Toward merge networks

example/concatenate.f90

@@ -0,0 +1,63 @@
+program concatenate
+  use nf, only: dense, input, network, sgd
+  implicit none
+
+  type(network) :: net1, net2, net3
+  real, allocatable :: x1(:), y1(:)
+  real, allocatable :: x2(:), y2(:)
+  real, allocatable :: x3(:), y3(:)
+  integer, parameter :: num_iterations = 500
+  integer :: n
+
+  ! Network 1
+  net1 = network([ &
+    input(3), &
+    dense(2) &
+  ])
+
+  x1 = [0.2, 0.4, 0.6]
+  y1 = [0.123456, 0.246802]
+
+  do n = 1, num_iterations
+    call net1 % forward(x1)
+    call net1 % backward(y1)
+    call net1 % update(optimizer=sgd(learning_rate=1.))
+  end do
+
+  print *, "net1 output: ", net1 % predict(x1)
+
+  ! Network 2
+  net2 = network([ &
+    input(3), &
+    dense(3) &
+  ])
+
+  x2 = [0.7, 0.5, 0.3]
+  y2 = [0.369258, 0.482604, 0.505050]
+
+  do n = 1, num_iterations
+    call net2 % forward(x2)
+    call net2 % backward(y2)
+    call net2 % update(optimizer=sgd(learning_rate=1.))
+  end do
+
+  print *, "net2 output: ", net2 % predict(x2)
+
+  ! Network 3
+  net3 = network([ &
+    input(size(net1 % predict(x1)) + size(net2 % predict(x2))), &
+    dense(5) & 
+  ])
+
+  x3 = [net1 % predict(x1), net2 % predict(x2)]
+  y3 = [0.111111, 0.222222, 0.333333, 0.444444, 0.555555]
+
+  do n = 1, num_iterations
+    call net3 % forward(x3)
+    call net3 % backward(y3)
+    call net3 % update(optimizer=sgd(learning_rate=1.))
+  end do
+
+  print *, "net3 output: ", net3 % predict(x3)
+
+end program concatenate