Toward merge networks

example/merge_networks.f90

@@ -0,0 +1,73 @@
+program merge_networks
+  use nf, only: dense, input, network, sgd
+  use nf_dense_layer, only: dense_layer
+  implicit none
+
+  type(network) :: net1, net2, net3
+  real, allocatable :: x1(:), x2(:)
+  real, pointer :: y1(:), y2(:)
+  real, allocatable :: y(:)
+  integer, parameter :: num_iterations = 500
+  integer :: n, nn
+  integer :: net1_output_size, net2_output_size
+
+  x1 = [0.1, 0.3, 0.5]
+  x2 = [0.2, 0.4]
+  y = [0.123456, 0.246802, 0.369258, 0.482604, 0.505050, 0.628406, 0.741852]
+
+  net1 = network([ &
+    input(3), &
+    dense(2), &
+    dense(3), &
+    dense(2) &
+  ])
+
+  net2 = network([ &
+    input(2), &
+    dense(5), &
+    dense(3) &
+  ])
+
+  net1_output_size = product(net1 % layers(size(net1 % layers)) % layer_shape)
+  net2_output_size = product(net2 % layers(size(net2 % layers)) % layer_shape)
+
+  ! Network 3
+  net3 = network([ &
+    input(net1_output_size + net2_output_size), &
+    dense(7) &
+  ])
+
+  do n = 1, num_iterations
+
+    ! Forward propagate two network branches
+    call net1 % forward(x1)
+    call net2 % forward(x2)
+
+    ! Get outputs of net1 and net2, concatenate, and pass to net3
+    call net1 % get_output(y1)
+    call net2 % get_output(y2)
+    call net3 % forward([y1, y2])
+
+    ! First compute the gradients on net3, then pass the gradients from the first
+    ! hidden layer on net3 to net1 and net2, and compute their gradients.
+    call net3 % backward(y)
+
+    select type (next_layer => net3 % layers(2) % p)
+      type is (dense_layer)
+        call net1 % backward(y, gradient=next_layer % gradient(1:net1_output_size))
+        call net2 % backward(y, gradient=next_layer % gradient(net1_output_size+1:size(next_layer % gradient)))
+    end select
+
+    ! Gradients are now computed on all networks and we can update the weights
+    call net1 % update(optimizer=sgd(learning_rate=1.))
+    call net2 % update(optimizer=sgd(learning_rate=1.))
+    call net3 % update(optimizer=sgd(learning_rate=1.))
+
+    if (mod(n, 50) == 0) then
+      print *, "Iteration ", n, ", output RMSE = ", &
+        sqrt(sum((net3 % predict([net1 % predict(x1), net2 % predict(x2)]) - y)**2) / size(y))
+    end if
+
+  end do
+
+end program merge_networks

src/nf/nf_network.f90

@@ -33,6 +33,7 @@ module nf_network
     procedure, private :: forward_1d_int
     procedure, private :: forward_2d
     procedure, private :: forward_3d
+    procedure, private :: get_output_1d
     procedure, private :: predict_1d
     procedure, private :: predict_1d_int
     procedure, private :: predict_2d
@@ -42,6 +43,7 @@ module nf_network
 
     generic :: evaluate => evaluate_batch_1d
     generic :: forward => forward_1d, forward_1d_int, forward_2d, forward_3d
+    generic :: get_output => get_output_1d
     generic :: predict => predict_1d, predict_1d_int, predict_2d, predict_3d
     generic :: predict_batch => predict_batch_1d, predict_batch_3d
 
@@ -131,7 +133,7 @@ end subroutine forward_3d
 
   end interface forward
 
-  interface output
+  interface predict
 
     module function predict_1d(self, input) result(res)
       !! Return the output of the network given the input 1-d array.
@@ -169,9 +171,10 @@ module function predict_3d(self, input) result(res)
       real, allocatable :: res(:)
         !! Output of the network
     end function predict_3d
-  end interface output
 
-  interface output_batch
+  end interface predict
+
+  interface predict_batch
     module function predict_batch_1d(self, input) result(res)
       !! Return the output of the network given an input batch of 3-d data.
       class(network), intent(in out) :: self
@@ -191,11 +194,18 @@ module function predict_batch_3d(self, input) result(res)
       real, allocatable :: res(:,:)
         !! Output of the network; the last dimension is the batch
     end function predict_batch_3d
-  end interface output_batch
+  end interface predict_batch
+
+  interface get_output
+    module subroutine get_output_1d(self, output)
+      class(network), intent(in), target :: self
+      real, pointer, intent(out) :: output(:)
+    end subroutine get_output_1d
+  end interface get_output
 
   interface
 
-    module subroutine backward(self, output, loss)
+    module subroutine backward(self, output, loss, gradient)
       !! Apply one backward pass through the network.
       !! This changes the state of layers on the network.
       !! Typically used only internally from the `train` method,
@@ -206,6 +216,12 @@ module subroutine backward(self, output, loss)
         !! Output data
       class(loss_type), intent(in), optional :: loss
         !! Loss instance to use. If not provided, the default is quadratic().
+      real, intent(in), optional :: gradient(:)
+        !! Gradient to use for the output layer.
+        !! If not provided, the gradient in the last layer is computed using
+        !! the loss function.
+        !! Passing the gradient is useful for merging/concatenating multiple
+        !! networks.
     end subroutine backward
 
     module integer function get_num_params(self)

src/nf/nf_network_submodule.f90

@@ -115,10 +115,11 @@ module function network_from_layers(layers) result(res)
   end function network_from_layers
 
 
-  module subroutine backward(self, output, loss)
+  module subroutine backward(self, output, loss, gradient)
     class(network), intent(in out) :: self
     real, intent(in) :: output(:)
     class(loss_type), intent(in), optional :: loss
+    real, intent(in), optional :: gradient(:)
     integer :: n, num_layers
 
     ! Passing the loss instance is optional. If not provided, and if the
@@ -140,58 +141,71 @@ module subroutine backward(self, output, loss)
 
     ! Iterate backward over layers, from the output layer
     ! to the first non-input layer
-    do n = num_layers, 2, -1
-
-      if (n == num_layers) then
-        ! Output layer; apply the loss function
-        select type(this_layer => self % layers(n) % p)
-          type is(dense_layer)
-            call self % layers(n) % backward( &
-              self % layers(n - 1), &
-              self % loss % derivative(output, this_layer % output) &
-            )
-          type is(flatten_layer)
-            call self % layers(n) % backward( &
-              self % layers(n - 1), &
-              self % loss % derivative(output, this_layer % output) &
-            )
-        end select
-      else
-        ! Hidden layer; take the gradient from the next layer
-        select type(next_layer => self % layers(n + 1) % p)
-          type is(dense_layer)
-            call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
-          type is(dropout_layer)
-            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)
-            else
-              call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient_3d)
-            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)
-            call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
-          type is(self_attention_layer)
-            call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
-          type is(maxpool1d_layer)
-            call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
-          type is(reshape2d_layer)
-            call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
-          type is(conv1d_layer)
-            call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
-          type is(locally_connected2d_layer)
-            call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
-          type is(layernorm_layer)
-            call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
-        end select
-      end if
 
+    ! Output layer first
+    n = num_layers
+    if (present(gradient)) then
+
+      ! If the gradient is passed, use it directly for the output layer
+      select type(this_layer => self % layers(n) % p)
+        type is(dense_layer)
+          call self % layers(n) % backward(self % layers(n - 1), gradient)
+        type is(flatten_layer)
+          call self % layers(n) % backward(self % layers(n - 1), gradient)
+      end select
+
+    else
+
+      ! Apply the loss function
+      select type(this_layer => self % layers(n) % p)
+        type is(dense_layer)
+          call self % layers(n) % backward( &
+            self % layers(n - 1), &
+            self % loss % derivative(output, this_layer % output) &
+          )
+        type is(flatten_layer)
+          call self % layers(n) % backward( &
+            self % layers(n - 1), &
+            self % loss % derivative(output, this_layer % output) &
+          )
+      end select
+
+    end if
+
+    ! Hidden layers; take the gradient from the next layer
+    do n = num_layers - 1, 2, -1
+      select type(next_layer => self % layers(n + 1) % p)
+        type is(dense_layer)
+          call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
+        type is(dropout_layer)
+          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)
+          else
+            call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient_3d)
+          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)
+          call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
+        type is(self_attention_layer)
+          call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
+        type is(maxpool1d_layer)
+          call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
+        type is(reshape2d_layer)
+          call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
+        type is(conv1d_layer)
+          call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
+        type is(locally_connected2d_layer)
+          call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
+        type is(layernorm_layer)
+          call self % layers(n) % backward(self % layers(n - 1), next_layer % gradient)
+      end select
     end do
 
   end subroutine backward
@@ -497,6 +511,28 @@ module subroutine print_info(self)
   end subroutine print_info
 
 
+  module subroutine get_output_1d(self, output)
+    class(network), intent(in), target :: self
+    real, pointer, intent(out) :: output(:)
+    integer :: last
+
+    last = size(self % layers)
+
+    select type(output_layer => self % layers(last) % p)
+      type is(dense_layer)
+        output => output_layer % output
+      type is(dropout_layer)
+        output => output_layer % output
+      type is(flatten_layer)
+        output => output_layer % output
+      class default
+        error stop 'network % get_output not implemented for ' // &
+          trim(self % layers(last) % name) // ' layer'
+    end select
+
+  end subroutine get_output_1d
+
+
   module function get_num_params(self)
     class(network), intent(in) :: self
     integer :: get_num_params