NumRec Fixes
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IlyaShurupov
Ilya Shurupov
parent
90ce453b60
commit
4804e51ad7
5 changed files with 181 additions and 148 deletions
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@ -1,4 +1,4 @@
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#include "FullyConnectedNN.hpp"
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#include "FCNN.hpp"
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#include "LocalConnection.hpp"
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#include "NewPlacement.hpp"
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@ -11,19 +11,6 @@ struct Dataset {
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Buffer<Buffer<uint1>> images;
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};
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void displayImage(const Dataset& dataset, ualni idx) {
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auto& image = dataset.images[idx];
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auto label = dataset.labels[idx];
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printf("Image : %i\n", int(label));
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for (auto i : Range(dataset.imageSize.x)) {
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for (auto j : Range(dataset.imageSize.y)) {
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printf("%c", image[j * dataset.imageSize.x + i]);
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}
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printf("\n");
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}
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}
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bool loadDataset(Dataset& out, const String& location) {
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LocalConnection dataset;
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dataset.connect(LocalConnection::Location(location), LocalConnection::Type(true));
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@ -59,114 +46,121 @@ bool loadDataset(Dataset& out, const String& location) {
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return true;
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}
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halnf test(const Dataset& dataset, FullyConnectedNN& nn, Range<ualni> range) {
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ualni numFailed = 0;
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struct NumberRec {
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NumberRec() {
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Buffer<halni> layers = { 784, 128, 10 };
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nn.initializeRandom(layers);
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for (auto i : range) {
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auto& image = dataset.images[i];
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auto label = dataset.labels[i];
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Dataset dataset;
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Buffer<halnf> results;
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Buffer<halnf> input;
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results.reserve(10);
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input.reserve(image.size());
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for (auto pixelIdx : Range(image.size())) {
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input[pixelIdx] = (halnf) image[pixelIdx] / 255.f;
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if (!loadDataset(dataset, "rsc/mnist")) {
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printf("Cant Load Mnist Dataset\n");
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return;
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}
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nn.evaluate(input, results);
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mTestcases.reserve(dataset.images.size());
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for (auto i : Range(dataset.images.size())) {
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auto& image = dataset.images[i];
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auto label = dataset.labels[i];
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ualni resultNumber = 0;
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for (auto resIdx : Range(results.size())) {
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if (results[resIdx] > results[resultNumber]) {
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resultNumber = resIdx;
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auto& testcase = mTestcases[i];
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testcase.output.reserve(10);
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for (auto dig : Range(10)) {
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testcase.output[dig] = label == dig ? 1 : 0;
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}
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testcase.input.reserve(image.size());
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for (auto pxl : Range(image.size())) {
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testcase.input[pxl] = (halnf) image[pxl] / 255.f;
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}
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}
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if (resultNumber != label) {
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numFailed++;
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output.reserve(10);
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}
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halnf eval(ualni idx) {
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nn.evaluate(mTestcases[idx].input, output);
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return nn.calcCost(mTestcases[idx].output);
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}
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void applyGrad(ualni idx) {
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nn.calcGrad(mTestcases[idx].output);
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nn.applyGrad(step);
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}
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static halni getMaxIdx(const Buffer<halnf>& in) {
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halni out = 0;
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for (auto i : Range(in.size())) {
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if (in[i] > in[out]) {
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out = i;
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}
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}
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return out;
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}
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bool testIncorrect(ualni idx) {
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nn.evaluate(mTestcases[idx].input, output);
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return getMaxIdx(mTestcases[idx].output) != getMaxIdx(output);
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}
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void debLog(halni idx) {
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printf("\n Got %i - ", getMaxIdx(output));
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for (auto val : output) {
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printf("%f ", val.data());
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}
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printf("\n Expected %i - ", getMaxIdx(mTestcases[idx].output));
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for (auto val : mTestcases[idx].output) {
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printf("%f ", val.data());
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}
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printf("\n\n");
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}
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void displayImage(ualni idx) {
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auto& testcase = mTestcases[idx];
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printf("Image : %i\n", int(getMaxIdx(testcase.output)));
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for (auto i : Range(28)) {
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for (auto j : Range(28)) {
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printf("%c", char(testcase.input[j * 28 + i] * 255));
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}
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printf("\n");
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}
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}
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return (halnf) numFailed / (halnf) range.idxDiff();
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}
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halnf test(const Range<halni>& range) {
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halnf avgCost = 0;
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for (auto i : range) {
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avgCost += eval(i);
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}
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avgCost /= (halnf) range.idxDiff();
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return avgCost;
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}
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void testTraining(const Dataset& dataset, FullyConnectedNN& nn) {
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void trainStep(const Range<halni>& range) {
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nn.clearGrad();
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for (auto i : range) {
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nn.evaluate(mTestcases[i].input, output);
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nn.calcGrad(mTestcases[i].output);
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}
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nn.applyGrad(step);
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}
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auto propagate = [&](ualni idx) {
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auto& image = dataset.images[idx];
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auto label = dataset.labels[idx];
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Buffer<halnf> results;
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public:
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struct Image {
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Buffer<halnf> input;
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results.reserve(10);
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input.reserve(image.size());
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for (auto pixelIdx : Range(image.size())) {
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input[pixelIdx] = (halnf) image[pixelIdx] / 255.f;
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}
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nn.evaluate(input, results);
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ualni resultNumber = 0;
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for (auto resIdx : Range(results.size())) {
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if (results[resIdx] > results[resultNumber]) {
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resultNumber = resIdx;
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}
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}
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Buffer<halnf> resultsExpected;
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resultsExpected.reserve(10);
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for (auto resIdx : Range(results.size())) {
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resultsExpected[resIdx] = (resIdx == label) ? 1 : 0;
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}
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nn.calcGrad(resultsExpected);
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return resultNumber;
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Buffer<halnf> output;
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};
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displayImage(dataset, 2);
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public:
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Buffer<Image> mTestcases;
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propagate(0);
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// nn.applyGrad();
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FCNN nn;
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Buffer<halnf> output;
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propagate(0);
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// nn.applyGrad();
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propagate(0);
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// nn.applyGrad();
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auto errorPercentage = test(dataset, nn, { 0, 1 });
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printf("Percentage error Trained on first image: %f\n", errorPercentage);
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}
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void numRec() {
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Dataset dataset;
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FullyConnectedNN nn;
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// settings
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Buffer<halni> layers;
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layers = { 784, 128, 10 };
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halnf trainBatchPercentage = 0.1f;
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halnf testSizePercentage = 0.1f;
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if (!loadDataset(dataset, "rsc/mnist")) {
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printf("Cant Load Mnist Dataset\n");
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return;
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}
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nn.initializeRandom(layers);
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auto errorPercentage = test(dataset, nn, { 0, 100 });
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printf("Percentage error : %f\n", errorPercentage);
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testTraining(dataset, nn);
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}
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halnf step = 0.01f;
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};
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int main() {
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ModuleManifest* deps[] = { &gModuleDataAnalysis, &gModuleConnection, nullptr };
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@ -176,9 +170,32 @@ int main() {
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return 1;
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}
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numRec();
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{
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NumberRec app;
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auto trainRange = Range(0, 100);
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auto testRange = Range(0, 100);
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halnf cost = 100;
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while (cost > 0.1f) {
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cost = app.test(trainRange);
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app.trainStep(trainRange);
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printf("Cost - %f\n", cost);
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}
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auto errors = 0;
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for (auto i : testRange) {
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if (app.testIncorrect(i)) {
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errors++;
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}
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// app.debLog(i);
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// app.displayImage(i);
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}
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printf("\n\nIncorrect - %i out of %i\n\n", errors, testRange.idxDiff());
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}
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module.deinitialize();
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return 0;
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}
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}
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