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Slow learning time solved with equal number of positive and negative training samples.

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This commit is contained in:
davidjacnogueira
2016-11-03 02:59:32 +00:00
parent ff7bfe1fa2
commit 9ff33f7b65
6 changed files with 124 additions and 119 deletions
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39
src/MLP.cpp
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@@ -20,8 +20,7 @@ bool MLP::ImportNNWeights(const std::vector<double> & weights) {
void MLP::GetOutput(const std::vector<double> &input,
std::vector<double> * output,
std::vector<std::vector<double>> * all_layers_activations,
bool apply_softmax) const {
std::vector<std::vector<double>> * all_layers_activations) const {
assert(input.size() == m_num_inputs);
int temp_size;
if (m_num_hidden_layers == 0)
@@ -50,7 +49,7 @@ void MLP::GetOutput(const std::vector<double> &input,
m_layers[i].GetOutputAfterSigmoid(temp_in, &temp_out);
}
if (apply_softmax && temp_out.size() > 1)
if (temp_out.size() > 1)
utils::Softmax(&temp_out);
*output = temp_out;
@@ -105,11 +104,11 @@ void MLP::UpdateMiniBatch(const std::vector<TrainingSample> &training_sample_set
}
}
}
for (int i = 0; i < max_iterations; i++) {
std::cout << "******************************" << std::endl;
std::cout << "******** ITER " << i << std::endl;
std::cout << "******************************" << std::endl;
size_t i = 0;
for ( i = 0; i < max_iterations; i++) {
//std::cout << "******************************" << std::endl;
//std::cout << "******** ITER " << i << std::endl;
//std::cout << "******************************" << std::endl;
double current_iteration_cost_function = 0.0;
for (auto & training_sample_with_bias : training_sample_set_with_bias) {
std::vector<double> predicted_output;
@@ -123,16 +122,16 @@ void MLP::UpdateMiniBatch(const std::vector<TrainingSample> &training_sample_set
assert(correct_output.size() == predicted_output.size());
std::vector<double> deriv_error_output(predicted_output.size());
std::cout << training_sample_with_bias << "\t\t";
{
std::cout << "Predicted output: [";
for (int i = 0; i < predicted_output.size(); i++) {
if (i != 0)
std::cout << ", ";
std::cout << predicted_output[i];
}
std::cout << "]" << std::endl;
}
//std::cout << training_sample_with_bias << "\t\t";
//{
// std::cout << "Predicted output: [";
// for (int i = 0; i < predicted_output.size(); i++) {
// if (i != 0)
// std::cout << ", ";
// std::cout << predicted_output[i];
// }
// std::cout << "]" << std::endl;
//}
for (int j = 0; j < predicted_output.size(); j++) {
current_iteration_cost_function +=
@@ -146,7 +145,8 @@ void MLP::UpdateMiniBatch(const std::vector<TrainingSample> &training_sample_set
learning_rate);
}
std::cout << "Iteration cost function f(error): "
if((i% (max_iterations/100))==0)
std::cout << "Iteration "<< i << " cost function f(error): "
<< current_iteration_cost_function << std::endl;
if (current_iteration_cost_function < min_error_cost)
break;
@@ -173,6 +173,7 @@ void MLP::UpdateMiniBatch(const std::vector<TrainingSample> &training_sample_set
std::cout << "******************************" << std::endl;
std::cout << "******* TRAINING ENDED *******" << std::endl;
std::cout << "******* " << i << " iters *******" << std::endl;
std::cout << "******************************" << std::endl;
{
int layer_i = -1;