Intel® oneAPI Deep Neural Network Developer Guide and Reference
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enum dnnl::algorithm
Overview
Kinds of algorithms. More…
#include <dnnl.hpp>
enum algorithm
{
undef = dnnl_alg_kind_undef,
convolution_auto = dnnl_convolution_auto,
convolution_direct = dnnl_convolution_direct,
convolution_winograd = dnnl_convolution_winograd,
deconvolution_direct = dnnl_deconvolution_direct,
deconvolution_winograd = dnnl_deconvolution_winograd,
eltwise_relu = dnnl_eltwise_relu,
eltwise_tanh = dnnl_eltwise_tanh,
eltwise_elu = dnnl_eltwise_elu,
eltwise_square = dnnl_eltwise_square,
eltwise_abs = dnnl_eltwise_abs,
eltwise_sqrt = dnnl_eltwise_sqrt,
eltwise_swish = dnnl_eltwise_swish,
eltwise_linear = dnnl_eltwise_linear,
eltwise_soft_relu = dnnl_eltwise_soft_relu,
eltwise_mish = dnnl_eltwise_mish,
eltwise_logistic = dnnl_eltwise_logistic,
eltwise_exp = dnnl_eltwise_exp,
eltwise_gelu_tanh = dnnl_eltwise_gelu_tanh,
eltwise_gelu_erf = dnnl_eltwise_gelu_erf,
eltwise_log = dnnl_eltwise_log,
eltwise_clip = dnnl_eltwise_clip,
eltwise_clip_v2 = dnnl_eltwise_clip_v2,
eltwise_pow = dnnl_eltwise_pow,
eltwise_round = dnnl_eltwise_round,
eltwise_hardswish = dnnl_eltwise_hardswish,
eltwise_hardsigmoid = dnnl_eltwise_hardsigmoid,
eltwise_relu_use_dst_for_bwd = dnnl_eltwise_relu_use_dst_for_bwd,
eltwise_tanh_use_dst_for_bwd = dnnl_eltwise_tanh_use_dst_for_bwd,
eltwise_elu_use_dst_for_bwd = dnnl_eltwise_elu_use_dst_for_bwd,
eltwise_sqrt_use_dst_for_bwd = dnnl_eltwise_sqrt_use_dst_for_bwd,
eltwise_logistic_use_dst_for_bwd = dnnl_eltwise_logistic_use_dst_for_bwd,
eltwise_exp_use_dst_for_bwd = dnnl_eltwise_exp_use_dst_for_bwd,
eltwise_clip_v2_use_dst_for_bwd = dnnl_eltwise_clip_v2_use_dst_for_bwd,
lrn_across_channels = dnnl_lrn_across_channels,
lrn_within_channel = dnnl_lrn_within_channel,
pooling_max = dnnl_pooling_max,
pooling_avg_include_padding = dnnl_pooling_avg_include_padding,
pooling_avg_exclude_padding = dnnl_pooling_avg_exclude_padding,
vanilla_rnn = dnnl_vanilla_rnn,
vanilla_lstm = dnnl_vanilla_lstm,
vanilla_gru = dnnl_vanilla_gru,
lbr_gru = dnnl_lbr_gru,
vanilla_augru = dnnl_vanilla_augru,
lbr_augru = dnnl_lbr_augru,
binary_add = dnnl_binary_add,
binary_mul = dnnl_binary_mul,
binary_max = dnnl_binary_max,
binary_min = dnnl_binary_min,
binary_div = dnnl_binary_div,
binary_sub = dnnl_binary_sub,
binary_ge = dnnl_binary_ge,
binary_gt = dnnl_binary_gt,
binary_le = dnnl_binary_le,
binary_lt = dnnl_binary_lt,
binary_eq = dnnl_binary_eq,
binary_ne = dnnl_binary_ne,
resampling_nearest = dnnl_resampling_nearest,
resampling_linear = dnnl_resampling_linear,
reduction_max = dnnl_reduction_max,
reduction_min = dnnl_reduction_min,
reduction_sum = dnnl_reduction_sum,
reduction_mul = dnnl_reduction_mul,
reduction_mean = dnnl_reduction_mean,
reduction_norm_lp_max = dnnl_reduction_norm_lp_max,
reduction_norm_lp_sum = dnnl_reduction_norm_lp_sum,
reduction_norm_lp_power_p_max = dnnl_reduction_norm_lp_power_p_max,
reduction_norm_lp_power_p_sum = dnnl_reduction_norm_lp_power_p_sum,
softmax_accurate = dnnl_softmax_accurate,
softmax_log = dnnl_softmax_log,
};
Detailed Documentation
Kinds of algorithms.
Enum Values
undef
Undefined algorithm.
convolution_auto
Convolution algorithm that is chosen to be either direct or Winograd automatically.
convolution_direct
Direct convolution.
convolution_winograd
Winograd convolution.
deconvolution_direct
Direct deconvolution.
deconvolution_winograd
Winograd deconvolution.
eltwise_relu
Elementwise: rectified linear unit (ReLU)
eltwise_tanh
Elementwise: hyperbolic tangent non-linearity (tanh)
eltwise_elu
Elementwise: exponential linear unit (ELU)
eltwise_square
Elementwise: square.
eltwise_abs
Elementwise: abs.
eltwise_sqrt
Elementwise: square root.
eltwise_swish
Elementwise: swish (
)
eltwise_linear
Elementwise: linear.
eltwise_soft_relu
Elementwise: soft_relu.
eltwise_mish
Elementwise: mish.
eltwise_logistic
Elementwise: logistic.
eltwise_exp
Elementwise: exponent.
eltwise_gelu_tanh
Elementwise: tanh-based gelu.
eltwise_gelu_erf
Elementwise: erf-based gelu.
eltwise_log
Elementwise: natural logarithm.
eltwise_clip
Elementwise: clip.
eltwise_clip_v2
Eltwise: clip version 2.
eltwise_pow
Elementwise: pow.
eltwise_round
Elementwise: round.
eltwise_hardswish
Elementwise: hardswish.
eltwise_hardsigmoid
Elementwise: hardsigmoid.
eltwise_relu_use_dst_for_bwd
Elementwise: rectified linar unit (ReLU) (dst for backward)
eltwise_tanh_use_dst_for_bwd
Elementwise: hyperbolic tangent non-linearity (tanh) (dst for backward)
eltwise_elu_use_dst_for_bwd
Elementwise: exponential linear unit (ELU) (dst for backward)
eltwise_sqrt_use_dst_for_bwd
Elementwise: square root (dst for backward)
eltwise_logistic_use_dst_for_bwd
Elementwise: logistic (dst for backward)
eltwise_exp_use_dst_for_bwd
Elementwise: exponent (dst for backward)
eltwise_clip_v2_use_dst_for_bwd
Elementwise: clip version 2 (dst for backward)
lrn_across_channels
Local response normalization (LRN) across multiple channels.
lrn_within_channel
LRN within a single channel.
pooling_max
Max pooling.
pooling_avg_include_padding
Average pooling include padding.
pooling_avg_exclude_padding
Average pooling exclude padding.
vanilla_rnn
RNN cell.
vanilla_lstm
LSTM cell.
vanilla_gru
GRU cell.
lbr_gru
GRU cell with linear before reset.
Differs from the vanilla GRU in how the new memory gate is calculated: 
LRB GRU expects 4 bias tensors on input: 
vanilla_augru
AUGRU cell.
lbr_augru
AUGRU cell with linear before reset.
binary_add
Binary add.
binary_mul
Binary mul.
binary_max
Binary max.
binary_min
Binary min.
binary_div
Binary div.
binary_sub
Binary sub.
binary_ge
Binary greater than or equal.
binary_gt
Binary greater than.
binary_le
Binary less than or equal.
binary_lt
Binary less than.
binary_eq
Binary equal.
binary_ne
Binary not equal.
resampling_nearest
Nearest Neighbor resampling method.
resampling_linear
Linear (Bilinear, Trilinear) resampling method.
reduction_max
Reduction using max operation.
reduction_min
Reduction using min operation.
reduction_sum
Reduction using sum operation.
reduction_mul
Reduction using mul operation.
reduction_mean
Reduction using mean operation.
reduction_norm_lp_max
Reduction using norm_lp_max operation.
reduction_norm_lp_sum
Reduction using norm_lp_sum operation.
reduction_norm_lp_power_p_max
Reduction using norm_lp_power_p_max operation.
reduction_norm_lp_power_p_sum
Reduction using norm_lp_power_p_sum operation.
softmax_accurate
Softmax, numerically stable.
softmax_log
LogSoftmax, numerically stable.