When it comes down to it, deep learning is an extremely complex set of algorithms that require large amounts of data to work. However, with the right balance, you can create some incredible AI applications!
The tricky part about using DL for AI purposes is finding the optimal amount of depth in your network architecture. Too much depth and your model overfits, but not enough depth and you don’t get good results.
In this article we will talk about how to find the optimal number of layers in your netowrk by testing different models against the same dataset. We will also discuss other ways to speed up training times, such as use GPU or faster CPUs, using batch sizes larger than what is typically advised, and minimizing memory overhead.
Why are test accuracy and train time important?
Test accuracy is very important because it tells us if our model works well or not. If it does not, then trying to use it in production might be a waste of resources so we should look into alternatives.
For example, when talking about image classification tasks like object recognition or scene understanding, sometimes too many convolutional layers (the kind used in most modern neural networks) cause poor performance due to overfitting.
On the opposite end, a shallow network usually takes longer to train, which may negatively affect usability since it could take hours, days, or even weeks to achieve decent performance.
Use more samples
A very common way to improve the speed of your neural network is by using larger batches. This can be done in two ways- either through batch size or training time.
By having a bigger batch, you are giving the computer more data it can process per set amount of time. For example, if your batch size is 64 then it will take twice as long to train than if the batch size was 32!
This applies even more when there are fewer examples in the dataset. If the number of examples drops lower than half the batch size, then the accuracy may suffer too!
However, you do not want to use too large a batch because this could result in overfitting, where the model becomes extremely good at predicting on the sample data that it is trained with.
Reduce data loss
When your neural network is training, it needs lots of raw data to work with to create its models.
One of the most difficult things that can slow down deep learning training is something called data loss. Data loss happens when there’s not enough quality information in the raw material (called the dataset) being fed into the model.
This can happen for several reasons. Some examples are poor quality images or text, missing values, incorrect values, etc.
When this occurs, what happens is some parts of the model don’t get trained properly because there’s not enough good data to give them correct answers.
One of the most powerful features of deep learning is its ability to train neural networks through backpropagation. Neural network training can be quite time consuming, however!
That’s because every iteration requires forward propagation (running the trained model) and backward propagation (learning how much each layer impacts the next). The more layers there are in a net, the longer these iterations can take.
Fortunately, there are some tricks you can use to speed up the process. By using different optimizers and regularization strategies, you can reduce the number of total epochs needed per retraining set.
In this article, we will discuss two such strategies that work well with almost any net architecture.
Use more neurons
Neurons play an important role in deep learning by helping your model process information as it learns.
Neurons can be either fully connected or sparse, but the most effective networks have both. A fully connected neuron receives input from all other parts of the network, making it easier to learn complex concepts. Sparsely connected neurons receive input only directly adjacent layers, which helps reduce overfitting by limiting how much the neuron is able to connect with past knowledge.
Sparsely connected neurons are also efficient because they don’t need to store as many connections. Only those necessary are stored in memory, leaving room for other things. This also reduces power consumption since you don’t need to constantly refresh the connection settings when training is done.
Deep neural nets typically use somewhere around 10-20% sparsity at test time, but during training this drops down to 0%. You will want to make sure that your net has enough capacity to utilize all of its neurons while training so it does not suffer from undertraining due to lack of usage.
Use transfer learning
When you are starting off, using deep neural networks can be tricky. You have to invest in expensive software or pay monthly fees for an AI training platform!
Luckily, there is another way to use these networks. Transfer learning! This article will give you all of the tips on how to do this effectively.
Transfer learning uses concepts and settings that have worked before and applies them to new problems. By applying it here, we make sure that our model is already optimized and won’t waste time re-learning things that we have already learned!
This article will go into more detail about different types of transfer learning and what situations each one works best in.
Freeze some layers
One of the most important steps in speeding up deep learning training is freezing certain layers. When performing image classification, for example, you will typically need a very accurate classifier that can distinguish between all of the possible categories.
However, there are cases where this distinction is not needed. For instance, if your model does not discriminate between two different types of cars, then it is unnecessary to have separate classes for car make and models.
By skipping these less important features, you can save GPU resources to train the rest of the network. Similarly, by removing the number feature set (numbers like 0-100k or M, L, S, etc.), you do not need an additional layer to determine size.
This article will discuss how to effectively use freeze modes in TensorFlow. You will learn about each layer and what effect they have on efficiency.
When training your neural network, you will run into issues with overfitting. This is when your model becomes very good at predicting data that it has been trained on, but cannot generalize well to new examples.
Generalizing means being able to predict outcomes for samples that are not in the same category as those used during training! Having a strong net requires preventing it from fitting too closely to the train set.
One of the most common ways to do this is through *regularization*. Regularization can be seen as adding an imbalance to the system by introducing some sort of error.
For example, if your net was really fit to the train set, then it would probably underestimate how much credit people give others in workplace reviews. By using regularization, the net learns that’s wrong so it adds additional terms to correct for that.
There are many types of regularizations, such as weight decay (where weights get smaller), dropout (randomly dropping out parts of the network) or constraint-based regularization (layers need to satisfy certain conditions to be added or removed).
Use a larger optimizer
When training your neural networks, you will run into issues eventually! This is due to the size of the optimizer, which determines how quickly the model learns.
As your dataset gets bigger, it can take longer for the optimizer to find its best settings because there are more parameters to search through.
At this stage, the algorithm may not be changing at all or even getting stuck in a bad local minimum. Both of these situations cause the loss to stay flat or increase instead of decreasing, making the model less likely to achieve its goal.
By having a large enough optimizer, it gives the machine time to explore all possible solutions before choosing one. Once it has done so, it will begin improving the cost efficiently, helping the model learn faster.