tf.math.reduce_sum

Computes the sum of elements across dimensions of a tensor.

tf.math.reduce_sum(
    input_tensor, axis=None, keepdims=False, name=None
)

This is the reduction operation for the elementwise tf.math.add op.

Reduces input_tensor along the dimensions given in axis. Unless keepdims is true, the rank of the tensor is reduced by 1 for each of the entries in axis, which must be unique. If keepdims is true, the reduced dimensions are retained with length 1.

If axis is None, all dimensions are reduced, and a tensor with a single element is returned.

For example:

# x has a shape of (2, 3) (two rows and three columns):
x = tf.constant([[1, 1, 1], [1, 1, 1]])
x.numpy()
array([[1, 1, 1],
       [1, 1, 1]], dtype=int32)
# sum all the elements
# 1 + 1 + 1 + 1 + 1+ 1 = 6
tf.reduce_sum(x).numpy()
6
# reduce along the first dimension
# the result is [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
tf.reduce_sum(x, 0).numpy()
array([2, 2, 2], dtype=int32)
# reduce along the second dimension
# the result is [1, 1] + [1, 1] + [1, 1] = [3, 3]
tf.reduce_sum(x, 1).numpy()
array([3, 3], dtype=int32)
# keep the original dimensions
tf.reduce_sum(x, 1, keepdims=True).numpy()
array([[3],
       [3]], dtype=int32)
# reduce along both dimensions
# the result is 1 + 1 + 1 + 1 + 1 + 1 = 6
# or, equivalently, reduce along rows, then reduce the resultant array
# [1, 1, 1] + [1, 1, 1] = [2, 2, 2]
# 2 + 2 + 2 = 6
tf.reduce_sum(x, [0, 1]).numpy()
6

input_tensor The tensor to reduce. Should have numeric type.
axis The dimensions to reduce. If None (the default), reduces all dimensions. Must be in the range [-rank(input_tensor), rank(input_tensor)].
keepdims If true, retains reduced dimensions with length 1.
name A name for the operation (optional).

The reduced tensor, of the same dtype as the input_tensor.

Numpy Compatibility

Equivalent to np.sum apart the fact that numpy upcast uint8 and int32 to int64 while tensorflow returns the same dtype as the input.