Merge pull request #43 from BerkeleyLab/fix-json-writer

Fix JSON reader and writer

example/icar-qr-network.f90

@@ -4,13 +4,14 @@ program icar_qr_network
   !! This program demonstrates how to read a neural network from a JSON file.
   !! query the network for a some of its properties.
   use command_line_m, only : command_line_t
-  use inference_engine_m, only : inference_engine_t, inputs_t
+  use inference_engine_m, only : inference_engine_t
   use string_m, only : string_t
   use matmul_m, only : matmul_t
   use file_m, only : file_t
+  use kind_parameters_m, only : rkind
   implicit none
 
-  type(string_t) input_file_name
+  type(string_t) input_file_name, activation_name
   type(command_line_t) command_line
   type(inference_engine_t) inference_engine
 
@@ -27,9 +28,18 @@ program icar_qr_network
   print *, "num_outputs = ", inference_engine%num_outputs()
   print *, "num_hidden_layers = ", inference_engine%num_hidden_layers()
   print *, "neurons_per_layer = ", inference_engine%neurons_per_layer()
+  activation_name = inference_engine%activation_function_name()
+  print *, "activation function: ", activation_name%string()
+  print *, "using skip connections: ", merge("true ", "false", inference_engine%skip())
 
-  print *, inference_engine%infer([5.7010256568901241e-05, 3.6012468740409531e-07, 1.4284904636951978e-07, &
-     0.0000000000000000e+00, 0.0000000000000000e+00, 1.8107765197753906e+01, 1.9922698868413136e-07, &
-      4.8543064679051895e-08, 2.3069851398468018e+00, 5.4544006347656250e+02], matmul_t())*120.
+  block
+    real(rkind), parameter :: inputs(*) = [ real(rkind) :: &
+      5.4041727707954124e-05_rkind, 1.8311046012797760e-09_rkind, 5.4236606525370767e-13_rkind, 0._rkind, 0._rkind, &
+      2.3016514256596565e-02_rkind, 1.0734779465337851e-07_rkind, 3.9603170742807947e-10_rkind, 2.3047807216644287e+00_rkind, &
+      5.3973675537109375e+02_rkind &
+    ]
+    print *, "inputs: ", inputs
+    print *, "outputs: ", inference_engine%infer(inputs, matmul_t())
+  end block
 
 end program

example/read.f90

@@ -13,6 +13,7 @@ program read_json
   type(string_t) input_file_name
   type(command_line_t) command_line
   type(inference_engine_t) inference_engine
+  type(string_t) activation_name
 
   input_file_name = string_t(command_line%flag_value("--input-file"))
 
@@ -23,8 +24,11 @@ program read_json
 
   print *, "Constructing a new inference_engine_t object by parameters from '"//input_file_name%string()//"'."
   inference_engine = inference_engine_t(file_t(input_file_name))
-  print *, "num_inputs = ", inference_engine%num_inputs()
-  print *, "num_outputs = ", inference_engine%num_outputs()
-  print *, "num_hidden_layers = ", inference_engine%num_hidden_layers()
-  print *, "neurons_per_layer = ", inference_engine%neurons_per_layer()
+  print *, "number of inputs: ", inference_engine%num_inputs()
+  print *, "number of outputs: ", inference_engine%num_outputs()
+  print *, "number of hidden layers: ", inference_engine%num_hidden_layers()
+  print *, "number of neurons per layer: ", inference_engine%neurons_per_layer()
+  activation_name = inference_engine%activation_function_name()
+  print *, "activation function: ", activation_name%string()
+  print *, "using skip connections: ", merge("true ", "false", inference_engine%skip())
 end program

example/write-read-infer.f90

@@ -30,6 +30,7 @@ program write_read_infer
 
   subroutine write_read_query_infer(output_file_name)
     type(string_t), intent(in) :: output_file_name
+    type(string_t) activation_name
     integer i, j
     integer, parameter :: num_inputs = 2, num_outputs = 1, num_neurons = 3, num_hidden_layers = 2
     integer, parameter :: identity(*,*,*) = & 
@@ -59,10 +60,12 @@ subroutine write_read_query_infer(output_file_name)
     inference_engine = inference_engine_t(json_input_file)
 
     print *, "Querying the new inference_engine_t object for several properties:"
-    print *, "num_outputs = ", inference_engine%num_outputs()
-    print *, "num_hidden_layers = ", inference_engine%num_hidden_layers()
-    print *, "neurons_per_layer = ", inference_engine%neurons_per_layer()
-
+    print *, "number of outputs:", inference_engine%num_outputs()
+    print *, "number of hidden layers:", inference_engine%num_hidden_layers()
+    print *, "number of neurons per layer:", inference_engine%neurons_per_layer()
+    activation_name = inference_engine%activation_function_name()
+    print *, "activation function: ", activation_name%string()
+    print *, "using skip connections: ", merge("true ", "false", inference_engine%skip())
     print *, "Performing inference:"
     print *, "inference_engine%infer([0.,1.]) =",inference_engine%infer([real(rkind):: 0.,1.], matmul_t())
     print *, "Correct answer for the XOR neural network: ", 1.

src/inference_engine_m.f90

@@ -49,6 +49,7 @@ module inference_engine_m
     procedure, private :: subtract
     generic :: operator(-) => subtract
     procedure :: skip
+    procedure :: activation_function_name
   end type
 
   interface inference_engine_t
@@ -143,6 +144,12 @@ elemental module function skip(self) result(use_skip_connections)
       logical use_skip_connections
     end function
 
+    elemental module function activation_function_name(self) result(activation_name)
+      implicit none
+      class(inference_engine_t), intent(in) :: self
+      type(string_t) activation_name
+    end function
+
   end interface
 
 end module inference_engine_m

src/inference_engine_s.f90

@@ -45,7 +45,7 @@
       case("step")
         inference_engine%activation_strategy_ = step_t()
       case default
-        error stop "inference_engine_t construct_from_json: unrecognized activation strategy"
+        error stop "inference_engine_t construct_from_components: unrecognized activation strategy"
     end select
 
     call assert_consistent(inference_engine)
@@ -157,8 +157,9 @@ function get_key_value(line) result(value_)
           associate(closing_value_quotes => opening_value_quotes + index(text_after_colon(opening_value_quotes+1:), '"'))
             if (any([opening_value_quotes, closing_value_quotes] == 0)) then
               value_ = string_t(trim(adjustl((text_after_colon))))
+            else
+              value_ = string_t(text_after_colon(opening_value_quotes+1:closing_value_quotes-1))
             end if
-            value_ = string_t(text_after_colon(opening_value_quotes+1:closing_value_quotes-1))
           end associate
         end associate
       end associate
@@ -339,8 +340,8 @@ pure subroutine assert_consistent(self)
         lines(line) = string_t('        "activationFunction": "' // &
                                                        self%metadata_(findloc(key, "activationFunction", dim=1))%string() // '",')
         line = line + 1
-        lines(line) = string_t('        "usingSkipConnections": "' // &
-                                                       self%metadata_(findloc(key, "usingSkipConnections", dim=1))%string() // '",')
+        lines(line) = string_t('        "usingSkipConnections": ' // &
+                                                       self%metadata_(findloc(key, "usingSkipConnections", dim=1))%string())
 
         line = line + 1
         lines(line) = string_t('    },')
@@ -428,4 +429,8 @@ pure subroutine assert_consistent(self)
     use_skip_connections = self%metadata_(findloc(key, "usingSkipConnections", dim=1))%string() == "true"
   end procedure
 
+  module procedure activation_function_name
+    activation_name = self%metadata_(findloc(key, "activationFunction", dim=1))
+  end procedure
+
 end submodule inference_engine_s

test/asymmetric_engine_test_m.f90

@@ -46,10 +46,12 @@ function results() result(test_results)
         "counting the number of hidden layers", &
         "counting the number of neurons per layer", &
         "counting the number of inputs", &
-        "counting the number of outputs" &
+        "counting the number of outputs", &
+        "getting the activation function name" &
       ], &
       [ xor_and_2nd_input_truth_table(concurrent_dot_products_t()), xor_and_2nd_input_truth_table(matmul_t()), &
-        test_num_hidden_layers(),  test_neurons_per_layer(), test_num_inputs(), test_num_outputs() &
+        test_num_hidden_layers(),  test_neurons_per_layer(), test_num_inputs(), test_num_outputs(), &
+        test_activation_name() &
       ] & 
     )   
   end function
@@ -142,4 +144,14 @@ function test_num_outputs() result(test_passes)
     test_passes = inference_engine%num_outputs() == 1
   end function
 
+  function test_activation_name() result(test_passes)
+    logical test_passes
+    type(string_t) :: function_name
+    type(inference_engine_t) inference_engine
+
+    inference_engine = xor_and_2nd_input_network()
+    function_name = inference_engine%activation_function_name() 
+    test_passes = function_name%string() == "step"
+  end function
+
 end module asymmetric_engine_test_m