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Diffstat (limited to 'dev-cpp/tiny-dnn/metadata.xml')
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diff --git a/dev-cpp/tiny-dnn/metadata.xml b/dev-cpp/tiny-dnn/metadata.xml deleted file mode 100644 index abe5e46975a6..000000000000 --- a/dev-cpp/tiny-dnn/metadata.xml +++ /dev/null @@ -1,86 +0,0 @@ -<?xml version="1.0" encoding="UTF-8"?> -<!DOCTYPE pkgmetadata SYSTEM "https://liguros.gitlab.io/dtd/metadata.dtd"> -<pkgmetadata> - <maintainer type="person"> - <email>lssndrbarbieri@gmail.com</email> - <name>Alessandro Barbieri</name> - </maintainer> - <longdescription lang="en"> -Features - -Reasonably fast, without GPU: -With TBB threading and SSE/AVX vectorization. -98.8% accuracy on MNIST in 13 minutes training (@Core i7-3520M). -Portable and header-only: -Runs anywhere as long as you have a compiler which supports C++14. -Just include tiny_dnn.h and write your model in C++. There is nothing to install. -Easy to integrate with real applications: -No output to stdout/stderr. -A constant throughput (simple parallelization model, no garbage collection). -Works without throwing an exception. -Can import caffe's model. -Simply implemented: -A good library for learning neural networks. - -Supported networks - -layer-types - -core -fully connected -dropout -linear operation -zero padding -power -convolution -convolutional -average pooling -max pooling -deconvolutional -average unpooling -max unpooling -normalization -contrast normalization (only forward pass) -batch normalization -split/merge -concat -slice -elementwise-add -activation functions - -tanh -asinh -sigmoid -softmax -softplus -softsign -rectified linear(relu) -leaky relu -identity -scaled tanh -exponential linear units(elu) -scaled exponential linear units (selu) -loss functions - -cross-entropy -mean squared error -mean absolute error -mean absolute error with epsilon range -optimization algorithms - -stochastic gradient descent (with/without L2 normalization) -momentum and Nesterov momentum -adagrad -rmsprop -adam -adamax - </longdescription> - - <use> - <flag name="double-precision">Build tiny-dnn with double precision computations</flag> - <flag name="opencl">Build tiny-dnn with OpenCL library support</flag> - <flag name="serialization">Build tiny-dnn with Serialization support</flag> - <flag name="tbb">Build tiny-dnn with TBB library support</flag> - </use> - <origin>gentoo-guru-overlay</origin> -</pkgmetadata>
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