fix links

.github/workflows/docs.yml

@@ -10,7 +10,7 @@ jobs:
   build:
     runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v3
 
       - uses: julia-actions/setup-julia@latest
 

.github/workflows/runtests.yml

@@ -25,7 +25,7 @@ jobs:
 
     steps:
       - name: Checkout InvertibleNetworks.jl
-        uses: actions/checkout@v2
+        uses: actions/checkout@v3
 
       - name: Setup julia
         uses: julia-actions/setup-julia@v1

Project.toml

@@ -1,7 +1,7 @@
 name = "InvertibleNetworks"
 uuid = "b7115f24-5f92-4794-81e8-23b0ddb121d3"
 authors = ["Philipp Witte <[email protected]>", "Ali Siahkoohi <[email protected]>", "Mathias Louboutin <[email protected]>", "Gabrio Rizzuti <[email protected]>", "Rafael Orozco <[email protected]>", "Felix J. herrmann <[email protected]>"]
-version = "2.2.1"
+version = "2.2.2"
 
 [deps]
 CUDA = "052768ef-5323-5732-b1bb-66c8b64840ba"

docs/src/api.md

@@ -1,36 +1,48 @@
 
-## Invertible Layers
+# Invertible Networks API reference
 
-### Types
+```@autodocs
+Modules = [InvertibleNetworks]
+Order  = [:function]
+Pages = ["neuralnet.jl", "parameter.jl"]
+```
+
+## Activations functions
 
 ```@autodocs
 Modules = [InvertibleNetworks]
-Order  = [:type]
-Filter = t -> t<:NeuralNetLayer
+Order   = [:function]
+Pages = ["activation_functions.jl"]
 ```
 
-## Invertible Networks
+## Dimensions manipulation
+
+```@autodocs
+Modules = [InvertibleNetworks]
+Order   = [:function]
+Pages = ["dimensionality_operations.jl"]
+```
 
-### Types
+## Layers
 
 ```@autodocs
 Modules = [InvertibleNetworks]
-Order   = [:type]
-Filter = t -> t<:InvertibleNetwork
+Order  = [:type]
+Filter = t -> t<:NeuralNetLayer
 ```
 
-## Activations functions
+## Networks
 
 ```@autodocs
 Modules = [InvertibleNetworks]
-Order   = [:function]
-Pages = ["activation_functions.jl"]
+Order   = [:type]
+Filter = t -> t<:InvertibleNetwork
 ```
 
-## Dimensions manipulation
+## AD Integration
 
 ```@autodocs
 Modules = [InvertibleNetworks]
-Order   = [:function]
-Pages = ["dimensionality_operations.jl"]
+Order  = [:function]
+Pages = ["chainrules.jl"]
 ```

docs/src/index.md

@@ -17,65 +17,49 @@ This package is developped and maintained by Felix J. Herrmann's [SlimGroup](htt
 
 ## Installation
 
-To install this package you can either directly install it from its url:
+THis package is registered in the Julia general registry and can be directly installed in the julia REPL package manager (`]`):
 
 
 ```julia
-] add https://github.com/slimgroup/InvertibleNetworks.jl
-```
-
-or if you wish to have access to all [slimgroup](https://github.com/slimgroup/)' softwares you can add our registry to have access to our packages in the standard julia way:
-
-
-```julia
-] registry add https://Github.com/slimgroup/SLIMregistryJL.git
-] add InvertibleNetworks
+] add/dev InvertibleNetworks
 ```
 
 ## References
 
- - Yann Dauphin, Angela Fan, Michael Auli and David Grangier, "Language modeling with gated convolutional networks", Proceedings of the 34th International Conference on Machine Learning, 2017. https://arxiv.org/pdf/1612.08083.pdf
+ - Yann Dauphin, Angela Fan, Michael Auli and David Grangier, "Language modeling with gated convolutional networks", Proceedings of the 34th International Conference on Machine Learning, 2017. [ArXiv](https://arxiv.org/pdf/1612.08083.pdf)
 
- - Laurent Dinh, Jascha Sohl-Dickstein and Samy Bengio, "Density estimation using Real NVP",  International Conference on Learning Representations, 2017, https://arxiv.org/abs/1605.08803
+ - Laurent Dinh, Jascha Sohl-Dickstein and Samy Bengio, "Density estimation using Real NVP",  International Conference on Learning Representations, 2017, [ArXiv](https://arxiv.org/abs/1605.08803)
 
- - Diederik P. Kingma and Prafulla Dhariwal, "Glow: Generative Flow with Invertible 1x1 Convolutions", Conference on Neural Information Processing Systems, 2018. https://arxiv.org/abs/1807.03039
+ - Diederik P. Kingma and Prafulla Dhariwal, "Glow: Generative Flow with Invertible 1x1 Convolutions", Conference on Neural Information Processing Systems, 2018. [ArXiv](https://arxiv.org/abs/1807.03039)
 
- - Keegan Lensink, Eldad Haber and Bas Peters, "Fully Hyperbolic Convolutional Neural Networks", arXiv Computer Vision and Pattern Recognition, 2019. https://arxiv.org/abs/1905.10484
+ - Keegan Lensink, Eldad Haber and Bas Peters, "Fully Hyperbolic Convolutional Neural Networks", arXiv Computer Vision and Pattern Recognition, 2019. [ArXiv](https://arxiv.org/abs/1905.10484)
 
- - Patrick Putzky and Max Welling, "Invert to learn to invert", Advances in Neural Information Processing Systems, 2019. https://arxiv.org/abs/1911.10914
+ - Patrick Putzky and Max Welling, "Invert to learn to invert", Advances in Neural Information Processing Systems, 2019. [ArXiv](https://arxiv.org/abs/1911.10914)
 
- - Jakob Kruse, Gianluca Detommaso, Robert Scheichl and Ullrich Köthe, "HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference", arXiv Statistics and Machine Learning, 2020. https://arxiv.org/abs/1905.10687
+ - Jakob Kruse, Gianluca Detommaso, Robert Scheichl and Ullrich Köthe, "HINT: Hierarchical Invertible Neural Transport for Density Estimation and Bayesian Inference", arXiv Statistics and Machine Learning, 2020. [ArXiv](https://arxiv.org/abs/1905.10687)
 
 ## Related work and publications
 
 The following publications use [InvertibleNetworks.jl]:
 
-- **[“Preconditioned training of normalizing flows for variational inference in inverse problems”]**
+- **[“Preconditioned training of normalizing flows for variational inference in inverse problems”](https://slim.gatech.edu/content/preconditioned-training-normalizing-flows-variational-inference-inverse-problems)**
     - paper: [https://arxiv.org/abs/2101.03709](https://arxiv.org/abs/2101.03709)
     - [presentation](https://slim.gatech.edu/Publications/Public/Conferences/AABI/2021/siahkoohi2021AABIpto/siahkoohi2021AABIpto_pres.pdf)
-    - code: [FastApproximateInference.jl]
+    - code: [FastApproximateInference.jl](https://github.com/slimgroup/Software.siahkoohi2021AABIpto)
 
-- **["Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization"]**
+- **["Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization"](https://slim.gatech.edu/content/parameterizing-uncertainty-deep-invertible-networks-application-reservoir-characterization)**
     - paper: [https://arxiv.org/abs/2004.07871](https://arxiv.org/abs/2004.07871)
     - [presentation](https://slim.gatech.edu/Publications/Public/Conferences/SEG/2020/rizzuti2020SEGuqavp/rizzuti2020SEGuqavp_pres.pdf)
     - code: [https://github.com/slimgroup/Software.SEG2020](https://github.com/slimgroup/Software.SEG2020)
 
-- **["Generalized Minkowski sets for the regularization of inverse problems"]**
+- **["Generalized Minkowski sets for the regularization of inverse problems"](https://slim.gatech.edu/content/generalized-minkowski-sets-regularization-inverse-problems-1)**
     - paper: [http://arxiv.org/abs/1903.03942](http://arxiv.org/abs/1903.03942)
-    - code: [SetIntersectionProjection.jl]
+    - code: [SetIntersectionProjection.jl](https://github.com/slimgroup/SetIntersectionProjection.jl)
 
 
 ## Acknowledgments
 
 This package uses functions from [NNlib.jl](https://github.com/FluxML/NNlib.jl), [Flux.jl](https://github.com/FluxML/Flux.jl) and [Wavelets.jl](https://github.com/JuliaDSP/Wavelets.jl)
 
-[Flux]:https://fluxml.ai
-[Julia]:https://julialang.org
-[Zygote]:https://github.com/FluxML/Zygote.jl
-[ChainRules]:https://github.com/JuliaDiff/ChainRules.jl
-[InvertibleNetworks.jl]:https://github.com/slimgroup/InvertibleNetworks.jl
-[“Preconditioned training of normalizing flows for variational inference in inverse problems”]:https://slim.gatech.edu/content/preconditioned-training-normalizing-flows-variational-inference-inverse-problems
-[FastApproximateInference.jl]:https://github.com/slimgroup/Software.siahkoohi2021AABIpto
-["Generalized Minkowski sets for the regularization of inverse problems"]:https://slim.gatech.edu/content/generalized-minkowski-sets-regularization-inverse-problems-1
-[SetIntersectionProjection.jl]:https://github.com/slimgroup/SetIntersectionProjection.jl
-["Parameterizing uncertainty by deep invertible networks, an application to reservoir characterization"]:https://slim.gatech.edu/content/parameterizing-uncertainty-deep-invertible-networks-application-reservoir-characterization
+
+

examples/networks/network_glow.jl

@@ -6,6 +6,7 @@ using InvertibleNetworks, LinearAlgebra, Flux
 import Flux.Optimise.update!
 
 device = InvertibleNetworks.CUDA.functional() ? gpu : cpu
+
 # Define network
 nx = 64     # must be multiple of 2
 ny = 64
@@ -31,6 +32,7 @@ end
 
 # Evaluate loss
 f = loss(X)
+@time loss(X)
 
 # Update weights
 opt = Flux.ADAM()