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PaddleRec
提交 0f78b254 编写于 作者: Z zhangwenhui03
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Merge branch 'develop' into 'develop' 

add multitask ssr gru4rec

See merge request !18
上级 3c1f6d2f 4cdbb3e6
展开全部 隐藏空白更改
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Showing with 1817 addition and 0 deletion
models/multitask/esmm/__init__.py 0 → 100755
浏览文件 @ 0f78b254
# 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/multitask/esmm/config.yaml 0 → 100644
浏览文件 @ 0f78b254
# 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.
train:
trainer:
# for cluster training
strategy: "async"
epochs: 3
workspace: "fleetrec.models.multitask.esmm"
reader:
batch_size: 2
class: "{workspace}/esmm_reader.py"
train_data_path: "{workspace}/data/train"
model:
models: "{workspace}/model.py"
hyper_parameters:
vocab_size: 10000
embed_size: 128
learning_rate: 0.001
optimizer: adam
save:
increment:
dirname: "increment"
epoch_interval: 2
save_last: True
inference:
dirname: "inference"
epoch_interval: 4
save_last: True
models/multitask/esmm/data/test/small_test.txt 0 → 100644
浏览文件 @ 0f78b254
0 16
475 473 155
491 21
96 185 96
29 14 13
5 481 11 21 470
70 5 70 11
167 42 167 217
72 15 73 161 172
82 82
97 297 97
193 182 186 183 184 177 214
152 152
163 298 7
39 73 71
490 23 23 496 488 74 23 74 486 23 23 74
17 17
170 170 483 444 443 234
25 472
5 5 11 70 69
149 149 455
356 68 477 468 17 479 66
159 172 6 71 6 6 158 13 494 169
155 44 438 144 500
156 9 9
146 146
173 10 10 461
7 6 6
269 48 268
50 100
323 174 18
69 69 22 98
38 171
22 29 489 10
0 0
11 5
29 13 14 232 231 451 289 452 229
260 11 156
166 160 166 39
223 134 134 420
66 401 68 132 17 84 287 5
39 304
65 84 132
400 211
145 144
16 28 254 48 50 100 42 154 262 133 17
0 0
28 28
11 476 464
61 61 86 86
38 38
463 478
437 265
22 39 485 171 98
434 51 344
16 16
67 67 67 448
22 12 161
15 377 147 147 374
119 317 0
38 484
403 499
432 442
28 0 16 50 465 42
163 487 7 162
99 99 325 423 83 83
154 133
5 37 492 235 160 279
10 10 457 493 10 460
441 4 4 4 4 4 4 4
153 153
159 164 164
328 37
65 65 404 347 431 459
80 80 44 44
61 446
162 495 7 453
157 21 204 68 37 66 469 145
37 151 230 206 240 205 264 87 409 87 288 270 280 329 157 296 454 474
430 445 433
449 14
9 9 9 9
440 238 226
148 148
266 267 181
48 498
263 255 256
458 158 7
72 168 12 165 71 73 173 49
0 0
7 7 6
14 29 13 6 15 14 15 13
480 439 21
450 21 151
12 12 49 14 13 165 12 169 72 15 15
91 91
22 12 49 168
497 101 30 411 30 482 30 53 30 101 176 415 53 447
462 150 150
471 456 131 435 131 467 436 412 227 218 190 466 429 213 326
models/multitask/esmm/data/train/small.csv 0 → 100644
浏览文件 @ 0f78b254
此差异已折叠。
models/multitask/esmm/esmm_reader.py 0 → 100644
浏览文件 @ 0f78b254
# 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.
from __future__ import print_function
from fleetrec.core.reader import Reader
from fleetrec.core.utils import envs
from collections import defaultdict
import numpy as np
class TrainReader(Reader):
def init(self):
all_field_id = ['101', '109_14', '110_14', '127_14', '150_14', '121', '122', '124', '125', '126', '127', '128', '129',
'205', '206', '207', '210', '216', '508', '509', '702', '853', '301']
self.all_field_id_dict = defaultdict(int)
for i,field_id in enumerate(all_field_id):
self.all_field_id_dict[field_id] = [False,i]
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.strip().split(',')
#ctr = list(map(int, features[1]))
#cvr = list(map(int, features[2]))
ctr = int(features[1])
cvr = int(features[2])
padding = 0
output = [(field_id,[]) for field_id in self.all_field_id_dict]
for elem in features[4:]:
field_id,feat_id = elem.strip().split(':')
if field_id not in self.all_field_id_dict:
continue
self.all_field_id_dict[field_id][0] = True
index = self.all_field_id_dict[field_id][1]
#feat_id = list(map(int, feat_id))
output[index][1].append(int(feat_id))
for field_id in self.all_field_id_dict:
visited,index = self.all_field_id_dict[field_id]
if visited:
self.all_field_id_dict[field_id][0] = False
else:
output[index][1].append(padding)
output.append(('ctr', [ctr]))
output.append(('cvr', [cvr]))
yield output
return reader
models/multitask/esmm/model.py 0 → 100644
浏览文件 @ 0f78b254
# 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 math
import paddle.fluid as fluid
from fleetrec.core.utils import envs
from fleetrec.core.model import Model as ModelBase
import numpy as np
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def fc(self,tag, data, out_dim, active='prelu'):
init_stddev = 1.0
scales = 1.0 / np.sqrt(data.shape[1])
p_attr = fluid.param_attr.ParamAttr(name='%s_weight' % tag,
initializer=fluid.initializer.NormalInitializer(loc=0.0, scale=init_stddev * scales))
b_attr = fluid.ParamAttr(name='%s_bias' % tag, initializer=fluid.initializer.Constant(0.1))
out = fluid.layers.fc(input=data,
size=out_dim,
act=active,
param_attr=p_attr,
bias_attr =b_attr,
name=tag)
return out
def input_data(self):
sparse_input_ids = [
fluid.data(name="field_" + str(i), shape=[-1, 1], dtype="int64", lod_level=1) for i in range(0,23)
]
label_ctr = fluid.data(name="ctr", shape=[-1, 1], dtype="int64")
label_cvr = fluid.data(name="cvr", shape=[-1, 1], dtype="int64")
inputs = sparse_input_ids + [label_ctr] + [label_cvr]
self._data_var.extend(inputs)
return inputs
def net(self, inputs):
vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, self._namespace)
embed_size = envs.get_global_env("hyper_parameters.embed_size", None, self._namespace)
emb = []
for data in inputs[0:-2]:
feat_emb = fluid.embedding(input=data,
size=[vocab_size, embed_size],
param_attr=fluid.ParamAttr(name='dis_emb',
learning_rate=5,
initializer=fluid.initializer.Xavier(fan_in=embed_size,fan_out=embed_size)
),
is_sparse=True)
field_emb = fluid.layers.sequence_pool(input=feat_emb,pool_type='sum')
emb.append(field_emb)
concat_emb = fluid.layers.concat(emb, axis=1)
# ctr
active = 'relu'
ctr_fc1 = self.fc('ctr_fc1', concat_emb, 200, active)
ctr_fc2 = self.fc('ctr_fc2', ctr_fc1, 80, active)
ctr_out = self.fc('ctr_out', ctr_fc2, 2, 'softmax')
# cvr
cvr_fc1 = self.fc('cvr_fc1', concat_emb, 200, active)
cvr_fc2 = self.fc('cvr_fc2', cvr_fc1, 80, active)
cvr_out = self.fc('cvr_out', cvr_fc2, 2,'softmax')
ctr_clk = inputs[-2]
ctcvr_buy = inputs[-1]
ctr_prop_one = fluid.layers.slice(ctr_out, axes=[1], starts=[1], ends=[2])
cvr_prop_one = fluid.layers.slice(cvr_out, axes=[1], starts=[1], ends=[2])
ctcvr_prop_one = fluid.layers.elementwise_mul(ctr_prop_one, cvr_prop_one)
ctcvr_prop = fluid.layers.concat(input=[1-ctcvr_prop_one,ctcvr_prop_one], axis = 1)
loss_ctr = fluid.layers.cross_entropy(input=ctr_out, label=ctr_clk)
loss_ctcvr = fluid.layers.cross_entropy(input=ctcvr_prop, label=ctcvr_buy)
cost = loss_ctr + loss_ctcvr
avg_cost = fluid.layers.mean(cost)
auc_ctr, batch_auc_ctr, auc_states_ctr = fluid.layers.auc(input=ctr_out, label=ctr_clk)
auc_ctcvr, batch_auc_ctcvr, auc_states_ctcvr = fluid.layers.auc(input=ctcvr_prop, label=ctcvr_buy)
self._cost = avg_cost
self._metrics["AUC_ctr"] = auc_ctr
self._metrics["BATCH_AUC_ctr"] = batch_auc_ctr
self._metrics["AUC_ctcvr"] = auc_ctcvr
self._metrics["BATCH_AUC_ctcvr"] = batch_auc_ctcvr
def train_net(self):
input_data = self.input_data()
self.net(input_data)
def infer_net(self):
pass
models/multitask/mmoe/__init__.py 0 → 100755
浏览文件 @ 0f78b254
# 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/multitask/mmoe/census_reader.py 0 → 100644
浏览文件 @ 0f78b254
# 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.
from __future__ import print_function
from fleetrec.core.reader import Reader
from fleetrec.core.utils import envs
import numpy as np
class TrainReader(Reader):
def init(self):
pass
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
"""
l = line.strip().split(',')
l = list(map(float, l))
label_income = []
label_marital = []
data = l[2:]
if int(l[1]) == 0:
label_income = [1, 0]
elif int(l[1]) == 1:
label_income = [0, 1]
if int(l[0]) == 0:
label_marital = [1, 0]
elif int(l[0]) == 1:
label_marital = [0, 1]
#label_income = np.array(label_income)
#label_marital = np.array(label_marital)
feature_name = ["input", "label_income", "label_marital"]
yield zip(feature_name, [data] + [label_income] + [label_marital])
return reader
models/multitask/mmoe/config.yaml 0 → 100644
浏览文件 @ 0f78b254
# 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.
train:
trainer:
# for cluster training
strategy: "async"
epochs: 3
workspace: "fleetrec.models.multitask.mmoe"
reader:
batch_size: 2
class: "{workspace}/census_reader.py"
train_data_path: "{workspace}/data/train"
model:
models: "{workspace}/model.py"
hyper_parameters:
feature_size: 499
expert_num: 8
gate_num: 2
expert_size: 16
tower_size: 8
learning_rate: 0.001
optimizer: adam
save:
increment:
dirname: "increment"
epoch_interval: 2
save_last: True
inference:
dirname: "inference"
epoch_interval: 4
save_last: True
models/multitask/mmoe/data/test/small_test.txt 0 → 100644
浏览文件 @ 0f78b254
0 16
475 473 155
491 21
96 185 96
29 14 13
5 481 11 21 470
70 5 70 11
167 42 167 217
72 15 73 161 172
82 82
97 297 97
193 182 186 183 184 177 214
152 152
163 298 7
39 73 71
490 23 23 496 488 74 23 74 486 23 23 74
17 17
170 170 483 444 443 234
25 472
5 5 11 70 69
149 149 455
356 68 477 468 17 479 66
159 172 6 71 6 6 158 13 494 169
155 44 438 144 500
156 9 9
146 146
173 10 10 461
7 6 6
269 48 268
50 100
323 174 18
69 69 22 98
38 171
22 29 489 10
0 0
11 5
29 13 14 232 231 451 289 452 229
260 11 156
166 160 166 39
223 134 134 420
66 401 68 132 17 84 287 5
39 304
65 84 132
400 211
145 144
16 28 254 48 50 100 42 154 262 133 17
0 0
28 28
11 476 464
61 61 86 86
38 38
463 478
437 265
22 39 485 171 98
434 51 344
16 16
67 67 67 448
22 12 161
15 377 147 147 374
119 317 0
38 484
403 499
432 442
28 0 16 50 465 42
163 487 7 162
99 99 325 423 83 83
154 133
5 37 492 235 160 279
10 10 457 493 10 460
441 4 4 4 4 4 4 4
153 153
159 164 164
328 37
65 65 404 347 431 459
80 80 44 44
61 446
162 495 7 453
157 21 204 68 37 66 469 145
37 151 230 206 240 205 264 87 409 87 288 270 280 329 157 296 454 474
430 445 433
449 14
9 9 9 9
440 238 226
148 148
266 267 181
48 498
263 255 256
458 158 7
72 168 12 165 71 73 173 49
0 0
7 7 6
14 29 13 6 15 14 15 13
480 439 21
450 21 151
12 12 49 14 13 165 12 169 72 15 15
91 91
22 12 49 168
497 101 30 411 30 482 30 53 30 101 176 415 53 447
462 150 150
471 456 131 435 131 467 436 412 227 218 190 466 429 213 326
models/multitask/mmoe/data/train/train_data 0 → 100644
浏览文件 @ 0f78b254
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0,0,47,876,0,0,0,1661.53,5,0,2,52,95,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0
0,0,34,0,0,0,0,1146.79,6,0,2,52,94,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0
models/multitask/mmoe/model.py 0 → 100644
浏览文件 @ 0f78b254
# 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 math
import paddle.fluid as fluid
from fleetrec.core.utils import envs
from fleetrec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def MMOE(self):
feature_size = envs.get_global_env("hyper_parameters.feature_size", None, self._namespace)
expert_num = envs.get_global_env("hyper_parameters.expert_num", None, self._namespace)
gate_num = envs.get_global_env("hyper_parameters.gate_num", None, self._namespace)
expert_size = envs.get_global_env("hyper_parameters.expert_size", None, self._namespace)
tower_size = envs.get_global_env("hyper_parameters.tower_size", None, self._namespace)
input_data = fluid.data(name="input", shape=[-1, feature_size], dtype="float32")
label_income = fluid.data(name="label_income", shape=[-1, 2], dtype="float32", lod_level=0)
label_marital = fluid.data(name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0)
self._data_var.extend([input_data, label_income, label_marital])
# f_{i}(x) = activation(W_{i} * x + b), where activation is ReLU according to the paper
expert_outputs = []
for i in range(0, expert_num):
expert_output = fluid.layers.fc(input=input_data,
size=expert_size,
act='relu',
bias_attr=fluid.ParamAttr(learning_rate=1.0),
name='expert_' + str(i))
expert_outputs.append(expert_output)
expert_concat = fluid.layers.concat(expert_outputs, axis=1)
expert_concat = fluid.layers.reshape(expert_concat,[-1, expert_num, expert_size])
# g^{k}(x) = activation(W_{gk} * x + b), where activation is softmax according to the paper
output_layers = []
for i in range(0, gate_num):
cur_gate = fluid.layers.fc(input=input_data,
size=expert_num,
act='softmax',
bias_attr=fluid.ParamAttr(learning_rate=1.0),
name='gate_' + str(i))
# f^{k}(x) = sum_{i=1}^{n}(g^{k}(x)_{i} * f_{i}(x))
cur_gate_expert = fluid.layers.elementwise_mul(expert_concat, cur_gate, axis=0)
cur_gate_expert = fluid.layers.reduce_sum(cur_gate_expert, dim=1)
# Build tower layer
cur_tower = fluid.layers.fc(input=cur_gate_expert,
size=tower_size,
act='relu',
name='task_layer_' + str(i))
out = fluid.layers.fc(input=cur_tower,
size=2,
act='softmax',
name='out_' + str(i))
output_layers.append(out)
pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15)
pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15)
cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True)
cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True)
label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2])
label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2])
auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income, label=fluid.layers.cast(x=label_income_1, dtype='int64'))
auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital, label=fluid.layers.cast(x=label_marital_1, dtype='int64'))
avg_cost_income = fluid.layers.mean(x=cost_income)
avg_cost_marital = fluid.layers.mean(x=cost_marital)
cost = avg_cost_income + avg_cost_marital
self._cost = cost
self._metrics["AUC_income"] = auc_income
self._metrics["BATCH_AUC_income"] = batch_auc_1
self._metrics["AUC_marital"] = auc_marital
self._metrics["BATCH_AUC_marital"] = batch_auc_2
def train_net(self):
self.MMOE()
def infer_net(self):
pass
models/multitask/share-bottom/__init__.py 0 → 100755
浏览文件 @ 0f78b254
# 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/multitask/share-bottom/census_reader.py 0 → 100644
浏览文件 @ 0f78b254
# 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.
from __future__ import print_function
from fleetrec.core.reader import Reader
from fleetrec.core.utils import envs
import numpy as np
class TrainReader(Reader):
def init(self):
pass
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
"""
l = line.strip().split(',')
l = list(map(float, l))
label_income = []
label_marital = []
data = l[2:]
if int(l[1]) == 0:
label_income = [1, 0]
elif int(l[1]) == 1:
label_income = [0, 1]
if int(l[0]) == 0:
label_marital = [1, 0]
elif int(l[0]) == 1:
label_marital = [0, 1]
#label_income = np.array(label_income)
#label_marital = np.array(label_marital)
feature_name = ["input", "label_income", "label_marital"]
yield zip(feature_name, [data] + [label_income] + [label_marital])
return reader
models/multitask/share-bottom/config.yaml 0 → 100644
浏览文件 @ 0f78b254
# 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.
train:
trainer:
# for cluster training
strategy: "async"
epochs: 3
workspace: "fleetrec.models.multitask.share-bottom"
reader:
batch_size: 2
class: "{workspace}/census_reader.py"
train_data_path: "{workspace}/data/train"
model:
models: "{workspace}/model.py"
hyper_parameters:
feature_size: 499
bottom_size: 117
tower_nums: 2
tower_size: 8
learning_rate: 0.001
optimizer: adam
save:
increment:
dirname: "increment"
epoch_interval: 2
save_last: True
inference:
dirname: "inference"
epoch_interval: 4
save_last: True
models/multitask/share-bottom/data/test/small_test.txt 0 → 100644
浏览文件 @ 0f78b254
0 16
475 473 155
491 21
96 185 96
29 14 13
5 481 11 21 470
70 5 70 11
167 42 167 217
72 15 73 161 172
82 82
97 297 97
193 182 186 183 184 177 214
152 152
163 298 7
39 73 71
490 23 23 496 488 74 23 74 486 23 23 74
17 17
170 170 483 444 443 234
25 472
5 5 11 70 69
149 149 455
356 68 477 468 17 479 66
159 172 6 71 6 6 158 13 494 169
155 44 438 144 500
156 9 9
146 146
173 10 10 461
7 6 6
269 48 268
50 100
323 174 18
69 69 22 98
38 171
22 29 489 10
0 0
11 5
29 13 14 232 231 451 289 452 229
260 11 156
166 160 166 39
223 134 134 420
66 401 68 132 17 84 287 5
39 304
65 84 132
400 211
145 144
16 28 254 48 50 100 42 154 262 133 17
0 0
28 28
11 476 464
61 61 86 86
38 38
463 478
437 265
22 39 485 171 98
434 51 344
16 16
67 67 67 448
22 12 161
15 377 147 147 374
119 317 0
38 484
403 499
432 442
28 0 16 50 465 42
163 487 7 162
99 99 325 423 83 83
154 133
5 37 492 235 160 279
10 10 457 493 10 460
441 4 4 4 4 4 4 4
153 153
159 164 164
328 37
65 65 404 347 431 459
80 80 44 44
61 446
162 495 7 453
157 21 204 68 37 66 469 145
37 151 230 206 240 205 264 87 409 87 288 270 280 329 157 296 454 474
430 445 433
449 14
9 9 9 9
440 238 226
148 148
266 267 181
48 498
263 255 256
458 158 7
72 168 12 165 71 73 173 49
0 0
7 7 6
14 29 13 6 15 14 15 13
480 439 21
450 21 151
12 12 49 14 13 165 12 169 72 15 15
91 91
22 12 49 168
497 101 30 411 30 482 30 53 30 101 176 415 53 447
462 150 150
471 456 131 435 131 467 436 412 227 218 190 466 429 213 326
models/multitask/share-bottom/data/train/train_data 0 → 100644
浏览文件 @ 0f78b254
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0,0,47,876,0,0,0,1661.53,5,0,2,52,95,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,1,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0
0,0,34,0,0,0,0,1146.79,6,0,2,52,94,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,1,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,1,1,0,0,0,0,0,0,0,0,0,0,1,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,1,0,0,0,0,1,0,0,1,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0,1,0,0,0,0,0,0,1,0,1,0
models/multitask/share-bottom/model.py 0 → 100644
浏览文件 @ 0f78b254
# 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 math
import paddle.fluid as fluid
from fleetrec.core.utils import envs
from fleetrec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def train(self):
feature_size = envs.get_global_env("hyper_parameters.feature_size", None, self._namespace)
bottom_size = envs.get_global_env("hyper_parameters.bottom_size", None, self._namespace)
tower_size = envs.get_global_env("hyper_parameters.tower_size", None, self._namespace)
tower_nums = envs.get_global_env("hyper_parameters.tower_nums", None, self._namespace)
input_data = fluid.data(name="input", shape=[-1, feature_size], dtype="float32")
label_income = fluid.data(name="label_income", shape=[-1, 2], dtype="float32", lod_level=0)
label_marital = fluid.data(name="label_marital", shape=[-1, 2], dtype="float32", lod_level=0)
self._data_var.extend([input_data, label_income, label_marital])
bottom_output = fluid.layers.fc(input=input_data,
size=bottom_size,
act='relu',
bias_attr=fluid.ParamAttr(learning_rate=1.0),
name='bottom_output')
# Build tower layer from bottom layer
output_layers = []
for index in range(tower_nums):
tower_layer = fluid.layers.fc(input=bottom_output,
size=tower_size,
act='relu',
name='task_layer_' + str(index))
output_layer = fluid.layers.fc(input=tower_layer,
size=2,
act='softmax',
name='output_layer_' + str(index))
output_layers.append(output_layer)
pred_income = fluid.layers.clip(output_layers[0], min=1e-15, max=1.0 - 1e-15)
pred_marital = fluid.layers.clip(output_layers[1], min=1e-15, max=1.0 - 1e-15)
cost_income = fluid.layers.cross_entropy(input=pred_income, label=label_income,soft_label = True)
cost_marital = fluid.layers.cross_entropy(input=pred_marital, label=label_marital,soft_label = True)
label_income_1 = fluid.layers.slice(label_income, axes=[1], starts=[1], ends=[2])
label_marital_1 = fluid.layers.slice(label_marital, axes=[1], starts=[1], ends=[2])
auc_income, batch_auc_1, auc_states_1 = fluid.layers.auc(input=pred_income, label=fluid.layers.cast(x=label_income_1, dtype='int64'))
auc_marital, batch_auc_2, auc_states_2 = fluid.layers.auc(input=pred_marital, label=fluid.layers.cast(x=label_marital_1, dtype='int64'))
cost = fluid.layers.elementwise_add(cost_income, cost_marital, axis=1)
avg_cost = fluid.layers.mean(x=cost)
self._cost = avg_cost
self._metrics["AUC_income"] = auc_income
self._metrics["BATCH_AUC_income"] = batch_auc_1
self._metrics["AUC_marital"] = auc_marital
self._metrics["BATCH_AUC_marital"] = batch_auc_2
def train_net(self):
self.train()
def infer_net(self):
pass
models/recall/gru4rec/__init__.py 0 → 100755
浏览文件 @ 0f78b254
# 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/recall/gru4rec/config.yaml 0 → 100644
浏览文件 @ 0f78b254
# 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.
train:
trainer:
# for cluster training
strategy: "async"
epochs: 3
workspace: "fleetrec.models.recall.gru4rec"
reader:
batch_size: 5
class: "{workspace}/rsc15_reader.py"
train_data_path: "{workspace}/data/train"
model:
models: "{workspace}/model.py"
hyper_parameters:
vocab_size: 1000
hid_size: 100
emb_lr_x: 10.0
gru_lr_x: 1.0
fc_lr_x: 1.0
init_low_bound: -0.04
init_high_bound: 0.04
learning_rate: 0.01
optimizer: adagrad
save:
increment:
dirname: "increment"
epoch_interval: 2
save_last: True
inference:
dirname: "inference"
epoch_interval: 4
save_last: True
models/recall/gru4rec/data/test/small_test.txt 0 → 100644
浏览文件 @ 0f78b254
0 16
475 473 155
491 21
96 185 96
29 14 13
5 481 11 21 470
70 5 70 11
167 42 167 217
72 15 73 161 172
82 82
97 297 97
193 182 186 183 184 177 214
152 152
163 298 7
39 73 71
490 23 23 496 488 74 23 74 486 23 23 74
17 17
170 170 483 444 443 234
25 472
5 5 11 70 69
149 149 455
356 68 477 468 17 479 66
159 172 6 71 6 6 158 13 494 169
155 44 438 144 500
156 9 9
146 146
173 10 10 461
7 6 6
269 48 268
50 100
323 174 18
69 69 22 98
38 171
22 29 489 10
0 0
11 5
29 13 14 232 231 451 289 452 229
260 11 156
166 160 166 39
223 134 134 420
66 401 68 132 17 84 287 5
39 304
65 84 132
400 211
145 144
16 28 254 48 50 100 42 154 262 133 17
0 0
28 28
11 476 464
61 61 86 86
38 38
463 478
437 265
22 39 485 171 98
434 51 344
16 16
67 67 67 448
22 12 161
15 377 147 147 374
119 317 0
38 484
403 499
432 442
28 0 16 50 465 42
163 487 7 162
99 99 325 423 83 83
154 133
5 37 492 235 160 279
10 10 457 493 10 460
441 4 4 4 4 4 4 4
153 153
159 164 164
328 37
65 65 404 347 431 459
80 80 44 44
61 446
162 495 7 453
157 21 204 68 37 66 469 145
37 151 230 206 240 205 264 87 409 87 288 270 280 329 157 296 454 474
430 445 433
449 14
9 9 9 9
440 238 226
148 148
266 267 181
48 498
263 255 256
458 158 7
72 168 12 165 71 73 173 49
0 0
7 7 6
14 29 13 6 15 14 15 13
480 439 21
450 21 151
12 12 49 14 13 165 12 169 72 15 15
91 91
22 12 49 168
497 101 30 411 30 482 30 53 30 101 176 415 53 447
462 150 150
471 456 131 435 131 467 436 412 227 218 190 466 429 213 326
models/recall/gru4rec/data/train/small_train.txt 0 → 100644
浏览文件 @ 0f78b254
197 196 198 236
93 93 384 362 363 43
336 364 407
421 322
314 388
128 58
138 138
46 46 46
34 34 57 57 57 342 228 321 346 357 59 376
110 110
135 94 135
27 250 27
129 118
18 18 18
81 81 89 89
27 27
20 20 20 20 20 212
33 33 33 33
62 62 62 63 63 55 248 124 381 428 383 382 43 43 261 63
90 90 78 78
399 397 202 141 104 104 245 192 191 271
239 332 283 88
187 313
136 136 324
41 41
352 128
413 414
410 45 45 45 1 1 1 1 1 1 1 1 31 31 31 31
92 334 92
95 285
215 249
390 41
116 116
300 252
2 2 2 2 2
8 8 8 8 8 8
53 241 259
118 129 126 94 137 208 216 299
209 368 139 418 419
311 180
303 302 203 284
369 32 32 32 32 337
207 47 47 47
106 107
143 143
179 178
109 109
405 79 79 371 246
251 417 427
333 88 387 358 123 348 394 360 36 365
3 3 3 3 3
189 188
398 425
107 406
281 201 141
2 2 2
359 54
395 385 293
60 60 60 121 121 233 58 58
24 199 175 24 24 24 351 386 106
115 294
122 122 127 127
35 35
282 393
277 140 140 343 225 123 36 36 36 221 114 114 59 59 117 117 247 367 219 258 222 301 375 350 353 111 111
275 272 273 274 331 330 305 108 76 76 108
26 26 26 408 26
290 18 210 291
372 139 424 113
341 340 335
120 370
224 200
426 416
137 319
402 55
54 54
327 119
125 125
391 396 354 355 389
142 142
295 320
113 366
253 85 85
56 56 310 309 308 307 278 25 25 19 19 3 312 19 19 19 3 25
220 338
34 130
130 120 380 315
339 422
379 378
95 56 392 115
55 124
126 34
349 373 361
195 194
75 75
64 64 64
35 35
40 40 40 242 77 244 77 243
257 316
103 306 102 51 52 103 105 52 52 292 318 112 286 345 237 276 112 51 102 105
models/recall/gru4rec/model.py 0 → 100644
浏览文件 @ 0f78b254
# 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 math
import paddle.fluid as fluid
from fleetrec.core.utils import envs
from fleetrec.core.model import Model as ModelBase
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def all_vocab_network(self):
""" network definition """
recall_k = envs.get_global_env("hyper_parameters.recall_k", None, self._namespace)
vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, self._namespace)
hid_size = envs.get_global_env("hyper_parameters.hid_size", None, self._namespace)
init_low_bound = envs.get_global_env("hyper_parameters.init_low_bound", None, self._namespace)
init_high_bound = envs.get_global_env("hyper_parameters.init_high_bound", None, self._namespace)
emb_lr_x = envs.get_global_env("hyper_parameters.emb_lr_x", None, self._namespace)
gru_lr_x = envs.get_global_env("hyper_parameters.gru_lr_x", None, self._namespace)
fc_lr_x = envs.get_global_env("hyper_parameters.fc_lr_x", None, self._namespace)
# Input data
src_wordseq = fluid.data(
name="src_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
dst_wordseq = fluid.data(
name="dst_wordseq", shape=[None, 1], dtype="int64", lod_level=1)
emb = fluid.embedding(
input=src_wordseq,
size=[vocab_size, hid_size],
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
low=init_low_bound, high=init_high_bound),
learning_rate=emb_lr_x),
is_sparse=True)
fc0 = fluid.layers.fc(input=emb,
size=hid_size * 3,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
low=init_low_bound, high=init_high_bound),
learning_rate=gru_lr_x))
gru_h0 = fluid.layers.dynamic_gru(
input=fc0,
size=hid_size,
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
low=init_low_bound, high=init_high_bound),
learning_rate=gru_lr_x))
fc = fluid.layers.fc(input=gru_h0,
size=vocab_size,
act='softmax',
param_attr=fluid.ParamAttr(
initializer=fluid.initializer.Uniform(
low=init_low_bound, high=init_high_bound),
learning_rate=fc_lr_x))
cost = fluid.layers.cross_entropy(input=fc, label=dst_wordseq)
acc = fluid.layers.accuracy(input=fc, label=dst_wordseq, k=recall_k)
avg_cost = fluid.layers.mean(x=cost)
self._data_var.append(src_wordseq)
self._data_var.append(dst_wordseq)
self._cost = avg_cost
self._metrics["cost"] = avg_cost
self._metrics["acc"] = acc
def train_net(self):
self.all_vocab_network()
def infer_net(self):
pass
models/recall/gru4rec/rsc15_reader.py 0 → 100644
浏览文件 @ 0f78b254
# 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.
from __future__ import print_function
from fleetrec.core.reader import Reader
from fleetrec.core.utils import envs
class TrainReader(Reader):
def init(self):
pass
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
"""
l = line.strip().split()
l = [w for w in l]
src_seq = l[:len(l) - 1]
src_seq = [int(e) for e in src_seq]
trg_seq = l[1:]
trg_seq = [int(e) for e in trg_seq]
feature_name = ["src_wordseq", "dst_wordseq"]
yield zip(feature_name, [src_seq] + [trg_seq])
return reader
models/recall/ssr/__init__.py 0 → 100755
浏览文件 @ 0f78b254
# 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/recall/ssr/config.yaml 0 → 100644
浏览文件 @ 0f78b254
# 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.
train:
trainer:
# for cluster training
strategy: "async"
epochs: 3
workspace: "fleetrec.models.recall.ssr"
reader:
batch_size: 5
class: "{workspace}/ssr_reader.py"
train_data_path: "{workspace}/data/train"
model:
models: "{workspace}/model.py"
hyper_parameters:
vocab_size: 1000
emb_dim: 128
hidden_size: 100
learning_rate: 0.01
optimizer: adagrad
save:
increment:
dirname: "increment"
epoch_interval: 2
save_last: True
inference:
dirname: "inference"
epoch_interval: 4
save_last: True
models/recall/ssr/data/test/small_test.txt 0 → 100644
浏览文件 @ 0f78b254
0 16
475 473 155
491 21
96 185 96
29 14 13
5 481 11 21 470
70 5 70 11
167 42 167 217
72 15 73 161 172
82 82
97 297 97
193 182 186 183 184 177 214
152 152
163 298 7
39 73 71
490 23 23 496 488 74 23 74 486 23 23 74
17 17
170 170 483 444 443 234
25 472
5 5 11 70 69
149 149 455
356 68 477 468 17 479 66
159 172 6 71 6 6 158 13 494 169
155 44 438 144 500
156 9 9
146 146
173 10 10 461
7 6 6
269 48 268
50 100
323 174 18
69 69 22 98
38 171
22 29 489 10
0 0
11 5
29 13 14 232 231 451 289 452 229
260 11 156
166 160 166 39
223 134 134 420
66 401 68 132 17 84 287 5
39 304
65 84 132
400 211
145 144
16 28 254 48 50 100 42 154 262 133 17
0 0
28 28
11 476 464
61 61 86 86
38 38
463 478
437 265
22 39 485 171 98
434 51 344
16 16
67 67 67 448
22 12 161
15 377 147 147 374
119 317 0
38 484
403 499
432 442
28 0 16 50 465 42
163 487 7 162
99 99 325 423 83 83
154 133
5 37 492 235 160 279
10 10 457 493 10 460
441 4 4 4 4 4 4 4
153 153
159 164 164
328 37
65 65 404 347 431 459
80 80 44 44
61 446
162 495 7 453
157 21 204 68 37 66 469 145
37 151 230 206 240 205 264 87 409 87 288 270 280 329 157 296 454 474
430 445 433
449 14
9 9 9 9
440 238 226
148 148
266 267 181
48 498
263 255 256
458 158 7
72 168 12 165 71 73 173 49
0 0
7 7 6
14 29 13 6 15 14 15 13
480 439 21
450 21 151
12 12 49 14 13 165 12 169 72 15 15
91 91
22 12 49 168
497 101 30 411 30 482 30 53 30 101 176 415 53 447
462 150 150
471 456 131 435 131 467 436 412 227 218 190 466 429 213 326
models/recall/ssr/data/train/small_train.txt 0 → 100644
浏览文件 @ 0f78b254
197 196 198 236
93 93 384 362 363 43
336 364 407
421 322
314 388
128 58
138 138
46 46 46
34 34 57 57 57 342 228 321 346 357 59 376
110 110
135 94 135
27 250 27
129 118
18 18 18
81 81 89 89
27 27
20 20 20 20 20 212
33 33 33 33
62 62 62 63 63 55 248 124 381 428 383 382 43 43 261 63
90 90 78 78
399 397 202 141 104 104 245 192 191 271
239 332 283 88
187 313
136 136 324
41 41
352 128
413 414
410 45 45 45 1 1 1 1 1 1 1 1 31 31 31 31
92 334 92
95 285
215 249
390 41
116 116
300 252
2 2 2 2 2
8 8 8 8 8 8
53 241 259
118 129 126 94 137 208 216 299
209 368 139 418 419
311 180
303 302 203 284
369 32 32 32 32 337
207 47 47 47
106 107
143 143
179 178
109 109
405 79 79 371 246
251 417 427
333 88 387 358 123 348 394 360 36 365
3 3 3 3 3
189 188
398 425
107 406
281 201 141
2 2 2
359 54
395 385 293
60 60 60 121 121 233 58 58
24 199 175 24 24 24 351 386 106
115 294
122 122 127 127
35 35
282 393
277 140 140 343 225 123 36 36 36 221 114 114 59 59 117 117 247 367 219 258 222 301 375 350 353 111 111
275 272 273 274 331 330 305 108 76 76 108
26 26 26 408 26
290 18 210 291
372 139 424 113
341 340 335
120 370
224 200
426 416
137 319
402 55
54 54
327 119
125 125
391 396 354 355 389
142 142
295 320
113 366
253 85 85
56 56 310 309 308 307 278 25 25 19 19 3 312 19 19 19 3 25
220 338
34 130
130 120 380 315
339 422
379 378
95 56 392 115
55 124
126 34
349 373 361
195 194
75 75
64 64 64
35 35
40 40 40 242 77 244 77 243
257 316
103 306 102 51 52 103 105 52 52 292 318 112 286 345 237 276 112 51 102 105
models/recall/ssr/model.py 0 → 100644
浏览文件 @ 0f78b254
# 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 math
import paddle.fluid as fluid
from fleetrec.core.utils import envs
from fleetrec.core.model import Model as ModelBase
import paddle.fluid.layers.tensor as tensor
import paddle.fluid.layers.io as io
import paddle.fluid.layers.control_flow as cf
class BowEncoder(object):
""" bow-encoder """
def __init__(self):
self.param_name = ""
def forward(self, emb):
return fluid.layers.sequence_pool(input=emb, pool_type='sum')
class GrnnEncoder(object):
""" grnn-encoder """
def __init__(self, param_name="grnn", hidden_size=128):
self.param_name = param_name
self.hidden_size = hidden_size
def forward(self, emb):
fc0 = fluid.layers.fc(input=emb,
size=self.hidden_size * 3,
param_attr=self.param_name + "_fc.w",
bias_attr=False)
gru_h = fluid.layers.dynamic_gru(
input=fc0,
size=self.hidden_size,
is_reverse=False,
param_attr=self.param_name + ".param",
bias_attr=self.param_name + ".bias")
return fluid.layers.sequence_pool(input=gru_h, pool_type='max')
class PairwiseHingeLoss(object):
def __init__(self, margin=0.8):
self.margin = margin
def forward(self, pos, neg):
loss_part1 = fluid.layers.elementwise_sub(
tensor.fill_constant_batch_size_like(
input=pos, shape=[-1, 1], value=self.margin, dtype='float32'),
pos)
loss_part2 = fluid.layers.elementwise_add(loss_part1, neg)
loss_part3 = fluid.layers.elementwise_max(
tensor.fill_constant_batch_size_like(
input=loss_part2, shape=[-1, 1], value=0.0, dtype='float32'),
loss_part2)
return loss_part3
class Model(ModelBase):
def __init__(self, config):
ModelBase.__init__(self, config)
def get_correct(self, x, y):
less = tensor.cast(cf.less_than(x, y), dtype='float32')
correct = fluid.layers.reduce_sum(less)
return correct
def train(self):
vocab_size = envs.get_global_env("hyper_parameters.vocab_size", None, self._namespace)
emb_dim = envs.get_global_env("hyper_parameters.emb_dim", None, self._namespace)
hidden_size = envs.get_global_env("hyper_parameters.hidden_size", None, self._namespace)
emb_shape = [vocab_size, emb_dim]
self.user_encoder = GrnnEncoder()
self.item_encoder = BowEncoder()
self.pairwise_hinge_loss = PairwiseHingeLoss()
user_data = fluid.data(
name="user", shape=[None, 1], dtype="int64", lod_level=1)
pos_item_data = fluid.data(
name="p_item", shape=[None, 1], dtype="int64", lod_level=1)
neg_item_data = fluid.data(
name="n_item", shape=[None, 1], dtype="int64", lod_level=1)
self._data_var.extend([user_data, pos_item_data, neg_item_data])
user_emb = fluid.embedding(
input=user_data, size=emb_shape, param_attr="emb.item")
pos_item_emb = fluid.embedding(
input=pos_item_data, size=emb_shape, param_attr="emb.item")
neg_item_emb = fluid.embedding(
input=neg_item_data, size=emb_shape, param_attr="emb.item")
user_enc = self.user_encoder.forward(user_emb)
pos_item_enc = self.item_encoder.forward(pos_item_emb)
neg_item_enc = self.item_encoder.forward(neg_item_emb)
user_hid = fluid.layers.fc(input=user_enc,
size=hidden_size,
param_attr='user.w',
bias_attr="user.b")
pos_item_hid = fluid.layers.fc(input=pos_item_enc,
size=hidden_size,
param_attr='item.w',
bias_attr="item.b")
neg_item_hid = fluid.layers.fc(input=neg_item_enc,
size=hidden_size,
param_attr='item.w',
bias_attr="item.b")
cos_pos = fluid.layers.cos_sim(user_hid, pos_item_hid)
cos_neg = fluid.layers.cos_sim(user_hid, neg_item_hid)
hinge_loss = self.pairwise_hinge_loss.forward(cos_pos, cos_neg)
avg_cost = fluid.layers.mean(hinge_loss)
correct = self.get_correct(cos_neg, cos_pos)
self._cost = avg_cost
self._metrics["correct"] = correct
self._metrics["hinge_loss"] = hinge_loss
def train_net(self):
self.train()
def infer_net(self):
pass
models/recall/ssr/ssr_reader.py 0 → 100644
浏览文件 @ 0f78b254
# 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.
from __future__ import print_function
from fleetrec.core.reader import Reader
from fleetrec.core.utils import envs
import random
class TrainReader(Reader):
def init(self):
pass
def sample_neg_from_seq(self, seq):
return seq[random.randint(0, len(seq) - 1)]
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
"""
ids = line.strip().split()
conv_ids = [int(i) for i in ids]
boundary = len(ids) - 1
src = conv_ids[:boundary]
pos_tgt = [conv_ids[boundary]]
neg_tgt = [self.sample_neg_from_seq(src)]
feature_name = ["user", "p_item", "n_item"]
yield zip(feature_name, [src] + [pos_tgt] + [neg_tgt])
return reader
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