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Train Network Using Custom Training Loop

This example shows how to train a network that classifies handwritten digits with a custom learning rate schedule.

You can train most types of neural networks using the trainnet and trainingOptions functions. If the trainingOptions function does not provide the options you need (for example, a custom learning rate schedule), then you can define your own custom training loop using dlarray and dlnetwork objects for automatic differentiation. For an example showing how to retrain a pretrained deep learning network using the trainnet function, see Retrain Neural Network to Classify New Images .

Training a deep neural network is an optimization task. By considering a neural network as a function f ( X ; θ ) , where X is the network input, and θ is the set of learnable parameters, you can optimize θ so that it minimizes some loss value based on the training data. For example, optimize the learnable parameters θ such that for a given inputs X with a corresponding targets T , they minimize the error between the predictions Y = f ( X ; θ ) and T .

The loss function used depends on the type of task. For example:

For classification tasks, you can minimize the cross entropy error between the predictions and targets.

For regression tasks, you can minimize the mean squared error between the predictions and targets.

You can optimize the objective using gradient descent: minimize the loss L by iteratively updating the learnable parameters θ by taking steps towards the minimum using the gradients of the loss with respect to the learnable parameters. Gradient descent algorithms typically update the learnable parameters by using a variant of an update step of the form θ t + 1 = θ t - ρ ∇ L , where t is the iteration number, ρ is the learning rate, and ∇ L denotes the gradients (the derivatives of the loss with respect to the learnable parameters).

This example trains a network to classify handwritten digits with the time-based decay learning rate schedule: for each iteration, the solver uses the learning rate given by ρ t = ρ 0 1 + k   t , where t is the iteration number, ρ 0 is the initial learning rate, and k is the decay.

Load Training Data

Load the digits data as an image datastore using the imageDatastore function and specify the folder containing the image data.

Partition the data into training and validation sets. Set aside 10% of the data for validation using the splitEachLabel function.

The network used in this example requires input images of size 28-by-28-by-1. To automatically resize the training images, use an augmented image datastore. Specify additional augmentation operations to perform on the training images: randomly translate the images up to 5 pixels in the horizontal and vertical axes. Data augmentation helps prevent the network from overfitting and memorizing the exact details of the training images.

To automatically resize the validation images without performing further data augmentation, use an augmented image datastore without specifying any additional preprocessing operations.

Determine the number of classes in the training data.

Define Network

Define the network for image classification.

For image input, specify an image input layer with input size matching the training data.

Do not normalize the image input, set the Normalization option of the input layer to "none" .

Specify three convolution-batchnorm-ReLU blocks.

Pad the input to the convolution layers such that the output has the same size by setting the Padding option to "same" .

For the first convolution layer specify 20 filters of size 5. For the remaining convolution layers specify 20 filters of size 3.

For classification, specify a fully connected layer with size matching the number of classes

To map the output to probabilities, include a softmax layer.

When training a network using a custom training loop, do not include an output layer.

Create a dlnetwork object from the layer array.

Define Model Loss Function

Create the function modelLoss , listed in the Model Loss Function section of the example, that takes as input the dlnetwork object, a mini-batch of input data with corresponding targets, and returns the loss, the gradients of the loss with respect to the learnable parameters, and the network state.

Specify Training Options

Train for ten epochs with a mini-batch size of 128.

Specify the options for SGDM optimization. Specify an initial learn rate of 0.01 with a decay of 0.01, and momentum 0.9.

Train Model

Create a minibatchqueue object that processes and manages mini-batches of images during training. For each mini-batch:

Use the custom mini-batch preprocessing function preprocessMiniBatch (defined at the end of this example) to convert the labels to one-hot encoded variables.

Format the image data with the dimension labels "SSCB" (spatial, spatial, channel, batch). By default, the minibatchqueue object converts the data to dlarray objects with underlying type single . Do not format the class labels.

Discard partial mini-batches.

Train on a GPU if one is available. By default, the minibatchqueue object converts each output to a gpuArray if a GPU is available. Using a GPU requires Parallel Computing Toolbox™ and a supported GPU device. For information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox) .

Initialize the velocity parameter for the SGDM solver.

Calculate the total number of iterations for the training progress monitor.

Initialize the TrainingProgressMonitor object. Because the timer starts when you create the monitor object, make sure that you create the object close to the training loop.

Train the network using a custom training loop. For each epoch, shuffle the data and loop over mini-batches of data. For each mini-batch:

Evaluate the model loss, gradients, and state using the dlfeval and modelLoss functions and update the network state.

Determine the learning rate for the time-based decay learning rate schedule.

Update the network parameters using the sgdmupdate function.

Update the loss, learn rate, and epoch values in the training progress monitor.

Stop if the Stop property is true. The Stop property value of the TrainingProgressMonitor object changes to true when you click the Stop button.

Test the classification accuracy of the model by comparing the predictions on the validation set with the true labels.

After training, making predictions on new data does not require the labels. Create minibatchqueue object containing only the predictors of the test data:

To ignore the labels for testing, set the number of outputs of the mini-batch queue to 1.

Specify the same mini-batch size used for training.

Preprocess the predictors using the preprocessMiniBatchPredictors function, listed at the end of the example.

For the single output of the datastore, specify the mini-batch format "SSCB" (spatial, spatial, channel, batch).

Loop over the mini-batches and classify the images using modelPredictions function, listed at the end of the example.

Evaluate the classification accuracy.

Visualize the predictions in a confusion chart.

Large values on the diagonal indicate accurate predictions for the corresponding class. Large values on the off-diagonal indicate strong confusion between the corresponding classes.

Supporting Functions

Model loss function.

The modelLoss function takes a dlnetwork object net , a mini-batch of input data X with corresponding targets T and returns the loss, the gradients of the loss with respect to the learnable parameters in net , and the network state. To compute the gradients automatically, use the dlgradient function.

Model Predictions Function

The modelPredictions function takes a dlnetwork object net , a minibatchqueue of input data mbq , and the network classes, and computes the model predictions by iterating over all data in the minibatchqueue object. The function uses the onehotdecode function to find the predicted class with the highest score.

Mini Batch Preprocessing Function

The preprocessMiniBatch function preprocesses a mini-batch of predictors and labels using the following steps:

Preprocess the images using the preprocessMiniBatchPredictors function.

Extract the label data from the incoming cell array and concatenate into a categorical array along the second dimension.

One-hot encode the categorical labels into numeric arrays. Encoding into the first dimension produces an encoded array that matches the shape of the network output.

Mini-Batch Predictors Preprocessing Function

The preprocessMiniBatchPredictors function preprocesses a mini-batch of predictors by extracting the image data from the input cell array and concatenate into a numeric array. For grayscale input, concatenating over the fourth dimension adds a third dimension to each image, to use as a singleton channel dimension.

trainingProgressMonitor | dlarray | dlgradient | dlfeval | dlnetwork | forward | adamupdate | predict | minibatchqueue | onehotencode | onehotdecode

Related Topics

• Train Generative Adversarial Network (GAN)
• Define Model Loss Function for Custom Training Loop
• Update Batch Normalization Statistics in Custom Training Loop
• Define Custom Training Loops, Loss Functions, and Networks
• Specify Training Options in Custom Training Loop
• Monitor Custom Training Loop Progress
• List of Deep Learning Layers
• List of Functions with dlarray Support

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Divide Data for Optimal Neural Network Training

This topic presents part of a typical multilayer network workflow. For more information and other steps, see Multilayer Shallow Neural Networks and Backpropagation Training .

When training multilayer networks, the general practice is to first divide the data into three subsets. The first subset is the training set, which is used for computing the gradient and updating the network weights and biases. The second subset is the validation set. The error on the validation set is monitored during the training process. The validation error normally decreases during the initial phase of training, as does the training set error. However, when the network begins to overfit the data, the error on the validation set typically begins to rise. The network weights and biases are saved at the minimum of the validation set error. This technique is discussed in more detail in Improve Shallow Neural Network Generalization and Avoid Overfitting .

The test set error is not used during training, but it is used to compare different models. It is also useful to plot the test set error during the training process. If the error on the test set reaches a minimum at a significantly different iteration number than the validation set error, this might indicate a poor division of the data set.

There are four functions provided for dividing data into training, validation and test sets. They are dividerand (the default), divideblock , divideint , and divideind . The data division is normally performed automatically when you train the network.

You can access or change the division function for your network with this property:

Each of the division functions takes parameters that customize its behavior. These values are stored and can be changed with the following network property:

The divide function is accessed automatically whenever the network is trained, and is used to divide the data into training, validation and testing subsets. If net.divideFcn is set to ' dividerand ' (the default), then the data is randomly divided into the three subsets using the division parameters net.divideParam.trainRatio , net.divideParam.valRatio , and net.divideParam.testRatio . The fraction of data that is placed in the training set is trainRatio /( trainRatio+valRatio+testRatio ), with a similar formula for the other two sets. The default ratios for training, testing and validation are 0.7, 0.15 and 0.15, respectively.

If net.divideFcn is set to ' divideblock ' , then the data is divided into three subsets using three contiguous blocks of the original data set (training taking the first block, validation the second and testing the third). The fraction of the original data that goes into each subset is determined by the same three division parameters used for dividerand .

If net.divideFcn is set to ' divideint ' , then the data is divided by an interleaved method, as in dealing a deck of cards. It is done so that different percentages of data go into the three subsets. The fraction of the original data that goes into each subset is determined by the same three division parameters used for dividerand .

When net.divideFcn is set to ' divideind ' , the data is divided by index. The indices for the three subsets are defined by the division parameters net.divideParam.trainInd , net.divideParam.valInd and net.divideParam.testInd . The default assignment for these indices is the null array, so you must set the indices when using this option.

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• Deep Learning and Neural Networks in Machine Learning Assignments: A MATLAB Approach

From Theory to Practice: Deep Learning and Neural Networks in MATLAB Assignments

In today's technologically advanced world, the study of machine learning has quickly become essential. For students pursuing their master's degrees across a range of disciplines, mastering machine learning concepts has become crucial as the demand for intelligent systems rises. If you need help with machine learning assignment , mastering Deep Learning and Neural Networks is vital for tackling difficult tasks. We will examine how Deep Learning and Neural Networks can be applied to difficult assignments for master's students in this blog. Students can effectively build and train neural networks for image recognition, natural language processing, and other challenging tasks by utilizing MATLAB's simple API and a wide range of pre-trained models.  If you are looking for a MATLAB assignment expert , the platform's support for transfer learning enables students to modify existing models to meet the requirements of their individual assignments. We will explore how to build neural networks in MATLAB, how to work with text and image data, and how to use cutting-edge methods like recurrent neural networks and convolutional neural networks.

Deep Learning with MATLAB's practical applications in fields like healthcare, autonomous vehicles, and natural language processing serve as additional illustrations of the importance of this methodology in contemporary research and development. Mastering Deep Learning opens up countless opportunities for the future of machine learning and artificial intelligence with MATLAB as a potent ally.

Understanding Deep Learning and Neural Networks

Modern machine learning algorithms, which are based on deep learning and neural networks, have made incredible strides in artificial intelligence. To learn hierarchical representations of data, artificial neural networks with many hidden layers are trained in deep learning, which enables the model to recognize intricate patterns and make complex decisions. Neural networks are made up of interconnected nodes that process information through weighted connections, imitating the behavior of neurons. They are inspired by the structure and operation of the human brain. The importance of neural networks lies in their capacity to generalize from data, which enables them to excel in a variety of tasks like time series prediction, image recognition, and natural language understanding. Researchers and developers can fully utilize deep learning and neural networks' enormous potential to successfully address real-world problems by understanding their fundamentals. A thorough understanding of these ideas is becoming more and more important as the field develops in order to spur innovation and push the limits of artificial intelligence.

What is Deep Learning?

Deep learning is a branch of machine learning that uses artificial neural networks to mimic how the human brain learns. These networks are made up of layers of interconnected nodes, where each node stands in for a neuron. To learn hierarchical patterns and enable feature extraction and decision-making, the data is passed through these layers.

The Significance of Neural Networks

Deep learning models' fundamental building blocks are neural networks. They are excellent for resolving complex issues because of their capacity to learn from data and generalize to fresh, unexplored instances. Neural networks can handle both straightforward and complex calculations because they have an input layer, one or more hidden layers, and an output layer.

Key Architectures in Deep Learning

Over time, a number of well-liked deep learning architectures have been created. These include Generative Adversarial Networks (GANs) for creating fresh data samples, Recurrent Neural Networks (RNNs) for sequential data, and Convolutional Neural Networks (CNNs) for analyzing images and videos. For MATLAB designers to create efficient solutions, understanding these architectures is essential. These architectures provide special benefits for various tasks while addressing particular difficulties. For instance, CNNs are excellent at extracting features from images, which makes them suitable for tasks requiring image recognition. RNNs, on the other hand, are frequently employed in natural language processing and can handle sequential data. GANs have become more and more common because they are good at producing accurate data, which helps with applications like image synthesis and style transfer. Researchers and developers can select the best model for their deep learning projects and make the most of MATLAB's capabilities by becoming familiar with these key architectures.

Leveraging MATLAB for Deep Learning Assignments

Due to its expertise in deep learning, MATLAB is a useful and adaptable tool for handling challenging machine-learning assignments. Users are provided with a wide range of functions and utilities by the Deep Learning Toolbox, a comprehensive library within MATLAB, to easily build, train, and deploy deep learning models. Both novice and experienced researchers can easily delve into the world of deep learning thanks to its user-friendly interface. For students and professionals looking to implement cutting-edge deep learning algorithms, MATLAB is the best option due to its extensive documentation and support. Additionally, MATLAB gives users access to deep learning models that have already been trained, saving both time and computational resources. These models can be tailored by researchers to their particular tasks, hastening the development process. MATLAB's visualization tools are essential for gaining an understanding of how deep learning models function. Researchers can examine neural network architectures, track training development, and spot potential improvement opportunities. This visualization-driven methodology helps with model optimization and understanding, resulting in better performance and outcomes in machine learning assignments.

MATLAB's Deep Learning Toolbox

The Deep Learning Toolbox in MATLAB is a comprehensive library that offers many tools and functions for creating, developing, and deploying deep learning models. It is suitable for both beginners and experts due to its user-friendly interface and thorough documentation. Users can build complex neural networks and experiment with various architectures with just a few lines of code. The toolbox provides researchers with the freedom to create and modify their models in accordance with particular needs because it is furnished with a variety of layers, activation functions, and optimization algorithms. The development process is further streamlined by the seamless integration of MATLAB's deep learning capabilities with its preexisting functionalities for data preprocessing and visualization, enabling a more effective workflow for handling challenging machine learning assignments.

Access to Pre-trained Models

Access to pre-trained deep learning models is one of MATLAB's key advantages. These models can be customized for particular tasks after being trained on large datasets, saving time and computational resources. MATLAB is an effective option for projects with short deadlines because researchers can concentrate on their special problem without starting from scratch. Additionally, the availability of pre-trained models makes it easier to benchmark and compare various approaches, allowing researchers to quickly experiment with different approaches. The range of pre-trained models that can be used is further increased by MATLAB's seamless integration with well-known deep learning libraries, such as TensorFlow and PyTorch, offering a variety of options for addressing various machine learning challenges.

Visualization Tools for Model Insights

To enhance the performance of deep learning models, it is essential to comprehend how they operate. To examine neural network architectures, track training progress, and spot potential bottlenecks, MATLAB provides a variety of visualization tools. Researchers can diagnose problems like overfitting or vanishing gradients by using visualizations to gain insights into how the model learns and changes during training. Additionally, being able to see feature maps and activations in various layers helps with understanding how the model gathers and processes data, which aids in model optimization. These insights support decision-making while helping to tailor the models for particular tasks, ultimately resulting in more effective and precise answers to machine learning problems.

Practical Applications of MATLAB in Deep Learning Assignments

For researchers and students alike, MATLAB's extensive deep-learning capabilities open up a world of practical applications. The classification and object detection of images is a well-known area where MATLAB excels. Convolutional neural networks (CNNs) and transfer learning are two techniques that MATLAB users can use to obtain cutting-edge outcomes on image datasets like ImageNet. From healthcare for medical image analysis to autonomous vehicles for object detection on roads, this capability is essential in a variety of industries. Another area where MATLAB's neural networks shine is Natural Language Processing (NLP). Researchers can easily carry out sentiment analysis, language translation, and text generation tasks using recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. The support of RNNs and LSTM networks in MATLAB also significantly improves time series analysis and forecasting, allowing for precise weather forecasting, stock market analysis, and financial forecasting. These real-world applications highlight MATLAB's enormous value in tackling contemporary problems and spurring deep learning innovation.

Image Classification and Object Detection

In tasks like object detection and image classification, MATLAB's Deep Learning Toolbox excels. On benchmark datasets like ImageNet, users can achieve cutting-edge results by utilizing Convolutional Neural Networks (CNNs) and transfer learning. Research opportunities in computer vision and image processing are made possible by this capability. Innovative applications, like autonomous vehicles that can detect objects on the road, medical imaging systems that can diagnose illnesses, and surveillance systems that can spot suspicious activity, are open for research. Users can fine-tune pre-trained models to make them more suitable for particular domains, which eliminates the need for extensive training on large datasets. The performance of the model can be examined further using MATLAB's visualization tools, and this understanding can help model developers gain new insights and improve their models.

Natural Language Processing (NLP)

Another area where MATLAB's neural networks excel is NLP. Tasks involving sentiment analysis, language translation, and text generation can be successfully completed using recurrent neural networks (RNNs) and long short-term memory (LSTM) networks. Working with textual data is made simpler by MATLAB's availability of pre-trained word embeddings. Pre-trained models can be used by researchers to extract meaningful representations of words and sentences, saving them a lot of time and money. Language translation models make it easier to communicate across different languages, while sentiment analysis can help businesses understand customer feedback. The expressive power of LSTM networks helps text generation tasks, like chatbots and creative writing, to be more effective. The simplicity of MATLAB's integration with natural language processing libraries and visualization tools enables researchers to quickly create state-of-the-art NLP solutions.

Time Series Analysis and Prediction

Models with the ability to handle time series data are necessary for financial forecasting, stock market analysis, and weather forecasting. It is a useful tool for these applications because MATLAB has strong support for RNNs and LSTM networks. These networks are capable of capturing temporal dependencies and producing precise forecasts based on past data. To create stock price prediction models and make wise investment choices, researchers and financial analysts can use MATLAB. LSTM networks can forecast weather patterns by analyzing historical climate data, which helps meteorologists and climate scientists. The time series analysis is enhanced by the potent data preprocessing and visualization capabilities of MATLAB, giving researchers a complete set of tools for efficiently exploring, modeling, and interpreting temporal data.

Best Practices and Tips for MATLAB Assignments on Deep Learning

Best practices and practical advice can greatly improve the quality and effectiveness of work when working on deep learning assignments in MATLAB. In order to ensure that the input data is correctly formatted and scaled before feeding it into the neural network, data preprocessing is essential. Data normalization, augmentation, and feature extraction are crucial steps in enhancing model performance, and MATLAB offers a variety of functions for these tasks. Another crucial component of deep learning assignments is hyperparameter tuning. To find the best configuration for their particular task, researchers should test out various learning rates, batch sizes, and activation functions. The Global Optimisation Toolbox in MATLAB can be especially useful in this situation. Regularisation strategies like L1 and L2 regularisation as well as dropout should be used to avoid overfitting. Regularisation enhances the model's ability to generalize and enhances its performance on new data. Last but not least, researchers can detect potential problems early on and make necessary adjustments thanks to continuous monitoring and visualization of the training process using MATLAB's built-in tools, leading to more successful and effective deep learning assignments.

Data Preprocessing and Augmentation

A deep learning model must be properly preprocessed before being fed data. Data preprocessing functions in MATLAB include normalization, resizing, and data augmentation. The input data is scaled to a standard range during normalization to stop some features from overpowering the learning process. In order for the model to process data samples uniformly, resizing makes sure that each sample has the same dimensions. Techniques for enhancing data, such as flipping or rotating images, can help expand the training set and enhance the generalization of the model. The model becomes more robust and can handle various testing scenarios by including variations in the training data. In order to create a trustworthy and effective deep learning model, proper data preprocessing and augmentation are essential.

Hyperparameter Tuning

Hyperparameters like learning rate, batch size, and number of hidden units have a significant impact on how well deep learning models perform. Researchers can find the best set of hyperparameters for their particular tasks with the aid of MATLAB's tools for hyperparameter tuning, like the Global Optimisation Toolbox. An essential step in enhancing the model's functionality and generalizability is hyperparameter tuning. Researchers can find the best configuration that produces the highest accuracy on the validation set by methodically examining various combinations of hyperparameters. This procedure is streamlined by the optimization algorithms in MATLAB, saving time and effort in determining the appropriate hyperparameter values. Researchers can improve their models' ability to generalize to new data and increase their dependability for practical applications by fine-tuning their hyperparameters.

Regularization and Dropout

Regularisation methods like L1 and L2 regularisation and dropout are frequently used to avoid overfitting. When a model gets too complicated and picks up noise from training data, it is said to overfit, which causes it to perform poorly on new data. Large weights are penalized by regularisation, which encourages the model to concentrate on important features and generalize more effectively. The adaptable way that these techniques are implemented in MATLAB enables users to improve the generalization and robustness of their models. Researchers can strike a balance between preventing overfitting and maintaining the model's ability to learn significant patterns by varying the regularisation strength. Another regularisation technique is a dropout, whereby random neurons are momentarily removed from the model during training to avoid the model overly depending on any particular neurons and to improve generalization. In order to guarantee the model's dependability and flexibility with different datasets, regularisation, and dropout must be used properly.

In conclusion, master's students pursuing machine learning assignments will find the integration of Deep Learning and Neural Networks with MATLAB to be an exciting journey. Students can successfully complete challenging tasks and contribute to cutting-edge research in a variety of fields by utilizing MATLAB's intuitive interface and extensive pre-trained models. Natural language processing, autonomous vehicles, and healthcare are just a few of the fields where MATLAB's versatility makes it possible to handle image and textual data with ease. Master's students who use this MATLAB approach will not only improve their academic performance but also arm themselves with useful skills that will help them succeed in their future careers. They are in a position to revolutionize machine learning and have a significant impact on the field of artificial intelligence thanks to the strength of Deep Learning and Neural Networks.

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Image Processing

Control systems, power system, signal processing, electronics, convolutional neural network assignment help.

In neural networks, Convolutional neural network (ConvNets or CNNs) is one of the main categories to do images recognition, images classifications. Objects detections, recognition faces etc., are some of the areas where CNNs are widely used.

CNN image classifications takes an input image, process it and classify it under certain categories (Eg., Dog, Cat, Tiger, Lion). Computers sees an input image as array of pixels and it depends on the image resolution. Based on the image resolution, it will see h x w x d( h = Height, w = Width, d = Dimension ). Eg., An image of 6 x 6 x 3 array of matrix of RGB (3 refers to RGB values) and an image of 4 x 4 x 1 array of matrix of grayscale image.

Technically, deep learning CNN models to train and test, each input image will pass it through a series of convolution layers with filters (Kernals), Pooling, fully connected layers (FC) and apply Softmax function to classify an object with probabilistic values between 0 and 1. The below figure is a complete flow of CNN to process an input image and classifies the objects based on values.

What is Convolution Layer?

Convolution is the first layer to extract features from an input image. Convolution preserves the relationship between pixels by learning image features using small squares of input data. It is a mathematical operation that takes two inputs such as image matrix and a filter or kernal

Consider a 5 x 5 whose image pixel values are 0, 1 and filter matrix 3 x 3 as shown in below

Then the convolution of 5 x 5 image matrix multiplies with 3 x 3 filter matrix which is called “Feature Map” as output shown in below

Convolution of an image with different filters can perform operations such as edge detection, blur and sharpen by applying filters. The below example shows various convolution image after applying different types of filters (Kernels).

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