Comments (4)
What output would you like here? There is no data, of course:
julia> x = randn(Float32, 4, 0, 6)
4×0×6 Array{Float32, 3}
If you're trying to propagate size information, then see Flux.outputsize
.
from flux.jl.
The expected output is an array of size (5, 0, 6)
in this example. Of course, there is no data but I sometimes slice an array with x[:,i+1:i+k,:]
, where k
is non-negative and hence may be zero. I don't want to do special handling in such a case.
from flux.jl.
Also note that the following works as expected. The behavior should be consistent with this.
julia> dense = Dense(4 => 5); x = randn(Float32, 4, 0);
julia> dense(x)
5×0 Matrix{Float32}
from flux.jl.
Yes that's not crazy, I suspect that this one could be made to work without too much effort. But there are likely to be many other places where similar things assume nonempty input, and I don't relish the thought of tracking them all down.
julia> Conv((2,2), 1=>3)(randn32(10,10,1,0))
ERROR: TaskFailedException
nested task error: ArgumentError: cannot create partitions of length 0
julia> GlobalMeanPool()(randn32(10,0,1,2))
ERROR: DivideError: integer division error
from flux.jl.
Related Issues (20)
- Illegal Memory Access Error During Gradient Calculation of predefined losses on GPU RTX 4050 HOT 1
- Unnecessarily using shared GPU memory HOT 8
- Flux installation error under Julia 1.10 on Apple Silicon HOT 2
- Given that DataLoader implements `length` shouldn't it also be able to provide size? HOT 4
- The dedicated tutorial on DataLoader is missing HOT 2
- Incorrect link on docs HOT 4
- Hard error using dice loss HOT 2
- Compilation time of Flux models HOT 1
- Flux.setup buggy and broken in latest v.0.13.17 HOT 3
- example for using apple GPU with flux HOT 4
- Dimensions check for `Conv` is incomplete, leading to confusing error HOT 1
- 2x performance regression due to 5e80211c3302b5e7b79b4f670498f5a68af6659b HOT 2
- Why is Flux.destructure type unstable? HOT 3
- bad formatting for PairwiseFusion docstring HOT 1
- Adding Simple Recurrent Unit as a recurrent layer
- Collecting PyTorch -> Flux migration notes HOT 1
- tests are failing due to ComponentArrays HOT 2
- deprecate Flux.params HOT 7
- Significant time spent moving medium-size arrays to GPU, type instability HOT 10
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from flux.jl.