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# tf.math.reduce_sum

Computes the sum of elements across dimensions of a tensor.

### Aliases:

• `tf.compat.v2.math.reduce_sum`
• `tf.compat.v2.reduce_sum`
• `tf.reduce_sum`
``````tf.math.reduce_sum(
input_tensor,
axis=None,
keepdims=False,
name=None
)
``````

### Used in the tutorials:

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.