本文整理汇总了Python中tensorflow.python.ops.gen_array_ops._broadcast_gradient_args方法的典型用法代码示例。如果您正苦于以下问题:Python gen_array_ops._broadcast_gradient_args方法的具体用法?Python gen_array_ops._broadcast_gradient_args怎么用?Python gen_array_ops._broadcast_gradient_args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tensorflow.python.ops.gen_array_ops
的用法示例。
在下文中一共展示了gen_array_ops._broadcast_gradient_args方法的15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: _BetaincGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _BetaincGrad(op, grad):
"""Returns gradient of betainc(a, b, x) with respect to x."""
# TODO(ebrevdo): Perhaps add the derivative w.r.t. a, b
a, b, x = op.inputs
# two cases: x is a scalar and a/b are same-shaped tensors, or vice
# versa; so its sufficient to check against shape(a).
sa = array_ops.shape(a)
sx = array_ops.shape(x)
# pylint: disable=protected-access
_, rx = gen_array_ops._broadcast_gradient_args(sa, sx)
# pylint: enable=protected-access
# Perform operations in log space before summing, because terms
# can grow large.
log_beta = (gen_math_ops.lgamma(a) + gen_math_ops.lgamma(b)
- gen_math_ops.lgamma(a + b))
partial_x = math_ops.exp(
(b - 1) * math_ops.log(1 - x) + (a - 1) * math_ops.log(x) - log_beta)
# TODO(b/36815900): Mark None return values as NotImplemented
return (None, # da
None, # db
array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx))
示例2: _ZetaGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _ZetaGrad(op, grad):
"""Returns gradient of zeta(x, q) with respect to x and q."""
# TODO(tillahoffmann): Add derivative with respect to x
x = op.inputs[0]
q = op.inputs[1]
# Broadcast gradients
sx = array_ops.shape(x)
sq = array_ops.shape(q)
unused_rx, rq = gen_array_ops._broadcast_gradient_args(sx, sq)
# Evaluate gradient
with ops.control_dependencies([grad.op]):
x = math_ops.conj(x)
q = math_ops.conj(q)
partial_q = -x * math_ops.zeta(x + 1, q)
# TODO(b/36815900): Mark None return values as NotImplemented
return (None,
array_ops.reshape(math_ops.reduce_sum(partial_q * grad, rq), sq))
示例3: _PolygammaGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _PolygammaGrad(op, grad):
"""Returns gradient of psi(n, x) with respect to n and x."""
# TODO(tillahoffmann): Add derivative with respect to n
n = op.inputs[0]
x = op.inputs[1]
# Broadcast gradients
sn = array_ops.shape(n)
sx = array_ops.shape(x)
unused_rn, rx = gen_array_ops._broadcast_gradient_args(sn, sx)
# Evaluate gradient
with ops.control_dependencies([grad.op]):
n = math_ops.conj(n)
x = math_ops.conj(x)
partial_x = math_ops.polygamma(n + 1, x)
# TODO(b/36815900): Mark None return values as NotImplemented
return (None,
array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx))
示例4: _RealDivGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _RealDivGrad(op, grad):
"""RealDiv op gradient."""
x = op.inputs[0]
y = op.inputs[1]
sx = array_ops.shape(x)
sy = array_ops.shape(y)
# pylint: disable=protected-access
rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
# pylint: enable=protected-access
x = math_ops.conj(x)
y = math_ops.conj(y)
return (array_ops.reshape(
math_ops.reduce_sum(math_ops.realdiv(grad, y), rx),
sx), array_ops.reshape(
math_ops.reduce_sum(grad * math_ops.realdiv(math_ops.realdiv(-x, y), y),
ry), sy))
示例5: _PowGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _PowGrad(op, grad):
"""Returns grad * (y*x^(y-1), z*log(x))."""
x = op.inputs[0]
y = op.inputs[1]
z = op.outputs[0]
sx = array_ops.shape(x)
sy = array_ops.shape(y)
rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
x = math_ops.conj(x)
y = math_ops.conj(y)
z = math_ops.conj(z)
gx = array_ops.reshape(
math_ops.reduce_sum(grad * y * math_ops.pow(x, y - 1), rx), sx)
# Avoid false singularity at x = 0
if x.dtype.is_complex:
# real(x) < 0 is fine for the complex case
log_x = array_ops.where(
math_ops.not_equal(x, 0), math_ops.log(x), array_ops.zeros_like(x))
else:
# There's no sensible real value to return if x < 0, so return 0
log_x = array_ops.where(x > 0, math_ops.log(x), array_ops.zeros_like(x))
gy = array_ops.reshape(math_ops.reduce_sum(grad * z * log_x, ry), sy)
return gx, gy
示例6: _MaximumMinimumGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _MaximumMinimumGrad(op, grad, selector_op):
"""Factor out the code for the gradient of Maximum or Minimum."""
x = op.inputs[0]
y = op.inputs[1]
gdtype = grad.dtype
sx = array_ops.shape(x)
sy = array_ops.shape(y)
gradshape = array_ops.shape(grad)
zeros = array_ops.zeros(gradshape, gdtype)
xmask = selector_op(x, y)
rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
xgrad = array_ops.where(xmask, grad, zeros)
ygrad = array_ops.where(math_ops.logical_not(xmask), grad, zeros)
gx = array_ops.reshape(math_ops.reduce_sum(xgrad, rx), sx)
gy = array_ops.reshape(math_ops.reduce_sum(ygrad, ry), sy)
return (gx, gy)
示例7: _ZetaGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _ZetaGrad(op, grad):
"""Returns gradient of zeta(x, q) with respect to x and q."""
# TODO(tillahoffmann): Add derivative with respect to x
x = op.inputs[0]
q = op.inputs[1]
# Broadcast gradients
sx = array_ops.shape(x)
sq = array_ops.shape(q)
unused_rx, rq = gen_array_ops._broadcast_gradient_args(sx, sq)
# Evaluate gradient
with ops.control_dependencies([grad.op]):
x = math_ops.conj(x)
q = math_ops.conj(q)
partial_q = -x * math_ops.zeta(x + 1, q)
return (None,
array_ops.reshape(math_ops.reduce_sum(partial_q * grad, rq), sq))
示例8: _PolygammaGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _PolygammaGrad(op, grad):
"""Returns gradient of psi(n, x) with respect to n and x."""
# TODO(tillahoffmann): Add derivative with respect to n
n = op.inputs[0]
x = op.inputs[1]
# Broadcast gradients
sn = array_ops.shape(n)
sx = array_ops.shape(x)
unused_rn, rx = gen_array_ops._broadcast_gradient_args(sn, sx)
# Evaluate gradient
with ops.control_dependencies([grad.op]):
n = math_ops.conj(n)
x = math_ops.conj(x)
partial_x = math_ops.polygamma(n + 1, x)
return (None,
array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx))
示例9: _SquaredDifferenceGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _SquaredDifferenceGrad(op, grad):
"""Returns the gradient for (x-y)^2."""
x = op.inputs[0]
y = op.inputs[1]
sx = array_ops.shape(x)
sy = array_ops.shape(y)
# pylint: disable=protected-access
rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
# pylint: enable=protected-access
# .op works with Tensors or IndexedSlices
with ops.control_dependencies([grad.op]):
# The parens ensure that if grad is IndexedSlices, it'll get multiplied by
# Tensor (not a number like 2.0) which causes it to convert to Tensor.
x_grad = math_ops.scalar_mul(2.0, grad) * (x - y)
return (array_ops.reshape(math_ops.reduce_sum(x_grad, rx), sx),
-array_ops.reshape(math_ops.reduce_sum(x_grad, ry), sy))
# Logical operations have no gradients.
示例10: _PowGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _PowGrad(op, grad):
"""Returns grad * (y*x^(y-1), z*log(x))."""
x = op.inputs[0]
y = op.inputs[1]
z = op.outputs[0]
sx = array_ops.shape(x)
sy = array_ops.shape(y)
rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
x = math_ops.conj(x)
y = math_ops.conj(y)
z = math_ops.conj(z)
gx = array_ops.reshape(
math_ops.reduce_sum(grad * y * math_ops.pow(x, y - 1), rx), sx)
# Avoid false singularity at x = 0
if x.dtype.is_complex:
# real(x) < 0 is fine for the complex case
log_x = math_ops.select(
math_ops.not_equal(x, 0), math_ops.log(x), array_ops.zeros_like(x))
else:
# There's no sensible real value to return if x < 0, so return 0
log_x = math_ops.select(x > 0, math_ops.log(x), array_ops.zeros_like(x))
gy = array_ops.reshape(
math_ops.reduce_sum(grad * z * log_x, ry), sy)
return gx, gy
示例11: _MaximumMinimumGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _MaximumMinimumGrad(op, grad, selector_op):
"""Factor out the code for the gradient of Maximum or Minimum."""
x = op.inputs[0]
y = op.inputs[1]
gdtype = grad.dtype
sx = array_ops.shape(x)
sy = array_ops.shape(y)
gradshape = array_ops.shape(grad)
zeros = array_ops.zeros(gradshape, gdtype)
xmask = selector_op(x, y)
rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
xgrad = math_ops.select(xmask, grad, zeros)
ygrad = math_ops.select(math_ops.logical_not(xmask), grad, zeros)
gx = array_ops.reshape(math_ops.reduce_sum(xgrad, rx), sx)
gy = array_ops.reshape(math_ops.reduce_sum(ygrad, ry), sy)
return (gx, gy)
示例12: _IgammaGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _IgammaGrad(op, grad):
"""Returns gradient of igamma(a, x) with respect to x."""
# TODO(ebrevdo): Perhaps add the derivative w.r.t. a
a = op.inputs[0]
x = op.inputs[1]
sa = array_ops.shape(a)
sx = array_ops.shape(x)
# pylint: disable=protected-access
unused_ra, rx = gen_array_ops._broadcast_gradient_args(sa, sx)
# pylint: enable=protected-access
# Perform operations in log space before summing, because Gamma(a)
# and Gamma'(a) can grow large.
partial_x = math_ops.exp(-x + (a - 1) * math_ops.log(x) - math_ops.lgamma(a))
# TODO(b/36815900): Mark None return values as NotImplemented
return (None,
array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx))
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:19,代码来源:math_grad.py
示例13: _ZetaGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _ZetaGrad(op, grad):
"""Returns gradient of zeta(x, q) with respect to x and q."""
# TODO(tillahoffmann): Add derivative with respect to x
x = op.inputs[0]
q = op.inputs[1]
# Broadcast gradients
sx = array_ops.shape(x)
sq = array_ops.shape(q)
# pylint: disable=protected-access
unused_rx, rq = gen_array_ops._broadcast_gradient_args(sx, sq)
# pylint: enable=protected-access
# Evaluate gradient
with ops.control_dependencies([grad]):
x = math_ops.conj(x)
q = math_ops.conj(q)
partial_q = -x * math_ops.zeta(x + 1, q)
# TODO(b/36815900): Mark None return values as NotImplemented
return (None,
array_ops.reshape(math_ops.reduce_sum(partial_q * grad, rq), sq))
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:21,代码来源:math_grad.py
示例14: _PolygammaGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _PolygammaGrad(op, grad):
"""Returns gradient of psi(n, x) with respect to n and x."""
# TODO(tillahoffmann): Add derivative with respect to n
n = op.inputs[0]
x = op.inputs[1]
# Broadcast gradients
sn = array_ops.shape(n)
sx = array_ops.shape(x)
# pylint: disable=protected-access
unused_rn, rx = gen_array_ops._broadcast_gradient_args(sn, sx)
# pylint: enable=protected-access
# Evaluate gradient
with ops.control_dependencies([grad]):
n = math_ops.conj(n)
x = math_ops.conj(x)
partial_x = math_ops.polygamma(n + 1, x)
# TODO(b/36815900): Mark None return values as NotImplemented
return (None,
array_ops.reshape(math_ops.reduce_sum(partial_x * grad, rx), sx))
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:21,代码来源:math_grad.py
示例15: _SquaredDifferenceGrad
# 需要导入模块: from tensorflow.python.ops import gen_array_ops [as 别名]
# 或者: from tensorflow.python.ops.gen_array_ops import _broadcast_gradient_args [as 别名]
def _SquaredDifferenceGrad(op, grad):
"""Returns the gradient for (x-y)^2."""
x = op.inputs[0]
y = op.inputs[1]
sx = array_ops.shape(x)
sy = array_ops.shape(y)
# pylint: disable=protected-access
rx, ry = gen_array_ops._broadcast_gradient_args(sx, sy)
# pylint: enable=protected-access
with ops.control_dependencies([grad]):
# The parens ensure that if grad is IndexedSlices, it'll get multiplied by
# Tensor (not a number like 2.0) which causes it to convert to Tensor.
x_grad = math_ops.scalar_mul(2.0, grad) * (x - y)
return (array_ops.reshape(math_ops.reduce_sum(x_grad, rx), sx),
-array_ops.reshape(math_ops.reduce_sum(x_grad, ry), sy))
# Logical operations have no gradients.
开发者ID:PacktPublishing,项目名称:Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda,代码行数:20,代码来源:math_grad.py