我无法使用来自 fastai.text 的 pretrained_model=URLs.WT103

Question

我正在尝试创建一个模型来预测单词作为输入和输出作为段落。尝试将 fastai|text 上给出的相同示例应用到我自己的数据集时出现错误。它在以下步骤中给出错误。当您查看该站点时，在您获得以下代码之前，这并不重要。但是这段代码给出了一个错误。导致此错误的原因可能是什么？

代码：

from fastai import *
from fastai.text import * 

path = untar_data(URLs.IMDB_SAMPLE)

df = pd.read_csv(path/'texts.csv')

# Language model data
data_lm = TextLMDataBunch.from_csv(path, 'texts.csv')
# Classifier model data
data_clas = TextClasDataBunch.from_csv(path, 'texts.csv', 
vocab=data_lm.train_ds.vocab, bs=32)

data_lm.save()
data_clas.save()

data_lm = TextLMDataBunch.load(path)
data_clas = TextClasDataBunch.load(path, bs=32)

learn = language_model_learner(data_lm, pretrained_model=URLs.WT103, drop_mult=0.5)
learn.fit_one_cycle(1, 1e-2)

错误代码：

learn = language_model_learner(data_lm, pretrained_model=URLs.WT103, drop_mult=0.5)

输出：

    102     if not ps: return None
    103     if b is None: return ps[0].requires_grad
--> 104     for p in ps: p.requires_grad=b
    105 
    106 def trainable_params(m:nn.Module)->ParamList:

RuntimeError: you can only change requires_grad flags of leaf variables. If you want to use a computed variable in a subgraph that doesn't require differentiation use var_no_grad = var.detach().

Answer 1

使用以下命令将 grad 设置为 false：torch.set_grad_enabled(False) （在创建学习者对象之前使用它）

并用 torch.enable_grad():

包装函数 (learn.fit cycle()) 的调用

我无法使用来自 fastai.text 的 pretrained_model=URLs.WT103

i can't use pretrained_model=URLs.WT103 from fastai.text

python

machine-learning

python-3.x

deep-learning

fast-ai