Does the TensorFlow save function automatically overwrite old models? If not, how does the save/load system work?
I think Eyal's answer is a good point to start. However, if you want to be sure you can let your program delete the previous model or change it's name on the fly. I also observed different results when deleting a model and not, but this could also be effects of the different training process, due to random initialization and updating the weights.
I've tried finding information regarding this online but the word overwrite does not show up at all in the official Tensorflow documentation and all the Stack Overflow questions are related to changing the number of copies saved by the model.
I would just like to know whether or not the save function overwrites at all. If I re-train a model and would like to re-run the save function will the newer model load in when I use the load_model function? Or will it be a model that is trained on the same data twice? Do older iterations get stored somewhere?