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ML
ffm-baseline
Commits
19905a47
Commit
19905a47
authored
Jun 05, 2019
by
Your Name
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test esmm predict
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aaatest.py
aaatest.py
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dist_predict.py
eda/esmm/Model_pipline/dist_predict.py
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19905a47
def
predict
(
1
):
pass
nodeRDD
=
paritdition
(
1
)
predict
\ No newline at end of file
eda/esmm/Model_pipline/dist_predict.py
View file @
19905a47
from
datetime
import
date
,
timedelta
import
tensorflow
as
tf
def
model_fn
(
features
,
labels
,
mode
,
params
):
"""Bulid Model function f(x) for Estimator."""
#------hyperparameters----
field_size
=
params
[
"field_size"
]
feature_size
=
params
[
"feature_size"
]
embedding_size
=
params
[
"embedding_size"
]
l2_reg
=
params
[
"l2_reg"
]
learning_rate
=
params
[
"learning_rate"
]
#optimizer = params["optimizer"]
layers
=
list
(
map
(
int
,
params
[
"deep_layers"
]
.
split
(
','
)))
dropout
=
list
(
map
(
float
,
params
[
"dropout"
]
.
split
(
','
)))
ctr_task_wgt
=
params
[
"ctr_task_wgt"
]
common_dims
=
field_size
*
embedding_size
#------bulid weights------
Feat_Emb
=
tf
.
get_variable
(
name
=
'embeddings'
,
shape
=
[
feature_size
,
embedding_size
],
initializer
=
tf
.
glorot_normal_initializer
())
feat_ids
=
features
[
'ids'
]
app_list
=
features
[
'app_list'
]
level2_list
=
features
[
'level2_list'
]
level3_list
=
features
[
'level3_list'
]
tag1_list
=
features
[
'tag1_list'
]
tag2_list
=
features
[
'tag2_list'
]
tag3_list
=
features
[
'tag3_list'
]
tag4_list
=
features
[
'tag4_list'
]
tag5_list
=
features
[
'tag5_list'
]
tag6_list
=
features
[
'tag6_list'
]
tag7_list
=
features
[
'tag7_list'
]
#------build f(x)------
with
tf
.
variable_scope
(
"Shared-Embedding-layer"
):
embedding_id
=
tf
.
nn
.
embedding_lookup
(
Feat_Emb
,
feat_ids
)
app_id
=
tf
.
nn
.
embedding_lookup_sparse
(
Feat_Emb
,
sp_ids
=
app_list
,
sp_weights
=
None
,
combiner
=
"sum"
)
level2
=
tf
.
nn
.
embedding_lookup_sparse
(
Feat_Emb
,
sp_ids
=
level2_list
,
sp_weights
=
None
,
combiner
=
"sum"
)
level3
=
tf
.
nn
.
embedding_lookup_sparse
(
Feat_Emb
,
sp_ids
=
level3_list
,
sp_weights
=
None
,
combiner
=
"sum"
)
tag1
=
tf
.
nn
.
embedding_lookup_sparse
(
Feat_Emb
,
sp_ids
=
tag1_list
,
sp_weights
=
None
,
combiner
=
"sum"
)
tag2
=
tf
.
nn
.
embedding_lookup_sparse
(
Feat_Emb
,
sp_ids
=
tag2_list
,
sp_weights
=
None
,
combiner
=
"sum"
)
tag3
=
tf
.
nn
.
embedding_lookup_sparse
(
Feat_Emb
,
sp_ids
=
tag3_list
,
sp_weights
=
None
,
combiner
=
"sum"
)
tag4
=
tf
.
nn
.
embedding_lookup_sparse
(
Feat_Emb
,
sp_ids
=
tag4_list
,
sp_weights
=
None
,
combiner
=
"sum"
)
tag5
=
tf
.
nn
.
embedding_lookup_sparse
(
Feat_Emb
,
sp_ids
=
tag5_list
,
sp_weights
=
None
,
combiner
=
"sum"
)
tag6
=
tf
.
nn
.
embedding_lookup_sparse
(
Feat_Emb
,
sp_ids
=
tag6_list
,
sp_weights
=
None
,
combiner
=
"sum"
)
tag7
=
tf
.
nn
.
embedding_lookup_sparse
(
Feat_Emb
,
sp_ids
=
tag7_list
,
sp_weights
=
None
,
combiner
=
"sum"
)
# x_concat = tf.reshape(embedding_id,shape=[-1, common_dims]) # None * (F * K)
x_concat
=
tf
.
concat
([
tf
.
reshape
(
embedding_id
,
shape
=
[
-
1
,
common_dims
]),
app_id
,
level2
,
level3
,
tag1
,
tag2
,
tag3
,
tag4
,
tag5
,
tag6
,
tag7
],
axis
=
1
)
with
tf
.
name_scope
(
"CVR_Task"
):
if
mode
==
tf
.
estimator
.
ModeKeys
.
TRAIN
:
train_phase
=
True
else
:
train_phase
=
False
x_cvr
=
x_concat
for
i
in
range
(
len
(
layers
)):
x_cvr
=
tf
.
contrib
.
layers
.
fully_connected
(
inputs
=
x_cvr
,
num_outputs
=
layers
[
i
],
\
weights_regularizer
=
tf
.
contrib
.
layers
.
l2_regularizer
(
l2_reg
),
scope
=
'cvr_mlp
%
d'
%
i
)
y_cvr
=
tf
.
contrib
.
layers
.
fully_connected
(
inputs
=
x_cvr
,
num_outputs
=
1
,
activation_fn
=
tf
.
identity
,
\
weights_regularizer
=
tf
.
contrib
.
layers
.
l2_regularizer
(
l2_reg
),
scope
=
'cvr_out'
)
y_cvr
=
tf
.
reshape
(
y_cvr
,
shape
=
[
-
1
])
with
tf
.
name_scope
(
"CTR_Task"
):
if
mode
==
tf
.
estimator
.
ModeKeys
.
TRAIN
:
train_phase
=
True
else
:
train_phase
=
False
x_ctr
=
x_concat
for
i
in
range
(
len
(
layers
)):
x_ctr
=
tf
.
contrib
.
layers
.
fully_connected
(
inputs
=
x_ctr
,
num_outputs
=
layers
[
i
],
\
weights_regularizer
=
tf
.
contrib
.
layers
.
l2_regularizer
(
l2_reg
),
scope
=
'ctr_mlp
%
d'
%
i
)
y_ctr
=
tf
.
contrib
.
layers
.
fully_connected
(
inputs
=
x_ctr
,
num_outputs
=
1
,
activation_fn
=
tf
.
identity
,
\
weights_regularizer
=
tf
.
contrib
.
layers
.
l2_regularizer
(
l2_reg
),
scope
=
'ctr_out'
)
y_ctr
=
tf
.
reshape
(
y_ctr
,
shape
=
[
-
1
])
with
tf
.
variable_scope
(
"MTL-Layer"
):
pctr
=
tf
.
sigmoid
(
y_ctr
)
pcvr
=
tf
.
sigmoid
(
y_cvr
)
pctcvr
=
pctr
*
pcvr
predictions
=
{
"pcvr"
:
pcvr
,
"pctr"
:
pctr
,
"pctcvr"
:
pctcvr
}
export_outputs
=
{
tf
.
saved_model
.
signature_constants
.
DEFAULT_SERVING_SIGNATURE_DEF_KEY
:
tf
.
estimator
.
export
.
PredictOutput
(
predictions
)}
# Provide an estimator spec for `ModeKeys.PREDICT`
if
mode
==
tf
.
estimator
.
ModeKeys
.
PREDICT
:
return
tf
.
estimator
.
EstimatorSpec
(
mode
=
mode
,
predictions
=
predictions
,
export_outputs
=
export_outputs
)
def
input_fn
(
filenames
,
batch_size
=
32
,
num_epochs
=
1
,
perform_shuffle
=
False
):
print
(
'Parsing'
,
filenames
)
def
_parse_fn
(
record
):
features
=
{
"y"
:
tf
.
FixedLenFeature
([],
tf
.
float32
),
"z"
:
tf
.
FixedLenFeature
([],
tf
.
float32
),
"ids"
:
tf
.
FixedLenFeature
([
15
],
tf
.
int64
),
"app_list"
:
tf
.
VarLenFeature
(
tf
.
int64
),
"level2_list"
:
tf
.
VarLenFeature
(
tf
.
int64
),
"level3_list"
:
tf
.
VarLenFeature
(
tf
.
int64
),
"tag1_list"
:
tf
.
VarLenFeature
(
tf
.
int64
),
"tag2_list"
:
tf
.
VarLenFeature
(
tf
.
int64
),
"tag3_list"
:
tf
.
VarLenFeature
(
tf
.
int64
),
"tag4_list"
:
tf
.
VarLenFeature
(
tf
.
int64
),
"tag5_list"
:
tf
.
VarLenFeature
(
tf
.
int64
),
"tag6_list"
:
tf
.
VarLenFeature
(
tf
.
int64
),
"tag7_list"
:
tf
.
VarLenFeature
(
tf
.
int64
)
}
parsed
=
tf
.
parse_single_example
(
record
,
features
)
y
=
parsed
.
pop
(
'y'
)
z
=
parsed
.
pop
(
'z'
)
return
parsed
,
{
"y"
:
y
,
"z"
:
z
}
# Extract lines from input files using the Dataset API, can pass one filename or filename list
dataset
=
tf
.
data
.
TFRecordDataset
(
filenames
)
.
map
(
_parse_fn
,
num_parallel_calls
=
10
)
.
prefetch
(
500000
)
# multi-thread pre-process then prefetch
# Randomizes input using a window of 256 elements (read into memory)
if
perform_shuffle
:
dataset
=
dataset
.
shuffle
(
buffer_size
=
256
)
# epochs from blending together.
dataset
=
dataset
.
repeat
(
num_epochs
)
dataset
=
dataset
.
batch
(
batch_size
)
# Batch size to use
# dataset = dataset.padded_batch(batch_size, padded_shapes=({"feeds_ids": [None], "feeds_vals": [None], "title_ids": [None]}, [None])) #不定长补齐
#return dataset.make_one_shot_iterator()
iterator
=
dataset
.
make_one_shot_iterator
()
batch_features
,
batch_labels
=
iterator
.
get_next
()
#return tf.reshape(batch_ids,shape=[-1,field_size]), tf.reshape(batch_vals,shape=[-1,field_size]), batch_labels
#print("-"*100)
#print(batch_features,batch_labels)
return
batch_features
,
batch_labels
def
esmm_predict
():
dt_dir
=
(
date
.
today
()
+
timedelta
(
-
1
))
.
strftime
(
'
%
Y
%
m
%
d'
)
model_dir
=
"hdfs://172.16.32.4:8020/strategy/esmm/model_ckpt/DeepCvrMTL/"
+
dt_dir
te_files
=
[
"hdfs://172.16.32.4:8020/strategy/esmm/part-r-00000"
]
model_params
=
{
"field_size"
:
15
,
"feature_size"
:
600000
,
"embedding_size"
:
32
,
"learning_rate"
:
0.0001
,
"l2_reg"
:
0.005
,
"deep_layers"
:
'512,256,128,64,32'
,
"dropout"
:
'0.3,0.3,0.3,0.3,0.3'
,
"ctr_task_wgt"
:
0.5
}
config
=
tf
.
estimator
.
RunConfig
()
.
replace
(
session_config
=
tf
.
ConfigProto
(
device_count
=
{
'GPU'
:
0
,
'CPU'
:
36
}),
log_step_count_steps
=
100
,
save_summary_steps
=
100
)
Estimator
=
tf
.
estimator
.
Estimator
(
model_fn
=
model_fn
,
model_dir
=
"hdfs://172.16.32.4:8020/strategy/esmm/model_ckpt/DeepCvrMTL/"
,
params
=
model_params
,
config
=
config
)
preds
=
Estimator
.
predict
(
input_fn
=
lambda
:
input_fn
(
te_files
,
num_epochs
=
1
,
batch_size
=
10000
),
predict_keys
=
[
"pctcvr"
,
"pctr"
,
"pcvr"
])
with
open
(
"/home/gmuser/esmm/nearby"
+
"/pred.txt"
,
"w"
)
as
fo
:
for
prob
in
preds
:
fo
.
write
(
"
%
f
\t
%
f
\t
%
f
\n
"
%
(
prob
[
'pctr'
],
prob
[
'pcvr'
],
prob
[
'pctcvr'
]))
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