def average(a, axis=None, weights=None, returned=False): # pylint: disable=missing-docstring
if axis is not None and not isinstance(axis, six.integer_types):
# TODO(wangpeng): Support tuple of ints as `axis`
raise ValueError('`axis` must be an integer. Tuple of ints is not '
'supported yet. Got type: %s' % type(axis))
a = array_ops.array(a)
if weights is None: # Treat all weights as 1
if not np.issubdtype(a.dtype, np.inexact):
a = a.astype(
utils.result_type(a.dtype, dtypes.default_float_type()))
avg = tf.reduce_mean(a.data, axis=axis)
if returned:
if axis is None:
weights_sum = tf.size(a.data)
else:
weights_sum = tf.shape(a.data)[axis]
weights_sum = tf.cast(weights_sum, a.data.dtype)
else:
if np.issubdtype(a.dtype, np.inexact):
out_dtype = utils.result_type(a.dtype, weights)
else:
out_dtype = utils.result_type(a.dtype, weights,
dtypes.default_float_type())
a = array_ops.array(a, out_dtype).data
weights = array_ops.array(weights, out_dtype).data
def rank_equal_case():
tf.debugging.Assert(
tf.reduce_all(tf.shape(a) == tf.shape(weights)),
[tf.shape(a), tf.shape(weights)])
weights_sum = tf.reduce_sum(weights, axis=axis)
avg = tf.reduce_sum(a * weights, axis=axis) / weights_sum
return avg, weights_sum
if axis is None:
avg, weights_sum = rank_equal_case()
else:
def rank_not_equal_case():
tf.debugging.Assert(tf.rank(weights) == 1, [tf.rank(weights)])
weights_sum = tf.reduce_sum(weights)
axes = tf.convert_to_tensor([[axis], [0]])
avg = tf.tensordot(a, weights, axes) / weights_sum
return avg, weights_sum
# We condition on rank rather than shape equality, because if we do the
# latter, when the shapes are partially unknown but the ranks are known
# and different, utils.cond will run shape checking on the true branch,
# which will raise a shape-checking error.
avg, weights_sum = utils.cond(
tf.rank(a) == tf.rank(weights), rank_equal_case,
rank_not_equal_case)
avg = array_ops.array(avg)
if returned:
weights_sum = array_ops.broadcast_to(weights_sum, tf.shape(avg.data))
return avg, weights_sum
return avg
def run_test(arr, shape):
for fn in self.array_transforms:
arg1 = fn(arr)
self.match(array_ops.broadcast_to(arg1, shape),
np.broadcast_to(arg1, shape))
def replicate(x, num_devices=2): return array_ops.broadcast_to(x, (num_devices,) + x.shape)