Read & perform inference on networks for which the hidden-layer width varies across layers

example/read-query-infer.f90

@@ -0,0 +1,56 @@
+! Copyright (c), The Regents of the University of California
+! Terms of use are as specified in LICENSE.txt
+program read_query_infer
+  !! This program demonstrates how to read a neural network from a JSON file,
+  !! query the network object for some of its properties, print those properties,
+  !! and use the network to perform inference.  
+  use inference_engine_m, only : inference_engine_t, relu_t, tensor_t
+  use julienne_m, only : string_t, command_line_t, file_t
+  use kind_parameters_m, only : rkind
+  implicit none
+
+  type(command_line_t) command_line
+  type(inference_engine_t) inference_engine
+
+  associate(file_name => string_t(command_line%flag_value("--input-file")))
+
+    if (len(file_name%string())==0) then
+      error stop new_line('a') // new_line('a') // &
+        'Usage: fpm run --example read-query -- --input-file "<file-name>"' 
+    end if
+
+    print *, "Reading an inference_engine_t object from the same JSON file '"//file_name%string()//"'."
+    associate(inference_engine => inference_engine_t(file_t(file_name)))
+
+      print *, "Querying the new inference_engine_t object for several properties:"
+      associate(activation_name => inference_engine%activation_function_name())
+        print *, "Activation function: ", activation_name%string()
+      end associate
+      print *, "Number of outputs:", inference_engine%num_outputs()
+      print *, "Number of inputs:", inference_engine%num_inputs()
+      print *, "Nodes per layer:", inference_engine%nodes_per_layer()
+      print *, "Performing inference:"
+
+      block
+        integer, parameter :: tensor_size = 2, num_tests = 3
+        real, parameter :: tensor_range = 11.
+        real harvest(tensor_size)
+        integer i
+
+        call random_init(repeatable=.false., image_distinct=.true.)
+
+        print *, "Inputs                 |       Outputs  "
+
+        do i = 1, num_tests
+          call random_number(harvest)
+          associate(inputs => tensor_t(tensor_range*harvest))
+            associate(outputs => inference_engine%infer(inputs))
+              print '(2(2g12.5,a,2x))', inputs%values(), "|",  outputs%values()
+            end associate
+          end associate
+        end do
+
+      end block
+    end associate ! associate(inference_engine => ...)
+  end associate ! associate(file_name => ...)
+end program

src/inference_engine/inference_engine_m_.f90

@@ -6,6 +6,7 @@ module inference_engine_m_
   use julienne_file_m, only : file_t
   use julienne_string_m, only : string_t
   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 differentiable_activation_strategy_m, only :differentiable_activation_strategy_t
@@ -24,7 +25,7 @@ module inference_engine_m_
     !! Encapsulate the minimal information needed to perform inference
     private
     type(tensor_range_t) input_range_, output_range_
-    type(string_t) metadata_(size(key))
+    type(metadata_t) metadata_
     real(rkind), allocatable :: weights_(:,:,:), biases_(:,:)
     integer, allocatable :: nodes_(:)
     class(activation_strategy_t), allocatable :: activation_strategy_ ! Strategy Pattern facilitates elemental activation
@@ -46,7 +47,7 @@ module inference_engine_m_
 
   type exchange_t
     type(tensor_range_t) input_range_, output_range_
-    type(string_t) metadata_(size(key))
+    type(metadata_t) metadata_
     real(rkind), allocatable :: weights_(:,:,:), biases_(:,:)
     integer, allocatable :: nodes_(:)
     class(activation_strategy_t), allocatable :: activation_strategy_ ! Strategy Pattern facilitates elemental activation
@@ -72,7 +73,7 @@ impure module function construct_from_padded_arrays(metadata, weights, biases, n
       type(inference_engine_t) inference_engine
     end function
 
-    impure elemental module function construct_from_json(file_) result(inference_engine)
+    impure elemental module function from_json(file_) result(inference_engine)
       implicit none
       type(file_t), intent(in) :: file_
       type(inference_engine_t) inference_engine