Currently, saving the traind model does not include the optimizer state, so that resuming training from a checkpointed model is impossible.
In this branch, this issue will solved by explicitly saving the optimizer states of the generator- and critic-models. Additionally, a load_checkpoint-method will be integrated as well as a corresponding parsing argument to main_train.py.
Currently, saving the traind model does not include the optimizer state, so that resuming training from a checkpointed model is impossible. In this branch, this issue will solved by explicitly saving the optimizer states of the
generator
- andcritic
-models. Additionally, aload_checkpoint
-method will be integrated as well as a corresponding parsing argument tomain_train.py
.