Refactor tensor_map_m to improve nomenclature & move phase_space_bin_t to cloud-microphysics

cloud-microphysics/app/train-cloud-microphysics.f90

@@ -13,10 +13,11 @@ program train_on_flat_distribution
   use julienne_m, only : string_t, file_t, command_line_t, bin_t
   use assert_m, only : assert, intrinsic_array_t
   use inference_engine_m, only : &
-    inference_engine_t, mini_batch_t, input_output_pair_t, tensor_t, trainable_engine_t, rkind, tensor_range_t, &
-    training_configuration_t, shuffle, phase_space_bin_t
+    inference_engine_t, mini_batch_t, input_output_pair_t, tensor_t, trainable_engine_t, rkind, tensor_map_t, &
+    training_configuration_t, shuffle
 
   !! Internal dependencies;
+  use phase_space_bin_m, only : phase_space_bin_t
   use NetCDF_file_m, only: NetCDF_file_t
   use ubounds_m, only : ubounds_t
   implicit none
@@ -279,12 +280,11 @@ subroutine read_train_write(training_configuration, base_name, plot_unit, previo
           ), lon = 1, size(qv_in,1))], lat = 1, size(qv_in,2))], level = 1, size(qv_in,3))], time = start_step, end_step, stride)]
 
         print *,"Calculating output tensor component ranges."
-        associate(output_range => tensor_range_t( &
-          layer  = "outputs", &
-          minima = [minval(dpt_dt), minval(dqv_dt), minval(dqc_dt), minval(dqr_dt), minval(dqs_dt)], &
-          maxima = [maxval(dpt_dt), maxval(dqv_dt), maxval(dqc_dt), maxval(dqr_dt), maxval(dqs_dt)], &
-          num_bins = num_bins &
-        ))
+        associate( &
+          output_minima => [minval(dpt_dt), minval(dqv_dt), minval(dqc_dt), minval(dqr_dt), minval(dqs_dt)], &
+          output_maxima => [maxval(dpt_dt), maxval(dqv_dt), maxval(dqc_dt), maxval(dqr_dt), maxval(dqs_dt)] &
+        )
+        associate( output_map => tensor_map_t(layer = "outputs", minima = output_minima, maxima = output_maxima))
           read_or_initialize_engine: &
           if (io_status==0) then
             print *,"Reading network from file " // network_file
@@ -301,29 +301,26 @@ subroutine read_train_write(training_configuration, base_name, plot_unit, previo
 
               print *,"Calculating input tensor component ranges."
               associate( &
-                input_range => tensor_range_t( &
+                input_map => tensor_map_t( &
                   layer  = "inputs", &
                   minima = [minval(pressure_in), minval(potential_temperature_in), minval(temperature_in), &
                     minval(qv_in), minval(qc_in), minval(qr_in), minval(qs_in)], &
                   maxima = [maxval(pressure_in), maxval(potential_temperature_in), maxval(temperature_in), &
-                    maxval(qv_in), maxval(qc_in), maxval(qr_in), maxval(qs_in)], &
-                  num_bins = num_bins &
-                ), &
-                date_string => string_t(date) &
-              )
+                    maxval(qv_in), maxval(qc_in), maxval(qr_in), maxval(qs_in)] &
+              ) ) 
                 associate(activation => training_configuration%differentiable_activation_strategy())
                   associate(residual_network => string_t(trim(merge("true ", "false", training_configuration%skip_connections())))) 
                     trainable_engine = trainable_engine_t( &
                       training_configuration,  &
                       perturbation_magnitude = 0.05, &
                       metadata = [ &
-                        string_t("Simple microphysics"), string_t("train-on-flat-dist"), date_string, activation%function_name(), &
+                        string_t("Simple microphysics"), string_t("train-on-flat-dist"), string_t(date), activation%function_name(), &
                         residual_network &
-                      ], input_range = input_range, output_range = output_range &
+                      ], input_map = input_map, output_map = output_map &
                     )
                   end associate
                 end associate
-              end associate ! input_range, date_string
+              end associate ! input_map, date_string
             end block initialize_network
           end if read_or_initialize_engine
 
@@ -336,7 +333,7 @@ subroutine read_train_write(training_configuration, base_name, plot_unit, previo
             occupied = .false.
             keepers = .false.
 
-            bin = [(output_range%bin(outputs(i), num_bins), i=1,size(outputs))]
+            bin = [(phase_space_bin_t(outputs(i), output_minima, output_maxima, num_bins), i=1,size(outputs))]
 
             do i = 1, size(outputs)
               if (occupied(bin(i)%loc(1),bin(i)%loc(2),bin(i)%loc(3),bin(i)%loc(4),bin(i)%loc(5))) cycle
@@ -347,7 +344,8 @@ subroutine read_train_write(training_configuration, base_name, plot_unit, previo
             print *, "Kept ", size(input_output_pairs), " out of ", size(outputs, kind=int64), " input/output pairs " // &
                      " in ", count(occupied)," out of ", size(occupied, kind=int64), " bins."
           end block
-        end associate ! output_range
+        end associate ! output_map
+        end associate 
 
         print *,"Normalizing the remaining input and output tensors"
         input_output_pairs = trainable_engine%map_to_training_ranges(input_output_pairs)

cloud-microphysics/src/phase_space_bin_m.f90

@@ -0,0 +1,39 @@
+module phase_space_bin_m
+  use kind_parameters_m, only : rkind
+  use tensor_map_m, only : tensor_map_t
+  use tensor_m, only : tensor_t
+  implicit none
+
+  public :: phase_space_bin_t
+
+  type phase_space_bin_t
+    integer, allocatable :: loc(:)
+  end type
+
+  interface phase_space_bin_t
+
+    pure module function bin(tensor, minima, maxima, num_bins) result(phase_space_bin)
+      implicit none
+      type(tensor_t), intent(in) :: tensor
+      real(rkind), intent(in) :: minima(:), maxima(:)
+      integer, intent(in) :: num_bins
+      type(phase_space_bin_t) phase_space_bin
+    end function
+
+  end interface
+
+contains
+
+  module procedure bin
+
+    real(rkind), parameter :: half = 0.5_rkind
+
+    associate(bin_widths => (maxima - minima)/real(num_bins,rkind))
+      associate(tensor_values => min(tensor%values(), maxima - half*bin_widths))
+        phase_space_bin%loc = (tensor_values - minima)/bin_widths + 1
+      end associate
+    end associate
+
+  end procedure
+
+end module phase_space_bin_m

src/inference_engine/inference_engine_m_.f90

@@ -8,7 +8,7 @@ module inference_engine_m_
   use kind_parameters_m, only : rkind
   use metadata_m, only : metadata_t
   use tensor_m, only : tensor_t
-  use tensor_range_m, only : tensor_range_t
+  use tensor_map_m, only : tensor_map_t
   implicit none
 
   private
@@ -20,7 +20,7 @@ module inference_engine_m_
   type inference_engine_t
     !! Encapsulate the minimal information needed to perform inference
     private
-    type(tensor_range_t) input_range_, output_range_
+    type(tensor_map_t) input_map_, output_map_
     type(metadata_t) metadata_
     real(rkind), allocatable :: weights_(:,:,:), biases_(:,:)
     integer, allocatable :: nodes_(:)
@@ -43,7 +43,7 @@ module inference_engine_m_
   end type
 
   type exchange_t
-    type(tensor_range_t) input_range_, output_range_
+    type(tensor_map_t) input_map_, output_map_
     type(metadata_t) metadata_
     real(rkind), allocatable :: weights_(:,:,:), biases_(:,:)
     integer, allocatable :: nodes_(:)
@@ -60,13 +60,13 @@ module inference_engine_m_
 
   interface inference_engine_t
 
-    impure module function construct_from_padded_arrays(metadata, weights, biases, nodes, input_range, output_range) &
+    impure module function construct_from_padded_arrays(metadata, weights, biases, nodes, input_map, output_map) &
       result(inference_engine)
       implicit none
       type(string_t), intent(in) :: metadata(:)
       real(rkind), intent(in) :: weights(:,:,:), biases(:,:)
       integer, intent(in) :: nodes(0:)
-      type(tensor_range_t), intent(in), optional :: input_range, output_range
+      type(tensor_map_t), intent(in), optional :: input_map, output_map
       type(inference_engine_t) inference_engine
     end function
 

src/inference_engine/inference_engine_s.F90

@@ -21,16 +21,16 @@
 contains
 
   module procedure map_to_input_range
-    normalized_tensor = self%input_range_%map_to_training_range(tensor)
+    normalized_tensor = self%input_map_%map_to_training_range(tensor)
   end procedure
 
   module procedure map_from_output_range
-    tensor = self%output_range_%map_from_training_range(normalized_tensor)
+    tensor = self%output_map_%map_from_training_range(normalized_tensor)
   end procedure
 
   module procedure to_exchange
-    exchange%input_range_ = self%input_range_
-    exchange%output_range_ = self%output_range_
+    exchange%input_map_ = self%input_map_
+    exchange%output_map_ = self%output_map_
     associate(strings => self%metadata_%strings())
       exchange%metadata_ = metadata_t(strings(1),strings(2),strings(3),strings(4),strings(5))
     end associate
@@ -53,13 +53,13 @@
       allocate(a(maxval(n), input_layer:output_layer))
 
 #ifndef _CRAYFTN
-      associate(normalized_inputs => self%input_range_%map_to_training_range(inputs))
+      associate(normalized_inputs => self%input_map_%map_to_training_range(inputs))
         a(1:n(input_layer),input_layer) = normalized_inputs%values()
       end associate
 #else
       block
         type(tensor_t) normalized_inputs
-        normalized_inputs = self%input_range_%map_to_training_range(inputs)
+        normalized_inputs = self%input_map_%map_to_training_range(inputs)
         a(1:n(input_layer),input_layer) = normalized_inputs%values()
       end block
 #endif
@@ -78,7 +78,7 @@
 #else
       associate(normalized_outputs => tensor_t(a(1:n(output_layer), output_layer)))
 #endif
-        outputs = self%output_range_%map_from_training_range(normalized_outputs)
+        outputs = self%output_map_%map_from_training_range(normalized_outputs)
 #ifdef _CRAYFTN
       end block
 #else
@@ -162,22 +162,22 @@ impure function activation_factory_method(activation_name) result(activation)
     block
       integer i
 
-      if (present(input_range)) then
-        inference_engine%input_range_ = input_range
+      if (present(input_map)) then
+        inference_engine%input_map_ = input_map
       else
         associate(num_inputs => nodes(lbound(nodes,1)))
           associate(default_minima => [(0., i=1,num_inputs)], default_maxima => [(1., i=1,num_inputs)])
-            inference_engine%input_range_ = tensor_range_t("inputs", default_minima, default_maxima)
+            inference_engine%input_map_ = tensor_map_t("inputs", default_minima, default_maxima)
           end associate
         end associate
       end if
 
-      if (present(output_range)) then
-        inference_engine%output_range_ = output_range
+      if (present(output_map)) then
+        inference_engine%output_map_ = output_map
       else
         associate(num_outputs => nodes(ubound(nodes,1)))
           associate(default_minima => [(0., i=1,num_outputs)], default_maxima => [(1., i=1,num_outputs)])
-            inference_engine%output_range_ = tensor_range_t("outputs", default_minima, default_maxima)
+            inference_engine%output_map_ = tensor_map_t("outputs", default_minima, default_maxima)
           end associate
         end associate
       end if
@@ -193,15 +193,15 @@ impure function activation_factory_method(activation_name) result(activation)
   module procedure from_json
 
     type(string_t), allocatable :: lines(:)
-    type(tensor_range_t) input_range, output_range
+    type(tensor_map_t) input_map, output_map
     type(layer_t) hidden_layers, output_layer
     character(len=:), allocatable :: justified_line
     integer l
 #ifdef _CRAYFTN
-    type(tensor_range_t) proto_range
+    type(tensor_map_t) proto_map
     type(metadata_t) proto_meta
     type(neuron_t) proto_neuron
-    proto_range = tensor_range_t("",[0.],[1.])
+    proto_map = tensor_map_t("",[0.],[1.])
     proto_meta = metadata_t(string_t(""),string_t(""),string_t(""),string_t(""),string_t(""))
     proto_neuron = neuron_t(weights=[0.], bias=0.)
 #endif
@@ -213,25 +213,25 @@ impure function activation_factory_method(activation_name) result(activation)
     associate(num_lines => size(lines))
 
 #ifndef _CRAYFTN
-      associate(proto_range => tensor_range_t("",[0.],[1.]))
+      associate(proto_map => tensor_map_t("",[0.],[1.]))
 #endif
-        associate(range_lines => size(proto_range%to_json()))
+        associate(map_lines => size(proto_map%to_json()))
 
-          find_inputs_range: &
+          find_inputs_map: &
           do l = 1, num_lines
             justified_line = adjustl(lines(l)%string())
-            if (justified_line == '"inputs_range": {') exit 
-          end do find_inputs_range
-          call assert(justified_line =='"inputs_range": {', 'from_json: expecting "inputs_range": {', justified_line)
-          input_range = tensor_range_t(lines(l:l+range_lines-1))
+            if (justified_line == '"inputs_map": {') exit
+          end do find_inputs_map
+          call assert(justified_line =='"inputs_map": {', 'from_json: expecting "inputs_map": {', justified_line)
+          input_map = tensor_map_t(lines(l:l+map_lines-1))
 
-          find_outputs_range: &
+          find_outputs_map: &
           do l = 1, num_lines
             justified_line = adjustl(lines(l)%string())
-            if (justified_line == '"outputs_range": {') exit 
-          end do find_outputs_range
-          call assert(justified_line =='"outputs_range": {', 'from_json: expecting "outputs_range": {', justified_line)
-          output_range = tensor_range_t(lines(l:l+range_lines-1))
+            if (justified_line == '"outputs_map": {') exit
+          end do find_outputs_map
+          call assert(justified_line =='"outputs_map": {', 'from_json: expecting "outputs_map": {', justified_line)
+          output_map = tensor_map_t(lines(l:l+map_lines-1))
 
         end associate
 #ifndef _CRAYFTN
@@ -283,7 +283,7 @@ impure function activation_factory_method(activation_name) result(activation)
 #endif
        associate(metadata => metadata_t(lines(l:l+size(proto_meta%to_json())-1)))
          associate(metadata_strings => metadata%strings())
-           inference_engine = hidden_layers%inference_engine(metadata_strings, output_layer, input_range, output_range)
+           inference_engine = hidden_layers%inference_engine(metadata_strings, output_layer, input_map, output_map)
            if (allocated(inference_engine%activation_strategy_)) deallocate(inference_engine%activation_strategy_)
            allocate(inference_engine%activation_strategy_, source = activation_factory_method(metadata_strings(4)%string()))
          end associate
@@ -433,10 +433,10 @@ function get_key_value(line) result(value_)
   module procedure to_json
 
 #ifdef _CRAYFTN
-    type(tensor_range_t) proto_range
+    type(tensor_map_t) proto_map
     type(metadata_t) proto_meta
     type(neuron_t) proto_neuron
-    proto_range = tensor_range_t("",[zero],[one])
+    proto_map = tensor_map_t("",[zero],[one])
     proto_meta = metadata_t(string_t(""),string_t(""),string_t(""),string_t(""),string_t(""))
     proto_neuron = neuron_t([zero],one)
 #endif
@@ -450,14 +450,14 @@ function get_key_value(line) result(value_)
       ,first_hidden => lbound(self%nodes_,1) + 1 &
       ,last_hidden => ubound(self%nodes_,1) - 1 &
 #ifndef _CRAYFTN
-      ,proto_range => tensor_range_t("",[zero],[one]) &
+      ,proto_map => tensor_map_t("",[zero],[one]) &
       ,proto_meta => metadata_t(string_t(""),string_t(""),string_t(""),string_t(""),string_t("")) &
       ,proto_neuron => neuron_t([zero],zero) &
 #endif
     )
       associate( &
         metadata_lines => size(proto_meta%to_json()), &
-        tensor_range_lines => size(proto_range%to_json()), &
+        tensor_map_lines => size(proto_map%to_json()), &
         neuron_lines => size(proto_neuron%to_json()) &
       )
         block
@@ -468,8 +468,8 @@ function get_key_value(line) result(value_)
           associate( json_lines => &
             brace + &                                                          ! { 
               metadata_lines + &                                               !   "metadata": ...
-              tensor_range_lines + &                                           !   "inputs_tensor_range": ...
-              tensor_range_lines + &                                           !   "outputs_tensor_range": ...
+              tensor_map_lines + &                                           !   "inputs_tensor_map": ...
+              tensor_map_lines + &                                           !   "outputs_tensor_map": ...
                 bracket_hidden_layers_array + &                                !   "hidden_layers": [
                   bracket_layer*num_hidden_layers + &                          !      [
                     neuron_lines*sum(self%nodes_(first_hidden:last_hidden))+ & !        neuron ...
@@ -485,14 +485,14 @@ function get_key_value(line) result(value_)
             associate(meta_start => brace + 1,  meta_end => brace + metadata_lines)
               lines(meta_start:meta_end) = self%metadata_%to_json()
               lines(meta_end) = lines(meta_end) // ","
-              associate(input_range_start => meta_end + 1,  input_range_end => meta_end + tensor_range_lines)
-                lines(input_range_start:input_range_end) =  self%input_range_%to_json()
-                lines(input_range_end) = lines(input_range_end) // ","
-                associate(output_range_start => input_range_end + 1,  output_range_end => input_range_end + tensor_range_lines)
-                  lines(output_range_start:output_range_end) =  self%output_range_%to_json()
-                  lines(output_range_end) = lines(output_range_end) // ","
-                  lines(output_range_end + 1) = string_t('     "hidden_layers": [')
-                  line= output_range_end + 1
+              associate(input_map_start => meta_end + 1,  input_map_end => meta_end + tensor_map_lines)
+                lines(input_map_start:input_map_end) =  self%input_map_%to_json()
+                lines(input_map_end) = lines(input_map_end) // ","
+                associate(output_map_start => input_map_end + 1,  output_map_end => input_map_end + tensor_map_lines)
+                  lines(output_map_start:output_map_end) =  self%output_map_%to_json()
+                  lines(output_map_end) = lines(output_map_end) // ","
+                  lines(output_map_end + 1) = string_t('     "hidden_layers": [')
+                  line= output_map_end + 1
                 end associate
               end associate
             end associate

src/inference_engine/layer_m.f90

@@ -4,7 +4,7 @@ module layer_m
   use neuron_m, only : neuron_t
   use julienne_string_m, only : string_t
   use inference_engine_m_, only : inference_engine_t
-  use tensor_range_m, only :  tensor_range_t
+  use tensor_map_m, only :  tensor_map_t
   implicit none
 
   private
@@ -39,12 +39,12 @@ recursive module function construct_layer(layer_lines, start) result(layer)
 
   interface
 
-    module function inference_engine(hidden_layers, metadata, output_layer, input_range, output_range) result(inference_engine_)
+    module function inference_engine(hidden_layers, metadata, output_layer, input_map, output_map) result(inference_engine_)
       implicit none
       class(layer_t), intent(in), target :: hidden_layers
       type(layer_t), intent(in), target :: output_layer
       type(string_t), intent(in) :: metadata(:)
-      type(tensor_range_t), intent(in) :: input_range, output_range
+      type(tensor_map_t), intent(in) :: input_map, output_map
       type(inference_engine_t) inference_engine_
     end function