Bug fixes; now reshape_generalized works. Added test for reshape_generalized

Author: Riccardo231

example/cnn_mnist_1d.f90

@@ -20,11 +20,8 @@ program cnn_mnist
 
     net = network([ &
       input(784), &
-      reshape([1, 28, 28]), &
-      conv2d(filters=8, kernel_size=3, activation=relu()), &
-      maxpool2d(pool_size=2), &
-      conv2d(filters=16, kernel_size=3, activation=relu()), &
-      maxpool2d(pool_size=2), &
+      reshape_generalized([28, 28]), &
+      locally_connected_1d(filters=8, kernel_size=3, activation=relu()), &
       dense(10, activation=softmax()) &
     ])
 

src/nf/nf_network_submodule.f90

@@ -8,6 +8,7 @@
   use nf_locally_connected_1d_layer, only: locally_connected_1d_layer
   use nf_maxpool2d_layer, only: maxpool2d_layer
   use nf_reshape_layer, only: reshape3d_layer
+  use nf_reshape_layer_generalized, only: reshape_generalized_layer
   use nf_layer, only: layer
   use nf_layer_constructors, only: conv2d, dense, flatten, input, locally_connected_1d, maxpool2d, reshape, reshape_generalized
   use nf_loss, only: quadratic
@@ -76,6 +77,9 @@ module function network_from_layers(layers) result(res)
             type is(reshape3d_layer)
               res % layers = [res % layers(:n-1), flatten(), res % layers(n:)]
               n = n + 1
+            type is(reshape_generalized_layer)
+              res % layers = [res % layers(:n-1), flatten(), res % layers(n:)]
+              n = n + 1
             class default
               n = n + 1
           end select
@@ -143,6 +147,8 @@ module subroutine backward(self, output, loss)
             call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
           type is(reshape3d_layer)
             call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
+          type is(reshape_generalized_layer)
+            call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
         end select
       end if
 

test/CMakeLists.txt

@@ -8,6 +8,7 @@ foreach(execid
   flatten_layer
   insert_flatten
   reshape_layer
+  reshape_generalized_layer
   dense_network
   get_set_network_params
   conv2d_network

test/test_reshape_generalized_layer.f90

@@ -0,0 +1,77 @@
+program test_reshape_layer
+
+    use iso_fortran_env, only: stderr => error_unit
+    use nf, only: input, network, reshape_generalized  ! Check if this is the correct function
+    use nf_datasets, only: download_and_unpack, keras_reshape_url
+
+    implicit none
+
+    type(network) :: net
+    real, allocatable :: sample_input(:), output(:,:,:)
+    integer, parameter :: output_shape_first(2)  = [64, 32]
+    integer, parameter :: output_shape_second(6) = [8, 8, 4, 2, 2, 2]
+    integer, parameter :: output_shape_third(5)  = [4, 4, 4, 4, 8]
+    integer :: input_size  ! Removed parameter
+    character(*), parameter :: keras_reshape_path = 'keras_reshape.h5'
+    logical :: ok = .true.
+    integer :: i
+    integer, dimension(:), allocatable :: output_shape
+
+    ! Test multiple reshape configurations
+    do i = 1, 3
+        select case (i)
+            case (1)
+                output_shape = output_shape_first
+            case (2)
+                output_shape = output_shape_second
+            case (3)
+                output_shape = output_shape_third
+        end select
+
+        ! Update input size
+        input_size = product(output_shape)
+
+        ! Create network with reshape_generalized
+        net = network([ &
+            input(input_size), &
+            reshape_generalized(output_shape) &  ! Make sure the function name is correct
+        ])
+
+        if (.not. size(net % layers) == 2) then
+            write(stderr, '(a, i0)') 'Test case ', i, ': the network should have 2 layers.. failed'
+            ok = .false.
+        end if
+
+        ! Initialize test data
+        allocate(sample_input(input_size))
+        call random_number(sample_input)
+
+        ! Allocate output correctly before reshaping
+        allocate(output(output_shape(1), output_shape(2), output_shape(3)))  
+        output = reshape(sample_input, shape(output))
+
+        ! Check shape
+        if (.not. all(shape(output) == output_shape)) then
+            write(stderr, '(a, i0)') 'Test case ', i, ': the reshape layer produces expected output shape.. failed'
+            ok = .false.
+        end if
+
+        ! Check values
+        if (.not. all(output == reshape(sample_input, shape(output)))) then
+            write(stderr, '(a, i0)') 'Test case ', i, ': the reshape layer produces expected output values.. failed'
+            ok = .false.
+        end if
+
+        ! Deallocate for next test case
+        deallocate(sample_input, output)
+    end do
+
+    ! Final test result
+    if (ok) then
+        print '(a)', 'test_reshape_generalized_layer: All tests passed.'
+    else
+        write(stderr, '(a)') 'test_reshape_generalized_layer: One or more tests failed.'
+        stop 1
+    end if
+
+end program test_reshape_layer