paddle 高级api 如何冻结某一层使其不参与训练

[LZH-0225]

import paddle
import paddle.nn as nn
import paddle.vision.transforms as T
from paddle.static import InputSpec

class Mnist(nn.Layer):

    def __init__(self):
        super(Mnist, self).__init__()
        self.net = nn.Sequential(
            nn.Flatten(1),
            nn.Linear(784, 200),
            nn.Tanh(),
            nn.Linear(200, 10),
            nn.Softmax())
    def forward(self, x):
        return self.net(x)

dynamic = True  # False
if not dynamic:
    paddle.enable_static()

input = InputSpec([None, 784], 'float32', 'x')
label = InputSpec([None, 1], 'int64', 'label')
model = paddle.Model(Mnist(), input, label)
optim = paddle.optimizer.SGD(learning_rate=1e-3,
    parameters=model.parameters())
model.prepare(optim, paddle.nn.CrossEntropyLoss())

transform = T.Compose([
    T.Transpose(),
    T.Normalize([127.5], [127.5])
])
data = paddle.vision.datasets.MNIST(mode='train', transform=transform)

model.fit(data, epochs=1, batch_size=32, verbose=0)

以上面代码为例，比如我只想冻结[784,200]的那个linear层，其他层正常训练，我应该怎么做呢？

[yt605155624]

https://aistudio.baidu.com/aistudio/projectdetail/438026

[yt605155624]

paddle issue openend by author: PaddlePaddle/Paddle#44732

[yt605155624]

PaddlePaddle/Paddle#44732

def freeze_layer(layer):
    for param in layer.parameters():
        param.trainable = False

linear_layer = model.network.net[1]
freeze_layer(linear_layer)