ParameterList

class paddle.fluid.dygraph.ParameterList(parameters=None)[source]

ParameterList Container.

This container acts like a Python list, but parameters it contains will be properly added.

Parameters

parameters (iterable, optional) – Iterable Parameters to be added

Examples

import paddle.fluid as fluid
import numpy as np

class MyLayer(fluid.Layer):
    def __init__(self, num_stacked_param):
        super(MyLayer, self).__init__()
        # create ParameterList with iterable Parameters
        self.params = fluid.dygraph.ParameterList(
            [fluid.layers.create_parameter(
                shape=[2, 2], dtype='float32')] * num_stacked_param)

    def forward(self, x):
        for i, p in enumerate(self.params):
            tmp = self._helper.create_variable_for_type_inference('float32')
            self._helper.append_op(
                type="mul",
                inputs={"X": x,
                        "Y": p},
                outputs={"Out": tmp},
                attrs={"x_num_col_dims": 1,
                       "y_num_col_dims": 1})
            x = tmp
        return x

data_np = np.random.uniform(-1, 1, [5, 2]).astype('float32')
with fluid.dygraph.guard():
    x = fluid.dygraph.to_variable(data_np)
    num_stacked_param = 4
    model = MyLayer(num_stacked_param)
    print(len(model.params))  # 4
    res = model(x)
    print(res.shape)  # [5, 2]

    replaced_param = fluid.layers.create_parameter(shape=[2, 3], dtype='float32')
    model.params[num_stacked_param - 1] = replaced_param  # replace last param
    res = model(x)
    print(res.shape)  # [5, 3]
    model.params.append(fluid.layers.create_parameter(shape=[3, 4], dtype='float32'))  # append param
    print(len(model.params))  # 5
    res = model(x)
    print(res.shape)  # [5, 4]
append(parameter)

Appends a given parameter at the end of the list.

Parameters

parameter (Parameter) – parameter to append