Right now, I meet the issue like: 1. I have a pre-trained model, e.g. resnet trained on my own datasets. Assume it is saved by the function ```save_parameters()```. The key of the saved function would be the format such as ```features.0.0.mean```
2.1. If I just use exactly model but only initialize the features using the pretrained model, the previous function does not work: ```model.features.initialize(model_path)```. 2.2. Based on this pretrained model, I would like to expand the functionalities or modify the model based on the pre-trained model. Let's say I have the several ```hyridizesequences``` such as ```features0, features1, ...```, which change the structure of the models and the key. 3. In both cases, the initialization would fail because it cannot find the corresponding key from the ndarray formated pre-trained model and the modified pre-trained models. For example, ndarray pretrained model is ```features.0.0.mean``` but the corresponding key of parameters in the case 2.1 is ```0.0.mean``` and the key of parameters in case 2.2 might be ```1.0.mean``` However, in the deprecated function ```save_params()```, which uses the layer name as the key, I can find the corresponding key in both cases. Hope the mxnet team can address this issue. Otherwise, it would be the terrible user experience when the users face both cases. [ Full content available at: https://github.com/apache/incubator-mxnet/issues/12334 ] This message was relayed via gitbox.apache.org for [email protected]
