slim.variance_scaling_initializer()

def xavier_initializer(uniform=True, seed=None, dtype=dtypes.float32):
  """Returns an initializer performing "Xavier" initialization for weights.

  This function implements the weight initialization from:

  Xavier Glorot and Yoshua Bengio (2010):
           [Understanding the difficulty of training deep feedforward neural
           networks. International conference on artificial intelligence and
           statistics.](
           http://www.jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf)

  This initializer is designed to keep the scale of the gradients roughly the
  same in all layers. In uniform distribution this ends up being the range:
  `x = sqrt(6. / (in + out)); [-x, x]` and for normal distribution a standard
  deviation of `sqrt(2. / (in + out))` is used.

  Args:
    uniform: Whether to use uniform or normal distributed random initialization.
    seed: A Python integer. Used to create random seeds. See
          `tf.set_random_seed` for behavior.
    dtype: The data type. Only floating point types are supported.
  Returns:
    An initializer for a weight matrix.
  Raises:
    ValueError: if `dtype` is not a floating point type.
    TypeError: if `mode` is not in ['FAN_IN', 'FAN_OUT', 'FAN_AVG'].
  """
  if not dtype.is_floating:
    raise TypeError('Cannot create initializer for non-floating point type.')
  if mode not in ['FAN_IN', 'FAN_OUT', 'FAN_AVG']:
    raise TypeError('Unknown mode %s [FAN_IN, FAN_OUT, FAN_AVG]', mode)