# tf.reduce_sum(input_tensor, axis=None, keep_dims=False, name=None, reduction_indices=None)

### tf.reduce_sum(input_tensor, axis=None, keep_dims=False, name=None, reduction_indices=None)

See the guide: Math > Reduction

Computes the sum of elements across dimensions of a tensor.

Reduces input_tensor along the dimensions given in axis. Unless keep_dims is true, the rank of the tensor is reduced by 1 for each entry in axis. If keep_dims is true, the reduced dimensions are retained with length 1.

If axis has no entries, all dimensions are reduced, and a tensor with a single element is returned.

For example:

# 'x' is [[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, keep_dims=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.
• keep_dims: If true, retains reduced dimensions with length 1.
• name: A name for the operation (optional).
• reduction_indices: The old (deprecated) name for axis.

#### Returns:

The reduced tensor.

#### numpy compatibility

Equivalent to np.sum

Defined in tensorflow/python/ops/math_ops.py.