tf.math.accumulate_n

Returns the element-wise sum of a list of tensors. (deprecated)

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_sum on the other hand calls tf.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.

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).

A Tensor of same shape and type as the elements of inputs.

ValueError If inputs don't all have same shape and dtype or the shape cannot be inferred.