sum

paddle.fluid.layers.sum(x)[source]

This OP is used to sum one or more Tensor or LoDTensor of the input. If the input is LoDTensor, the output only shares LoD information with the first input.

Case 1:

System Message: ERROR/3 (/usr/local/lib/python2.7/dist-packages/paddle/fluid/layers/nn.py:docstring of paddle.fluid.layers.sum, line 5)

Unexpected indentation.

Input:
    Input. Shape = [2, 3]
    Input = [[1, 2, 3],
             [4, 5, 6]]

Output:
    The output. Shape = [2, 3]
    Output = [[1, 2, 3],
              [4, 5, 6]]

Case 2:

System Message: ERROR/3 (/usr/local/lib/python2.7/dist-packages/paddle/fluid/layers/nn.py:docstring of paddle.fluid.layers.sum, line 17)

Unexpected indentation.

Input:
    First input:
    Input1. Shape = [2, 3]
    Input1 = [[1, 2, 3],
              [4, 5, 6]]

The second input:
    Input2. Shape = [2, 3]
    Input2 = [[7, 8, 9],
              [10, 11, 12]]

Output:
    The output. Shape = [2, 3]
    Output = [[8, 10, 12],
              [14, 16, 18]]
Parameters

x (Variable|list(Variable)) – A Varaible list. The shape and data type of the list elementsshould be consistent. Variable can be multi-dimensional Tensoror LoDTensor, and data types can be: float32, float64, int32, int64

Returns

the sum of input x. its shape and data types are consistent with x

Return type

Variable

Examples

import paddle.fluid as fluid

input0 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=5)
input1 = fluid.layers.fill_constant(shape=[2, 3], dtype='int64', value=3)
sum = fluid.layers.sum([input0, input1])

# You can print out 'sum' via executor.
out = fluid.layers.Print(sum, message="the sum of input0 and input1: ")
exe = fluid.Executor(fluid.CPUPlace())
exe.run(fluid.default_main_program())

# The printed result is:
# 1570701754        the sum of input0 and input1:   The place is:CPUPlace
# Tensor[sum_0.tmp_0]
#    shape: [2,3,]
#    dtype: l
#    data: 8,8,8,8,8,8,

# the sum of input0 and input1 is 2-D Tensor with shape [2,3].
# dtype is the corresponding C++ data type, which may vary in different environments.
# Eg: if the data type of tensor is int64, then the corresponding C++ data type is int64_t,
#       so the dtype value is typeid(int64_t).Name(), which is 'x' on MacOS, 'l' on Linux,
#       and '__int64' on Windows. They both represent 64-bit integer variables.