Recent developments in deep learning have led to incredible results across many applications, including speech recognition, object detection, and natural language processing. While there are some studies that claim that using deeper networks helps accuracy, it is not always easy to achieve this depth!
By adding more layers to the network, you increase the amount of parameters (we use the term “parameter” very broadly here to include things like weights or biases) that your model has. Too much information flow is sometimes referred to as overfitting because the model fits too closely to the data.
Overfit models may work well for the training set, but they do not generalize well when you test them. Generalization means applying their knowledge to new examples outside of the training set.
Generalizing beyond the training set is one of the most important skills we can develop as AI practitioners!
This article will go into detail about how to improve the accuracy of your neural net by reducing overfitting through careful parameter tuning and experimental testing methods.
Ensure your model is not overfitting
A common beginner’s mistake when training neural networks is to use too much data, or to try to fit every example with no noise in it.
A heavily-overfit network will almost always perform worse than one that does not overfit as well, however powerful it may seem.
It will also often produce more confusing results, because there are so many different patterns in the data that the algorithm does not understand.
Instead, you should test how well the network works on new examples by putting some noise into the dataset.
Add some random numbers or images to the datasets occasionally, this will prevent your machine learning model from getting very good scores on everything due to pattern matching.
Alternatively, add some basic statistical features (such as average, variance, etc.) to the dataset and see if the system can learn them automatically without having to worry about overfitting.
Reduce bias
A very common cause of overfitting is having too many parameters in your model. As mentioned before, the more parameters you have, the better your model will perform on data that was used for training, but this also means it can’t generalize well beyond the data it was trained with!
Generalizing a machine learning algorithm means applying its knowledge to new examples or scenarios not included during training. When algorithms are overfit, they become dependent only on the patterns of their own internal workings and fail to work when these patterns aren’t present anymore.
This is why there is such a thing as test-and-retrain. After creating your initial deep neural network, you can take some time to see how it performs on different datasets before adding more layers and/or going through other optimization steps.
Reducing bias via greater diversity in your dataset is one way to avoid overfitting. Another way is using a pretrained model instead of starting from scratch every time. What both of those do is limit the amount of information the model uses to determine what goes into each layer and what comes out, so it must learn extra information itself.
By introducing more variability into the data, we reduce the effectiveness of the underlying assumptions about the nature of the relationships in the given problem domain. This helps prevent the model from making similar assumptions which may no longer apply since the input has changed.
Use data augmentation
Data augmentation is an integral part in improving the accuracy of any deep learning model. This includes changing the input images, adjusting the image dimensions or size, and adding random effects such as moving the objects around, flipping the image, or introducing noise.
Data augmentation was first introduced in 2015 by CaffeNet, which improved the performance of VGG networks. Since then, it has become one of the most common ways to improve the accuracy of almost every neural network architecture.
By incorporating this into your training process, you are giving the machine more examples of how to recognize patterns in the dataset. Because there are so many, the system does not get overwhelmed when it encounters one that is slightly different than the others.
There are several types of data augmentation that can be done, but the two most popular ones are scale-up and translation. With scaling up, the image is expanded using additional space, while with translation, the position of the object being recognized is adjusted. Both of these methods increase diversity in the data set.
Use a learning rate adjustment
One important factor in improving accuracy is using an appropriate learning rate. Your model will not be able to learn as quickly if you don’t increase your initial learning rate.
As your neural network gets closer and closer to convergence, however, it becomes more difficult to achieve good results due to the increased number of parameters in the network. This means that it takes longer for the computer software to converge which can result in poor quality models or no convergance at all.
By having too low of a starting learning rate, you may need to spend lots of time getting a decent output which can waste valuable training time. You also cannot make any progress towards achieving convergence because there is not enough momentum built up with the current settings!
Too high of a startle rate can cause overfitting which will reduce overall performance. When this happens, the model will become very accurate but does not generalize well.
A common way to address this problem is to use an exponential decay method to decrease the learning rate. More advanced users can also use different strategies such as step-decay or cosine decays to optimize their networks even further. All these methods work by decreasing the learning rate proportionally per iteration.
The ratio between each successive drop can easily be calculated and applied automatically so that you do not have to worry about making mistakes when doing it. There are many free tools available online that can apply these types of optimizer schemes for you.
Test and validate
The more times you train your model, the better it will get! However, this also means that if your model is not improving after a set number of attempts, then something may be wrong.
The first step in improving the accuracy of any algorithm is testing and validating it! This can be done by either using the same data set to test against or creating new ones.
By testing different versions of your algorithm, you will find which parts are causing errors and how to fix them!
Testing can be done with both similar and completely different datasets so there’s no need to worry about running out of ideas. It is very common to use the test set as development material to improve upon the algorithms internal processes!
There are many ways to check the accuracy of a neural network, some of the most well-known being k-fold cross validation and holdout sets. Both of these methods have their benefits and they must be used properly to ensure accurate results!
Cross validation
A cross validation method divides your dataset into several groups or folds. Each group is treated independently and all groups together make one complete experiment for determining an error rate.
This way, every fold uses a part of your data to determine the final result while the other parts are used for training. Cross validation creates consistency because each group is tested separately, but still gets the whole picture at the end.
Use a good regularization technique
One important factor in improving accuracy of your model is using a good regularization technique. This can be done at either the layer level, parameter level, or both!
At the layer level, you can apply weight dropout where some layers are allowed to lose part of their weights so that they do not overfit the data too much. At the parameter level, you can set parameters to values which reduce the optimization speed but eventually lead to better results due to reduced effect of overfitting.
Both these strategies work by throwing out parts of the learning process, effectively weakening it. By doing this at different levels, you can learn about the individual layers and how well they fit the data, as well as if there were excessive amounts of noise in the data that needed to be filtered out.
Use a conservative activation function
When creating your neural network, you will need to pick an activation function for your hidden layers. This is one of the most important decisions that affect the accuracy of your model!
The activation function determines whether messages get boosted or suppressed as they traverse the layer. Some common functions are sigmoid (like the familiar logistic regression), softmax (classification), and ReLU (real-valued nonlinearity).
The reason many people use ReLU in deep learning models is because it produces fast results and does not overcompensate by suppressing information. By this reasoning, it learns more from its inputs than other functions.
That said, too much suppression can hinder training since there is no longer feedback coming back through the neuron. If this happens, then the algorithm may “lose” part of the input signal and cannot determine what was inputted next.
This effect becomes even worse as the depth of the networks increase – very thin layers do not have this problem as much. Because of this, engineers often add another layer just before the output to help regulate the activity so that the system does not become unstable.
It is best to be aware of which types of activation functions work well with different architectures and how changing them might influence overall performance.
Use a deeper network
Recent developments in deep learning have led to very successful applications, with state-of-the-art performance across many domains. However, not all networks are created equal!
Some researchers make models that perform well but require large amounts of data to train effectively, limiting applicability to datasets with enough examples. Other groups develop architectures that achieve excellent accuracy, but at great cost in terms of efficiency, requiring significant time to run inference tasks.
This article will discuss some strategies for improving the overall accuracy of your neural networks while keeping their runtime relatively efficient. These strategies can be applied to both small scale projects as well as production level AI technology.
Deep networks are characterized by several layers of computation where information is passed along sequentially. Each layer learns an underlying pattern or feature of the input it receives, before passing it onto the next layer.
The number of layers, how they’re connected to each other, and what types of activations (functions) are used determine which features the model finds important and how much each layer contributes to the final prediction.
There are two main reasons why most current convolutional neural nets (ConvNets) suffer from poor test accuracy. First, they may rely too heavily on only one type of activation function, such as the ReLu nonlinearity, or may use batch normalization, which helps mitigate this effect slightly.
