Neural Network
D
/**
* @file
* @author [Deep Raval](https://github.com/imdeep2905)
*
* @brief Implementation of [Multilayer Perceptron]
* (https://en.wikipedia.org/wiki/Multilayer_perceptron).
*
* @details
* A multilayer perceptron (MLP) is a class of feedforward artificial neural
* network (ANN). The term MLP is used ambiguously, sometimes loosely to any
* feedforward ANN, sometimes strictly to refer to networks composed of multiple
* layers of perceptrons (with threshold activation). Multilayer perceptrons are
* sometimes colloquially referred to as "vanilla" neural networks, especially
* when they have a single hidden layer.
*
* An MLP consists of at least three layers of nodes: an input layer, a hidden
* layer and an output layer. Except for the input nodes, each node is a neuron
* that uses a nonlinear activation function. MLP utilizes a supervised learning
* technique called backpropagation for training. Its multiple layers and
* non-linear activation distinguish MLP from a linear perceptron. It can
* distinguish data that is not linearly separable.
*
* See [Backpropagation](https://en.wikipedia.org/wiki/Backpropagation) for
* training algorithm.
*
* \note This implementation uses mini-batch gradient descent as optimizer and
* MSE as loss function. Bias is also not included.
*/
#include <algorithm>
#include <cassert>
#include <chrono>
#include <cmath>
#include <fstream>
#include <iostream>
#include <sstream>
#include <string>
#include <valarray>
#include <vector>
#include "vector_ops.hpp" // Custom header file for vector operations
/** \namespace machine_learning
* \brief Machine learning algorithms
*/
namespace machine_learning {
/** \namespace neural_network
* \brief Neural Network or Multilayer Perceptron
*/
namespace neural_network {
/** \namespace activations
* \brief Various activation functions used in Neural network
*/
namespace activations {
/**
* Sigmoid function
* @param X Value
* @return Returns sigmoid(x)
*/
double sigmoid(const double &x) { return 1.0 / (1.0 + std::exp(-x)); }
/**
* Derivative of sigmoid function
* @param X Value
* @return Returns derivative of sigmoid(x)
*/
double dsigmoid(const double &x) { return x * (1 - x); }
/**
* Relu function
* @param X Value
* @returns relu(x)
*/
double relu(const double &x) { return std::max(0.0, x); }
/**
* Derivative of relu function
* @param X Value
* @returns derivative of relu(x)
*/
double drelu(const double &x) { return x >= 0.0 ? 1.0 : 0.0; }
/**
* Tanh function
* @param X Value
* @return Returns tanh(x)
*/
double tanh(const double &x) { return 2 / (1 + std::exp(-2 * x)) - 1; }
/**
* Derivative of Sigmoid function
* @param X Value
* @return Returns derivative of tanh(x)
*/
double dtanh(const double &x) { return 1 - x * x; }
} // namespace activations
/** \namespace util_functions
* \brief Various utility functions used in Neural network
*/
namespace util_functions {
/**
* Square function
* @param X Value
* @return Returns x * x
*/
double square(const double &x) { return x * x; }
/**
* Identity function
* @param X Value
* @return Returns x
*/
double identity_function(const double &x) { return x; }
} // namespace util_functions
/** \namespace layers
* \brief This namespace contains layers used
* in MLP.
*/
namespace layers {
/**
* neural_network::layers::DenseLayer class is used to store all necessary
* information about the layers (i.e. neurons, activation and kernel). This
* class is used by NeuralNetwork class to store layers.
*
*/
class DenseLayer {
public:
// To store activation function and it's derivative
double (*activation_function)(const double &);
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>> 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 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> &kernel_shape,
const bool &random_kernel) {
// Choosing activation (and it's derivative)
if (activation == "sigmoid") {
activation_function = neural_network::activations::sigmoid;
dactivation_function = neural_network::activations::sigmoid;
} else if (activation == "relu") {
activation_function = neural_network::activations::relu;
dactivation_function = neural_network::activations::drelu;
} else if (activation == "tanh") {
activation_function = neural_network::activations::tanh;
dactivation_function = neural_network::activations::dtanh;
} else if (activation == "none") {
// Set identity function in casse of none is supplied
activation_function =
neural_network::util_functions::identity_function;
dactivation_function =
neural_network::util_functions::identity_function;
} else {
// If supplied activation is invalid
std::cerr << "ERROR (" << __func__ << ") : ";
std::cerr << "Invalid argument. Expected {none, sigmoid, relu, "
"tanh} got ";
std::cerr << activation << std::endl;
std::exit(EXIT_FAILURE);
}
this->activation = activation; // Setting activation name
this->neurons = neurons; // Setting number of neurons
// Initialize kernel according to flag
if (random_kernel) {
uniform_random_initialization(kernel, kernel_shape, -1.0, 1.0);
} else {
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 kernel values of kernel (useful in loading model)
*/
DenseLayer(const int &neurons, const std::string &activation,
const std::vector<std::valarray<double>> &kernel) {
// Choosing activation (and it's derivative)
if (activation == "sigmoid") {
activation_function = neural_network::activations::sigmoid;
dactivation_function = neural_network::activations::sigmoid;
} else if (activation == "relu") {
activation_function = neural_network::activations::relu;
dactivation_function = neural_network::activations::drelu;
} else if (activation == "tanh") {
activation_function = neural_network::activations::tanh;
dactivation_function = neural_network::activations::dtanh;
} else if (activation == "none") {
// Set identity function in casse of none is supplied
activation_function =
neural_network::util_functions::identity_function;
dactivation_function =
neural_network::util_functions::identity_function;
} else {
// If supplied activation is invalid
std::cerr << "ERROR (" << __func__ << ") : ";
std::cerr << "Invalid argument. Expected {none, sigmoid, relu, "
"tanh} got ";
std::cerr << activation << std::endl;
std::exit(EXIT_FAILURE);
}
this->activation = activation; // Setting activation name
this->neurons = neurons; // Setting number of neurons
this->kernel = kernel; // Setting supplied kernel values
}
/**
* Copy Constructor for class DenseLayer.
*
* @param model instance of class to be copied.
*/
DenseLayer(const DenseLayer &layer) = default;
/**
* Destructor for class DenseLayer.
*/
~DenseLayer() = default;
/**
* Copy assignment operator for class DenseLayer
*/
DenseLayer &operator=(const DenseLayer &layer) = default;
/**
* Move constructor for class DenseLayer
*/
DenseLayer(DenseLayer &&) = default;
/**
* Move assignment operator for class DenseLayer
*/
DenseLayer &operator=(DenseLayer &&) = default;
};
} // namespace layers
/**
* NeuralNetwork class is implements MLP. This class is
* used by actual user to create and train networks.
*
*/
class NeuralNetwork {
private:
std::vector<neural_network::layers::DenseLayer> layers; // To store layers
/**
* Private Constructor for class NeuralNetwork. This constructor
* is used internally to load model.
* @param config vector containing pair (neurons, activation)
* @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>>> &kernels) {
// First layer should not have activation
if (config.begin()->second != "none") {
std::cerr << "ERROR (" << __func__ << ") : ";
std::cerr
<< "First layer can't have activation other than none got "
<< config.begin()->second;
std::cerr << std::endl;
std::exit(EXIT_FAILURE);
}
// Network should have atleast two layers
if (config.size() <= 1) {
std::cerr << "ERROR (" << __func__ << ") : ";
std::cerr << "Invalid size of network, ";
std::cerr << "Atleast two layers are required";
std::exit(EXIT_FAILURE);
}
// 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, kernels[i]));
}
std::cout << "INFO: Network constructed successfully" << std::endl;
}
/**
* Private function to get detailed predictions (i.e.
* activated neuron values). This function is used in
* backpropagation, single predict and batch predict.
* @param X input vector
*/
std::vector<std::vector<std::valarray<double>>>
__detailed_single_prediction(const std::vector<std::valarray<double>> &X) {
std::vector<std::vector<std::valarray<double>>> details;
std::vector<std::valarray<double>> current_pass = X;
details.emplace_back(X);
for (const auto &l : layers) {
current_pass = multiply(current_pass, l.kernel);
current_pass = apply_function(current_pass, l.activation_function);
details.emplace_back(current_pass);
}
return details;
}
public:
/**
* Default Constructor for class NeuralNetwork. This constructor
* is used to create empty variable of type NeuralNetwork class.
*/
NeuralNetwork() = default;
/**
* Constructor for class NeuralNetwork. This constructor
* is used by user.
* @param config vector containing pair (neurons, activation)
*/
explicit NeuralNetwork(
const std::vector<std::pair<int, std::string>> &config) {
// First layer should not have activation
if (config.begin()->second != "none") {
std::cerr << "ERROR (" << __func__ << ") : ";
std::cerr
<< "First layer can't have activation other than none got "
<< config.begin()->second;
std::cerr << std::endl;
std::exit(EXIT_FAILURE);
}
// Network should have atleast two layers
if (config.size() <= 1) {
std::cerr << "ERROR (" << __func__ << ") : ";
std::cerr << "Invalid size of network, ";
std::cerr << "Atleast two layers are required";
std::exit(EXIT_FAILURE);
}
// Separately creating first layer so it can have unit matrix
// as kernel.
layers.push_back(neural_network::layers::DenseLayer(
config[0].first, config[0].second,
{config[0].first, config[0].first}, false));
// Creating remaining layers
for (size_t i = 1; i < config.size(); i++) {
layers.push_back(neural_network::layers::DenseLayer(
config[i].first, config[i].second,
{config[i - 1].first, config[i].first}, true));
}
std::cout << "INFO: Network constructed successfully" << std::endl;
}
/**
* Copy Constructor for class NeuralNetwork.
*
* @param model instance of class to be copied.
*/
NeuralNetwork(const NeuralNetwork &model) = default;
/**
* Destructor for class NeuralNetwork.
*/
~NeuralNetwork() = default;
/**
* Copy assignment operator for class NeuralNetwork
*/
NeuralNetwork &operator=(const NeuralNetwork &model) = default;
/**
* Move constructor for class NeuralNetwork
*/
NeuralNetwork(NeuralNetwork &&) = default;
/**
* Move assignment operator for class NeuralNetwork
*/
NeuralNetwork &operator=(NeuralNetwork &&) = default;
/**
* Function to get X and Y from csv file (where X = data, Y = label)
* @param file_name csv file name
* @param last_label flag for whether label is in first or last column
* @param normalize flag for whether to normalize data
* @param slip_lines number of lines to skip
* @return returns pair of X and Y
*/
std::pair<std::vector<std::vector<std::valarray<double>>>,
std::vector<std::vector<std::valarray<double>>>>
get_XY_from_csv(const std::string &file_name, const bool &last_label,
const bool &normalize, const int &slip_lines = 1) {
std::ifstream in_file; // Ifstream to read file
in_file.open(file_name.c_str(), std::ios::in); // Open file
// If there is any problem in opening file
if (!in_file.is_open()) {
std::cerr << "ERROR (" << __func__ << ") : ";
std::cerr << "Unable to open file: " << file_name << std::endl;
std::exit(EXIT_FAILURE);
}
std::vector<std::vector<std::valarray<double>>> X,
Y; // To store X and Y
std::string line; // To store each line
// Skip lines
for (int i = 0; i < slip_lines; i++) {
std::getline(in_file, line, '\n'); // Ignore line
}
// While file has information
while (!in_file.eof() && std::getline(in_file, line, '\n')) {
std::valarray<double> x_data,
y_data; // To store single sample and label
std::stringstream ss(line); // Constructing stringstream from line
std::string token; // To store each token in line (seprated by ',')
while (std::getline(ss, token, ',')) { // For each token
// Insert numerical value of token in x_data
x_data = insert_element(x_data, std::stod(token));
}
// If label is in last column
if (last_label) {
y_data.resize(this->layers.back().neurons);
// If task is classification
if (y_data.size() > 1) {
y_data[x_data[x_data.size() - 1]] = 1;
}
// If task is regrssion (of single value)
else {
y_data[0] = x_data[x_data.size() - 1];
}
x_data = pop_back(x_data); // Remove label from x_data
} else {
y_data.resize(this->layers.back().neurons);
// If task is classification
if (y_data.size() > 1) {
y_data[x_data[x_data.size() - 1]] = 1;
}
// If task is regrssion (of single value)
else {
y_data[0] = x_data[x_data.size() - 1];
}
x_data = pop_front(x_data); // Remove label from x_data
}
// Push collected X_data and y_data in X and Y
X.push_back({x_data});
Y.push_back({y_data});
}
// Normalize training data if flag is set
if (normalize) {
// Scale data between 0 and 1 using min-max scaler
X = minmax_scaler(X, 0.01, 1.0);
}
in_file.close(); // Closing file
return make_pair(X, Y); // Return pair of X and Y
}
/**
* Function to get prediction of model on single sample.
* @param X array of feature vectors
* @return returns predictions as vector
*/
std::vector<std::valarray<double>> single_predict(
const std::vector<std::valarray<double>> &X) {
// Get activations of all layers
auto activations = this->__detailed_single_prediction(X);
// Return activations of last layer (actual predicted values)
return activations.back();
}
/**
* Function to get prediction of model on batch
* @param X array of feature vectors
* @return returns predicted values as vector
*/
std::vector<std::vector<std::valarray<double>>> batch_predict(
const std::vector<std::vector<std::valarray<double>>> &X) {
// Store predicted values
std::vector<std::vector<std::valarray<double>>> predicted_batch(
X.size());
for (size_t i = 0; i < X.size(); i++) { // For every sample
// Push predicted values
predicted_batch[i] = this->single_predict(X[i]);
}
return predicted_batch; // Return predicted values
}
/**
* Function to fit model on supplied data
* @param X array of feature vectors
* @param Y array of target values
* @param epochs number of epochs (default = 100)
* @param learning_rate learning rate (default = 0.01)
* @param batch_size batch size for gradient descent (default = 32)
* @param shuffle flag for whether to shuffle data (default = true)
*/
void fit(const std::vector<std::vector<std::valarray<double>>> &X_,
const std::vector<std::vector<std::valarray<double>>> &Y_,
const int &epochs = 100, const double &learning_rate = 0.01,
const size_t &batch_size = 32, const bool &shuffle = true) {
std::vector<std::vector<std::valarray<double>>> X = X_, Y = Y_;
// Both label and input data should have same size
if (X.size() != Y.size()) {
std::cerr << "ERROR (" << __func__ << ") : ";
std::cerr << "X and Y in fit have different sizes" << std::endl;
std::exit(EXIT_FAILURE);
}
std::cout << "INFO: Training Started" << std::endl;
for (int epoch = 1; epoch <= epochs; epoch++) { // For every epoch
// Shuffle X and Y if flag is set
if (shuffle) {
equal_shuffle(X, Y);
}
auto start =
std::chrono::high_resolution_clock::now(); // Start clock
double loss = 0,
acc = 0; // Intialize performance metrics with zero
// For each starting index of batch
for (size_t batch_start = 0; batch_start < X.size();
batch_start += batch_size) {
for (size_t i = batch_start;
i < std::min(X.size(), batch_start + batch_size); i++) {
std::vector<std::valarray<double>> grad, cur_error,
predicted;
auto activations = this->__detailed_single_prediction(X[i]);
// Gradients vector to store gradients for all layers
// 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].kernel));
}
predicted = activations.back(); // Predicted vector
cur_error = predicted - Y[i]; // Absoulute error
// Calculating loss with MSE
loss += sum(apply_function(
cur_error, neural_network::util_functions::square));
// If prediction is correct
if (argmax(predicted) == argmax(Y[i])) {
acc += 1;
}
// For every layer (except first) starting from last one
for (size_t j = this->layers.size() - 1; j >= 1; j--) {
// Backpropogating errors
cur_error = hadamard_product(
cur_error,
apply_function(
activations[j + 1],
this->layers[j].dactivation_function));
// Calculating gradient for current layer
grad = multiply(transpose(activations[j]), cur_error);
// Change error according to current kernel values
cur_error = multiply(cur_error,
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 kernel (aka weights)
this->layers[j].kernel = this->layers[j].kernel -
gradients[j] * learning_rate;
}
}
}
auto stop =
std::chrono::high_resolution_clock::now(); // Stoping the clock
// Calculate time taken by epoch
auto duration =
std::chrono::duration_cast<std::chrono::microseconds>(stop -
start);
loss /= X.size(); // Averaging loss
acc /= X.size(); // Averaging accuracy
std::cout.precision(4); // set output precision to 4
// Printing training stats
std::cout << "Training: Epoch " << epoch << '/' << epochs;
std::cout << ", Loss: " << loss;
std::cout << ", Accuracy: " << acc;
std::cout << ", Taken time: " << duration.count() / 1e6
<< " seconds";
std::cout << std::endl;
}
return;
}
/**
* Function to fit model on data stored in csv file
* @param file_name csv file name
* @param last_label flag for whether label is in first or last column
* @param epochs number of epochs
* @param learning_rate learning rate
* @param normalize flag for whether to normalize data
* @param slip_lines number of lines to skip
* @param batch_size batch size for gradient descent (default = 32)
* @param shuffle flag for whether to shuffle data (default = true)
*/
void fit_from_csv(const std::string &file_name, const bool &last_label,
const int &epochs, const double &learning_rate,
const bool &normalize, const int &slip_lines = 1,
const size_t &batch_size = 32,
const bool &shuffle = true) {
// Getting training data from csv file
auto data =
this->get_XY_from_csv(file_name, last_label, normalize, slip_lines);
// Fit the model on training data
this->fit(data.first, data.second, epochs, learning_rate, batch_size,
shuffle);
return;
}
/**
* Function to evaluate model on supplied data
* @param X array of feature vectors (input data)
* @param Y array of target values (label)
*/
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
for (size_t i = 0; i < X.size(); i++) { // For every sample in input
// Get predictions
std::vector<std::valarray<double>> pred =
this->single_predict(X[i]);
// If predicted class is correct
if (argmax(pred) == argmax(Y[i])) {
acc += 1; // Increment accuracy
}
// Calculating loss - Mean Squared Error
loss += sum(apply_function((Y[i] - pred),
neural_network::util_functions::square) *
0.5);
}
acc /= X.size(); // Averaging accuracy
loss /= X.size(); // Averaging loss
// Prinitng performance of the model
std::cout << "Evaluation: Loss: " << loss;
std::cout << ", Accuracy: " << acc << std::endl;
return;
}
/**
* Function to evaluate model on data stored in csv file
* @param file_name csv file name
* @param last_label flag for whether label is in first or last column
* @param normalize flag for whether to normalize data
* @param slip_lines number of lines to skip
*/
void evaluate_from_csv(const std::string &file_name, const bool &last_label,
const bool &normalize, const int &slip_lines = 1) {
// Getting training data from csv file
auto data =
this->get_XY_from_csv(file_name, last_label, normalize, slip_lines);
// Evaluating model
this->evaluate(data.first, data.second);
return;
}
/**
* Function to save current model.
* @param file_name file name to save model (*.model)
*/
void save_model(const std::string &_file_name) {
std::string file_name = _file_name;
// Adding ".model" extension if it is not already there in name
if (file_name.find(".model") == file_name.npos) {
file_name += ".model";
}
std::ofstream out_file; // Ofstream to write in file
// Open file in out|trunc mode
out_file.open(file_name.c_str(),
std::ofstream::out | std::ofstream::trunc);
// If there is any problem in opening file
if (!out_file.is_open()) {
std::cerr << "ERROR (" << __func__ << ") : ";
std::cerr << "Unable to open file: " << file_name << std::endl;
std::exit(EXIT_FAILURE);
}
/**
Format in which model is saved:
total_layers
neurons(1st neural_network::layers::DenseLayer) activation_name(1st
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) kernel_shape(Nth
neural_network::layers::DenseLayer) kernel_value
For Example, pretrained model with 3 layers:
<pre>
3
4 none
4 4
1 0 0 0
0 1 0 0
0 0 1 0
0 0 0 1
6 relu
4 6
-1.88963 -3.61165 1.30757 -0.443906 -2.41039 -2.69653
-0.684753 0.0891452 0.795294 -2.39619 2.73377 0.318202
-2.91451 -4.43249 -0.804187 2.51995 -6.97524 -1.07049
-0.571531 -1.81689 -1.24485 1.92264 -2.81322 1.01741
3 sigmoid
6 3
0.390267 -0.391703 -0.0989607
0.499234 -0.564539 -0.28097
0.553386 -0.153974 -1.92493
-2.01336 -0.0219682 1.44145
1.72853 -0.465264 -0.705373
-0.908409 -0.740547 0.376416
</pre>
*/
// Saving model in the same format
out_file << layers.size();
out_file << std::endl;
for (const auto &layer : this->layers) {
out_file << layer.neurons << ' ' << layer.activation << std::endl;
const auto shape = get_shape(layer.kernel);
out_file << shape.first << ' ' << shape.second << std::endl;
for (const auto &row : layer.kernel) {
for (const auto &val : row) {
out_file << val << ' ';
}
out_file << std::endl;
}
}
std::cout << "INFO: Model saved successfully with name : ";
std::cout << file_name << std::endl;
out_file.close(); // Closing file
return;
}
/**
* Function to load earlier saved model.
* @param file_name file from which model will be loaded (*.model)
* @return instance of NeuralNetwork class with pretrained weights
*/
NeuralNetwork load_model(const std::string &file_name) {
std::ifstream in_file; // Ifstream to read file
in_file.open(file_name.c_str()); // Openinig file
// If there is any problem in opening file
if (!in_file.is_open()) {
std::cerr << "ERROR (" << __func__ << ") : ";
std::cerr << "Unable to open file: " << file_name << std::endl;
std::exit(EXIT_FAILURE);
}
std::vector<std::pair<int, std::string>> config; // To store config
std::vector<std::vector<std::valarray<double>>>
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>> 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];
}
kernel.push_back(row);
}
config.emplace_back(make_pair(neurons, activation));
;
kernels.emplace_back(kernel);
}
std::cout << "INFO: Model loaded successfully" << std::endl;
in_file.close(); // Closing file
return NeuralNetwork(
config, kernels); // Return instance of NeuralNetwork class
}
/**
* Function to print summary of the network.
*/
void summary() {
// Printing Summary
std::cout
<< "==============================================================="
<< std::endl;
std::cout << "\t\t+ MODEL SUMMARY +\t\t\n";
std::cout
<< "==============================================================="
<< std::endl;
for (size_t i = 1; i <= layers.size(); i++) { // For every layer
std::cout << i << ")";
std::cout << " Neurons : "
<< layers[i - 1].neurons; // number of neurons
std::cout << ", Activation : "
<< layers[i - 1].activation; // activation
std::cout << ", kernel Shape : "
<< get_shape(layers[i - 1].kernel); // kernel shape
std::cout << std::endl;
}
std::cout
<< "==============================================================="
<< std::endl;
return;
}
};
} // namespace neural_network
} // namespace machine_learning
/**
* Function to test neural network
* @returns none
*/
static void test() {
// Creating network with 3 layers for "iris.csv"
machine_learning::neural_network::NeuralNetwork myNN =
machine_learning::neural_network::NeuralNetwork({
{4, "none"}, // First layer with 3 neurons and "none" as activation
{6,
"relu"}, // Second layer with 6 neurons and "relu" as activation
{3, "sigmoid"} // Third layer with 3 neurons and "sigmoid" as
// activation
});
// Printing summary of model
myNN.summary();
// Training Model
myNN.fit_from_csv("iris.csv", true, 100, 0.3, false, 2, 32, true);
// Testing predictions of model
assert(machine_learning::argmax(
myNN.single_predict({{5, 3.4, 1.6, 0.4}})) == 0);
assert(machine_learning::argmax(
myNN.single_predict({{6.4, 2.9, 4.3, 1.3}})) == 1);
assert(machine_learning::argmax(
myNN.single_predict({{6.2, 3.4, 5.4, 2.3}})) == 2);
return;
}
/**
* @brief Main function
* @returns 0 on exit
*/
int main() {
// Testing
test();
return 0;
}
