array_write¶

paddle.fluid.layers. array_write ( x, i, array=None ) [源代码] ¶

该OP将输入的变量 x 写入到数组 cn_api_fluid_LoDTensorArray array 的第i个位置，并返回修改后的LoDTensorArray，如果 array 为None，则创建一个新的LoDTensorArray。常与 array_read OP联合使用对LoDTensorArray进行读写。

参数¶

x (Variable) – 待写入的数据，多维Tensor或LoDTensor，数据类型支持float32，float64，int32，int64

i (Variable) – shape为[1]的1-D Tensor，表示写入到输出数组LoDTensorArray的位置，数据类型为int64

array (Variable，可选) – 指定写入 x 的数组LoDTensorArray。默认值为None，此时将创建新的LoDTensorArray并作为结果返回

返回¶

写入输入 x 之后的LoDTensorArray

返回类型¶

Variable

代码示例¶

          import paddle.fluid as fluid
tmp = fluid.layers.fill_constant(shape=[3, 2], dtype='int64', value=5)
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
#将tmp写入数组arr下标为10的位置，并返回arr
arr = fluid.layers.array_write(tmp, i=i)

#此时arr是长度为11的LoDTensorArray，可以通过array_read来查看下标为10的LoDTensor，并将之打印出来
item = fluid.layers.array_read(arr, i=i)
input = fluid.layers.Print(item, message="The content of i-th LoDTensor:")
main_program = fluid.default_main_program()
exe = fluid.Executor(fluid.CPUPlace())
exe.run(main_program)

         

输出结果

          import paddle.fluid as fluid
tmp = fluid.layers.fill_constant(shape=[3, 2], dtype='int64', value=5)
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
# Write tmp into the position of arr with subscript 10 and return arr.
arr = fluid.layers.array_write(tmp, i=i)

# Now, arr is a LoDTensorArray with length 11. We can use array_read OP to read
# the data at subscript 10 and print it out.
item = fluid.layers.array_read(arr, i=i)
input = fluid.layers.Print(item, message="The content of i-th LoDTensor:")
main_program = fluid.default_main_program()
exe = fluid.Executor(fluid.CPUPlace())
exe.run(main_program)

# The printed result is:
# 1570533133    The content of i-th LoDTensor:  The place is:CPUPlace
# Tensor[array_read_0.tmp_0]
#    shape: [3,2,]
#    dtype: l
#    data: 5,5,5,5,5,5,

# the output is 2-D Tensor with shape [3,2], which is tmp above.
# 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.

         