TensorFlow 2 version | View source on GitHub |
Returns the element-wise sum of a list of tensors.
tf.math.accumulate_n(
inputs, shape=None, tensor_dtype=None, name=None
)
Optionally, pass shape
and tensor_dtype
for shape and type checking,
otherwise, these are inferred.
accumulate_n
performs the same operation as tf.math.add_n
, but
does not wait for all of its inputs to be ready before beginning to sum.
This approach can save memory if inputs are ready at different times, since
minimum temporary storage is proportional to the output size rather than the
inputs' size.
accumulate_n
is differentiable (but wasn't previous to TensorFlow 1.7).
For example:
a = tf.constant([[1, 2], [3, 4]])
b = tf.constant([[5, 0], [0, 6]])
tf.math.accumulate_n([a, b, a]) # [[7, 4], [6, 14]]
# Explicitly pass shape and type
tf.math.accumulate_n([a, b, a], shape=[2, 2], tensor_dtype=tf.int32)
# [[7, 4],
# [6, 14]]
Args | |
---|---|
inputs
|
A list of Tensor objects, each with same shape and type.
|
shape
|
Expected shape of elements of inputs (optional). Also controls the
output shape of this op, which may affect type inference in other ops. A
value of None means "infer the input shape from the shapes in inputs ".
|
tensor_dtype
|
Expected data type of inputs (optional). A value of None
means "infer the input dtype from inputs[0] ".
|
name
|
A name for the operation (optional). |
Returns | |
---|---|
A Tensor of same shape and type as the elements of inputs .
|
Raises | |
---|---|
ValueError
|
If inputs don't all have same shape and dtype or the shape
cannot be inferred.
|