tf.compat.v1.reduce_sum

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

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Compat aliases for migration

See Migration guide for more details.

tf.compat.v1.math.reduce_sum

tf.compat.v1.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 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 = 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.