Improving Deep Learning Performance
Deep Learning is one of the branches of machine learning in which we deal with Artificial Neural Networks. A model (algorithm) is trained for a problem that predicts the solution. The question is how to improve the performance of prediction? We will discuss some of the steps which are very important to improve the performance of Deep Learning models.
It is said that the larger the dataset you have, the more is the information. Data is very important. The more dataset you have means better will be the performance of your model. The larger dataset covers more features and this makes the model more general than the one trained from a small dataset.
When you are preparing your dataset, the first step is collecting data. This data is not ready to use at this level. We need to remove the dirty data and make the data clean so, that we can use this to train the model. Cleaning means removing the data which is irrelevant and which can decrease the performance. Likewise, labeling is also important. This is the process of tagging objects with specific labels. Another thing is data shuffling while training. Without shuffling if you are training the model with the data then there will issue of bias and the leaning will be low and stops quickly. Feature selection is also very important. Those features are very important, on which the output depends. Sometimes when there are overlapping features then this also decreases the performance of the model. Last but not least in this section is that you should have a balanced dataset which means the number of objects (samples) in each class should be equal and there should be similar data in all the classes. Otherwise, the model will be biased toward the class having a larger number of samples. Most of the time while working on the data set from collection to labelling you better understand the problem you are trying to solve. So, at this level you can better reframe the problem.
When we compare machine learning with human learning, one of the properties which human has is that he is a continuous learner. On the other side, at some point the machine learning model stops learning. On further training, its performance decreases not increases. So, it’s recommended to stop training further before the performance starts decreasing.
Batch size is also important. If the batch size is very small then the model will learn very slowly and loss oscillates. The gradient descent will not be smooth. Opposite to this if the batch size is so large then it will take a larger time to complete one iteration (training). Typical batch sizes are 8, 16,32 or 64. So, while selecting a batch size you need to take care that it should be not either so large or so small. You need to minimize the tradeoff by adjusting the batch size.
You may be familiar with the terms Overfitting and Underfitting in machine learning. The term underfitting means this model will give you poor performance for the training data as well as the generalized data. On the other side, Overfit is the one which gives you the best performance on trained data and poor performance on generalized data. The best model is the one that should perform best on general data. So, try to minimize underfit and overfit and try to achieve the best fit through regularization.
No one can claim the exact number of layers which will have the best performance in an artificial neural network. The same is the case for the number of neurons. The only thing is to test by adding the number of layers until the performance starts decreasing. Do the same for the number of neurons. You can also go for a combination of both too. While training at some point the learning stops and after that, the performance can decrease further learning. So, it is recommended to stop the training before the performance starts decreasing.
It’s always difficult for a programmer to increase the performance of the Deep learning model. The good news is that now Accentedge is solving this problem for you. Accented has Deep Learning Experts as well as state-of-the-art technology, who will help you to maximize your model’s performance. So, feel free to contact Accentedge.