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03a03ab1
编写于
7月 29, 2020
作者:
X
Xiaoda Zhang
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
changing mix-precision level
上级
e2739ce5
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
2 addition
and
2 deletion
+2
-2
tutorials/source_en/advanced_use/mixed_precision.md
tutorials/source_en/advanced_use/mixed_precision.md
+1
-1
tutorials/source_zh_cn/advanced_use/mixed_precision.md
tutorials/source_zh_cn/advanced_use/mixed_precision.md
+1
-1
未找到文件。
tutorials/source_en/advanced_use/mixed_precision.md
浏览文件 @
03a03ab1
...
@@ -84,7 +84,7 @@ label = Tensor(np.zeros([64, 128]).astype(np.float32))
...
@@ -84,7 +84,7 @@ label = Tensor(np.zeros([64, 128]).astype(np.float32))
# Define Loss and Optimizer
# Define Loss and Optimizer
loss
=
nn
.
SoftmaxCrossEntropyWithLogits
()
loss
=
nn
.
SoftmaxCrossEntropyWithLogits
()
optimizer
=
Momentum
(
params
=
net
.
trainable_params
(),
learning_rate
=
0.1
,
momentum
=
0.9
)
optimizer
=
Momentum
(
params
=
net
.
trainable_params
(),
learning_rate
=
0.1
,
momentum
=
0.9
)
train_network
=
amp
.
build_train_network
(
net
,
optimizer
,
loss
,
level
=
"O
2
"
,
loss_scale_manager
=
None
)
train_network
=
amp
.
build_train_network
(
net
,
optimizer
,
loss
,
level
=
"O
3
"
,
loss_scale_manager
=
None
)
# Run training
# Run training
output
=
train_network
(
predict
,
label
)
output
=
train_network
(
predict
,
label
)
...
...
tutorials/source_zh_cn/advanced_use/mixed_precision.md
浏览文件 @
03a03ab1
...
@@ -83,7 +83,7 @@ label = Tensor(np.zeros([64, 128]).astype(np.float32))
...
@@ -83,7 +83,7 @@ label = Tensor(np.zeros([64, 128]).astype(np.float32))
# Define Loss and Optimizer
# Define Loss and Optimizer
loss
=
nn
.
SoftmaxCrossEntropyWithLogits
()
loss
=
nn
.
SoftmaxCrossEntropyWithLogits
()
optimizer
=
Momentum
(
params
=
net
.
trainable_params
(),
learning_rate
=
0.1
,
momentum
=
0.9
)
optimizer
=
Momentum
(
params
=
net
.
trainable_params
(),
learning_rate
=
0.1
,
momentum
=
0.9
)
train_network
=
amp
.
build_train_network
(
net
,
optimizer
,
loss
,
level
=
"O
2
"
,
loss_scale_manager
=
None
)
train_network
=
amp
.
build_train_network
(
net
,
optimizer
,
loss
,
level
=
"O
3
"
,
loss_scale_manager
=
None
)
# Run training
# Run training
output
=
train_network
(
predict
,
label
)
output
=
train_network
(
predict
,
label
)
...
...
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