Comments (2)
alerem18
commented on May 18, 2024
the most time consuming parts are train_discriminator!() and train_generator!()
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CarloLucibello
commented on May 18, 2024
this seems a nice example where to investigate performances, but unless you identify a more specific performance issue (e.g. regarding the forward or the backward pass of a specific layer) this conversion is better suited for discord
from flux.jl.
Related Issues (20)
- Warning: `sort(d::Dict; args...)` is deprecated, use `sort!(OrderedDict(d); args...)` instead. HOT 2
- Does `withgradient` have lower precicision than simply calling the function? HOT 3
- Warning: sort(d::Dict; args...) is deprecated, use sort!(OrderedDict(d); args...) instead. HOT 1
- `show` is confused by shared parameters
- No cudnn implementation of Conv((1,) N=>M) HOT 12
- Flux docs missing withgradient() call for multi-objective loss functions HOT 6
- can't use masks in multi-head-attention layer HOT 6
- Adapt saving & loading example to CuArrays HOT 2
- Segmentation fault when doing a forward pass with a model saved with BSON HOT 2
- Flux new explicit API does not work but old implicit API works for a simple RNN HOT 4
- Intel Arc GPU support. HOT 3
- `using Flux, cuDNN` freezes, but `using Flux, CUDA, cuDNN` works HOT 1
- Convolutional network slower than tensorflow on CPU HOT 13
- Problem with RNN and CUDA. HOT 7
- precompilation issue on Julia 1.10 HOT 1
- Android/iOS support HOT 1
- since new version: Flux throws error when for train! / update! even on quick start problem HOT 4
- 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
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from flux.jl.