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Fix paths to test data #236

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2 changes: 1 addition & 1 deletion cpp/neural_networks/mnist_batch_norm/mnist_batch_norm.cpp
Original file line number Diff line number Diff line change
Expand Up @@ -171,7 +171,7 @@ int main()
// The original file could be download from
// https://www.kaggle.com/c/digit-recognizer/data

data::Load("../data/mnist_test.csv", dataset, true);
data::Load("../../../data/mnist_test.csv", dataset, true);
mat testY = dataset.row(0);
dataset.shed_row(0); // Remove labels.

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2 changes: 1 addition & 1 deletion cpp/neural_networks/mnist_cnn_f32/mnist_cnn_f32.cpp
Original file line number Diff line number Diff line change
Expand Up @@ -213,7 +213,7 @@ int main()
// Get predictions on test data points.
// The original file could be download from
// https://www.kaggle.com/c/digit-recognizer/data
data::Load("../data/mnist_test.csv", dataset, true);
data::Load("../../../data/mnist_test.csv", dataset, true);
const arma::fmat testX = dataset.submat(1, 0, dataset.n_rows - 1, dataset.n_cols - 1)
/ 256.0;
const arma::fmat testY = dataset.row(0);
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2 changes: 1 addition & 1 deletion cpp/neural_networks/mnist_simple/mnist_simple.cpp
Original file line number Diff line number Diff line change
Expand Up @@ -161,7 +161,7 @@ int main()

// Loading test dataset (the one whose predicted labels
// should be sent to kaggle website).
data::Load("../data/mnist_test.csv", dataset, true);
data::Load("../../../data/mnist_test.csv", dataset, true);
arma::mat testY = dataset.row(0);
dataset.shed_row(0); // Strip labels before predicting.
dataset /= 255.0; // Apply the same normalization as to the training data.
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2 changes: 1 addition & 1 deletion cpp/neural_networks/mnist_simple_f32/mnist_simple_f32.cpp
Original file line number Diff line number Diff line change
Expand Up @@ -166,7 +166,7 @@ int main()

// Loading test dataset (the one whose predicted labels
// should be sent to kaggle website).
data::Load("../data/mnist_test.csv", dataset, true);
data::Load("../../../data/mnist_test.csv", dataset, true);
arma::fmat testY = dataset.row(dataset.n_rows - 1);
dataset.shed_row(dataset.n_rows - 1); // Strip labels before predicting.

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