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提交 16a48fc4 编写于 作者: O overlordmax
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add fibinet

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models/rank/fibinet/__init__.py 0 → 100644
浏览文件 @ 16a48fc4
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
models/rank/fibinet/config.yaml 0 → 100644
浏览文件 @ 16a48fc4
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# workspace
workspace: "paddlerec.models.rank.fibinet"
# list of dataset
dataset:
- name: dataloader_train # name of dataset to distinguish different datasets
batch_size: 2
type: DataLoader # or QueueDataset
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
dense_slots: "dense_var:13"
- name: dataset_train # name of dataset to distinguish different datasets
batch_size: 2
type: QueueDataset # or DataLoader
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
dense_slots: "dense_var:13"
- name: dataset_infer # name
batch_size: 2
type: DataLoader # or QueueDataset
data_path: "{workspace}/data/sample_data/train"
sparse_slots: "click 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26"
dense_slots: "dense_var:13"
# hyper parameters of user-defined network
hyper_parameters:
# optimizer config
optimizer:
class: Adam
learning_rate: 0.001
strategy: async
# user-defined <key, value> pairs
sparse_inputs_slots: 27
sparse_feature_number: 1000001
sparse_feature_dim: 9
dense_input_dim: 13
bilinear_type: 'all'
reduction_ratio: 3
dropout_rate: 0.5
# select runner by name
mode: [single_cpu_train, single_cpu_infer]
# config of each runner.
# runner is a kind of paddle training class, which wraps the train/infer process.
runner:
- name: single_cpu_train
class: train
# num of epochs
epochs: 4
# device to run training or infer
device: cpu
save_checkpoint_interval: 2 # save model interval of epochs
save_inference_interval: 4 # save inference
save_checkpoint_path: "increment_model" # save checkpoint path
save_inference_path: "inference" # save inference path
save_inference_feed_varnames: [] # feed vars of save inference
save_inference_fetch_varnames: [] # fetch vars of save inference
init_model_path: "" # load model path
print_interval: 10
phases: [phase1]
- name: single_cpu_infer
class: infer
# num of epochs
epochs: 1
# device to run training or infer
device: cpu
init_model_path: "increment_model" # load model path
phases: [phase2]
# runner will run all the phase in each epoch
phase:
- name: phase1
model: "{workspace}/model.py" # user-defined model
dataset_name: dataloader_train # select dataset by name
thread_num: 1
- name: phase2
model: "{workspace}/model.py" # user-defined model
dataset_name: dataset_infer # select dataset by name
thread_num: 1
models/rank/fibinet/data/download.sh 0 → 100644
浏览文件 @ 16a48fc4
wget --no-check-certificate https://fleet.bj.bcebos.com/ctr_data.tar.gz
tar -zxvf ctr_data.tar.gz
mv ./raw_data ./train_data_full
mkdir train_data && cd train_data
cp ../train_data_full/part-0 ../train_data_full/part-1 ./ && cd ..
mv ./test_data ./test_data_full
mkdir test_data && cd test_data
cp ../test_data_full/part-220 ./ && cd ..
echo "Complete data download."
echo "Full Train data stored in ./train_data_full "
echo "Full Test data stored in ./test_data_full "
echo "Rapid Verification train data stored in ./train_data "
echo "Rapid Verification test data stored in ./test_data "
models/rank/fibinet/data/get_slot_data.py 0 → 100644
浏览文件 @ 16a48fc4
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.fluid.incubate.data_generator as dg
cont_min_ = [0, -3, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]
cont_max_ = [20, 600, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
cont_diff_ = [20, 603, 100, 50, 64000, 500, 100, 50, 500, 10, 10, 10, 50]
hash_dim_ = 1000001
continuous_range_ = range(1, 14)
categorical_range_ = range(14, 40)
class CriteoDataset(dg.MultiSlotDataGenerator):
"""
DacDataset: inheritance MultiSlotDataGeneratior, Implement data reading
Help document: http://wiki.baidu.com/pages/viewpage.action?pageId=728820675
"""
def generate_sample(self, line):
"""
Read the data line by line and process it as a dictionary
"""
def reader():
"""
This function needs to be implemented by the user, based on data format
"""
features = line.rstrip('\n').split('\t')
dense_feature = []
sparse_feature = []
for idx in continuous_range_:
if features[idx] == "":
dense_feature.append(0.0)
else:
dense_feature.append(
(float(features[idx]) - cont_min_[idx - 1]) /
cont_diff_[idx - 1])
for idx in categorical_range_:
sparse_feature.append(
[hash(str(idx) + features[idx]) % hash_dim_])
label = [int(features[0])]
process_line = dense_feature, sparse_feature, label
feature_name = ["dense_feature"]
for idx in categorical_range_:
feature_name.append("C" + str(idx - 13))
feature_name.append("label")
s = "click:" + str(label[0])
for i in dense_feature:
s += " dense_feature:" + str(i)
for i in range(1, 1 + len(categorical_range_)):
s += " " + str(i) + ":" + str(sparse_feature[i - 1][0])
print(s.strip())
yield None
return reader
d = CriteoDataset()
d.run_from_stdin()
models/rank/fibinet/data/run.sh 0 → 100644
浏览文件 @ 16a48fc4
sh download.sh
mkdir slot_train_data_full
for i in `ls ./train_data_full`
do
cat train_data_full/$i | python get_slot_data.py > slot_train_data_full/$i
done
mkdir slot_test_data_full
for i in `ls ./test_data_full`
do
cat test_data_full/$i | python get_slot_data.py > slot_test_data_full/$i
done
mkdir slot_train_data
for i in `ls ./train_data`
do
cat train_data/$i | python get_slot_data.py > slot_train_data/$i
done
mkdir slot_test_data
for i in `ls ./test_data`
do
cat test_data/$i | python get_slot_data.py > slot_test_data/$i
done
models/rank/fibinet/data/sample_data/train/sample_train.txt 0 → 100644
浏览文件 @ 16a48fc4
click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:0.05 dense_feature:0.08 dense_feature:0.207421875 dense_feature:0.028 dense_feature:0.35 dense_feature:0.08 dense_feature:0.082 dense_feature:0.0 dense_feature:0.4 dense_feature:0.0 dense_feature:0.08 1:737395 2:210498 3:903564 4:286224 5:286835 6:906818 7:906116 8:67180 9:27346 10:51086 11:142177 12:95024 13:157883 14:873363 15:600281 16:812592 17:228085 18:35900 19:880474 20:984402 21:100885 22:26235 23:410878 24:798162 25:499868 26:306163
click:1 dense_feature:0.0 dense_feature:0.932006633499 dense_feature:0.02 dense_feature:0.14 dense_feature:0.0395625 dense_feature:0.328 dense_feature:0.98 dense_feature:0.12 dense_feature:1.886 dense_feature:0.0 dense_feature:1.8 dense_feature:0.0 dense_feature:0.14 1:715353 2:761523 3:432904 4:892267 5:515218 6:948614 7:266726 8:67180 9:27346 10:266081 11:286126 12:789480 13:49621 14:255651 15:47663 16:79797 17:342789 18:616331 19:880474 20:984402 21:242209 22:26235 23:669531 24:26284 25:269955 26:187951
click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.08 dense_feature:0.06 dense_feature:0.14125 dense_feature:0.076 dense_feature:0.05 dense_feature:0.22 dense_feature:0.208 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.06 1:737395 2:952384 3:511141 4:271077 5:286835 6:948614 7:903547 8:507110 9:27346 10:56047 11:612953 12:747707 13:977426 14:671506 15:158148 16:833738 17:342789 18:427155 19:880474 20:537425 21:916237 22:26235 23:468277 24:676936 25:751788 26:363967
click:0 dense_feature:0.0 dense_feature:0.124378109453 dense_feature:0.02 dense_feature:0.04 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.08 dense_feature:0.024 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.04 1:210127 2:286436 3:183920 4:507656 5:286835 6:906818 7:199553 8:67180 9:502607 10:708281 11:809876 12:888238 13:375164 14:202774 15:459895 16:475933 17:555571 18:847163 19:26230 20:26229 21:808836 22:191474 23:410878 24:315120 25:26224 26:26223
click:0 dense_feature:0.1 dense_feature:0.0149253731343 dense_feature:0.34 dense_feature:0.32 dense_feature:0.016421875 dense_feature:0.098 dense_feature:0.04 dense_feature:0.96 dense_feature:0.202 dense_feature:0.1 dense_feature:0.2 dense_feature:0.0 dense_feature:0.32 1:230803 2:817085 3:539110 4:388629 5:286835 6:948614 7:586040 8:67180 9:27346 10:271155 11:176640 12:827381 13:36881 14:202774 15:397299 16:411672 17:342789 18:474060 19:880474 20:984402 21:216871 22:26235 23:761351 24:787115 25:884722 26:904135
click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.13 dense_feature:0.04 dense_feature:0.246203125 dense_feature:0.108 dense_feature:0.05 dense_feature:0.04 dense_feature:0.03 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.04 1:737395 2:64837 3:259267 4:336976 5:515218 6:154084 7:847938 8:67180 9:27346 10:708281 11:776766 12:964800 13:324323 14:873363 15:212708 16:637238 17:681378 18:895034 19:673458 20:984402 21:18600 22:26235 23:410878 24:787115 25:884722 26:355412
click:0 dense_feature:0.0 dense_feature:0.028192371476 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0245625 dense_feature:0.016 dense_feature:0.04 dense_feature:0.12 dense_feature:0.016 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:737395 2:554760 3:661483 4:263696 5:938478 6:906818 7:786926 8:67180 9:27346 10:245862 11:668197 12:745676 13:432600 14:413795 15:751427 16:272410 17:342789 18:422136 19:26230 20:26229 21:452501 22:26235 23:51381 24:776636 25:26224 26:26223
click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:1.95 dense_feature:0.28 dense_feature:0.092828125 dense_feature:0.57 dense_feature:0.06 dense_feature:0.4 dense_feature:0.4 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.4 1:371155 2:817085 3:773609 4:555449 5:938478 6:906818 7:166117 8:507110 9:27346 10:545822 11:316654 12:172765 13:989600 14:255651 15:792372 16:606361 17:342789 18:566554 19:880474 20:984402 21:235256 22:191474 23:700326 24:787115 25:884722 26:569095
click:0 dense_feature:0.0 dense_feature:0.0912106135987 dense_feature:0.01 dense_feature:0.02 dense_feature:0.06625 dense_feature:0.018 dense_feature:0.05 dense_feature:0.06 dense_feature:0.098 dense_feature:0.0 dense_feature:0.4 dense_feature:0.0 dense_feature:0.04 1:230803 2:531472 3:284417 4:661677 5:938478 6:553107 7:21150 8:49466 9:27346 10:526914 11:164508 12:631773 13:882348 14:873363 15:523948 16:687081 17:342789 18:271301 19:26230 20:26229 21:647160 22:26235 23:410878 24:231695 25:26224 26:26223
click:1 dense_feature:0.0 dense_feature:0.00663349917081 dense_feature:0.01 dense_feature:0.02 dense_feature:0.02153125 dense_feature:0.092 dense_feature:0.05 dense_feature:0.68 dense_feature:0.472 dense_feature:0.0 dense_feature:0.3 dense_feature:0.0 dense_feature:0.02 1:737395 2:532829 3:320762 4:887282 5:286835 6:25207 7:640357 8:67180 9:27346 10:695831 11:739268 12:835325 13:402539 14:873363 15:125813 16:168896 17:342789 18:374414 19:26230 20:26229 21:850229 22:26235 23:410878 24:480027 25:26224 26:26223
click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:0.05 dense_feature:0.04 dense_feature:0.086125 dense_feature:0.098 dense_feature:0.15 dense_feature:0.06 dense_feature:0.228 dense_feature:0.0 dense_feature:0.2 dense_feature:0.0 dense_feature:0.04 1:210127 2:999497 3:646348 4:520638 5:938478 6:906818 7:438398 8:67180 9:27346 10:975902 11:532544 12:708828 13:815045 14:255651 15:896230 16:663630 17:342789 18:820094 19:687226 20:537425 21:481536 22:26235 23:761351 24:888170 25:250729 26:381125
click:1 dense_feature:0.1 dense_feature:0.00331674958541 dense_feature:0.02 dense_feature:0.02 dense_feature:0.00078125 dense_feature:0.002 dense_feature:0.73 dense_feature:0.08 dense_feature:0.254 dense_feature:0.1 dense_feature:1.4 dense_feature:0.0 dense_feature:0.02 1:715353 2:342833 3:551901 4:73418 5:286835 6:446063 7:219517 8:67180 9:27346 10:668726 11:40711 12:921745 13:361076 14:15048 15:214564 16:400893 17:228085 18:393370 19:26230 20:26229 21:383046 22:26235 23:700326 24:369764 25:26224 26:26223
click:0 dense_feature:0.0 dense_feature:0.142620232172 dense_feature:0.04 dense_feature:0.1 dense_feature:0.08853125 dense_feature:0.028 dense_feature:0.01 dense_feature:0.1 dense_feature:0.028 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.1 1:737395 2:583707 3:519411 4:19103 5:286835 6:906818 7:801403 8:67180 9:27346 10:35743 11:626052 12:142351 13:988058 14:873363 15:617333 16:850339 17:276641 18:696084 19:26230 20:26229 21:121620 22:191474 23:468277 24:18340 25:26224 26:26223
click:0 dense_feature:0.0 dense_feature:0.00995024875622 dense_feature:0.0 dense_feature:0.22 dense_feature:0.0251875 dense_feature:0.0 dense_feature:0.0 dense_feature:0.8 dense_feature:0.182 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.84 1:737395 2:19359 3:166075 4:381832 5:286835 6:446063 7:816009 8:67180 9:27346 10:708281 11:619790 12:524128 13:826787 14:202774 15:371495 16:392894 17:644532 18:271180 19:26230 20:26229 21:349978 22:26235 23:761351 24:517170 25:26224 26:26223
click:0 dense_feature:0.0 dense_feature:0.0149253731343 dense_feature:0.52 dense_feature:0.1 dense_feature:6.25153125 dense_feature:0.0 dense_feature:0.0 dense_feature:0.3 dense_feature:0.03 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.1 1:230803 2:24784 3:519411 4:19103 5:843054 6:948614 7:529143 8:67180 9:502607 10:708281 11:430027 12:142351 13:529101 14:202774 15:618316 16:850339 17:644532 18:95370 19:880474 20:31181 21:121620 22:26235 23:744389 24:18340 25:269955 26:683431
click:0 dense_feature:0.0 dense_feature:0.0480928689884 dense_feature:0.12 dense_feature:0.22 dense_feature:0.541703125 dense_feature:1.062 dense_feature:0.01 dense_feature:0.24 dense_feature:0.054 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.22 1:737395 2:378661 3:21539 4:552097 5:286835 6:553107 7:512138 8:67180 9:27346 10:708281 11:91094 12:516991 13:150114 14:873363 15:450569 16:353024 17:228085 18:539379 19:26230 20:26229 21:410733 22:26235 23:700326 24:272703 25:26224 26:26223
click:0 dense_feature:0.0 dense_feature:0.016583747927 dense_feature:0.06 dense_feature:0.0 dense_feature:0.209625 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.09 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 dense_feature:0.0 1:737395 2:750131 3:807749 4:905739 5:286835 6:906818 7:11935 8:67180 9:27346 10:708281 11:505199 12:285350 13:724106 14:255651 15:625913 16:511836 17:644532 18:102288 19:26230 20:26229 21:726818 22:179327 23:744389 24:176417 25:26224 26:26223
click:0 dense_feature:0.0 dense_feature:0.00663349917081 dense_feature:0.05 dense_feature:0.14 dense_feature:0.226703125 dense_feature:0.12 dense_feature:0.05 dense_feature:0.14 dense_feature:0.112 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.14 1:736218 2:690313 3:757279 4:763330 5:286835 6:553107 7:89560 8:642551 9:27346 10:128328 11:281593 12:246510 13:200341 14:255651 15:899145 16:807138 17:342789 18:659853 19:26230 20:26229 21:399608 22:26235 23:669531 24:787115 25:26224 26:26223
click:0 dense_feature:0.0 dense_feature:0.00829187396352 dense_feature:0.3 dense_feature:0.2 dense_feature:0.021296875 dense_feature:0.83 dense_feature:0.2 dense_feature:0.56 dense_feature:1.122 dense_feature:0.0 dense_feature:0.5 dense_feature:0.0 dense_feature:0.2 1:715353 2:283434 3:523722 4:590869 5:286835 6:948614 7:25472 8:67180 9:27346 10:340404 11:811342 12:679454 13:897590 14:813514 15:578769 16:962576 17:342789 18:267210 19:310188 20:537425 21:746185 22:179327 23:761351 24:416923 25:253255 26:249672
click:1 dense_feature:0.05 dense_feature:0.0149253731343 dense_feature:0.03 dense_feature:0.24 dense_feature:0.0 dense_feature:0.008 dense_feature:0.4 dense_feature:0.62 dense_feature:0.82 dense_feature:0.1 dense_feature:1.4 dense_feature:0.0 dense_feature:0.08 1:715353 2:532829 3:716475 4:940968 5:286835 6:948614 7:38171 8:67180 9:27346 10:619455 11:515541 12:779426 13:711791 14:255651 15:881750 16:408550 17:342789 18:612540 19:26230 20:26229 21:23444 22:26235 23:410878 24:88425 25:26224 26:26223
click:0 dense_feature:0.0 dense_feature:0.00497512437811 dense_feature:0.11 dense_feature:0.08 dense_feature:0.135265625 dense_feature:0.426 dense_feature:0.06 dense_feature:0.06 dense_feature:0.42 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.08 1:737395 2:817085 3:506158 4:48876 5:286835 6:948614 7:95506 8:67180 9:27346 10:75825 11:220591 12:613471 13:159874 14:255651 15:121379 16:889290 17:681378 18:532453 19:880474 20:537425 21:717912 22:26235 23:270873 24:450199 25:884722 26:382723
click:0 dense_feature:0.0 dense_feature:0.0829187396352 dense_feature:0.0 dense_feature:0.0 dense_feature:0.555859375 dense_feature:0.318 dense_feature:0.03 dense_feature:0.0 dense_feature:0.02 dense_feature:0.0 dense_feature:0.1 dense_feature:0.0 dense_feature:0.0 1:715353 2:465222 3:974451 4:892661 5:938478 6:948614 7:651987 8:67180 9:27346 10:708281 11:229311 12:545057 13:875629 14:149134 15:393524 16:213237 17:681378 18:540092 19:26230 20:26229 21:483290 22:26235 23:700326 24:946673 25:26224 26:26223
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models/rank/fibinet/model.py 0 → 100644
浏览文件 @ 16a48fc4
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.fluid as fluid
import itertools
from paddlerec.core.utils import envs
from paddlerec.core.model import ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def _init_hyper_parameters(self):
self.is_distributed = True if envs.get_fleet_mode().upper(
) == "PSLIB" else False
self.sparse_feature_number = envs.get_global_env(
"hyper_parameters.sparse_feature_number")
self.sparse_feature_dim = envs.get_global_env(
"hyper_parameters.sparse_feature_dim")
self.learning_rate = envs.get_global_env(
"hyper_parameters.optimizer.learning_rate")
def _SENETLayer(self, inputs, filed_size, reduction_ratio=3):
reduction_size = max(1, filed_size // reduction_ratio)
Z = fluid.layers.reduce_mean(inputs, dim=-1)
A_1 = fluid.layers.fc(
input=Z,
size=reduction_size,
param_attr=fluid.initializer.Xavier(uniform=False),
act='relu',
name='W_1')
A_2 = fluid.layers.fc(
input=A_1,
size=filed_size,
param_attr=fluid.initializer.Xavier(uniform=False),
act='relu',
name='W_2')
V = fluid.layers.elementwise_mul(
inputs, y=fluid.layers.unsqueeze(
input=A_2, axes=[2]))
return fluid.layers.split(V, num_or_sections=filed_size, dim=1)
def _BilinearInteraction(self,
inputs,
filed_size,
embedding_size,
bilinear_type="interaction"):
if bilinear_type == "all":
p = [
fluid.layers.elementwise_mul(
fluid.layers.fc(
input=v_i,
size=embedding_size,
param_attr=fluid.initializer.Xavier(uniform=False),
act=None,
name=None),
fluid.layers.squeeze(
input=v_j, axes=[1]))
for v_i, v_j in itertools.combinations(inputs, 2)
]
else:
raise NotImplementedError
return fluid.layers.concat(input=p, axis=1)
def _DNNLayer(self, inputs, dropout_rate=0.5):
deep_input = inputs
for i, hidden_unit in enumerate([400, 400, 400]):
fc_out = fluid.layers.fc(
input=deep_input,
size=hidden_unit,
param_attr=fluid.initializer.Xavier(uniform=False),
act='relu',
name='d_' + str(i))
fc_out = fluid.layers.dropout(fc_out, dropout_prob=dropout_rate)
deep_input = fc_out
return deep_input
def net(self, input, is_infer=False):
self.sparse_inputs = self._sparse_data_var[1:]
self.dense_input = self._dense_data_var[0]
self.label_input = self._sparse_data_var[0]
emb = []
for data in self.sparse_inputs:
feat_emb = fluid.embedding(
input=data,
size=[self.sparse_feature_number, self.sparse_feature_dim],
param_attr=fluid.ParamAttr(
name='dis_emb',
learning_rate=5,
initializer=fluid.initializer.Xavier(
fan_in=self.sparse_feature_dim,
fan_out=self.sparse_feature_dim)),
is_sparse=True)
emb.append(feat_emb)
concat_emb = fluid.layers.concat(emb, axis=1)
filed_size = len(self.sparse_inputs)
bilinear_type = envs.get_global_env("hyper_parameters.bilinear_type")
reduction_ratio = envs.get_global_env(
"hyper_parameters.reduction_ratio")
dropout_rate = envs.get_global_env("hyper_parameters.dropout_rate")
senet_output = self._SENETLayer(concat_emb, filed_size,
reduction_ratio)
senet_bilinear_out = self._BilinearInteraction(
senet_output, filed_size, self.sparse_feature_dim, bilinear_type)
concat_emb = fluid.layers.split(
concat_emb, num_or_sections=filed_size, dim=1)
bilinear_out = self._BilinearInteraction(
concat_emb, filed_size, self.sparse_feature_dim, bilinear_type)
dnn_input = fluid.layers.concat(
input=[senet_bilinear_out, bilinear_out, self.dense_input], axis=1)
dnn_output = self._DNNLayer(dnn_input, dropout_rate)
y_pred = fluid.layers.fc(
input=dnn_output,
size=1,
param_attr=fluid.initializer.Xavier(uniform=False),
act='sigmoid',
name='logit')
self.predict = y_pred
auc, batch_auc, _ = fluid.layers.auc(input=self.predict,
label=self.label_input,
num_thresholds=2**12,
slide_steps=20)
if is_infer:
self._infer_results["AUC"] = auc
self._infer_results["BATCH_AUC"] = batch_auc
return
self._metrics["AUC"] = auc
self._metrics["BATCH_AUC"] = batch_auc
cost = fluid.layers.log_loss(
input=self.predict,
label=fluid.layers.cast(
x=self.label_input, dtype='float32'))
avg_cost = fluid.layers.reduce_mean(cost)
self._cost = avg_cost
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