def _kl_blockwise_blockwise(b0, b1, name=None):
  """Calculate the batched KL divergence KL(b0 || b1) with b0 and b1 Blockwise distributions.

  Args:
    b0: instance of a Blockwise distribution object.
    b1: instance of a Blockwise distribution object.
    name: (optional) Name to use for created operations. Default is
      "kl_blockwise_blockwise".

  Returns:
    kl_blockwise_blockwise: `Tensor`. The batchwise KL(b0 || b1).
  """
  if len(b0.distributions) != len(b1.distributions):
    raise ValueError(
        'Can only compute KL divergence between Blockwise distributions with '
        'the same number of component distributions.')

  # We also need to check that the event shapes match for each one.
  b0_event_sizes = [_event_size(d) for d in b0.distributions]
  b1_event_sizes = [_event_size(d) for d in b1.distributions]

  assertions = []
  message = ('Can only compute KL divergence between Blockwise distributions '
             'with the same pairwise event shapes.')

  if (all(isinstance(event_size, int) for event_size in b0_event_sizes) and
      all(isinstance(event_size, int) for event_size in b1_event_sizes)):
    if b0_event_sizes != b1_event_sizes:
      raise ValueError(message)
  else:
    if b0.validate_args or b1.validate_args:
      assertions.extend(
          assert_util.assert_equal(  # pylint: disable=g-complex-comprehension
              e1, e2, message=message)
          for e1, e2 in zip(b0_event_sizes, b1_event_sizes))

  with tf.name_scope(name or 'kl_blockwise_blockwise'):
    with tf.control_dependencies(assertions):
      return sum([
          kullback_leibler.kl_divergence(d1, d2) for d1, d2 in zip(
              b0.distributions, b1.distributions)])
def _kl_blockwise_blockwise(b0, b1, name=None):
  """Calculate the batched KL divergence KL(b0 || b1) with b0 and b1 Blockwise distributions.

  Args:
    b0: instance of a Blockwise distribution object.
    b1: instance of a Blockwise distribution object.
    name: (optional) Name to use for created operations. Default is
      "kl_blockwise_blockwise".

  Returns:
    kl_blockwise_blockwise: `Tensor`. The batchwise KL(b0 || b1).
  """
  if len(b0.distributions) != len(b1.distributions):
    raise ValueError(
        'Can only compute KL divergence between Blockwise distributions with '
        'the same number of component distributions.')

  # We also need to check that the event shapes match for each one.
  b0_event_sizes = [_event_size(d) for d in b0.distributions]
  b1_event_sizes = [_event_size(d) for d in b1.distributions]

  assertions = []
  message = ('Can only compute KL divergence between Blockwise distributions '
             'with the same pairwise event shapes.')

  if (all(isinstance(event_size, int) for event_size in b0_event_sizes) and
      all(isinstance(event_size, int) for event_size in b1_event_sizes)):
    if b0_event_sizes != b1_event_sizes:
      raise ValueError(message)
  else:
    if b0.validate_args or b1.validate_args:
      assertions.extend(
          assert_util.assert_equal(  # pylint: disable=g-complex-comprehension
              e1, e2, message=message)
          for e1, e2 in zip(b0_event_sizes, b1_event_sizes))

  with tf.name_scope(name or 'kl_blockwise_blockwise'):
    with tf.control_dependencies(assertions):
      return sum([
          kullback_leibler.kl_divergence(d1, d2) for d1, d2 in zip(
              b0.distributions, b1.distributions)])
def _kl_blockwise_blockwise ( b0 , b1 , name = None ) : if len ( b0 . distributions ) != len ( b1 . distributions ) : raise ValueError ( 'Can only compute KL divergence between Blockwise distributions with ' 'the same number of component distributions.' ) # We also need to check that the event shapes match for each one. b0_event_sizes = [ _event_size ( d ) for d in b0 . distributions ] b1_event_sizes = [ _event_size ( d ) for d in b1 . distributions ] assertions = [ ] message = ( 'Can only compute KL divergence between Blockwise distributions ' 'with the same pairwise event shapes.' ) if ( all ( isinstance ( event_size , int ) for event_size in b0_event_sizes ) and all ( isinstance ( event_size , int ) for event_size in b1_event_sizes ) ) : if b0_event_sizes != b1_event_sizes : raise ValueError ( message ) else : if b0 . validate_args or b1 . validate_args : assertions . extend ( assert_util . assert_equal ( # pylint: disable=g-complex-comprehension e1 , e2 , message = message ) for e1 , e2 in zip ( b0_event_sizes , b1_event_sizes ) ) with tf . name_scope ( name or 'kl_blockwise_blockwise' ) : with tf . control_dependencies ( assertions ) : return sum ( [ kullback_leibler . kl_divergence ( d1 , d2 ) for d1 , d2 in zip ( b0 . distributions , b1 . distributions ) ] )
Calculate the batched KL divergence KL(b0 || b1) with b0 and b1 Blockwise distributions.

  Args:
    b0: instance of a Blockwise distribution object.
    b1: instance of a Blockwise distribution object.
    name: (optional) Name to use for created operations. Default is
      "kl_blockwise_blockwise".

  Returns:
    kl_blockwise_blockwise: `Tensor`. The batchwise KL(b0 || b1).
Calculate the batched KL divergence KL ( b0 || b1 ) with b0 and b1 Blockwise distributions .