分类目录：《深入浅出PaddlePaddle函数》总目录
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· 深入浅出PaddlePaddle函数——paddle.sum
对指定维度上的Tensor元素进行求和运算，并输出相应的计算结果。
语法
paddle.sum(x, axis=None, dtype=None, keepdim=False, name=None)
参数
x：[Tensor] 输入变量为多维Tensor，支持数据类型为float32、float64、int32、int64。axis：[可选,int/list/tuple] 求和运算的维度。如果为None，则计算所有元素的和并返回包含单个元素的Tensor变量，否则必须在范围内。如果
，则维度将变为
，默认值为
None。dtype：[可选,str] 输出变量的数据类型。若参数为空，则输出变量的数据类型和输入变量相同，默认值为None。keepdim：[bool] 是否在输出Tensor中保留减小的维度。如keepdim=True，否则结果张量的维度将比输入张量小，默认值为False。name：[可选,str] 具体用法参见Name，一般无需设置，默认值为None。
返回值
Tensor，在指定维度上进行求和运算的Tensor，数据类型和输入数据类型一致。
实例
import paddle
# x is a Tensor with following elements:
# [[0.2, 0.3, 0.5, 0.9]
# [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the corresponding output tensor.
x = paddle.to_tensor([[0.2, 0.3, 0.5, 0.9],
[0.1, 0.2, 0.6, 0.7]])
out1 = paddle.sum(x) # [3.5]
out2 = paddle.sum(x, axis=0) # [0.3, 0.5, 1.1, 1.6]
out3 = paddle.sum(x, axis=-1) # [1.9, 1.6]
out4 = paddle.sum(x, axis=1, keepdim=True) # [[1.9], [1.6]]
# y is a Tensor with shape [2, 2, 2] and elements as below:
# [[[1, 2], [3, 4]],
# [[5, 6], [7, 8]]]
# Each example is followed by the corresponding output tensor.
y = paddle.to_tensor([[[1, 2], [3, 4]],
[[5, 6], [7, 8]]])
out5 = paddle.sum(y, axis=[1, 2]) # [10, 26]
out6 = paddle.sum(y, axis=[0, 1]) # [16, 20]
# x is a Tensor with following elements:
# [[True, True, True, True]
# [False, False, False, False]]
# Each example is followed by the corresponding output tensor.
x = paddle.to_tensor([[True, True, True, True],
[False, False, False, False]])
out7 = paddle.sum(x) # [4]
out8 = paddle.sum(x, axis=0) # [1, 1, 1, 1]
out9 = paddle.sum(x, axis=1) # [4, 0]
函数实现
def sum(x, axis=None, dtype=None, keepdim=False, name=None):
"""
Computes the sum of tensor elements over the given dimension.
Args:
x (Tensor): An N-D Tensor, the data type is bool, float16, float32, float64, int32 or int64.
axis (int|list|tuple, optional): The dimensions along which the sum is performed. If
:attr:`None`, sum all elements of :attr:`x` and return a
Tensor with a single element, otherwise must be in the
range :math:`[-rank(x), rank(x))`. If :math:`axis[i] < 0`,
the dimension to reduce is :math:`rank + axis[i]`.
dtype (str, optional): The dtype of output Tensor. The default value is None, the dtype
of output is the same as input Tensor `x`.
keepdim (bool, optional): Whether to reserve the reduced dimension in the
output Tensor. The result Tensor will have one fewer dimension
than the :attr:`x` unless :attr:`keepdim` is true, default
value is False.
name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`.
Returns:
Tensor: Results of summation operation on the specified axis of input Tensor `x`,
if `x.dtype='bool'`, `x.dtype='int32'`, it's data type is `'int64'`,
otherwise it's data type is the same as `x`.
Examples:
.. code-block:: python
import paddle
# x is a Tensor with following elements:
# [[0.2, 0.3, 0.5, 0.9]
# [0.1, 0.2, 0.6, 0.7]]
# Each example is followed by the corresponding output tensor.
x = paddle.to_tensor([[0.2, 0.3, 0.5, 0.9],
[0.1, 0.2, 0.6, 0.7]])
out1 = paddle.sum(x) # [3.5]
out2 = paddle.sum(x, axis=0) # [0.3, 0.5, 1.1, 1.6]
out3 = paddle.sum(x, axis=-1) # [1.9, 1.6]
out4 = paddle.sum(x, axis=1, keepdim=True) # [[1.9], [1.6]]
# y is a Tensor with shape [2, 2, 2] and elements as below:
# [[[1, 2], [3, 4]],
# [[5, 6], [7, 8]]]
# Each example is followed by the corresponding output tensor.
y = paddle.to_tensor([[[1, 2], [3, 4]],
[[5, 6], [7, 8]]])
out5 = paddle.sum(y, axis=[1, 2]) # [10, 26]
out6 = paddle.sum(y, axis=[0, 1]) # [16, 20]
# x is a Tensor with following elements:
# [[True, True, True, True]
# [False, False, False, False]]
# Each example is followed by the corresponding output tensor.
x = paddle.to_tensor([[True, True, True, True],
[False, False, False, False]])
out7 = paddle.sum(x) # [4]
out8 = paddle.sum(x, axis=0) # [1, 1, 1, 1]
out9 = paddle.sum(x, axis=1) # [4, 0]
"""
if isinstance(axis, Variable):
reduce_all_flag = True if axis.shape[0] == len(x.shape) else False
else:
if axis is not None and not isinstance(axis, (list, tuple)):
axis = [axis]
if not axis:
axis = []
if len(axis) == 0:
reduce_all_flag = True
else:
if len(axis) == len(x.shape):
reduce_all_flag = True
else:
reduce_all_flag = False
dtype_flag = False
if dtype is not None:
dtype_flag = True
dtype = convert_np_dtype_to_dtype_(dtype)
if in_dygraph_mode():
return _C_ops.sum(x, axis, dtype, keepdim)
if not isinstance(axis, Variable):
axis = axis if axis != None and axis != [] and axis != () else [0]
if utils._contain_var(axis):
axis = utils._convert_to_tensor_list(axis)
if _in_legacy_dygraph():
if dtype_flag:
return _legacy_C_ops.reduce_sum(x, 'dim', axis, 'keep_dim', keepdim,
'reduce_all', reduce_all_flag, 'in_dtype',
x.dtype, 'out_dtype', dtype)
else:
return _legacy_C_ops.reduce_sum(x, 'dim', axis, 'keep_dim', keepdim,
'reduce_all', reduce_all_flag)
attrs = {
'dim': axis,
'keep_dim': keepdim,
'reduce_all': reduce_all_flag
}
if dtype_flag:
attrs.update({
'in_dtype': x.dtype,
'out_dtype': dtype
})
check_variable_and_dtype(
x, 'x', ['bool', 'float16', 'float32', 'float64',
'int16', 'int32', 'int64', 'complex64', 'complex128',
u'bool', u'float16', u'float32', u'float64',
u'int32', u'int64', u'complex64', u'complex128'], 'sum')
check_type(axis, 'axis', (int, list, tuple, type(None), Variable), 'sum')
helper = LayerHelper('sum', **locals())
if dtype_flag:
out = helper.create_variable_for_type_inference(
dtype=dtype)
else:
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(
type='reduce_sum',
inputs={'X': x},
outputs={'Out': out},
attrs=attrs)
return
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