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corrected paths for images in tutorials (#70)
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* corrected paths for images in tutorials

* reduced chunk_size for Julia 1.6

* minor bug fix
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@ThummeTo
ThummeTo committed Jan 4, 2023
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7 changes: 4 additions & 3 deletions examples/src/advanced_hybrid_ME.ipynb
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Expand Up @@ -41,7 +41,7 @@
"\n",
"NeuralFMUs do not need to be as easy as in this example. Basically a NeuralFMU can combine different ANN topologies that manipulate any FMU-input (system state, system inputs, time) and any FMU-output (system state derivative, system outputs, other system variables). However, for this example a NeuralFMU topology as shown in the following picture is used.\n",
"\n",
"![NeuralFMU.svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/pics/NeuralFMU.svg?raw=true)\n",
"![NeuralFMU.svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/img/NeuralFMU.svg?raw=true)\n",
"\n",
"*NeuralFMU (ME) from* [[1]](#Source).\n",
"\n",
Expand Down Expand Up @@ -109,18 +109,19 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"After importing the packages, the path to the *Functional Mock-up Units* (FMUs) is set. The FMU is a model exported meeting the *Functional Mock-up Interface* (FMI) Standard. The FMI is a free standard ([fmi-standard.org](http://fmi-standard.org/)) that defines a container and an interface to exchange dynamic models using a combination of XML files, binaries and C code zipped into a single file. \n",
"\n",
"The object-orientated structure of the *SpringPendulum1D* (*simpleFMU*) can be seen in the following graphic and corresponds to a simple modeling.\n",
"\n",
"![svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/pics/SpringPendulum1D.svg?raw=true)\n",
"![svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/img/SpringPendulum1D.svg?raw=true)\n",
"\n",
"In contrast, the model *SpringFrictionPendulum1D* (*realFMU*) is somewhat more accurate, because it includes a friction component. \n",
"\n",
"![svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/pics/SpringFrictionPendulum1D.svg?raw=true)"
"![svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/img/SpringFrictionPendulum1D.svg?raw=true)"
]
},
{
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12 changes: 6 additions & 6 deletions examples/src/mdpi_2022.ipynb
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Expand Up @@ -44,7 +44,7 @@
"\n",
"In this example, a real-world simulation model is enhanced in terms of accuracy using a so called *physics-enhanced neural ordinary differential equation* (PeNode). Basically, this is an extension to the NeuralODE concept and looks as can be seen in Fig. 1.\n",
"\n",
"![NeuralFMU.svg](https://github.com/ThummeTo/FMIFlux.jl/tree/main/examples/img/mdpi_2022/neuralfmu.svg?raw=true)\n",
"![NeuralFMU.svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/img/mdpi_2022/neuralfmu.svg?raw=true)\n",
"\n",
"*Fig.1: A possible representation for a physics-enhanced neural ordinary differential equation (PeNODE). PeNODEs that include FMUs instead of symbolic ODEs are called NeuralFMUs.*\n",
"\n",
Expand Down Expand Up @@ -371,9 +371,9 @@
"\n",
"These considerations lead to the following topology:\n",
"\n",
"![usedneuralfmu.svg](https://github.com/ThummeTo/FMIFlux.jl/tree/main/examples/img/mdpi_2022/usedneuralfmu.svg?raw=true)\n",
"![usedneuralfmu.svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/img/mdpi_2022/usedneuralfmu.svg?raw=true)\n",
"\n",
"*Fig.2: The used topology for a NeuralFMU in this example. The ANN operates based on the signals $\\dot{x}_4, \\dot{x}_5, \\dot{x}_6$ from the FMU, but only modifies the signal $\\hat{\\dot{x}}_5$.*\n",
"*Fig.2: The used topology for a NeuralFMU in this example. The ANN operates based on the signals $\\dot{x}_4, \\dot{x}_5, \\dot{x}_6$ from the FMU, but only modifies the signal $\\hat{\\dot{x}}_5$. The gates $p_{FMU}$ and $p_{ANN}$ control how much the dynamics of FMU and ANN contribute to the NeuralFMU dynamics. Starting with $p_{FMU}=1.0$ and $p_{ANN}=0.0$ makes an initialization routine obsolete, because the dynamics of the NeuralFMU equal the dynamics of the FMU, see [[1]](#Source) for further details.*\n",
"\n",
"### Part 2c: Translating topology to Julia\n",
"To implement the signal bypass in a layer sequence, two layers named `CacheLayer` and `CacheRetrieveLayer` are used to cache and retrieve arbitrary values:"
Expand Down Expand Up @@ -567,12 +567,12 @@
"metadata": {},
"outputs": [],
"source": [
"# Load parameters \n",
"# fmiLoadParameters(neuralFMU, paramsPath)\n",
"\n",
"# check what had been learned by the NeuralFMU, simulate it ...\n",
"resultNFMU_train = neuralFMU(x0, (tStart, tStop); parameters=data.params, showProgress=true, recordValues=manipulatedDerVars, maxiters=1e7) # [120s]\n",
"\n",
"# Load parameters \n",
"fmiLoadParameters(neuralFMU, paramsPath)\n",
"\n",
"# are we better?\n",
"mse_NFMU = FMIFlux.Losses.mse(data.consumption_val, fmiGetSolutionState(resultNFMU_train, 6; isIndex=true))\n",
"mse_FMU = FMIFlux.Losses.mse(data.consumption_val, fmiGetSolutionState(resultFMU, 6; isIndex=true))\n",
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7 changes: 4 additions & 3 deletions examples/src/modelica_conference_2021.ipynb
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Expand Up @@ -41,7 +41,7 @@
"\n",
"NeuralFMUs do not need to be as easy as in this example. Basically a NeuralFMU can combine different ANN topologies that manipulate any FMU-input (system state, system inputs, time) and any FMU-output (system state derivative, system outputs, other system variables). However, for this example a NeuralFMU topology as shown in the following picture is used.\n",
"\n",
"![NeuralFMU.svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/pics/NeuralFMU.svg?raw=true)\n",
"![NeuralFMU.svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/img/NeuralFMU.svg?raw=true)\n",
"\n",
"*NeuralFMU (ME) from* [[1]](#Source).\n",
"\n",
Expand Down Expand Up @@ -107,18 +107,19 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"After importing the packages, the path to the *Functional Mock-up Units* (FMUs) is set. The exported FMU is a model meeting the *Functional Mock-up Interface* (FMI) Standard. The FMI is a free standard ([fmi-standard.org](http://fmi-standard.org/)) that defines a container and an interface to exchange dynamic models using a combination of XML files, binaries and C code zipped into a single file. \n",
"\n",
"The object-orientated structure of the *SpringPendulum1D* (*simpleFMU*) can be seen in the following graphic and corresponds to a simple modeling.\n",
"\n",
"![svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/pics/SpringPendulum1D.svg?raw=true)\n",
"![svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/img/SpringPendulum1D.svg?raw=true)\n",
"\n",
"In contrast, the model *SpringFrictionPendulum1D* (*realFMU*) is somewhat more accurate, because it includes a friction component. \n",
"\n",
"![svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/pics/SpringFrictionPendulum1D.svg?raw=true)"
"![svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/img/SpringFrictionPendulum1D.svg?raw=true)"
]
},
{
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5 changes: 3 additions & 2 deletions examples/src/simple_hybrid_CS.ipynb
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Expand Up @@ -41,7 +41,7 @@
"\n",
"NeuralFMUs do not need to be as easy as in this example. Basically a NeuralFMU can combine different ANN topologies that manipulate any FMU-input (system state, system inputs, time) and any FMU-output (system state derivative, system outputs, other system variables). However, for this example a NeuralFMU topology as shown in the following picture is used.\n",
"\n",
"![CS-NeuralFMU.svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/pics/CSNeuralFMU.svg?raw=true)\n",
"![CS-NeuralFMU.svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/img/CSNeuralFMU.svg?raw=true)\n",
"\n",
"*NeuralFMU (CS) from* [[1]](#Source).\n",
"\n",
Expand Down Expand Up @@ -107,14 +107,15 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"After importing the packages, the path to the *Functional Mock-up Units* (FMUs) is set. The FMU is a model exported meeting the *Functional Mock-up Interface* (FMI) Standard. The FMI is a free standard ([fmi-standard.org](http://fmi-standard.org/)) that defines a container and an interface to exchange dynamic models using a combination of XML files, binaries and C code zipped into a single file. \n",
"\n",
"The objec-orientated structure of the *SpringPendulumExtForce1D* can be seen in the following graphic. This model is a simple spring pendulum without friction, but with an external force. \n",
"\n",
"![svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/pics/SpringPendulumExtForce1D.svg?raw=true)"
"![svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/img/SpringPendulumExtForce1D.svg?raw=true)"
]
},
{
Expand Down
7 changes: 4 additions & 3 deletions examples/src/simple_hybrid_ME.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@
"\n",
"NeuralFMUs do not need to be as easy as in this example. Basically a NeuralFMU can combine different ANN topologies that manipulate any FMU-input (system state, system inputs, time) and any FMU-output (system state derivative, system outputs, other system variables). However, for this example a NeuralFMU topology as shown in the following picture is used.\n",
"\n",
"![NeuralFMU.svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/pics/NeuralFMU.svg?raw=true)\n",
"![NeuralFMU.svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/img/NeuralFMU.svg?raw=true)\n",
"\n",
"*NeuralFMU (ME) from* [[1]](#Source).\n",
"\n",
Expand Down Expand Up @@ -107,18 +107,19 @@
]
},
{
"attachments": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"After importing the packages, the path to the *Functional Mock-up Units* (FMUs) is set. The FMU is a model exported meeting the *Functional Mock-up Interface* (FMI) Standard. The FMI is a free standard ([fmi-standard.org](http://fmi-standard.org/)) that defines a container and an interface to exchange dynamic models using a combination of XML files, binaries and C code zipped into a single file. \n",
"\n",
"The object-orientated structure of the *SpringPendulum1D* (*simpleFMU*) can be seen in the following graphic and corresponds to a simple modeling.\n",
"\n",
"![svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/pics/SpringPendulum1D.svg?raw=true)\n",
"![svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/img/SpringPendulum1D.svg?raw=true)\n",
"\n",
"In contrast, the model *SpringFrictionPendulum1D* (*realFMU*) is somewhat more accurate, because it includes a friction component. \n",
"\n",
"![svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/pics/SpringFrictionPendulum1D.svg?raw=true)"
"![svg](https://github.com/thummeto/FMIFlux.jl/blob/main/docs/src/examples/img/SpringFrictionPendulum1D.svg?raw=true)"
]
},
{
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10 changes: 7 additions & 3 deletions src/layers.jl
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Expand Up @@ -63,7 +63,7 @@ struct ScaleShift{T}
end

# init ScaleShift with inverse transformation of a given ShiftScale
function ScaleShift(l::ShiftScale{T}; indices=1:length(data)) where {T}
function ScaleShift(l::ShiftScale{T}; indices=1:length(l.scale)) where {T}
return ScaleShift{T}(1.0 / l.scale[indices], -1.0 * l.shift[indices])
end

Expand Down Expand Up @@ -140,10 +140,14 @@ struct CacheRetrieveLayer
end
export CacheRetrieveLayer

function (l::CacheRetrieveLayer)(idxBefore, x, idxAfter=nothing)
function (l::CacheRetrieveLayer)(idxBefore, x=nothing, idxAfter=nothing)
# Zygote doesn't like empty arrays
if idxAfter == nothing
if idxAfter == nothing && x == nothing
return l.cacheLayer.cache[idxBefore]
elseif idxAfter == nothing
return [l.cacheLayer.cache[idxBefore]..., x...]
elseif x == nothing
return [l.cacheLayer.cache[idxBefore]..., l.cacheLayer.cache[idxAfter]...]
else
return [l.cacheLayer.cache[idxBefore]..., x..., l.cacheLayer.cache[idxAfter]...]
end
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6 changes: 5 additions & 1 deletion src/neural.jl
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Expand Up @@ -1340,6 +1340,10 @@ function train!(loss, params::Union{Flux.Params, Zygote.Params, Vector{Vector{Fl

to_differentiate = p -> loss(p)

if VERSION < v"1.7.0"
@warn "Training under Julia 1.6 is very slow, please consider using Julia 1.7 or newer."
end

for i in 1:length(data)

try
Expand All @@ -1356,7 +1360,7 @@ function train!(loss, params::Union{Flux.Params, Zygote.Params, Vector{Vector{Fl
chunk_size = ceil(Int, sqrt( Sys.total_memory()/(2^30) ))*32

else
chunk_size = ceil(Int, sqrt( Sys.total_memory()/(2^30) ))*4
chunk_size = ceil(Int, sqrt( Sys.total_memory()/(2^30) ))*2
#grad = ForwardDiff.gradient(to_differentiate, params[j]);
end

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Registration pull request created: JuliaRegistries/General/75053

After the above pull request is merged, it is recommended that a tag is created on this repository for the registered package version.

This will be done automatically if the Julia TagBot GitHub Action is installed, or can be done manually through the github interface, or via:

git tag -a v0.9.1 -m "<description of version>" 6cd38f7dae85290d0f0fcf62fb347afcb40098f7
git push origin v0.9.1

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