Bug fix

Author: Riccardo231

src/nf/nf_layer_constructors.f90

@@ -208,22 +208,21 @@ module function conv2d(filters, kernel_size, activation) result(res)
     end function conv2d
 
     module function locally_connected_1d(filters, kernel_size, activation) result(res)
-      !! CHANGE THE COMMENTS!!!
-      !! 2-d convolutional layer constructor.
+      !! 1-d locally connected network constructor
       !!
-      !! This layer is for building 2-d convolutional network.
-      !! Although the established convention is to call these layers 2-d,
-      !! the shape of the data is actuall 3-d: image width, image height,
+      !! This layer is for building 1-d locally connected network.
+      !! Although the established convention is to call these layers 1-d,
+      !! the shape of the data is actuall 2-d: image width,
       !! and the number of channels.
-      !! A conv2d layer must not be the first layer in the network.
+      !! A locally connected 1d layer must not be the first layer in the network.
       !!
       !! Example:
       !!
       !! ```
-      !! use nf, only :: conv2d, layer
-      !! type(layer) :: conv2d_layer
-      !! conv2d_layer = dense(filters=32, kernel_size=3)
-      !! conv2d_layer = dense(filters=32, kernel_size=3, activation='relu')
+      !! use nf, only :: locally_connected_1d, layer
+      !! type(layer) :: locally_connected_1d_layer
+      !! locally_connected_1d_layer = dense(filters=32, kernel_size=3)
+      !! locally_connected_1d_layer = dense(filters=32, kernel_size=3, activation='relu')
       !! ```
       integer, intent(in) :: filters
         !! Number of filters in the output of the layer
@@ -236,17 +235,17 @@ module function locally_connected_1d(filters, kernel_size, activation) result(re
     end function locally_connected_1d
 
     module function maxpool1d(pool_size, stride) result(res)
-      !! 2-d maxpooling layer constructor.
+      !! 1-d maxpooling layer constructor.
       !!
-      !! This layer is for downscaling other layers, typically `conv2d`.
+      !! This layer is for downscaling other layers, typically `conv1d`.
       !!
       !! Example:
       !!
       !! ```
-      !! use nf, only :: maxpool2d, layer
-      !! type(layer) :: maxpool2d_layer
-      !! maxpool2d_layer = maxpool2d(pool_size=2)
-      !! maxpool2d_layer = maxpool2d(pool_size=2, stride=3)
+      !! use nf, only :: maxpool1d, layer
+      !! type(layer) :: maxpool1d_layer
+      !! maxpool1d_layer = maxpool1d(pool_size=2)
+      !! maxpool1d_layer = maxpool1d(pool_size=2, stride=3)
       !! ```
       integer, intent(in) :: pool_size
         !! Width of the pooling window, commonly 2
@@ -292,9 +291,9 @@ end function reshape
 
     module function reshape2d(output_shape) result(res)
       !! Rank-1 to rank-any reshape layer constructor.
-      !! Currently implemented is only rank-3 for the output of the reshape.
+      !! Currently implemented is only rank-2 for the output of the reshape.
       !!
-      !! This layer is for connecting 1-d inputs to conv2d or similar layers.
+      !! This layer is for connecting 1-d inputs to conv1d or similar layers.
       integer, intent(in) :: output_shape(:)
         !! Shape of the output
       type(layer) :: res

src/nf/nf_network_submodule.f90

@@ -92,9 +92,9 @@ module function network_from_layers(layers) result(res)
             type is(conv1d_layer)
               res % layers = [res % layers(:n-1), flatten(), res % layers(n:)]
               n = n + 1
-            !type is(reshape2d_layer)
-            !  res % layers = [res % layers(:n-1), flatten(), res % layers(n:)]
-            !  n = n + 1
+            type is(reshape2d_layer)
+              res % layers = [res % layers(:n-1), flatten(), res % layers(n:)]
+              n = n + 1
             class default
               n = n + 1
           end select
@@ -163,7 +163,6 @@ module subroutine backward(self, output, loss)
             call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
           type is(conv2d_layer)
             call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
-
           type is(flatten_layer)
             if (size(self % layers(n) % layer_shape) == 2) then
               call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient_2d)
@@ -172,7 +171,6 @@ module subroutine backward(self, output, loss)
             end if
           type is(maxpool2d_layer)
             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(linear2d_layer)

test/test_conv1d_network.f90

@@ -41,7 +41,7 @@ program test_conv1d_network
       real :: y(1)
       real :: tolerance = 1e-4
       integer :: n
-      integer, parameter :: num_iterations = 1500
+      integer, parameter :: num_iterations = 1000
 
       ! Test training of a minimal constant mapping
       allocate(sample_input(1, 5))
@@ -60,6 +60,7 @@ program test_conv1d_network
         call cnn % forward(sample_input)
         call cnn % backward(y)
         call cnn % update(optimizer=sgd(learning_rate=1.))
+        print *, cnn % predict(sample_input), y
         if (all(abs(cnn % predict(sample_input) - y) < tolerance)) exit
       end do
 
@@ -79,7 +80,7 @@ program test_conv1d_network
       real :: y(1)
       real :: tolerance = 1e-4
       integer :: n
-      integer, parameter :: num_iterations = 1500
+      integer, parameter :: num_iterations = 1000
 
       call random_number(x)
       y = [0.1234567]