Yan Zhu 003f97cefa fc layer accept axis argument (#13822) ... Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/13822 as title Reviewed By: xianjiec Differential Revision: D12996338 fbshipit-source-id: 1aa61e71e2d79535325ea7034c82e1cb6bf3a9f6		2018-11-11 13:44:57 -08:00
..
__init__.py
adaptive_weight.py	fbshipit-source-id: ba600fcd2b5cefc7621357bdeb05e24cea02e5af	2018-06-27 04:50:56 -07:00
add_bias.py
arc_cosine_feature_map.py
batch_distill_lr_loss.py	Co-disitillation with different archs and/or feature set (#9793)	2018-07-25 10:10:27 -07:00
batch_lr_loss.py
batch_mse_loss.py
batch_normalization.py
batch_sigmoid_cross_entropy_loss.py
batch_softmax_loss.py
blob_weighted_sum.py
bucket_weighted.py	Implement bucket-based attention pooling for IdScoreList features (#13004)	2018-10-25 18:04:08 -07:00
build_index.py
concat.py	refactor preproc, support dense in TumHistory layer	2018-09-05 16:10:13 -07:00
constant_weight.py
conv.py
dropout.py
fc.py	fc layer accept axis argument (#13822)	2018-11-11 13:44:57 -08:00
fc_without_bias.py
feature_sparse_to_dense.py	support generic feature in DPER2 (#10197)	2018-08-04 15:25:13 -07:00
functional.py
gather_record.py
homotopy_weight.py
label_smooth.py
last_n_window_collector.py
layer_normalization.py	Allow use substitute ops for LayerNorm (#12177)	2018-10-11 17:36:10 -07:00
layers.py
margin_rank_loss.py
merge_id_lists.py
pairwise_similarity.py	move matrix formation for dot products to precompute/request-only (#10531)	2018-08-15 11:02:10 -07:00
position_weighted.py	Remove unused code base for distributed training (#10282)	2018-08-16 20:10:17 -07:00
random_fourier_features.py
reservoir_sampling.py
sampling_train.py
sampling_trainable_mixin.py
select_record_by_context.py
semi_random_features.py
sparse_feature_hash.py
sparse_lookup.py	Revert D10123465: Set the correct engine name for position weighted pooling when fp16 is used for training	2018-10-15 01:53:48 -07:00
split.py
tags.py	Remove unused code base for distributed training (#10282)	2018-08-16 20:10:17 -07:00
uniform_sampling.py