Recent developments in artificial intelligence have *brought us something new*, which is referred to as deep learning. Neural networks are an algorithm that work by having multiple layers of functioning nodes (layers within the system). These layers are connected to each other and trained using data.

By incorporating more advanced mathematical techniques into this model type, it was able to achieve impressive results when applied to various tasks. When companies use these models for predictive analysis or classification, it is **called neural network classifiers**, pre-trained models, or sometimes just plain old deep learning!

While there are many types of neural networks out there, one of the most popular uses of this technique is in the area of natural language processing (NLP) or chatbot technology. Companies can apply this method to understand what content people want to read, how they want to be spoken to, and then create their own talking bot or service.

Given enough training data, you could even create your very own AI personal assistant! This article will go over some basics about evaluating different architectures and features of a CNN (Convolutional Neuronal Network), a common type of neural net used for NLP applications. You will also learn about some additional software tools to test and evaluate your model performance.

## Use validation set

A very common way to evaluate the performance of a model is using a separate test set that it has access to for evaluating its accuracy. This method, however, has been criticized because the **test sets usually** have less instances than the dataset as a whole, which makes the results difficult to trust.

By comparing the models’ accuracies on *different sized test sets*, we can get an idea about how well their generalization capabilities are but this will not give us a true picture of how good these specific models are.

Because there is no **widely accepted definition** of what constitutes a deep neural network, it is hard to compare one model with another. Different people may use slightly different definitions when talking about depth so it is important to make sure you understand those definitions before drawing any conclusions.

Another problem with test-set evaluations is data imbalance. Because most datasets contain more instances of one class over all others, just taking the majority vote does not tell you much about how effective the model was at distinguishing between classes.

## Use the test set

A very important part of any AI software is the model! This **includes things like chatbots**, voice assistants such as Alexa or Siri, and even self-driving cars.

A deep neural network (DNN) is one type of machine learning algorithm that has become increasingly popular in recent years. These networks are designed to learn complex patterns by feeding them large amounts of data.

They do this through what’s called an “end-to-end training process,” which means they train all parts of the DNN at the same time. That way, it learns how to work together as a whole.

Since most companies don’t have access to lots of natural language data, there are tools that can be used to create your own DNN. One of these is PyTorch, a Python scripting tool that makes developing DL models easier.

There are many ways to evaluate the performance of a DNN, but using the test set is usually considered the best approach. By doing so, you get a more accurate picture of just how well the model works without being influenced too much by possible biases in the data it was trained with.

## Look at the accuracy of the model

A very important thing to look into when evaluating a deep learning model is how well it predicts different categories or classes of data. If your model does not perform well in this area, then you may want to reconsider whether it is **worth investing time** in it.

The more examples the model has seen, the better it will predict for new instances. The more instances it has seen, the better it will generalize its predictions.

By looking at precision, recall, and F1-score, we can get a good idea of how well a model performs in these areas.

Precision measures the proportion of times the model correctly identifies an item as being positive (or true) over all the times it was told to be positive.

Measures the degree to which the model agrees with other models. A **higher value means** that the model tends to agree more with similar models than different ones.

Recall similarly calculates the ratio between correct and false positives, but instead looks at false negatives. This gives us an understanding of whether the model is missing out features that are needed to make a prediction.

And finally, the F1 score is the weighted average of both precision and recall, where the weights are determined by the prevalence of each class in the dataset.

So if there are far more negative examples than positive, the **scores become heavily influenced** by recall, while if most cases were either positive or negative, the **focus shifts onto precision**.

## Look at the loss function

The most important part of any neural network is how it trains and optimizes its layers. There are two main components in deep learning, which are called the *backbone* and the *head*.

The backbone is typically referred to as the “architecture” or “structure” of the model. This is usually a collection of mathematical functions (or *layers*) that take inputs and produce outputs.

These layers can be simple like fully-connected perceptrons with only linear output or they could be more complex like convolutional networks where **input features map onto internal representations using matrix multiplication** before being processed by subsequent layers.

It’s very common to find something in between these extremes so we will focus our attention on those types here!

Intermediate layer types such as convolutions and pooling operators allow the network to learn abstract representation patterns from the data. These patterns can then be applied directly to new datasets without having to re-train the entire model!

That’s one of the reasons why models with lots of **intermediate layers often perform better** than ones with fewer – you get longer time horizons for transferability.1

However, too *many intermediaries may hurt overall performance* because there’s less ability to identify underlying structure in the data.*2 3 4 5 6 7*

There are several different metrics used to evaluate the quality of a DNN.

## Use confusion matrices

A good way to evaluate the performance of a deep learning model is by using a confusion matrix. This will compare your model’s predictions with those from a baseline method, such as a traditional algorithm or neural network without gated structures like Layers NN (see our article here for more information). By comparing these two sets of numbers, you can determine how well each model performs over the other.

The accuracy metric in most cases is precision-or true positives-this measures how many samples the **model correctly identified** as positive. Recall, or true positives, is similar to accuracy but instead calculates how many samples the model predicted were positive. The difference between them is that **recall includes false negatives**, which are samples that the model did not predict as being positive, whereas precision does not account for this.

By looking at both precision and recall, we get a better understanding of how successful the model was. For example, if a model has high precision but low recall, it may be identifying almost all instances as being positive, which is great, but it is failing to identify any negative examples. Or, it could be that the model is very accurate at predicting everything as being positive, so it is only able to tell whether a sample is positive or negative, it cannot discriminate between them.

A *commonly used measure* to assess models is F1 score, which averages precision and recall together, weighting each term equally.

## Use entropy to check if the model is overfitting

One of the most important things to evaluate when trying to determine how well a neural network is learning your dataset is whether or not it has become overly complicated.

When you train a neural net, the system will constantly be updating itself as it learns. This process can cause the network to get more complex as it tries to *find every possible way* to achieve its goal.

After it has learned all these possibilities, it computes an accuracy score by counting how many times it could have predicted each outcome and how many it actually did, then divides the first number by the second.

This ratio is called the accuracy score because it tells you how likely it was that the prediction was correct.

The higher this ratio gets, the better the model is doing its job- but only up until a certain point! When it reaches this limit, the model becomes too clever and is no longer able to accurately identify what it is being asked about.

This is referred to as over fitting the data and **reducing predictive power**. [2] It **may also indicate** there’s something wrong with the architecture of the model.

You should always strive for a balance between having very high accuracies and having a meaningful representation of the data, but there are some ways to tell when a model has crossed this line.

One of the easiest ways to do this is to **use information theory**.

## Use AUC to check model accuracy

A very popular metric for evaluating the performance of any classification model is the area under the receiver operating characteristic (ROC) curve (AUC). The ROC graph plots true positive rate against false positive rate for *various cutoff values used* to determine if an object or event is present or not.

The area under the ROC curve can take a value in the range [0, 1]. Higher numbers indicate better overall classifier performance.

By calculating the AUC for several different cutoffs, one may be able to find the optimal threshold that maximizes the ratio of true positives to false positives. For example, say we are trying to classify whether there is someone at the door or not. If we set our cutoff low, then we will get lots of *false alarms – people walking* up to the house would result in a “there’s a person at the door” prediction. However, if we set our cutoff high, then we will miss some actual intrusions because these instances won’t reach our threshold and so we’ll predict it as being calm.

By finding the optimal balance between those two types of errors, we can compute the AUC for our model. In this case, the AUC equals 0.75, which means our model performed well since it had a moderate amount of error.

There are many software packages that allow you to evaluate models using the AUC metric.

## Use regression to see how the model predicts values

A common way to evaluate the accuracy of a deep learning model is by comparing its predictions with those from other models or methods that are considered more accurate.

A **commonly used method** for evaluation is called regression. With this approach, the model being evaluated is given an input value (such as 10 or “how much money do you earn?”) and then it produces a *predicted output value*.

The difference between the *actual output value* and the **predicted one gives us** a number we can refer to as error. This error can be calculated in two ways: mean absolute error (MAE) and root-mean squared error (RMSE).

We will use MAE as our metric to determine the quality of your model.