Adds all input tensors element-wise.
tf.math.add_n(
inputs, name=None
)
Used in the notebooks
| Used in the guide |
Used in the tutorials |
|
|
|
tf.math.add_n performs the same operation as tf.math.accumulate_n, but it
waits for all of its inputs to be ready before beginning to sum.
This buffering can result in higher memory consumption when inputs are ready
at different times, since the minimum temporary storage required is
proportional to the input size rather than the output size.
This op does not broadcast
its inputs. If you need broadcasting, use tf.math.add (or the + operator)
instead.
For example:
a = tf.constant([[3, 5], [4, 8]])
b = tf.constant([[1, 6], [2, 9]])
tf.math.add_n([a, b, a])
<tf.Tensor: shape=(2, 2), dtype=int32, numpy=
array([[ 7, 16],
[10, 25]], dtype=int32)>
Args |
|---|
inputs
|
A list of tf.Tensor or tf.IndexedSlices objects, each with the
same shape and type. tf.IndexedSlices objects will be converted into
dense tensors prior to adding.
|
name
|
A name for the operation (optional).
|
Returns |
|---|
A tf.Tensor of the 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.
|
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TensorFlow 1 version
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