Built-In Training

Train deep learning networks for sequence and tabular data using built-in training functions

After defining the network architecture, you can define training parameters using the trainingOptions function. You can then train the network using trainNetwork or trainnet. Use the trained network to predict class labels or numeric responses, or forecast future time steps.

You can train a neural network on a CPU, a GPU, multiple CPUs or GPUs, or in parallel on a cluster or in the cloud. Training on a GPU or in parallel requires Parallel Computing Toolbox™. Using a GPU requires a supported GPU device (for information on supported devices, see GPU Computing Requirements (Parallel Computing Toolbox)). Specify the execution environment using the trainingOptions function.

Apps

Deep Network Designer

Design, visualize, and train deep learning networks

Functions

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Train Network

`trainingOptions`	Options for training deep learning neural network
`trainNetwork`	Train neural network
`trainnet`	Train deep learning neural network (Since R2023b)
`analyzeNetwork`	Analyze deep learning network architecture

Prediction

`classify`	Classify data using trained deep learning neural network
`predict`	Predict responses using trained deep learning neural network
`activations`	Compute deep learning network layer activations
`predictAndUpdateState`	Predict responses using a trained recurrent neural network and update the network state
`classifyAndUpdateState`	Classify data using a trained recurrent neural network and update the network state
`resetState`	Reset state parameters of neural network
`confusionchart`	Create confusion matrix chart for classification problem
`sortClasses`	Sort classes of confusion matrix chart

Topics

Multilayer Perceptron Networks

Train Network with Numeric Features
This example shows how to create and train a simple neural network for deep learning feature data classification.
Compare Deep Learning Networks for Credit Default Prediction
Create, train, and compare three deep learning networks for predicting credit default probability.
Battery State of Charge Workflow

An example workflow for training, compressing, and using a deep learning network in Simulink^®.
- STEP 1: Predict Battery State of Charge Using Deep Learning
- STEP 2: Compress Network for Estimating Battery State of Charge
- STEP 3: Battery State of Charge Estimation in Simulink Using Deep Learning Network

Recurrent Networks

Create Simple Sequence Classification Network Using Deep Network Designer
This example shows how to create a simple long short-term memory (LSTM) classification network using Deep Network Designer.
Sequence-to-Sequence Classification Using Deep Learning
This example shows how to classify each time step of sequence data using a long short-term memory (LSTM) network.
Sequence-to-Sequence Regression Using Deep Learning
This example shows how to predict the remaining useful life (RUL) of engines by using deep learning.
Sequence-to-One Regression Using Deep Learning
This example shows how to predict the frequency of a waveform using a long short-term memory (LSTM) neural network.
Train Network with LSTM Projected Layer
Train a deep learning network with an LSTM projected layer for sequence-to-label classification.
Classify Videos Using Deep Learning
This example shows how to create a network for video classification by combining a pretrained image classification model and an LSTM network.
Train Network Using Custom Mini-Batch Datastore for Sequence Data
This example shows how to train a deep learning network on out-of-memory sequence data using a custom mini-batch datastore.

Convolutional Networks

Sequence Classification Using 1-D Convolutions
This example shows how to classify sequence data using a 1-D convolutional neural network.
Time Series Anomaly Detection Using Deep Learning
This example shows how to detect anomalies in sequence or time series data.
Train Sequence Classification Network Using Data With Imbalanced Classes
This example shows how to classify sequences with a 1-D convolutional neural network using class weights to modify the training to account for imbalanced classes.
Sequence-to-Sequence Classification Using 1-D Convolutions
This example shows how to classify each time step of sequence data using a generic temporal convolutional network (TCN).
Train Network with Complex-Valued Data
This example shows how to predict the frequency of a complex-valued waveform using a 1-D convolutional neural network.
Sequence Classification Using CNN-LSTM Network
This example shows how to create a 2-D CNN-LSTM network for speech classification tasks by combining a 2-D convolutional neural network (CNN) with a long short-term memory (LSTM) layer.
Train Network on Image and Feature Data
This example shows how to train a network that classifies handwritten digits using both image and feature input data.

Deep Learning with MATLAB

Deep Learning in MATLAB
Discover deep learning capabilities in MATLAB^® using convolutional neural networks for classification and regression, including pretrained networks and transfer learning, and training on GPUs, CPUs, clusters, and clouds.
Deep Learning Tips and Tricks
Learn how to improve the accuracy of deep learning networks.
Data Sets for Deep Learning
Discover data sets for various deep learning tasks.

Featured Examples

Time Series Forecasting Using Deep Learning

Forecast time series data using a long short-term memory (LSTM) network.

Open Live Script

Train Speech Command Recognition Model Using Deep Learning

Train a deep learning model that detects the presence of speech commands in audio. The example uses the Speech Commands Dataset [1] to train a convolutional neural network to recognize a set of commands.

Open Live Script

Chemical Process Fault Detection Using Deep Learning

Use simulation data to train a neural network than can detect faults in a chemical process.

Open Live Script