ParameterList

class paddle.nn.ParameterList ( parameters=None ) [源代码]

参数列表容器。此容器的行为类似于Python列表，但它包含的参数将被正确地注册和添加。

参数：

  • parameters (iterable，可选) - 可迭代的Parameters。

返回：无

代码示例

  1. import paddle
  2. import numpy as np
  4. class MyLayer(paddle.nn.Layer):
  5.     def __init__(self, num_stacked_param):
  6.         super(MyLayer, self).__init__()
  7.         # create ParameterList with iterable Parameters
  8.         self.params = paddle.nn.ParameterList(
  9.             [paddle.create_parameter(
  10.                 shape=[2, 2], dtype='float32')] * num_stacked_param)
  12.     def forward(self, x):
  13.         for i, p in enumerate(self.params):
  14.             tmp = self._helper.create_variable_for_type_inference('float32')
  15.             self._helper.append_op(
  16.                 type="mul",
  17.                 inputs={"X": x,
  18.                         "Y": p},
  19.                 outputs={"Out": tmp},
  20.                 attrs={"x_num_col_dims": 1,
  21.                         "y_num_col_dims": 1})
  22.             x = tmp
  23.         return x
  25. data_np = np.random.uniform(-1, 1, [5, 2]).astype('float32')
  26. x = paddle.to_tensor(data_np)
  27. num_stacked_param = 4
  28. model = MyLayer(num_stacked_param)
  29. print(len(model.params))  # 4
  30. res = model(x)
  31. print(res.shape)  # [5, 2]
  33. replaced_param = paddle.create_parameter(shape=[2, 3], dtype='float32')
  34. model.params[num_stacked_param - 1] = replaced_param  # replace last param
  35. res = model(x)
  36. print(res.shape)  # [5, 3]
  37. model.params.append(paddle.create_parameter(shape=[3, 4], dtype='float32'))  # append param
  38. print(len(model.params))  # 5
  39. res = model(x)
  40. print(res.shape)  # [5, 4]