tensorflow_probability/python/math/linalg.py
def _maybe_validate_matrix(a, validate_args):
  """Checks that input is a `float` matrix."""
  assertions = []
  if not a.dtype.is_floating:
    raise TypeError('Input `a` must have `float`-like `dtype` '
                    '(saw {}).'.format(a.dtype.name))
  if a.shape.ndims is not None:
    if a.shape.ndims < 2:
      raise ValueError('Input `a` must have at least 2 dimensions '
                       '(saw: {}).'.format(a.shape.ndims))
  elif validate_args:
    assertions.append(tf.compat.v1.assert_rank_at_least(
        a, rank=2, message='Input `a` must have at least 2 dimensions.'))
  return assertions
def _maybe_validate_matrix(a, validate_args):
  assertions = []
  if not a.dtype.is_floating:
    raise TypeError('Input `a` must have `float`-like `dtype` '
                    '(saw {}).'.format(a.dtype.name))
  if a.shape.ndims is not None:
    if a.shape.ndims < 2:
      raise ValueError('Input `a` must have at least 2 dimensions '
                       '(saw: {}).'.format(a.shape.ndims))
  elif validate_args:
    assertions.append(tf.compat.v1.assert_rank_at_least(
        a, rank=2, message='Input `a` must have at least 2 dimensions.'))
  return assertions
def _maybe_validate_matrix ( a , validate_args ) : assertions = [ ] if not a . dtype . is_floating : raise TypeError ( 'Input `a` must have `float`-like `dtype` ' '(saw {}).' . format ( a . dtype . name ) ) if a . shape . ndims is not None : if a . shape . ndims < 2 : raise ValueError ( 'Input `a` must have at least 2 dimensions ' '(saw: {}).' . format ( a . shape . ndims ) ) elif validate_args : assertions . append ( tf . compat . v1 . assert_rank_at_least ( a , rank = 2 , message = 'Input `a` must have at least 2 dimensions.' ) ) return assertions
Checks that input is a `float` matrix.
Checks that input is a float matrix .