本文整理汇总了Python中pylearn2.sandbox.cuda_convnet.filter_acts.FilterActs.min方法的典型用法代码示例。如果您正苦于以下问题:Python FilterActs.min方法的具体用法?Python FilterActs.min怎么用?Python FilterActs.min使用的例子?那么, 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类pylearn2.sandbox.cuda_convnet.filter_acts.FilterActs
的用法示例。
在下文中一共展示了FilterActs.min方法的4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,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: test_match_valid_conv
# 需要导入模块: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 别名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts.FilterActs import min [as 别名]
def test_match_valid_conv():
# Tests that running FilterActs with no padding is the same as running
# theano's conv2D in valid mode
rng = np.random.RandomState([2012, 10, 9])
batch_size = 5
rows = 10
cols = 9
channels = 3
filter_rows = 4
filter_cols = filter_rows
num_filters = 16
images = shared(rng.uniform(-1.0, 1.0, (channels, rows, cols, batch_size)).astype("float32"), name="images")
filters = shared(
rng.uniform(-1.0, 1.0, (channels, filter_rows, filter_cols, num_filters)).astype("float32"), name="filters"
)
gpu_images = gpu_from_host(images)
gpu_filters = gpu_from_host(filters)
output = FilterActs()(gpu_images, gpu_filters)
output = host_from_gpu(output)
images_bc01 = images.dimshuffle(3, 0, 1, 2)
filters_bc01 = filters.dimshuffle(3, 0, 1, 2)
filters_bc01 = filters_bc01[:, :, ::-1, ::-1]
output_conv2d = conv2d(images_bc01, filters_bc01, border_mode="valid")
output_conv2d = output_conv2d.dimshuffle(1, 2, 3, 0)
f = function([], [output, output_conv2d])
output, output_conv2d = f()
warnings.warn(
"""test_match_valid_conv success criterion is not very strict. Can we verify that this is OK?
One possibility is that theano is numerically unstable and Alex's code is better.
Probably theano CPU 64 bit is OK but it's worth checking the others."""
)
if np.abs(output - output_conv2d).max() > 2.4e-6:
assert type(output) == type(output_conv2d)
assert output.dtype == output_conv2d.dtype
if output.shape != output_conv2d.shape:
print "cuda-convnet shape: ", output.shape
print "theano shape: ", output_conv2d.shape
assert False
err = np.abs(output - output_conv2d)
print "absolute error range: ", (err.min(), err.max())
print "mean absolute error: ", err.mean()
print "cuda-convnet value range: ", (output.min(), output.max())
print "theano value range: ", (output_conv2d.min(), output_conv2d.max())
assert False
示例2: test_match_valid_conv_strided
# 需要导入模块: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 别名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts.FilterActs import min [as 别名]
def test_match_valid_conv_strided():
# Tests that running FilterActs with stride is the same as running
# theano's conv2D in valid mode and then downsampling
rng = np.random.RandomState([2012,10,9])
batch_size = 5
rows = 9
cols = 9
channels = 3
filter_rows = 3
filter_cols = filter_rows
stride = 3
num_filters = 16
images = shared(rng.uniform(-1., 1., (channels, rows, cols,
batch_size)).astype('float32'), name='images')
filters = shared(rng.uniform(-1., 1., (channels, filter_rows,
filter_cols, num_filters)).astype('float32'), name='filters')
gpu_images = gpu_from_host(images)
gpu_filters = gpu_from_host(filters)
output = FilterActs(stride=stride)(gpu_images, gpu_filters)
output = host_from_gpu(output)
images_bc01 = images.dimshuffle(3,0,1,2)
filters_bc01 = filters.dimshuffle(3,0,1,2)
filters_bc01 = filters_bc01[:,:,::-1,::-1]
output_conv2d = conv2d(images_bc01, filters_bc01,
border_mode='valid', subsample=(stride, stride))
output_conv2d_orig = output_conv2d.dimshuffle(1,2,3,0)
output_conv2d = output_conv2d_orig # [:, ::stride, ::stride, :]
f = function([], [output, output_conv2d, output_conv2d_orig])
output, output_conv2d, output_conv2d_orig = f()
warnings.warn("""test_match_valid_conv success criterion is not very strict. Can we verify that this is OK?
One possibility is that theano is numerically unstable and Alex's code is better.
Probably theano CPU 64 bit is OK but it's worth checking the others.""")
if np.abs(output - output_conv2d).max() > 2.4e-6:
assert type(output) == type(output_conv2d)
assert output.dtype == output_conv2d.dtype
if output.shape != output_conv2d.shape:
print 'cuda-convnet shape: ',output.shape
print 'theano shape: ',output_conv2d.shape
assert False
err = np.abs(output - output_conv2d)
print 'absolute error range: ', (err.min(), err.max())
print 'mean absolute error: ', err.mean()
print 'cuda-convnet value range: ', (output.min(), output.max())
print 'theano value range: ', (output_conv2d.min(), output_conv2d.max())
assert False
示例3: test_match_valid_conv
# 需要导入模块: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 别名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts.FilterActs import min [as 别名]
def test_match_valid_conv():
# Tests that running FilterActs with no padding is the same as running
# theano's conv2D in valid mode
rng = np.random.RandomState([2012,10,9])
batch_size = 5
rows = 10
cols = 9
channels = 3
filter_rows = 4
filter_cols = filter_rows
num_filters = 16
images = shared(rng.uniform(-1., 1., (channels, rows, cols,
batch_size)).astype('float32'), name='images')
filters = shared(rng.uniform(-1., 1., (channels, filter_rows,
filter_cols, num_filters)).astype('float32'), name='filters')
gpu_images = gpu_from_host(images)
gpu_filters = gpu_from_host(filters)
output = FilterActs()(gpu_images, gpu_filters)
output = host_from_gpu(output)
images_bc01 = images.dimshuffle(3,0,1,2)
filters_bc01 = filters.dimshuffle(3,0,1,2)
filters_bc01 = filters_bc01[:,:,::-1,::-1]
output_conv2d = conv2d(images_bc01, filters_bc01,
border_mode='valid')
output_conv2d = output_conv2d.dimshuffle(1,2,3,0)
try:
f = function([], [output, output_conv2d])
except:
raise KnownFailureTest("cuda-convnet code depends on an unmerged theano feature.")
output, output_conv2d = f()
warnings.warn("test_match_valid_conv success criterion is not very strict. Can we verify that this is OK?")
if np.abs(output - output_conv2d).max() > 2.4e-6:
assert type(output) == type(output_conv2d)
assert output.dtype == output_conv2d.dtype
if output.shape != output_conv2d.shape:
print 'cuda-convnet shape: ',output.shape
print 'theano shape: ',output_conv2d.shape
assert False
err = np.abs(output - output_conv2d)
print 'absolute error range: ', (err.min(), err.max())
print 'mean absolute error: ', err.mean()
print 'cuda-convnet value range: ', (output.min(), output.max())
print 'theano value range: ', (output_conv2d.min(), output_conv2d.max())
assert False
示例4: test_match_grad_valid_conv
# 需要导入模块: from pylearn2.sandbox.cuda_convnet.filter_acts import FilterActs [as 别名]
# 或者: from pylearn2.sandbox.cuda_convnet.filter_acts.FilterActs import min [as 别名]
def test_match_grad_valid_conv():
# Tests that weightActs is the gradient of FilterActs
# with respect to the weights.
for partial_sum in [0, 1, 4]:
rng = np.random.RandomState([2012, 10, 9])
batch_size = 3
rows = 7
cols = 9
channels = 8
filter_rows = 4
filter_cols = filter_rows
num_filters = 16
images = shared(rng.uniform(-1., 1., (channels, rows, cols,
batch_size)).astype('float32'),
name='images')
filters = rng.uniform(-1., 1.,
(channels, filter_rows,
filter_cols, num_filters)).astype('float32')
filters = shared(filters, name='filters')
gpu_images = gpu_from_host(images)
gpu_filters = gpu_from_host(filters)
output = FilterActs(partial_sum=partial_sum)(gpu_images, gpu_filters)
output = host_from_gpu(output)
images_bc01 = images.dimshuffle(3, 0, 1, 2)
filters_bc01 = filters.dimshuffle(3, 0, 1, 2)
filters_bc01 = filters_bc01[:, :, ::-1, ::-1]
output_conv2d = conv2d(images_bc01, filters_bc01,
border_mode='valid')
output_conv2d = output_conv2d.dimshuffle(1, 2, 3, 0)
theano_rng = MRG_RandomStreams(2013 + 1 + 31)
coeffs = theano_rng.normal(avg=0., std=1.,
size=output_conv2d.shape, dtype='float32')
cost_conv2d = (coeffs * output_conv2d).sum()
weights_grad_conv2d = T.grad(cost_conv2d, filters)
cost = (coeffs * output).sum()
hid_acts_grad = T.grad(cost, output)
weights_grad = WeightActs(partial_sum=partial_sum)(
gpu_images,
gpu_from_host(hid_acts_grad),
as_tensor_variable((4, 4))
)[0]
weights_grad = host_from_gpu(weights_grad)
f = function([], [output, output_conv2d, weights_grad,
weights_grad_conv2d])
output, output_conv2d, weights_grad, weights_grad_conv2d = f()
if np.abs(output - output_conv2d).max() > 8e-6:
assert type(output) == type(output_conv2d)
assert output.dtype == output_conv2d.dtype
if output.shape != output_conv2d.shape:
print('cuda-convnet shape: ', output.shape)
print('theano shape: ', output_conv2d.shape)
assert False
err = np.abs(output - output_conv2d)
print('absolute error range: ', (err.min(), err.max()))
print('mean absolute error: ', err.mean())
print('cuda-convnet value range: ', (output.min(), output.max()))
print('theano value range: ', (output_conv2d.min(),
output_conv2d.max()))
assert False
warnings.warn(
"test_match_grad_valid_conv success criterion is not very strict."
" Can we verify that this is OK? One possibility is that theano"
" is numerically unstable and Alex's code is better. Probably"
" theano CPU 64 bit is OK but it's worth checking the others.")
if np.abs(weights_grad - weights_grad_conv2d).max() > 8.6e-6:
if type(weights_grad) != type(weights_grad_conv2d):
raise AssertionError("weights_grad is of type " +
str(weights_grad))
assert weights_grad.dtype == weights_grad_conv2d.dtype
if weights_grad.shape != weights_grad_conv2d.shape:
print('cuda-convnet shape: ', weights_grad.shape)
print('theano shape: ', weights_grad_conv2d.shape)
assert False
err = np.abs(weights_grad - weights_grad_conv2d)
print('absolute error range: ', (err.min(), err.max()))
print('mean absolute error: ', err.mean())
print('cuda-convnet value range: ', (weights_grad.min(),
weights_grad.max()))
print('theano value range: ', (weights_grad_conv2d.min(),
weights_grad_conv2d.max()))
#.........这里部分代码省略.........