Definitive bug fixes

Author: Riccardo231

example/cnn_mnist.f90

@@ -12,7 +12,7 @@ program cnn_mnist
   real, allocatable :: validation_images(:,:), validation_labels(:)
   real, allocatable :: testing_images(:,:), testing_labels(:)
   integer :: n
-  integer, parameter :: num_epochs = 10
+  integer, parameter :: num_epochs = 250
 
   call load_mnist(training_images, training_labels, &
                   validation_images, validation_labels, &
@@ -37,7 +37,7 @@ program cnn_mnist
       label_digits(training_labels), &
       batch_size=16, &
       epochs=1, &
-      optimizer=sgd(learning_rate=0.003) &
+      optimizer=sgd(learning_rate=0.001) &
     )
 
     print '(a,i2,a,f5.2,a)', 'Epoch ', n, ' done, Accuracy: ', accuracy( &

example/cnn_mnist_1d.f90

@@ -1,7 +1,7 @@
 program cnn_mnist_1d
 
     use nf, only: network, sgd, &
-      input, conv1d, conv2d, maxpool1d, maxpool2d, flatten, dense, reshape, reshape2d, locally_connected_1d, &
+      input, conv1d, maxpool1d, flatten, dense, reshape, reshape2d, locally_connected_1d, &
       load_mnist, label_digits, softmax, relu
 
     implicit none
@@ -12,7 +12,7 @@ program cnn_mnist_1d
     real, allocatable :: validation_images(:,:), validation_labels(:)
     real, allocatable :: testing_images(:,:), testing_labels(:)
     integer :: n
-    integer, parameter :: num_epochs = 25
+    integer, parameter :: num_epochs = 250
 
     call load_mnist(training_images, training_labels, &
                     validation_images, validation_labels, &
@@ -37,7 +37,7 @@ program cnn_mnist_1d
         label_digits(training_labels), &
         batch_size=16, &
         epochs=1, &
-        optimizer=sgd(learning_rate=0.005) &
+        optimizer=sgd(learning_rate=0.01) &
       )
 
       print '(a,i2,a,f5.2,a)', 'Epoch ', n, ' done, Accuracy: ', accuracy( &

src/nf/nf_conv1d_layer_submodule.f90

@@ -59,7 +59,7 @@ pure module subroutine forward(self, input)
     class(conv1d_layer), intent(in out) :: self
     real, intent(in) :: input(:,:)
     integer :: input_channels, input_width
-    integer :: j, n
+    integer :: j, n, a, b
     integer :: iws, iwe, half_window
 
     input_channels = size(input, dim=1)
@@ -95,7 +95,7 @@ pure module subroutine backward(self, input, gradient)
     real, intent(in) :: gradient(:,:)
 
     integer :: input_channels, input_width, output_width
-    integer :: j, n, k
+    integer :: j, n, k, a, b, c
     integer :: iws, iwe, half_window
     real :: gdz_val
 

src/nf/nf_datasets_mnist_submodule.f90

@@ -50,9 +50,9 @@ module subroutine load_mnist(training_images, training_labels, &
     real, allocatable, intent(in out), optional :: testing_labels(:)
 
     integer, parameter :: dtype = 4, image_size = 784
-    integer, parameter :: num_training_images = 500
-    integer, parameter :: num_validation_images = 100
-    integer, parameter :: num_testing_images = 100
+    integer, parameter :: num_training_images = 50000
+    integer, parameter :: num_validation_images = 10000
+    integer, parameter :: num_testing_images = 10000
     logical :: file_exists
 
     ! Check if MNIST data is present and download it if not.

src/nf/nf_network.f90

@@ -201,6 +201,7 @@ module integer function get_num_params(self)
       !! Network instance
     end function get_num_params
 
+
     module function get_params(self) result(params)
       !! Get the network parameters (weights and biases).
       class(network), intent(in) :: self

src/nf/nf_network_submodule.f90

@@ -460,7 +460,6 @@ module function get_num_params(self)
 
   end function get_num_params
 
-
   module function get_params(self) result(params)
     class(network), intent(in) :: self
     real, allocatable :: params(:)
@@ -480,7 +479,6 @@ module function get_params(self) result(params)
 
   end function get_params
 
-
   module function get_gradients(self) result(gradients)
     class(network), intent(in) :: self
     real, allocatable :: gradients(:)
@@ -640,6 +638,12 @@ module subroutine update(self, optimizer, batch_size)
         type is(conv2d_layer)
           call co_sum(this_layer % dw)
           call co_sum(this_layer % db)
+        type is(conv1d_layer)
+          call co_sum(this_layer % dw)
+          call co_sum(this_layer % db)
+        type is(locally_connected_1d_layer)
+          call co_sum(this_layer % dw)
+          call co_sum(this_layer % db)
       end select
     end do
 #endif
@@ -657,6 +661,12 @@ module subroutine update(self, optimizer, batch_size)
         type is(conv2d_layer)
           this_layer % dw = 0
           this_layer % db = 0
+        type is(conv1d_layer)
+          this_layer % dw = 0
+          this_layer % db = 0
+        type is(locally_connected_1d_layer)
+          this_layer % dw = 0
+          this_layer % db = 0
       end select
     end do
 

test/test_conv1d_network.f90

@@ -60,8 +60,7 @@ program test_conv1d_network
         call cnn % forward(sample_input)
         call cnn % backward(y)
         call cnn % update(optimizer=sgd(learning_rate=1.))
-        o = cnn % layers(2) % get_params()
-        print *, o
+
         if (all(abs(cnn % predict(sample_input) - y) < tolerance)) exit
       end do