array_read( array, i )
This OP is used to read data at the specified position from the input array api_fluid_LoDTensorArray .
arrayis the input array and
iis the specified read position. This OP is often used together with api_fluid_layers_array_write OP.
Input: The shape of first three tensors are , and that of the last one is [1,2]: array = ([0.6], [0.1], [0.3], [0.4, 0.2]) And: i =  Output: output = [0.4, 0.2]
array (LoDTensorArray) – The input LoDTensorArray.
i (Variable) – 1-D Tensor, whose shape is  and dtype is int64. It represents the specified read position of
The LoDTensor or Tensor that is read at the specified position of
- Return type
# 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=, 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.