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synced 2024-09-11 06:15:56 +03:00
renamed num of data to BATCH_SIZE
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@ -12,13 +12,13 @@ int main(int argc, char** argv) {
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const size_t IMAGE_SIZE = 784;
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const size_t LABEL_SIZE = 10;
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int BATCH_SIZE = 10000;
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std::cerr << "Loading test set...";
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std::vector<float> testImages = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", numofdata, IMAGE_SIZE);
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std::vector<float> testLabels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", numofdata, LABEL_SIZE);
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std::vector<float> testImages = datasets::mnist::ReadImages("../examples/mnist/t10k-images-idx3-ubyte", BATCH_SIZE, IMAGE_SIZE);
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std::vector<float> testLabels = datasets::mnist::ReadLabels("../examples/mnist/t10k-labels-idx1-ubyte", BATCH_SIZE, LABEL_SIZE);
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std::cerr << "Done." << std::endl;
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const size_t BATCH_SIZE = testLabels.size();
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std::cerr << "Loading model params...";
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NpzConverter converter("../scripts/test_model/model.npz");
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@ -43,19 +43,18 @@ int main(int argc, char** argv) {
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axis=1, name="pred");
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auto graph = -mean(sum(y * log(predict), axis=1),
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axis=0, name="cost");
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std::cerr << "Done." << std::endl;
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Tensor xt({numofdata, IMAGE_SIZE});
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Tensor yt({numofdata, LABEL_SIZE});
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Tensor xt({BATCH_SIZE, IMAGE_SIZE});
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Tensor yt({BATCH_SIZE, LABEL_SIZE});
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x = xt << testImages;
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y = yt << testLabels;
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graph.forward(BATCH_SIZE);
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auto results = predict.val();
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graph.backward();
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auto results = predict.val();
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std::vector<float> resultsv(results.size());
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resultsv << results;
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