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Merge pull request #242 from htm-community/mnist-example
MNIST example
2 parents 9123f7c + d26299a commit b39ffa9

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13 files changed

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‎external/CMakeLists.txt

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@@ -102,4 +102,6 @@ if(match)
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endif()
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#################
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# mnist data
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include(mnist_data.cmake)

‎external/bootstrap.cmake

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@@ -97,6 +97,7 @@ set(EXTERNAL_INCLUDES
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${yaml-cpp_INCLUDE_DIRS}
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${Boost_INCLUDE_DIRS}
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${eigen_INCLUDE_DIRS}
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${mnist_INCLUDE_DIRS}
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${REPOSITORY_DIR}/external/common/include
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)
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‎external/mnist_data.cmake

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# -----------------------------------------------------------------------------
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# Numenta Platform for Intelligent Computing (NuPIC)
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# Copyright (C) 2016, Numenta, Inc. Unless you have purchased from
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# Numenta, Inc. a separate commercial license for this software code, the
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# following terms and conditions apply:
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#
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# This program is free software: you can redistribute it and/or modify
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# it under the terms of the GNU Affero Public License version 3 as
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# published by the Free Software Foundation.
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
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# See the GNU Affero Public License for more details.
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#
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# You should have received a copy of the GNU Affero Public License
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# along with this program. If not, see http://www.gnu.org/licenses.
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#
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# http://numenta.org/licenses/
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# -----------------------------------------------------------------------------
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# Fetch MNIST dataset from online archive
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#
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if(EXISTS ${REPOSITORY_DIR}/build/ThirdParty/share/mnist.zip)
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set(URL ${REPOSITORY_DIR}/build/ThirdParty/share/mnist.zip)
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else()
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set(URL "https://github.com/wichtounet/mnist/archive/master.zip")
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set(HASH "855cb8c60f84e2fc6bea08c4a9df9a3cbd6230bddc55def635a938665c512ffc")
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endif()
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message(STATUS "obtaining MNIST data")
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include(DownloadProject/DownloadProject.cmake)
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download_project(PROJ mnist
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PREFIX ${EP_BASE}/mnist_data
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URL ${URL}
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URL_HASH SHA256=${HASH}
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UPDATE_DISCONNECTED 1
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# QUIET
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)
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# No build. This is a data only package
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# But we do need to run its CMakeLists.txt to unpack the files.
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add_subdirectory(${mnist_SOURCE_DIR}/example/ ${mnist_BINARY_DIR})
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FILE(APPEND "${EXPORT_FILE_NAME}" "mnist_INCLUDE_DIRS@@@${mnist_SOURCE_DIR}/include\n")
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FILE(APPEND "${EXPORT_FILE_NAME}" "mnist_SOURCE_DIR@@@${mnist_SOURCE_DIR}\n")
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# includes will be found with #include <mnist/mnist_reader_less.hpp>
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# data will be found in folder ${mnist_SOURCE_DIR}

‎src/CMakeLists.txt

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Original file line numberDiff line numberDiff line change
@@ -223,9 +223,10 @@ set(utils_files
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)
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set(examples_files
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examples/hotgym/Hotgym.cpp
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examples/hotgym/Hotgym.cpp # contains conflicting main()
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examples/hotgym/HelloSPTP.cpp
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examples/hotgym/HelloSPTP.hpp
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examples/mnist/MNIST_SP.cpp
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)
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#set up file tabs in Visual Studio
@@ -261,7 +262,6 @@ add_library(${src_objlib} OBJECT
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${regions_files}
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${types_files}
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${utils_files}
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${examples_files}
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)
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# shared libraries need PIC
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target_compile_options( ${src_objlib} PUBLIC ${INTERNAL_CXX_FLAGS})
@@ -348,13 +348,8 @@ add_subdirectory(test)
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#
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## Setup benchmark_hotgym
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#
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source_group("examples" FILES
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examples/hotgym/Hotgym.cpp
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examples/hotgym/HelloSPTP.hpp
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)
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set(src_executable_hotgym benchmark_hotgym)
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add_executable(${src_executable_hotgym} examples/hotgym/Hotgym.cpp examples/hotgym/HelloSPTP.hpp)
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add_executable(${src_executable_hotgym} examples/hotgym/Hotgym.cpp examples/hotgym/HelloSPTP.hpp examples/hotgym/HelloSPTP.cpp)
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if(MSVC)
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# for Windows, link with the static library
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target_link_libraries(${src_executable_hotgym}
@@ -381,21 +376,46 @@ add_custom_target(hotgym
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VERBATIM)
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#########################################################
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## MNIST Spatial Pooler Example
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#
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set(src_executable_mnistsp mnist_sp)
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add_executable(${src_executable_mnistsp} examples/mnist/MNIST_SP.cpp)
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target_link_libraries(${src_executable_mnistsp}
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${core_library}
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${COMMON_OS_LIBS}
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)
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target_compile_options(${src_executable_mnistsp} PUBLIC ${INTERNAL_CXX_FLAGS})
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target_compile_definitions(${src_executable_mnistsp} PRIVATE ${COMMON_COMPILER_DEFINITIONS})
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# Pass MNIST data directory to main.cpp
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target_compile_definitions(${src_executable_mnistsp} PRIVATE MNIST_DATA_LOCATION=${mnist_SOURCE_DIR})
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target_include_directories(${src_executable_mnistsp} PRIVATE
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${CORE_LIB_INCLUDES}
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${EXTERNAL_INCLUDES}
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)
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add_custom_target(mnist
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COMMAND ${src_executable_mnistsp}
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DEPENDS ${src_executable_mnistsp}
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COMMENT "Executing ${src_executable_mnistsp}"
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VERBATIM)
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############ INSTALL ######################################
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#
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# Install targets into CMAKE_INSTALL_PREFIX
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#
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install(TARGETS
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${core_library}
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${src_executable_hotgym}
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RUNTIME DESTINATION bin
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${src_executable_mnistsp}
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RUNTIME DESTINATION bin
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LIBRARY DESTINATION lib
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ARCHIVE DESTINATION lib)
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if(MSVC)
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else()
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install(TARGETS
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${src_lib_shared}
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${src_lib_shared}
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RUNTIME DESTINATION bin
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LIBRARY DESTINATION lib
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ARCHIVE DESTINATION lib)
@@ -415,7 +435,6 @@ install(DIRECTORY "${REPOSITORY_DIR}/external/common/include/"
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MESSAGE_NEVER
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DESTINATION include)
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#
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# `make package` results in

‎src/examples/hotgym/HelloSPTP.cpp

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@@ -56,6 +56,7 @@ using TM = nupic::algorithms::temporal_memory::TemporalMemory;
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using nupic::algorithms::anomaly::Anomaly;
5757
using nupic::algorithms::anomaly::AnomalyMode;
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// work-load
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Real64 BenchmarkHotgym::run(UInt EPOCHS, bool useSPlocal, bool useSPglobal, bool useTP, bool useBackTM, bool useTM, const UInt COLS, const UInt DIM_INPUT, const UInt CELLS) {
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#ifndef NDEBUG

‎src/examples/mnist/MNIST_SP.cpp

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/* ---------------------------------------------------------------------
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* Copyright (C) 2018, David McDougall.
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*
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* This program is free software: you can redistribute it and/or modify
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* it under the terms of the GNU Affero Public License version 3 as
6+
* published by the Free Software Foundation.
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*
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* This program is distributed in the hope that it will be useful,
9+
* but WITHOUT ANY WARRANTY; without even the implied warranty of
10+
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
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* See the GNU Affero Public License for more details.
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*
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* You should have received a copy of the GNU Affero Public License
14+
* along with this program. If not, see http://www.gnu.org/licenses.
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* ----------------------------------------------------------------------
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*/
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/**
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* Solving the MNIST dataset with Spatial Pooler.
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*
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* This consists of a simple black & white image encoder, a spatial pool, and an
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* SDR classifier. The task is to recognise images of hand written numbers 0-9.
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* This should score at least 95%.
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*/
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#include <algorithm>
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#include <cstdint> //uint8_t
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#include <iostream>
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#include <vector>
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#include <nupic/algorithms/SpatialPooler.hpp>
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#include <nupic/algorithms/SDRClassifier.hpp>
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#include <nupic/algorithms/ClassifierResult.hpp>
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#include <nupic/utils/SdrMetrics.hpp>
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#include <mnist/mnist_reader.hpp> // MNIST data itself + read methods, namespace mnist::
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38+
namespace examples {
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40+
using namespace std;
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using namespace nupic;
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43+
using nupic::algorithms::spatial_pooler::SpatialPooler;
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using nupic::algorithms::sdr_classifier::SDRClassifier;
45+
using nupic::algorithms::cla_classifier::ClassifierResult;
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47+
class MNIST {
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private:
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SpatialPooler sp;
51+
SDR input;
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SDR columns;
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SDRClassifier clsr;
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mnist::MNIST_dataset<std::vector, std::vector<uint8_t>, uint8_t> dataset;
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public:
57+
UInt verbosity = 1;
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const UInt train_dataset_iterations = 1u;
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60+
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void setup() {
62+
63+
input.initialize({28, 28});
64+
sp.initialize(
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/* inputDimensions */ input.dimensions,
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/* columnDimensions */ {28, 28}, //mostly affects speed, to some threshold accuracy only marginally
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/* potentialRadius */ 5u,
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/* potentialPct */ 0.5f,
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/* globalInhibition */ false,
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/* localAreaDensity */ 0.20f, //% active bits, //quite important variable (speed x accuracy)
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/* numActiveColumnsPerInhArea */ -1,
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/* stimulusThreshold */ 6u,
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/* synPermInactiveDec */ 0.005f,
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/* synPermActiveInc */ 0.01f,
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/* synPermConnected */ 0.4f,
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/* minPctOverlapDutyCycles */ 0.001f,
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/* dutyCyclePeriod */ 1402,
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/* boostStrength */ 2.5f, //boosting does help
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/* seed */ 93u,
80+
/* spVerbosity */ 1u,
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/* wrapAround */ false); //wrap is false for this problem
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83+
columns.initialize({sp.getNumColumns()});
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85+
clsr.initialize(
86+
/* steps */ {0},
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/* alpha */ .001,
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/* actValueAlpha */ .3,
89+
verbosity);
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91+
dataset = mnist::read_dataset<std::vector, std::vector, uint8_t, uint8_t>(string("../ThirdParty/mnist_data/mnist-src/")); //from CMake
92+
}
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void train() {
95+
// Train
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97+
if(verbosity)
98+
cout << "Training for " << (train_dataset_iterations * dataset.training_labels.size())
99+
<< " cycles ..." << endl;
100+
size_t i = 0;
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102+
SDR_Metrics inputStats(input, 1402);
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SDR_Metrics columnStats(columns, 1402);
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105+
for(auto epoch = 0u; epoch < train_dataset_iterations; epoch++) {
106+
NTA_INFO << "epoch " << epoch;
107+
// Shuffle the training data.
108+
vector<UInt> index( dataset.training_labels.size() );
109+
index.assign(dataset.training_labels.cbegin(), dataset.training_labels.cend());
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Random().shuffle( index.begin(), index.end() );
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for(const auto idx : index) { // index = order of label (shuffeled)
113+
// Get the input & label
114+
const auto image = dataset.training_images.at(idx);
115+
const UInt label = dataset.training_labels.at(idx);
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117+
// Compute & Train
118+
input.setDense( image );
119+
sp.compute(input, true, columns);
120+
ClassifierResult result;
121+
clsr.compute(sp.getIterationNum(), columns.getFlatSparse(),
122+
/* bucketIdxList */ {label},
123+
/* actValueList */ {(Real)label},
124+
/* category */ true,
125+
/* learn */ true,
126+
/* infer */ false,
127+
&result);
128+
if( verbosity && (++i % 1000 == 0) ) cout << "." << flush;
129+
}
130+
if( verbosity ) cout << endl;
131+
}
132+
cout << "epoch ended" << endl;
133+
cout << inputStats << endl;
134+
cout << columnStats << endl;
135+
}
136+
137+
void test() {
138+
// Test
139+
Real score = 0;
140+
UInt n_samples = 0;
141+
if(verbosity)
142+
cout << "Testing for " << dataset.test_labels.size() << " cycles ..." << endl;
143+
for(UInt i = 0; i < dataset.test_labels.size(); i++) {
144+
// Get the input & label
145+
const auto image = dataset.test_images.at(i);
146+
const UInt label = dataset.test_labels.at(i);
147+
148+
// Compute
149+
input.setDense( image );
150+
sp.compute(input, false, columns);
151+
sp.stripUnlearnedColumns(columns);
152+
ClassifierResult result;
153+
clsr.compute(sp.getIterationNum(), columns.getFlatSparse(),
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/* bucketIdxList */ {},
155+
/* actValueList */ {},
156+
/* category */ true,
157+
/* learn */ false,
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/* infer */ true,
159+
&result);
160+
// Check results
161+
for(auto iter : result) {
162+
if( iter.first == 0 ) {
163+
const auto *pdf = iter.second;
164+
const auto max = std::max_element(pdf->cbegin(), pdf->cend());
165+
const UInt cls = max - pdf->cbegin();
166+
if(cls == label)
167+
score += 1;
168+
n_samples += 1;
169+
}
170+
}
171+
if( verbosity && i % 1000 == 0 ) cout << "." << flush;
172+
}
173+
if( verbosity ) cout << endl;
174+
cout << "Score: " << 100.0 * score / n_samples << "% " << endl;
175+
}
176+
177+
}; // End class MNIST
178+
} // End namespace examples
179+
180+
int main(int argc, char **argv) {
181+
examples::MNIST m;
182+
m.setup();
183+
m.train();
184+
m.test();
185+
186+
return 0;
187+
}
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‎src/nupic/algorithms/Connections.cpp

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@@ -472,6 +472,7 @@ void Connections::raisePermanencesToThreshold(
472472
if( segmentThreshold == 0 ) //no synapses requested to be connected, done.
473473
return;
474474

475+
NTA_ASSERT(segment < segments_.size()) << "Accessing segment out of bounds.";
475476
auto &segData = segments_[segment];
476477
if( segData.numConnected >= segmentThreshold ) //the segment already satisfies the requirement, done.
477478
return;
@@ -496,7 +497,7 @@ void Connections::raisePermanencesToThreshold(
496497
// permance by such that it becomes a connected synapse.
497498
// After that there will be at least N synapses connected.
498499

499-
auto minPermSynPtr = synapses.begin() + threshold - 1;
500+
auto minPermSynPtr = synapses.begin() + threshold - 1; //threshold is ensured to be >=1 by condition at very beginning if(thresh == 0)...
500501
// Do a partial sort, it's faster than a full sort. Only minPermSynPtr is in
501502
// its final sorted position.
502503
const auto permanencesGreater = [&](const Synapse &A, const Synapse &B)
@@ -515,8 +516,9 @@ void Connections::raisePermanencesToThreshold(
515516

516517
void Connections::bumpSegment(const Segment segment, const Permanence delta) {
517518
const vector<Synapse> &synapses = synapsesForSegment(segment);
518-
for( const auto &syn : synapses )
519+
for( const auto &syn : synapses ) {
519520
updateSynapsePermanence(syn, synapses_[syn].permanence + delta);
521+
}
520522
}
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