How do I make my neural network converge faster?

How do I make my neural network converge faster?

The authors point out that neural networks often learn faster when the examples in the training dataset sum to zero. This can be achieved by subtracting the mean value from each input variable, called centering. Convergence is usually faster if the average of each input variable over the training set is close to zero.

Will network converge faster if I have very large learning rate?

A learning rate that is too large can cause the model to converge too quickly to a suboptimal solution, whereas a learning rate that is too small can cause the process to get stuck.

Why is my neural network not working properly?

Shuffle the dataset If your dataset hasn’t been shuffled and has a particular order to it (ordered by label) this could negatively impact the learning. Shuffle your dataset to avoid this. Make sure you are shuffling input and labels together.

How much data do you need to train a neural network?

If you are training a net from scratch (i.e. not finetuning), you probably need lots of data. For image classification, people say you need a 1000 images per class or more. 10. Make sure your batches don’t contain a single label This can happen in a sorted dataset (i.e. the first 10k samples contain the same class).

What causes a neural network to underfit?

Augmentation has a regularizing effect. Too much of this combined with other forms of regularization (weight L2, dropout, etc.) can cause the net to underfit. 14. Check the preprocessing of your pretrained model If you are using a pretrained model, make sure you are using the same normalization and preprocessing as the model was when training.

Can my standard laptop be used to run deep learning?

If you run the code you pull on github, you might have to spend a reasonable amount of time downgrading the code to work on “cpu only mode” (Its not merely a flip of a switch, you might need to hack with the actual code, even those you might get from reliable places like FB research..I am speaking from experience)