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tf.math.reduce_sum

Computes the sum of elements across dimensions of a tensor.

Used in the notebooks

Used in the guide Used in the tutorials

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