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c051fb37
编写于
5月 15, 2020
作者:
Z
zhangwenhui03
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Merge branch 'develop' into 'develop'
add ssr infer See merge request
!35
上级
183ff0ad
3b59e3c4
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
93 addition
and
1 deletion
+93
-1
models/recall/ssr/config.yaml
models/recall/ssr/config.yaml
+8
-0
models/recall/ssr/model.py
models/recall/ssr/model.py
+41
-1
models/recall/ssr/ssr_infer_reader.py
models/recall/ssr/ssr_infer_reader.py
+44
-0
未找到文件。
models/recall/ssr/config.yaml
浏览文件 @
c051fb37
...
...
@@ -12,6 +12,14 @@
# See the License for the specific language governing permissions and
# limitations under the License.
evaluate
:
reader
:
batch_size
:
1
class
:
"
{workspace}/ssr_infer_reader.py"
test_data_path
:
"
{workspace}/data/train"
is_return_numpy
:
True
train
:
trainer
:
# for cluster training
...
...
models/recall/ssr/model.py
浏览文件 @
c051fb37
...
...
@@ -134,5 +134,45 @@ class Model(ModelBase):
self
.
train
()
def
infer
(
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
)
user_data
=
fluid
.
data
(
name
=
"user"
,
shape
=
[
None
,
1
],
dtype
=
"int64"
,
lod_level
=
1
)
all_item_data
=
fluid
.
data
(
name
=
"all_item"
,
shape
=
[
None
,
vocab_size
],
dtype
=
"int64"
)
pos_label
=
fluid
.
data
(
name
=
"pos_label"
,
shape
=
[
None
,
1
],
dtype
=
"int64"
)
self
.
_infer_data_var
=
[
user_data
,
all_item_data
,
pos_label
]
self
.
_infer_data_loader
=
fluid
.
io
.
DataLoader
.
from_generator
(
feed_list
=
self
.
_infer_data_var
,
capacity
=
64
,
use_double_buffer
=
False
,
iterable
=
False
)
user_emb
=
fluid
.
embedding
(
input
=
user_data
,
size
=
[
vocab_size
,
emb_dim
],
param_attr
=
"emb.item"
)
all_item_emb
=
fluid
.
embedding
(
input
=
all_item_data
,
size
=
[
vocab_size
,
emb_dim
],
param_attr
=
"emb.item"
)
all_item_emb_re
=
fluid
.
layers
.
reshape
(
x
=
all_item_emb
,
shape
=
[
-
1
,
emb_dim
])
user_encoder
=
GrnnEncoder
()
user_enc
=
user_encoder
.
forward
(
user_emb
)
user_hid
=
fluid
.
layers
.
fc
(
input
=
user_enc
,
size
=
hidden_size
,
param_attr
=
'user.w'
,
bias_attr
=
"user.b"
)
user_exp
=
fluid
.
layers
.
expand
(
x
=
user_hid
,
expand_times
=
[
1
,
vocab_size
])
user_re
=
fluid
.
layers
.
reshape
(
x
=
user_exp
,
shape
=
[
-
1
,
hidden_size
])
all_item_hid
=
fluid
.
layers
.
fc
(
input
=
all_item_emb_re
,
size
=
hidden_size
,
param_attr
=
'item.w'
,
bias_attr
=
"item.b"
)
cos_item
=
fluid
.
layers
.
cos_sim
(
X
=
all_item_hid
,
Y
=
user_re
)
all_pre_
=
fluid
.
layers
.
reshape
(
x
=
cos_item
,
shape
=
[
-
1
,
vocab_size
])
acc
=
fluid
.
layers
.
accuracy
(
input
=
all_pre_
,
label
=
pos_label
,
k
=
20
)
self
.
_infer_results
[
'recall20'
]
=
acc
def
infer_net
(
self
):
pass
self
.
infer
()
models/recall/ssr/ssr_infer_reader.py
0 → 100644
浏览文件 @
c051fb37
# 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
paddlerec.core.reader
import
Reader
from
paddlerec.core.utils
import
envs
import
random
import
numpy
as
np
class
EvaluateReader
(
Reader
):
def
init
(
self
):
self
.
vocab_size
=
envs
.
get_global_env
(
"vocab_size"
,
10
,
"train.model.hyper_parameters"
)
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
]]
feature_name
=
[
"user"
,
"all_item"
,
"p_item"
]
yield
zip
(
feature_name
,
[
src
]
+
[
np
.
arange
(
self
.
vocab_size
).
astype
(
"int64"
).
tolist
()]
+
[
pos_tgt
])
return
reader
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