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fix: typo in neural_network.cpp #2689

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Merged
merged 3 commits into from
Aug 31, 2024
Merged

fix: typo in neural_network.cpp #2689

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a5a4de4
refactor: fix typo in neural_network.cpp
eltociear Apr 15, 2024
a2eaf6e
Merge branch 'master' into patch-2
realstealthninja Aug 31, 2024
1a8a3c9
Merge branch 'master' into patch-2
realstealthninja Aug 31, 2024
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8 changes: 4 additions & 4 deletions machine_learning/neural_network.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -136,7 +136,7 @@ class DenseLayer {
* @param neurons number of neurons
* @param activation activation function for layer
* @param kernel_shape shape of kernel
* @param random_kernel flag for whether to intialize kernel randomly
* @param random_kernel flag for whether to initialize kernel randomly
*/
DenseLayer(const int &neurons, const std::string &activation,
const std::pair<size_t, size_t> &kernel_shape,
Expand Down Expand Up @@ -502,7 +502,7 @@ class NeuralNetwork {
auto start =
std::chrono::high_resolution_clock::now(); // Start clock
double loss = 0,
acc = 0; // Intialize performance metrics with zero
acc = 0; // Initialize performance metrics with zero
// For each starting index of batch
for (size_t batch_start = 0; batch_start < X.size();
batch_start += batch_size) {
Expand All @@ -515,7 +515,7 @@ class NeuralNetwork {
// They will be averaged and applied to kernel
std::vector<std::vector<std::valarray<double>>> gradients;
gradients.resize(this->layers.size());
// First intialize gradients to zero
// First initialize gradients to zero
for (size_t i = 0; i < gradients.size(); i++) {
zeroes_initialization(
gradients[i], get_shape(this->layers[i].kernel));
Expand Down Expand Up @@ -606,7 +606,7 @@ class NeuralNetwork {
void evaluate(const std::vector<std::vector<std::valarray<double>>> &X,
const std::vector<std::vector<std::valarray<double>>> &Y) {
std::cout << "INFO: Evaluation Started" << std::endl;
double acc = 0, loss = 0; // intialize performance metrics with zero
double acc = 0, loss = 0; // initialize performance metrics with zero
for (size_t i = 0; i < X.size(); i++) { // For every sample in input
// Get predictions
std::vector<std::valarray<double>> pred =
Expand Down
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