Data Science Asked on February 11, 2021
I’ve created my own neural network in python. I am tracking the performance of each run. What are some things I can investigate when my performance results are inconsistent? When I compare my network results against my expect results (during cross-validation) I can get everything from 0.46 to 0.51 all the way up to 0.56 and various numbers in between.
Is this a sign of bad data? Not enough training epochs? Need for a different learning rate?
Data: It depends on how distinguishable your classes are. Like cats vs dogs is quite easy, while disgust expression vs angry expression is quite hard.
Epochs: Over-fit your model and do early stopping. To handle over-fitting, increase regularization or decrease the complexity of model by decreasing the number of hidden neurons / layers. See what's best for you given the time and performance.
Learning rate: Start with the largest learning rate that allows the model to learn. From there, experiment by dividing that learning rate by 10 or 2, observe the results, see which learning rate allows your model to converge faster and leads to higher accuracy.
There are still a lot of things to consider to achieve the maximum performance such as learning rate decay, gradient descent optimization algorithms, weight initializations, etc.
More observations = more productivity. Be mindful of the time and speed in choosing the best hyperparameters.
To know more, I suggest reading this by Michael Nielsen - Neural Networks and Deep Learning
Answered by Renz on February 11, 2021
Before briefing about the other things, I would like to suggest you something good. It is always better to set a seed number in machine learning and deep learning problems to get a reproducible result and by that I mean that every time when you try to run your model anywhere, it can produce the same best result which you got on your machine while experimenting. So, you should always set numpy.random.seed(*anynumber*)
.
Now how to compare your result to your expected result? There are a number of parameters that you can look upon during training your model. Some of these are:
Answered by enterML on February 11, 2021
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