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Merge branch 'master' of https://github.com/mlpack/mlpack into iss2071

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This commit is contained in:
NippunSharma
2020-12-06 17:12:45 +05:30
parent ed274d7a8b d7cb723d23
commit f8bf36626e
38 changed files with 1251 additions and 593 deletions
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.appveyor.yml
-1
View File
@@ -71,7 +71,6 @@ build_script:
-DARMADILLO_LIBRARY:FILEPATH=%ARMADILLO_LIBRARY%
-DCEREAL_INCLUDE_DIR="C:/projects/mlpack/unofficial-flayan-cereal.1.2.2/build/native/include"
-DBOOST_INCLUDEDIR:PATH=%BOOST_INCLUDE%
-DBOOST_LIBRARYDIR:PATH="C:/projects/mlpack/boost_libs"
-DDEBUG=OFF
-DPROFILE=OFF
-DBUILD_PYTHON_BINDINGS=OFF
.ci/ci.yaml
-19
View File
@@ -59,25 +59,6 @@ jobs:
steps:
- template: macos-steps.yaml
# - job: WindowsVS15
# timeoutInMinutes: 360
# displayName: Windows VS15
# pool:
# vmImage: vs2017-win2016
# strategy:
# matrix:
# Plain:
# CMakeArgs: '-DDEBUG=ON -DPROFILE=OFF -DBUILD_PYTHON_BINDINGS=OFF -DBUILD_GO_BINDINGS=OFF -DBUILD_R_BINDINGS=OFF'
# python.version: '2.7'
# CMakeGenerator: '-G "Visual Studio 15 2017 Win64"'
# MSBuildVersion: '15.0'
# ArchiveNoLibs: 'mlpack-windows-vs15-no-libs.zip'
# ArchiveLibs: 'mlpack-windows-vs15.zip'
# ArchiveTests: 'mlpack_test-vs15.xml'
# steps:
# - template: windows-steps.yaml
- job: WindowsVS16
timeoutInMinutes: 360
displayName: Windows VS16
.ci/windows-steps.yaml
+55
View File
@@ -116,6 +116,56 @@ steps:
replaceExistingArchive: true
displayName: 'Build artifacts'
# Build MSI installer.
- powershell: |
# Pull the documentation for the installer.
try {
$url = "http://ci.mlpack.org/job/mlpack%20-%20doxygen%20build/lastSuccessfulBuild/artifact/build/doc/html/*zip*/html.zip"
(new-object net.webclient).DownloadFile($url, 'dist\win-installer\jenkinsdoc.zip')
}
catch {
Write-Output "Unable to download precompiled Doxygen documentation from Jenkins!"
}
try {
(Add-Type -AssemblyName System.IO.Compression.FileSystem);
[System.IO.Compression.ZipFile]::ExtractToDirectory('dist\win-installer\jenkinsdoc.zip', 'dist\win-installer\mlpack-win-installer\Sources\doc')
}
catch{Write-Output "Unable to add doc to installer, skipping!"}
# Preparing installer staging.
mkdir dist\win-installer\mlpack-win-installer\Sources\lib
cp build\Release\*.lib dist\win-installer\mlpack-win-installer\Sources\lib\
cp build\Release\*.exp dist\win-installer\mlpack-win-installer\Sources\lib\
cp build\Release\*.dll dist\win-installer\mlpack-win-installer\Sources\
cp build\Release\*.exe dist\win-installer\mlpack-win-installer\Sources\
cp $(Agent.ToolsDirectory)\OpenBLAS.0.2.14.1\lib\native\bin\x64\*.dll dist\win-installer\mlpack-win-installer\Sources\
cp build\include\mlpack dist\win-installer\mlpack-win-installer\Sources -recurse
cp doc\examples dist\win-installer\mlpack-win-installer\Sources -recurse
cp src\mlpack\tests\data\german.csv dist\win-installer\mlpack-win-installer\Sources\examples\sample-ml-app\sample-ml-app\data\
# Check current git version or mlpack version.
$ver = (Get-Content "src\mlpack\core\util\version.hpp" | where {$_ -like "*MLPACK_VERSION*"});
$env:MLPACK_VERSION += $ver[0].substring($ver[0].length - 1, 1) + '.';
$env:MLPACK_VERSION += $ver[1].substring($ver[1].length - 1, 1) + '.';
$env:MLPACK_VERSION += $ver[2].substring($ver[2].length - 1, 1);
if (Test-Path "src/mlpack/core/util/gitversion.hpp")
{
$ver = (Get-Content "src/mlpack/core/util/gitversion.hpp");
$env:INSTALL_VERSION = $ver.Split('"')[1].Split(' ')[1];
}
else
{
$env:INSTALL_VERSION = $env:MLPACK_VERSION;
}
# Build the MSI installer.
cd dist\win-installer\mlpack-win-installer
& 'C:\Program Files (x86)\Microsoft Visual Studio\2019\Enterprise\MSBuild\Current\Bin\MSBuild.exe' `
-t:rebuild `
-p:Configuration=Release `
-p:TreatWarningsAsErrors=True `
mlpack-win-installer.wixproj
displayName: 'Build MSI Windows installer'
# Publish artifacts to Azure Pipelines
- task: PublishBuildArtifacts@1
inputs:
@@ -132,6 +182,11 @@ steps:
pathtoPublish: 'build/Testing/'
artifactName: 'Tests'
displayName: 'Publish artifacts test results'
- task: PublishBuildArtifacts@1
inputs:
pathtoPublish: 'dist\win-installer\mlpack-win-installer\bin\Release\mlpack-windows.msi'
artifactName: mlpack-windows-installer
displayName: 'Publish Windows MSI installer'
# Publish test results to Azure Pipelines
- task: PublishTestResults@2
CMakeLists.txt
+3 -24
View File
@@ -289,7 +289,6 @@ endif()
# ARMADILLO_INCLUDE_DIRS - directories necessary for Armadillo includes
# BOOST_ROOT - root of Boost installation
# BOOST_INCLUDEDIR - include directory for Boost
# BOOST_LIBRARYDIR - library directory for Boost
# ENSMALLEN_INCLUDE_DIR - include directory for ensmallen
# STB_IMAGE_INCLUDE_DIR - include directory for STB image library
# MATHJAX_ROOT - root of MathJax installation
@@ -443,31 +442,11 @@ set(Boost_ADDITIONAL_VERSIONS
# TODO for the brave: transition all mlpack's CMake to 'target-based modern
# CMake'. Good luck! You'll need it.
set(Boost_NO_BOOST_CMAKE 1)
find_package(Boost "${BOOST_VERSION}"
COMPONENTS
REQUIRED
)
link_directories(${Boost_LIBRARY_DIRS})
# In Visual Studio, automatic linking is performed, so we don't need to worry
# about it. Clear the list of libraries to link against and let Visual Studio
# handle it.
if (MSVC)
link_directories(${Boost_LIBRARY_DIRS})
set(CMAKE_MSVCIDE_RUN_PATH ${CMAKE_MSVCIDE_RUN_PATH} ${Boost_LIBRARY_DIRS})
message("boost lib dirs ${Boost_LIBRARY_DIRS}")
set(Boost_LIBRARIES "")
endif ()
find_package(Boost "${BOOST_VERSION}")
set(MLPACK_INCLUDE_DIRS ${MLPACK_INCLUDE_DIRS} ${Boost_INCLUDE_DIRS})
set(MLPACK_LIBRARIES ${MLPACK_LIBRARIES} ${Boost_LIBRARIES})
set(MLPACK_LIBRARY_DIRS ${MLPACK_LIBRARY_DIRS} ${Boost_LIBRARY_DIRS})
# For Boost testing framework (will have no effect on non-testing executables).
# This specifies to Boost that we are dynamically linking to the Boost test
# library.
add_definitions(-DBOOST_TEST_DYN_LINK)
set(MLPACK_LIBRARIES ${MLPACK_LIBRARIES})
set(MLPACK_LIBRARY_DIRS ${MLPACK_LIBRARY_DIRS})
# Detect OpenMP support in a compiler. If the compiler supports OpenMP, flags
# to compile with OpenMP are returned and added and the HAS_OPENMP definition
COPYRIGHT.txt
+2
View File
@@ -136,6 +136,8 @@ Copyright:
Copyright 2020, Aakash Kaushik <kaushikaakash7539@gmail.com>
Copyright 2020, Anush Kini <anushkini@gmail.com>
Copyright 2020, Nippun Sharma <inbox.nippun@gmail.com>
Copyright 2020, Rishabh Garg <rishabhgarg108@gmail.com>
Copyright 2020, Sudhakar Brar <dxhrmhall1449@tutanota.com>
License: BSD-3-clause
All rights reserved.
dist/win-installer/mlpack-win-installer/Product.wxs
Vendored
+30 -39
View File
@@ -2,47 +2,38 @@
<Wix xmlns="http://schemas.microsoft.com/wix/2006/wi">
<!-- 1) DO NOT CHANGE the product GUID! It is forever -->
<!-- 2) %MLPACK_VERSION env var is set by .appveyor.yml -->
<Product Id="02A00C77-197D-4E91-B7D9-5836220E92E9"
UpgradeCode="6C2D7EC0-6F10-40CB-9703-1DC160A62662"
Name="mlpack"
Language="1033"
Version="$(env.MLPACK_VERSION)"
Manufacturer="mlpack">
<Package InstallerVersion="200"
Description="mlpack Windows Installer"
Compressed="yes"
InstallScope="perMachine"
Platform="x64"/>
<Product Id="02A00C77-197D-4E91-B7D9-5836220E92E9"
UpgradeCode="6C2D7EC0-6F10-40CB-9703-1DC160A62662"
Name="mlpack"
Language="1033"
Version="$(env.MLPACK_VERSION)"
Manufacturer="mlpack">
<Package InstallerVersion="200"
Description="mlpack Windows Installer"
Compressed="yes"
InstallScope="perMachine"
Platform="x64"/>
<MajorUpgrade DowngradeErrorMessage="A newer version of [ProductName] is already installed." />
<MediaTemplate EmbedCab="yes"/>
<MajorUpgrade DowngradeErrorMessage="A newer version of [ProductName] is already installed." />
<MediaTemplate EmbedCab="yes"/>
<Feature Id="ProductFeature" Title="mlpackWindows" Level="1">
<ComponentGroupRef Id="ProductComponents" />
</Feature>
<Property Id="MLPACK_VERSION">$(env.MLPACK_VERSION)</Property>
<Property Id="WIXUI_INSTALLDIR" Value="INSTALLFOLDER" />
<WixVariable Id="WixUILicenseRtf" Value="..\staging\license.rtf"/>
<WixVariable Id="WixUIBannerBmp" Value="..\res\banner.jpg"/>
<WixVariable Id="WixUIDialogBmp" Value="..\res\dialog_white.jpg"/>
<UIRef Id="WixUI_InstallDir" />
</Product>
<Directory Id="TARGETDIR" Name="SourceDir">
<Directory Id="ProgramFilesFolder" Name="PFiles">
<Directory Id="INSTALLDIR" Name="mlpack">
<Directory Id="Sources" />
</Directory>
</Directory>
</Directory>
<Fragment>
<Directory Id="TARGETDIR" Name="SourceDir">
<Directory Id="ProgramFiles64Folder">
<Directory Id="INSTALLFOLDER" Name="mlpack" />
</Directory>
</Directory>
</Fragment>
<Fragment>
<ComponentGroup Id="ProductComponents" Directory="INSTALLFOLDER">
<!-- This references the list of mlpack files automatically generated using Heat (see .wixproj BeforeBuild Target) -->
<ComponentGroupRef Id="HeatGenerated"/>
</ComponentGroup>
</Fragment>
<Feature Id="ProductFeature" Title="mlpackWindows" ConfigurableDirectory="INSTALLDIR" Level="1">
<ComponentGroupRef Id="Sources" />
</Feature>
<Property Id="MLPACK_VERSION">$(env.MLPACK_VERSION)</Property>
<Property Id="WIXUI_INSTALLDIR" Value="INSTALLDIR" />
<WixVariable Id="WixUILicenseRtf" Value="..\staging\license.rtf"/>
<WixVariable Id="WixUIBannerBmp" Value="..\res\banner.jpg"/>
<WixVariable Id="WixUIDialogBmp" Value="..\res\dialog_white.jpg"/>
<UIRef Id="WixUI_InstallDir" />
</Product>
</Wix>
dist/win-installer/mlpack-win-installer/mlpack-win-installer.wixproj
Vendored
+12 -19
View File
@@ -9,33 +9,36 @@
<OutputName>mlpack-windows</OutputName>
<OutputType>Package</OutputType>
<Name>mlpack-win-installer</Name>
<DefineSolutionProperties>false</DefineSolutionProperties>
<DefineConstants>SourceDir=.\Sources</DefineConstants>
<WixTargetsPath Condition=" '$(WixTargetsPath' == '' ">$(MSBuildExtensionsPath)\Microsoft\WiX\v3.x\Wix.targets</WixTargetsPath>
</PropertyGroup>
<PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Debug|x86' ">
<OutputPath>bin\$(Configuration)\</OutputPath>
<IntermediateOutputPath>obj\$(Configuration)\</IntermediateOutputPath>
<DefineConstants>Debug</DefineConstants>
<DefineConstants>Debug;$(DefineConstants)</DefineConstants>
</PropertyGroup>
<PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Release|x86' ">
<OutputPath>bin\$(Configuration)\</OutputPath>
<IntermediateOutputPath>obj\$(Configuration)\</IntermediateOutputPath>
</PropertyGroup>
<PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Debug|x64' ">
<DefineConstants>Debug</DefineConstants>
<OutputPath>bin\$(Platform)\$(Configuration)\</OutputPath>
<IntermediateOutputPath>obj\$(Platform)\$(Configuration)\</IntermediateOutputPath>
<DefineConstants>Debug;$(DefineConstants)</DefineConstants>
</PropertyGroup>
<PropertyGroup Condition=" '$(Configuration)|$(Platform)' == 'Release|x64' ">
<OutputPath>bin\$(Platform)\$(Configuration)\</OutputPath>
<IntermediateOutputPath>obj\$(Platform)\$(Configuration)\</IntermediateOutputPath>
</PropertyGroup>
<PropertyGroup>
<DefineConstants>HarvestPath=..\staging</DefineConstants>
</PropertyGroup>
<ItemGroup>
<Compile Include="Product.wxs" />
<Compile Include="HeatGeneratedFileList.wxs" />
</ItemGroup>
<ItemGroup>
<HarvestDirectory Include=".\Sources">
<DirectoryRefId>Sources</DirectoryRefId>
<ComponentGroupName>Sources</ComponentGroupName>
<PreprocessorVariable>var.SourceDir</PreprocessorVariable>
<SuppressRegistry>true</SuppressRegistry>
</HarvestDirectory>
<WixExtension Include="WixUIExtension">
<HintPath>$(WixExtDir)\WixUIExtension.dll</HintPath>
<Name>WixUIExtension</Name>
@@ -46,14 +49,4 @@
<Target Name="EnsureWixToolsetInstalled" Condition=" '$(WixTargetsImported)' != 'true' ">
<Error Text="The WiX Toolset v3.11 (or newer) build tools must be installed to build this project. To download the WiX Toolset, see http://wixtoolset.org/releases/" />
</Target>
<!--
To modify your build process, add your task inside one of the targets below and uncomment it.
Other similar extension points exist, see Wix.targets.-->
<Target Name="BeforeBuild">
<HeatDirectory Directory="..\staging" PreprocessorVariable="var.HarvestPath" OutputFile="HeatGeneratedFileList.wxs" ComponentGroupName="HeatGenerated" DirectoryRefId="INSTALLFOLDER" AutogenerateGuids="true" ToolPath="$(WixToolPath)" SuppressFragments="true" SuppressRegistry="true" SuppressRootDirectory="true" />
</Target>
<!--
<Target Name="AfterBuild">
</Target>
-->
</Project>
</Project>
doc/guide/build.hpp
+12 -12
View File
@@ -2,10 +2,10 @@
@section build_buildintro Introduction
This document discusses how to build mlpack from source. These build directions
This document discusses how to build mlpack from source. These build directions
will work for any Linux-like shell environment (for example Ubuntu, macOS,
FreeBSD etc). However, mlpack is in the repositories of many Linux distributions
and so it may be easier to use the package manager for your system. For example,
FreeBSD etc). However, mlpack is in the repositories of many Linux distributions
and so it may be easier to use the package manager for your system. For example,
on Ubuntu, you can install mlpack with the following command:
@code
@@ -25,7 +25,7 @@ mlpack uses CMake as a build system and allows several flexible build
configuration options. One can consult any of numerous CMake tutorials for
further documentation, but this tutorial should be enough to get mlpack built
and installed on most Linux and UNIX-like systems (including OS X). If you want
to build mlpack on Windows, see \ref build_windows (alternatively, you can read
to build mlpack on Windows, see \ref build_windows (alternatively, you can read
<a href="https://keon.io/mlpack-on-windows/">Keon's excellent tutorial</a> which
is based on older versions).
@@ -78,7 +78,7 @@ mlpack depends on the following libraries, which need to be installed on the
system and have headers present:
- Armadillo >= 8.400.0 (with LAPACK support)
- Boost (math_c99, unit_test_framework, heap, spirit) >= 1.58
- Boost (math_c99, spirit) >= 1.58
- cereal >= 1.1.2
- ensmallen >= 2.10.0 (will be downloaded if not found)
@@ -95,11 +95,11 @@ For Python bindings, the following packages are required:
- pandas >= 0.15.0
- pytest-runner
In Ubuntu (>= 18.04) and Debian (>= 10) all of these dependencies can be
In Ubuntu (>= 18.04) and Debian (>= 10) all of these dependencies can be
installed through apt:
@code
# apt-get install libboost-math-dev libboost-test-dev libcereal-dev
# apt-get install libboost-math-dev libcereal-dev
libarmadillo-dev binutils-dev python3-pandas python3-numpy cython3
python3-setuptools
@endcode
@@ -112,18 +112,18 @@ packages:
# apt-get install libensmallen-dev libstb-dev
@endcode
@note For older versions of Ubuntu and Debian, Armadillo needs to be built from
source as apt installs an older version. So you need to omit
@note For older versions of Ubuntu and Debian, Armadillo needs to be built from
source as apt installs an older version. So you need to omit
\c libarmadillo-dev from the code snippet above and instead use
<a href="http://arma.sourceforge.net/download.html">this link</a>
to download the required file. Extract this file and follow the README in the
to download the required file. Extract this file and follow the README in the
uncompressed folder to build and install Armadillo.
On Fedora, Red Hat, or CentOS, these same dependencies can be obtained via dnf:
@code
# dnf install boost-devel boost-test boost-math armadillo-devel binutils-devel
python3-Cython python3-setuptools python3-numpy python3-pandas ensmallen-devel
# dnf install boost-devel boost-math armadillo-devel binutils-devel
python3-Cython python3-setuptools python3-numpy python3-pandas ensmallen-devel
stbi-devel cereal-devel
@endcode
doc/guide/build_windows.hpp
+115 -10
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@@ -9,8 +9,13 @@
@section build_windows_intro Introduction
This tutorial will show you how to build mlpack for Windows from source, so you can
later create your own C++ applications. Before you try building mlpack, you may
This tutorial will show you how to build mlpack for Windows from source, so
you can later create your own C++ applications, using two different ways:
- Using CMake to generate an intermeditate Visual Studio solution (`.sln`).
- @ref build_visual_studio_cmake_integration "Use Visual Studio's CMake integration to directly build from the `CMakeLists`."
Before you try building mlpack, you may
want to install mlpack using vcpkg for Windows. If you don't want to install
using vcpkg, skip this section and continue with the build tutorial.
@@ -78,6 +83,23 @@ system environment variables or manually set the PATH before running CMake)
- Click on OpenBlas and check the mlpack project, then click Install
- Once it has finished installing, close Visual Studio
<b> Building OpenBLAS from Source </b>
Unfortunately, the support for building `LAPACK` and `BLAS` on Windows is quite poor, due to the need for Fortran
compiler and libraries. The easiest method to get the necessary `BLAS/LAPACK` libraries built on Windows is to
compile OpenBLAS with LLVM's `clang-cl` and `flang` to produce the required static library (`.lib`) files
compatible with the MSVC compiler. A comprehensive guide on the
<a href="https://github.com/xianyi/OpenBLAS/wiki/How-to-use-OpenBLAS-in-Microsoft-Visual-Studio">compilation
of OpenBLAS for Windows can be found here</a>.
One could always download prebuilt `LAPACK` and `BLAS` libraries for Windows. However, there are few official
sources, and some of those libraries may require further `dll`s at runtime which may not be available in your
system.
It you choose to build `OpenBLAS` from source, make sure that `LAPACK` functions are also built. Finally, make
sure that the `openblas.lib` library is linked in your `Armadillo` build (see below), as well as the library
path used for the CMake options `BLAS_LIBRARIES` and `LAPACK_LIBRARIES` in the mlpack CMake project.
<b> Boost Dependency </b>
You can either get Boost via NuGet or you can download the prebuilt Windows binaries separately.
@@ -110,7 +132,7 @@ compiler version, check if the Visual Studio compiler and Windows SDK are instal
- Build > Build Solution
- Once it has successfully finished, close Visual Studio
@section build_windows_mlpack Building mlpack
@section build_windows_mlpack Building mlpack with CMake-Generated Solution
- Create a "build" directory into "C:\mlpack\mlpack\"
- You can generate the project using either cmake via command line or GUI. If you prefer to use GUI, refer to the \ref build_windows_appendix "appendix"
@@ -129,6 +151,96 @@ cmake -G "Visual Studio 16 2019" -A x64 -DBLAS_LIBRARIES:FILEPATH="C:/mlpack/mlp
You are ready to create your first application, take a look at the @ref sample_ml_app "Sample C++ ML App"
@section build_visual_studio_cmake_integration Building mlpack with Visual Studio's CMake Integration
This project can be directly built from the `CMakeLists.txt` with the latest version of MS Visual Studio,
given you have CMake integration via the
<a href="https://docs.microsoft.com/en-us/cpp/build/cmake-projects-in-visual-studio?view=msvc-160">C++
CMake tools for Windows</a>. To open the CMake project with Visual Studio, select File->Open->CMake
in the top menu, followed by selecting the root `CMakeLists.txt` located in mlpack's root directory.
In order to allow Visual Studio to configure the CMake project, the CMake configuration json will have
to be edited to provide the <a href="https://github.com/mlpack/mlpack#3-dependencies">relevant options
shown in the `README`</a> needed to find all the dependencies. The options that you
must provide to Visual Studio's CMake are:
- `ARMADILLO_INCLUDE_DIR`
- `ARMADILLO_LIBRARY`
- `BOOST_ROOT`
- `CEREAL_INCLUDE_DIR`
- `BLAS_LIBRARIES`
- `LAPACK_LIBRARIES`
The CMake configuration json can be editted in Visual Studio by right clicking the root `CMakeLists.txt`
in the project view, selecting <b>CMake settings for mlpack</b> and finally clicking on <b>edit JSON</b>.
Adding a new CMake option can be done by adding object fields with the following format to the variables
array in the `CMakeSettings.json`:
@code
{
"name": "options_name_string",
"value": "options_value_string",
"type" : "{BOOL|FILEPATH|PATH|STRING}"
}
@endcode
Here is a full example of the `CMakeSettings.json`file:
@code
{
"configurations": [
{
"name": "x64-Debug (default)",
"generator": "Ninja",
"configurationType": "Debug",
"inheritEnvironments": [ "msvc_x64_x64" ],
"buildRoot": "${projectDir}\\out\\build\\${name}",
"installRoot": "${projectDir}\\out\\install\\${name}",
"cmakeCommandArgs": "",
"buildCommandArgs": "",
"ctestCommandArgs": "",
"variables": [
{
"name": "ARMADILLO_INCLUDE_DIR",
"value": "PATH/TO/CPP/DEPENDENCY/armadillo-10.1.2/include",
"type": "PATH"
},
{
"name": "ARMADILLO_LIBBRARY",
"value": "PATH/TO/CPP/DEPENDENCY/armadillo-10.1.2/lib/armadillo.lib",
"type": "PATH"
},
{
"name": "CEREAL_INCLUDE_DIR",
"value": "PATH/TO/CPP/DEPENDENCY/cereal-1.3.0/include",
"type": "PATH"
},
{
"name": "BUILD_ROOT",
"value": "PATH/TO/CPP/DEPENDENCY/boost_1_66_0",
"type": "PATH"
},
{
"name": "BOOST_INCLUDEDIR",
"value": "PATH/TO/CPP/DEPENDENCY/boost_1_66_0",
"type": "PATH"
},
{
"name": "BLAS_LIBRARIES",
"value": "PATH/TO/CPP/DEPENDENCY/OpenBLAS/lib/openblas.lib",
"type": "PATH"
},
{
"name": "LAPACK_LIBRARIES",
"value": "PATH/TO/CPP/DEPENDENCY/OpenBLAS/lib/openblas.lib",
"type": "PATH"
}
]
}
]
}
@endcode
@section build_windows_appendix Appendix
If you prefer to use cmake GUI, follow these instructions:
@@ -147,13 +259,6 @@ If you prefer to use cmake GUI, follow these instructions:
following variables and reconfigure:
- Name: `BOOST_INCLUDEDIR`; type `PATH`; value `C:/boost/`
- Name: `BOOST_LIBRARYDIR`; type `PATH`; value `C:/boost/lib64-msvc-14.2`
- If Boost is still not found, try adding the following variables and
reconfigure:
- Name: `Boost_INCLUDE_DIR`; type `PATH`; value `C:/boost/`
- Name: `Boost_SERIALIZATION_LIBRARY_DEBUG`; type `FILEPATH`; value should be `C:/boost/lib64-msvc-14.2/boost_serialization-vc142-mt-gd-x64-1_71.lib`
- Name: `Boost_SERIALIZATION_LIBRARY_RELEASE`; type `FILEPATH`; value should be `C:/boost/lib64-msvc-14.2/boost_serialization-vc142-mt-x64-1_71.lib`
- Name: `Boost_UNIT_TEST_FRAMEWORK_LIBRARY_DEBUG`; type `FILEPATH`; value should be `C:/boost/lib64-msvc-14.2/boost_unit_test_framework-vc142-mt-gd-x64-1_71.lib`
- Name: `Boost_UNIT_TEST_FRAMEWORK_LIBRARY_RELEASE`; type `FILEPATH`; value should be `C:/boost/lib64-msvc-14.2/boost_unit_test_framework-vc142-mt-x64-1_71.lib`
- Once CMake has configured successfully, hit "Generate" to create the `.sln` file.
@section build_windows_additional_information Additional Information
doc/guide/sample_ml_app.hpp
-1
View File
@@ -34,7 +34,6 @@ mlpack and dependencies in Release Mode).
- Under Linker > Input > Additional Dependencies add:
@code
- C:\mlpack\mlpack-3.4.2\build\Debug\mlpack.lib
- C:\boost\boost_1_71_0\lib64-msvc-14.2\libboost_serialization-vc142-mt-gd-x64-1_71.lib
@endcode
- Under Build Events > Post-Build Event > Command Line add:
@code
src/mlpack/bindings/cli/CMakeLists.txt
-1
View File
@@ -53,7 +53,6 @@ if (BUILD_CLI_EXECUTABLES)
target_link_libraries(mlpack_${name}
mlpack
${ARMADILLO_LIBRARIES}
${Boost_LIBRARIES}
${COMPILER_SUPPORT_LIBRARIES}
)
# Make sure that we set BINDING_TYPE to cli so the command-line program is
src/mlpack/bindings/python/CMakeLists.txt
-9
View File
@@ -18,7 +18,6 @@ macro (post_python_bindings)
-D GENERATE_CPP_IN=${CMAKE_SOURCE_DIR}/src/mlpack/bindings/python/setup.py.in
-D GENERATE_CPP_OUT=${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/setup.py
-D PACKAGE_VERSION="${PACKAGE_VERSION}"
-D Boost_LIBRARY_DIRS="${Boost_LIBRARY_DIRS}"
-D ARMADILLO_LIBRARIES="${ARMADILLO_LIBRARIES}"
-D MLPACK_LIBRARY=$<TARGET_LINKER_FILE:mlpack>
-D MLPACK_LIBDIR=$<TARGET_LINKER_FILE_DIR:mlpack>
@@ -240,14 +239,6 @@ if (WIN32)
foreach (dll ${DLL_COPY_LIBS})
file(COPY ${dll} DESTINATION ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/mlpack/)
endforeach ()
# We also need to copy the boost DLLs over.
file(GLOB boost_ser_dll_files "${Boost_LIBRARY_DIRS}/*serialization*.dll")
file(COPY ${boost_ser_dll_files} DESTINATION ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/mlpack/)
file(GLOB boost_po_dll_files "${Boost_LIBRARY_DIRS}/*program*options*.dll")
file(COPY ${boost_po_dll_files} DESTINATION ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/mlpack/)
file(GLOB boost_utf_dll_files "${Boost_LIBRARY_DIRS}/*unit*test*framework*.dll")
file(COPY ${boost_utf_dll_files} DESTINATION ${CMAKE_BINARY_DIR}/src/mlpack/bindings/python/mlpack/)
endif ()
# Add a macro to build a python binding.
src/mlpack/bindings/python/setup.py.in
+1 -2
View File
@@ -34,8 +34,7 @@ else:
# directories with a (valid) space in the name will be given to us as '\ '; so,
# in order to split these right, we first convert all spaces to ';', then
# convert '\;' back to ' ', then split on ';'.
library_dirs = list(filter(None, ['${MLPACK_LIBDIR}'] +
'${Boost_LIBRARY_DIRS}'.replace(' ', ';').replace('\;', ' ').split(' ')))
library_dirs = ['${MLPACK_LIBDIR}']
# We'll link with the exact paths to each library using extra_objects, instead
# of linking with 'libraries' and 'library_dirs', because of differences in
src/mlpack/core/data/image_info_impl.hpp
+76 -76
View File
@@ -1,77 +1,77 @@
/**
/**
* @file core/data/image_info_impl.hpp
* @author Mehul Kumar Nirala
*
* An image information holder implementation.
*
* mlpack is free software; you may redistribute it and/or modify it under the
* terms of the 3-clause BSD license. You should have received a copy of the
* 3-clause BSD license along with mlpack. If not, see
* http://www.opensource.org/licenses/BSD-3-Clause for more information.
*/
#ifndef MLPACK_CORE_DATA_IMAGE_INFO_IMPL_HPP
#define MLPACK_CORE_DATA_IMAGE_INFO_IMPL_HPP
#ifdef HAS_STB // Compile this only if stb is present.
// In case it hasn't been included yet.
#include "image_info.hpp"
namespace mlpack {
namespace data {
static const std::vector<std::string> loadFileTypes({"jpg", "png", "tga",
"bmp", "psd", "gif", "hdr", "pic", "pnm", "jpeg"});
static const std::vector<std::string> saveFileTypes({"jpg", "png", "tga",
"bmp", "hdr"});
inline bool ImageFormatSupported(const std::string& fileName, const bool save)
{
if (save)
{
// Iterate over all supported file types that can be saved.
for (auto extension : saveFileTypes)
{
if (extension == Extension(fileName))
return true;
}
}
else
{
// Iterate over all supported file types that can be loaded.
for (auto extension : loadFileTypes)
{
if (extension == Extension(fileName))
return true;
}
}
return false;
}
} // namespace data
} // namespace mlpack
#endif // HAS_STB.
namespace mlpack {
namespace data {
inline ImageInfo::ImageInfo(const size_t width,
const size_t height,
const size_t channels,
const size_t quality) :
width(width),
height(height),
channels(channels),
quality(quality)
{
// Do nothing.
}
} // namespace data
} // namespace mlpack
#endif
* @author Mehul Kumar Nirala
*
* An image information holder implementation.
*
* mlpack is free software; you may redistribute it and/or modify it under the
* terms of the 3-clause BSD license. You should have received a copy of the
* 3-clause BSD license along with mlpack. If not, see
* http://www.opensource.org/licenses/BSD-3-Clause for more information.
*/
#ifndef MLPACK_CORE_DATA_IMAGE_INFO_IMPL_HPP
#define MLPACK_CORE_DATA_IMAGE_INFO_IMPL_HPP
#ifdef HAS_STB // Compile this only if stb is present.
// In case it hasn't been included yet.
#include "image_info.hpp"
namespace mlpack {
namespace data {
static const std::vector<std::string> loadFileTypes({"jpg", "png", "tga",
"bmp", "psd", "gif", "hdr", "pic", "pnm", "jpeg"});
static const std::vector<std::string> saveFileTypes({"jpg", "png", "tga",
"bmp", "hdr"});
inline bool ImageFormatSupported(const std::string& fileName, const bool save)
{
if (save)
{
// Iterate over all supported file types that can be saved.
for (auto extension : saveFileTypes)
{
if (extension == Extension(fileName))
return true;
}
}
else
{
// Iterate over all supported file types that can be loaded.
for (auto extension : loadFileTypes)
{
if (extension == Extension(fileName))
return true;
}
}
return false;
}
} // namespace data
} // namespace mlpack
#endif // HAS_STB.
namespace mlpack {
namespace data {
inline ImageInfo::ImageInfo(const size_t width,
const size_t height,
const size_t channels,
const size_t quality) :
width(width),
height(height),
channels(channels),
quality(quality)
{
// Do nothing.
}
} // namespace data
} // namespace mlpack
#endif
src/mlpack/core/data/load_image_impl.hpp
+95 -95
View File
@@ -1,96 +1,96 @@
/**
/**
* @file core/data/load_image_impl.hpp
* @author Mehul Kumar Nirala
*
* An image loading utility implementation.
*
* mlpack is free software; you may redistribute it and/or modify it under the
* terms of the 3-clause BSD license. You should have received a copy of the
* 3-clause BSD license along with mlpack. If not, see
* http://www.opensource.org/licenses/BSD-3-Clause for more information.
*/
#ifndef MLPACK_CORE_DATA_LOAD_IMAGE_IMPL_HPP
#define MLPACK_CORE_DATA_LOAD_IMAGE_IMPL_HPP
// In case it hasn't been included yet.
#include "load.hpp"
namespace mlpack {
namespace data {
// Image loading API.
template<typename eT>
bool Load(const std::string& filename,
arma::Mat<eT>& matrix,
ImageInfo& info,
const bool fatal)
{
Timer::Start("loading_image");
// STB loads into unsigned char matrices, so we may have to convert once
// loaded.
arma::Mat<unsigned char> tempMatrix;
const bool result = LoadImage(filename, tempMatrix, info, fatal);
// If fatal is true, then the program will have already thrown an exception.
if (!result)
{
Timer::Stop("loading_image");
return false;
}
matrix = arma::conv_to<arma::Mat<eT>>::from(tempMatrix);
Timer::Stop("loading_image");
return true;
}
// Image loading API for multiple files.
template<typename eT>
bool Load(const std::vector<std::string>& files,
arma::Mat<eT>& matrix,
ImageInfo& info,
const bool fatal)
{
if (files.size() == 0)
{
std::ostringstream oss;
oss << "Load(): vector of image files is empty." << std::endl;
if (fatal)
Log::Fatal << oss.str();
else
Log::Warn << oss.str();
return false;
}
arma::Mat<unsigned char> img;
bool status = LoadImage(files[0], img, info, fatal);
if (!status)
return false;
// Decide matrix dimension using the image height and width.
arma::Mat<unsigned char> tmpMatrix(
info.Width() * info.Height() * info.Channels(), files.size());
tmpMatrix.col(0) = img;
for (size_t i = 1; i < files.size() ; ++i)
{
arma::Mat<unsigned char> colImg(tmpMatrix.colptr(i), tmpMatrix.n_rows, 1,
false, true);
status = LoadImage(files[i], colImg, info, fatal);
if (!status)
return false;
}
matrix = arma::conv_to<arma::Mat<eT>>::from(tmpMatrix);
return true;
}
} // namespace data
} // namespace mlpack
#endif
* @author Mehul Kumar Nirala
*
* An image loading utility implementation.
*
* mlpack is free software; you may redistribute it and/or modify it under the
* terms of the 3-clause BSD license. You should have received a copy of the
* 3-clause BSD license along with mlpack. If not, see
* http://www.opensource.org/licenses/BSD-3-Clause for more information.
*/
#ifndef MLPACK_CORE_DATA_LOAD_IMAGE_IMPL_HPP
#define MLPACK_CORE_DATA_LOAD_IMAGE_IMPL_HPP
// In case it hasn't been included yet.
#include "load.hpp"
namespace mlpack {
namespace data {
// Image loading API.
template<typename eT>
bool Load(const std::string& filename,
arma::Mat<eT>& matrix,
ImageInfo& info,
const bool fatal)
{
Timer::Start("loading_image");
// STB loads into unsigned char matrices, so we may have to convert once
// loaded.
arma::Mat<unsigned char> tempMatrix;
const bool result = LoadImage(filename, tempMatrix, info, fatal);
// If fatal is true, then the program will have already thrown an exception.
if (!result)
{
Timer::Stop("loading_image");
return false;
}
matrix = arma::conv_to<arma::Mat<eT>>::from(tempMatrix);
Timer::Stop("loading_image");
return true;
}
// Image loading API for multiple files.
template<typename eT>
bool Load(const std::vector<std::string>& files,
arma::Mat<eT>& matrix,
ImageInfo& info,
const bool fatal)
{
if (files.size() == 0)
{
std::ostringstream oss;
oss << "Load(): vector of image files is empty." << std::endl;
if (fatal)
Log::Fatal << oss.str();
else
Log::Warn << oss.str();
return false;
}
arma::Mat<unsigned char> img;
bool status = LoadImage(files[0], img, info, fatal);
if (!status)
return false;
// Decide matrix dimension using the image height and width.
arma::Mat<unsigned char> tmpMatrix(
info.Width() * info.Height() * info.Channels(), files.size());
tmpMatrix.col(0) = img;
for (size_t i = 1; i < files.size() ; ++i)
{
arma::Mat<unsigned char> colImg(tmpMatrix.colptr(i), tmpMatrix.n_rows, 1,
false, true);
status = LoadImage(files[i], colImg, info, fatal);
if (!status)
return false;
}
matrix = arma::conv_to<arma::Mat<eT>>::from(tmpMatrix);
return true;
}
} // namespace data
} // namespace mlpack
#endif
src/mlpack/methods/ann/layer/concatenate.hpp
+13 -1
View File
@@ -41,6 +41,18 @@ class Concatenate
*/
Concatenate();
//! Copy constructor.
Concatenate(const Concatenate& layer);
//! Move constructor.
Concatenate(Concatenate&& layer);
//! Operator= copy constructor.
Concatenate& operator=(const Concatenate& layer);
//! Operator= move constructor.
Concatenate& operator=(Concatenate&& layer);
/**
* Ordinary feed forward pass of a neural network, evaluating the function
* f(x) by propagating the activity forward through f.
@@ -82,7 +94,7 @@ class Concatenate
//! Get the concat matrix.
OutputDataType const& Concat() const { return concat; }
//! Modify the delta.
//! Modify the concat.
OutputDataType& Concat() { return concat; }
/**
src/mlpack/methods/ann/layer/concatenate_impl.hpp
+53 -1
View File
@@ -20,11 +20,63 @@ namespace mlpack {
namespace ann /** Artificial Neural Network. */ {
template<typename InputDataType, typename OutputDataType>
Concatenate<InputDataType, OutputDataType>::Concatenate()
Concatenate<InputDataType, OutputDataType>::Concatenate() :
inRows(0)
{
// Nothing to do here.
}
template<typename InputDataType, typename OutputDataType>
Concatenate<InputDataType, OutputDataType>::Concatenate(const Concatenate& layer) :
inRows(layer.inRows),
weights(layer.weights),
delta(layer.delta),
concat(layer.concat)
{
// Nothing to to here.
}
template<typename InputDataType, typename OutputDataType>
Concatenate<InputDataType, OutputDataType>::Concatenate(Concatenate&& layer) :
inRows(layer.inRows),
weights(std::move(layer.weights)),
delta(std::move(layer.delta)),
concat(std::move(layer.concat))
{
// Nothing to do here.
}
template<typename InputDataType, typename OutputDataType>
Concatenate<InputDataType, OutputDataType>&
Concatenate<InputDataType, OutputDataType>::
operator=(const Concatenate& layer)
{
if (this != &layer)
{
inRows = layer.inRows;
weights = layer.weights;
delta = layer.delta;
concat = layer.concat;
}
return *this;
}
template<typename InputDataType, typename OutputDataType>
Concatenate<InputDataType, OutputDataType>&
Concatenate<InputDataType, OutputDataType>::
operator=(Concatenate&& layer)
{
if (this != &layer)
{
inRows = layer.inRows;
weights = std::move(layer.weights);
delta = std::move(layer.delta);
concat = std::move(layer.concat);
}
return *this;
}
template<typename InputDataType, typename OutputDataType>
template<typename eT>
void Concatenate<InputDataType, OutputDataType>::Forward(
src/mlpack/methods/ann/layer/fast_lstm.hpp
+12
View File
@@ -73,6 +73,18 @@ class FastLSTM
//! Create the Fast LSTM object.
FastLSTM();
//! Copy Constructor
FastLSTM(const FastLSTM& layer);
//! Move Constructor
FastLSTM(FastLSTM&& layer);
//! Copy assignment operator
FastLSTM& operator=(const FastLSTM& layer);
//! Move assignment operator
FastLSTM& operator=(FastLSTM&& layer);
/**
* Create the Fast LSTM layer object using the specified parameters.
*
src/mlpack/methods/ann/layer/fast_lstm_impl.hpp
+84
View File
@@ -45,6 +45,90 @@ FastLSTM<InputDataType, OutputDataType>::FastLSTM(
weights.set_size(WeightSize(), 1);
}
template<typename InputDataType, typename OutputDataType>
FastLSTM<InputDataType, OutputDataType>::FastLSTM(const FastLSTM& layer) :
inSize(layer.inSize),
outSize(layer.outSize),
rho(layer.rho),
forwardStep(layer.forwardStep),
backwardStep(layer.backwardStep),
gradientStep(layer.gradientStep),
weights(layer.weights),
batchSize(layer.batchSize),
batchStep(layer.batchStep),
gradientStepIdx(layer.gradientStepIdx),
grad(layer.grad),
rhoSize(layer.rho),
bpttSteps(layer.bpttSteps)
{
// Nothing to do here.
}
template<typename InputDataType, typename OutputDataType>
FastLSTM<InputDataType, OutputDataType>::FastLSTM(FastLSTM&& layer) :
inSize(std::move(layer.inSize)),
outSize(std::move(layer.outSize)),
rho(std::move(layer.rho)),
forwardStep(std::move(layer.forwardStep)),
backwardStep(std::move(layer.backwardStep)),
gradientStep(std::move(layer.gradientStep)),
weights(std::move(layer.weights)),
batchSize(std::move(layer.batchSize)),
batchStep(std::move(layer.batchStep)),
gradientStepIdx(std::move(layer.gradientStepIdx)),
grad(std::move(layer.grad)),
rhoSize(std::move(layer.rho)),
bpttSteps(std::move(layer.bpttSteps))
{
// Nothing to do here.
}
template<typename InputDataType, typename OutputDataType>
FastLSTM<InputDataType, OutputDataType>&
FastLSTM<InputDataType, OutputDataType>::operator=(const FastLSTM& layer)
{
if (this != &layer)
{
inSize = layer.inSize;
outSize = layer.outSize;
rho = layer.rho;
forwardStep = layer.forwardStep;
backwardStep = layer.backwardStep;
gradientStep = layer.gradientStep;
weights = layer.weights;
batchSize = layer.batchSize;
batchStep = layer.batchStep;
gradientStepIdx = layer.gradientStepIdx;
grad = layer.grad;
rhoSize = layer.rho;
bpttSteps = layer.bpttSteps;
}
return *this;
}
template<typename InputDataType, typename OutputDataType>
FastLSTM<InputDataType, OutputDataType>&
FastLSTM<InputDataType, OutputDataType>::operator=(FastLSTM&& layer)
{
if (this != &layer)
{
inSize = std::move(layer.inSize);
outSize = std::move(layer.outSize);
rho = std::move(layer.rho);
forwardStep = std::move(layer.forwardStep);
backwardStep = std::move(layer.backwardStep);
gradientStep = std::move(layer.gradientStep);
weights = std::move(layer.weights);
batchSize = std::move(layer.batchSize);
batchStep = std::move(layer.batchStep);
gradientStepIdx = std::move(layer.gradientStepIdx);
grad = std::move(layer.grad);
rhoSize = std::move(layer.rho);
bpttSteps = std::move(layer.bpttSteps);
}
return *this;
}
template<typename InputDataType, typename OutputDataType>
void FastLSTM<InputDataType, OutputDataType>::Reset()
{
src/mlpack/methods/ann/layer/linear3d.hpp
+12
View File
@@ -54,6 +54,18 @@ class Linear3D
const size_t outSize,
RegularizerType regularizer = RegularizerType());
//! Copy constructor.
Linear3D(const Linear3D& layer);
//! Move constructor.
Linear3D(Linear3D&&);
//! Copy assignment operator.
Linear3D& operator=(const Linear3D& layer);
//! Move assignment operator.
Linear3D& operator=(Linear3D&& layer);
/*
* Reset the layer parameter.
*/
src/mlpack/methods/ann/layer/linear3d_impl.hpp
+56
View File
@@ -40,6 +40,62 @@ Linear3D<InputDataType, OutputDataType, RegularizerType>::Linear3D(
weights.set_size(outSize * inSize + outSize, 1);
}
template<typename InputDataType, typename OutputDataType,
typename RegularizerType>
Linear3D<InputDataType, OutputDataType, RegularizerType>::Linear3D(
const Linear3D& layer) :
inSize(layer.inSize),
outSize(layer.outSize),
weights(layer.weights),
regularizer(layer.regularizer)
{
// Nothing to do here.
}
template<typename InputDataType, typename OutputDataType,
typename RegularizerType>
Linear3D<InputDataType, OutputDataType, RegularizerType>::Linear3D(
Linear3D&& layer) :
inSize(0),
outSize(0),
weights(std::move(layer.weights)),
regularizer(std::move(layer.regularizer))
{
// Nothing to do here.
}
template<typename InputDataType, typename OutputDataType,
typename RegularizerType>
Linear3D<InputDataType, OutputDataType, RegularizerType>&
Linear3D<InputDataType, OutputDataType, RegularizerType>::
operator=(const Linear3D& layer)
{
if (this != &layer)
{
inSize = layer.inSize;
outSize = layer.outSize;
weights = layer.weights;
regularizer = layer.regularizer;
}
return *this;
}
template<typename InputDataType, typename OutputDataType,
typename RegularizerType>
Linear3D<InputDataType, OutputDataType, RegularizerType>&
Linear3D<InputDataType, OutputDataType, RegularizerType>::
operator=(Linear3D&& layer)
{
if (this != &layer)
{
inSize = 0;
outSize = 0;
weights = std::move(layer.weights);
regularizer = std::move(layer.regularizer);
}
return *this;
}
template<typename InputDataType, typename OutputDataType,
typename RegularizerType>
void Linear3D<InputDataType, OutputDataType, RegularizerType>::Reset()
src/mlpack/methods/ann/layer/noisylinear.hpp
+9
View File
@@ -48,6 +48,15 @@ class NoisyLinear
//! Copy constructor.
NoisyLinear(const NoisyLinear&);
//! Move constructor.
NoisyLinear(NoisyLinear&&);
//! Operator= copy constructor.
NoisyLinear& operator=(const NoisyLinear& layer);
//! Operator= move constructor.
NoisyLinear& operator=(NoisyLinear&& layer);
/*
* Reset the layer parameter.
*/
src/mlpack/methods/ann/layer/noisylinear_impl.hpp
+44
View File
@@ -48,6 +48,50 @@ NoisyLinear<InputDataType, OutputDataType>::NoisyLinear(
biasEpsilon.set_size(outSize, 1);
}
template<typename InputDataType, typename OutputDataType>
NoisyLinear<InputDataType, OutputDataType>::NoisyLinear(
NoisyLinear&& layer) :
inSize(std::move(layer.inSize)),
outSize(std::move(layer.outSize)),
weights(std::move(layer.weights))
{
layer.inSize = 0;
layer.outSize = 0;
layer.weights = nullptr;
Reset();
}
template<typename InputDataType, typename OutputDataType>
NoisyLinear<InputDataType, OutputDataType>&
NoisyLinear<InputDataType, OutputDataType>::operator=(const NoisyLinear& layer)
{
if (this != &layer)
{
inSize = layer.inSize;
outSize = layer.outSize;
weights = layer.weights;
Reset();
}
return *this;
}
template<typename InputDataType, typename OutputDataType>
NoisyLinear<InputDataType, OutputDataType>&
NoisyLinear<InputDataType, OutputDataType>::operator=(NoisyLinear&& layer)
{
if (this != &layer)
{
inSize = std::move(layer.inSize);
layer.inSize = 0;
outSize = std::move(layer.outSize);
layer.outSize = 0;
weights = std::move(layer.weights);
layer.weights = nullptr;
Reset();
}
return *this;
}
template<typename InputDataType, typename OutputDataType>
void NoisyLinear<InputDataType, OutputDataType>::Reset()
{
src/mlpack/methods/ann/layer/recurrent.hpp
+1 -2
View File
@@ -2,8 +2,7 @@
* @file methods/ann/layer/recurrent.hpp
* @author Marcus Edel
*
* Definition of the LinearLayer class also known as fully-connected layer or
* affine transformation.
* Definition of the Recurrent class.
*
* mlpack is free software; you may redistribute it and/or modify it under the
* terms of the 3-clause BSD license. You should have received a copy of the
src/mlpack/methods/ann/layer/recurrent_impl.hpp
+1 -2
View File
@@ -2,8 +2,7 @@
* @file methods/ann/layer/recurrent_impl.hpp
* @author Marcus Edel
*
* Implementation of the LinearLayer class also known as fully-connected layer
* or affine transformation.
* Implementation of the Recurrent class.
*
* mlpack is free software; you may redistribute it and/or modify it under the
* terms of the 3-clause BSD license. You should have received a copy of the
src/mlpack/methods/ann/layer/spatial_dropout_impl.hpp
+3
View File
@@ -51,6 +51,9 @@ template<typename eT>
void SpatialDropout<InputDataType, OutputDataType>::Forward(
const arma::Mat<eT>& input, arma::Mat<eT>& output)
{
Log::Assert(input.n_rows % size == 0, "Input features must be divisible \
by feature maps.");
if (!reset)
{
batchSize = input.n_cols;
src/mlpack/methods/ann/layer/virtual_batch_norm_impl.hpp
+3
View File
@@ -67,6 +67,9 @@ template<typename eT>
void VirtualBatchNorm<InputDataType, OutputDataType>::Forward(
const arma::Mat<eT>& input, arma::Mat<eT>& output)
{
Log::Assert(input.n_rows % size == 0, "Input features must be divisible \
by feature maps.");
inputParameter = input;
arma::mat inputMean = arma::mean(input, 1);
arma::mat inputMeanSquared = arma::mean(arma::square(input), 1);
src/mlpack/methods/ann/loss_functions/mean_squared_error.hpp
+11 -9
View File
@@ -42,24 +42,26 @@ class MeanSquaredError
/**
* Computes the mean squared error function.
*
* @param input Input data used for evaluating the specified function.
* @param prediction Predictions used for evaluating the specified loss
* function.
* @param target The target vector.
*/
template<typename InputType, typename TargetType>
typename InputType::elem_type Forward(const InputType& input,
const TargetType& target);
template<typename PredictionType, typename TargetType>
typename PredictionType::elem_type Forward(const PredictionType& prediction,
const TargetType& target);
/**
* Ordinary feed backward pass of a neural network.
*
* @param input The propagated input activation.
* @param prediction Predictions used for evaluating the specified loss
* function
* @param target The target vector.
* @param output The calculated error.
* @param loss The calculated error.
*/
template<typename InputType, typename TargetType, typename OutputType>
void Backward(const InputType& input,
template<typename PredictionType, typename TargetType, typename LossType>
void Backward(const PredictionType& prediction,
const TargetType& target,
OutputType& output);
LossType& loss);
//! Get the output parameter.
OutputDataType& OutputParameter() const { return outputParameter; }
src/mlpack/methods/ann/loss_functions/mean_squared_error_impl.hpp
+8 -8
View File
@@ -25,23 +25,23 @@ MeanSquaredError<InputDataType, OutputDataType>::MeanSquaredError()
}
template<typename InputDataType, typename OutputDataType>
template<typename InputType, typename TargetType>
typename InputType::elem_type
template<typename PredictionType, typename TargetType>
typename PredictionType::elem_type
MeanSquaredError<InputDataType, OutputDataType>::Forward(
const InputType& input,
const PredictionType& prediction,
const TargetType& target)
{
return arma::accu(arma::square(input - target)) / target.n_cols;
return arma::accu(arma::square(prediction - target)) / target.n_cols;
}
template<typename InputDataType, typename OutputDataType>
template<typename InputType, typename TargetType, typename OutputType>
template<typename PredictionType, typename TargetType, typename LossType>
void MeanSquaredError<InputDataType, OutputDataType>::Backward(
const InputType& input,
const PredictionType& prediction,
const TargetType& target,
OutputType& output)
LossType& loss)
{
output = 2 * (input - target) / target.n_cols;
loss = 2 * (prediction - target) / target.n_cols;
}
template<typename InputDataType, typename OutputDataType>
src/mlpack/methods/ann/rnn.hpp
+15
View File
@@ -70,6 +70,18 @@ class RNN
OutputLayerType outputLayer = OutputLayerType(),
InitializationRuleType initializeRule = InitializationRuleType());
//! Copy constructor.
RNN(const RNN&);
//! Move constructor.
RNN(RNN&&);
//! Copy assignment operator.
RNN& operator=(const RNN&);
//! Move assignment operator
RNN& operator=(RNN&&);
//! Destructor to release allocated memory.
~RNN();
@@ -412,6 +424,9 @@ class RNN
//! Locally-stored weight size visitor.
WeightSizeVisitor weightSizeVisitor;
//! Locally-stored copy visitor
CopyVisitor<CustomLayers...> copyVisitor;
//! Locally-stored reset visitor.
ResetVisitor resetVisitor;
src/mlpack/methods/ann/rnn_impl.hpp
+44
View File
@@ -51,6 +51,50 @@ RNN<OutputLayerType, InitializationRuleType, CustomLayers...>::RNN(
/* Nothing to do here */
}
template<typename OutputLayerType, typename InitializationRuleType,
typename... CustomLayers>
RNN<OutputLayerType, InitializationRuleType, CustomLayers...>::RNN(
const RNN& network) :
rho(network.rho),
outputLayer(network.outputLayer),
initializeRule(network.initializeRule),
inputSize(network.inputSize),
outputSize(network.outputSize),
targetSize(network.targetSize),
reset(network.reset),
single(network.single),
parameter(network.parameter),
numFunctions(network.numFunctions),
deterministic(network.deterministic)
{
for (size_t i = 0; i < network.network.size(); ++i)
{
this->network.push_back(boost::apply_visitor(copyVisitor,
network.network[i]));
boost::apply_visitor(resetVisitor, this->network.back());
}
}
template<typename OutputLayerType, typename InitializationRuleType,
typename... CustomLayers>
RNN<OutputLayerType, InitializationRuleType, CustomLayers...>::RNN(
RNN&& network) :
rho(std::move(network.rho)),
outputLayer(std::move(network.outputLayer)),
initializeRule(std::move(network.initializeRule)),
inputSize(std::move(network.inputSize)),
outputSize(std::move(network.outputSize)),
targetSize(std::move(network.targetSize)),
reset(std::move(network.reset)),
single(std::move(network.single)),
parameter(std::move(network.parameter)),
numFunctions(std::move(network.numFunctions)),
deterministic(std::move(network.deterministic)),
network(std::move(network.network))
{
// Nothing to do here.
}
template<typename OutputLayerType, typename InitializationRuleType,
typename... CustomLayers>
RNN<OutputLayerType, InitializationRuleType, CustomLayers...>::~RNN()
src/mlpack/tests/CMakeLists.txt
-1
View File
@@ -179,7 +179,6 @@ add_executable(mlpack_test
target_link_libraries(mlpack_test
mlpack
${ARMADILLO_LIBRARIES}
${BOOST_LIBRARIES}
${COMPILER_SUPPORT_LIBRARIES}
)
src/mlpack/tests/ann_layer_test.cpp
+80
View File
@@ -31,6 +31,52 @@
using namespace mlpack;
using namespace mlpack::ann;
// network1 should be allocated with `new`, and trained on some data.
template<typename MatType = arma::cube, typename ModelType>
void CheckRNNCopyFunction(ModelType* network1,
MatType& trainData,
MatType& trainLabels,
const size_t maxEpochs)
{
arma::cube predictions1;
arma::cube predictions2;
ens::StandardSGD opt(0.1, 1, maxEpochs * trainData.n_slices, -100, false);
network1->Train(trainData, trainLabels, opt);
network1->Predict(trainData, predictions1);
RNN<> network2 = *network1;
delete network1;
// Deallocating all of network1's memory, so that network2 does not use any
// of that memory.
network2.Predict(trainData, predictions2);
CheckMatrices(predictions1, predictions2);
}
// network1 should be allocated with `new`, and trained on some data.
template<typename MatType = arma::cube, typename ModelType>
void CheckRNNMoveFunction(ModelType* network1,
MatType& trainData,
MatType& trainLabels,
const size_t maxEpochs)
{
arma::cube predictions1;
arma::cube predictions2;
ens::StandardSGD opt(0.1, 1, maxEpochs * trainData.n_slices, -100, false);
network1->Train(trainData, trainLabels, opt);
network1->Predict(trainData, predictions1);
RNN<> network2(std::move(*network1));
delete network1;
// Deallocating all of network1's memory, so that network2 does not use any
// of that memory.
network2.Predict(trainData, predictions2);
CheckMatrices(predictions1, predictions2);
}
/**
* Simple add module test.
*/
@@ -1183,6 +1229,40 @@ TEST_CASE("FastLSTMLayerParametersTest", "[ANNLayerTest]")
REQUIRE(layer1.Rho() == layer2.Rho());
}
/**
* Check whether copying and moving network with FastLSTM is working or not.
*/
TEST_CASE("CheckCopyMoveFastLSTMTest", "[ANNLayerTest]")
{
arma::cube input = arma::randu(1, 1, 5);
arma::cube target = arma::ones(1, 1, 5);
const size_t rho = 5;
RNN<NegativeLogLikelihood<> > *model1 =
new RNN<NegativeLogLikelihood<> >(rho);
model1->Predictors() = input;
model1->Responses() = target;
model1->Add<IdentityLayer<> >();
model1->Add<Linear<> >(1, 10);
model1->Add<FastLSTM<> >(10, 3, rho);
model1->Add<LogSoftMax<> >();
RNN<NegativeLogLikelihood<> > *model2 =
new RNN<NegativeLogLikelihood<> >(rho);
model2->Predictors() = input;
model2->Responses() = target;
model2->Add<IdentityLayer<> >();
model2->Add<Linear<> >(1, 10);
model2->Add<FastLSTM<> >(10, 3, rho);
model2->Add<LogSoftMax<> >();
// Check whether copy constructor is working or not.
CheckRNNCopyFunction<>(model1, input, target, 1);
// Check whether move constructor is working or not.
CheckRNNMoveFunction<>(model2, input, target, 1);
}
/**
* Testing the overloaded Forward() of the LSTM layer, for retrieving the cell
* state. Besides output, the overloaded function provides read access to cell
src/mlpack/tests/callback_test.cpp
+4 -4
View File
@@ -42,9 +42,9 @@ TEST_CASE("FFNCallbackTest", "[CallbackTest]")
arma::mat data;
arma::mat labels;
if (!data::Load("lab1.csv", data, true))
if (!data::Load("lab1.csv", data))
FAIL("Cannot load test dataset lab1.csv!");
if (!data::Load("lab3.csv", labels, true))
if (!data::Load("lab3.csv", labels))
FAIL("Cannot load test dataset lab3.csv!");
FFN<MeanSquaredError<>, RandomInitialization> model;
@@ -68,9 +68,9 @@ TEST_CASE("FFNWithOptimizerCallbackTest", "[CallbackTest]")
arma::mat data;
arma::mat labels;
if (!data::Load("lab1.csv", data, true))
if (!data::Load("lab1.csv", data))
FAIL("Cannot load test dataset lab1.csv!");
if (!data::Load("lab3.csv", labels, true))
if (!data::Load("lab3.csv", labels))
FAIL("Cannot load test dataset lab3.csv!");
FFN<MeanSquaredError<>, RandomInitialization> model;
src/mlpack/tests/data/nbc_high_dim_test_labels.csv
+50 -50
View File
@@ -1,50 +1,50 @@
3
2
0
0
0
1
2
3
3
2
4
2
1
2
3
1
2
4
4
1
3
0
2
0
0
2
0
1
3
3
2
2
2
3
3
3
3
3
0
0
4
3
3
0
3
2
3
2
1
1
3
2
0
0
0
1
2
3
3
2
4
2
1
2
3
1
2
4
4
1
3
0
2
0
0
2
0
1
3
3
2
2
2
3
3
3
3
3
0
0
4
3
3
0
3
2
3
2
1
1
1 3
2 2
3 0
4 0
5 0
6 1
7 2
8 3
9 3
10 2
11 4
12 2
13 1
14 2
15 3
16 1
17 2
18 4
19 4
20 1
21 3
22 0
23 2
24 0
25 0
26 2
27 0
28 1
29 3
30 3
31 2
32 2
33 2
34 3
35 3
36 3
37 3
38 3
39 0
40 0
41 4
42 3
43 3
44 0
45 3
46 2
47 3
48 2
49 1
50 1
src/mlpack/tests/data/nbc_high_dim_train_labels.csv
+200 -200
View File
@@ -1,200 +1,200 @@
1
4
2
2
1
0
1
0
0
4
0
4
3
4
3
2
4
2
2
2
4
1
2
1
3
0
4
1
4
4
4
0
3
4
3
1
3
2
3
0
4
1
4
1
4
2
1
4
2
1
2
0
2
2
4
2
0
2
0
3
3
3
0
2
1
4
3
1
2
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0
1
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4
4
4
2
4
2
3
4
4
3
2
3
3
4
3
4
2
4
0
3
3
1
3
4
2
1
2
3
1
3
3
0
4
0
0
3
2
1
0
3
2
1
0
0
1
0
2
2
4
2
3
1
4
4
2
3
4
0
2
2
0
4
0
3
1
4
4
2
0
0
0
0
3
4
3
2
0
4
3
3
4
0
3
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3
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2
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0
0
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1
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1
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1
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1 1
2 4
3 2
4 2
5 1
6 0
7 1
8 0
9 0
10 4
11 0
12 4
13 3
14 4
15 3
16 2
17 4
18 2
19 2
20 2
21 4
22 1
23 2
24 1
25 3
26 0
27 4
28 1
29 4
30 4
31 4
32 0
33 3
34 4
35 3
36 1
37 3
38 2
39 3
40 0
41 4
42 1
43 4
44 1
45 4
46 2
47 1
48 4
49 2
50 1
51 2
52 0
53 2
54 2
55 4
56 2
57 0
58 2
59 0
60 3
61 3
62 3
63 0
64 2
65 1
66 4
67 3
68 1
69 2
70 2
71 4
72 0
73 1
74 3
75 4
76 4
77 4
78 2
79 4
80 2
81 3
82 4
83 4
84 3
85 2
86 3
87 3
88 4
89 3
90 4
91 2
92 4
93 0
94 3
95 3
96 1
97 3
98 4
99 2
100 1
101 2
102 3
103 1
104 3
105 3
106 0
107 4
108 0
109 0
110 3
111 2
112 1
113 0
114 3
115 2
116 1
117 0
118 0
119 1
120 0
121 2
122 2
123 4
124 2
125 3
126 1
127 4
128 4
129 2
130 3
131 4
132 0
133 2
134 2
135 0
136 4
137 0
138 3
139 1
140 4
141 4
142 2
143 0
144 0
145 0
146 0
147 3
148 4
149 3
150 2
151 0
152 4
153 3
154 3
155 4
156 0
157 3
158 1
159 3
160 4
161 3
162 2
163 2
164 4
165 0
166 0
167 0
168 0
169 1
170 4
171 0
172 3
173 4
174 3
175 1
176 4
177 0
178 1
179 4
180 3
181 2
182 1
183 3
184 2
185 4
186 3
187 2
188 0
189 1
190 4
191 2
192 0
193 2
194 3
195 0
196 0
197 2
198 1
199 3
200 1
src/mlpack/tests/feedforward_network_test.cpp
+143 -5
View File
@@ -71,8 +71,8 @@ void CheckCopyFunction(ModelType* network1,
network2 = *network1;
delete network1;
// Deallocating all of network1's memory, so that
// if network2 is trying to use any of that memory.
// Deallocating all of network1's memory, so that network2 does not use any
// of that memory.
arma::mat predictions2;
network2.Predict(trainData, predictions2);
CheckMatrices(predictions1, predictions2);
@@ -93,8 +93,8 @@ void CheckMoveFunction(ModelType* network1,
FFN<> network2(std::move(*network1));
delete network1;
// Deallocating all of network1's memory, so that
// if network2 is trying to use any of that memory.
// Deallocating all of network1's memory, so that network2 does not use any
// of that memory.
arma::mat predictions2;
network2.Predict(trainData, predictions2);
CheckMatrices(predictions1, predictions2);
@@ -154,7 +154,145 @@ TEST_CASE("CheckCopyMovingVanillaNetworkTest", "[FeedForwardNetworkTest]")
}
/**
* Check whether copying and moving network with dropout is working or not.
* Check whether copying and moving network with linear3d is working or not.
*/
TEST_CASE("CheckCopyMovingLinear3DNetworkTest", "[FeedForwardNetworkTest]")
{
// Load the dataset.
arma::mat trainData;
data::Load("thyroid_train.csv", trainData, true);
arma::mat trainLabels = trainData.row(trainData.n_rows - 1);
trainData.shed_row(trainData.n_rows - 1);
/*
* Construct a feed forward network with trainData.n_rows input nodes,
* hiddenLayerSize hidden nodes and trainLabels.n_rows output nodes. The
* network structure looks like:
*
* Input Hidden Output
* Layer Layer Layer
* +-----+ +-----+ +-----+
* | | | | | |
* | +------>| +------>| |
* | | +>| | +>| |
* +-----+ | +--+--+ | +-----+
* | |
* Bias | Bias |
* Layer | Layer |
* +-----+ | +-----+ |
* | | | | | |
* | +-----+ | +-----+
* | | | |
* +-----+ +-----+
*/
FFN<NegativeLogLikelihood<> > *model = new FFN<NegativeLogLikelihood<> >;
model->Add<Linear<> >(trainData.n_rows, 8);
model->Add<SigmoidLayer<> >();
model->Add<Linear3D<> >(8, 3);
model->Add<LogSoftMax<> >();
FFN<NegativeLogLikelihood<> > *model1 = new FFN<NegativeLogLikelihood<> >;
model1->Add<Linear<> >(trainData.n_rows, 8);
model1->Add<SigmoidLayer<> >();
model1->Add<Linear3D<> >(8, 3);
model1->Add<LogSoftMax<> >();
// Check whether copy constructor is working or not.
CheckCopyFunction<>(model, trainData, trainLabels, 1);
// Check whether move constructor is working or not.
CheckMoveFunction<>(model1, trainData, trainLabels, 1);
}
/**
* Check whether copying and moving of Noisy Linear layer is working or not.
*/
TEST_CASE("CheckCopyMovingNoisyLinearTest", "[FeedForwardNetworkTest]")
{
// Create training input by 5x5 matrix.
arma::mat input = arma::randu(10,1);
// Create training output by 1 matrix.
arma::mat output = arma::mat("1");
// Check copying constructor.
FFN<NegativeLogLikelihood<>> *model1 = new FFN<NegativeLogLikelihood<>>();
model1->Predictors() = input;
model1->Responses() = output;
model1->Add<IdentityLayer<>>();
model1->Add<NoisyLinear<>>(10, 5);
model1->Add<Linear<> >(5, 1);
model1->Add<LogSoftMax<>>();
// Check whether copy constructor is working or not.
CheckCopyFunction<>(model1, input, output, 1);
// Check moving constructor.
FFN<NegativeLogLikelihood<>> *model2 = new FFN<NegativeLogLikelihood<>>();
model2->Predictors() = input;
model2->Responses() = output;
model2->Add<IdentityLayer<>>();
model2->Add<NoisyLinear<>>(10, 5);
model2->Add<Linear<> >(5, 1);
model2->Add<LogSoftMax<>>();
// Check whether move constructor is working or not.
CheckMoveFunction<>(model2, input, output, 1);
}
/**
* Check whether copying and moving of concatenate layer is working or not.
*/
TEST_CASE("CheckCopyMovingConcatenateTest", "[FeedForwardNetworkTest]")
{
// Create training input by 5x5 matrix.
arma::mat input = arma::randu(10,1);
// Create training output by 1 matrix.
arma::mat output = arma::mat("1");
// Check copying constructor.
FFN<NegativeLogLikelihood<>> *model1 = new FFN<NegativeLogLikelihood<>>();
model1->Predictors() = input;
model1->Responses() = output;
model1->Add<IdentityLayer<>>();
model1->Add<Linear<>>(10, 5);
// Create concatenate layer.
arma::mat concatMatrix = arma::ones(5, 1);
Concatenate<>* concatLayer = new Concatenate<>();
concatLayer->Concat() = concatMatrix;
// Add concatenate layer to the current network.
model1->Add(concatLayer);
model1->Add<Linear<> >(10, 5);
model1->Add<LogSoftMax<>>();
// Check whether copy constructor is working or not.
CheckCopyFunction<>(model1, input, output, 1);
// Check moving constructor.
FFN<NegativeLogLikelihood<>> *model2 = new FFN<NegativeLogLikelihood<>>();
model2->Predictors() = input;
model2->Responses() = output;
model2->Add<IdentityLayer<>>();
model2->Add<Linear<>>(10, 5);
// Create new concat layer.
Concatenate<>* concatLayer2 = new Concatenate<>();
concatLayer2->Concat() = concatMatrix;
// Add concatenate layer to the current network.
model2->Add(concatLayer2);
model2->Add<Linear<> >(10, 5);
model2->Add<LogSoftMax<>>();
// Check whether move constructor is working or not.
CheckMoveFunction<>(model2, input, output, 1);
}
/**
* Check whether copying and moving of Dropout network is working or not.
*/
TEST_CASE("CheckCopyMovingDropoutNetworkTest", "[FeedForwardNetworkTest]")
{
src/mlpack/tests/svd_batch_test.cpp
+4 -2
View File
@@ -70,7 +70,8 @@ class SpecificRandomInitialization
TEST_CASE("SVDBatchMomentumTest", "[SVDBatchTest]")
{
mat dataset;
data::Load("GroupLensSmall.csv", dataset);
if (!data::Load("GroupLensSmall.csv", dataset))
FAIL("Cannot load dataset GroupLensSmall.csv!");
// Generate list of locations for batch insert constructor for sparse
// matrices.
@@ -117,7 +118,8 @@ TEST_CASE("SVDBatchMomentumTest", "[SVDBatchTest]")
TEST_CASE("SVDBatchRegularizationTest", "[SVDBatchTest]")
{
mat dataset;
data::Load("GroupLensSmall.csv", dataset);
if (!data::Load("GroupLensSmall.csv", dataset))
FAIL("Cannot load dataset GroupLensSmall.csv!");
// Generate list of locations for batch insert constructor for sparse
// matrices.