Python tensorflow.python.ops.math_ops.zeta() Examples

The following are 7 code examples of tensorflow.python.ops.math_ops.zeta(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module tensorflow.python.ops.math_ops , or try the search function .
Example #1
Source File: math_grad.py    From lambda-packs with MIT License 6 votes vote down vote up 
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))
Example #2
Source File: math_grad.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up 
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))
Example #3
Source File: core_test.py    From auto-alt-text-lambda-api with MIT License 6 votes vote down vote up 
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
Example #4
Source File: math_grad.py    From deep_image_model with Apache License 2.0 6 votes vote down vote up 
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))
Example #5
Source File: math_grad.py    From Serverless-Deep-Learning-with-TensorFlow-and-AWS-Lambda with MIT License 6 votes vote down vote up 
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))
Example #6
Source File: math_grad.py    From keras-lambda with MIT License 6 votes vote down vote up 
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))
Example #7
Source File: core_test.py    From keras-lambda with MIT License 6 votes vote down vote up 
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