Comments (4)
@jklaise I don't think higher order derivatives can be calculated this way in Flux. IMO, it's best to use Hessians to obtain second order derivatives. You can do something like this:
julia> using Flux
julia> using ReverseDiff
julia> f(x) = @. 3*x^2 + 2*x + 1
f (generic function with 1 method)
julia> ReverseDiff.hessian(f,[5])
1×1 Array{Int64,2}:
6
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You can do this but you'll want to follow the syntax given in the latest version of the docs.
We should update the tutorial, but FWIW you should be able to activate the project and get a compatible version of Flux.
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@MikeInnes thanks for the pointer, I think I got confused between several different versions of the API. I'll see if I can finish the blitz successfully and update the tutorial.
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This was fixed by #143 . This can be closed
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Related Issues (20)
- Adding custom Model for one of the newly added dataset in MLDatasets.jl HOT 8
- 60 minute blitz uses stacked Dense layers with no activation function HOT 1
- Add literature background to the models HOT 8
- flatten is no longer exported from Flux
- Use Metalhead.jl for the vision examples HOT 1
- MethodError using the ResNet transfer learning tutorial HOT 6
- diffusion_mnist broken throws CUDNNError: CUDNN_STATUS_BAD_PARAM (code 3) HOT 5
- [Question] how to understand out_conv_size in LeNet5 ? HOT 1
- `char-rnn.jl` is broken on Flux `v0.13.9` HOT 1
- vae_mnist increasing runtime and memory usage after each epoch HOT 5
- mlp_mnist can not be instantiated
- Manifest files have the old format
- Need to update the tutorial "Deep Learning with Flux - A 60 Minute Blitz" HOT 1
- Problem with MNIST DCGANs tutorial HOT 5
- Transfer Learning Example lacks the depth required to showcase Flux HOT 2
- RNN Issue, it doesn't work as expected
- Trebuchet differentiable programming example throws ForwardDiff error HOT 2
- char-rnn example throws MethodError: no method matching ndims(::SubString{String})
- Update to Flux 0.14 HOT 2
- Scalar indexing is disallowed...... and missing ')' at last line @show lenet2.... HOT 7
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