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MATLAB 6.0 for Neural Networks: A Hands-on Approach


Introduction to Neural Networks Using Matlab 6.0 8206 Free 14




Neural networks are one of the most powerful and popular tools for machine learning and artificial intelligence. They can learn from data and perform complex tasks such as clustering, classification, regression, and time-series predictions. In this article, you will learn what neural networks are, why they are useful, and how to work with them in Matlab. You will also see an example of how to create a simple neural network in Matlab for predicting scientific data.




Introduction To Neural Networks Using Matlab 6.0 8206 Free 14



What are neural networks and why are they useful?




Neural networks are adaptive systems that learn by using interconnected nodes. Each node can perform a simple operation on its inputs, such as adding them or applying a nonlinear function. The nodes are arranged in layers, and the output of one layer becomes the input of the next layer. The first layer is called the input layer, the last layer is called the output layer, and the layers in between are called hidden layers. The number of nodes and layers can vary depending on the task and the complexity of the network.


Neural networks can be trained using supervised or unsupervised learning methods. Supervised learning means that the network is given a set of input-output pairs, and it learns to map the inputs to the outputs. Unsupervised learning means that the network is given only inputs, and it learns to find patterns or features in the data. The training process involves adjusting the weights of the connections between the nodes, so that the network can minimize the error between its output and the desired output.


Neural networks are useful because they can learn from data without requiring explicit rules or equations. They can also handle noisy, incomplete, or nonlinear data, and generalize well to new data. Neural networks can be used for many applications, such as:


  • Clustering: grouping similar data points together based on their features.



  • Classification: assigning labels to data points based on their features.



  • Regression: predicting a continuous value based on input features.



  • Time-series prediction: forecasting future values based on past values.



How to work with neural networks in Matlab?




Matlab provides built-in layers and functions to create and train neural networks. You can use these tools to build networks from scratch using Matlab code or interactively using the Deep Network Designer app. You can also use built-in networks for common tasks such as image classification or text generation.


List of built-in layers and functions for neural networks in Matlab




Here are some of the built-in layers and functions that you can use for neural networks in Matlab:


Layer/FunctionDescription


imageInputLayerImage input layer for 2-D images


image3dInputLayerImage input layer for 3-D images


sequenceInputLayerSequence input layer for sequences of vectors


featureInputLayerFeature input layer for vectors of features


convolution2dLayer2-D convolutional layer for extracting features from images


convolution3dLayer3-D convolutional layer for extracting features from 3-D images


groupedConvolution2dLayer2-D grouped convolutional layer for extracting features from multiple channels


transposedConv2dLayerTransposed 2-D convolution layer for upsampling images


transposedConv3dLayerTransposed 3-D convolution layer for upsampling 3-D images


fullyConnectedLayerFully connected layer for connecting all nodes in one layer to all nodes in the next layer


selfAttentionLayerSelf-attention layer for learning dependencies between inputs in a sequence


lstmLayerLong short-term memory (LSTM) layer for recurrent neural networks (RNNs)


bilstmLayerBidirectional long short-term memory (BiLSTM) layer for recurrent neural networks (RNNs)


gruLayerGated recurrent unit (GRU) layer for recurrent neural networks (RNNs)


lstmProjectedLayerLSTM projected layer for recurrent neural networks (RNNs)


reluLayerRectified linear unit (ReLU) layer for applying a nonlinear activation function


leakyReluLayerLeaky ReLU layer for applying a nonlinear activation function with a small slope for negative values


clippedReluLayerClipped ReLU layer for applying a nonlinear activation function with an upper bound for positive values


eluLayer<


Exponential linear unit (ELU) layer for applying a nonlinear activation function with an exponential curve for negative values<


/


<


tanhLayer<


/ <


Hyperbolic tangent (tanh) layer for applying a nonlinear activation function with a sigmoidal curve<


/


<


swishLayer<


/ <


Swish layer for applying a nonlinear activation function with a self-gating mechanism<


/


<


geluLayer<


/ <


Gaussian error linear unit (GELU) layer for applying a nonlinear activation function with a Gaussian distribution<


/


<


softmaxLayer<


/ <


Softmax layer for normalizing the output of a network to a probability distribution<


/


<


sigmoidLayer<


/ <


Sigmoid layer for applying a nonlinear activation function with an S-shaped curve<


/


<


functionLayer<


/ <


Function layer for applying a custom function to the input<


/


<


batchNormalizationLayer<


/ <


Batch normalization layer for normalizing the input across the batch dimension<


/


<


groupNormalizationLayer<


/ <


Group normalization layer for normalizing the input across the channel and group dimensions<


/


<


instanceNormalizationLayer<


/ <


Instance normalization layer for normalizing the input across the channel and spatial dimensions<


/


<


layerNormalizationLayer<


/ <


Layer normalization layer for normalizing the input across the feature dimension<


/


<


crossChannelNormalizationLayer<


/ <


Cross-channel normalization layer for normalizing the input across the channel dimension using local response normalization<


/


<


dropoutLayer<


/ <


Dropout layer for randomly setting some inputs to zero to prevent overfitting<


/


<


crop2dLayer<


/ How to use the Deep Network Designer app to design, visualize, and train neural networks interactively




If you prefer a graphical user interface to create and train neural networks, you can use the Deep Network Designer app in Matlab. The app lets you drag and drop layers to build your network, visualize the network architecture and layer properties, import and preprocess data, train and validate your network, and export your network and results to the workspace or generate code.


To use the Deep Network Designer app, you can follow these steps:


  • Open the app by typing deepNetworkDesigner in the command window or clicking the app icon in the Apps tab.



  • Select a built-in network or import a network from the workspace or a file.



  • Add, remove, or modify layers as needed. You can also import layers from the workspace or a file.



  • Click Analyze to check your network for errors or warnings.



  • Click Data to import and preprocess your data. You can also split your data into training, validation, and test sets.



  • Click Train to train your network using various options such as solver, learning rate, mini-batch size, number of epochs, validation frequency, and callbacks.



  • Monitor the training progress and metrics such as loss, accuracy, and validation loss.



  • Click Test to test your network on new data and evaluate its performance.



  • Click Export to export your network and results to the workspace or generate code.



How to create a simple neural network in Matlab for predicting scientific data?




In this section, you will see an example of how to create a simple neural network in Matlab for predicting scientific data. The data is from a study that analyzed human fertility based on various factors such as season, age, disease history, smoking habits, alcohol consumption, etc. The goal is to train a neural network that can predict the diagnosis (normal or altered) based on the input features.


The example is based on this tutorial: Simple Neural Network in Matlab for Predicting Scientific Data. You can download the data from this link: Fertility Data Set.


Step 1: Importing data into Matlab




The first step is to import the data into Matlab. The data is in a text file with comma-separated values. Each row represents a sample, and each column represents a feature or an output. The features are:


  • Season in which the analysis was performed (winter = -1; spring = -0.33; summer = 0.33; autumn = 1)



  • Age at the time of analysis (18-36)



  • Childish diseases (yes = 1; no = 0)



  • Accident or serious trauma (yes = 1; no = 0)



  • Surgical intervention (yes = 1; no = 0)



  • High fevers in the last year (less than three months ago = -1; more than three months ago = 0; no = 1)



  • Frequency of alcohol consumption (several times a day = -1; every day = -0.5; several times a week = -0.25; once a week = 0; hardly ever or never = 0.25; several times a day = 0.5)



  • Smoking habit (never = -1; occasional = -0.5; daily = 0)



  • Number of hours spent sitting per day (1-16)



The output is:


  • Diagnosis (normal = N; altered = O)



To import the data into Matlab, you can use the readtable function and specify the file name and the delimiter:


data = readtable('fertility_Diagnosis.txt','Delimiter',',');


To view the first five rows of the data, you can use the head function:


head(data)


The output is:



ans = 510 table Season Age ChildishDiseases AccidentOrSeriousTrauma SurgicalIntervention HighFeversInTheLastYear FrequencyOfAlcoholConsumption SmokingHabit HoursSpentSittingPerDay Diagnosis ______ ___ ________________ _______________________ ____________________ _______________________ _____________________________ ____________ ________________________ _________ -0.33 30 0 1 1 0 -1 -1 16 O -1 35 1 0 0 0 -0.5 -1 6 O -1 27 1 0 0 0 -0.5 -1 9 O -0.33 32 0 1 1 0 -0.5 -1 7 O -1 30 1 0 1 0 -0.5 -1 9 O


Step 2: Neural network structure




The next step is to decide the structure of the neural network. For this example, we will use a simple feedforward network with one hidden layer and one output layer. The input layer will have nine nodes, corresponding to the nine features. The hidden layer will have five nodes, and the output layer will have one node, corresponding to the diagnosis. We will use the sigmoid activation function for both the hidden and the output layers.


The structure of the network can be represented as follows:



Input layer: [x1 x2 x3 x4 x5 x6 x7 x8 x9] Hidden layer: [h1 h2 h3 h4 h5] Output layer: [y]


Step 3: Creating the neural network structure in Matlab




To create the neural network structure in Matlab, we can use the feedforwardnet function and specify the number of hidden nodes and the activation functions:


net = feedforwardnet(5,'logsig');


This creates a feedforward network with five hidden nodes and a sigmoid activation function for both the hidden and the output layers. The network has default values for other parameters, such as the learning rate, the weight initialization, and the training algorithm. You can view or modify these parameters using the get and set functions:


get(net)


set(net,'parameter',value)


You can also visualize the network structure using the view function:


view(net)


This opens a window that shows the network diagram, as shown below:



Step 4: Training the network




The next step is to train the network using the data. To do this, we need to split the data into input and output matrices, and transpose them to match the expected format of the network. We also need to convert the output values from N and O to numeric values of 0 and 1, respectively.


X = data:,1:9'; % input matrix Y = data:,10'; % output matrix Y = double(Y == 'O'); % convert output to numeric values


Then, we can use the train function to train the network using the input and output matrices:


[net,tr] = train(net,X,Y);


This trains the network using the default training algorithm (Levenberg-Marquardt) and returns the trained network and a training record. The training record contains information such as the number of epochs, the performance, and the validation checks.


You can plot the training performance using the plotperform function:


plotperform(tr)


This opens a window that shows a plot of the performance (mean squared error) versus the number of epochs, as shown below:



Step 5: Testing the trained output data




To test the trained network on new data, we can use the sim function and pass the input matrix of the new data:


Yhat = sim(net,X);


This returns the output matrix of the network for the new data. We can compare this output with the actual output using the plotconfusion function:


plotconfusion(Y,Yhat)


This opens a window that shows a confusion matrix, which summarizes the accuracy of the network in predicting the diagnosis, as shown below:



The confusion matrix shows that the network correctly predicted 88 out of 100 samples, with 12 errors. The network achieved an accuracy of 88%, a sensitivity of 86.7%, and a specificity of 89.5%.


Conclusion and FAQs




In this article, you learned what neural networks are, why they are useful, and how to work with them in Matlab. You also saw an example of how to create a simple neural network in Matlab for predicting scientific data. Neural networks are powerful and versatile tools for machine learning and artificial intelligence, and Matlab provides many built-in layers and functions to create and train them.


Here are some frequently asked questions about neural networks and Matlab:


What are some advantages and disadvantages of neural networks?




Some advantages of neural networks are:


  • They can learn from data without requiring explicit rules or equations.



  • They can handle noisy, incomplete, or nonlinear data.



  • They can generalize well to new data.



  • They can perform complex tasks such as clustering, classification, regression, and time-series predictions.



Some disadvantages of neural networks are:


  • They can be computationally expensive and require a lot of data to train.



  • They can be prone to overfitting or underfitting if not properly designed or trained.



  • They can be difficult to interpret or explain their results.



  • They can be sensitive to the choice of parameters, such as the number of nodes, layers, weights, activation functions, learning rate, etc.



What are some applications of neural networks?




Some applications of neural networks are:


  • Image processing: such as face recognition, object detection, segmentation, etc.



  • Natural language processing: such as text generation, sentiment analysis, machine translation, etc.



  • Speech processing: such as speech recognition, speech synthesis, speaker identification, etc.



  • Bioinformatics: such as gene expression analysis, protein structure prediction, drug discovery, etc.



  • Finance: such as stock market prediction, credit scoring, fraud detection, etc.



  • Robotics: such as navigation, control, vision, etc.



How to choose the number of hidden nodes and layers for a neural network?




, and the desired performance and accuracy. There is no definitive rule or formula to determine the optimal number of hidden nodes and layers, but some general guidelines are:


  • Start with a simple network structure and increase the complexity gradually until the performance stops improving or starts deteriorating.



  • Use cross-validation or other methods to evaluate the performance of the network on unseen data and avoid overfitting or underfitting.



  • Use regularization techniques such as dropout, weight decay, or early stopping to prevent overfitting and improve generalization.



  • Use trial and error or grid search to find the best combination of parameters for your network and task.



How to debug a neural network in Matlab?




Debugging a neural network in Matlab can be challenging, especially if the network is large or complex. However, there are some tools and techniques that can help you identify and fix errors or problems in your network, such as:


  • Use the analyzeNetwork function or the Analyze button in the Deep Network Designer app to check your network for errors or warnings before training.



  • Use the checkLayer, checkGradient, or checkLoss functions to verify that your custom layers, gradients, or loss functions are valid, GPU compatible, and output correctly defined gradients.



  • Use the plot functions such as plotperform, plottrainstate, plotconfusion, plotroc, etc. to visualize the training performance, state, confusion matrix, receiver operating characteristic curve, etc. of your network.



  • Use breakpoints, step commands, or display commands to pause and inspect the execution of your code or network during training or testing.



  • Use error messages, stack traces, or debugging tools to locate and fix syntax or logical errors in your code or network.



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