- paddle.fluid.layers.control_flow. array_write ( x, i, array=None )
This OP writes the input
xinto the i-th position of the
arrayapi_fluid_LoDTensorArray and returns the modified array. If
arrayis none, a new LoDTensorArray will be created and returned. This OP is often used together with api_fluid_layers_array_read OP.
x (Variable) – The input data to be written into array. It’s multi-dimensional Tensor or LoDTensor. Data type: float32, float64, int32, int64.
i (Variable) – 1-D Tensor with shape , which represents the position into which
xis written. Data type: int64.
array (LoDTensorArray, optional) – The LoDTensorArray into which
xis written. The default value is None, when a new LoDTensorArray will be created and returned as a result.
xis written into.
- Return type
import paddle.fluid as fluid tmp = fluid.layers.fill_constant(shape=[3, 2], dtype='int64', value=5) i = fluid.layers.fill_constant(shape=, 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.