Neural Network Matlab

[Ernest Babin]

Aneural network (also called an artificial neural network or ANN) is an adaptive system that learns by using interconnected nodes or neurons in a layered structure that resembles a human brain. A neural network can learn from data, so it can be trained to recognize patterns, classify data, and forecast future events. A neural network breaks down the input into layers of abstraction. It can be trained using many examples to recognize patterns in speech or images just as the human brain does. The neural network behavior is defined by the way its individual elements are connected and by the strength, or weights, of those connections. These weights are automatically adjusted during training according to a specified learning rule until the artificial neural network performs the desired task correctly.

Neural networks are a type of machine learning approach inspired by how neurons signal to each other in the human brain. Neural networks are especially suitable for modeling nonlinear relationships, and they are typically used to perform pattern recognition and classify objects or signals in speech, vision, and control systems.

Neural networks, particularly deep neural networks, have become known for their proficiency at complex identification applications such as face recognition, text translation, and voice recognition. These approaches are a key technology driving innovation in advanced driver assistance systems and tasks, including lane classification and traffic sign recognition.

Like other machine learning algorithms, neural networks can be used for classification or regression tasks. Model parameters are set by weighting the neural network through learning on training data, typically by optimizing weights to minimize prediction error.

Deep learning refers to neural networks with many layers, whereas neural networks with only two or three layers of connected neurons are also known as shallow neural networks. Deep learning has become popular because it eliminates the need to extract features from images, which previously challenged the application of machine learning to image and signal processing. Although feature extraction can be omitted in image processing applications, some form of feature extraction is still commonly applied to signal processing tasks to improve model accuracy.

With just a few lines of code, you can create neural networks in MATLAB without being an expert. You can get started quickly, train and visualize neural network models, and integrate neural networks into your existing system and deploy them to servers, enterprise systems, clusters, clouds, and embedded devices.

For example, you could create a network with more hidden layers, or a deep neural network. There are many types of deep networks supported in MATLAB and resources for deep learning. For more info, check out the links in the description below.

You can generate a MATLAB function or Simulink diagram for simulating your neural network. Use genfunction to create the neural network including all settings, weight and bias values, functions, and calculations in one MATLAB function file.

Hi. My name is Gabriel Ha, and I'm here to show you how MATLAB makes it straightforward to create a deep neural network from scratch. Our demo has specific application-to-image processing and recognition, but we feel like images are pretty easy to relate to. And it's a fairly well-known application of neural networks. Most importantly, we want to make deep learning accessible to everyone, and you'll be able to get your hands on everything that we show you, and build on them, and start using your own networks on your own.

So for those of you who are super familiar with training networks, along with techniques to make them more accurate, MATLAB is going to be great for you because, as you would expect, we provide you with the intuitive syntax and functions which will allow you to easily implement your refinements. For those of you new to the deep learning field and want to get your feet wet with this technology, the extent of what you can immediately do might be limited to image recognition, but I'm confident that it will provide you with more than enough material to get started and have a lot of fun with neural networks.

So here's what we're going do. We want to train a network to recognize four different animals: cats, dogs, frogs, and deer. To do that, we're going to introduce images of each animal to our network, define the layers of our network, and then, using a single line of code, tell MATLAB to train and create our network from scratch. Then we'll test out our network by showing it new images that it hasn't seen before and check its accuracy.

In this case, we got all of our images from the publicly available CIFAR-10 dataset, which literally just involves downloading and extracting one big ZIP file. So thankfully, setting up this demo is only dependent on your network speeds and processor power, respectively. That being said, training a network from scratch does require quite a bit of data, so always look for opportunities to build on previous work like this demo.

Let's take a look at the core code required to execute our training. You can see this part, which specifies the animal names, and then this part, pointing MATLAB to the folder containing that training data. And as far as setup is concerned, that's it.

So now we're going to tell MATLAB how we want the deep network to be trained. Every neural network has a series of layers, and the more layers it has, the deeper the network. Now each layer takes in data from the previous layer, transforms the data, and then passes it on. So the first layer takes in the raw input image, and by the time we get to the last layer, it's going to hopefully spit out the correct name of the animal in the original image.

So here are the layers that we've chosen to implement for this example. For those of you completely new to this field, you would not be expected to be able to come up with all these layers from scratch. On the other end, if you're a deep learning expert, we provide you with the tools to precisely implement your layers.

But in either case, if you want to build off this example, just replace the training data with your own, tweak the layers if you feel like you're up to the task, and with one line of code, MATLAB will give you a neural network trained on whatever you want, whether it's animals or faces of your friends, which is totally not a creepy thing that I did on company time.

So of course, it'll take some time to train. If you just have the CPU, it'll take a while, but if you have a wicked decked-out GPU like this machine, it takes about 45 seconds. Once it's done, we can move on to testing our network.

As a caveat, you'll notice that the CIFAR-10 images are really small, and the first layer of our network requires images that are 32 by 32 by 3. While our code does resize your image, you'll have to determine whether that makes sense for your data. But if you have a whole bunch of images that you want to classify with the neural network, here's how to do it with MATLAB, and you can get started right away.

Click the links in the description below to get your hands on the code and check out documentation on using Neural Network Toolbox. Don't hesitate to leave us a question or comment. And as always, thanks for watching.

To train a neural network classification model, use the Classification Learner app. For greater flexibility, train a neural network classifier using fitcnet in the command-line interface. After training, you can classify new data by passing the model and the new predictor data to predict.

Deep Learning is a very hot topic these days especially in computer vision applications and you probably see it in the news and get curious. Now the question is, how do you get started with it? Today's guest blogger, Toshi Takeuchi, gives us a quick tutorial on artificial neural networks as a starting point for your study of deep learning.

Many of us tend to learn better with a concrete example. Let me give you a quick step-by-step tutorial to get intuition using a popular MNIST handwritten digit dataset. Kaggle happens to use this very dataset in the Digit Recognizer tutorial competition. Let's use it in this example. You can download the competition dataset from "Get the Data" page:

The first column is the label that shows the correct digit for each sample in the dataset, and each row is a sample. In the remaining columns, a row represents a 28 x 28 image of a handwritten digit, but all pixels are placed in a single row, rather than in the original rectangular form. To visualize the digits, we need to reshape the rows into 28 x 28 matrices. You can use reshape for that, except that we need to transpose the data, because reshape operates by column-wise rather than row-wise.

The dataset stores samples in rows rather than in columns, so you need to transpose it. Then you will partition the data so that you hold out 1/3 of the data for model evaluation, and you will only use 2/3 for training our artificial neural network model.

W in the diagram stands for weights and b for bias units, which are part of individual neurons. Individual neurons in the hidden layer look like this - 784 inputs and corresponding weights, 1 bias unit, and 10 activation outputs.

If you look inside myNNfun.m, you see variables like IW1_1 and x1_step1_keep that represent the weights your artificial neural network model learned through training. Because we have 784 inputs and 100 neurons, the full layer 1 weights will be a 100 x 784 matrix. Let's visualize them. This is what our neurons are learning!

Now you are ready to use myNNfun.m to predict labels for the heldout data in Xtest and compare them to the actual labels in Ytest. That gives you a realistic predictive performance against unseen data. This is also the metric Kaggle uses to score submissions.

First, you see the actual output from the network, which shows the probability for each possible label. You simply choose the most probable label as your prediction and then compare it to the actual label. You should see 95% categorization accuracy.

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