Add CNN from Keras example; rename the dense from Keras example

Author: milancurcic

example/CMakeLists.txt

@@ -1,4 +1,16 @@
-foreach(execid cnn mnist mnist_from_keras simple sine)
+foreach(execid
+  cnn
+  cnn_from_keras
+  dense_from_keras
+  mnist
+  simple
+  sine
+)
   add_executable(${execid} ${execid}.f90)
-  target_link_libraries(${execid} PRIVATE neural h5fortran::h5fortran jsonfortran::jsonfortran ${LIBS})
+  target_link_libraries(${execid} PRIVATE
+    neural
+    h5fortran::h5fortran
+    jsonfortran::jsonfortran
+    ${LIBS}
+  )
 endforeach()

example/cnn_from_keras.f90

@@ -0,0 +1,58 @@
+program cnn_from_keras
+
+  ! This example demonstrates loading a convolutional model
+  ! pre-trained on the MNIST dataset from a Keras HDF5
+  ! file and running an inferrence on the testing dataset.
+
+  use nf, only: network, label_digits, load_mnist
+  use nf_datasets, only: download_and_unpack, keras_cnn_mnist_url
+
+  implicit none
+
+  type(network) :: net
+  real, allocatable :: training_images(:,:), training_labels(:)
+  real, allocatable :: validation_images(:,:), validation_labels(:)
+  real, allocatable :: testing_images(:,:), testing_labels(:)
+  character(*), parameter :: keras_cnn_path = 'keras_cnn_mnist.h5'
+  logical :: file_exists
+  real :: acc
+
+  inquire(file=keras_cnn_path, exist=file_exists)
+  if (.not. file_exists) call download_and_unpack(keras_cnn_mnist_url)
+
+  call load_mnist(training_images, training_labels, &
+                  validation_images, validation_labels, &
+                  testing_images, testing_labels)
+
+  print '("Loading a pre-trained CNN model from Keras")'
+  print '(60("="))'
+
+  net = network(keras_cnn_path)
+
+  call net % print_info()
+
+  if (this_image() == 1) then
+    acc = accuracy( &
+      net, &
+      reshape(testing_images(:,:), shape=[1,28,28,size(testing_images,2)]), &
+      label_digits(testing_labels) &
+    )
+    print '(a,f5.2,a)', 'Accuracy: ', acc * 100, ' %'
+  end if
+
+contains
+
+  real function accuracy(net, x, y)
+    type(network), intent(in out) :: net
+    real, intent(in) :: x(:,:,:,:), y(:,:)
+    integer :: i, good
+    good = 0
+    do i = 1, size(x, dim=4)
+      if (all(maxloc(net % output(x(:,:,:,i))) == maxloc(y(:,i)))) then
+        good = good + 1
+      end if
+    end do
+    accuracy = real(good) / size(x, dim=4)
+  end function accuracy
+
+end program cnn_from_keras

example/mnist_from_keras.f90 renamed to example/dense_from_keras.f90

@@ -1,7 +1,8 @@
-program mnist_from_keras
+program dense_from_keras
 
-  ! This example demonstrates loading a pre-trained MNIST model from Keras
-  ! from an HDF5 file and running an inferrence on the testing dataset.
+  ! This example demonstrates loading a dense model
+  ! pre-trained on the MNIST dataset from a Keras HDF5
+  ! file and running an inferrence on the testing dataset.
 
   use nf, only: network, label_digits, load_mnist
   use nf_datasets, only: download_and_unpack, keras_dense_mnist_url
@@ -22,7 +23,7 @@ program mnist_from_keras
                   validation_images, validation_labels, &
                   testing_images, testing_labels)
 
-  print '("Loading a pre-trained MNIST model from Keras")'
+  print '("Loading a pre-trained dense model from Keras")'
   print '(60("="))'
 
   net = network(keras_dense_path)
@@ -48,4 +49,4 @@ real function accuracy(net, x, y)
     accuracy = real(good) / size(x, dim=2)
   end function accuracy
 
-end program mnist_from_keras
+end program dense_from_keras