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## ❓ Questions and Help
I transcribed this repository in order to understand representation learning and graph neural network.
I implemented in singularity and the environment I made follows:
Pyt…
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Thanks for such wonderful project, I get some troubles in follow the idea of GCNConv layer code.
Allow me to begin by presenting my understanding of the GCN. From the GCN paper [Semi-supervised Cl…
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Chris
NeuralPDE refuses to compile
I'm using julia 18.5 and trying just to run the tutorial example https://docs.sciml.ai/NeuralPDE/stable/tutorials/pdesystem/
Naceur
Here is the error outp…
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Examples is the core of Lux.jl ecosystem
Hope to add more examples:
1. As an example codes to study
2. Demonstrate the power and ease of use of Lux
3. Attract users and developers
Hope to re…
zsz00 updated
2 years ago
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TypeError: in converted code:
/home/jupyter-tide1994/tide/embedding/GraphNeuralNetwork-master/gnn/gcn.py:73 call *
TypeError: Input 'pred' of 'Switch' Op has type float32 that does not match…
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We currently have the following definitions
```julia
# DataLoader compatibility passing a vector of graphs and
# effectively using `batch` as a collated function.
MLUtils.numobs(data::Vector{ shuf…
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Related to https://github.com/FluxML/NNlib.jl/issues/411
```julia
using GraphNeuralNetworks
using CUDA
using Flux: gpu
g = rand_graph(10, 30) |> gpu
g = add_edges(g,[10],[10])
x = CUDA.rand(3, …
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We currently have `SimpleGraph`/`SimpleDiGraph` -> `GNNGraph`. We should add the other direction.
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There are quite a few useful datasets, benchmarks and leaderboards in [ogb](https://ogb.stanford.edu/). The paper is [here](https://arxiv.org/abs/2005.00687).
From what I can see it plays nicely w…
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```julia
using GraphNeuralNetworks
using Flux
function test_nn(nn, x)
@show nn
println("CPU")
@show nn(x)
println("GPU")
x = Flux.gpu(x)
nn = Flux.gpu(nn)
@show…