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体验新版 GitCode,发现更多精彩内容 >>
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3529c6c3
编写于
2月 13, 2017
作者:
Y
Yi Wang
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差异文件
Put all layers and costs in package paddle.layer
上级
8b70f0f3
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1
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13 deletion
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-13
doc/design/api.md
doc/design/api.md
+13
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未找到文件。
doc/design/api.md
浏览文件 @
3529c6c3
...
...
@@ -16,11 +16,11 @@ Some essential concepts that our API have to provide include:
1.
In some topologies, layers share parameters. For
example,
[
the network for training a ranking model
](
https://github.com/PaddlePaddle/Paddle/issues/1311#issuecomment-279121850
)
.
1.
At programming time, users specify topologies and possible sharing
of parameters. PaddlePaddle can figure out and create parameters
required (and possibly shared) by one or more topologies.
## Starting from Examples
...
...
@@ -59,9 +59,9 @@ fA = f(paddle.layer.data(input_name="A"))
fB
=
f
(
paddle
.
layer
.
data
(
input_name
=
"B"
))
fQ
=
f
(
paddle
.
layer
.
data
(
input_name
=
"Q"
))
topology
=
paddle
.
cost
.
less_than
(
paddle
.
cost
.
cross_entropy
(
fA
,
fQ
),
paddle
.
cost
.
corss_entropy
(
fB
,
fQ
))
topology
=
paddle
.
layer
.
less_than
(
paddle
.
layer
.
cross_entropy
(
fA
,
fQ
),
paddle
.
layer
.
corss_entropy
(
fB
,
fQ
))
# Derive parameters required in topology and create them in model.
parameters
=
paddle
.
parameters
.
create
(
topology
)
...
...
@@ -86,7 +86,7 @@ correspond to the two networks in the following figure:
```
python
def
G
(
in
):
# over-simplified example as G has only one layers:
return
paddle
.
layer
.
fc
(
in
,
parameter_name
=
"G"
)
return
paddle
.
layer
.
fc
(
in
,
parameter_name
=
"G"
)
def
D
(
in
);
# again, over-simplified:
...
...
@@ -94,12 +94,12 @@ def D(in);
# Construct the first topology, which contains both D and G.
# By learning this topology, we update parameters of G.
d0
=
paddle
.
cost
.
should_be_false
(
D
(
G
(
paddle
.
layer
.
data
())))
d0
=
paddle
.
layer
.
should_be_false
(
D
(
G
(
paddle
.
layer
.
data
())))
# Construct a second topology d1, which contains only D. By
# training this topology, we update parameters of D. Note
# training this topology, we update parameters of D. Note
# that d1 share parameters with d0.
d1
=
paddle
.
cost
.
should_be_true
(
D
(
paddle
.
layer
.
data
()))
d1
=
paddle
.
layer
.
should_be_true
(
D
(
paddle
.
layer
.
data
()))
# Create parameters from a list of multiple topologies (models) for
# the chance to share parameters between these topologies.
...
...
@@ -132,16 +132,16 @@ Above two programs reveal some important design concerns:
1.
At training and inference time,
`paddle.train`
and
`paddle.infer`
requires both a topology and the parameter set that holds the parameters of that topology. There are some reasons:
1.
This prevents users from forgetting to call
`paddle.parameters.create`
.
1.
`paddle.train`
needs to know which parameter set to update.
1.
Users could load another (pre-trained) parameter set and use it
with a topology in
`train.infer`
.
1.
By specifying the
`immutable_parameters`
parameter of
`paddle.train`
, we can forbid the update of these parameters.
## Reader
...
...
@@ -190,7 +190,7 @@ access a Kubernetes cluster, s/he should be able to call
```
python
paddle
.
dist_train
(
model
,
trainer
=
paddle
.
trainer
.
SGD
(...,
trainer
=
paddle
.
trainer
.
SGD
(...,
paddle
.
updater
.
Adam
(...)),
reader
=
read
,
k8s_user
=
"yi"
,
...
...
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