# \[solved\] KeyError: 'unexpected key "module.encoder.embedding.weight" in state\_dict'

**URL:** <https://discuss.pytorch.org/t/solved-keyerror-unexpected-key-module-encoder-embedding-weight-in-state-dict/1686>\
**Category:** Uncategorized\
**Created:** [April 6, 2017, 8:11am UTC](https://discuss.pytorch.org/t/solved-keyerror-unexpected-key-module-encoder-embedding-weight-in-state-dict/1686 "2017-04-06T08:11:10Z")\
**Posts on this page:** 1\
**Showing post:** 3

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**Author:** ![wasiahmad](https://discuss.pytorch.org/user_avatar/discuss.pytorch.org/wasiahmad/32/248_2.png) [@wasiahmad](https://discuss.pytorch.org/u/wasiahmad)\
**Post date:** [April 6, 2017, 9:14am UTC](https://discuss.pytorch.org/t/solved-keyerror-unexpected-key-module-encoder-embedding-weight-in-state-dict/1686/3 "2017-04-06T09:14:53Z")

</div>

Yes, I used `nn.DataParallel`. I didn’t understand your second suggestion. Loading the weights file, create a new ordered dict without the module prefix and load it back. (Can you provide an example?)

Are you suggesting something like this? (example taken from here - [https://github.com/OpenNMT/OpenNMT-py/blob/master/train.py](https://github.com/OpenNMT/OpenNMT-py/blob/master/train.py))

```
model_state_dict = model.module.state_dict() if len(opt.gpus) > 1 else model.state_dict()
model_state_dict = {k: v for k, v in model_state_dict.items() if 'generator' not in k}
generator_state_dict = model.generator.module.state_dict() if len(opt.gpus) > 1 else model.generator.state_dict()
# (4) drop a checkpoint
checkpoint = {
	'model': model_state_dict,
	'generator': generator_state_dict,
	'dicts': dataset['dicts'],
	'opt': opt,
	'epoch': epoch,
	'optim': optim
}
torch.save(checkpoint,
		   '%s_acc_%.2f_ppl_%.2f_e%d.pt' % (opt.save_model, 100*valid_acc, valid_ppl, epoch))  

```

May I ask you one question about the above code snippet, what is `generator` here?

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