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Computes the sum of elements across dimensions of a tensor.
tf.compat.v2.reduce_sum(
input_tensor, axis=None, keepdims=False, name=None
)
Reduces input_tensor
along the dimensions given in axis
.
Unless keepdims
is true, the rank of the tensor is reduced by 1 for each
entry in axis
. 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 = 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) # [[3], [3]]
tf.reduce_sum(x, [0, 1]) # 6
Args | |
---|---|
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). |
Returns | |
---|---|
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.