Moved remaining tests from docstring tests to build_function directory.

ps -> nn.params.

Vector can't be used on Tuple of Vectors (apparently).

src/build_function/build_function_arrays.jl

@@ -44,25 +44,21 @@ Return a function that takes an input, (optionally) an output and neural network
 
 ```jldoctest
 using SymbolicNeuralNetworks: build_nn_function, SymbolicNeuralNetwork
-using AbstractNeuralNetworks: Chain, Dense, initialparameters, NeuralNetworkParameters
+using AbstractNeuralNetworks: Chain, Dense, NeuralNetwork
 import Random
 Random.seed!(123)
 
 c = Chain(Dense(2, 1, tanh))
-nn = SymbolicNeuralNetwork(c)
-eqs = (a = c(nn.input, nn.params), b = c(nn.input, nn.params).^2)
-funcs = build_nn_function(eqs, nn.params, nn.input)
+nn = NeuralNetwork(c)
+snn = SymbolicNeuralNetwork(nn)
+eqs = (a = c(snn.input, snn.params), b = c(snn.input, snn.params).^2)
+funcs = build_nn_function(eqs, snn.params, snn.input)
 input = [1., 2.]
-ps = initialparameters(c) |> NeuralNetworkParameters
-a = c(input, ps)
-b = c(input, ps).^2
-funcs_evaluated = funcs(input, ps)
-
-(funcs_evaluated.a, funcs_evaluated.b) .≈ (a, b)
+funcs_evaluated = funcs(input, nn.params)
 
 # output
 
-(true, true)
+(a = [-0.9999386280616135], b = [0.9998772598897417])
 ```
 
 # Implementation

test/build_function/build_function_arrays.jl

@@ -1,4 +1,4 @@
-using SymbolicNeuralNetworks: build_nn_function, SymbolicNeuralNetwork
+using SymbolicNeuralNetworks: build_nn_function, SymbolicNeuralNetwork, function_valued_parameters
 using AbstractNeuralNetworks: Chain, Dense, NeuralNetwork
 using Test
 import Random
@@ -21,8 +21,44 @@ function build_function_for_array_valued_equation(input_dim::Integer=2, output_d
     @test funcs_evaluated_as_vector ≈ result_of_standard_computation
 end
 
+function build_function_for_named_tuple(input_dim::Integer=2, output_dim::Integer=1)
+    c = Chain(Dense(input_dim, output_dim, tanh))
+    nn = NeuralNetwork(c)
+    snn = SymbolicNeuralNetwork(nn)
+    eqs = (a = c(snn.input, snn.params), b = c(snn.input, snn.params).^2)
+    funcs = build_nn_function(eqs, snn.params, snn.input)
+    input = [1., 2.]
+    a = c(input, nn.params)
+    b = c(input, nn.params).^2
+    funcs_evaluated = funcs(input, nn.params)
+
+    funcs_evaluated_as_vector = [funcs_evaluated.a, funcs_evaluated.b]
+    result_of_standard_computation = [a, b]
+
+    @test funcs_evaluated_as_vector ≈ result_of_standard_computation
+end
+
+function function_valued_parameters_for_named_tuple(input_dim::Integer=2, output_dim::Integer=1)
+    c = Chain(Dense(input_dim, output_dim, tanh))
+    nn = NeuralNetwork(c)
+    snn = SymbolicNeuralNetwork(nn)
+    eqs = (a = c(snn.input, snn.params), b = c(snn.input, snn.params).^2)
+    funcs = function_valued_parameters(eqs, snn.params, snn.input)
+    input = [1., 2.]
+    a = c(input, nn.params)
+    b = c(input, nn.params).^2
+
+    funcs_evaluated_as_vector = [funcs.a(input, nn.params), funcs.b(input, nn.params)]
+    result_of_standard_computation = [a, b]
+
+    @test funcs_evaluated_as_vector ≈ result_of_standard_computation
+end
+
+# we test in the following order: `function_valued_parameters` → `build_function` (for `NamedTuple`) → `build_function` (for `Array` of `NamedTuple`s) as this is also how the functions are built.
 for input_dim ∈ (2, 3)
     for output_dim ∈ (1, 2)
+        function_valued_parameters_for_named_tuple(input_dim, output_dim)
+        build_function_for_named_tuple(input_dim, output_dim)
         build_function_for_array_valued_equation(input_dim, output_dim)
     end
 end

test/runtests.jl

@@ -1,7 +1,7 @@
 using SymbolicNeuralNetworks
 using SafeTestsets
 
-@safetestset "Docstring tests.                                                                       " begin include("doctest.jl") end
+# @safetestset "Docstring tests.                                                                       " begin include("doctest.jl") end
 @safetestset "Symbolic gradient                                                                      " begin include("derivatives/symbolic_gradient.jl") end
 @safetestset "Symbolic Neural network                                                                " begin include("derivatives/jacobian.jl") end
 @safetestset "Symbolic Params                                                                        " begin include("symbolic_neuralnet/symbolize.jl") end