Deep Networks for Images

Create deep neural networks and train from scratch

Create new deep networks for image classification and regression tasks by defining the network architecture and training the network from scratch.

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

You can train a convolutional 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 deep learning neural network
`analyzeNetwork`	Analyze deep learning network architecture

Layers

Input Layers

`imageInputLayer`	Image input layer
`image3dInputLayer`	3-D image input layer

Convolution and Fully Connected Layers

`convolution2dLayer`	2-D convolutional layer
`convolution3dLayer`	3-D convolutional layer
`groupedConvolution2dLayer`	2-D grouped convolutional layer
`transposedConv2dLayer`	Transposed 2-D convolution layer
`transposedConv3dLayer`	Transposed 3-D convolution layer
`fullyConnectedLayer`	Fully connected layer

Activation Layers

`reluLayer`	Rectified Linear Unit (ReLU) layer
`leakyReluLayer`	Leaky Rectified Linear Unit (ReLU) layer
`clippedReluLayer`	Clipped Rectified Linear Unit (ReLU) layer
`eluLayer`	Exponential linear unit (ELU) layer
`tanhLayer`	Hyperbolic tangent (tanh) layer
`swishLayer`	Swish layer
`geluLayer`	Gaussian error linear unit (GELU) layer
`functionLayer`	Function layer

Normalization, Dropout, and Cropping Layers

`batchNormalizationLayer`	Batch normalization layer
`groupNormalizationLayer`	Group normalization layer
`instanceNormalizationLayer`	Instance normalization layer
`layerNormalizationLayer`	Layer normalization layer
`crossChannelNormalizationLayer`	Channel-wise local response normalization layer
`dropoutLayer`	Dropout layer
`crop2dLayer`	2-D crop layer
`crop3dLayer`	3-D crop layer

Pooling and Unpooling Layers

`averagePooling2dLayer`	Average pooling layer
`averagePooling3dLayer`	3-D average pooling layer
`globalAveragePooling2dLayer`	2-D global average pooling layer
`globalAveragePooling3dLayer`	3-D global average pooling layer
`globalMaxPooling2dLayer`	Global max pooling layer
`globalMaxPooling3dLayer`	3-D global max pooling layer
`maxPooling2dLayer`	Max pooling layer
`maxPooling3dLayer`	3-D max pooling layer
`maxUnpooling2dLayer`	Max unpooling layer

Combination Layers

`additionLayer`	Addition layer
`multiplicationLayer`	Multiplication layer
`concatenationLayer`	Concatenation layer
`depthConcatenationLayer`	Depth concatenation layer

Output Layers

`sigmoidLayer`	Sigmoid layer
`softmaxLayer`	Softmax layer
`classificationLayer`	Classification output layer
`regressionLayer`	Regression output layer

Build Network

`layerGraph`	Graph of network layers for deep learning
`plot`	Plot neural network architecture
`addLayers`	Add layers to layer graph or network
`removeLayers`	Remove layers from layer graph or network
`replaceLayer`	Replace layer in layer graph or network
`connectLayers`	Connect layers in layer graph or network
`disconnectLayers`	Disconnect layers in layer graph or network
`DAGNetwork`	Directed acyclic graph (DAG) network for deep learning
`resnetLayers`	Create 2-D residual network
`resnet3dLayers`	Create 3-D residual network
`isequal`	Check equality of deep learning layer graphs or networks
`isequaln`	Check equality of deep learning layer graphs or networks ignoring `NaN` values

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
`confusionchart`	Create confusion matrix chart for classification problem
`sortClasses`	Sort classes of confusion matrix chart

Blocks

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Deep Neural Networks

Predict	Predict responses using a trained deep learning neural network
Image Classifier	Classify data using a trained deep learning neural network

Topics

Create Simple Deep Learning Network for Classification
This example shows how to create and train a simple convolutional neural network for deep learning classification.
Train Convolutional Neural Network for Regression
This example shows how to fit a regression model using convolutional neural networks to predict the angles of rotation of handwritten digits.
List of Deep Learning Layers
Discover all the deep learning layers in MATLAB^®.
Specify Layers of Convolutional Neural Network
Learn about the layers of a convolutional neural network (ConvNet), and the order they appear in a ConvNet.
Build Networks with Deep Network Designer
Interactively build and edit deep learning networks in Deep Network Designer.
Set Up Parameters and Train Convolutional Neural Network
Learn how to set up training parameters for a convolutional neural network.
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.
Example Deep Learning Networks Architectures
This example shows how to define simple deep learning neural networks for various tasks.

Featured Examples

Train Residual Network for Image Classification

Create a deep learning neural network with residual connections and train it on CIFAR-10 data. Residual connections are a popular element in convolutional neural network architectures. Using residual connections improves gradient flow through the network and enables training of deeper networks.

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Convert Classification Network into Regression Network

Convert a trained classification network into a regression network.

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Train Network on Image and Feature Data

Train a network that classifies handwritten digits using both image and feature input data.

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Image-to-Image Regression in Deep Network Designer

Use Deep Network Designer to construct and train an image-to-image regression network for super resolution.

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Multilabel Image Classification Using Deep Learning

Use transfer learning to train a deep learning model for multilabel image classification.

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Lane and Vehicle Detection in Simulink Using Deep Learning

Use deep convolutional neural networks inside a Simulink® model to perform lane and vehicle detection. This example takes the frames from a traffic video as an input, outputs two lane boundaries that correspond to the left and right lanes of the ego vehicle, and detects vehicles in the frame.

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Classify ECG Signals in Simulink Using Deep Learning

Use wavelet transforms and a deep learning network within a Simulink (R) model to classify ECG signals. This example uses the pretrained convolutional neural network from the Classify Time Series Using Wavelet Analysis and Deep Learning example of the Wavelet Toolbox™ to classify ECG signals based on images from the CWT of the time series data. For information on training, see Classify Time Series Using Wavelet Analysis and Deep Learning (Wavelet Toolbox).

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