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5103fd81
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前往新版Gitcode,体验更适合开发者的 AI 搜索 >>
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5103fd81
编写于
7月 14, 2018
作者:
G
Gabriel de Marmiesse
提交者:
François Chollet
7月 14, 2018
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差异文件
Refactoring the `layer_test` function. (#10660)
上级
8fe86302
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
30 addition
and
44 deletion
+30
-44
keras/utils/test_utils.py
keras/utils/test_utils.py
+30
-44
未找到文件。
keras/utils/test_utils.py
浏览文件 @
5103fd81
...
...
@@ -81,6 +81,32 @@ def layer_test(layer_cls, kwargs={}, input_shape=None, input_dtype=None,
kwargs
[
'weights'
]
=
weights
layer
=
layer_cls
(
**
kwargs
)
expected_output_shape
=
layer
.
compute_output_shape
(
input_shape
)
def
_layer_in_model_test
(
model
):
actual_output
=
model
.
predict
(
input_data
)
actual_output_shape
=
actual_output
.
shape
for
expected_dim
,
actual_dim
in
zip
(
expected_output_shape
,
actual_output_shape
):
if
expected_dim
is
not
None
:
assert
expected_dim
==
actual_dim
if
expected_output
is
not
None
:
assert_allclose
(
actual_output
,
expected_output
,
rtol
=
1e-3
)
# test serialization, weight setting at model level
model_config
=
model
.
get_config
()
recovered_model
=
model
.
__class__
.
from_config
(
model_config
)
if
model
.
weights
:
weights
=
model
.
get_weights
()
recovered_model
.
set_weights
(
weights
)
_output
=
recovered_model
.
predict
(
input_data
)
assert_allclose
(
_output
,
actual_output
,
rtol
=
1e-3
)
# test training mode (e.g. useful for dropout tests)
model
.
compile
(
'rmsprop'
,
'mse'
)
model
.
train_on_batch
(
input_data
,
actual_output
)
return
actual_output
# test in functional API
if
fixed_batch_size
:
x
=
Input
(
batch_shape
=
input_shape
,
dtype
=
input_dtype
)
...
...
@@ -89,59 +115,19 @@ def layer_test(layer_cls, kwargs={}, input_shape=None, input_dtype=None,
y
=
layer
(
x
)
assert
K
.
dtype
(
y
)
==
expected_output_dtype
# check
shape inference
# check
with the functional API
model
=
Model
(
x
,
y
)
expected_output_shape
=
layer
.
compute_output_shape
(
input_shape
)
actual_output
=
model
.
predict
(
input_data
)
actual_output_shape
=
actual_output
.
shape
for
expected_dim
,
actual_dim
in
zip
(
expected_output_shape
,
actual_output_shape
):
if
expected_dim
is
not
None
:
assert
expected_dim
==
actual_dim
if
expected_output
is
not
None
:
assert_allclose
(
actual_output
,
expected_output
,
rtol
=
1e-3
)
# test serialization, weight setting at model level
model_config
=
model
.
get_config
()
recovered_model
=
Model
.
from_config
(
model_config
)
if
model
.
weights
:
weights
=
model
.
get_weights
()
recovered_model
.
set_weights
(
weights
)
_output
=
recovered_model
.
predict
(
input_data
)
assert_allclose
(
_output
,
actual_output
,
rtol
=
1e-3
)
# test training mode (e.g. useful for dropout tests)
model
.
compile
(
'rmsprop'
,
'mse'
)
model
.
train_on_batch
(
input_data
,
actual_output
)
_layer_in_model_test
(
model
)
# test as first layer in Sequential API
layer_config
=
layer
.
get_config
()
layer_config
[
'batch_input_shape'
]
=
input_shape
layer
=
layer
.
__class__
.
from_config
(
layer_config
)
# check with the sequential API
model
=
Sequential
()
model
.
add
(
layer
)
actual_output
=
model
.
predict
(
input_data
)
actual_output_shape
=
actual_output
.
shape
for
expected_dim
,
actual_dim
in
zip
(
expected_output_shape
,
actual_output_shape
):
if
expected_dim
is
not
None
:
assert
expected_dim
==
actual_dim
if
expected_output
is
not
None
:
assert_allclose
(
actual_output
,
expected_output
,
rtol
=
1e-3
)
# test serialization, weight setting at model level
model_config
=
model
.
get_config
()
recovered_model
=
Sequential
.
from_config
(
model_config
)
if
model
.
weights
:
weights
=
model
.
get_weights
()
recovered_model
.
set_weights
(
weights
)
_output
=
recovered_model
.
predict
(
input_data
)
assert_allclose
(
_output
,
actual_output
,
rtol
=
1e-3
)
# test training mode (e.g. useful for dropout tests)
model
.
compile
(
'rmsprop'
,
'mse'
)
model
.
train_on_batch
(
input_data
,
actual_output
)
actual_output
=
_layer_in_model_test
(
model
)
# for further checks in the caller function
return
actual_output
...
...
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