array_read¶

paddle.fluid.layers. array_read ( array, i ) [源代码] ¶

该OP用于读取输入数组 cn_api_fluid_LoDTensorArray 中指定位置的数据，array 为输入的数组，i 为指定的读取位置。常与 array_write OP配合使用进行LoDTensorArray的读写。

例1:

         输入：
    包含4个Tensor的LoDTensorArray，前3个shape为[1]，最后一个shape为[1,2]:
        input = ([0.6], [0.1], [0.3], [0.4, 0.2])
    并且：
        i = [3]

输出：
    output = [0.4, 0.2]

        

参数¶

array (Variable) - 输入的数组LoDTensorArray

i (Variable) - shape为[1]的1-D Tensor，表示从 array 中读取数据的位置，数据类型为int64

返回¶

从 array 中指定位置读取的LoDTensor或Tensor

返回类型¶

Variable

代码示例¶

          #先创建一个LoDTensorArray，再在指定位置写入Tensor，然后从该位置读取Tensor
import paddle.fluid as fluid
arr = fluid.layers.create_array(dtype='float32')
tmp = fluid.layers.fill_constant(shape=[3, 2], dtype='int64', value=5)
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
#tmp是shape为[3,2]的Tensor，将其写入空数组arr的下标10的位置，则arr的长度变为11
arr = fluid.layers.array_write(tmp, i, array=arr)
#读取arr的下标10的位置的数据
item = fluid.layers.array_read(arr, i)

#可以通过executor打印出该数据
input = fluid.layers.Print(item, message="The LoDTensor of the i-th position:")
main_program = fluid.default_main_program()
exe = fluid.Executor(fluid.CPUPlace())
exe.run(main_program)

         

输出结果

          # First we're going to create a LoDTensorArray, then we're going to write the Tensor into
# the specified position, and finally we're going to read the Tensor at that position.
import paddle.fluid as fluid
arr = fluid.layers.create_array(dtype='float32')
tmp = fluid.layers.fill_constant(shape=[3, 2], dtype='int64', value=5)
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
# tmp is the Tensor with shape [3,2], and if we write it into the position with subscript 10
# of the empty-array: arr, then the length of arr becomes 11.
arr = fluid.layers.array_write(tmp, i, array=arr)
# Read the data of the position with subscript 10.
item = fluid.layers.array_read(arr, i)

# You can print out the data via executor.
input = fluid.layers.Print(item, message="The LoDTensor of the i-th position:")
main_program = fluid.default_main_program()
exe = fluid.Executor(fluid.CPUPlace())
exe.run(main_program)

# The printed result is:

# 1569588169  The LoDTensor of the i-th position: 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].
# 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.

         