void noise_data_transformer::transform(
const void * input_data,
void * output_data,
neuron_data_type::input_type type,
const layer_configuration_specific& original_config)
{
if (type != neuron_data_type::type_byte)
throw neural_network_exception("noise_data_transformer is implemented for data stored as bytes only");
unsigned char * data = static_cast<unsigned char *>(output_data);
unsigned int elem_count = original_config.get_neuron_count();
for(unsigned char * data_it = data; data_it != (data + elem_count); data_it++)
{
int shift = max_noise_distribution(generator);
*data_it = static_cast<unsigned char>(std::min<int>(std::max<int>((shift + static_cast<int>(*data_it)), 0), 255));
}
}
void noise_data_transformer::transform(
const float * data,
float * data_transformed,
const layer_configuration_specific& original_config,
unsigned int sample_id)
{
unsigned int elem_count = original_config.get_neuron_count();
{
std::lock_guard<std::mutex> lock(gen_stream_mutex);
for(unsigned int elem_id = 0; elem_id < elem_count; ++elem_id)
{
float shift = max_noise_distribution.min();
if (max_noise_distribution.max() > max_noise_distribution.min())
shift = max_noise_distribution(generator);
data_transformed[elem_id] = data[elem_id] + shift;
}
}
}
