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b8589509
编写于
9月 28, 2020
作者:
Q
Qdriving
提交者:
GitHub
9月 28, 2020
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差异文件
Update model.py
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1 changed file
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models/rank/dien/model.py
models/rank/dien/model.py
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models/rank/dien/model.py
浏览文件 @
b8589509
...
@@ -230,7 +230,7 @@ class Model(ModelBase):
...
@@ -230,7 +230,7 @@ class Model(ModelBase):
pad_value
)
pad_value
)
seq_shape
=
fluid
.
layers
.
shape
(
pos_seq_pad
)
seq_shape
=
fluid
.
layers
.
shape
(
pos_seq_pad
)
if
seq_shape
[
1
]
<
2
:
if
(
seq_shape
[
1
]
==
1
)
:
aux_loss
=
0
aux_loss
=
0
else
:
else
:
test_pos
=
fluid
.
layers
.
reduce_sum
(
test_pos
=
fluid
.
layers
.
reduce_sum
(
...
@@ -239,28 +239,26 @@ class Model(ModelBase):
...
@@ -239,28 +239,26 @@ class Model(ModelBase):
fluid
.
layers
.
sigmoid
(
fluid
.
layers
.
sigmoid
(
fluid
.
layers
.
reduce_sum
(
fluid
.
layers
.
reduce_sum
(
gru_out_pad
[:,
start_value
:
seq_shape
[
1
]
-
1
,
:]
gru_out_pad
[:,
start_value
:
seq_shape
[
1
]
-
1
,
:]
*
pos_seq_pad
[:,
start_value
+
1
:
seq_shape
[
*
pos_seq_pad
[:,
start_value
+
1
],
:],
1
:
seq_shape
[
1
],
:],
dim
=
2
,
dim
=
2
,
keep_dim
=
True
))),
keep_dim
=
True
))),
dim
=
2
),
dim
=
2
),
dim
=
1
,
dim
=
1
,
keep_dim
=
True
)
keep_dim
=
True
)
test_neg
=
fluid
.
layers
.
reduce_sum
(
test_neg
=
fluid
.
layers
.
reduce_sum
(
fluid
.
layers
.
reduce_sum
(
fluid
.
layers
.
reduce_sum
(
fluid
.
layers
.
log
(
fluid
.
layers
.
log
(
fluid
.
layers
.
sigmoid
(
fluid
.
layers
.
sigmoid
(
fluid
.
layers
.
reduce_sum
(
fluid
.
layers
.
reduce_sum
(
gru_out_pad
[:,
start_value
:
seq_shape
[
1
]
-
1
,
:]
gru_out_pad
[:,
start_value
:
seq_shape
[
1
]
-
1
,
:]
*
neg_seq_pad
[:,
start_value
+
1
:
seq_shape
[
*
neg_seq_pad
[:,
start_value
+
1
],
:],
1
:
seq_shape
[
1
],
:],
dim
=
2
,
dim
=
2
,
keep_dim
=
True
))),
keep_dim
=
True
))),
dim
=
2
),
dim
=
2
),
dim
=
1
,
dim
=
1
,
keep_dim
=
True
)
keep_dim
=
True
)
aux_loss
=
fluid
.
layers
.
mean
(
test_neg
+
test_pos
)
aux_loss
=
fluid
.
layers
.
mean
(
test_neg
+
test_pos
)
# ------------------------- Interest Evolving Layer (GRU with attentional input (AIGRU)) --------------------------
# ------------------------- Interest Evolving Layer (GRU with attentional input (AIGRU)) --------------------------
...
...
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