Yu Shi 43a2fd0e24 Support focal loss in MTML ... Summary: [Not in need of review at this time] Support focal loss in MTML (effectively dper2 in general) as described in https://arxiv.org/pdf/1708.02002.pdf. Adopt approach similar to Yuchen He's WIP diff D14008545 Test Plan: Passed the following unit tests buck test //caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test -- test_lr_loss_based_focal_loss buck test //caffe2/caffe2/fb/dper/layer_models/tests:mtml_test_2 -- test_mtml_with_lr_loss_based_focal_loss buck test //caffe2/caffe2/fb/dper/layer_models/tests/split_1:sparse_nn_test -- test_lr_loss_based_focal_loss_with_stop_grad_in_focal_factor Passed ./fblearner/flow/projects/dper/canary.sh; URL to track workflow runs: https://fburl.com/fblearner/446ix5q6 Model based on V10 of this diff f133367092 Baseline model f133297603 Protobuf of train_net_1 https://our.intern.facebook.com/intern/everpaste/?color=0&handle=GEq30QIFW_7HJJoCAAAAAABMgz4Jbr0LAAAz Reviewed By: hychyc90, ellie-wen Differential Revision: D16795972 fbshipit-source-id: 7bacae3e2255293d337951c896e9104208235f33		2019-08-25 01:42:25 -07:00
..
__init__.py
adaptive_weight.py
add_bias.py
arc_cosine_feature_map.py
batch_huber_loss.py	Add new regression loss function type to FBLearner (#21080)	2019-06-17 17:43:00 -07:00
batch_lr_loss.py	Support focal loss in MTML	2019-08-25 01:42:25 -07:00
batch_mse_loss.py
batch_normalization.py
batch_sigmoid_cross_entropy_loss.py
batch_softmax_loss.py
blob_weighted_sum.py
bpr_loss.py	Add BPR loss to TTSN (#24439)	2019-08-15 23:20:15 -07:00
bucket_weighted.py	Make hashing default for bucket-weighted pooling (#24266)	2019-08-13 13:56:32 -07:00
build_index.py
concat.py
constant_weight.py
conv.py
dropout.py	add dropout during eval (#17549)	2019-02-28 23:21:29 -08:00
fc.py
fc_without_bias.py
feature_sparse_to_dense.py	Return list of AccessedFeatures from get_accessed_features (#23983)	2019-08-14 10:50:27 -07:00
functional.py
gather_record.py
homotopy_weight.py
label_smooth.py
last_n_window_collector.py
layer_normalization.py
layers.py	Return list of AccessedFeatures from get_accessed_features (#23983)	2019-08-14 10:50:27 -07:00
margin_rank_loss.py
merge_id_lists.py
pairwise_similarity.py
position_weighted.py
random_fourier_features.py
reservoir_sampling.py
sampling_train.py
sampling_trainable_mixin.py
select_record_by_context.py
semi_random_features.py
sparse_dropout_with_replacement.py	hook up dropout sparse with replacement operator	2019-07-23 14:34:25 -07:00
sparse_feature_hash.py	Refactor and expose metadata of tum_history layer for online prediction	2019-08-15 00:27:11 -07:00
sparse_lookup.py	Perform weight re-init for embedding table in sparse_lookup.py (#22348)	2019-07-03 10:33:40 -07:00
split.py
tags.py
uniform_sampling.py