tf.math.reduce_sum

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Computes the sum of elements across dimensions of a tensor. (deprecated arguments)

View aliases

Main aliases

tf.reduce_sum

Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.math.reduce_sum, tf.compat.v1.reduce_sum

tf.math.reduce_sum(
    input_tensor, axis=None, keepdims=None, name=None, reduction_indices=None,
    keep_dims=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).
reduction_indices	The old (deprecated) name for axis.
keep_dims	Deprecated alias for keepdims.

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.