问题
I am working on comparison of deep learning models with application in Vehicular network communication security. I want to know how I can compute the complexity of these models to know the performance of my proposed ones. I am making use of tensorflow
回答1:
You can compare the complexity of two deep networks with respect to space and time.
Regarding space complexity:
Number of parameters in your model -> this is directly proportional to the amount of memory consumed by your model.
Regarding time complexity:
- Amount of time it takes to train a single batch for a given batch size.
- Amount of time it takes for training to converge
- Amount of time it takes to perform inference on a single sample
Some papers also discuss the architecture complexity. For example, if GoogLeNet accuracy is only marginally higher than VGG-net, some people might prefer VGG-net as it is a lot easier to implement.
You can also discuss some analysis on tolerance of your network to hyperparameter tuning i.e. how your performance varies when you change the hyperparameters.
If your model is in a distributed setting, there are other things to mention such as the communication interval as it is the bottleneck sometimes.
In summary, you can discuss pretty much anything you feel that is implemented differently in another network and that is contributing additional complexity without much improvement in accuracy with respect to your network.
I don't think you would want it but there is also an open source project called deepBench to benchmark different deep network models.
来源：https://stackoverflow.com/questions/53384906/how-to-compute-the-complexity-of-machine-learning-models