tf.math.accumulate_n | TensorFlow v2.16.1 (original) (raw)
tf.math.accumulate_n
Stay organized with collections Save and categorize content based on your preferences.
Returns the element-wise sum of a list of tensors. (deprecated)
View aliases
Compat aliases for migration
SeeMigration guide for more details.
tf.compat.v1.accumulate_n, tf.compat.v1.math.accumulate_n
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.
For example:
a = tf.constant([[1, 2], [3, 4]])
b = tf.constant([[5, 0], [0, 6]])
tf.math.accumulate_n([a, b, a]).numpy()
array([[ 7, 4],
[ 6, 14]], dtype=int32)
# Explicitly pass shape and type
tf.math.accumulate_n(
[a, b, a], shape=[2, 2], tensor_dtype=tf.int32).numpy()
array([[ 7, 4],
[ 6, 14]], dtype=int32)
See Also:
- tf.reduce_sum(inputs, axis=0) - This performe the same mathematical operation, but tf.add_n may be more efficient because it sums the tensors directly.
reduce_sumon the other hand callstf.convert_to_tensor on the list of tensors, unncessairly stacking them into a single tensor before summing. - tf.add_n - This is another python wrapper for the same Op. It has nearly identical functionality.
| 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 Nonemeans "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. |