Merge pull request #10 from jvdp1/development

Addition of 2 methods and of 2 examples

Author: milancurcic

.gitignore

@@ -1,4 +1,4 @@
 *.o
 *.mod
 build
-data/mnist/*.dat
+data/*/*.dat

CMakeLists.txt

@@ -78,7 +78,7 @@ foreach(execid mnist network_save network_sync set_activation_function)
   add_test(test_${execid} bin/test_${execid})
 endforeach()
 
-foreach(execid mnist save_and_load simple sine)
+foreach(execid mnist montesinos_uni montesinos_multi save_and_load simple sine)
   add_executable(example_${execid} src/tests/example_${execid}.f90)
   target_link_libraries(example_${execid} neural ${LIBS})
   add_test(example_${execid} bin/example_${execid})

README.md

@@ -322,6 +322,45 @@ for example on 16 cores using [OpenCoarrays](https://github.com/sourceryinstitut
 $ cafrun -n 16 ./example_mnist
 ```
 
+### Montesinos-Lopez et al. (2018) univariate example
+
+The Montesinos-Lopez et al. (2018) univariate example is extracted from the study:
+
+```
+Montesinos-Lopez et al. 2018. Multi-environment genomic prediction of plant traits using deep learners with dense architecture. G3, 8, 3813-3828.
+```
+
+This example uses the data from the dataset "Data\_Maize\_1to3", and was extracted using the R code in the Appendix of this paper.
+
+
+The Montesinos-Lopez univariate data is included with the repo and you will have to unpack it first:
+
+```
+cd data/montesinos_uni
+tar xzvf montesinos_uni.tar.gz
+cd -
+```
+
+### Montesinos-Lopez et al. (2018) multivariate example
+
+The Montesinos-Lopez et al. (2018) multivariate example is extracted from the study:
+
+```
+Montesinos-Lopez et al. 2018. Multi-trait, multi-environment deep learning modeling for genomic-enabled prediction of plant traits. G3, 8, 3829-3840.
+```
+
+This example uses the data from the dataset "Data\_Maize\_set\_1", and was extracted using the R code in the Appendix B of this paper.
+
+
+The Montesinos-Lopez multivariate data is included with the repo and you will have to unpack it first:
+
+```
+cd data/montesinos_multi
+tar xzvf montesinos_multi.tar.gz
+cd -
+```
+
+
 ## Contributing
 
 neural-fortran is currently a proof-of-concept with potential for

data/montesinos_multi.tar.gz

@@ -23,15 +23,18 @@ module mod_network
     procedure, public, pass(self) :: init
     procedure, public, pass(self) :: load
     procedure, public, pass(self) :: loss
-    procedure, public, pass(self) :: output
+    procedure, public, pass(self) :: output_batch
+    procedure, public, pass(self) :: output_single
     procedure, public, pass(self) :: save
     procedure, public, pass(self) :: set_activation
     procedure, public, pass(self) :: sync
     procedure, public, pass(self) :: train_batch
+    procedure, public, pass(self) :: train_epochs
     procedure, public, pass(self) :: train_single
     procedure, public, pass(self) :: update
 
-    generic, public :: train => train_batch, train_single
+    generic, public :: output => output_batch, output_single
+    generic, public :: train => train_batch, train_epochs, train_single
 
   end type network_type
 
@@ -159,7 +162,7 @@ pure real(rk) function loss(self, x, y)
     loss = 0.5 * sum((y - self % output(x))**2) / size(x)
   end function loss
 
-  pure function output(self, x) result(a)
+  pure function output_single(self, x) result(a)
     ! Use forward propagation to compute the output of the network.
     class(network_type), intent(in) :: self
     real(rk), intent(in) :: x(:)
@@ -171,7 +174,21 @@ pure function output(self, x) result(a)
         a = self % layers(n) % activation(matmul(transpose(layers(n-1) % w), a) + layers(n) % b)
       end do
     end associate
-  end function output
+  end function output_single
+
+  pure function output_batch(self, x) result(a)
+    class(network_type), intent(in) :: self
+    real(rk), intent(in) :: x(:,:)
+    real(rk), allocatable :: a(:,:)
+
+    integer(ik) :: i
+
+    allocate(a(self%dims(size(self%dims)),size(x,dim=2)))
+    do i = 1, size(x, dim=2)
+     a(:,i)=self%output(x(:,i))
+    enddo
+ 
+  end function output_batch
 
   subroutine save(self, filename)
     ! Saves the network to a file.
@@ -255,6 +272,37 @@ subroutine train_batch(self, x, y, eta)
 
   end subroutine train_batch
 
+  subroutine train_epochs(self, x, y, eta,num_epochs,num_batch_size)
+    !Performs the training for nun_epochs epochs with mini-bachtes of size equal to num_batch_size
+    class(network_type), intent(in out) :: self
+    integer(ik),intent(in)::num_epochs,num_batch_size
+    real(rk), intent(in) :: x(:,:), y(:,:), eta
+
+    integer(ik)::i,n,nsamples,nbatch
+    integer(ik)::batch_start,batch_end
+
+    real(rk)::pos
+
+    nsamples=size(y,dim=2)
+
+    nbatch=nsamples/num_batch_size
+
+    epoch: do n=1,num_epochs
+     mini_batches: do i=1,nbatch
+      
+      !pull a random mini-batch from the dataset  
+      call random_number(pos)
+      batch_start=int(pos*(nsamples-num_batch_size+1))
+      if(batch_start.eq.0)batch_start=1
+      batch_end=batch_start+num_batch_size-1
+   
+      call self%train(x(:,batch_start:batch_end),y(:,batch_start:batch_end),eta)
+       
+     enddo mini_batches
+    enddo epoch
+
+  end subroutine train_epochs
+
   pure subroutine train_single(self, x, y, eta)
     ! Trains a network using a single set of input data x and output data y,
     ! and learning rate eta.

data/montesinos_uni.tar.gz

@@ -12,18 +12,15 @@ program example_mnist
 
   real(rk), allocatable :: tr_images(:,:), tr_labels(:)
   real(rk), allocatable :: te_images(:,:), te_labels(:)
-  !real(rk), allocatable :: va_images(:,:), va_labels(:)
-  real(rk), allocatable :: input(:,:), output(:,:)
 
   type(network_type) :: net
 
   integer(ik) :: i, n, num_epochs
-  integer(ik) :: batch_size, batch_start, batch_end
-  real(rk) :: pos
+  integer(ik) :: batch_size
 
   call load_mnist(tr_images, tr_labels, te_images, te_labels)
 
-  net = network_type([784, 10, 10])
+  net = network_type([size(tr_images,dim=1), 10, size(label_digits(te_labels),dim=1)])
 
   batch_size = 1000
   num_epochs = 10
@@ -33,28 +30,12 @@ program example_mnist
       net % accuracy(te_images, label_digits(te_labels)) * 100, ' %'
   end if
 
-  epochs: do n = 1, num_epochs
-    mini_batches: do i = 1, size(tr_labels) / batch_size
-
-      ! pull a random mini-batch from the dataset
-      call random_number(pos)
-      batch_start = int(pos * (size(tr_labels) - batch_size + 1))
-      batch_end = batch_start + batch_size - 1
-
-      ! prepare mini-batch
-      input = tr_images(:,batch_start:batch_end)
-      output = label_digits(tr_labels(batch_start:batch_end))
-
-      ! train the network on the mini-batch
-      call net % train(input, output, eta=3._rk)
-
-    end do mini_batches
-
-    if (this_image() == 1) then
-      write(*, '(a,i2,a,f5.2,a)') 'Epoch ', n, ' done, Accuracy: ',&
-        net % accuracy(te_images, label_digits(te_labels)) * 100, ' %'
-    end if
+  call net%train(tr_images,label_digits(tr_labels),3._rk,num_epochs,batch_size)
+   
+  if (this_image() == 1) then
+    write(*, '(a,f5.2,a)') 'Epochs done, Accuracy: ',&
+     net % accuracy(te_images, label_digits(te_labels)) * 100, ' %'
+  endif
 
-  end do epochs
 
 end program example_mnist

src/lib/mod_network.f90

@@ -0,0 +1,136 @@
+program example_montesinos_multi
+ use mod_kinds,only:ik,rk
+ use mod_network,only:network_type
+ implicit none
+ integer(ik)::ny1_tr,ny2_tr,nx1_tr,nx2_tr
+ integer(ik)::ny1_ts,ny2_ts,nx1_ts,nx2_ts
+
+ integer(ik)::batch_size,num_epochs
+
+ real(rk),allocatable::y_tr(:,:),x_tr(:,:)
+ real(rk),allocatable::y_ts(:,:),x_ts(:,:)
+
+ type(network_type)::net
+
+ call readfile('../data/montesinos_multi/y_tr.dat',ny1_tr,ny2_tr,y_tr)
+ call readfile('../data/montesinos_multi/x_tr.dat',nx1_tr,nx2_tr,x_tr)
+ 
+ net=network_type([nx1_tr,50,50,ny1_tr])
+
+ batch_size=50
+ num_epochs=50
+
+ !training
+ call net%train(x_tr,y_tr,3._rk,num_epochs,batch_size)
+
+ call net%sync(1)
+
+ !validation
+ call readfile('../data/montesinos_multi/y_ts.dat',ny1_ts,ny2_ts,y_ts)
+ call readfile('../data/montesinos_multi/x_ts.dat',nx1_ts,nx2_ts,x_ts)
+
+ if(this_image().eq.1)then
+  write(*,*)'Correlation(s): ',corr_array(net%output(x_ts),y_ts)
+ endif
+
+contains
+
+subroutine readfile(filename,n,m,array)
+ character(len=*),intent(in)::filename
+ integer(ik),intent(out)::n,m
+ real(rk),allocatable,intent(out)::array(:,:)
+
+ integer(ik)::un,i,io
+
+ open(newunit=un,file=filename,status='old',action='read')
+ call numlines(un,m)
+ call numcol(un,n) 
+
+ allocate(array(n,m))
+ rewind(un)
+ do i=1,m
+  read(un,*,iostat=io)array(:,i)
+  if(io.ne.0)exit
+ enddo
+ close(un)
+ 
+end subroutine
+
+pure function corr_array(array1,array2) result(a)
+ real(rk),intent(in)::array1(:,:),array2(:,:)
+ real(rk),allocatable::a(:)
+ 
+ integer(ik)::i,n
+ 
+ n=size(array1,dim=1)
+ 
+ allocate(a(n))
+ a=0.0_rk
+ do i=1,n
+  a(i)=corr(array1(i,:),array2(i,:))
+ enddo
+
+end function
+
+pure real(rk) function corr(array1,array2)
+ real(rk),intent(in)::array1(:),array2(:)
+ 
+ real(rk)::mean1,mean2
+
+ !brute force
+ 
+ mean1=sum(array1)/size(array1)
+ mean2=sum(array2)/size(array2)
+ corr=dot_product(array1-mean1,array2-mean2)/sqrt(sum((array1-mean1)**2)*sum((array2-mean2)**2))
+
+end function
+
+subroutine numlines(unfile,n)
+ implicit none
+ integer::io
+ integer,intent(in)::unfile
+ integer,intent(out)::n
+ rewind(unfile)
+ n=0
+ do
+  read(unfile,*,iostat=io)
+  if (io.ne.0) exit
+  n=n+1
+ enddo
+ rewind(unfile)
+end subroutine
+
+subroutine numcol(unfile,n)
+ implicit none
+ integer,intent(in)::unfile
+ character(len=1000000)::a
+ integer,intent(out)::n
+ integer::curr,first,last,lena,stat,i
+ rewind(unfile)
+ read(unfile,"(a)")a
+ curr=1;lena=len(a);n=0
+ do
+  first=0
+  do i=curr,lena
+   if (a(i:i) /= " ") then
+    first=i
+    exit
+   endif
+  enddo
+  if (first == 0) exit
+  curr=first+1
+  last=0
+  do i=curr,lena
+   if (a(i:i) == " ") then
+    last=i
+    exit
+   endif
+  enddo
+  if (last == 0) last=lena
+  n=n+1
+  curr=last+1
+ enddo
+ rewind(unfile)
+end subroutine
+
+end program