Added Adam optimizer implementation (#150)

* Added Adam optimizer implementation

* called rms from optimizers module

* Minor fixes to improve adam performance

* Suggested Changes

* Remove dead code; format to <=80 columns

* Use associate instead of explicit allocation for m_hat and v_hat; formatting

* Added convergency test for Adam

* AdamW: Adam with decay weights modification

* AdamW Modifications

* Fixed failing test

* Add notes; clean up; make more internal parameters private

* AdamW changes

* flexible weight decay regularization

* Formatting

---------

Co-authored-by: milancurcic <[email protected]>

Author: Spnetic-5

README.md

@@ -18,7 +18,7 @@ Read the paper [here](https://arxiv.org/abs/1902.06714).
 * Training and inference of dense (fully connected) and convolutional neural
   networks
 * Stochastic gradient descent optimizers: Classic, momentum, Nesterov momentum,
-  and RMSProp
+  RMSProp, Adam, AdamW
 * More than a dozen activation functions and their derivatives
 * Loading dense and convolutional models from Keras HDF5 (.h5) files
 * Data-based parallelism
@@ -287,4 +287,4 @@ group.
 
 Neural-fortran has been used successfully in over a dozen published studies.
 See all papers that cite it
-[here](https://scholar.google.com/scholar?cites=7315840714744905948).
+[here](https://scholar.google.com/scholar?cites=7315840714744905948).

example/quadratic.f90

@@ -4,10 +4,10 @@ program quadratic_fit
   ! descent.
   use nf, only: dense, input, network
   use nf_dense_layer, only: dense_layer
-  use nf_optimizers, only: sgd
+  use nf_optimizers, only: sgd, rmsprop, adam
 
   implicit none
-  type(network) :: net(6)
+  type(network) :: net(9)
 
   ! Training parameters
   integer, parameter :: num_epochs = 1000
@@ -16,6 +16,9 @@ program quadratic_fit
   integer, parameter :: batch_size = 100
   real, parameter :: learning_rate = 0.01
   real, parameter :: decay_rate = 0.9
+  real, parameter :: beta1 = 0.85
+  real, parameter :: beta2 = 0.95
+  real, parameter :: epsilon = 1e-8
 
   ! Input and output data
   real, allocatable :: x(:), y(:) ! training data
@@ -51,19 +54,46 @@ program quadratic_fit
   call sgd_optimizer(net(1), x, y, xtest, ytest, learning_rate, num_epochs)
 
   ! SGD, momentum
-  call sgd_optimizer(net(2), x, y, xtest, ytest, learning_rate, num_epochs, momentum=0.9)
+  call sgd_optimizer( &
+    net(2), x, y, xtest, ytest, learning_rate, num_epochs, momentum=0.9 &
+  )
 
   ! SGD, momentum with Nesterov
-  call sgd_optimizer(net(3), x, y, xtest, ytest, learning_rate, num_epochs, momentum=0.9, nesterov=.true.)
+  call sgd_optimizer( &
+    net(3), x, y, xtest, ytest, learning_rate, num_epochs, &
+    momentum=0.9, nesterov=.true. &
+  )
 
   ! Batch SGD optimizer
   call batch_gd_optimizer(net(4), x, y, xtest, ytest, learning_rate, num_epochs)
 
   ! Mini-batch SGD optimizer
-  call minibatch_gd_optimizer(net(5), x, y, xtest, ytest, learning_rate, num_epochs, batch_size)
+  call minibatch_gd_optimizer( &
+    net(5), x, y, xtest, ytest, learning_rate, num_epochs, batch_size &
+  )
 
   ! RMSProp optimizer
-  call rmsprop_optimizer(net(6), x, y, xtest, ytest, learning_rate, num_epochs, decay_rate)
+  call rmsprop_optimizer( &
+    net(6), x, y, xtest, ytest, learning_rate, num_epochs, decay_rate &
+  )
+
+  ! Adam optimizer
+  call adam_optimizer( &
+    net(7), x, y, xtest, ytest, learning_rate, num_epochs, &
+    beta1, beta2, epsilon &
+  )
+
+  ! Adam optimizer with L2 regularization
+  call adam_optimizer( &
+    net(8), x, y, xtest, ytest, learning_rate, num_epochs, &
+    beta1, beta2, epsilon, weight_decay_l2=1e-4 &
+  )
+
+  ! Adam optimizer with decoupled weight decay regularization
+  call adam_optimizer( &
+    net(9), x, y, xtest, ytest, learning_rate, num_epochs, &
+    beta1, beta2, epsilon, weight_decay_decoupled=1e-5 &
+  )
 
 contains
 
@@ -73,7 +103,9 @@ real elemental function quadratic(x) result(y)
     y = (x**2 / 2 + x / 2 + 1) / 2
   end function quadratic
 
-  subroutine sgd_optimizer(net, x, y, xtest, ytest, learning_rate, num_epochs, momentum, nesterov)
+  subroutine sgd_optimizer( &
+    net, x, y, xtest, ytest, learning_rate, num_epochs, momentum, nesterov &
+  )
     ! In the stochastic gradient descent (SGD) optimizer, we run the forward
     ! and backward passes and update the weights for each training sample,
     ! one at a time.
@@ -109,12 +141,19 @@ subroutine sgd_optimizer(net, x, y, xtest, ytest, learning_rate, num_epochs, mom
       do i = 1, size(x)
         call net % forward([x(i)])
         call net % backward([y(i)])
-        call net % update(sgd(learning_rate=learning_rate, momentum=momentum_value, nesterov=nesterov_value))
+        call net % update( &
+          sgd( &
+            learning_rate=learning_rate, &
+            momentum=momentum_value, &
+            nesterov=nesterov_value &
+          ) &
+        )
       end do
 
       if (mod(n, num_epochs / 10) == 0) then
         ypred = [(net % predict([xtest(i)]), i = 1, size(xtest))]
-        print '("Epoch: ", i4,"/",i4,", RMSE = ", f9.6)', n, num_epochs, sum((ypred - ytest)**2) / size(ytest)
+        print '("Epoch: ", i4,"/",i4,", RMSE = ", f9.6)', &
+          n, num_epochs, sum((ypred - ytest)**2) / size(ytest)
       end if
 
     end do
@@ -123,7 +162,9 @@ subroutine sgd_optimizer(net, x, y, xtest, ytest, learning_rate, num_epochs, mom
 
   end subroutine sgd_optimizer
 
-  subroutine batch_gd_optimizer(net, x, y, xtest, ytest, learning_rate, num_epochs)
+  subroutine batch_gd_optimizer( &
+    net, x, y, xtest, ytest, learning_rate, num_epochs &
+  )
     ! Like the stochastic gradient descent (SGD) optimizer, except that here we
     ! accumulate the weight gradients for all training samples and update the
     ! weights once per epoch.
@@ -147,7 +188,8 @@ subroutine batch_gd_optimizer(net, x, y, xtest, ytest, learning_rate, num_epochs
 
       if (mod(n, num_epochs / 10) == 0) then
         ypred = [(net % predict([xtest(i)]), i = 1, size(xtest))]
-        print '("Epoch: ", i4,"/",i4,", RMSE = ", f9.6)', n, num_epochs, sum((ypred - ytest)**2) / size(ytest)
+        print '("Epoch: ", i4,"/",i4,", RMSE = ", f9.6)', &
+          n, num_epochs, sum((ypred - ytest)**2) / size(ytest)
       end if
 
     end do
@@ -156,7 +198,9 @@ subroutine batch_gd_optimizer(net, x, y, xtest, ytest, learning_rate, num_epochs
 
   end subroutine batch_gd_optimizer
 
-  subroutine minibatch_gd_optimizer(net, x, y, xtest, ytest, learning_rate, num_epochs, batch_size)
+  subroutine minibatch_gd_optimizer( &
+    net, x, y, xtest, ytest, learning_rate, num_epochs, batch_size &
+  )
     ! Like the batch SGD optimizer, except that here we accumulate the weight
     ! over a number of mini batches and update the weights once per mini batch.
     !
@@ -203,7 +247,8 @@ subroutine minibatch_gd_optimizer(net, x, y, xtest, ytest, learning_rate, num_ep
 
       if (mod(n, num_epochs / 10) == 0) then
         ypred = [(net % predict([xtest(i)]), i = 1, size(xtest))]
-        print '("Epoch: ", i4,"/",i4,", RMSE = ", f9.6)', n, num_epochs, sum((ypred - ytest)**2) / size(ytest)
+        print '("Epoch: ", i4,"/",i4,", RMSE = ", f9.6)', &
+          n, num_epochs, sum((ypred - ytest)**2) / size(ytest)
       end if
 
     end do
@@ -212,79 +257,36 @@ subroutine minibatch_gd_optimizer(net, x, y, xtest, ytest, learning_rate, num_ep
 
   end subroutine minibatch_gd_optimizer
 
-  subroutine rmsprop_optimizer(net, x, y, xtest, ytest, learning_rate, num_epochs, decay_rate)
+  subroutine rmsprop_optimizer( &
+    net, x, y, xtest, ytest, learning_rate, num_epochs, decay_rate &
+  )
     ! RMSprop optimizer for updating weights using root mean square
     type(network), intent(inout) :: net
     real, intent(in) :: x(:), y(:)
     real, intent(in) :: xtest(:), ytest(:)
     real, intent(in) :: learning_rate, decay_rate
     integer, intent(in) :: num_epochs
     integer :: i, j, n
-    real, parameter :: epsilon = 1e-8 ! Small constant to avoid division by zero
     real, allocatable :: ypred(:)
 
-    ! Define a dedicated type to store the RMSprop gradients.
-    ! This is needed because array sizes vary between layers and we need to
-    ! keep track of gradients for each layer over time.
-    ! For now this works only for dense layers.
-    ! We will need to define a similar type for conv2d layers.
-    type :: rms_gradient_dense
-      real, allocatable :: dw(:,:)
-      real, allocatable :: db(:)
-    end type rms_gradient_dense
-
-    type(rms_gradient_dense), allocatable :: rms(:)
-
     print '(a)', 'RMSProp optimizer'
     print '(34("-"))'
 
-    ! Here we allocate the array or RMS gradient derived types.
-    ! We need one for each dense layer, however we will allocate it to the
-    ! length of all layers as it will make housekeeping easier.
-    allocate(rms(size(net % layers)))
-
     do n = 1, num_epochs
 
       do i = 1, size(x)
         call net % forward([x(i)])
         call net % backward([y(i)])
       end do
 
-      ! RMSprop update rule
-      do j = 1, size(net % layers)
-        select type (this_layer => net % layers(j) % p)
-          type is (dense_layer)
-
-            ! If this is our first time here for this layer, allocate the
-            ! internal RMS gradient arrays and initialize them to zero.
-            if (.not. allocated(rms(j) % dw)) then
-              allocate(rms(j) % dw, mold=this_layer % dw)
-              allocate(rms(j) % db, mold=this_layer % db)
-              rms(j) % dw = 0
-              rms(j) % db = 0
-            end if
-
-            ! Update the RMS gradients using the RMSprop moving average rule
-            rms(j) % dw = decay_rate * rms(j) % dw + (1 - decay_rate) * this_layer % dw**2
-            rms(j) % db = decay_rate * rms(j) % db + (1 - decay_rate) * this_layer % db**2
-
-            ! Update weights and biases using the RMSprop update rule
-            this_layer % weights = this_layer % weights - learning_rate &
-              / sqrt(rms(j) % dw + epsilon) * this_layer % dw
-            this_layer % biases = this_layer % biases - learning_rate &
-              / sqrt(rms(j) % db + epsilon) * this_layer % db
-
-            ! We have updated the weights and biases, so we need to reset the
-            ! gradients to zero for the next epoch.
-            this_layer % dw = 0
-            this_layer % db = 0
-
-        end select
-      end do
+      call net % update( &
+        rmsprop(learning_rate=learning_rate, decay_rate=decay_rate) &
+      )
 
       if (mod(n, num_epochs / 10) == 0) then
         ypred = [(net % predict([xtest(i)]), i = 1, size(xtest))]
-        print '("Epoch: ", i4,"/",i4,", RMSE = ", f9.6)', n, num_epochs, sum((ypred - ytest)**2) / size(ytest)
+        print '("Epoch: ", i4,"/",i4,", RMSE = ", f9.6)', &
+          n, num_epochs, sum((ypred - ytest)**2) / size(ytest)
       end if
 
     end do
@@ -293,6 +295,69 @@ subroutine rmsprop_optimizer(net, x, y, xtest, ytest, learning_rate, num_epochs,
 
   end subroutine rmsprop_optimizer
 
+  subroutine adam_optimizer( &
+    net, x, y, xtest, ytest, learning_rate, num_epochs, beta1, beta2, epsilon, &
+    weight_decay_l2, weight_decay_decoupled &
+  )
+    ! Adam optimizer
+    type(network), intent(inout) :: net
+    real, intent(in) :: x(:), y(:)
+    real, intent(in) :: xtest(:), ytest(:)
+    real, intent(in) :: learning_rate, beta1, beta2, epsilon
+    real, intent(in), optional :: weight_decay_l2
+    real, intent(in), optional :: weight_decay_decoupled
+    integer, intent(in) :: num_epochs
+    real, allocatable :: ypred(:)
+    integer :: i, n
+    real :: weight_decay_l2_val
+    real :: weight_decay_decoupled_val
+
+    ! Set default values for weight_decay_l2
+    if (.not. present(weight_decay_l2)) then
+      weight_decay_l2_val = 0.0
+    else
+      weight_decay_l2_val = weight_decay_l2
+    end if
+
+    ! Set default values for weight_decay_decoupled
+    if (.not. present(weight_decay_decoupled)) then
+      weight_decay_decoupled_val = 0.0
+    else
+      weight_decay_decoupled_val = weight_decay_decoupled
+    end if
+
+    print '(a)', 'Adam optimizer'
+    print '(34("-"))'
+
+    do n = 1, num_epochs
+      do i = 1, size(x)
+        call net % forward([x(i)])
+        call net % backward([y(i)])
+      end do
+
+      call net % update( &
+        adam( &
+          learning_rate=learning_rate, &
+          beta1=beta1, &
+          beta2=beta2, &
+          epsilon=epsilon, &
+          weight_decay_l2=weight_decay_l2_val, &
+          weight_decay_decoupled=weight_decay_decoupled_val &
+        ) &
+      )
+
+      if (mod(n, num_epochs / 10) == 0) then
+        ypred = [(net % predict([xtest(i)]), i = 1, size(xtest))]
+        print '("Epoch: ", i4,"/",i4,", RMSE = ", f9.6)', &
+          n, num_epochs, sum((ypred - ytest)**2) / size(ytest)
+      end if
+
+    end do
+
+    print *, ''
+
+  end subroutine adam_optimizer
+
   subroutine shuffle(arr)
     ! Shuffle an array using the Fisher-Yates algorithm.
     integer, intent(inout) :: arr(:)

fpm.toml

@@ -1,5 +1,5 @@
 name = "neural-fortran"
-version = "0.13.0"
+version = "0.14.0"
 license = "MIT"
 author = "Milan Curcic"
 maintainer = "[email protected]"

src/nf.f90

@@ -5,7 +5,7 @@ module nf
   use nf_layer_constructors, only: &
     conv2d, dense, flatten, input, maxpool2d, reshape
   use nf_network, only: network
-  use nf_optimizers, only: sgd, rmsprop
+  use nf_optimizers, only: sgd, rmsprop, adam
   use nf_activation, only: activation_function, elu, exponential,  &
                            gaussian, linear, relu, leaky_relu,     &
                            sigmoid, softmax, softplus, step, tanhf, &