|TensorFlow 2 version||View source on GitHub|
Computes the sum of elements across dimensions of a tensor. (deprecated arguments)
See Migration guide for more details.
tf.math.reduce_sum( input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None, keep_dims=None )
input_tensor along the dimensions given in
keepdims is true, the rank of the tensor is reduced by 1 for each
keepdims is true, the reduced dimensions
are retained with length 1.
axis is None, all dimensions are reduced, and a
tensor with a single element is returned.
x = tf.constant([[1, 1, 1], [1, 1, 1]]) tf.reduce_sum(x) # 6 tf.reduce_sum(x, 0) # [2, 2, 2] tf.reduce_sum(x, 1) # [3, 3] tf.reduce_sum(x, 1, keepdims=True) # [, ] tf.reduce_sum(x, [0, 1]) # 6
||The tensor to reduce. Should have numeric type.|
The dimensions to reduce. If
||If true, retains reduced dimensions with length 1.|
||A name for the operation (optional).|
||The old (deprecated) name for axis.|
Deprecated alias for
|The reduced tensor, of the same dtype as the input_tensor.|
Equivalent to np.sum apart the fact that numpy upcast uint8 and int32 to int64 while tensorflow returns the same dtype as the input.