Python math_ops.div方法代碼示例

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def _safe_scalar_div(numerator, denominator, name):
  """Divides two values, returning 0 if the denominator is 0.

  Args:
    numerator: A scalar `float64` `Tensor`.
    denominator: A scalar `float64` `Tensor`.
    name: Name for the returned op.

  Returns:
    0 if `denominator` == 0, else `numerator` / `denominator`
  """
  numerator.get_shape().with_rank_at_most(1)
  denominator.get_shape().with_rank_at_most(1)
  return control_flow_ops.cond(
      math_ops.equal(
          array_ops.constant(0.0, dtype=dtypes.float64), denominator),
      lambda: array_ops.constant(0.0, dtype=dtypes.float64),
      lambda: math_ops.div(numerator, denominator),
      name=name) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def _MinOrMaxGrad(op, grad):
  """Gradient for Min or Max. Amazingly it's precisely the same code."""
  input_shape = array_ops.shape(op.inputs[0])
  output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
  y = op.outputs[0]
  y = array_ops.reshape(y, output_shape_kept_dims)
  grad = array_ops.reshape(grad, output_shape_kept_dims)

  # Compute the number of selected (maximum or minimum) elements in each
  # reduction dimension. If there are multiple minimum or maximum elements
  # then the gradient will be divided between them.
  indicators = math_ops.cast(math_ops.equal(y, op.inputs[0]), grad.dtype)
  num_selected = array_ops.reshape(
      math_ops.reduce_sum(indicators, op.inputs[1]), output_shape_kept_dims)

  return [math_ops.div(indicators, num_selected) * grad, None] 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def _SegmentMinOrMaxGrad(op, grad, is_sorted):
  """Gradient for SegmentMin and (unsorted) SegmentMax. They share similar code."""
  zeros = array_ops.zeros(array_ops.shape(op.inputs[0]),
                          dtype=op.inputs[0].dtype)

  # Get the number of selected (minimum or maximum) elements in each segment.
  gathered_outputs = array_ops.gather(op.outputs[0], op.inputs[1])
  is_selected = math_ops.equal(op.inputs[0], gathered_outputs)
  if is_sorted:
    num_selected = math_ops.segment_sum(math_ops.cast(is_selected, grad.dtype),
                                        op.inputs[1])
  else:
    num_selected = math_ops.unsorted_segment_sum(math_ops.cast(is_selected, grad.dtype),
                                                 op.inputs[1], op.inputs[2])

  # Compute the gradient for each segment. The gradient for the ith segment is
  # divided evenly among the selected elements in that segment.
  weighted_grads = math_ops.div(grad, num_selected)
  gathered_grads = array_ops.gather(weighted_grads, op.inputs[1])

  if is_sorted:
    return array_ops.where(is_selected, gathered_grads, zeros), None
  else:
    return array_ops.where(is_selected, gathered_grads, zeros), None, None 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def _safe_div(numerator, denominator, name="value"):
  """Computes a safe divide which returns 0 if the denominator is zero.

  Note that the function contains an additional conditional check that is
  necessary for avoiding situations where the loss is zero causing NaNs to
  creep into the gradient computation.

  Args:
    numerator: An arbitrary `Tensor`.
    denominator: `Tensor` whose shape matches `numerator` and whose values are
      assumed to be non-negative.
    name: An optional name for the returned op.

  Returns:
    The element-wise value of the numerator divided by the denominator.
  """
  return array_ops.where(
      math_ops.greater(denominator, 0),
      math_ops.div(numerator, array_ops.where(
          math_ops.equal(denominator, 0),
          array_ops.ones_like(denominator), denominator)),
      array_ops.zeros_like(numerator),
      name=name) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def _length_penalty(sequence_lengths, penalty_factor):
  """Calculates the length penalty. See https://arxiv.org/abs/1609.08144.

  Args:
    sequence_lengths: The sequence length of all hypotheses, a tensor
      of shape [beam_size, vocab_size].
    penalty_factor: A scalar that weights the length penalty.

  Returns:
    The length penalty factor, a tensor fo shape [beam_size].
  """
  penalty_factor = ops.convert_to_tensor(penalty_factor, name="penalty_factor")
  penalty_factor.set_shape(())  # penalty should be a scalar.
  static_penalty = tensor_util.constant_value(penalty_factor)
  if static_penalty is not None and static_penalty == 0:
    return 1.0
  return math_ops.div((5. + math_ops.to_float(sequence_lengths))
                      **penalty_factor, (5. + 1.)**penalty_factor) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def _sample_n(self, n, seed=None):
    sample_shape = array_ops.concat([[n], array_ops.shape(self.logits)], 0)
    logits = self.logits * array_ops.ones(sample_shape)
    logits_2d = array_ops.reshape(logits, [-1, self.event_size])
    # Uniform variates must be sampled from the open-interval `(0, 1)` rather
    # than `[0, 1)`. To do so, we use `np.finfo(self.dtype.as_numpy_dtype).tiny`
    # because it is the smallest, positive, "normal" number. A "normal" number
    # is such that the mantissa has an implicit leading 1. Normal, positive
    # numbers x, y have the reasonable property that, `x + y >= max(x, y)`. In
    # this case, a subnormal number (i.e., np.nextafter) can cause us to sample
    # 0.
    uniform = random_ops.random_uniform(
        shape=array_ops.shape(logits_2d),
        minval=np.finfo(self.dtype.as_numpy_dtype).tiny,
        maxval=1.,
        dtype=self.dtype,
        seed=seed)
    gumbel = -math_ops.log(-math_ops.log(uniform))
    noisy_logits = math_ops.div(gumbel + logits_2d, self._temperature_2d)
    samples = nn_ops.log_softmax(noisy_logits)
    ret = array_ops.reshape(samples, sample_shape)
    return ret 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def _optimal_step_size(last_step,
                       error_ratio,
                       safety=0.9,
                       ifactor=10.0,
                       dfactor=0.2,
                       order=5,
                       name=None):
  """Calculate the optimal size for the next Runge-Kutta step."""
  with ops.name_scope(
      name, 'optimal_step_size', [last_step, error_ratio]) as scope:
    error_ratio = math_ops.cast(error_ratio, last_step.dtype)
    exponent = math_ops.cast(1 / order, last_step.dtype)
    # this looks more complex than necessary, but importantly it keeps
    # error_ratio in the numerator so we can't divide by zero:
    factor = math_ops.maximum(
        1 / ifactor,
        math_ops.minimum(error_ratio ** exponent / safety, 1 / dfactor))
    return math_ops.div(last_step, factor, name=scope) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def setUp(self):
    self.a = variables.Variable(2.0, name="a")
    self.b = variables.Variable(3.0, name="b")

    self.c = math_ops.multiply(self.a, self.b, name="c")  # Should be 6.0.
    self.d = math_ops.multiply(self.a, self.a, name="d")  # Should be 4.0.

    self.e = math_ops.multiply(self.d, self.c, name="e")  # Should be 24.0.

    self.f_y = constant_op.constant(0.30, name="f_y")
    self.f = math_ops.div(self.b, self.f_y, name="f")  # Should be 10.0.

    # The there nodes x, y and z form a graph with "cross-links" in. I.e., x
    # and y are both direct inputs to z, but x is also a direct input to y.
    self.x = variables.Variable(2.0, name="x")  # Should be 2.0
    self.y = math_ops.negative(self.x, name="y")  # Should be -2.0.

    self.z = math_ops.multiply(self.x, self.y, name="z")  # Should be -4.0.

    self.sess = session.Session()
    self.sess.run(variables.global_variables_initializer())

    self.sess = session.Session()
    self.sess.run(variables.global_variables_initializer()) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def _SegmentMinOrMaxGrad(op, grad):
  """Gradient for SegmentMin and SegmentMax. Both share the same code."""
  zeros = array_ops.zeros(
      array_ops.shape(op.inputs[0]), dtype=op.inputs[0].dtype)

  # Get the number of selected (minimum or maximum) elements in each segment.
  gathered_outputs = array_ops.gather(op.outputs[0], op.inputs[1])
  is_selected = math_ops.equal(op.inputs[0], gathered_outputs)
  num_selected = math_ops.segment_sum(
      math_ops.cast(is_selected, grad.dtype), op.inputs[1])

  # Compute the gradient for each segment. The gradient for the ith segment is
  # divided evenly among the selected elements in that segment.
  weighted_grads = math_ops.div(grad, num_selected)
  gathered_grads = array_ops.gather(weighted_grads, op.inputs[1])

  return array_ops.where(is_selected, gathered_grads, zeros), None 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def _sample_n(self, n, seed=None):
    sample_shape = array_ops.concat(([n], array_ops.shape(self.logits)), 0)
    logits = self.logits * array_ops.ones(sample_shape)
    if logits.get_shape().ndims == 2:
      logits_2d = logits
    else:
      logits_2d = array_ops.reshape(logits, [-1, self.num_classes])
    np_dtype = self.dtype.as_numpy_dtype()
    minval = np.nextafter(np_dtype(0), np_dtype(1))
    uniform = random_ops.random_uniform(shape=array_ops.shape(logits_2d),
                                        minval=minval,
                                        maxval=1,
                                        dtype=self.dtype,
                                        seed=seed)
    gumbel = - math_ops.log(- math_ops.log(uniform))
    noisy_logits = math_ops.div(gumbel + logits_2d, self.temperature)
    samples = nn_ops.log_softmax(noisy_logits)
    ret = array_ops.reshape(samples, sample_shape)
    return ret 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def _safe_div(numerator, denominator, name="value"):
  """Computes a safe divide which returns 0 if the denominator is zero.

  Note that the function contains an additional conditional check that is
  necessary for avoiding situations where the loss is zero causing NaNs to
  creep into the gradient computation.

  Args:
    numerator: An arbitrary `Tensor`.
    denominator: A `Tensor` whose shape matches `numerator` and whose values are
      assumed to be non-negative.
    name: An optional name for the returned op.

  Returns:
    The element-wise value of the numerator divided by the denominator.
  """
  return array_ops.where(
      math_ops.greater(denominator, 0),
      math_ops.div(numerator, array_ops.where(
          math_ops.equal(denominator, 0),
          array_ops.ones_like(denominator), denominator)),
      array_ops.zeros_like(numerator),
      name=name) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def _MinOrMaxGrad(op, grad):
  """Gradient for Min or Max. Amazingly it's precisely the same code."""
  input_shape = array_ops.shape(op.inputs[0])
  output_shape_kept_dims = math_ops.reduced_shape(input_shape, op.inputs[1])
  y = op.outputs[0]
  y = array_ops.reshape(y, output_shape_kept_dims)
  grad = array_ops.reshape(grad, output_shape_kept_dims)

  # Compute the number of selected (maximum or minimum) elements in each
  # reduction dimension. If there are multiple minimum or maximum elements
  # then the gradient will be divided between them.
  indicators = math_ops.cast(math_ops.equal(y, op.inputs[0]), grad.dtype)
  num_selected = array_ops.reshape(
      math_ops.reduce_sum(indicators, op.inputs[1]),
      output_shape_kept_dims)

  return [math_ops.div(indicators, num_selected) * grad, None] 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def _safe_div(numerator, denominator, name="value"):
  """Computes a safe divide which returns 0 if the denominator is zero.

  Note that the function contains an additional conditional check that is
  necessary for avoiding situations where the loss is zero causing NaNs to
  creep into the gradient computation.

  Args:
    numerator: An arbitrary `Tensor`.
    denominator: A `Tensor` whose shape matches `numerator` and whose values are
      assumed to be non-negative.
    name: An optional name for the returned op.

  Returns:
    The element-wise value of the numerator divided by the denominator.
  """
  return math_ops.select(
      math_ops.greater(denominator, 0),
      math_ops.div(numerator, math_ops.select(
          math_ops.equal(denominator, 0),
          array_ops.ones_like(denominator), denominator)),
      array_ops.zeros_like(numerator),
      name=name) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def _safe_div(numerator, denominator, name='safe_div'):
    """Computes a safe divide which returns 0 if the denominator is zero.

    Args:
      numerator: An arbitrary `Tensor`.
      denominator: `Tensor` whose shape matches `numerator`.
      name: An optional name for the returned op.

    Returns:
      The element-wise value of the numerator divided by the denominator.
    """
    return array_ops.where(
        math_ops.equal(denominator, 0),
        array_ops.zeros_like(numerator),
        math_ops.div(numerator, denominator),
        name=name) 

# 需要導入模塊: from tensorflow.python.ops import math_ops [as 別名]
# 或者: from tensorflow.python.ops.math_ops import div [as 別名]
def tf_safe_div(numerator, denominator):
    """ Computes a safe division which returns 0 if the denominator is zero.
    Note that the function contains an additional conditional check that is
    necessary for avoiding situations where the loss is zero causing NaNs to
    creep into the gradient computation.

    Args:
      numerator: tf.tensor
      denominator: tf.tensor

    Returns: tf.tensor

    """

    return array_ops.where(
        math_ops.greater(denominator, 0),
        math_ops.div(numerator,
                     array_ops.where(
                         math_ops.equal(denominator, 0),
                         array_ops.ones_like(denominator), denominator)),
        array_ops.zeros_like(numerator))