Knet 1.2.5 released
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Deniz Yuret
Sep 5, 2019, 4:51:46 PM9/5/19
to knet-users, knet-dev
Highlights:
* Unsupported KnetArray shapes for getindex/setindex!, cat/hcat/vcat and permutedims have now fallback kernels from CuArrays. permutedims speed for ndims>=2 greatly improved. This addresses issues #198, #319, #368, #400, #470.
* Memory manager made faster and more robust using attention based nmt benchmarks.
* Improved stability problems with CuArrays on some devices (e.g. gitlab-ci) using CUDAnative.initialize().
* Addressed different device ids used by cudart, cuda, and nvml using PCIBusIds with cuid() and nvmlid().
* RNN fixes: init speed improved, default forget bias=1, allocates own workspace, no longer a parametric type RNN{T}, fixed issue #482 with size 1 input.
* nll/accuracy now use a 0 value for masking, return (total,count) pair when average=false.
* progress now takes a function argument and runs it periodically either every n seconds or n steps.
* minimize and friends (adam etc.) return Result instead of plain loss to allow looking at gradients.
* Use IterTools in tutorial instead of redefining the same functions.
* Use loggamma instead of deprecated lgamma.
* Memory manager made faster and more robust using attention based nmt benchmarks.
* Improved stability problems with CuArrays on some devices (e.g. gitlab-ci) using CUDAnative.initialize().
* Addressed different device ids used by cudart, cuda, and nvml using PCIBusIds with cuid() and nvmlid().
* RNN fixes: init speed improved, default forget bias=1, allocates own workspace, no longer a parametric type RNN{T}, fixed issue #482 with size 1 input.
* nll/accuracy now use a 0 value for masking, return (total,count) pair when average=false.
* progress now takes a function argument and runs it periodically either every n seconds or n steps.
* minimize and friends (adam etc.) return Result instead of plain loss to allow looking at gradients.
* Use IterTools in tutorial instead of redefining the same functions.
* Use loggamma instead of deprecated lgamma.
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