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Python math_ops.zeta方法代码示例

本文整理汇总了Python中tensorflow.python.ops.math_ops.zeta方法的典型用法代码示例。如果您正苦于以下问题:Python math_ops.zeta方法的具体用法?Python math_ops.zeta怎么用?Python math_ops.zeta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tensorflow.python.ops.math_ops的用法示例。


在下文中一共展示了math_ops.zeta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: _ZetaGrad

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import zeta [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)) 
开发者ID:ryfeus,项目名称:lambda-packs,代码行数:19,代码来源:math_grad.py

示例2: _ZetaGrad

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import zeta [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)) 
开发者ID:abhisuri97,项目名称:auto-alt-text-lambda-api,代码行数:18,代码来源:math_grad.py

示例3: setUp

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import zeta [as 别名]
def setUp(self):
    super(FloatBinaryOpsTest, self).setUp()

    self.ops = [
        ('igamma', None, math_ops.igamma, core.igamma),
        ('igammac', None, math_ops.igammac, core.igammac),
        ('zeta', None, math_ops.zeta, core.zeta),
        ('polygamma', None, math_ops.polygamma, core.polygamma),
        ('maximum', None, math_ops.maximum, core.maximum),
        ('minimum', None, math_ops.minimum, core.minimum),
        ('squared_difference', None, math_ops.squared_difference,
         core.squared_difference),
    ]
    total_size = np.prod([v.size for v in self.original_lt.axes.values()])
    test_lt = core.LabeledTensor(
        math_ops.cast(self.original_lt, dtypes.float32) / total_size,
        self.original_lt.axes)
    self.test_lt_1 = test_lt
    self.test_lt_2 = 1.0 - test_lt
    self.test_lt_1_broadcast = self.test_lt_1.tensor
    self.test_lt_2_broadcast = self.test_lt_2.tensor
    self.broadcast_axes = self.test_lt_1.axes 
开发者ID:abhisuri97,项目名称:auto-alt-text-lambda-api,代码行数:24,代码来源:core_test.py

示例4: _ZetaGrad

# 需要导入模块: from tensorflow.python.ops import math_ops [as 别名]
# 或者: from tensorflow.python.ops.math_ops import zeta [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


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