C++ const_additional_buffer_smart_ptr类代码示例

		void softmax_layer_hessian_plain::backprop(
			additional_buffer_smart_ptr input_errors,
			const_additional_buffer_smart_ptr output_errors,
			const_additional_buffer_smart_ptr output_neurons,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const_layer_data_smart_ptr data,
			const_layer_data_custom_smart_ptr data_custom,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int entry_count) const
		{
			const unsigned int input_neuron_count = input_configuration_specific.get_neuron_count();
			const unsigned int input_neuron_count_per_feature_map = input_configuration_specific.get_neuron_count_per_feature_map();
			const unsigned int feature_map_count = static_cast<unsigned int>(input_configuration_specific.feature_map_count);

			const std::vector<float>::iterator input_errors_it = input_errors->begin();
			const std::vector<float>::const_iterator output_errors_it = output_errors->begin();
			const std::vector<float>::const_iterator output_neurons_it = output_neurons->begin();

			const int total_workload = entry_count * input_neuron_count_per_feature_map;
			const int openmp_thread_count = plain_config->openmp_thread_count;
			
			#pragma omp parallel default(none) shared(additional_buffers) num_threads(openmp_thread_count)
			{
				int thread_id = 0;
				#ifdef _OPENMP
				thread_id = omp_get_thread_num();
				#endif

				#pragma omp for schedule(guided)
				for(int workload_id = 0; workload_id < total_workload; ++workload_id)
				{
					int entry_id = workload_id / input_neuron_count_per_feature_map;
					int neuron_id = workload_id - (entry_id * input_neuron_count_per_feature_map);

					const std::vector<float>::iterator in_errors_it = input_errors_it + (entry_id * input_neuron_count) + neuron_id;
					const std::vector<float>::const_iterator out_errors_it = output_errors_it + (entry_id * input_neuron_count) + neuron_id;
					const std::vector<float>::const_iterator out_neurons_it = output_neurons_it + (entry_id * input_neuron_count) + neuron_id;

					float sum = 0.0F;
					for(unsigned int feature_map_id = 0; feature_map_id < feature_map_count; ++feature_map_id)
					{
						unsigned int offset = feature_map_id * input_neuron_count_per_feature_map;
						float val = (*(out_neurons_it + offset));
						sum += val * val * (*(out_errors_it + offset));
					}

					for(unsigned int feature_map_id = 0; feature_map_id < feature_map_count; ++feature_map_id)
					{
						unsigned int offset = feature_map_id * input_neuron_count_per_feature_map;
						float y = *(out_neurons_it + offset);
						float y2 = y * y;
						*(in_errors_it + offset) = y2 * ((*(out_errors_it + offset)) * (2.0F * (y2 - y) + 1.0F) - sum);
					}
				} // for(int workload_id
			} // #pragma parallel
		}

		void dropout_layer_updater_plain::test(
			const_additional_buffer_smart_ptr input_buffer,
			additional_buffer_smart_ptr output_buffer,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const_layer_data_smart_ptr data,
			const_layer_data_custom_smart_ptr data_custom,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int updater_count,
			unsigned int offset_input_entry_id,
			bool force_deterministic) const
		{
			if (offset_input_entry_id > 0)
				throw neural_network_exception("dropout_layer_updater_plain is not able to run using offset");

			if (force_deterministic)
			{
				memcpy(&(output_buffer->at(0)), &(input_buffer->at(0)), input_configuration_specific.get_neuron_count() * updater_count * sizeof(float));
			}
			else
			{
				const std::vector<float>::const_iterator in_it_global = input_buffer->begin();
				const std::vector<float>::iterator out_it_global = output_buffer->begin();
				unsigned char * keep_elem_ptr = reinterpret_cast<unsigned char *>(&(additional_buffers[0]->at(0)));

				nnforge_shared_ptr<const dropout_layer> layer_derived = nnforge_dynamic_pointer_cast<const dropout_layer>(layer_schema);
				const float dropout_rate = layer_derived->dropout_rate;
				const float keep_rate = 1.0F - dropout_rate;
				const float mult = 1.0F / keep_rate;

				const int total_workload = input_configuration_specific.get_neuron_count() * updater_count;

				nnforge_uniform_real_distribution<float> dist(0.0F, 1.0F);

				for(int i = 0; i < total_workload; ++i)
					keep_elem_ptr[i] = (dist(gen) <= keep_rate ? (unsigned char)1 : (unsigned char)0);

				#pragma omp parallel default(none) num_threads(plain_config->openmp_thread_count) shared(keep_elem_ptr)
				{
					#pragma omp for schedule(guided)
					for(int workload_id = 0; workload_id < total_workload; ++workload_id)
					{
						int elem_id = workload_id;
						*(out_it_global + elem_id) = *(in_it_global + elem_id) * (keep_elem_ptr[elem_id] ? mult : 0.0F);
					}
				}
			}
		}

		void hyperbolic_tangent_layer_updater_plain::test(
			const_additional_buffer_smart_ptr input_buffer,
			additional_buffer_smart_ptr output_buffer,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const_layer_data_smart_ptr data,
			const_layer_data_custom_smart_ptr data_custom,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int updater_count,
			unsigned int offset_input_entry_id) const
		{
			if (offset_input_entry_id > 0)
				throw neural_network_exception("hyperbolic_tangent_layer_updater_plain is not able to run using offset");

			const int elem_count = static_cast<int>(updater_count * input_configuration_specific.get_neuron_count());
			const std::vector<float>::const_iterator in_it = input_buffer->begin();
			const std::vector<float>::iterator out_it = output_buffer->begin();

			nnforge_shared_ptr<const hyperbolic_tangent_layer> layer_derived = nnforge_dynamic_pointer_cast<const hyperbolic_tangent_layer>(layer_schema);
			const float hyperbolic_tangent_steepness2 = layer_derived->steepness * 2.0F;
			const float hyperbolic_tangent_major_multiplier = layer_derived->major_multiplier;

			#pragma omp parallel for default(none) schedule(guided) num_threads(plain_config->openmp_thread_count)
			for(int i = 0; i < elem_count; ++i)
			{
				float inp = *(in_it + i);
				float inp2 = expf(inp * hyperbolic_tangent_steepness2);
				float res = (inp2 - 1.0F) / (inp2 + 1.0F) * hyperbolic_tangent_major_multiplier;
				*(out_it + i) = res;
			}
		}

		void hyperbolic_tangent_layer_updater_plain::backprop(
			additional_buffer_smart_ptr input_errors,
			const_additional_buffer_smart_ptr input_neurons,
			const_additional_buffer_smart_ptr output_errors,
			const_additional_buffer_smart_ptr output_neurons,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const_layer_data_smart_ptr data,
			const_layer_data_custom_smart_ptr data_custom,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int updater_count) const
		{
			const int elem_count = static_cast<int>(updater_count * input_configuration_specific.get_neuron_count());
			const std::vector<float>::iterator in_err_it = input_errors->begin();
			const std::vector<float>::const_iterator out_it = output_neurons->begin();

			nnforge_shared_ptr<const hyperbolic_tangent_layer> layer_derived = nnforge_dynamic_pointer_cast<const hyperbolic_tangent_layer>(layer_schema);
			const float hyperbolic_tangent_major_multiplier_reverse = 1.0F / layer_derived->major_multiplier;
			const float hyperbolic_tangent_steepness3 = layer_derived->steepness * layer_derived->major_multiplier;
			#pragma omp parallel for default(none) schedule(guided) num_threads(plain_config->openmp_thread_count)
			for(int i = 0; i < elem_count; ++i)
			{
				float out_neuron = *(out_it + i);
				float normalized_value = out_neuron * hyperbolic_tangent_major_multiplier_reverse;
				float der1st = hyperbolic_tangent_steepness3 * (1.0F - (normalized_value * normalized_value));
				*(in_err_it + i) *= der1st;
			}
		}

		void sigmoid_layer_updater_plain::test(
			const_additional_buffer_smart_ptr input_buffer,
			additional_buffer_smart_ptr output_buffer,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const layer_data_list& data,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int updater_count,
			int offset_input_entry_id) const
		{
			if (offset_input_entry_id >= 0)
				throw neural_network_exception("sigmoid_layer_updater_plain is not able to run using the same input");

			const int elem_count = static_cast<int>(updater_count * input_configuration_specific.get_neuron_count());
			const std::vector<float>::const_iterator in_it = input_buffer->begin();
			const std::vector<float>::iterator out_it = output_buffer->begin();

			#pragma omp parallel for default(none) schedule(guided) num_threads(plain_config->openmp_thread_count)
			for(int i = 0; i < elem_count; ++i)
			{
				float inp = *(in_it + i);
				float res = 1.0F / (expf(-inp) + 1.0F);
				*(out_it + i) = res;
			}
		}

		void softmax_layer_hessian_plain::test(
			const_additional_buffer_smart_ptr input_buffer,
			additional_buffer_smart_ptr output_buffer,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const_layer_data_smart_ptr data,
			const_layer_data_custom_smart_ptr data_custom,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int entry_count) const
		{
			const unsigned int input_neuron_count = input_configuration_specific.get_neuron_count();
			const unsigned int input_neuron_count_per_feature_map = input_configuration_specific.get_neuron_count_per_feature_map();
			const unsigned int feature_map_count = static_cast<unsigned int>(input_configuration_specific.feature_map_count);

			const std::vector<float>::const_iterator input_buffer_it = input_buffer->begin();
			const std::vector<float>::iterator output_buffer_it = output_buffer->begin();

			const int total_workload = entry_count * input_neuron_count_per_feature_map;
			const int openmp_thread_count = plain_config->openmp_thread_count;
			
			#pragma omp parallel default(none) shared(additional_buffers) num_threads(openmp_thread_count)
			{
				int thread_id = 0;
				#ifdef _OPENMP
				thread_id = omp_get_thread_num();
				#endif

				std::vector<float>& local_additional_buffer = *(additional_buffers[thread_id]);

				#pragma omp for schedule(guided)
				for(int workload_id = 0; workload_id < total_workload; ++workload_id)
				{
					int entry_id = workload_id / input_neuron_count_per_feature_map;
					int neuron_id = workload_id - (entry_id * input_neuron_count_per_feature_map);

					const std::vector<float>::const_iterator in_it = input_buffer_it + (entry_id * input_neuron_count) + neuron_id;
					const std::vector<float>::iterator out_it = output_buffer_it + (entry_id * input_neuron_count) + neuron_id;

					float max_val = -1.0e+37F;
					for(unsigned int feature_map_id = 0; feature_map_id < feature_map_count; ++feature_map_id)
					{
						float val = *(in_it + (feature_map_id * input_neuron_count_per_feature_map));
						max_val = std::max(max_val, val);
					}

					float sum = 0.0F;
					for(unsigned int feature_map_id = 0; feature_map_id < feature_map_count; ++feature_map_id)
					{
						float val = expf((*(in_it + (feature_map_id * input_neuron_count_per_feature_map))) - max_val);
						sum += val;
						local_additional_buffer[feature_map_id] = val;
					}
					float mult = 1.0F / sum;
					for(unsigned int feature_map_id = 0; feature_map_id < feature_map_count; ++feature_map_id)
						*(out_it + (feature_map_id * input_neuron_count_per_feature_map)) = local_additional_buffer[feature_map_id] * mult;
				} // for(int workload_id
			} // #pragma parallel
		}

		void absolute_layer_updater_plain::backprop(
			additional_buffer_smart_ptr input_errors,
			const_additional_buffer_smart_ptr input_neurons,
			const_additional_buffer_smart_ptr output_errors,
			const_additional_buffer_smart_ptr output_neurons,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const_layer_data_smart_ptr data,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int updater_count) const
		{
			const int elem_count = static_cast<int>(updater_count * input_configuration_specific.get_neuron_count());
			const std::vector<float>::const_iterator in_it = input_neurons->begin();
			const std::vector<float>::iterator in_err_it = input_errors->begin();

			#pragma omp parallel for default(none) schedule(guided) num_threads(plain_config->openmp_thread_count)
			for(int i = 0; i < elem_count; ++i)
			{
				float val = *(in_it + i);
				if (val < 0.0F)
				{
					*(in_err_it + i) = - *(in_err_it + i);
				}
			}
		}

		void absolute_layer_updater_plain::test(
			const_additional_buffer_smart_ptr input_buffer,
			additional_buffer_smart_ptr output_buffer,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const_layer_data_smart_ptr data,
			const_layer_data_custom_smart_ptr data_custom,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int updater_count,
			unsigned int offset_input_entry_id,
			bool force_deterministic) const
		{
			if (offset_input_entry_id > 0)
				throw neural_network_exception("absolute_layer_updater_plain is not able to run using offset");

			const int elem_count = static_cast<int>(updater_count * input_configuration_specific.get_neuron_count());
			const std::vector<float>::const_iterator in_it = input_buffer->begin();
			const std::vector<float>::iterator out_it = output_buffer->begin();

			#pragma omp parallel for default(none) schedule(guided) num_threads(plain_config->openmp_thread_count)
			for(int i = 0; i < elem_count; ++i)
				*(out_it + i) = fabs(*(in_it + i));
		}

		void soft_rectified_linear_layer_updater_plain::backprop(
			additional_buffer_smart_ptr input_errors,
			const_additional_buffer_smart_ptr input_neurons,
			const_additional_buffer_smart_ptr output_errors,
			const_additional_buffer_smart_ptr output_neurons,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const_layer_data_smart_ptr data,
			const_layer_data_custom_smart_ptr data_custom,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int updater_count) const
		{
			const int elem_count = static_cast<int>(updater_count * input_configuration_specific.get_neuron_count());
			const std::vector<float>::iterator in_err_it = input_errors->begin();
			const std::vector<float>::const_iterator out_it = output_neurons->begin();

			#pragma omp parallel for default(none) schedule(guided) num_threads(plain_config->openmp_thread_count)
			for(int i = 0; i < elem_count; ++i)
			{
				float out_neuron = *(out_it + i);
				float val = expf(out_neuron);
				float der1st = (val - 1.0F) / val;
				*(in_err_it + i) *= der1st;
			}
		}

		void sigmoid_layer_hessian_plain::test(
			const_additional_buffer_smart_ptr input_buffer,
			additional_buffer_smart_ptr output_buffer,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const_layer_data_smart_ptr data,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int entry_count) const
		{
			const int elem_count = static_cast<int>(entry_count * input_configuration_specific.get_neuron_count());
			const std::vector<float>::const_iterator in_it = input_buffer->begin();
			const std::vector<float>::iterator out_it = output_buffer->begin();

			#pragma omp parallel for default(none) schedule(guided) num_threads(plain_config->openmp_thread_count)
			for(int i = 0; i < elem_count; ++i)
			{
				float inp = *(in_it + i);
				float res = 1.0F / (expf(-inp) + 1.0F);
				*(out_it + i) = res;
			}
		}

		void max_subsampling_layer_updater_plain::backprop(
			additional_buffer_smart_ptr input_errors,
			const_additional_buffer_smart_ptr input_neurons,
			const_additional_buffer_smart_ptr output_errors,
			const_additional_buffer_smart_ptr output_neurons,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const_layer_data_smart_ptr data,
			const_layer_data_custom_smart_ptr data_custom,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int updater_count,
			bool force_deterministic) const
		{
			const std::vector<float>::iterator in_err_it_global = input_errors->begin();
			const std::vector<float>::const_iterator out_err_it_global = output_errors->begin();
			const std::vector<float>::const_iterator max_indexes_it_global = additional_buffers[0]->begin();

			const int total_clean_workload = updater_count * input_configuration_specific.get_neuron_count();

			#pragma omp parallel for default(none) schedule(guided) num_threads(plain_config->openmp_thread_count)
			for(int workload_id = 0; workload_id < total_clean_workload; ++workload_id)
			{
				*(in_err_it_global + workload_id) = 0.0F;
			}

			const int total_workload = updater_count * output_configuration_specific.get_neuron_count();

			#pragma omp parallel for default(none) schedule(guided) num_threads(plain_config->openmp_thread_count)
			for(int workload_id = 0; workload_id < total_workload; ++workload_id)
			{
				unsigned int max_index = *(((unsigned int *)(&(*max_indexes_it_global))) + workload_id);
				float err = *(out_err_it_global + workload_id);
				*(in_err_it_global + max_index) = err;
			}
		}

		void sigmoid_layer_hessian_plain::backprop(
			additional_buffer_smart_ptr input_errors,
			const_additional_buffer_smart_ptr output_errors,
			const_additional_buffer_smart_ptr output_neurons,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const_layer_data_smart_ptr data,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int entry_count) const
		{
			const int elem_count = static_cast<int>(entry_count * input_configuration_specific.get_neuron_count());
			const std::vector<float>::iterator in_err_it = input_errors->begin();
			const std::vector<float>::const_iterator out_it = output_neurons->begin();

			#pragma omp parallel for default(none) schedule(guided) num_threads(plain_config->openmp_thread_count)
			for(int i = 0; i < elem_count; ++i)
			{
				float out_neuron = *(out_it + i);
				float der1st = out_neuron * (1.0F - out_neuron);
				*(in_err_it + i) *= (der1st * der1st);
			}
		}

		void convolution_layer_hessian_plain::update_hessian(
			const_additional_buffer_smart_ptr input_neurons,
			const_additional_buffer_smart_ptr output_errors,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			layer_data_smart_ptr hessian_data,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int entry_count) const
		{
			const std::vector<float>::const_iterator in_it_global = input_neurons->begin();
			const std::vector<float>::const_iterator out_err_it_global = output_errors->begin();
			const unsigned int input_neuron_count = input_configuration_specific.get_neuron_count();
			const unsigned int input_neuron_count_per_feature_map = input_configuration_specific.get_neuron_count_per_feature_map();
			const unsigned int output_neuron_count = output_configuration_specific.get_neuron_count();
			const unsigned int output_neuron_count_per_feature_map = output_configuration_specific.get_neuron_count_per_feature_map();
			std::tr1::shared_ptr<const convolution_layer> layer_derived = std::tr1::dynamic_pointer_cast<const convolution_layer>(layer_schema);
			const std::vector<unsigned int>& window_sizes = layer_derived->window_sizes;
			const unsigned int dimension_count = static_cast<unsigned int>(window_sizes.size());
			std::vector<unsigned int> input_slices(input_configuration_specific.dimension_sizes.size());
			input_slices[0] = 1;
			for(unsigned int i = 0; i < dimension_count - 1; ++i)
				input_slices[i + 1] = input_slices[i] * input_configuration_specific.dimension_sizes[i];
			unsigned int window_elem_count = 1;
			for(unsigned int i = 0; i < dimension_count; ++i)
				window_elem_count *= window_sizes[i];
			const unsigned int const_window_elem_count = window_elem_count;
			const std::vector<float>::iterator weights = (*hessian_data)[0].begin();
			const std::vector<float>::iterator biases = (*hessian_data)[1].begin();

			std::vector<unsigned int> current_local_input_position(dimension_count, 0);
			std::vector<unsigned int> offset_list(window_elem_count);
			for(unsigned int i = 1; i < window_elem_count; ++i)
			{
				int offset = 0;
				for(unsigned int j = 0; j < dimension_count; ++j)
				{
					offset += static_cast<int>(input_slices[j]);
					if ((++current_local_input_position[j]) < window_sizes[j])
					{
						offset_list[i] = offset_list[i-1] + offset;
						break;
					}
					current_local_input_position[j] = 0;
					offset -= static_cast<int>(window_sizes[j] * input_slices[j]);
				}
			}

			const unsigned int output_feature_map_count = output_configuration_specific.feature_map_count;
			const unsigned int input_feature_map_count = input_configuration_specific.feature_map_count;
			const int total_workload = output_feature_map_count * input_feature_map_count;
			const unsigned int const_entry_count = entry_count;
			const std::vector<unsigned int>::const_iterator output_dimension_sizes_it = output_configuration_specific.dimension_sizes.begin();
			const std::vector<unsigned int>::const_iterator input_slices_it = input_slices.begin();
			const std::vector<unsigned int>::const_iterator offset_list_it = offset_list.begin();

			#pragma omp parallel default(none) num_threads(plain_config->openmp_thread_count)
			{
				std::tr1::array<unsigned int, max_dimension_count> current_output_position;
				std::vector<float> weights_global(const_window_elem_count, 0.0F);
				std::vector<float> weights_local(const_window_elem_count, 0.0F);

				#pragma omp for schedule(guided)
				for(int workload_id = 0; workload_id < total_workload; ++workload_id)
				{
					int output_feature_map_id = workload_id / input_feature_map_count;
					int input_feature_map_id = workload_id - (output_feature_map_id * input_feature_map_count);

					std::vector<float>::const_iterator in_it_base = in_it_global + (input_feature_map_id * input_neuron_count_per_feature_map);
					std::vector<float>::const_iterator out_err_it_base = out_err_it_global + (output_feature_map_id * output_neuron_count_per_feature_map);
					std::vector<float>::iterator weights_it_base = weights + (output_feature_map_id * (const_window_elem_count * input_feature_map_count)) + (const_window_elem_count * input_feature_map_id);

					std::fill_n(weights_global.begin(), const_window_elem_count, 0.0F);

					for(unsigned int entry_id = 0; entry_id < const_entry_count; ++entry_id)
					{
						std::vector<float>::const_iterator in_it_base2 = in_it_base + (entry_id * input_neuron_count);
						std::vector<float>::const_iterator out_err_it_base2 = out_err_it_base + (entry_id * output_neuron_count);

						std::fill_n(current_output_position.begin(), dimension_count, 0);
						std::fill_n(weights_local.begin(), const_window_elem_count, 0.0F);
						for(std::vector<float>::const_iterator out_err_it = out_err_it_base2; out_err_it != out_err_it_base2 + output_neuron_count_per_feature_map; ++out_err_it)
						{
							std::vector<float>::const_iterator in_it = in_it_base2;
							for(unsigned int i = 0; i < dimension_count; ++i)
								in_it += current_output_position[i] * (*(input_slices_it + i));

							float current_err = *out_err_it;
							for(unsigned int i = 0; i < const_window_elem_count; ++i)
							{
								float in_neuron = *(in_it + *(offset_list_it + i));
								weights_local[i] += (in_neuron * in_neuron * current_err);
							}

							// Go to the next output element
							for(unsigned int i = 0; i < dimension_count; ++i)
							{
								if ((++current_output_position[i]) < *(output_dimension_sizes_it + i))
									break;
//.........这里部分代码省略.........

		void sparse_convolution_layer_updater_plain::test(
			const_additional_buffer_smart_ptr input_buffer,
			additional_buffer_smart_ptr output_buffer,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const_layer_data_smart_ptr data,
			const_layer_data_custom_smart_ptr data_custom,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int updater_count,
			unsigned int offset_input_entry_id) const
		{
			const unsigned int input_neuron_count = input_configuration_specific.get_neuron_count();
			const unsigned int input_neuron_count_per_feature_map = input_configuration_specific.get_neuron_count_per_feature_map();
			const unsigned int output_neuron_count = output_configuration_specific.get_neuron_count();
			const unsigned int output_neuron_count_per_feature_map = output_configuration_specific.get_neuron_count_per_feature_map();
			const std::vector<float>::const_iterator in_it_global = input_buffer->begin() + input_neuron_count * offset_input_entry_id;
			const std::vector<float>::iterator out_it_global = output_buffer->begin();
			nnforge_shared_ptr<const sparse_convolution_layer> layer_derived = nnforge_dynamic_pointer_cast<const sparse_convolution_layer>(layer_schema);
			const std::vector<unsigned int>& window_sizes = layer_derived->window_sizes;
			const unsigned int dimension_count = static_cast<unsigned int>(window_sizes.size());
			std::vector<unsigned int> input_slices(input_configuration_specific.dimension_sizes.size());
			input_slices[0] = 1;
			for(unsigned int i = 0; i < dimension_count - 1; ++i)
				input_slices[i + 1] = input_slices[i] * input_configuration_specific.dimension_sizes[i];
			unsigned int window_elem_count = 1;
			for(unsigned int i = 0; i < dimension_count; ++i)
				window_elem_count *= window_sizes[i];
			const unsigned int const_window_elem_count = window_elem_count;

			const std::vector<float>::const_iterator weights = (*data)[0].begin();
			const std::vector<float>::const_iterator biases = (*data)[1].begin();

			const std::vector<int>::const_iterator column_indices = (*data_custom)[0].begin();
			const std::vector<int>::const_iterator row_indices = (*data_custom)[1].begin();

			std::vector<unsigned int> current_local_input_position(dimension_count, 0);
			std::vector<unsigned int> offset_list(window_elem_count);
			for(unsigned int i = 1; i < window_elem_count; ++i)
			{
				int offset = 0;
				for(unsigned int j = 0; j < dimension_count; ++j)
				{
					offset += static_cast<int>(input_slices[j]);
					if ((++current_local_input_position[j]) < window_sizes[j])
					{
						offset_list[i] = offset_list[i-1] + offset;
						break;
					}
					current_local_input_position[j] = 0;
					offset -= static_cast<int>(window_sizes[j] * input_slices[j]);
				}
			}

			const unsigned int output_feature_map_count = output_configuration_specific.feature_map_count;
			const unsigned int input_feature_map_count = input_configuration_specific.feature_map_count;
			const int total_workload = updater_count * output_feature_map_count;
			const std::vector<unsigned int>::const_iterator output_dimension_sizes_it = output_configuration_specific.dimension_sizes.begin();
			const std::vector<unsigned int>::const_iterator input_slices_it = input_slices.begin();
			const std::vector<unsigned int>::const_iterator offset_list_it = offset_list.begin();

			#pragma omp parallel default(none) num_threads(plain_config->openmp_thread_count)
			{
				nnforge_array<unsigned int, max_dimension_count> current_output_position;

				#pragma omp for schedule(guided)
				for(int workload_id = 0; workload_id < total_workload; ++workload_id)
				{
					int entry_id = workload_id / output_feature_map_count;
					int output_feature_map_id = workload_id - (entry_id * output_feature_map_count);

					std::vector<float>::iterator out_it_base = out_it_global + (entry_id * output_neuron_count) + (output_feature_map_id * output_neuron_count_per_feature_map);
					std::vector<float>::const_iterator in_it_base = in_it_global + entry_id * input_neuron_count;

					const int start_column_index = row_indices[output_feature_map_id];
					const int end_column_index = row_indices[output_feature_map_id + 1];

					std::fill_n(current_output_position.begin(), dimension_count, 0);
					for(std::vector<float>::iterator out_it = out_it_base; out_it != out_it_base + output_neuron_count_per_feature_map; ++out_it)
					{
						float sum = *(biases + output_feature_map_id);
						std::vector<float>::const_iterator weights_it = weights + start_column_index * const_window_elem_count;
						std::vector<float>::const_iterator in_it_base2 = in_it_base;
						for(unsigned int i = 0; i < dimension_count; ++i)
							in_it_base2 += current_output_position[i] * (*(input_slices_it + i));

						for(int column_index = start_column_index; column_index < end_column_index; ++column_index)
						{
							int input_feature_map_id = column_indices[column_index];

							// Define the starting position of the first input elem
							std::vector<float>::const_iterator in_it = in_it_base2 + (input_feature_map_id * input_neuron_count_per_feature_map);

							for(unsigned int i = 0; i < const_window_elem_count; ++i)
							{
								sum += (*(in_it + *(offset_list_it + i))) * (*weights_it);
								++weights_it;
							}
						}
//.........这里部分代码省略.........

		void sparse_convolution_layer_updater_plain::update_weights(
			const_additional_buffer_smart_ptr input_neurons,
			const_additional_buffer_smart_ptr output_errors,
			std::vector<additional_buffer_smart_ptr>& additional_buffers,
			layer_data_smart_ptr gradient,
			const_layer_data_custom_smart_ptr data_custom,
			plain_running_configuration_const_smart_ptr plain_config,
			const_layer_smart_ptr layer_schema,
			const layer_configuration_specific& input_configuration_specific,
			const layer_configuration_specific& output_configuration_specific,
			unsigned int updater_count,
			unsigned int offset_input_entry_id) const
		{
			const unsigned int input_neuron_count = input_configuration_specific.get_neuron_count();
			const unsigned int input_neuron_count_per_feature_map = input_configuration_specific.get_neuron_count_per_feature_map();
			const unsigned int output_neuron_count = output_configuration_specific.get_neuron_count();
			const unsigned int output_neuron_count_per_feature_map = output_configuration_specific.get_neuron_count_per_feature_map();
			const std::vector<float>::const_iterator in_it_global = input_neurons->begin() + input_neuron_count * offset_input_entry_id;
			const std::vector<float>::const_iterator out_err_it_global = output_errors->begin();
			nnforge_shared_ptr<const sparse_convolution_layer> layer_derived = nnforge_dynamic_pointer_cast<const sparse_convolution_layer>(layer_schema);
			const std::vector<unsigned int>& window_sizes = layer_derived->window_sizes;
			unsigned int feature_map_connection_count = layer_derived->feature_map_connection_count;
			const unsigned int dimension_count = static_cast<unsigned int>(window_sizes.size());
			std::vector<unsigned int> input_slices(input_configuration_specific.dimension_sizes.size());
			input_slices[0] = 1;
			for(unsigned int i = 0; i < dimension_count - 1; ++i)
				input_slices[i + 1] = input_slices[i] * input_configuration_specific.dimension_sizes[i];
			unsigned int window_elem_count = 1;
			for(unsigned int i = 0; i < dimension_count; ++i)
				window_elem_count *= window_sizes[i];
			const unsigned int const_window_elem_count = window_elem_count;

			const std::vector<float>::iterator gradient_weights = (*gradient)[0].begin();
			const std::vector<float>::iterator gradient_biases = (*gradient)[1].begin();

			const std::vector<int>::const_iterator column_indices = (*data_custom)[0].begin();
			const std::vector<int>::const_iterator row_indices = (*data_custom)[1].begin();

			std::vector<std::pair<int, int> > out_fm_in_fm_list(feature_map_connection_count);
			int i = 0;
			for(int output_feature_map_id = 0; output_feature_map_id < output_configuration_specific.feature_map_count; ++output_feature_map_id)
			{
				const int start_column_index = row_indices[output_feature_map_id];
				const int end_column_index = row_indices[output_feature_map_id + 1];
				for(int column_index = start_column_index; column_index < end_column_index; ++column_index)
				{
					int input_feature_map_id = column_indices[column_index];
					out_fm_in_fm_list[i].first = output_feature_map_id;
					out_fm_in_fm_list[i].second = input_feature_map_id;
					++i;
				}
			}

			std::vector<unsigned int> current_local_input_position(dimension_count, 0);
			std::vector<unsigned int> offset_list(window_elem_count);
			for(unsigned int i = 1; i < window_elem_count; ++i)
			{
				int offset = 0;
				for(unsigned int j = 0; j < dimension_count; ++j)
				{
					offset += static_cast<int>(input_slices[j]);
					if ((++current_local_input_position[j]) < window_sizes[j])
					{
						offset_list[i] = offset_list[i-1] + offset;
						break;
					}
					current_local_input_position[j] = 0;
					offset -= static_cast<int>(window_sizes[j] * input_slices[j]);
				}
			}

			const unsigned int output_feature_map_count = output_configuration_specific.feature_map_count;
			const unsigned int input_feature_map_count = input_configuration_specific.feature_map_count;
			const int total_workload = feature_map_connection_count;
			const unsigned int const_entry_count = updater_count;
			const std::vector<unsigned int>::const_iterator output_dimension_sizes_it = output_configuration_specific.dimension_sizes.begin();
			const std::vector<unsigned int>::const_iterator input_slices_it = input_slices.begin();
			const std::vector<unsigned int>::const_iterator offset_list_it = offset_list.begin();
			const std::vector<std::pair<int, int> >::const_iterator out_fm_in_fm_it = out_fm_in_fm_list.begin();
			const int const_updater_count = updater_count;

			#pragma omp parallel default(none) num_threads(plain_config->openmp_thread_count)
			{
				nnforge_array<unsigned int, max_dimension_count> current_output_position;
				std::vector<float> weights_local(const_window_elem_count, 0.0F);

				#pragma omp for schedule(guided)
				for(int workload_id = 0; workload_id < total_workload; ++workload_id)
				{
					int weight_block_id = workload_id;
					int output_feature_map_id = out_fm_in_fm_it[weight_block_id].first;
					int input_feature_map_id = out_fm_in_fm_it[weight_block_id].second;

					std::fill_n(weights_local.begin(), const_window_elem_count, 0.0F);

					for(int entry_id = 0; entry_id < const_updater_count; ++entry_id)
					{
						std::vector<float>::const_iterator in_it_base = in_it_global + (entry_id * input_neuron_count) + (input_feature_map_id * input_neuron_count_per_feature_map);
						std::vector<float>::const_iterator out_err_it_base = out_err_it_global + (entry_id * output_neuron_count) + (output_feature_map_id * output_neuron_count_per_feature_map);

//.........这里部分代码省略.........