From 4387175f6b4a0d6a696cddfd00ad161d53fbc214 Mon Sep 17 00:00:00 2001
From: "Liguros - Gitlab CI/CD [develop]" <gitlab@liguros.net>
Date: Tue, 26 Mar 2024 08:16:36 +0000
Subject: Adding metadata

---
 dev-cpp/tiny-dnn/metadata.xml | 86 -------------------------------------------
 1 file changed, 86 deletions(-)
 delete mode 100644 dev-cpp/tiny-dnn/metadata.xml

(limited to 'dev-cpp/tiny-dnn/metadata.xml')

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>
\ No newline at end of file
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