Intel(R) Math Kernel Library for Deep Neural Networks (Intel(R) MKL-DNN)
0.13
Performance library for Deep Learning
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Classes | |
struct | batch_normalization_backward |
struct | batch_normalization_forward |
struct | concat |
struct | convolution_backward_data |
struct | convolution_backward_weights |
struct | convolution_forward |
struct | convolution_relu_forward |
struct | eltwise_backward |
struct | eltwise_forward |
struct | engine |
An execution engine. More... | |
struct | error |
Intel(R) MKL-DNN exception class. More... | |
class | handle |
A class for wrapping an Intel(R) MKL-DNN handle. It is used as the base class for primitive (mkldnn_primitive_t), engine (mkldnn_engine_t), and stream (mkldnn_stream_t) handles. An object of the mkldnn::handle class can be passed by value. This class enables wrapping: More... | |
class | handle_traits |
A class that provides the destructor for an Intel(R) MKL-DNN C handle. More... | |
struct | inner_product_backward_data |
struct | inner_product_backward_weights |
struct | inner_product_forward |
struct | lrn_backward |
struct | lrn_forward |
struct | memory |
Memory primitive that describes the data. More... | |
struct | pooling_backward |
struct | pooling_forward |
struct | post_ops |
class | primitive |
Base class for all computational primitives. More... | |
struct | primitive_attr |
struct | reorder |
struct | softmax_forward |
struct | stream |
struct | sum |
struct | view |
Typedefs | |
typedef eltwise_forward | relu_forward |
typedef eltwise_backward | relu_backward |
Enumerations | |
enum | round_mode { round_nearest = mkldnn_round_nearest, round_down = mkldnn_round_down } |
enum | padding_kind { zero = mkldnn_padding_zero } |
enum | prop_kind { forward_training = mkldnn_forward_training, forward_scoring = mkldnn_forward_scoring, forward_inference = mkldnn_forward_inference, forward = mkldnn_forward, backward = mkldnn_backward, backward_data = mkldnn_backward_data, backward_weights = mkldnn_backward_weights, backward_bias = mkldnn_backward_bias } |
enum | algorithm { convolution_direct = mkldnn_convolution_direct, convolution_winograd = mkldnn_convolution_winograd, eltwise_relu = mkldnn_eltwise_relu, eltwise_tanh = mkldnn_eltwise_tanh, eltwise_elu = mkldnn_eltwise_elu, eltwise_square = mkldnn_eltwise_square, eltwise_abs = mkldnn_eltwise_abs, eltwise_sqrt = mkldnn_eltwise_sqrt, eltwise_linear = mkldnn_eltwise_linear, eltwise_bounded_relu = mkldnn_eltwise_bounded_relu, eltwise_soft_relu = mkldnn_eltwise_soft_relu, eltwise_logistic = mkldnn_eltwise_logistic, lrn_across_channels = mkldnn_lrn_across_channels, lrn_within_channel = mkldnn_lrn_within_channel, pooling_max = mkldnn_pooling_max, pooling_avg = mkldnn_pooling_avg, pooling_avg_include_padding = mkldnn_pooling_avg_include_padding, pooling_avg_exclude_padding = mkldnn_pooling_avg_exclude_padding } |
enum | batch_normalization_flag { use_global_stats = mkldnn_use_global_stats, use_scale_shift = mkldnn_use_scaleshift, omit_stats = mkldnn_omit_stats, fuse_bn_relu = mkldnn_fuse_bn_relu } |
enum | query { undef = mkldnn_query_undef, eengine = mkldnn_query_engine, primitive_kind = mkldnn_query_primitive_kind, num_of_inputs_s32 = mkldnn_query_num_of_inputs_s32, num_of_outputs_s32 = mkldnn_query_num_of_outputs_s32, time_estimate_f64 = mkldnn_query_time_estimate_f64, memory_consumption_s64 = mkldnn_query_memory_consumption_s64, impl_info_str = mkldnn_query_impl_info_str, memory_d = mkldnn_query_memory_d, convolution_d = mkldnn_query_convolution_d, eltwise_d = mkldnn_query_eltwise_d, relu_d = mkldnn_query_relu_d, softmax_d = mkldnn_query_softmax_d, pooling_d = mkldnn_query_pooling_d, lrn_d = mkldnn_query_lrn_d, batch_normalization_d = mkldnn_query_batch_normalization_d, inner_product_d = mkldnn_query_inner_product_d, convolution_relu_d = mkldnn_query_convolution_relu_d, input_pd = mkldnn_query_input_pd, output_pd = mkldnn_query_output_pd, src_pd = mkldnn_query_src_pd, diff_src_pd = mkldnn_query_diff_src_pd, weights_pd = mkldnn_query_weights_pd, diff_weights_pd = mkldnn_query_diff_weights_pd, dst_pd = mkldnn_query_dst_pd, diff_dst_pd = mkldnn_query_diff_dst_pd, workspace_pd = mkldnn_query_workspace_pd } |
Functions | |
mkldnn_primitive_kind_t | convert_to_c (primitive::kind akind) |
mkldnn_round_mode_t | convert_to_c (round_mode mode) |
mkldnn_padding_kind_t | convert_to_c (padding_kind kind) |
mkldnn_prop_kind_t | convert_to_c (prop_kind kind) |
mkldnn_alg_kind_t | convert_to_c (algorithm aalgorithm) |
mkldnn_batch_normalization_flag_t | convert_to_c (batch_normalization_flag aflag) |
mkldnn_query_t | convert_to_c (query aquery) |
bool | operator== (mkldnn_data_type_t a, memory::data_type b) |
bool | operator!= (mkldnn_data_type_t a, memory::data_type b) |
bool | operator== (memory::data_type a, mkldnn_data_type_t b) |
bool | operator!= (memory::data_type a, mkldnn_data_type_t b) |
bool | operator== (mkldnn_memory_format_t a, memory::format b) |
bool | operator!= (mkldnn_memory_format_t a, memory::format b) |
bool | operator== (memory::format a, mkldnn_memory_format_t b) |
bool | operator!= (memory::format a, mkldnn_memory_format_t b) |