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pytensor
Commits
dab7a1ed
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dab7a1ed
authored
4月 08, 2016
作者:
Mathieu Germain
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Fix docstring in old backend
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42a4e9ef
隐藏空白字符变更
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1 个修改的文件
包含
53 行增加
和
50 行删除
+53
-50
dnn.py
theano/sandbox/cuda/dnn.py
+53
-50
没有找到文件。
theano/sandbox/cuda/dnn.py
浏览文件 @
dab7a1ed
...
...
@@ -295,10 +295,8 @@ class GpuDnnConv(DnnBase, COp):
The convolution descriptor.
workmem
*deprecated*, use parameter algo instead.
algo
['none', 'small', 'large', 'fft', 'fft_tiling', 'guess_once', 'winograd',
'guess_on_shape_change', 'time_once', 'time_on_shape_change']
algo : {'none', 'small', 'large', 'fft', 'fft_tiling', 'guess_once', 'winograd',
'guess_on_shape_change', 'time_once', 'time_on_shape_change'}
Default is the value of :attr:`config.dnn.conv.algo_fwd`.
"""
...
...
@@ -481,17 +479,20 @@ class GpuDnnConv3d(GpuDnnConv):
"""
The forward convolution.
:param image:
:param kernel:
:param descr: the convolution descriptor
:param workmem:
Parameters
----------
image
kernel
descr
The convolution descriptor
workmem
*deprecated*, use parameter algo instead.
:param algo: ['none', 'small', 'fft_tiling', 'winograd',
'guess_once', 'guess_on_shape_change',
'time_once', 'time_on_shape_change']
algo : {'none', 'small', 'fft_tiling', 'winograd', 'guess_once',
'guess_on_shape_change', 'time_once', 'time_on_shape_change'}
Default is the value of :attr:`config.dnn.conv.algo_fwd`.
"""
__props__
=
(
'algo'
,
'inplace'
)
__input_name__
=
(
'image'
,
'kernel'
,
'output'
,
'descriptor'
,
'alpha'
,
'beta'
)
...
...
@@ -584,7 +585,8 @@ class GpuDnnConvGradW(DnnBase, COp):
The convolution descriptor.
workmem
*deprecated*, use parameter algo instead.
algo : {'none', 'deterministic', 'fft', 'small', 'guess_once', 'guess_on_shape_change', 'time_once', 'time_on_shape_change'}
algo : {'none', 'deterministic', 'fft', 'small', 'guess_once',
'guess_on_shape_change', 'time_once', 'time_on_shape_change'}
Default is the value of :attr:`config.dnn.conv.algo_bwd_filter`.
"""
...
...
@@ -719,17 +721,20 @@ class GpuDnnConv3dGradW(GpuDnnConvGradW):
"""
The convolution gradient with respect to the weights.
:param image:
:param kernel:
:param descr: the convolution descriptor
:param workmem:
Parameters
----------
image
kernel
descr
The convolution descriptor
workmem
*deprecated*, use parameter algo instead.
:param algo: ['none', 'small',
'guess_once', 'guess_on_shape_change',
'time_once', 'time_on_shape_change']
algo : {'none', 'small', 'guess_once', 'guess_on_shape_change',
'time_once', 'time_on_shape_change'}
Default is the value of :attr:`config.dnn.conv.algo_bwd_filter`.
"""
__props__
=
(
'algo'
,
'inplace'
,)
__input_name__
=
(
'image'
,
'grad'
,
'output'
,
'descriptor'
,
'alpha'
,
'beta'
)
...
...
@@ -801,10 +806,8 @@ class GpuDnnConvGradI(DnnBase, COp):
The convolution descriptor.
workmem
*deprecated*, use parameter algo instead.
algo
['none', 'deterministic', 'fft', 'fft_tiling', 'winograd', 'guess_once',
'guess_on_shape_change', 'time_once', 'time_on_shape_change']
algo : {'none', 'deterministic', 'fft', 'fft_tiling', 'winograd', 'guess_once',
'guess_on_shape_change', 'time_once', 'time_on_shape_change'}
Default is the value of :attr:`config.dnn.conv.algo_bwd_data`.
"""
...
...
@@ -956,17 +959,20 @@ class GpuDnnConv3dGradI(GpuDnnConvGradI):
"""
The convolution gradient with respect to the inputs.
:param image:
:param kernel:
:param descr: the convolution descriptor
:param workmem:
Parameters
----------
image
kernel
descr
The convolution descriptor
workmem
*deprecated*, use parameter algo instead.
:param algo: ['none', 'deterministic, 'fft_tiling', 'winograd', 'guess_once',
'guess_on_shape_change', 'time_once', 'time_on_shape_change']
algo : {'none', 'deterministic, 'fft_tiling', 'winograd', 'guess_once',
'guess_on_shape_change', 'time_once', 'time_on_shape_change'}
Default is the value of :attr:`config.dnn.conv.algo_bwd_data`.
"""
__props__
=
(
'algo'
,
'inplace'
,)
__input_name__
=
(
'kernel'
,
'grad'
,
'output'
,
'descriptor'
,
'alpha'
,
'beta'
)
...
...
@@ -1455,6 +1461,7 @@ class GpuDnnPool(DnnBase):
(padX, padY) padding information.
padX is the size of the left and right borders,
padY is the size of the top and bottom borders.
"""
__props__
=
(
"mode"
,)
...
...
@@ -1974,14 +1981,12 @@ class GpuDnnSoftmaxBase(DnnBase):
----------
tensor_format
Always set this to 'bc01'.
algo
'fast', 'accurate' or 'log' indicating whether, respectively, computations
should be optimized for speed, for accuracy, or if CuDNN should rather
compute the log-softmax instead.
mode
'instance' or 'channel' indicating whether the softmax should
be computed per image across 'c01' or per spatial location '01' per
image across 'c'.
algo : {'fast', 'accurate', 'log'}
Indicating whether, respectively, computations should be optimized for
speed, for accuracy, or if CuDNN should rather compute the log-softmax instead.
mode : {'instance', 'channel'}
Indicating whether the softmax should be computed per image across 'c01'
or per spatial location '01' per image across 'c'.
"""
...
...
@@ -2137,13 +2142,12 @@ class GpuDnnSoftmax(GpuDnnSoftmaxBase):
----------
tensor_format
Always set to 'bc01'.
algo
'fast' or 'accurate' i
ndicating whether computations should be
algo
: {'fast', 'accurate'}
I
ndicating whether computations should be
optimized for speed or accuracy respectively.
mode
'instance' or 'channel' indicating whether the softmax should
be computed per image across 'c01' or per spatial location '01' per
image across 'c'.
mode : {'instance', 'channel'}
Indicating whether the softmax should be computed per image across 'c01'
or per spatial location '01' per image across 'c'.
"""
...
...
@@ -2205,13 +2209,12 @@ class GpuDnnSoftmaxGrad(GpuDnnSoftmaxBase):
----------
tensor_format
Always set to 'bc01'.
algo
'fast' or 'accurate' i
ndicating whether computations should be
algo
: {'fast', 'accurate'}
I
ndicating whether computations should be
optimized for speed or accuracy respectively.
mode
'instance' or 'channel' indicating whether the softmax should
be computed per image across 'c01' or per spatial location '01' per
image across 'c'.
mode : {'instance', 'channel'}
Indicating whether the softmax should be computed per image across 'c01'
or per spatial location '01' per image across 'c'.
"""
...
...
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