % output)
       type is(conv2d_layer)
         allocate(output, source=this_layer % output)
+      type is(locally_connected_1d_layer)
+        allocate(output, source=this_layer % output)
       type is(maxpool2d_layer)
         allocate(output, source=this_layer % output)
       type is(reshape3d_layer)
@@ -277,6 +291,8 @@ impure elemental module subroutine init(self, input)
     select type(this_layer => self % p)
       type is(conv2d_layer)
         self % layer_shape = shape(this_layer % output)
+      type is(locally_connected_1d_layer)
+        self % layer_shape = shape(this_layer % output)
       type is(maxpool2d_layer)
         self % layer_shape = shape(this_layer % output)
       type is(flatten_layer)
@@ -320,6 +336,8 @@ elemental module function get_num_params(self) result(num_params)
         num_params = this_layer % get_num_params()
       type is (conv2d_layer)
         num_params = this_layer % get_num_params()
+      type is (locally_connected_1d_layer)
+        num_params = this_layer % get_num_params()
       type is (maxpool2d_layer)
         num_params = 0
       type is (flatten_layer)
@@ -349,6 +367,8 @@ module function get_params(self) result(params)
         params = this_layer % get_params()
       type is (conv2d_layer)
         params = this_layer % get_params()
+      type is (locally_connected_1d_layer)
+        params = this_layer % get_params()
       type is (maxpool2d_layer)
         ! No parameters to get.
       type is (flatten_layer)
@@ -378,6 +398,8 @@ module function get_gradients(self) result(gradients)
         gradients = this_layer % get_gradients()
       type is (conv2d_layer)
         gradients = this_layer % get_gradients()
+      type is (locally_connected_1d_layer)
+        gradients = this_layer % get_gradients()
       type is (maxpool2d_layer)
         ! No gradients to get.
       type is (flatten_layer)
@@ -432,6 +454,9 @@ module subroutine set_params(self, params)
 
       type is (conv2d_layer)
         call this_layer % set_params(params)
+      
+      type is (locally_connected_1d_layer)
+        call this_layer % set_params(params)
 
       type is (maxpool2d_layer)
         ! No parameters to set.

src/nf/nf_locally_connected_1d.f90

@@ -1,112 +1,119 @@
 module nf_locally_connected_1d_layer
-  !! This module provides a locally connected 1d layer type.
-
-  use nf_activation, only: activation_function
-  use nf_base_layer,   only: base_layer
-  implicit none
-
-  private
-  public :: locally_connected_1d_layer
-
-  type, extends(base_layer) :: locally_connected_1d_layer
-    ! For a 1D layer, we assume an input shape of [channels, input_length]
-    integer :: channels        ! number of input channels
-    integer :: input_length    ! length of the 1D input
-    integer :: output_length   ! computed as input_length - kernel_size + 1
-    integer :: kernel_size     ! size of the 1D window
-    integer :: filters         ! number of filters (output channels)
-
-    ! Parameters (unshared weights)
-    ! Kernel shape: (filters, output_length, channels, kernel_size)
-    real, allocatable :: kernel(:,:,:,:)
-    ! Biases shape: (filters, output_length)
-    real, allocatable :: biases(:,:)
-
-    ! Forward-pass arrays
-    ! Pre-activation values: shape (filters, output_length)
-    real, allocatable :: z(:,:)
-    ! Activated output: shape (filters, output_length)
-    real, allocatable :: output(:,:)
-
-    ! Gradients for backpropagation
-    ! Gradient for kernel, same shape as kernel
-    real, allocatable :: dw(:,:,:,:)
-    ! Gradient for biases, same shape as biases
-    real, allocatable :: db(:,:)
-    ! Gradient with respect to the input, shape (channels, input_length)
-    real, allocatable :: gradient(:,:)
-
-    ! Activation function
-    class(activation_function), allocatable :: activation
-  contains
-    procedure :: forward
-    procedure :: backward
-    procedure :: get_gradients
-    procedure :: get_num_params
-    procedure :: get_params
-    procedure :: init
-    procedure :: set_params
-  end type locally_connected_1d_layer
-
-  interface locally_connected_1d_layer
-    module function locally_connected_1d_layer_cons(filters, kernel_size, activation) result(res)
-      !! Constructor for the locally connected 1d layer.
-      integer, intent(in)                   :: filters
-      integer, intent(in)                   :: kernel_size
-      class(activation_function), intent(in):: activation
-      type(locally_connected_1d_layer)       :: res
-    end function locally_connected_1d_layer_cons
-  end interface locally_connected_1d_layer
-
-  interface
-    module subroutine init(self, input_shape)
-      !! Initialize the layer data structures.
-      !! input_shape: integer array of length 2, where
-      !!   input_shape(1) = number of channels
-      !!   input_shape(2) = input length
-      class(locally_connected_1d_layer), intent(inout) :: self
-      integer, intent(in) :: input_shape(:)
-    end subroutine init
-
-    pure module subroutine forward(self, input)
-      !! Apply the forward pass.
-      !! Input shape: (channels, input_length)
-      class(locally_connected_1d_layer), intent(inout) :: self
-      real, intent(in) :: input(:,:)
-    end subroutine forward
-
-    pure module subroutine backward(self, input, gradient)
-      !! Apply the backward pass.
-      !! input: shape (channels, input_length)
-      !! gradient: gradient w.r.t. output, shape (filters, output_length)
-      class(locally_connected_1d_layer), intent(inout) :: self
-      real, intent(in) :: input(:,:)
-      real, intent(in) :: gradient(:,:)
-    end subroutine backward
-
-    pure module function get_num_params(self) result(num_params)
-      !! Get the total number of parameters (kernel + biases)
-      class(locally_connected_1d_layer), intent(in) :: self
-      integer :: num_params
-    end function get_num_params
-
-    module function get_params(self) result(params)
-      !! Return a flattened vector of parameters (kernel then biases).
-      class(locally_connected_1d_layer), intent(in), target :: self
-      real, allocatable :: params(:)
-    end function get_params
-
-    module function get_gradients(self) result(gradients)
-      !! Return a flattened vector of gradients (dw then db).
-      class(locally_connected_1d_layer), intent(in), target :: self
-      real, allocatable :: gradients(:)
-    end function get_gradients
-
-    module subroutine set_params(self, params)
-      !! Set the parameters from a flattened vector.
-      class(locally_connected_1d_layer), intent(inout) :: self
-      real, intent(in) :: params(:)
-    end subroutine set_params
-  end interface
+    !! This modules provides a 1-d convolutional `locally_connected_1d` type.
+  
+    use nf_activation, only: activation_function
+    use nf_base_layer, only: base_layer
+    implicit none
+  
+    private
+    public :: locally_connected_1d_layer
+  
+    type, extends(base_layer) :: locally_connected_1d_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(:,:)
+  
+      class(activation_function), allocatable :: activation
+  
+    contains
+  
+      procedure :: forward
+      procedure :: backward
+      procedure :: get_gradients
+      procedure :: get_num_params
+      procedure :: get_params
+      procedure :: init
+      procedure :: set_params
+  
+    end type locally_connected_1d_layer
+  
+    interface locally_connected_1d_layer
+      module function locally_connected_1d_layer_cons(filters, kernel_size, activation) &
+        result(res)
+        !! `locally_connected_1d_layer` constructor function
+        integer, intent(in) :: filters
+        integer, intent(in) :: kernel_size
+        class(activation_function), intent(in) :: activation
+        type(locally_connected_1d_layer) :: res
+      end function locally_connected_1d_layer_cons
+    end interface locally_connected_1d_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(locally_connected_1d_layer), intent(in out) :: self
+          !! A `locally_connected_1d_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 `locally_connected_1d` layer.
+        class(locally_connected_1d_layer), intent(in out) :: self
+          !! A `locally_connected_1d_layer` instance
+        real, intent(in) :: input(:,:)
+          !! Input data
+      end subroutine forward
+  
+      pure module subroutine backward(self, input, gradient)
+        !! Apply a backward pass on the `locally_connected_1d` layer.
+        class(locally_connected_1d_layer), intent(in out) :: self
+          !! A `locally_connected_1d_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(num_params)
+        !! Get the number of parameters in the layer.
+        class(locally_connected_1d_layer), intent(in) :: self
+          !! A `locally_connected_1d_layer` instance
+        integer :: num_params
+          !! Number of parameters
+      end function get_num_params
+  
+      module function get_params(self) result(params)
+        !! Return the parameters (weights and biases) of this layer.
+        !! The parameters are ordered as weights first, biases second.
+        class(locally_connected_1d_layer), intent(in), target :: self
+          !! A `locally_connected_1d_layer` instance
+        real, allocatable :: params(:)
+          !! Parameters to get
+      end function get_params
+  
+      module function get_gradients(self) result(gradients)
+        !! Return the gradients of this layer.
+        !! The gradients are ordered as weights first, biases second.
+        class(locally_connected_1d_layer), intent(in), target :: self
+          !! A `locally_connected_1d_layer` instance
+        real, allocatable :: gradients(:)
+          !! Gradients to get
+      end function get_gradients
+  
+      module subroutine set_params(self, params)
+        !! Set the parameters of the layer.
+        class(locally_connected_1d_layer), intent(in out) :: self
+          !! A `locally_connected_1d_layer` instance
+        real, intent(in) :: params(:)
+          !! Parameters to set
+      end subroutine set_params
+  
+    end interface
 
 end module nf_locally_connected_1d_layer