GitLab

The thread pool was initialized after the network, so it lead to a
reorder warning in GCC 9.3.0

* Remove outdated comment

That comment was there from when I was using a dnn_trainer to train
the discriminator network.

* Fix case

* fix some warnings when running tests

* rever changes in CMakeLists.txt

* update example make use of newly promoted method

* update tests to make use of newly promoted methods

* remove branch from cuda kernel

* promote lambda to a global function

Promote some of the sub-network methods into the add_loss_layer interface so users don't have to write .subnet() so often.

* wip: dcgan-example

* wip: dcgan-example

* update example to use leaky_relu and remove bias from net

* wip

* it works!

* add more comments

* add visualization code

* add example documentation

* rename example

* fix comment

* better comment format

* fix the noise generator seed

* add message to hit enter for image generation

* fix srand, too

* add std::vector overload to update_parameters

* improve training stability

* better naming of variables

make sure it is clear we update the generator with the discriminator's
gradient using fake samples and true labels

* fix comment: generator -> discriminator

* update leaky_relu docs to match the relu ones

* replace not with !

* add Davis' suggestions to make training more stable

* use tensor instead of resizable_tensor

* do not use dnn_trainer for discriminator

* add leaky_relu activation layer

* add inplace case for leaky_relu and test_layer

* make clear that alpha is not learned by leaky_relu

* remove branch from cuda kernel

* Problem: The CUDA runtime allocates resources for each thread, and apparently those resources are not freed when the corresponding threads terminate. Therefore, each instantiation of dnn_trainer leaks a bit of GPU memory.

Solution: Add possibility to pass thread pools from outside. This way, subsequent dnn_trainer instances can use the same threads, and there's no memory leak.

* Add helpful comments

openblasp is a parallel implementation of openblas with pthreads found on Centos/Fedora

* add loss_multiclass_log_weighted

* fix class name in loss_abstract

* add loss_multiclass_log_weighted test

* rename test function to match class name

* fix typo

* reuse the weighted label struct across weighted losses

* do not break compatibility with loss_multiclass_log_per_pixel_weighted

* actually test the loss and fix docs

* fix build with gcc 9

* Prevention of compiler warning due to usage of int instead of a size type

* Conversion of status type to long to prevent compiler warnings

* The returned number of read items from a buffer is specified in numbers of type "streamsize"
Co-authored-by: Hartwig <git@skywind.eu>

Co-authored-by: Facundo Galan <fgalan@danaide.com.ar>

Co-authored-by: Hartwig <git@skywind.eu>

* simplify definition by reusing struct template parameter

* put resnet into its own namespace

* fix infer names

* rename struct impl to def

* imglab: add support for using chinese whispers for more automatic clustering

* widgets: refactor out zooming from wheel handling

* tools/imglab/src/metadata_editor.cpp

imglab: add keyboard shortcuts for zooming

* Add dnn_introduction3_ex

and the unit test servers don't even support it anymore.

Dlib does not use nsl symbols, why was this necessary ?
This make conda-forge build fail

* Add new loss layer for binary loss per pixel

cuda_data_ptr<T>.  Also moved some memcpy() functions to namespace scope
so that calling them like dlib::cuda::memcpy() can referene them.  It
was slightly annoting before.