Python tensorflow.python.ops.array_ops.broadcast_static_shape() Examples

def prefer_static_broadcast_shape(
    shape1, shape2, name="prefer_static_broadcast_shape"):
  """Convenience function which statically broadcasts shape when possible.

  Args:
    shape1:  `1-D` integer `Tensor`.  Already converted to tensor!
    shape2:  `1-D` integer `Tensor`.  Already converted to tensor!
    name:  A string name to prepend to created ops.

  Returns:
    The broadcast shape, either as `TensorShape` (if broadcast can be done
      statically), or as a `Tensor`.
  """
  with ops.name_scope(name, values=[shape1, shape2]):
    if (tensor_util.constant_value(shape1) is not None and
        tensor_util.constant_value(shape2) is not None):
      return array_ops.broadcast_static_shape(
          tensor_shape.TensorShape(tensor_util.constant_value(shape1)),
          tensor_shape.TensorShape(tensor_util.constant_value(shape2)))
    return array_ops.broadcast_dynamic_shape(shape1, shape2) 

def _check_shapes(self):
    """Static check that shapes are compatible."""
    # Broadcast shape also checks that u and v are compatible.
    uv_shape = array_ops.broadcast_static_shape(
        self.u.get_shape(), self.v.get_shape())

    batch_shape = array_ops.broadcast_static_shape(
        self.base_operator.batch_shape, uv_shape[:-2])

    self.base_operator.domain_dimension.assert_is_compatible_with(
        uv_shape[-2])

    if self._diag_update is not None:
      uv_shape[-1].assert_is_compatible_with(self._diag_update.get_shape()[-1])
      array_ops.broadcast_static_shape(
          batch_shape, self._diag_update.get_shape()[:-1]) 

def _get_batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.alpha.get_shape(), self.beta.get_shape()) 

def _get_batch_shape(self):
    return array_ops.broadcast_static_shape(
        self._mu.get_shape(), self.sigma.get_shape()) 

def _possibly_broadcast_batch_shape(self, x):
    """Return 'x', possibly after broadcasting the leading dimensions."""
    # If we have no batch shape, our batch shape broadcasts with everything!
    if self._batch_shape_arg is None:
      return x

    # Static attempt:
    #   If we determine that no broadcast is necessary, pass x through
    #   If we need a broadcast, add to an array of zeros.
    #
    # special_shape is the shape that, when broadcast with x's shape, will give
    # the correct broadcast_shape.  Note that
    #   We have already verified the second to last dimension of self.shape
    #   matches x's shape in assert_compatible_matrix_dimensions.
    #   Also, the final dimension of 'x' can have any shape.
    #   Therefore, the final two dimensions of special_shape are 1's.
    special_shape = self.batch_shape.concatenate([1, 1])
    bshape = array_ops.broadcast_static_shape(x.get_shape(), special_shape)
    if special_shape.is_fully_defined():
      # bshape.is_fully_defined iff special_shape.is_fully_defined.
      if bshape == x.get_shape():
        return x
      # Use the built in broadcasting of addition.
      zeros = array_ops.zeros(shape=special_shape, dtype=self.dtype)
      return x + zeros

    # Dynamic broadcast:
    #   Always add to an array of zeros, rather than using a "cond", since a
    #   cond would require copying data from GPU --> CPU.
    special_shape = array_ops.concat((self.batch_shape_dynamic(), [1, 1]), 0)
    zeros = array_ops.zeros(shape=special_shape, dtype=self.dtype)
    return x + zeros 

def _batch_shape(self):
        return array_ops.broadcast_static_shape(
            self._loc.get_shape(),
            self._scale.get_shape()) 

def _batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.concentration.get_shape(),
        self.rate.get_shape()) 

def _finish_log_prob_for_one_fiber(self, y, x, ildj):
    """Finish computation of log_prob on one element of the inverse image."""
    x = self._maybe_rotate_dims(x, rotate_right=True)
    log_prob = self.distribution.log_prob(x)
    if self._is_maybe_event_override:
      log_prob = math_ops.reduce_sum(log_prob, self._reduce_event_indices)
    log_prob = ildj + log_prob
    if self._is_maybe_event_override:
      log_prob.set_shape(array_ops.broadcast_static_shape(
          y.get_shape().with_rank_at_least(1)[:-1], self.batch_shape))
    return log_prob 

def _finish_prob_for_one_fiber(self, y, x, ildj):
    """Finish computation of prob on one element of the inverse image."""
    x = self._maybe_rotate_dims(x, rotate_right=True)
    prob = self.distribution.prob(x)
    if self._is_maybe_event_override:
      prob = math_ops.reduce_prod(prob, self._reduce_event_indices)
    prob *= math_ops.exp(ildj)
    if self._is_maybe_event_override:
      prob.set_shape(array_ops.broadcast_static_shape(
          y.get_shape().with_rank_at_least(1)[:-1], self.batch_shape))
    return prob 

def _batch_shape(self):
    return array_ops.broadcast_static_shape(
        array_ops.broadcast_static_shape(self.df.get_shape(),
                                         self.loc.get_shape()),
        self.scale.get_shape()) 

def _batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.low.get_shape(),
        self.high.get_shape()) 

def _batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.loc.get_shape(),
        self.scale.get_shape()) 

def _get_batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.n.get_shape(), self.p.get_shape()) 

def _get_batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.alpha.get_shape(), self.beta.get_shape()) 

def _get_batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.loc.get_shape(), self.scale.get_shape()) 

def _get_batch_shape(self):
    return array_ops.broadcast_static_shape(
        array_ops.broadcast_static_shape(self.df.get_shape(),
                                         self.mu.get_shape()),
        self.sigma.get_shape()) 

def _get_batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.loc.get_shape(), self.scale.get_shape()) 

def _get_batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.n.get_shape(), self.p.get_shape()) 

def _get_batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.loc.get_shape(), self.scale.get_shape()) 

def _get_batch_shape(self):
    return array_ops.broadcast_static_shape(
        self._mu.get_shape(), self.sigma.get_shape()) 

def _possibly_broadcast_batch_shape(self, x):
    """Return 'x', possibly after broadcasting the leading dimensions."""
    # If we have no batch shape, our batch shape broadcasts with everything!
    if self._batch_shape_arg is None:
      return x

    # Static attempt:
    #   If we determine that no broadcast is necessary, pass x through
    #   If we need a broadcast, add to an array of zeros.
    #
    # special_shape is the shape that, when broadcast with x's shape, will give
    # the correct broadcast_shape.  Note that
    #   We have already verified the second to last dimension of self.shape
    #   matches x's shape in assert_compatible_matrix_dimensions.
    #   Also, the final dimension of 'x' can have any shape.
    #   Therefore, the final two dimensions of special_shape are 1's.
    special_shape = self.batch_shape.concatenate([1, 1])
    bshape = array_ops.broadcast_static_shape(x.get_shape(), special_shape)
    if special_shape.is_fully_defined():
      # bshape.is_fully_defined iff special_shape.is_fully_defined.
      if bshape == x.get_shape():
        return x
      # Use the built in broadcasting of addition.
      zeros = array_ops.zeros(shape=special_shape, dtype=self.dtype)
      return x + zeros

    # Dynamic broadcast:
    #   Always add to an array of zeros, rather than using a "cond", since a
    #   cond would require copying data from GPU --> CPU.
    special_shape = array_ops.concat((self.batch_shape_dynamic(), [1, 1]), 0)
    zeros = array_ops.zeros(shape=special_shape, dtype=self.dtype)
    return x + zeros 

def _static_check_for_broadcastable_batch_shape(operators):
  """ValueError if operators determined to have non-broadcastable shapes."""
  if len(operators) < 2:
    return

  # This will fail if they cannot be broadcast together.
  batch_shape = operators[0].batch_shape
  for op in operators[1:]:
    batch_shape = array_ops.broadcast_static_shape(batch_shape, op.batch_shape) 

def _log_prob(self, y):
    x = self.bijector.inverse(y)
    ildj = self.bijector.inverse_log_det_jacobian(y)
    x = self._maybe_rotate_dims(x, rotate_right=True)
    log_prob = self.distribution.log_prob(x)
    if self._is_maybe_event_override:
      log_prob = math_ops.reduce_sum(log_prob, self._reduce_event_indices)
    log_prob = ildj + log_prob
    if self._is_maybe_event_override:
      log_prob.set_shape(array_ops.broadcast_static_shape(
          y.get_shape().with_rank_at_least(1)[:-1], self.batch_shape))
    return log_prob 

def _prob(self, y):
    x = self.bijector.inverse(y)
    ildj = self.bijector.inverse_log_det_jacobian(y)
    x = self._maybe_rotate_dims(x, rotate_right=True)
    prob = self.distribution.prob(x)
    if self._is_maybe_event_override:
      prob = math_ops.reduce_prod(prob, self._reduce_event_indices)
    prob *= math_ops.exp(ildj)
    if self._is_maybe_event_override:
      prob.set_shape(array_ops.broadcast_static_shape(
          y.get_shape().with_rank_at_least(1)[:-1], self.batch_shape))
    return prob 

def _batch_shape(self):
    return array_ops.broadcast_static_shape(
        array_ops.broadcast_static_shape(self.df.get_shape(),
                                         self.loc.get_shape()),
        self.scale.get_shape()) 

def _batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.low.get_shape(),
        self.high.get_shape()) 

def _batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.loc.get_shape(),
        self.scale.get_shape()) 

def _batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.concentration.get_shape(),
        self.rate.get_shape()) 

def _batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.loc.get_shape(), self.scale.get_shape()) 

def _batch_shape(self):
    return array_ops.broadcast_static_shape(
        self.loc.get_shape(), self.scale.get_shape())