本文整理汇总了Python中pylearn2.sandbox.cuda_convnet.filter_acts.FilterActs.sum方法的典型用法代码示例。如果您正苦于以下问题：Python FilterActs.sum方法的具体用法？Python FilterActs.sum怎么用？Python FilterActs.sum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.sum方法的1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_grad
# 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 sum [as 别名]
def test_grad():
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)
# XXX: use verify_grad
output_grad = grad(output.sum(), images)
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)
# XXX: use verify_grad
output_conv2d_grad = grad(output_conv2d.sum(), images)
f = function([], [output_grad, output_conv2d_grad])
output_grad, output_conv2d_grad = 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_grad - output_conv2d_grad).max() > 7.7e-6:
assert type(output_grad) == type(output_conv2d_grad)
assert output_grad.dtype == output_conv2d_grad.dtype
if output_grad.shape != output_conv2d_grad.shape:
print "cuda-convnet shape: ", output_grad.shape
print "theano shape: ", output_conv2d_grad.shape
assert False
err = np.abs(output_grad - output_conv2d_grad)
print "absolute error range: ", (err.min(), err.max())
print "mean absolute error: ", err.mean()
print "cuda-convnet value range: ", (output_grad.min(), output_grad.max())
print "theano value range: ", (output_conv2d_grad.min(), output_conv2d_grad.max())
assert False