dnn应用的python代码_Python dnn.dnn_available方法代码示例
本文整理汇总了Python中theano.sandbox.cuda.dnn.dnn_available方法的典型用法代码示例。如果您正苦于以下问题：Python dnn.dnn_available方法的具体用法？Python dnn.dnn_available怎么用？Python dnn.dnn_available使用的例子？那么恭喜您, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在模块theano.sandbox.cuda.dnn的用法示例。
在下文中一共展示了dnn.dnn_available方法的19个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于我们的系统推荐出更棒的Python代码示例。
示例1: test_old_pool_interface
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def test_old_pool_interface():
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
testfile_dir = os.path.dirname(os.path.realpath(__file__))
fname = 'old_pool_interface.pkl'
with open(os.path.join(testfile_dir, fname), 'rb') as fp:
try:
pickle.load(fp)
except ImportError:
# Windows sometimes fail with nonsensical errors like:
# ImportError: No module named type
# ImportError: No module named copy_reg
# when "type" and "copy_reg" are builtin modules.
if sys.platform == 'win32':
exc_type, exc_value, exc_trace = sys.exc_info()
reraise(SkipTest, exc_value, exc_trace)
raise
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:19，
示例2: test_dnn_conv_merge_mouts
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def test_dnn_conv_merge_mouts():
# make sure it doesn't attempt to output/alpha merge a convolution
# that has multiple clients.
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
img = T.ftensor4()
kern = T.ftensor4()
out = T.ftensor4()
conv = dnn.dnn_conv(img, kern)
lr = numpy.asarray(0.05, dtype='float32')
if cuda.dnn.version() == -1:
# Can't merge alpha with cudnn v1
fr = conv + out
else:
fr = lr * (conv + out)
rr = conv * lr
f = theano.function([img, kern, out], [fr, rr], mode=mode_with_gpu)
convs = [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, dnn.GpuDnnConv)]
assert len(convs) == 1
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:26，
示例3: test_dnn_conv_merge_broad
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def test_dnn_conv_merge_broad():
# Make sure that we don't apply output_merge on broadcasted values.
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
img = T.ftensor4()
kern = T.ftensor4()
conv = dnn.dnn_conv(img, kern)
lr = numpy.asarray(0.05, dtype='float32')
# this does broadcasting
fr = conv + lr
f = theano.function([img, kern], [fr])
convs = [n for n in f.maker.fgraph.toposort()
if isinstance(n.op, dnn.GpuDnnConv)]
assert len(convs) == 1
conv = convs[0]
# Assert output was not merged
assert isinstance(conv.inputs[2].owner.op, GpuAllocEmpty)
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:23，
示例4: get_output_for
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def get_output_for(self, input, *args, **kwargs):
if not dnn_available:
raise RuntimeError("cudnn is not available.")
# by default we assume 'cross', consistent with earlier versions of conv2d.
conv_mode = 'conv' if self.flip_filters else 'cross'
# if 'border_mode' is one of 'valid' or 'full' use that.
# else use pad directly.
border_mode = self.border_mode if (self.border_mode is not None) else self.pad
conved = dnn.dnn_conv(img=input,
kerns=self.W,
subsample=self.strides,
border_mode=border_mode,
conv_mode=conv_mode
)
if self.b is None:
activation = conved
elif self.untie_biases:
activation = conved + self.b.dimshuffle('x', 0, 1, 2)
else:
activation = conved + self.b.dimshuffle('x', 0, 'x', 'x')
return self.nonlinearity(activation)
开发者ID:benanne，项目名称:kaggle-ndsb，代码行数:25，
示例5: setUp
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def setUp(self):
"""
Set up a test image and filter to re-use.
"""
skip_if_no_gpu()
if not dnn_available():
raise SkipTest('Skipping tests cause cudnn is not available')
self.orig_floatX = theano.config.floatX
theano.config.floatX = 'float32'
self.image = np.random.rand(1, 1, 3, 3).astype(theano.config.floatX)
self.image_tensor = tensor.tensor4()
self.input_space = Conv2DSpace((3, 3), 1, axes=('b', 'c', 0, 1))
self.filters_values = np.ones(
(1, 1, 2, 2), dtype=theano.config.floatX
)
self.filters = sharedX(self.filters_values, name='filters')
self.batch_size = 1
self.cudnn2d = Cudnn2D(self.filters, self.batch_size, self.input_space)
开发者ID:zchengquan，项目名称:TextDetector，代码行数:21，
示例6: get_output
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def get_output(self, train=False):
X = self.get_input(train)
newshape = (X.shape[0]*X.shape[1], X.shape[2], X.shape[3], X.shape[4])
Y = theano.tensor.reshape(X, newshape) #collapse num_samples and num_timesteps
border_mode = self.border_mode
if on_gpu() and dnn.dnn_available():
if border_mode == 'same':
assert(self.subsample == (1, 1))
pad_x = (self.nb_row - self.subsample[0]) // 2
pad_y = (self.nb_col - self.subsample[1]) // 2
conv_out = dnn.dnn_conv(img=Y,
kerns=self.W,
border_mode=(pad_x, pad_y))
else:
conv_out = dnn.dnn_conv(img=Y,
kerns=self.W,
border_mode=border_mode,
subsample=self.subsample)
else:
if border_mode == 'same':
border_mode = 'full'
conv_out = theano.tensor.nnet.conv.conv2d(Y, self.W,
border_mode=border_mode, subsample=self.subsample)
if self.border_mode == 'same':
shift_x = (self.nb_row - 1) // 2
shift_y = (self.nb_col - 1) // 2
conv_out = conv_out[:, :, shift_x:Y.shape[2] + shift_x, shift_y:Y.shape[3] + shift_y]
output = self.activation(conv_out + self.b.dimshuffle('x', 0, 'x', 'x'))
newshape = (X.shape[0], X.shape[1], output.shape[1], output.shape[2], output.shape[3])
return theano.tensor.reshape(output, newshape)
开发者ID:textclf，项目名称:fancy-cnn，代码行数:35，
示例7: test_dnn_conv_desc_merge
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def test_dnn_conv_desc_merge():
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
img_shp = T.as_tensor_variable(
numpy.asarray([2, 1, 8, 8]).astype('int64'))
kern_shp = T.as_tensor_variable(
numpy.asarray([3, 1, 2, 2]).astype('int64'))
desc1 = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(2, 2),
conv_mode='conv')(img_shp, kern_shp)
desc2 = dnn.GpuDnnConvDesc(border_mode='full', subsample=(1, 1),
conv_mode='cross')(img_shp, kern_shp)
# CDataType is not DeepCopyable so this will crash if we don't use
# borrow=True
f = theano.function([], [theano.Out(desc1, borrow=True),
theano.Out(desc2, borrow=True)],
mode=mode_with_gpu)
d1, d2 = f()
# This will be the case if they are merged, which would be bad.
assert d1 != d2
desc1v2 = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(2, 2),
conv_mode='conv')(img_shp, kern_shp)
f = theano.function([], [theano.Out(desc1, borrow=True),
theano.Out(desc1v2, borrow=True)],
mode=mode_with_gpu)
assert len([n for n in f.maker.fgraph.apply_nodes
if isinstance(n.op, dnn.GpuDnnConvDesc)]) == 1
# CDATA type don't equal even if they represent the same object
# So we can't use debugmode with it.
if theano.config.mode not in ["DebugMode", "DEBUG_MODE"]:
d1, d2 = f()
# They won't be equal if they aren't merged.
assert d1 == d2
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:39，
示例8: test_dnn_conv_merge
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def test_dnn_conv_merge():
"""This test that we merge correctly multiple dnn_conv.
This can is more difficult due to GpuEmptyAlloc that aren't
merged.
"""
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
img_shp = [2, 5, 6, 8]
kern_shp = [3, 5, 5, 6]
img = T.ftensor4('img')
kern = T.ftensor4('kern')
out = T.ftensor4('out')
desc = dnn.GpuDnnConvDesc(
border_mode='valid')(img.shape, kern.shape)
# Test forward op
o1 = dnn.dnn_conv(img, kern)
o2 = dnn.dnn_conv(img, kern)
f = theano.function([img, kern], [o1, o2], mode=mode_with_gpu)
d1, d2 = f(numpy.random.rand(*img_shp).astype('float32'),
numpy.random.rand(*kern_shp).astype('float32'))
topo = f.maker.fgraph.toposort()
assert len([n for n in topo if isinstance(n.op, dnn.GpuDnnConv)]) == 1
# Test grad w op
o1 = dnn.GpuDnnConvGradW()(img, kern, out, desc)
o2 = dnn.GpuDnnConvGradW()(img, kern, out, desc)
f = theano.function([img, kern, out], [o1, o2], mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
assert len([n for n in topo if isinstance(n.op, dnn.GpuDnnConvGradW)]) == 1
# Test grad i op
o1 = dnn.GpuDnnConvGradI()(img, kern, out, desc)
o2 = dnn.GpuDnnConvGradI()(img, kern, out, desc)
f = theano.function([img, kern, out], [o1, o2], mode=mode_with_gpu)
topo = f.maker.fgraph.toposort()
assert len([n for n in topo if isinstance(n.op, dnn.GpuDnnConvGradI)]) == 1
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:41，
示例9: setUp
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def setUp(self):
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
utt.seed_rng()
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:6，
示例10: test_dnn_tag
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def test_dnn_tag():
"""
Test that if cudnn isn't avail we crash and that if it is avail, we use it.
"""
x = T.ftensor4()
old = theano.config.on_opt_error
theano.config.on_opt_error = "raise"
sio = StringIO()
handler = logging.StreamHandler(sio)
logging.getLogger('theano.compile.tests.test_dnn').addHandler(handler)
# Silence original handler when intentionnally generating warning messages
logging.getLogger('theano').removeHandler(theano.logging_default_handler)
raised = False
try:
f = theano.function(
[x],
pool_2d(x, ds=(2, 2), ignore_border=True),
mode=mode_with_gpu.including("cudnn"))
except (AssertionError, RuntimeError):
assert not cuda.dnn.dnn_available()
raised = True
finally:
theano.config.on_opt_error = old
logging.getLogger(
'theano.compile.tests.test_dnn').removeHandler(handler)
logging.getLogger('theano').addHandler(theano.logging_default_handler)
if not raised:
assert cuda.dnn.dnn_available()
assert any([isinstance(n.op, cuda.dnn.GpuDnnPool)
for n in f.maker.fgraph.toposort()])
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:34，
示例11: test_softmax
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def test_softmax(self):
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
t = T.ftensor4('t')
rand_tensor = numpy.asarray(
numpy.random.rand(5, 4, 3, 2),
dtype='float32'
)
self._compile_and_check(
[t],
[dnn.GpuDnnSoftmax('bc01', 'accurate', 'channel')(t)],
[rand_tensor],
dnn.GpuDnnSoftmax
)
self._compile_and_check(
[t],
[
T.grad(
dnn.GpuDnnSoftmax(
'bc01',
'accurate',
'channel'
)(t).mean(),
t
)
],
[rand_tensor],
dnn.GpuDnnSoftmaxGrad
)
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:32，
示例12: test_conv
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def test_conv(self):
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor4('img')
kerns = T.ftensor4('kerns')
out = T.ftensor4('out')
img_val = numpy.asarray(
numpy.random.rand(10, 2, 6, 4),
dtype='float32'
)
kern_vals = numpy.asarray(
numpy.random.rand(8, 2, 4, 3),
dtype='float32'
)
for params in product(
['valid', 'full', 'half'],
[(1, 1), (2, 2)],
['conv', 'cross']
):
out_vals = numpy.zeros(
dnn.GpuDnnConv.get_out_shape(img_val.shape, kern_vals.shape,
border_mode=params[0],
subsample=params[1]),
dtype='float32')
desc = dnn.GpuDnnConvDesc(
border_mode=params[0],
subsample=params[1],
conv_mode=params[2]
)(img.shape, kerns.shape)
conv = dnn.GpuDnnConv()(img, kerns, out, desc)
self._compile_and_check(
[img, kerns, out],
[conv],
[img_val, kern_vals, out_vals],
dnn.GpuDnnConv
)
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:39，
示例13: test_conv3d
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def test_conv3d(self):
if not (cuda.dnn.dnn_available() and dnn.version() >= (2000, 2000)):
raise SkipTest('"CuDNN 3D convolution requires CuDNN v2')
ftensor5 = T.TensorType(dtype="float32", broadcastable=(False,) * 5)
img = ftensor5('img')
kerns = ftensor5('kerns')
out = ftensor5('out')
img_val = numpy.asarray(
numpy.random.rand(10, 2, 6, 4, 11),
dtype='float32'
)
kern_vals = numpy.asarray(
numpy.random.rand(8, 2, 4, 3, 1),
dtype='float32'
)
for params in product(
['valid', 'full', 'half'],
[(1, 1, 1), (2, 2, 2)],
['conv', 'cross']
):
out_vals = numpy.zeros(
dnn.GpuDnnConv3d.get_out_shape(img_val.shape, kern_vals.shape,
border_mode=params[0],
subsample=params[1]),
dtype='float32')
desc = dnn.GpuDnnConvDesc(
border_mode=params[0],
subsample=params[1],
conv_mode=params[2]
)(img.shape, kerns.shape)
conv = dnn.GpuDnnConv3d()(img, kerns, out, desc)
self._compile_and_check(
[img, kerns, out],
[conv],
[img_val, kern_vals, out_vals],
dnn.GpuDnnConv3d
)
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:40，
示例14: test_pool
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def test_pool(self):
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor4('img')
img_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5),
dtype='float32'
)
# 'average_exc_pad' is disabled for versions < 4004
if cuda.dnn.version() < (4004, 4004):
modes = ['max', 'average_inc_pad']
else:
modes = ['max', 'average_inc_pad', 'average_exc_pad']
for params in product(
[(1, 1), (2, 2), (3, 3)],
[(1, 1), (2, 2), (3, 3)],
modes
):
self._compile_and_check(
[img],
[dnn.GpuDnnPool(mode=params[2])
(img, params[0], params[1], (0, 0))],
[img_val],
dnn.GpuDnnPool
)
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:29，
示例15: test_pool_grad
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def test_pool_grad(self):
if not dnn.dnn_available():
raise SkipTest(dnn.dnn_available.msg)
img = T.ftensor4('img')
img_grad = T.ftensor4('img_grad')
out = T.ftensor4('out')
img_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5),
dtype='float32'
)
img_grad_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5),
dtype='float32'
)
out_val = numpy.asarray(
numpy.random.rand(2, 3, 4, 5),
dtype='float32'
)
for params in product(
[(1, 1), (2, 2), (3, 3)],
[(1, 1), (2, 2), (3, 3)],
['max', 'average_inc_pad']
):
pool_grad = dnn.GpuDnnPoolGrad()(
img,
out,
img_grad,
params[0],
params[1],
(0, 0)
)
self._compile_and_check(
[img, img_grad, out],
[pool_grad],
[img_val, img_grad_val, out_val],
dnn.GpuDnnPoolGrad
)
# this has been a problem in the past
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:43，
示例16: test_dnn_conv_grad
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def test_dnn_conv_grad():
if not cuda.dnn.dnn_available() or dnn.version() == -1:
raise SkipTest('alpha != 1.0 not supported in cudnn v1')
b = 1
c = 4
f = 3
ih = 2
iw = 8
kh = 2
kw = 2
img_val = numpy.random.random((b, c, ih, iw)).astype('float32')
kern_val = numpy.random.random((f, c, kh, kw)).astype('float32')
out_val = numpy.random.random((b, f, ih - kw + 1,
iw - kw + 1)).astype('float32')
def dconv(img, kern, out):
desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
conv_mode='conv')(img.shape, kern.shape)
return dnn.GpuDnnConv()(img, kern, out, desc, alpha=0.5, beta=0.75)
def dconvi(img, kern, out):
desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
conv_mode='conv')(img.shape, kern.shape)
return dnn.GpuDnnConvGradI()(kern, out, img, desc, alpha=-1.0,
beta=0.0)
def dconvw(img, kern, out):
desc = dnn.GpuDnnConvDesc(border_mode='valid', subsample=(1, 1),
conv_mode='conv')(img.shape, kern.shape)
return dnn.GpuDnnConvGradW()(img, out, kern, desc, alpha=0.75,
beta=-1.0)
utt.verify_grad(dconv, [img_val, kern_val, out_val], mode=mode_with_gpu)
utt.verify_grad(dconvi, [img_val, kern_val, out_val], mode=mode_with_gpu)
utt.verify_grad(dconvw, [img_val, kern_val, out_val], mode=mode_with_gpu)
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:38，
示例17: get_conv3d_test_cases
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def get_conv3d_test_cases():
# Every element of test_shapes follows the format
# [input_shape, filter_shape, subsample]
test_shapes = [[(128, 3, 5, 5, 5), (64, 3, 1, 2, 4), (1, 1, 1)],
[(8, 4, 20, 12, 15), (5, 4, 6, 12, 4), (2, 2, 2)],
[(8, 1, 20, 12, 15), (5, 1, 6, 12, 4), (3, 3, 3)],
[(8, 1, 20, 12, 15), (5, 1, 6, 12, 4), (3, 2, 1)],
[(8, 1, 20, 12, 15), (5, 1, 6, 12, 4), (3, 2, 1)],
# Test with 1x1x1 filters
[(8, 1, 10, 10, 10), (10, 1, 1, 1, 1), (1, 1, 1)],
# Test with dimensions larger than 1024 (thread block dim)
[(1025, 1, 2, 3, 4), (5, 1, 1, 2, 3), (1, 1, 1)],
[(8, 1, 2, 3, 4), (1025, 1, 1, 2, 3), (1, 1, 1)],
[(8, 1025, 2, 3, 4), (5, 1025, 1, 1, 2), (1, 1, 1)],
[(8, 1, 1030, 3, 4), (5, 1, 1025, 1, 1), (1, 1, 1)],
[(8, 1, 2, 1030, 4), (5, 1, 2, 1025, 1), (1, 1, 1)],
[(8, 1, 2, 3, 1030), (5, 1, 1, 2, 1025), (1, 1, 1)],
# The equivalent of this caused a crash with conv2d
[(1, 1, 1, 44800, 1), (6, 1, 1, 1, 1), (1, 1, 1)]]
# With border mode 'full', test with kernel bigger than image in some/all
# dimensions
test_shapes_full = [[(6, 2, 2, 2, 2), (4, 2, 3, 1, 1), (1, 1, 1)],
[(6, 2, 2, 2, 2), (4, 2, 1, 3, 1), (1, 1, 1)],
[(6, 2, 2, 2, 2), (4, 2, 1, 1, 3), (1, 1, 1)],
[(6, 2, 2, 2, 2), (4, 2, 5, 5, 5), (1, 1, 1)]]
border_modes = ['valid', 'full', 'half', (1, 2, 3), (3, 2, 1), 1, 2]
conv_modes = ['conv', 'cross']
if cuda.dnn.dnn_available() and dnn.version() >= (3000, 3000):
itt = chain(product(test_shapes, border_modes, conv_modes),
product(test_shapes_full, ['full'], conv_modes))
else:
# CuDNN, before V3, did not support kernels larger than the inputs,
# even if the original inputs were padded so they would be larger than
# the kernels. If using a version older than V3 don't run the tests
# with kernels larger than the unpadded inputs.
itt = product(test_shapes, border_modes, conv_modes)
return itt
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:42，
示例18: test_version
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def test_version():
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
assert isinstance(cuda.dnn.version(), (int, tuple))
开发者ID:muhanzhang，项目名称:D-VAE，代码行数:6，
示例19: test_old_pool_interface
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# 需要导入模块: from theano.sandbox.cuda import dnn [as 别名]
# 或者: from theano.sandbox.cuda.dnn import dnn_available [as 别名]
def test_old_pool_interface():
if not cuda.dnn.dnn_available():
raise SkipTest(cuda.dnn.dnn_available.msg)
testfile_dir = os.path.dirname(os.path.realpath(__file__))
fname = 'old_pool_interface.pkl'
with open(os.path.join(testfile_dir, fname), 'rb') as fp:
pickle.load(fp)
开发者ID:rizar，项目名称:attention-lvcsr，代码行数:9，
注：本文中的theano.sandbox.cuda.dnn.dnn_available方法示例整理自Github/MSDocs等源码及文档管理平台，相关代码片段筛选自各路编程大神贡献的开源项目，源码版权归原作者所有，传播和使用请参考对应项目的License；未经允许，请勿转载。