Back to 1D concept

I'll try with 1D with a state memory and the option to reset state for
processing a new time series.

Author: castelao

src/nf/nf_rnn_layer.f90

@@ -20,7 +20,7 @@ module nf_rnn_layer
     integer :: output_size
 
     real, allocatable :: weights(:,:)
-    real, allocatable :: recurrent(:,:)
+    real, allocatable :: recurrent(:)
     real, allocatable :: biases(:)
     real, allocatable :: z(:) ! matmul(x, w) + b
     real, allocatable :: state(:)

src/nf/nf_rnn_layer_submodule.f90

@@ -134,17 +134,13 @@ module subroutine init(self, input_shape)
     call random_normal(self % weights)
     self % weights = self % weights / self % input_size
 
-    ! Recurrent weights are a 2-d square array of shape this layer size.
-    ! Each neuron is adjusted by each state times a recurrent weight.
-    allocate(self % recurrent(self % output_size, self % output_size))
-    call random_normal(self % recurrent)
-    self % recurrent = self % recurrent / self % output_size
-
     ! Broadcast weights to all other images, if any.
     call co_broadcast(self % weights, 1)
 
-    allocate(self % state(self % output_size))
-    self % state = 0
+    allocate(self % recurrent(self % output_size))
+    call random_normal(self % recurrent)
+    self % recurrent = self % recurrent / self % input_size
+
 
     allocate(self % biases(self % output_size))
     self % biases = 0
@@ -155,6 +151,9 @@ module subroutine init(self, input_shape)
     allocate(self % z(self % output_size))
     self % z = 0
 
+    allocate(self % state(self % output_size))
+    self % state = 0
+
     allocate(self % dw(self % input_size, self % output_size))
     self % dw = 0