I am using TF CNN segmentation model using the network edited and made compatible for tf 2.12.1. I use TensorFlow 2.12.1 on GPU with cuda/cudnn to segment my MRI images. The model works fine but does not produce 100% identical results. My results have a 0.94 of coefficient correlation with the previous study’s results. As I am using the same pipeline and data but with an upgraded TF version, I expect a correlation of around 0.99 or 1, which I am not getting. Can anyone please help or comment on it?
Hi @Chopraamhk ,
It’s natural to expect identical results when using the same pipeline and data, there are several factors that can lead to slight variations in the results:
- Use the same random seed
- Use the same hardware
- Use the same numerical precision
- Compare model architectures
- Compare data preprocessing pipelines
- Check Hyperparameters
- Library Versions: Ensure that all libraries and dependencies, including GPU drivers and cuDNN, are the same or compatible with the previous study’s environment.
- Postprocessing: If postprocessing steps are involved in generating the final results, make sure they are consistent.
However, it’s important to note that achieving an exact correlation of 1.0 may be challenging due to the inherent stochastic nature of neural network training. A correlation of 0.94 is already quite high and indicates strong similarity between the results.
I hope it helps!