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Merge pull request #242 from htm-community/mnist-example
MNIST example
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| ################# | ||
| # mnist data | ||
| include(mnist_data.cmake) | ||
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| # ----------------------------------------------------------------------------- | ||
| # Numenta Platform for Intelligent Computing (NuPIC) | ||
| # Copyright (C) 2016, Numenta, Inc. Unless you have purchased from | ||
| # Numenta, Inc. a separate commercial license for this software code, the | ||
| # following terms and conditions apply: | ||
| # | ||
| # This program is free software: you can redistribute it and/or modify | ||
| # it under the terms of the GNU Affero Public License version 3 as | ||
| # published by the Free Software Foundation. | ||
| # | ||
| # This program is distributed in the hope that it will be useful, | ||
| # but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| # MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. | ||
| # See the GNU Affero Public License for more details. | ||
| # | ||
| # You should have received a copy of the GNU Affero Public License | ||
| # along with this program. If not, see http://www.gnu.org/licenses. | ||
| # | ||
| # http://numenta.org/licenses/ | ||
| # ----------------------------------------------------------------------------- | ||
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| # Fetch MNIST dataset from online archive | ||
| # | ||
| if(EXISTS ${REPOSITORY_DIR}/build/ThirdParty/share/mnist.zip) | ||
| set(URL ${REPOSITORY_DIR}/build/ThirdParty/share/mnist.zip) | ||
| else() | ||
| set(URL "https://github.com/wichtounet/mnist/archive/master.zip") | ||
| set(HASH "855cb8c60f84e2fc6bea08c4a9df9a3cbd6230bddc55def635a938665c512ffc") | ||
| endif() | ||
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| message(STATUS "obtaining MNIST data") | ||
| include(DownloadProject/DownloadProject.cmake) | ||
| download_project(PROJ mnist | ||
| PREFIX ${EP_BASE}/mnist_data | ||
| URL ${URL} | ||
| URL_HASH SHA256=${HASH} | ||
| UPDATE_DISCONNECTED 1 | ||
| # QUIET | ||
| ) | ||
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| # No build. This is a data only package | ||
| # But we do need to run its CMakeLists.txt to unpack the files. | ||
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| add_subdirectory(${mnist_SOURCE_DIR}/example/ ${mnist_BINARY_DIR}) | ||
| FILE(APPEND "${EXPORT_FILE_NAME}" "mnist_INCLUDE_DIRS@@@${mnist_SOURCE_DIR}/include\n") | ||
| FILE(APPEND "${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> | ||
| # data will be found in folder ${mnist_SOURCE_DIR} |
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| /* --------------------------------------------------------------------- | ||
| * Copyright (C) 2018, David McDougall. | ||
| * | ||
| * This program is free software: you can redistribute it and/or modify | ||
| * it under the terms of the GNU Affero Public License version 3 as | ||
| * published by the Free Software Foundation. | ||
| * | ||
| * This program is distributed in the hope that it will be useful, | ||
| * but WITHOUT ANY WARRANTY; without even the implied warranty of | ||
| * MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. | ||
| * See the GNU Affero Public License for more details. | ||
| * | ||
| * You should have received a copy of the GNU Affero Public License | ||
| * along with this program. If not, see http://www.gnu.org/licenses. | ||
| * ---------------------------------------------------------------------- | ||
| */ | ||
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| /** | ||
| * Solving the MNIST dataset with Spatial Pooler. | ||
| * | ||
| * This consists of a simple black & white image encoder, a spatial pool, and an | ||
| * SDR classifier. The task is to recognise images of hand written numbers 0-9. | ||
| * This should score at least 95%. | ||
| */ | ||
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| #include <algorithm> | ||
| #include <cstdint> //uint8_t | ||
| #include <iostream> | ||
| #include <vector> | ||
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| #include <nupic/algorithms/SpatialPooler.hpp> | ||
| #include <nupic/algorithms/SDRClassifier.hpp> | ||
| #include <nupic/algorithms/ClassifierResult.hpp> | ||
| #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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| namespace examples { | ||
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| using namespace std; | ||
| using namespace nupic; | ||
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| using nupic::algorithms::spatial_pooler::SpatialPooler; | ||
| using nupic::algorithms::sdr_classifier::SDRClassifier; | ||
| using nupic::algorithms::cla_classifier::ClassifierResult; | ||
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| class MNIST { | ||
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| private: | ||
| SpatialPooler sp; | ||
| SDR input; | ||
| SDR columns; | ||
| SDRClassifier clsr; | ||
| mnist::MNIST_dataset<std::vector, std::vector<uint8_t>, uint8_t> dataset; | ||
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| public: | ||
| UInt verbosity = 1; | ||
| const UInt train_dataset_iterations = 1u; | ||
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| void setup() { | ||
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| input.initialize({28, 28}); | ||
| sp.initialize( | ||
| /* inputDimensions */ input.dimensions, | ||
| /* columnDimensions */ {28, 28}, //mostly affects speed, to some threshold accuracy only marginally | ||
| /* potentialRadius */ 5u, | ||
| /* potentialPct */ 0.5f, | ||
| /* globalInhibition */ false, | ||
| /* localAreaDensity */ 0.20f, //% active bits, //quite important variable (speed x accuracy) | ||
| /* numActiveColumnsPerInhArea */ -1, | ||
| /* stimulusThreshold */ 6u, | ||
| /* synPermInactiveDec */ 0.005f, | ||
| /* synPermActiveInc */ 0.01f, | ||
| /* synPermConnected */ 0.4f, | ||
| /* minPctOverlapDutyCycles */ 0.001f, | ||
| /* dutyCyclePeriod */ 1402, | ||
| /* boostStrength */ 2.5f, //boosting does help | ||
| /* seed */ 93u, | ||
| /* spVerbosity */ 1u, | ||
| /* wrapAround */ false); //wrap is false for this problem | ||
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| columns.initialize({sp.getNumColumns()}); | ||
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| clsr.initialize( | ||
| /* steps */ {0}, | ||
| /* alpha */ .001, | ||
| /* actValueAlpha */ .3, | ||
| verbosity); | ||
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| dataset = mnist::read_dataset<std::vector, std::vector, uint8_t, uint8_t>(string("../ThirdParty/mnist_data/mnist-src/")); //from CMake | ||
| } | ||
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| void train() { | ||
| // Train | ||
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| if(verbosity) | ||
| cout << "Training for " << (train_dataset_iterations * dataset.training_labels.size()) | ||
| << " cycles ..." << endl; | ||
| size_t i = 0; | ||
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| SDR_Metrics inputStats(input, 1402); | ||
| SDR_Metrics columnStats(columns, 1402); | ||
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| for(auto epoch = 0u; epoch < train_dataset_iterations; epoch++) { | ||
| NTA_INFO << "epoch " << epoch; | ||
| // Shuffle the training data. | ||
| vector<UInt> index( dataset.training_labels.size() ); | ||
| index.assign(dataset.training_labels.cbegin(), dataset.training_labels.cend()); | ||
| Random().shuffle( index.begin(), index.end() ); | ||
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| for(const auto idx : index) { // index = order of label (shuffeled) | ||
| // Get the input & label | ||
| const auto image = dataset.training_images.at(idx); | ||
| const UInt label = dataset.training_labels.at(idx); | ||
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| // Compute & Train | ||
| input.setDense( image ); | ||
| sp.compute(input, true, columns); | ||
| ClassifierResult result; | ||
| clsr.compute(sp.getIterationNum(), columns.getFlatSparse(), | ||
| /* bucketIdxList */ {label}, | ||
| /* actValueList */ {(Real)label}, | ||
| /* category */ true, | ||
| /* learn */ true, | ||
| /* infer */ false, | ||
| &result); | ||
| if( verbosity && (++i % 1000 == 0) ) cout << "." << flush; | ||
| } | ||
| if( verbosity ) cout << endl; | ||
| } | ||
| cout << "epoch ended" << endl; | ||
| cout << inputStats << endl; | ||
| cout << columnStats << endl; | ||
| } | ||
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| void test() { | ||
| // Test | ||
| Real score = 0; | ||
| UInt n_samples = 0; | ||
| if(verbosity) | ||
| cout << "Testing for " << dataset.test_labels.size() << " cycles ..." << endl; | ||
| for(UInt i = 0; i < dataset.test_labels.size(); i++) { | ||
| // Get the input & label | ||
| const auto image = dataset.test_images.at(i); | ||
| const UInt label = dataset.test_labels.at(i); | ||
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| // Compute | ||
| input.setDense( image ); | ||
| sp.compute(input, false, columns); | ||
| sp.stripUnlearnedColumns(columns); | ||
| ClassifierResult result; | ||
| clsr.compute(sp.getIterationNum(), columns.getFlatSparse(), | ||
| /* bucketIdxList */ {}, | ||
| /* actValueList */ {}, | ||
| /* category */ true, | ||
| /* learn */ false, | ||
| /* infer */ true, | ||
| &result); | ||
| // Check results | ||
| for(auto iter : result) { | ||
| if( iter.first == 0 ) { | ||
| const auto *pdf = iter.second; | ||
| const auto max = std::max_element(pdf->cbegin(), pdf->cend()); | ||
| const UInt cls = max - pdf->cbegin(); | ||
| if(cls == label) | ||
| score += 1; | ||
| n_samples += 1; | ||
| } | ||
| } | ||
| if( verbosity && i % 1000 == 0 ) cout << "." << flush; | ||
| } | ||
| if( verbosity ) cout << endl; | ||
| cout << "Score: " << 100.0 * score / n_samples << "% " << endl; | ||
| } | ||
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| }; // End class MNIST | ||
| } // End namespace examples | ||
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| int main(int argc, char **argv) { | ||
| examples::MNIST m; | ||
| m.setup(); | ||
| m.train(); | ||
| m.test(); | ||
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| return 0; | ||
| } | ||
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