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Add hasneuralnetwork and hasneuralnetworkps accessor functions #113

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f598437
re add hasneuralnetwork and hasneuralnetworkps
TorkelE Mar 26, 2026
53d6ad4
commit remaining files
TorkelE Mar 26, 2026
ca15025
Merge branch 'main' into readd_hasneuralnetwork_accessors
TorkelE Mar 26, 2026
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2 changes: 2 additions & 0 deletions docs/src/api.md
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,8 @@ SymbolicNeuralNetwork
@SymbolicNeuralNetwork
multi_layer_feed_forward
ModelingToolkitNeuralNets.isneuralnetwork
ModelingToolkitNeuralNets.hasneuralnetwork
ModelingToolkitNeuralNets.isneuralnetworkps
ModelingToolkitNeuralNets.hasneuralnetworkps
ModelingToolkitNeuralNets.get_nn_chain
```
38 changes: 38 additions & 0 deletions src/nn_par_accessors.jl
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Expand Up @@ -24,6 +24,25 @@ function isneuralnetwork(p::Symbolics.SymbolicT)
getmetadata(p, NeuralNetworkParameter, false)
end

"""
ModelingToolkitNeuralNets.hasneuralnetwork(p)

Returns `true` if the parameter has the `neuralnetwork` metadata set (whenever the value is `true` or `false`). This function is primarily intended for internal use within dependent packages.

Example:
```julia
@parameters d
@SymbolicNeuralNetwork NN, θ = chain
ModelingToolkitNeuralNets.hasneuralnetwork(d) # false
ModelingToolkitNeuralNets.hasneuralnetwork(NN) # true
ModelingToolkitNeuralNets.hasneuralnetwork(θ) # false
````
"""
hasneuralnetwork(p::Union{Symbolics.Num, Symbolics.Arr, Symbolics.CallAndWrap}) = hasneuralnetwork(Symbolics.unwrap(p))
function hasneuralnetwork(p::Symbolics.SymbolicT)
hasmetadata(p, NeuralNetworkParameter)
end

"""
ModelingToolkitNeuralNets.isneuralnetworkps(p)

Expand All @@ -43,6 +62,25 @@ function isneuralnetworkps(p::Symbolics.SymbolicT)
getmetadata(p, NeuralNetworkParametrisation, false)
end

"""
ModelingToolkitNeuralNets.hasneuralnetworkps(p)

Returns `true` if the parameter has the `neuralnetworkps` metadata set (whenever the value is `true` or `false`). This function is primarily intended for internal use within dependent packages.

Example:
```julia
@parameters d
@SymbolicNeuralNetwork NN, θ = chain
ModelingToolkitNeuralNets.hasneuralnetworkps(d) # false
ModelingToolkitNeuralNets.hasneuralnetworkps(NN) # false
ModelingToolkitNeuralNets.hasneuralnetworkps(θ) # true
````
"""
hasneuralnetworkps(p::Union{Symbolics.Num, Symbolics.Arr, Symbolics.CallAndWrap}) = hasneuralnetworkps(Symbolics.unwrap(p))
function hasneuralnetworkps(p::Symbolics.SymbolicT)
hasmetadata(p, NeuralNetworkParametrisation)
end


### Defines Other Accessors ###

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54 changes: 54 additions & 0 deletions test/nn_ps_accessors.jl
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Expand Up @@ -9,7 +9,9 @@ let
@parameters p q[1:3]
for s in [X, Y, Y[1], p, q, q[1]]
@test !ModelingToolkitNeuralNets.isneuralnetwork(s)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(s)
@test !ModelingToolkitNeuralNets.isneuralnetworkps(s)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(s)
end

# Tests on MTKNeuralNets parameters
Expand All @@ -23,9 +25,18 @@ let
@test !ModelingToolkitNeuralNets.isneuralnetwork(θ)
@test ModelingToolkitNeuralNets.isneuralnetwork(U)
@test !ModelingToolkitNeuralNets.isneuralnetwork(p)
@test ModelingToolkitNeuralNets.hasneuralnetwork(NN)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(θ)
@test ModelingToolkitNeuralNets.hasneuralnetwork(U)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(p)
@test !ModelingToolkitNeuralNets.isneuralnetworkps(NN)
@test ModelingToolkitNeuralNets.isneuralnetworkps(θ)
@test !ModelingToolkitNeuralNets.isneuralnetworkps(U)
@test ModelingToolkitNeuralNets.hasneuralnetworkps(p)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(NN)
@test ModelingToolkitNeuralNets.hasneuralnetworkps(θ)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(U)
@test ModelingToolkitNeuralNets.hasneuralnetworkps(p)
end

# Check that `isneuralnetwork` and `isneuralnetworkps` give correct input on parameters stored in a model created using symbolic approach.
Expand All @@ -49,10 +60,18 @@ let
@test !ModelingToolkitNeuralNets.isneuralnetwork(sys.d)
@test ModelingToolkitNeuralNets.isneuralnetwork(sys.NN)
@test !ModelingToolkitNeuralNets.isneuralnetwork(sys.θ)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(sys.X)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(sys.d)
@test ModelingToolkitNeuralNets.hasneuralnetwork(sys.NN)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(sys.θ)
@test !ModelingToolkitNeuralNets.isneuralnetworkps(sys.X)
@test !ModelingToolkitNeuralNets.isneuralnetworkps(sys.d)
@test !ModelingToolkitNeuralNets.isneuralnetworkps(sys.NN)
@test ModelingToolkitNeuralNets.isneuralnetworkps(sys.θ)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys.X)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys.d)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys.NN)
@test ModelingToolkitNeuralNets.hasneuralnetworkps(sys.θ)
end

# Check that `isneuralnetwork` and `isneuralnetworkps` give correct input on parameters stored in a model created using NNBlock approach.
Expand Down Expand Up @@ -83,6 +102,15 @@ let
@test !ModelingToolkitNeuralNets.isneuralnetwork(sys_nnblock.x)
@test !ModelingToolkitNeuralNets.isneuralnetwork(sys_nnblock.y)

@test ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.nn.lux_apply)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.nn.lux_model)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.nn.p)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.nn.T)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.α)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.δ)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.x)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(sys_nnblock.y)

@test !ModelingToolkitNeuralNets.isneuralnetworkps(sys_nnblock.nn.lux_apply)
@test !ModelingToolkitNeuralNets.isneuralnetworkps(sys_nnblock.nn.lux_model)
@test ModelingToolkitNeuralNets.isneuralnetworkps(sys_nnblock.nn.p)
Expand All @@ -91,6 +119,32 @@ let
@test !ModelingToolkitNeuralNets.isneuralnetworkps(sys_nnblock.δ)
@test !ModelingToolkitNeuralNets.isneuralnetworkps(sys_nnblock.x)
@test !ModelingToolkitNeuralNets.isneuralnetworkps(sys_nnblock.y)

@test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.nn.lux_apply)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.nn.lux_model)
@test ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.nn.p)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.nn.T)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.α)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.δ)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.x)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(sys_nnblock.y)
end

# Specific `hasneuralnetwork` and `hasneuralnetworkps` tests.
let
@parameters p1 [neuralnetwork = true] p2 [neuralnetwork = false] p3 [neuralnetworkps = true] p4 [neuralnetworkps = false] p5 p6
@test ModelingToolkitNeuralNets.hasneuralnetwork(p1)
@test ModelingToolkitNeuralNets.hasneuralnetwork(p2)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(p3)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(p4)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(p5)
@test !ModelingToolkitNeuralNets.hasneuralnetwork(p6)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(p1)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(p2)
@test ModelingToolkitNeuralNets.hasneuralnetworkps(p3)
@test ModelingToolkitNeuralNets.hasneuralnetworkps(p4)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(p5)
@test !ModelingToolkitNeuralNets.hasneuralnetworkps(p6)
end

# Checks the `get_nn_chain` accessor function.
Expand Down
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