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Fixed a typo (TheAlgorithms#1237)
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@imdeep2905
imdeep2905 authored Oct 11, 2020
1 parent 04e0acc commit 895ae31
Showing 1 changed file with 37 additions and 37 deletions.
74 changes: 37 additions & 37 deletions machine_learning/neural_network.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -118,7 +118,7 @@ double identity_function(const double &x) { return x; }
namespace layers {
/**
* neural_network::layers::DenseLayer class is used to store all necessary
* information about the layers (i.e. neurons, activation and kernal). This
* information about the layers (i.e. neurons, activation and kernel). This
* class is used by NeuralNetwork class to store layers.
*
*/
Expand All @@ -129,18 +129,18 @@ class DenseLayer {
double (*dactivation_function)(const double &);
int neurons; // To store number of neurons (used in summary)
std::string activation; // To store activation name (used in summary)
std::vector<std::valarray<double>> kernal; // To store kernal (aka weights)
std::vector<std::valarray<double>> kernel; // To store kernel (aka weights)

/**
* Constructor for neural_network::layers::DenseLayer class
* @param neurons number of neurons
* @param activation activation function for layer
* @param kernal_shape shape of kernal
* @param random_kernal flag for whether to intialize kernal randomly
* @param kernel_shape shape of kernel
* @param random_kernel flag for whether to intialize kernel randomly
*/
DenseLayer(const int &neurons, const std::string &activation,
const std::pair<size_t, size_t> &kernal_shape,
const bool &random_kernal) {
const std::pair<size_t, size_t> &kernel_shape,
const bool &random_kernel) {
// Choosing activation (and it's derivative)
if (activation == "sigmoid") {
activation_function = neural_network::activations::sigmoid;
Expand All @@ -167,21 +167,21 @@ class DenseLayer {
}
this->activation = activation; // Setting activation name
this->neurons = neurons; // Setting number of neurons
// Initialize kernal according to flag
if (random_kernal) {
uniform_random_initialization(kernal, kernal_shape, -1.0, 1.0);
// Initialize kernel according to flag
if (random_kernel) {
uniform_random_initialization(kernel, kernel_shape, -1.0, 1.0);
} else {
unit_matrix_initialization(kernal, kernal_shape);
unit_matrix_initialization(kernel, kernel_shape);
}
}
/**
* Constructor for neural_network::layers::DenseLayer class
* @param neurons number of neurons
* @param activation activation function for layer
* @param kernal values of kernal (useful in loading model)
* @param kernel values of kernel (useful in loading model)
*/
DenseLayer(const int &neurons, const std::string &activation,
const std::vector<std::valarray<double>> &kernal) {
const std::vector<std::valarray<double>> &kernel) {
// Choosing activation (and it's derivative)
if (activation == "sigmoid") {
activation_function = neural_network::activations::sigmoid;
Expand All @@ -208,7 +208,7 @@ class DenseLayer {
}
this->activation = activation; // Setting activation name
this->neurons = neurons; // Setting number of neurons
this->kernal = kernal; // Setting supplied kernal values
this->kernel = kernel; // Setting supplied kernel values
}

/**
Expand Down Expand Up @@ -251,11 +251,11 @@ class NeuralNetwork {
* Private Constructor for class NeuralNetwork. This constructor
* is used internally to load model.
* @param config vector containing pair (neurons, activation)
* @param kernals vector containing all pretrained kernals
* @param kernels vector containing all pretrained kernels
*/
NeuralNetwork(
const std::vector<std::pair<int, std::string>> &config,
const std::vector<std::vector<std::valarray<double>>> &kernals) {
const std::vector<std::vector<std::valarray<double>>> &kernels) {
// First layer should not have activation
if (config.begin()->second != "none") {
std::cerr << "ERROR (" << __func__ << ") : ";
Expand All @@ -275,7 +275,7 @@ class NeuralNetwork {
// Reconstructing all pretrained layers
for (size_t i = 0; i < config.size(); i++) {
layers.emplace_back(neural_network::layers::DenseLayer(
config[i].first, config[i].second, kernals[i]));
config[i].first, config[i].second, kernels[i]));
}
std::cout << "INFO: Network constructed successfully" << std::endl;
}
Expand All @@ -291,7 +291,7 @@ class NeuralNetwork {
std::vector<std::valarray<double>> current_pass = X;
details.emplace_back(X);
for (const auto &l : layers) {
current_pass = multiply(current_pass, l.kernal);
current_pass = multiply(current_pass, l.kernel);
current_pass = apply_function(current_pass, l.activation_function);
details.emplace_back(current_pass);
}
Expand Down Expand Up @@ -329,7 +329,7 @@ class NeuralNetwork {
std::exit(EXIT_FAILURE);
}
// Separately creating first layer so it can have unit matrix
// as kernal.
// as kernel.
layers.push_back(neural_network::layers::DenseLayer(
config[0].first, config[0].second,
{config[0].first, config[0].first}, false));
Expand Down Expand Up @@ -512,13 +512,13 @@ class NeuralNetwork {
predicted;
auto activations = this->__detailed_single_prediction(X[i]);
// Gradients vector to store gradients for all layers
// They will be averaged and applied to kernal
// 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
for (size_t i = 0; i < gradients.size(); i++) {
zeroes_initialization(
gradients[i], get_shape(this->layers[i].kernal));
gradients[i], get_shape(this->layers[i].kernel));
}
predicted = activations.back(); // Predicted vector
cur_error = predicted - Y[i]; // Absoulute error
Expand All @@ -539,16 +539,16 @@ class NeuralNetwork {
this->layers[j].dactivation_function));
// Calculating gradient for current layer
grad = multiply(transpose(activations[j]), cur_error);
// Change error according to current kernal values
// Change error according to current kernel values
cur_error = multiply(cur_error,
transpose(this->layers[j].kernal));
transpose(this->layers[j].kernel));
// Adding gradient values to collection of gradients
gradients[j] = gradients[j] + grad / double(batch_size);
}
// Applying gradients
for (size_t j = this->layers.size() - 1; j >= 1; j--) {
// Updating kernal (aka weights)
this->layers[j].kernal = this->layers[j].kernal -
// Updating kernel (aka weights)
this->layers[j].kernel = this->layers[j].kernel -
gradients[j] * learning_rate;
}
}
Expand Down Expand Up @@ -670,14 +670,14 @@ class NeuralNetwork {
total_layers
neurons(1st neural_network::layers::DenseLayer) activation_name(1st
neural_network::layers::DenseLayer) kernal_shape(1st
neural_network::layers::DenseLayer) kernal_values
neural_network::layers::DenseLayer) kernel_shape(1st
neural_network::layers::DenseLayer) kernel_values
.
.
.
neurons(Nth neural_network::layers::DenseLayer) activation_name(Nth
neural_network::layers::DenseLayer) kernal_shape(Nth
neural_network::layers::DenseLayer) kernal_value
neural_network::layers::DenseLayer) kernel_shape(Nth
neural_network::layers::DenseLayer) kernel_value
For Example, pretrained model with 3 layers:
<pre>
Expand Down Expand Up @@ -709,9 +709,9 @@ class NeuralNetwork {
out_file << std::endl;
for (const auto &layer : this->layers) {
out_file << layer.neurons << ' ' << layer.activation << std::endl;
const auto shape = get_shape(layer.kernal);
const auto shape = get_shape(layer.kernel);
out_file << shape.first << ' ' << shape.second << std::endl;
for (const auto &row : layer.kernal) {
for (const auto &row : layer.kernel) {
for (const auto &val : row) {
out_file << val << ' ';
}
Expand Down Expand Up @@ -740,31 +740,31 @@ class NeuralNetwork {
}
std::vector<std::pair<int, std::string>> config; // To store config
std::vector<std::vector<std::valarray<double>>>
kernals; // To store pretrained kernals
kernels; // To store pretrained kernels
// Loading model from saved file format
size_t total_layers = 0;
in_file >> total_layers;
for (size_t i = 0; i < total_layers; i++) {
int neurons = 0;
std::string activation;
size_t shape_a = 0, shape_b = 0;
std::vector<std::valarray<double>> kernal;
std::vector<std::valarray<double>> kernel;
in_file >> neurons >> activation >> shape_a >> shape_b;
for (size_t r = 0; r < shape_a; r++) {
std::valarray<double> row(shape_b);
for (size_t c = 0; c < shape_b; c++) {
in_file >> row[c];
}
kernal.push_back(row);
kernel.push_back(row);
}
config.emplace_back(make_pair(neurons, activation));
;
kernals.emplace_back(kernal);
kernels.emplace_back(kernel);
}
std::cout << "INFO: Model loaded successfully" << std::endl;
in_file.close(); // Closing file
return NeuralNetwork(
config, kernals); // Return instance of NeuralNetwork class
config, kernels); // Return instance of NeuralNetwork class
}

/**
Expand All @@ -785,8 +785,8 @@ class NeuralNetwork {
<< layers[i - 1].neurons; // number of neurons
std::cout << ", Activation : "
<< layers[i - 1].activation; // activation
std::cout << ", Kernal Shape : "
<< get_shape(layers[i - 1].kernal); // kernal shape
std::cout << ", kernel Shape : "
<< get_shape(layers[i - 1].kernel); // kernel shape
std::cout << std::endl;
}
std::cout
Expand Down

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