I ran a training job for some
n sample with a given architecture and save the model using
Now, I want to use the saved model (it’s weights) to run a different training job with
- exact same architecture
- a large number (m samples) of similar (but not same) data
- more epocs
However, when I use
model = torch.load(saved_model) as my model for re-training the new job, I get inconsistent results i.e. I get different predictions for different run (clear variables, restart kernel) of the new training job.
Sample code can be seen here: How to use pretrained weights for initializing the weights in next iteration?