Resnet-Retrain

This repository helps to train ResNet and dump the trained model - which can then be modified and reloaded to the network for retraining.

This can be used for purposes like Weight Quantization etc. It converts the TensorFlow ckpt checkpoint file to a readable numpy format(.npy). This allows us to perform any operations on the trained model/visualize the trained model. This repository allows you to re-load the same data and re-train. You can once again dump the trained model for your requirements.

The original ResNet model is taken from the official Tensorflow ResNet model.

Usage:

1. Run the prepare.sh by sh prepare.sh
2. Train the model : python cifar10_main.py
3. Run till the accuracy is good enough.
4. Check the latest check point file by ls data/model. For ex: model.ckpt-8000
5. Dump the weights using python cifar10_main.py --dump=True --ckpt_file=<Latest Checkpoint filename>
6. This should create a weights_cifar10.npy file, which contains the data.
7. You can use this to edit the data. (ex: Quantize)
8. If you would like to re-run the training with the changed data(replace the original .npy file), simply run python cifar10_main.py --retrain=True --ckpt_file=<Latest Checkpoint filename>

Note that, once you re-train, your check-point files are generated in data/model_retrain folder. If you would like to do one more iteration of retraining, pass the approprite --model_dir path.