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Number recognition Tuning

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IlyaShurupov 2023-10-25 22:42:20 +03:00 • committed by Ilya Shurupov
parent 4804e51ad7
commit ee8027055e
3 changed files with 24 additions and 12 deletions
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32
DataAnalysis/applications/NumberRecognition.cpp
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@ -48,7 +48,7 @@ bool loadDataset(Dataset& out, const String& location) {
struct NumberRec {
NumberRec() {
Buffer<halni> layers = { 784, 128, 10 };
Buffer<halni> layers = { 784, 10 };
nn.initializeRandom(layers);
Dataset dataset;
@ -159,7 +159,7 @@ public:
FCNN nn;
Buffer<halnf> output;
halnf step = 0.01f;
halnf step = 1.f;
};
int main() {
@ -173,14 +173,26 @@ int main() {
{
NumberRec app;
auto trainRange = Range(0, 100);
auto testRange = Range(0, 100);
auto numBatches = 10;
auto trainRange = Range(0, 50000);
auto testRange = Range(50000, 70000);
halnf cost = 100;
while (cost > 0.1f) {
cost = app.test(trainRange);
app.trainStep(trainRange);
printf("Cost - %f\n", cost);
auto batchSize = trainRange.idxDiff() / numBatches;
for (auto epoch : Range(10)) {
printf("Epoch %i\n", epoch.index());
for (auto batchIdx : Range(trainRange.idxDiff() / batchSize)) {
printf(" - Batch :%i \n", batchIdx.index());
auto batchRange = Range(trainRange.idxBegin() + batchSize * batchIdx, trainRange.idxBegin() + batchSize * (batchIdx + 1));
app.trainStep(batchRange);
printf("Cost on batch data : %f\n", app.test(batchRange));
}
printf("Cost on test data : %f\n\n", app.test(testRange));
}
auto errors = 0;
@ -192,7 +204,7 @@ int main() {
// app.displayImage(i);
}
printf("\n\nIncorrect - %i out of %i\n\n", errors, testRange.idxDiff());
printf("\n\nIncorrect - %i out of %i (%f)\n\n", errors, testRange.idxDiff(), (halnf) errors / (halnf) testRange.idxDiff() );
}
module.deinitialize();

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DataAnalysis/applications/rsc/mnist.gz
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4
DataAnalysis/private/FullyConnectedNN.cpp
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@ -22,9 +22,9 @@ static halnf relu(halnf val) { return val < 0 ? 0 : val; }
static halnf reluDerivative(halnf val) { return val < 0 ? 0 : 1; }
static halnf activationFunction(halnf val) { return relu(val); }
static halnf activationFunction(halnf val) { return sigmoid(val); }
static halnf activationFunctionDerivative(halnf val) { return reluDerivative(val); }
static halnf activationFunctionDerivative(halnf val) { return sigmoidDerivative(val); }
FCNN::FCNN(const Buffer<halni>& description) { initializeRandom(description); }