ParameterList¶
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¶
import paddle
import numpy as np
class MyLayer(paddle.nn.Layer):
def __init__(self, num_stacked_param):
super(MyLayer, self).__init__()
# create ParameterList with iterable Parameters
self.params = paddle.nn.ParameterList(
[paddle.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')
x = paddle.to_tensor(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 = paddle.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(paddle.create_parameter(shape=[3, 4], dtype='float32')) # append param
print(len(model.params)) # 5
res = model(x)
print(res.shape) # [5, 4]