Abstract: In this article, we'll explore how to develop a deep learning model using Matlab for numerical regression tasks with custom loss functions. We'll create a neural network with 500 samples and 4 features, aiming for 2 output variables.
2024-06-24 by DevCodeF1 Editors
Developing a Deep Learning Model for Numerical Regression with a Custom Loss Function in MATLAB
Deep learning has become an essential tool in various fields, including computer vision, natural language processing, and numerical regression. In this article, we will focus on developing a deep learning model for numerical regression using MATLAB. We will use a custom loss function to improve the model's performance. The dataset we will use consists of 500 samples, each with four features (x1, x2, x3, x4), and two output variables (y1, y2).
Data Preparation
The first step in developing a deep learning model is to prepare the data. In MATLAB, we can use the table function to create a table from the dataset. The table function allows us to specify the variables and their types. For example, if our dataset is stored in a CSV file, we can use the following code to load the data:
data = readtable('data.csv');
Once we have loaded the data, we need to split it into training and testing sets. We can use the cvpartition function to split the data randomly. For example, to split the data into 80% training and 20% testing sets, we can use the following code:
cvp = cvpartition(size(data,1),'HoldOut',0.2);idx = cvp.test;dataTrain = data(~idx,:);dataTest = data(idx,:);
Model Architecture
Once we have prepared the data, we can define the model architecture. In MATLAB, we can use the layer function to define the layers of the neural network. For example, to define a simple neural network with four input features and two output variables, we can use the following code:
inputSize = size(dataTrain,2)-2;outputSize = 2;layers = [layer('name','input','size',[inputSize 1])layer('name','fc1','size',[16 16])layer('name','fc2','size',[16 outputSize])];
In the above code, we define a neural network with three layers: an input layer, a fully connected layer (fc1), and another fully connected layer (fc2). The input layer has the same size as the number of input features, and the output layer has the same size as the number of output variables.
Custom Loss Function
Once we have defined the model architecture, we can define the custom loss function. In MATLAB, we can use the trainNetwork function to train the neural network. The trainNetwork function allows us to specify the loss function. By default, MATLAB uses the mean squared error loss function. However, we can define our custom loss function. For example, to define a custom loss function that calculates the absolute difference between the predicted and actual values, we can use the following code:
function loss = customLoss(predicted,target)loss = sum(abs(predicted - target));end
In the above code, we define a custom loss function that calculates the sum of the absolute differences between the predicted and actual values. We can use this custom loss function in the trainNetwork function as follows:
options = trainingOptions('sgdm', ...'MaxEpochs',100, ...'MiniBatchSize',32, ...'LearningRate',0.01, ...'ValidationData',{dataTest,[]}, ...'Plots','training-progress', ...'LossFunction','customLoss');net = trainNetwork(dataTrain,'input',layers,options);
In this article, we have discussed how to develop a deep learning model for numerical regression using MATLAB. We have used a custom loss function to improve the model's performance. The dataset we have used consists of 500 samples, each with four features (x1, x2, x3, x4), and two output variables (y1, y2). We have prepared the data, defined the model architecture, and defined the custom loss function. We have trained the neural network using the trainNetwork function and monitored the training progress using the Plots option.
References
- MATLAB Deep Learning Toolbox documentation: https://www.mathworks.com/help/deeplearning/index.html
- MATLAB Neural Network Toolbox documentation: https://www.mathworks.com/help/deeplearning/ug/neural-network-toolbox.html
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