float linear_sampler_layer::get_flops_per_entry(
const std::vector<layer_configuration_specific>& input_configuration_specific_list,
const layer_action& action) const
{
switch (action.get_action_type())
{
case layer_action::forward:
{
layer_configuration_specific output_config = get_output_layer_configuration_specific(input_configuration_specific_list);
unsigned int neuron_count_per_feature_map = output_config.get_neuron_count_per_feature_map();
unsigned int per_feature_map_work = 7;
unsigned int constant_work = 12;
return static_cast<float>(neuron_count_per_feature_map) * static_cast<float>(constant_work + per_feature_map_work * output_config.feature_map_count);
}
case layer_action::backward_data:
{
layer_configuration_specific output_config = get_output_layer_configuration_specific(input_configuration_specific_list);
unsigned int neuron_count_per_feature_map = output_config.get_neuron_count_per_feature_map();
unsigned int per_feature_map_work = 8;
unsigned int constant_work = 18;
return static_cast<float>(neuron_count_per_feature_map) * static_cast<float>(constant_work + per_feature_map_work * output_config.feature_map_count);
}
case layer_action::backward_weights:
default:
return 0.0F;
}
}
float convolution_layer::get_weights_update_flops_2nd(const layer_configuration_specific& input_configuration_specific) const
{
unsigned int neuron_count = get_output_layer_configuration_specific(input_configuration_specific).get_neuron_count();
unsigned int per_item_flops = input_feature_map_count * 2;
std::for_each(window_sizes.begin(), window_sizes.end(), per_item_flops *= boost::lambda::_1);
return (static_cast<float>(neuron_count) * static_cast<float>(per_item_flops)) + static_cast<float>(input_configuration_specific.get_neuron_count());
}
float average_subsampling_layer::get_forward_flops(const layer_configuration_specific& input_configuration_specific) const
{
unsigned int neuron_count = get_output_layer_configuration_specific(input_configuration_specific).get_neuron_count();
unsigned int per_item_flops = 1;
std::for_each(subsampling_sizes.begin(), subsampling_sizes.end(), per_item_flops *= boost::lambda::_1);
return static_cast<float>(neuron_count) * static_cast<float>(per_item_flops);
}
float accuracy_layer::get_flops_per_entry(
const std::vector<layer_configuration_specific>& input_configuration_specific_list,
const layer_action& action) const
{
switch (action.get_action_type())
{
case layer_action::forward:
{
unsigned int neuron_count = get_output_layer_configuration_specific(input_configuration_specific_list).get_neuron_count();
unsigned int per_item_flops = input_configuration_specific_list[0].feature_map_count * 2;
return static_cast<float>(neuron_count) * static_cast<float>(per_item_flops);
}
case layer_action::backward_data:
throw neural_network_exception("get_backward_flops is not implemented for accuracy_layer");
default:
return 0.0F;
}
}
float max_subsampling_layer::get_flops_per_entry(
const std::vector<layer_configuration_specific>& input_configuration_specific_list,
const layer_action& action) const
{
switch (action.get_action_type())
{
case layer_action::forward:
{
unsigned int neuron_count = get_output_layer_configuration_specific(input_configuration_specific_list).get_neuron_count();
unsigned int per_item_flops = feature_map_subsampling_size * entry_subsampling_size;
std::for_each(subsampling_sizes.begin(), subsampling_sizes.end(), [&per_item_flops] (unsigned int x) { per_item_flops *= x; });
per_item_flops -= 1;
return static_cast<float>(neuron_count) * static_cast<float>(per_item_flops);
}
case layer_action::backward_data:
return 0.0F;
default:
return 0.0F;
}
}
float negative_log_likelihood_layer::get_flops_per_entry(
const std::vector<layer_configuration_specific>& input_configuration_specific_list,
const layer_action& action) const
{
switch (action.get_action_type())
{
case layer_action::forward:
{
unsigned int neuron_count = get_output_layer_configuration_specific(input_configuration_specific_list).get_neuron_count();
unsigned int per_item_flops = input_configuration_specific_list[0].feature_map_count * 3;
return static_cast<float>(neuron_count) * static_cast<float>(per_item_flops);
}
case layer_action::backward_data:
{
unsigned int neuron_count = input_configuration_specific_list[action.get_backprop_index()].get_neuron_count();
unsigned int per_item_flops = 2;
return static_cast<float>(neuron_count) * static_cast<float>(per_item_flops);
}
default:
return 0.0F;
}
}
float average_subsampling_layer::get_backward_flops(const layer_configuration_specific& input_configuration_specific) const
{
unsigned int neuron_count = get_output_layer_configuration_specific(input_configuration_specific).get_neuron_count();
return static_cast<float>(neuron_count);
}