MATLAB Deep Learning Toolbox UG (R2024b)

Deep Networks Deep Learning in MATLAB What Is Deep Learning? Start Deep Learning Faster Using Transfer Learning Deep Learning Workflows Deep Learning Apps Train Classifiers Using Features Extracted from Pretrained Networks Deep Learning with Big Data on CPUs, GPUs, in Parallel, and on the Cloud Deep Learning Using Simulink Deep Learning Interpretability Deep Learning Customization Deep Learning Import and Export Pretrained Deep Neural Networks Compare Pretrained Neural Networks Load Pretrained Neural Networks Visualize Pretrained Neural Networks Feature Extraction Transfer Learning Import and Export Neural Networks Pretrained Neural Networks for Audio Applications Pretrained Neural Networks for Computer Vision Applications Pretrained Models on GitHub Learn About Convolutional Neural Networks Example Deep Learning Networks Architectures Multiple-Input and Multiple-Output Networks Multiple-Input Networks Multiple-Output Networks Use Datastores for Multiple-Input and Multiple-Output Networks List of Deep Learning Layers Deep Learning Layers List of Deep Learning Layer Blocks Deep Learning Layer Blocks Specify Layers of Convolutional Neural Network Set Up Parameters and Train Convolutional Neural Network Solvers Monitoring Options Data Format Options Stochastic Solver Options L-BFGS Solver Options Validation Options Regularization and Normalization Options Gradient Clipping Options Sequence Options Hardware and Acceleration Options Checkpoint Options Train Network with Numeric Features Train Neural Network with Tabular Data Train Network on Image and Feature Data Compare Activation Layers Deep Learning Tips and Tricks Choose Network Architecture Choose Training Options Improve Training Accuracy Fix Errors in Training Prepare and Preprocess Data Use Available Hardware Fix Errors With Loading from MAT-Files Speed Up Deep Neural Network Training Optimize Training Hyperparameters Use Transfer Learning Optimize Network Architecture Normalize Data Stop Training Early Disable Optional Visualizations Reduce Validation Time Preprocess Data in Advance Use Uniformly Sized Data Use GPUs and Parallel Computing Accelerate Custom Layers Optimize Custom Training Code Long Short-Term Memory Neural Networks LSTM Neural Network Architecture Layers Classification, Prediction, and Forecasting Sequence Padding and Truncation Normalize Sequence Data Out-of-Memory Data Visualization LSTM Layer Architecture Choose an AI Model Data Considerations Image Tasks Text Tasks Tabular Data and Small Time-Series Tasks Time-Series and Large Feature Data Tasks Implement Unsupported Deep Learning Layer Blocks Deep Network Designer Prepare Network for Transfer Learning Using Deep Network Designer Build Networks with Deep Network Designer Transfer Learning Image Classification Sequence Classification Numeric Data Classification Convert Classification Network into Regression Network Multiple-Input and Multiple-Output Networks Deep Networks Check Network Import Data into Deep Network Designer Import Data Image Augmentation Validation Data Build Time Series Forecasting Network Using Deep Network Designer Generate MATLAB Code from Deep Network Designer View Autogenerated Custom Layers Using Deep Network Designer Build Image-to-Image Regression Network Using Deep Network Designer Adapt Pretrained Audio Network for New Data Using Deep Network Designer Import PyTorch® Model Using Deep Network Designer Build Simple App For Deep Learning Inference Using App Designer Deep Learning with Images Classify Webcam Images Using Deep Learning Retrain Neural Network to Classify New Images Train Residual Network for Image Classification Classify Image Using GoogLeNet Extract Image Features Using Pretrained Network Transfer Learning Using AlexNet Create Simple Deep Learning Neural Network for Classification Train Convolutional Neural Network for Regression Train Network with Multiple Outputs Convert Classification Network into Regression Network Train Generative Adversarial Network (GAN) Train Conditional Generative Adversarial Network (CGAN) Train Wasserstein GAN with Gradient Penalty (WGAN-GP) Generate Images Using Diffusion Train Fast Style Transfer Network Train a Twin Neural Network to Compare Images Train a Twin Network for Dimensionality Reduction Train Neural ODE Network Train Variational Autoencoder (VAE) to Generate Images Convert Convolutional Network to Spiking Neural Network Lane and Vehicle Detection in Simulink Using Deep Learning Classify ECG Signals in Simulink Using Deep Learning Classify Images in Simulink Using GoogLeNet Multilabel Image Classification Using Deep Learning Profile Your Deep Learning Code to Improve Performance Acceleration for Simulink Deep Learning Models Run Acceleration Mode from the User Interface Run Acceleration Mode Programmatically Create and Train Network with Nested Layers Deep Learning with Time Series, Sequences, and Text Sequence Classification Using Deep Learning Sequence Classification Using 1-D Convolutions Time Series Forecasting Using Deep Learning Train Speech Command Recognition Model Using Deep Learning Sequence-to-Sequence Classification Using Deep Learning Sequence-to-Sequence Regression Using Deep Learning Sequence-to-One Regression Using Deep Learning Train Network with Complex-Valued Data Train Network with LSTM Projected Layer Simulate Calorie Burn Using Neural Network in Simulink Predict Battery State of Charge Using Deep Learning Evaluate Code Generation Inference Time of Compressed Deep Neural Network Battery State of Charge Estimation in Simulink Using Feedforward Neural Network Classify Videos Using Deep Learning Classify Videos Using Deep Learning with Custom Training Loop Train Sequence Classification Network Using Data with Imbalanced Classes Sequence-to-Sequence Classification Using 1-D Convolutions Time Series Anomaly Detection Using Deep Learning Sequence Classification Using CNN-LSTM Network Create Bidirectional LSTM (BiLSTM) Function Train Latent ODE Network with Irregularly Sampled Time-Series Data Solve PDE Using Fourier Neural Operator Multivariate Time Series Anomaly Detection Using Graph Neural Network Classify Text Data Using Deep Learning Classify Text Data Using Convolutional Neural Network Multilabel Text Classification Using Deep Learning Classify Text Data Using Custom Training Loop Generate Text Using Autoencoders Define Text Encoder Model Function Define Text Decoder Model Function Sequence-to-Sequence Translation Using Attention Generate Text Using Deep Learning Pride and Prejudice and MATLAB Word-by-Word Text Generation Using Deep Learning Image Captioning Using Attention Language Translation Using Deep Learning Predict and Update Network State in Simulink Classify and Update Network State in Simulink Time Series Prediction in Simulink Using Deep Learning Network Improve Performance of Deep Learning Simulations in Simulink Physical System Modeling Using LSTM Network in Simulink Export Network to FMU Deep Learning Tuning and Visualization Explore Network Predictions Using Deep Learning Visualization Techniques Deep Dream Images Using GoogLeNet Grad-CAM Reveals the Why Behind Deep Learning Decisions Interpret Deep Learning Time-Series Classifications Using Grad-CAM Understand Network Predictions Using Occlusion Investigate Classification Decisions Using Gradient Attribution Techniques Understand Network Predictions Using LIME Investigate Spectrogram Classifications Using LIME Interpret Deep Network Predictions on Tabular Data Using LIME Explore Semantic Segmentation Network Using Grad-CAM Investigate Audio Classifications Using Deep Learning Interpretability Techniques Verification of Neural Networks Neural Network Robustness Out-of-Distribution Detection Calculate Out-of-Distribution Threshold Other Techniques Generate Untargeted and Targeted Adversarial Examples for Image Classification Train Image Classification Network Robust to Adversarial Examples Generate Adversarial Examples for Semantic Segmentation Verify Robustness of Deep Learning Neural Network Verify Robustness of ONNX Network Out-of-Distribution Detection for Deep Neural Networks Out-of-Distribution Data Discriminator for YOLO v4 Object Detector Out-of-Distribution Detection for BERT Document Classifier Out-of-Distribution Detection for LSTM Document Classifier Verify an Airborne Deep Learning System Reproduce Network Training on a GPU Resume Training from Checkpoint Network Create Custom Deep Learning Training Plot Custom Stopping Criteria for Deep Learning Training Deep Learning Using Bayesian Optimization Train Deep Learning Networks in Parallel Monitor Deep Learning Training Progress Define Custom Metric Function Create Custom Metric Function Example Regression Metric Example Classification Metric Define Custom Deep Learning Metric Object Metric Template Metric Properties Constructor Function Initialization Function Reset Function Update Function Aggregation Function Evaluation Function Function Call Order Define Custom Metric Object Metric Template Metric Name Declare Properties Create Constructor Function Create Initialization Function Create Reset Function Create Update Function Create Aggregation Function Create Evaluation Function Completed Metric Use Custom Metric During Training Detect Issues During Deep Neural Network Training Detect Vanishing Gradients in Deep Neural Networks by Plotting Gradient Distributions Investigate Network Predictions Using Class Activation Mapping View Network Behavior Using tsne Visualize Activations of a Convolutional Neural Network Visualize Activations of LSTM Network Visualize Features of a Convolutional Neural Network Visualize Image Classifications Using Maximal and Minimal Activating Images Monitor GAN Training Progress and Identify Common Failure Modes Convergence Failure Mode Collapse Deep Learning Visualization Methods Visualization Methods Interpretability Methods for Nonimage Data ROC Curve and Performance Metrics Introduction to ROC Curve Performance Curve with MATLAB ROC Curve for Multiclass Classification Performance Metrics Classification Scores and Thresholds Pointwise Confidence Intervals Compare Deep Learning Models Using ROC Curves Manage Experiments Run Experiments in Parallel Run Multiple Simultaneous Trials Run Single Trial on Multiple Workers Set Up Parallel Environment Offload Experiments as Batch Jobs to a Cluster Create Batch Job on Cluster Track Progress of Batch Job Cancel Batch Job Download Training Results Delete Batch Job Debug Deep Learning Experiments Start Debugging Session Verify Your Results Debug Metric Functions Create a Deep Learning Experiment for Classification Create a Deep Learning Experiment for Regression Evaluate Deep Learning Experiments by Using Metric Functions Try Multiple Pretrained Networks for Transfer Learning Experiment with Weight Initializers for Transfer Learning Tune Experiment Hyperparameters by Using Bayesian Optimization Choose Training Configurations for LSTM Using Bayesian Optimization Run a Custom Training Experiment for Image Comparison Use Experiment Manager to Train Generative Adversarial Networks (GANs) Use Bayesian Optimization in Custom Training Experiments Custom Training with Multiple GPUs in Experiment Manager Deep Learning in Parallel and the Cloud Scale Up Deep Learning in Parallel, on GPUs, and in the Cloud Train Single Network in Parallel Train Multiple Networks in Parallel Batch Deep Learning Manage Cluster Profiles and Automatic Pool Creation Deep Learning Precision Reproducibility Deep Learning in the Cloud Access MATLAB in the Cloud Work with Big Data in the Cloud Work with Deep Learning Data in the Cloud Deep Learning with MATLAB on Multiple GPUs Use Multiple GPUs in Local Machine Use Multiple GPUs in Cluster Optimize Mini-Batch Size and Learning Rate Select Particular GPUs to Use for Training Train Multiple Networks on Multiple GPUs Advanced Support for Fast Multi-Node GPU Communication Resolve GPU Memory Issues Out of GPU Memory Errors Possible Solutions Deep Learning with Big Data Work with Big Data in Parallel Preprocess Data in the Background Work with Big Data in the Cloud Run Custom Training Loops on a GPU and in Parallel Train Network on GPU Train Single Network in Parallel Train Multiple Networks in Parallel Use Experiment Manager to Train in Parallel Cloud AI Workflow Using the Deep Learning Container Cloud AI Workflow Using MathWorks Cloud Center Train Network in the Cloud Using Automatic Parallel Support Use parfeval to Train Multiple Deep Learning Networks Send Deep Learning Batch Job to Cluster Train Network Using Automatic Multi-GPU Support Use parfor to Train Multiple Deep Learning Networks Work with Deep Learning Data in AWS Work with Deep Learning Data in Azure Train Network in Parallel with Custom Training Loop Train Network Using Federated Learning Train Network on Amazon Web Services Using MATLAB Deep Learning Container Use Amazon S3 Buckets with MATLAB Deep Learning Container Use Experiment Manager in the Cloud with MATLAB Deep Learning Container Train Network on Amazon Web Services Using MathWorks Cloud Center Use Experiment Manager in the Cloud with MathWorks Cloud Center AI Workflows Battery State of Charge Estimation Using Deep Learning Define Requirements for Battery State of Charge Estimation Prepare Data for Battery State of Charge Estimation Using Deep Learning Train Deep Learning Network for Battery State of Charge Estimation Compress Deep Learning Network for Battery State of Charge Estimation Test Deep Learning Network for Battery State of Charge Estimation Integrate AI Model into Simulink for Battery State of Charge Estimation Generate Code for Battery State of Charge Estimation Using Deep Learning Computer Vision Examples Gesture Recognition using Videos and Deep Learning Code Generation for Object Detection by Using Single Shot Multibox Detector Point Cloud Classification Using PointNet Deep Learning Activity Recognition from Video and Optical Flow Data Using Deep Learning Import Pretrained ONNX YOLO v2 Object Detector Export YOLO v2 Object Detector to ONNX Object Detection Using SSD Deep Learning Object Detection Using YOLO v3 Deep Learning Object Detection Using YOLO v4 Deep Learning Object Detection Using YOLO v2 Deep Learning Semantic Segmentation Using Deep Learning Semantic Segmentation Using Dilated Convolutions Semantic Segmentation of Multispectral Images Using Deep Learning 3-D Brain Tumor Segmentation Using Deep Learning Perform Instance Segmentation Using Mask R-CNN Perform Instance Segmentation Using SOLOv2 Perform 6-DoF Pose Estimation for Bin Picking Using Deep Learning Estimate Body Pose Using Deep Learning Generate Image from Segmentation Map Using Deep Learning Classify Defects on Wafer Maps Using Deep Learning Detect Defects on Printed Circuit Boards Using YOLOX Network Detect Image Anomalies Using Explainable FCDD Network Detect Image Anomalies Using Pretrained ResNet-18 Feature Embeddings Localize Industrial Defects Using PatchCore Anomaly Detector Reidentify People Throughout a Video Sequence Using ReID Network Train Vision Transformer Network for Image Classification Image Processing Examples Remove Noise from Color Image Using Pretrained Neural Network Increase Image Resolution Using Deep Learning JPEG Image Deblocking Using Deep Learning Image Processing Operator Approximation Using Deep Learning Develop Camera Processing Pipeline Using Deep Learning Brighten Extremely Dark Images Using Deep Learning Classify Tumors in Multiresolution Blocked Images Unsupervised Day-to-Dusk Image Translation Using UNIT Quantify Image Quality Using Neural Image Assessment Neural Style Transfer Using Deep Learning Ship Detection from Sentinel-1 C Band SAR Data Using YOLOX Object Detection Unsupervised Medical Image Denoising Using CycleGAN Unsupervised Medical Image Denoising Using UNIT Segment Lungs from CT Scan Using Pretrained Neural Network Brain MRI Segmentation Using Pretrained 3-D U-Net Network Breast Tumor Segmentation from Ultrasound Using Deep Learning Cardiac Left Ventricle Segmentation from Cine-MRI Images Using U-Net Network Segment CT Scan Using MONAI Label Choose Pretrained Cellpose Model for Cell Segmentation Refine Cellpose Segmentation by Tuning Model Parameters Detect Nuclei in Large Whole Slide Images Using Cellpose Train Custom Cellpose Model Automated Driving Examples Create Occupancy Grid Using Monocular Camera and Semantic Segmentation Train Deep Learning Semantic Segmentation Network Using 3-D Simulation Data Navigation Examples Train Deep Learning-Based Sampler for Motion Planning Accelerate Motion Planning with Deep-Learning-Based Sampler Lidar Examples Code Generation for Lidar Object Detection Using SqueezeSegV2 Network Lidar Object Detection Using Complex-YOLO v4 Network Aerial Lidar Semantic Segmentation Using PointNet++ Deep Learning Code Generation for Aerial Lidar Semantic Segmentation Using PointNet++ Deep Learning Lidar Point Cloud Semantic Segmentation Using PointSeg Deep Learning Network Lidar Point Cloud Semantic Segmentation Using SqueezeSegV2 Deep Learning Network Code Generation for Lidar Point Cloud Segmentation Network Lidar 3-D Object Detection Using PointPillars Deep Learning Signal Processing Examples Learn Pre-Emphasis Filter Using Deep Learning Hand Gesture Classification Using Radar Signals and Deep Learning Waveform Segmentation Using Deep Learning Classify ECG Signals Using Long Short-Term Memory Networks Generate Synthetic Signals Using Conditional GAN Classify Time Series Using Wavelet Analysis and Deep Learning Denoise Signals with Generative Adversarial Networks Deploy Signal Classifier on NVIDIA Jetson Using Wavelet Analysis and Deep Learning Deploy Signal Classifier Using Wavelets and Deep Learning on Raspberry Pi Deploy Signal Segmentation Deep Network on Raspberry Pi Anomaly Detection Using Autoencoder and Wavelets Fault Detection Using Wavelet Scattering and Recurrent Deep Networks Parasite Classification Using Wavelet Scattering and Deep Learning Detect Anomalies Using Wavelet Scattering with Autoencoders Denoise Signals with Adversarial Learning Denoiser Model Human Health Monitoring Using Continuous Wave Radar and Deep Learning Classify ECG Signals Using DAG Network Deployed to FPGA Code Generation for a Deep Learning Simulink Model to Classify ECG Signals Modulation Classification Using Wavelet Analysis on NVIDIA Jetson Crack Identification from Accelerometer Data Time-Frequency Feature Embedding with Deep Metric Learning Wireless Resource Allocation Using Graph Neural Network Time-Frequency Convolutional Network for EEG Data Classification Machine Learning and Deep Learning Classification Using Signal Feature Extraction Objects Feature Selection Based on Deep Learning Interpretability for Signal Classification Applications Export Labeled Data from Signal Labeler for Deep Learning Classification Detect Anomalies in Signals Using deepSignalAnomalyDetector Detect Anomalies in Machinery Using LSTM Autoencoder Anomaly Detection Using Convolutional Autoencoder with Wavelet Scattering Sequences Real-Time Noise Detection on Raspberry Pi Using Deep Signal Anomaly Detector Musical Instrument Classification with Joint Time-Frequency Scattering Wireless Comm Examples Compare Residual Recurrent Neural Network Structures for Digital Predistortion Design Data Preparation for Neural Network Digital Predistortion Design Power Amplifier Modeling Using Neural Networks Structurally Compress Neural Network DPD Using Projection Model-Free Training of AI-Based OFDM Wireless Systems Custom Training Loops and Loss Functions for AI-Based Wireless Systems Import TensorFlow Channel Feedback Compression Network and Deploy to GPU OFDM Autoencoder for Wireless Communications Train DQN Agent for Beam Selection CSI Feedback with Transformer Autoencoder CSI Feedback with Autoencoders Modulation Classification by Using FPGA Neural Network for Digital Predistortion Design - Online Training Neural Network for Digital Predistortion Design-Offline Training Neural Network for Beam Selection Spectrum Sensing with Deep Learning to Identify 5G and LTE Signals Autoencoders for Wireless Communications Modulation Classification with Deep Learning Training and Testing a Neural Network for LLR Estimation Design a Deep Neural Network with Simulated Data to Detect WLAN Router Impersonation Test a Deep Neural Network with Captured Data to Detect WLAN Router Impersonation Audio Examples Transfer Learning with Pretrained Audio Networks Speech Command Recognition in Simulink Speaker Identification Using Custom SincNet Layer and Deep Learning Dereverberate Speech Using Deep Learning Networks Speaker Recognition Using x-vectors Speaker Diarization Using x-vectors Train Spoken Digit Recognition Network Using Out-of-Memory Audio Data Train Spoken Digit Recognition Network Using Out-of-Memory Features Keyword Spotting in Noise Code Generation with Intel MKL-DNN Keyword Spotting in Noise Code Generation on Raspberry Pi Speech Command Recognition Code Generation on Raspberry Pi Speech Command Recognition Code Generation with Intel MKL-DNN Train Generative Adversarial Network (GAN) for Sound Synthesis Sequential Feature Selection for Audio Features Acoustic Scene Recognition Using Late Fusion Keyword Spotting in Noise Using MFCC and LSTM Networks Speech Emotion Recognition Spoken Digit Recognition with Wavelet Scattering and Deep Learning Cocktail Party Source Separation Using Deep Learning Networks Voice Activity Detection in Noise Using Deep Learning Denoise Speech Using Deep Learning Networks Accelerate Audio Deep Learning Using GPU-Based Feature Extraction Acoustics-Based Machine Fault Recognition Acoustics-Based Machine Fault Recognition Code Generation Acoustics-Based Machine Fault Recognition Code Generation on Raspberry Pi Train End-to-End Speaker Separation Model Train 3-D Sound Event Localization and Detection (SELD) Using Deep Learning 3-D Sound Event Localization and Detection Using Trained Recurrent Convolutional Neural Network Speech Command Recognition Code Generation with Intel MKL-DNN Using Simulink Speech Command Recognition on Raspberry Pi Using Simulink Audio-Based Anomaly Detection for Machine Health Monitoring 3-D Speech Enhancement Using Trained Filter and Sum Network Train 3-D Speech Enhancement Network Using Deep Learning Audio Transfer Learning Using Experiment Manager Audio Event Classification Using TensorFlow Lite on Raspberry Pi Compare Speaker Separation Models Compress Machine Fault Recognition Neural Network Using Projection Reinforcement Learning Examples Reinforcement Learning Using Deep Neural Networks Reinforcement Learning Workflow Reinforcement Learning Environments Reinforcement Learning Agents Create Deep Neural Network Policies and Value Functions Train Reinforcement Learning Agents Deploy Trained Policies Control Water Level in a Tank Using a DDPG Agent Train DDPG Agent to Swing Up and Balance Pendulum with Image Observation Create DQN Agent Using Deep Network Designer and Train Using Image Observations Imitate MPC Controller for Lane Keeping Assist Train DDPG Agent to Control Sliding Robot Train Biped Robot to Walk Using Reinforcement Learning Agents Train Humanoid Walker Train DDPG Agent for Adaptive Cruise Control Train DQN Agent for Lane Keeping Assist Using Parallel Computing Train DDPG Agent for Path-Following Control Train PPO Agent for Automatic Parking Valet Predictive Maintenance Examples Chemical Process Fault Detection Using Deep Learning Rolling Element Bearing Fault Diagnosis Using Deep Learning Remaining Useful Life Estimation Using Convolutional Neural Network Anomaly Detection in Industrial Machinery Using Three-Axis Vibration Data Battery Cycle Life Prediction Using Deep Learning Computational Finance Examples Compare Deep Learning Networks for Credit Default Prediction Interpret and Stress-Test Deep Learning Networks for Probability of Default Hedge Options Using Reinforcement Learning Toolbox Use Deep Learning to Approximate Barrier Option Prices with Heston Model Backtest Strategies Using Deep Learning Deep Reinforcement Learning for Optimal Trade Execution Multiperiod Goal-Based Wealth Management Using Reinforcement Learning Python Model Coexecution in Simulink Examples Classify Images Using TensorFlow Model Predict Block Predict Responses Using TensorFlow Model Predict Block Classify Images Using ONNX Model Predict Block Predict Responses Using ONNX Model Predict Block Classify Images Using PyTorch Model Predict Block Predict Responses Using PyTorch Model Predict Block Import, Export, and Customization Train Deep Learning Model in MATLAB Training Methods Decisions Define Custom Deep Learning Layers Neural Network Layer Architecture Custom Layer Template Formatted Inputs and Outputs Custom Layer Acceleration Custom Layer Properties Forward Functions Reset State Function Backward Function GPU Compatibility Code Generation Compatibility Network Composition Check Validity of Layer Define Custom Deep Learning Layer with Learnable Parameters Custom Layer Template Name Layer and Specify Superclasses Declare Properties and Learnable Parameters Create Constructor Function Create Initialize Function Create Forward Functions Completed Layer GPU Compatibility Check Validity of Custom Layer Using checkLayer Include Custom Layer in Network Define Custom Deep Learning Layer with Multiple Inputs Custom Layer Template Name Layer and Specify Superclasses Declare Properties and Learnable Parameters Create Constructor Function Create Forward Functions Completed Layer GPU Compatibility Check Validity of Layer with Multiple Inputs Use Custom Weighted Addition Layer in Network Define Custom Deep Learning Layer with Formatted Inputs Custom Layer Template Name Layer and Specify Superclasses Declare Properties and Learnable Parameters Create Constructor Function Create Initialize Function Create Forward Functions Completed Layer GPU Compatibility Include Custom Layer in Network Define Custom Recurrent Deep Learning Layer Custom Layer Template Name Layer Declare Properties, State, and Learnable Parameters Create Constructor Function Create Initialize Function Create Predict Function Create Reset State Function Completed Layer GPU Compatibility Include Custom Layer in Network Specify Custom Layer Backward Function Create Custom Layer Create Backward Function Complete Layer GPU Compatibility Custom Layer Function Acceleration Acceleration Considerations Deep Learning Network Composition Automatically Initialize Learnable dlnetwork Objects for Training Predict and Forward Functions GPU Compatibility Define Nested Deep Learning Layer Using Network Composition Custom Layer Template Name Layer and Specify Superclasses Declare Properties and Learnable Parameters Create Constructor Function Create Forward Functions Completed Layer GPU Compatibility Train Network with Custom Nested Layers Weight Tying Using Nested Layer Define Custom Deep Learning Layer for Code Generation Custom Layer Template Name Layer and Specify Superclasses Specify Code Generation Pragma Declare Properties and Learnable Parameters Create Constructor Function Create Initialize Function Create Forward Functions Completed Layer Check Custom Layer for Code Generation Compatibility Define Custom Deep Learning Output Layers Output Layer Architecture Output Layer Templates Custom Layer Acceleration Output Layer Properties Forward Loss Function Backward Loss Function GPU Compatibility Check Validity of Layer Define Custom Classification Output Layer Classification Output Layer Template Name the Layer and Specify Superclasses Declare Layer Properties Create Constructor Function Create Forward Loss Function Completed Layer GPU Compatibility Check Output Layer Validity Include Custom Classification Output Layer in Network Define Custom Regression Output Layer Regression Output Layer Template Name the Layer and Specify Superclasses Declare Layer Properties Create Constructor Function Create Forward Loss Function Completed Layer GPU Compatibility Check Output Layer Validity Include Custom Regression Output Layer in Network Specify Custom Output Layer Backward Loss Function Create Custom Layer Create Backward Loss Function Complete Layer GPU Compatibility Check Custom Layer Validity Check Custom Layer Validity List of Tests Generated Data Diagnostics Specify Custom Weight Initialization Function Compare Layer Weight Initializers Define Custom Learning Rate Schedule Custom Learning Rate Schedule Template Name Schedule and Specify Superclass Declare Properties Create Constructor Function Create Update Function Completed Learning Rate Schedule Train Using Custom Learning Rate Schedule Object Automatic Differentiation Background What Is Automatic Differentiation? Forward Mode Reverse Mode Use Automatic Differentiation In Deep Learning Toolbox Custom Training and Calculations Using Automatic Differentiation Use dlgradient and dlfeval Together for Automatic Differentiation Derivative Trace Characteristics of Automatic Derivatives Define Custom Training Loops, Loss Functions, and Networks Define Custom Loss Function Define Deep Learning Model for Custom Training Loop Define Custom Training Loop Loss Function Update Learnable Parameters Using Automatic Differentiation Specify Training Options in Custom Training Loop Solver Options Learn Rate Plots Verbose Output Mini-Batch Size Number of Epochs Validation L2 Regularization Gradient Clipping Single CPU or GPU Training Checkpoints Deep Learning Data Formats Train Network Using Custom Training Loop Train Sequence Classification Network Using Custom Training Loop Define Model Loss Function for Custom Training Loop Create Model Loss Function for Model Defined as dlnetwork Object Create Model Loss Function for Model Defined as Function Evaluate Model Loss Function Update Learnable Parameters Using Gradients Use Model Loss Function in Custom Training Loop Debug Model Loss Functions Update Batch Normalization Statistics in Custom Training L