fix: Conciliating with latest main state

src/nf/nf_network_submodule.f90

@@ -8,8 +8,6 @@
   use nf_maxpool2d_layer, only: maxpool2d_layer
   use nf_reshape_layer, only: reshape3d_layer
   use nf_rnn_layer, only: rnn_layer
-  use nf_io_hdf5, only: get_hdf5_dataset
-  use nf_keras, only: get_keras_h5_layers, keras_layer
   use nf_layer, only: layer
   use nf_layer_constructors, only: conv2d, dense, flatten, input, maxpool2d, reshape, rnn
   use nf_loss, only: quadratic
@@ -96,155 +94,6 @@ module function network_from_layers(layers) result(res)
   end function network_from_layers
 
 
-  module function network_from_keras(filename) result(res)
-    character(*), intent(in) :: filename
-    type(network) :: res
-    type(keras_layer), allocatable :: keras_layers(:)
-    type(layer), allocatable :: layers(:)
-    character(:), allocatable :: layer_name
-    character(:), allocatable :: object_name
-    integer :: n
-
-    keras_layers = get_keras_h5_layers(filename)
-
-    allocate(layers(size(keras_layers)))
-
-    do n = 1, size(layers)
-
-      select case(keras_layers(n) % class)
-
-        case('Conv2D')
-
-          if (keras_layers(n) % kernel_size(1) &
-            /= keras_layers(n) % kernel_size(2)) &
-            error stop 'Non-square kernel in conv2d layer not supported.'
-
-          layers(n) = conv2d( &
-            keras_layers(n) % filters, &
-            !FIXME add support for non-square kernel
-            keras_layers(n) % kernel_size(1), &
-            get_activation_by_name(keras_layers(n) % activation) &
-          )
-
-        case('Dense')
-
-          layers(n) = dense( &
-            keras_layers(n) % units(1), &
-            get_activation_by_name(keras_layers(n) % activation) &
-          )
-
-        case('Flatten')
-          layers(n) = flatten()
-
-        case('InputLayer')
-          if (size(keras_layers(n) % units) == 1) then
-            ! input1d
-            layers(n) = input(keras_layers(n) % units(1))
-          else
-            ! input3d
-            layers(n) = input(keras_layers(n) % units)
-          end if
-
-        case('MaxPooling2D')
-
-          if (keras_layers(n) % pool_size(1) &
-            /= keras_layers(n) % pool_size(2)) &
-            error stop 'Non-square pool in maxpool2d layer not supported.'
-
-          if (keras_layers(n) % strides(1) &
-            /= keras_layers(n) % strides(2)) &
-            error stop 'Unequal strides in maxpool2d layer are not supported.'
-
-          layers(n) = maxpool2d( &
-            !FIXME add support for non-square pool and stride
-            keras_layers(n) % pool_size(1), &
-            keras_layers(n) % strides(1) &
-          )
-
-        case('Reshape')
-          layers(n) = reshape(keras_layers(n) % target_shape)
-
-        case default
-          error stop 'This Keras layer is not supported'
-
-      end select
-
-    end do
-
-    res = network(layers)
-
-    ! Loop over layers and read weights and biases from the Keras h5 file
-    ! for each; currently only dense layers are implemented.
-    do n = 2, size(res % layers)
-
-      layer_name = keras_layers(n) % name
-
-      select type(this_layer => res % layers(n) % p)
-
-        type is(conv2d_layer)
-          ! Read biases from file
-          object_name = '/model_weights/' // layer_name // '/' &
-            // layer_name // '/bias:0'
-          call get_hdf5_dataset(filename, object_name, this_layer % biases)
-
-          ! Read weights from file
-          object_name = '/model_weights/' // layer_name // '/' &
-            // layer_name // '/kernel:0'
-          call get_hdf5_dataset(filename, object_name, this_layer % kernel)
-
-        type is(dense_layer)
-
-          ! Read biases from file
-          object_name = '/model_weights/' // layer_name // '/' &
-            // layer_name // '/bias:0'
-          call get_hdf5_dataset(filename, object_name, this_layer % biases)
-
-          ! Read weights from file
-          object_name = '/model_weights/' // layer_name // '/' &
-            // layer_name // '/kernel:0'
-          call get_hdf5_dataset(filename, object_name, this_layer % weights)
-
-        type is(flatten_layer)
-          ! Nothing to do
-          continue
-
-        type is(maxpool2d_layer)
-          ! Nothing to do
-          continue
-
-        type is(reshape3d_layer)
-          ! Nothing to do
-          continue
-
-        type is(rnn_layer)
-
-          ! Read biases from file
-          object_name = '/model_weights/' // layer_name // '/' &
-            // layer_name // '/simple_rnn_cell_23/bias:0'
-          call get_hdf5_dataset(filename, object_name, this_layer % biases)
-
-          ! Read weights from file
-          object_name = '/model_weights/' // layer_name // '/' &
-            // layer_name // '/simple_rnn_cell_23/kernel:0'
-          call get_hdf5_dataset(filename, object_name, this_layer % weights)
-
-          ! Read recurrent weights from file
-          object_name = '/model_weights/' // layer_name // '/' &
-            // layer_name // '/simple_rnn_cell_23/recurrent_kernel:0'
-          call get_hdf5_dataset(filename, object_name, this_layer % recurrent)
-
-        class default
-          error stop 'Internal error in network_from_keras(); ' &
-            // 'mismatch in layer types between the Keras and ' &
-            // 'neural-fortran model layers.'
-
-      end select
-
-  end do
-
-  end function network_from_keras
-
-
   pure function get_activation_by_name(activation_name) result(res)
   ! Workaround to get activation_function with some
   ! hardcoded default parameters by its name.
@@ -298,7 +147,7 @@ pure function get_activation_by_name(activation_name) result(res)
 
   end function get_activation_by_name
 
-  pure module subroutine backward(self, output, loss)
+  module subroutine backward(self, output, loss)
     class(network), intent(in out) :: self
     real, intent(in) :: output(:)
     class(loss_type), intent(in), optional :: loss