Fixed all docstrings that were broken after adjustment to ANN v0.6.

Author: Benedikt Brantner Admin

docs/src/double_derivative.md

@@ -18,7 +18,7 @@
 ```@example jacobian_gradient
 using AbstractNeuralNetworks
 using SymbolicNeuralNetworks
-using SymbolicNeuralNetworks: Jacobian, Gradient, derivative, params
+using SymbolicNeuralNetworks: Jacobian, Gradient, derivative
 using Latexify: latexify
 
 c = Chain(Dense(2, 1, tanh; use_bias = false))

src/build_function/build_function.jl

@@ -54,7 +54,7 @@ built_function([1. 2.; 3. 4.], params(nn), 1)
 # output
 
 1-element Vector{Float64}:
- -0.9999967113439513
+ 0.9912108161055604
 ```
 
 Note that we have to supply an extra argument (index) to `_build_nn_function` that we do not have to supply to [`build_nn_function`](@ref).

src/build_function/build_function_arrays.jl

@@ -22,8 +22,8 @@ funcs_evaluated = funcs(input, params(nn))
 # output
 
 2-element Vector{NamedTuple}:
- (a = [-0.9999386280616135], b = [0.9998772598897417])
- (c = [-0.9998158954841537],)
+ (a = [0.985678060655224], b = [0.9715612392570434])
+ (c = [0.9576465981186686],)
 ```
 """
 function build_nn_function(eqs::AbstractArray{<:Union{NamedTuple, NeuralNetworkParameters}}, sparams::NeuralNetworkParameters, sinput::Symbolics.Arr...)
@@ -58,7 +58,7 @@ funcs_evaluated = funcs(input, params(nn))
 
 # output
 
-(a = [-0.9999386280616135], b = [0.9998772598897417])
+(a = [0.985678060655224], b = [0.9715612392570434])
 ```
 
 # Implementation

src/custom_definitions_and_extensions/latexraw.jl

@@ -1,3 +1,7 @@
 function _latexraw(args::AbstractNeuralNetworks.GenericActivation; kwargs...)
     _latexraw(args.σ; kwargs...)
+end
+
+function _latexraw(args::AbstractNeuralNetworks.TanhActivation; kwargs...)
+    _latexraw(tanh; kwargs...)
 end

src/derivatives/jacobian.jl

@@ -45,7 +45,7 @@ We can use `Jacobian` together with [`build_nn_function`](@ref):
 ```jldoctest
 using SymbolicNeuralNetworks
 using SymbolicNeuralNetworks: Jacobian, derivative
-using AbstractNeuralNetworks: Dense, Chain, NeuralNetwork
+using AbstractNeuralNetworks: Dense, Chain, NeuralNetwork, params
 using Symbolics
 import Random
 

src/derivatives/pullback.jl

@@ -52,16 +52,15 @@ c = Chain(Dense(2, 1, tanh))
 nn = NeuralNetwork(c)
 snn = SymbolicNeuralNetwork(nn)
 loss = FeedForwardLoss()
-pb = SymbolicPullback(nn, loss)
-ps = AbstractNeuralNetworks.params(NeuralNetwork(c))
+pb = SymbolicPullback(snn, loss)
 input_output = (rand(2), rand(1))
 loss_and_pullback = pb(params(nn), nn.model, input_output)
 # note that we apply the second argument to another input `1`
 pb_values = loss_and_pullback[2](1)
 
 @variables soutput[1:SymbolicNeuralNetworks.output_dimension(nn.model)]
-symbolic_pullbacks = SymbolicNeuralNetworks.symbolic_pullback(loss(nn.model, SymbolicNeuralNetworks.params(nn), nn.input, soutput), nn)
-pv_values2 = build_nn_function(symbolic_pullbacks, SymbolicNeuralNetworks.params(nn), nn.input, soutput)(input_output[1], input_output[2], ps)
+symbolic_pullbacks = SymbolicNeuralNetworks.symbolic_pullback(loss(nn.model, params(snn), snn.input, soutput), snn)
+pb_values2 = build_nn_function(symbolic_pullbacks, params(snn), snn.input, soutput)(input_output[1], input_output[2], params(nn))
 
 pb_values == (pb_values2 |> SymbolicNeuralNetworks._get_contents |> SymbolicNeuralNetworks._get_params)