218 lines
7.8 KiB
Plaintext
218 lines
7.8 KiB
Plaintext
#include "core/omp_config.h"
|
|
|
|
#include "utils/utils.h"
|
|
#include "numerics/numerics.h"
|
|
#include "decomp/decomp.h"
|
|
|
|
#include "modules/neural_networks/neural_networks.h"
|
|
#include "random/random.h"
|
|
|
|
|
|
|
|
//#include <iostream>
|
|
//#include <stdexcept>
|
|
//#include <chrono>
|
|
|
|
|
|
|
|
|
|
|
|
int main(int argc, char const *argv[])
|
|
{
|
|
|
|
uint64_t number_of_classes = 2;
|
|
uint64_t number_of_samples = 1000;
|
|
uint64_t number_of_epochs = 10000;
|
|
|
|
utils::Mf X;
|
|
utils::Mf X_test;
|
|
utils::Matrix<float> y;
|
|
utils::Matrix<float> y_test;
|
|
float data_loss;
|
|
float regularization_loss;
|
|
float loss;
|
|
float accuracy;
|
|
|
|
utils::Vector<uint64_t> class_targets;
|
|
utils::Matrix<float> predictions;
|
|
|
|
|
|
// Create dataset
|
|
//neural_networks::create_spital_data<float, int64_t>(number_of_samples, number_of_classes, X, y);
|
|
//neural_networks::create_vertical_data<float, int64_t>(number_of_samples, number_of_classes, X, y);
|
|
neural_networks::create_sine_data(number_of_samples, // samples
|
|
2.0f* 3.1415f, // length
|
|
X,
|
|
y);
|
|
|
|
|
|
|
|
|
|
// Create Dense layer with 2 input featues and 3 output values
|
|
neural_networks::Dense_Layer<float> dense1(
|
|
1, 64, // input/output
|
|
0.0f, // weight L1
|
|
5e-5f, // weight L2
|
|
0.0f, // bias L1
|
|
5e-5f // bias L2
|
|
);
|
|
neural_networks::Activation_ReLU<float> activation1;
|
|
//neural_networks::Dropout_Layer<float> dropout1(0.1);
|
|
|
|
neural_networks::Dense_Layer<float> dense2(
|
|
64, 64, // input/output
|
|
0.0f, // weight L1
|
|
5e-5f, // weight L2
|
|
0.0f, // bias L1
|
|
5e-5f // bias L2
|
|
);
|
|
neural_networks::Activation_ReLU<float> activation2;
|
|
|
|
|
|
neural_networks::Dense_Layer<float> dense3(
|
|
64, 1, // input/output
|
|
0.0f, // weight L1
|
|
5e-5f, // weight L2
|
|
0.0f, // bias L1
|
|
5e-5f // bias L2
|
|
);
|
|
neural_networks::Activation_Linear<float> activation3;
|
|
|
|
|
|
neural_networks::Loss_MeanSquaredError<float> loss_function;
|
|
neural_networks::Optimizer_Adam<float> optimizer(
|
|
0.001, // Learning-rate
|
|
1e-3, // Learning-rate decay
|
|
1e-7, // epsilons
|
|
0.9, // beta 1
|
|
0.999 // beta 2
|
|
);
|
|
|
|
|
|
/* Accuracy precision for accuracy calculation
|
|
# There are no really accuracy factor for regression problem,
|
|
# but we can simulate/approximate it. We'll calculate it by checking
|
|
# how many values have a difference to their ground truth equivalent
|
|
# less than given precision
|
|
# We'll calculate this precision as a fraction of standard deviation
|
|
# of al the ground truth values */
|
|
// accuracy_precision = np.std(y) / 250
|
|
float accuracy_precision = numerics::standard_deviation(y)/ 250.0f;
|
|
|
|
|
|
// Train in loop
|
|
for (uint64_t epoch = 0; epoch < number_of_epochs+1; ++epoch){
|
|
|
|
// Perform a forward pass of our training data through this layer
|
|
dense1.forward(X);
|
|
activation1.forward(dense1.outputs);
|
|
//dropout1.forward(activation1.outputs);
|
|
|
|
dense2.forward(activation1.outputs);
|
|
activation2.forward(dense2.outputs);
|
|
|
|
dense3.forward(activation2.outputs);
|
|
activation3.forward(dense3.outputs);
|
|
|
|
// Perform a foard pass through the activation/loss function
|
|
// takes the output of the second dense layer here and returns loss
|
|
data_loss = loss_function.calculate(activation3.outputs, y);
|
|
|
|
// Calculate regularization penalty
|
|
regularization_loss = loss_function.regularization_loss(dense1) +
|
|
loss_function.regularization_loss(dense2);
|
|
|
|
loss = data_loss + regularization_loss;
|
|
|
|
predictions = activation3.outputs;
|
|
accuracy = numerics::mean(numerics::less( numerics::abs( numerics::sub(predictions, y)), accuracy_precision));
|
|
//accuracy = numerics::mean(numerics::equal_elementwise_serial(predictions, utils::veccast<float, int64_t>(y.get_col(0))));
|
|
|
|
|
|
if (!(epoch%100) && epoch != 0){
|
|
|
|
std::cout << "epoch: " << epoch;
|
|
std::cout << ", acc: " << accuracy;
|
|
std::cout << ", loss: " << loss;
|
|
std::cout << ", data_loss: " << data_loss;
|
|
std::cout << ", regularization_loss: " << regularization_loss;
|
|
std::cout << ", lr: " << optimizer.current_learning_rate;
|
|
std::cout << std::endl;
|
|
|
|
}
|
|
|
|
// Backward pass
|
|
loss_function.backward(activation3.outputs, y);
|
|
|
|
activation3.backward(loss_function.dinputs);
|
|
dense3.backward(activation3.dinputs);
|
|
|
|
activation2.backward(dense3.dinputs);
|
|
dense2.backward(activation2.dinputs);
|
|
|
|
activation1.backward(dense2.dinputs);
|
|
dense1.backward(activation1.dinputs);
|
|
|
|
// Update weights and biases
|
|
optimizer.pre_update_params();
|
|
optimizer.update_params(dense1);
|
|
optimizer.update_params(dense2);
|
|
optimizer.update_params(dense3);
|
|
optimizer.post_update_params();
|
|
|
|
}
|
|
/*
|
|
std::cout << "X, y, pred:" << std::endl;
|
|
|
|
for (uint64_t i = 0; i < X.rows(); ++i) {
|
|
std::cout << X(i, 0)
|
|
<< ", "
|
|
<< y(i, 0)
|
|
<< ", "
|
|
<< activation3.outputs(i, 0)
|
|
<< std::endl;
|
|
}
|
|
|
|
// Validate the model
|
|
|
|
// Create dataset
|
|
neural_networks::create_spital_data<float, int64_t>(100, number_of_classes, X_test, y_test);
|
|
|
|
// Perform a forward pass of our training data through this layer
|
|
dense1.forward(X_test);
|
|
activation1.forward(dense1.outputs);
|
|
//dropout1.forward(activation1.outputs);
|
|
|
|
dense2.forward(activation1.outputs);
|
|
activation2.forward(dense2.outputs);
|
|
|
|
dense3.forward(activation2.outputs);
|
|
activation3.forward(dense3.outputs);
|
|
|
|
// Perform a foard pass through the activation/loss function
|
|
// takes the output of the second dense layer here and returns loss
|
|
data_loss = loss_activation.calculate(activation3.outputs, y_test);
|
|
|
|
// Calculate regularization penalty
|
|
regularization_loss = loss_activation.regularization_loss(dense1) +
|
|
loss_activation.regularization_loss(dense2) +
|
|
loss_activation.regularization_loss(dense3);
|
|
|
|
loss = data_loss + regularization_loss;
|
|
|
|
|
|
// skal flyttes ned under loss functions.
|
|
predictions = activation3.outputs();
|
|
predictions = numerics::mean(numerics::abs(numerics::sub(predictions, y)));
|
|
std::cout << predictions << std::endl;
|
|
|
|
// Calculate accuracy from output of activation2 and targets
|
|
predictions = numerics::greater_than(activation3.outputs, 0.5f).get_col(0);
|
|
|
|
accuracy = numerics::mean(numerics::equal_elementwise_serial(predictions, utils::veccast<float, int64_t>(y_test.get_col(0))));
|
|
|
|
|
|
std::cout << "validation, acc: " << accuracy << ", loss: " << loss << std::endl;
|
|
*/
|
|
return 0;
|
|
} |