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gm_strategy_cvr
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gm_strategy_cvr
Commits
b75a4cda
Commit
b75a4cda
authored
Sep 03, 2020
by
赵威
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save diary
parent
cc22d34b
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2 changed files
with
4 additions
and
6 deletions
+4
-6
train_diary.py
src/train_diary.py
+3
-5
train_tractate.py
src/train_tractate.py
+1
-1
No files found.
src/train_diary.py
View file @
b75a4cda
...
...
@@ -65,8 +65,7 @@ def main():
estimator_config
=
tf
.
estimator
.
RunConfig
(
session_config
=
session_config
)
model
=
tf
.
estimator
.
Estimator
(
model_fn
=
esmm_model_fn
,
params
=
params
,
model_dir
=
model_path
,
config
=
estimator_config
)
# TODO 50000
train_spec
=
tf
.
estimator
.
TrainSpec
(
input_fn
=
lambda
:
esmm_input_fn
(
train_df
,
shuffle
=
True
),
max_steps
=
12000
)
train_spec
=
tf
.
estimator
.
TrainSpec
(
input_fn
=
lambda
:
esmm_input_fn
(
train_df
,
shuffle
=
True
),
max_steps
=
45000
)
eval_spec
=
tf
.
estimator
.
EvalSpec
(
input_fn
=
lambda
:
esmm_input_fn
(
val_df
,
shuffle
=
False
))
res
=
tf
.
estimator
.
train_and_evaluate
(
model
,
train_spec
,
eval_spec
)
print
(
"@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@"
)
...
...
@@ -81,9 +80,8 @@ def main():
model_export_path
=
str
(
Path
(
"/data/files/models/diary"
)
.
expanduser
())
save_path
=
model_export
(
model
,
all_features
,
model_export_path
)
print
(
"save to: "
+
save_path
)
# TODO save model
# set_essm_model_save_path("diary", save_path)
# record_esmm_auc_to_db("diary", ctr_auc, ctcvr_auc, total_time, save_path)
set_essm_model_save_path
(
"diary"
,
save_path
)
record_esmm_auc_to_db
(
"diary"
,
ctr_auc
,
ctcvr_auc
,
total_time
,
save_path
)
print
(
"============================================================"
)
# save_path = str(Path("~/Desktop/models/1596012827").expanduser()) # local
...
...
src/train_tractate.py
View file @
b75a4cda
...
...
@@ -62,7 +62,7 @@ def main():
estimator_config
=
tf
.
estimator
.
RunConfig
(
session_config
=
session_config
)
model
=
tf
.
estimator
.
Estimator
(
model_fn
=
esmm_model_fn
,
params
=
params
,
model_dir
=
model_path
,
config
=
estimator_config
)
train_spec
=
tf
.
estimator
.
TrainSpec
(
input_fn
=
lambda
:
esmm_input_fn
(
train_df
,
shuffle
=
True
),
max_steps
=
50
000
)
train_spec
=
tf
.
estimator
.
TrainSpec
(
input_fn
=
lambda
:
esmm_input_fn
(
train_df
,
shuffle
=
True
),
max_steps
=
45
000
)
eval_spec
=
tf
.
estimator
.
EvalSpec
(
input_fn
=
lambda
:
esmm_input_fn
(
val_df
,
shuffle
=
False
))
res
=
tf
.
estimator
.
train_and_evaluate
(
model
,
train_spec
,
eval_spec
)
print
(
"@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@"
)
...
...
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