本文整理匯總了Python中cntk.minus方法的典型用法代碼示例。如果您正苦於以下問題：Python cntk.minus方法的具體用法？Python cntk.minus怎麽用？Python cntk.minus使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類cntk的用法示例。
在下文中一共展示了cntk.minus方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: SmoothL1Loss
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import minus [as 別名]
def SmoothL1Loss(sigma, bbox_pred, bbox_targets, bbox_inside_weights, bbox_outside_weights):
"""
From https://github.com/smallcorgi/Faster-RCNN_TF/blob/master/lib/fast_rcnn/train.py
ResultLoss = outside_weights * SmoothL1(inside_weights * (bbox_pred - bbox_targets))
SmoothL1(x) = 0.5 * (sigma * x)^2, if |x| < 1 / sigma^2
|x| - 0.5 / sigma^2, otherwise
"""
sigma2 = sigma * sigma
inside_mul_abs = C.abs(C.element_times(bbox_inside_weights, C.minus(bbox_pred, bbox_targets)))
smooth_l1_sign = C.less(inside_mul_abs, 1.0 / sigma2)
smooth_l1_option1 = C.element_times(C.element_times(inside_mul_abs, inside_mul_abs), 0.5 * sigma2)
smooth_l1_option2 = C.minus(inside_mul_abs, 0.5 / sigma2)
smooth_l1_result = C.plus(C.element_times(smooth_l1_option1, smooth_l1_sign),
C.element_times(smooth_l1_option2, C.minus(1.0, smooth_l1_sign)))
return C.element_times(bbox_outside_weights, smooth_l1_result) 示例2: _moments
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import minus [as 別名]
def _moments(x, axes=None, shift=None, keep_dims=False):
_axes = tuple(axes)
if shift is None:
shift = x
# Compute true mean while keeping the dims for proper broadcasting.
for axis in _axes:
shift = C.reduce_mean(shift, axis=axis)
shift = C.stop_gradient(shift)
shifted_mean = C.minus(x, shift)
for axis in _axes:
shifted_mean = C.reduce_mean(shifted_mean, axis=axis)
variance_mean = C.square(C.minus(x, shift))
for axis in _axes:
variance_mean = C.reduce_mean(variance_mean, axis=axis)
variance = C.minus(variance_mean, C.square(shifted_mean))
mean = C.plus(shifted_mean, shift)
if not keep_dims:
mean = squeeze(mean, _axes)
variance = squeeze(variance, _axes)
return mean, variance 示例3: test_minus_1
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import minus [as 別名]
def test_minus_1():
cntk_op = C.minus([1, 2, 3], [4, 5, 6])
cntk_ret = cntk_op.eval()
ng_op, _ = CNTKImporter().import_model(cntk_op)
ng_ret = ng.transformers.make_transformer().computation(ng_op)()
assert np.array_equal(cntk_ret, ng_ret) 示例4: test_minus_2
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import minus [as 別名]
def test_minus_2():
cntk_op = C.minus([[1, 2, 3], [4, 5, 6]], [7, 8, 9])
cntk_ret = cntk_op.eval()
ng_op, _ = CNTKImporter().import_model(cntk_op)
ng_ret = ng.transformers.make_transformer().computation(ng_op)()
assert np.array_equal(cntk_ret, ng_ret) 示例5: test_minus_3
# 需要導入模塊: import cntk [as 別名]
# 或者: from cntk import minus [as 別名]
def test_minus_3():
cntk_op = C.minus([1, 2, 3], [[4, 5, 6], [7, 8, 9]])
cntk_ret = cntk_op.eval()
ng_op, _ = CNTKImporter().import_model(cntk_op)
ng_ret = ng.transformers.make_transformer().computation(ng_op)()
assert np.array_equal(cntk_ret, ng_ret)