Assign¶
该接口为参数初始化函数，使用 Numpy 数组、Python 列表、Tensor 来初始化参数。
参数¶
value （Tensor|numpy.ndarray|list） - 用于初始化参数的一个 Numpy 数组、Python 列表、Tensor。
name (str，可选) - 具体用法请参见 Name，一般无需设置，默认值为 None。
返回¶
由 Numpy 数组、Python 列表、Tensor 初始化的参数。
代码示例¶
>>> import paddle
>>> import numpy as np
>>> # numpy array
>>> data_1 = paddle.ones(shape=[1, 2], dtype='float32')
>>> weight_attr_1 = paddle.ParamAttr(
... name="linear_weight_1",
... initializer=paddle.nn.initializer.Assign(np.array([2, 2])))
>>> bias_attr_1 = paddle.ParamAttr(
... name="linear_bias_1",
... initializer=paddle.nn.initializer.Assign(np.array([2])))
>>> linear_1 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_1, bias_attr=bias_attr_1)
>>> print(linear_1.weight.numpy())
[2. 2.]
>>> print(linear_1.bias.numpy())
[2.]
>>> res_1 = linear_1(data_1)
>>> print(res_1.numpy())
[6.]
>>> # python list
>>> data_2 = paddle.ones(shape=[1, 2], dtype='float32')
>>> weight_attr_2 = paddle.ParamAttr(
... name="linear_weight_2",
... initializer=paddle.nn.initializer.Assign([2, 2]))
>>> bias_attr_2 = paddle.ParamAttr(
... name="linear_bias_2",
... initializer=paddle.nn.initializer.Assign([2]))
>>> linear_2 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_2, bias_attr=bias_attr_2)
>>> print(linear_2.weight.numpy())
[2. 2.]
>>> print(linear_2.bias.numpy())
[2.]
>>> res_2 = linear_2(data_2)
>>> print(res_2.numpy())
[6.]
>>> # tensor
>>> data_3 = paddle.ones(shape=[1, 2], dtype='float32')
>>> weight_attr_3 = paddle.ParamAttr(
... name="linear_weight_3",
... initializer=paddle.nn.initializer.Assign(paddle.full([2], 2)))
>>> bias_attr_3 = paddle.ParamAttr(
... name="linear_bias_3",
... initializer=paddle.nn.initializer.Assign(paddle.full([1], 2)))
>>> linear_3 = paddle.nn.Linear(2, 2, weight_attr=weight_attr_3, bias_attr=bias_attr_3)
>>> print(linear_3.weight.numpy())
[2. 2.]
>>> print(linear_3.bias.numpy())
[2.]
>>> res_3 = linear_3(data_3)
>>> print(res_3.numpy())
[6.]