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Build Deep Neural Networks

Build neural networks for image data using MATLAB^® code or interactively using Deep Network Designer

Create new deep networks for tasks such as image classification and regression by defining the network architecture from scratch. Build networks using MATLAB or interactively using Deep Network Designer.

For most tasks, you can use built-in layers. If there is not a built-in layer that you need for your task, then you can define your own custom layer. You can specify a custom loss function using a custom output layer and define custom layers with learnable and state parameters. After defining a custom layer, you can check that the layer is valid, GPU compatible, and outputs correctly defined gradients. For a list of supported layers, see List of Deep Learning Layers.

For models that layer graphs do not support, you can define a custom model as a function. To learn more, see Define Custom Training Loops, Loss Functions, and Networks.

Apps

Deep Network Designer

Design, visualize, and train deep learning networks

Functions

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Built-In Layers

Input Layers

`imageInputLayer`	Image input layer
`image3dInputLayer`	3-D image input layer (Since R2019a)

Convolution and Fully Connected Layers

`convolution2dLayer`	2-D convolutional layer
`convolution3dLayer`	3-D convolutional layer (Since R2019a)
`groupedConvolution2dLayer`	2-D grouped convolutional layer (Since R2019a)
`transposedConv2dLayer`	Transposed 2-D convolution layer
`transposedConv3dLayer`	Transposed 3-D convolution layer (Since R2019a)
`fullyConnectedLayer`	Fully connected layer

Transformer Layers

`selfAttentionLayer`	Self-attention layer (Since R2023a)
`positionEmbeddingLayer`	Position embedding layer (Since R2023b)
`sinusoidalPositionEncodingLayer`	Sinusoidal position encoding layer (Since R2023b)
`embeddingConcatenationLayer`	Embedding concatenation layer (Since R2023b)
`indexing1dLayer`	1-D indexing layer (Since R2023b)

Neural ODE Layers

neuralODELayer Neural ODE layer (Since R2023b)

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 (Since R2019a)
`tanhLayer`	Hyperbolic tangent (tanh) layer (Since R2019a)
`swishLayer`	Swish layer (Since R2021a)
`geluLayer`	Gaussian error linear unit (GELU) layer (Since R2022b)
`sigmoidLayer`	Sigmoid layer (Since R2020b)
`softmaxLayer`	Softmax layer
`functionLayer`	Function layer (Since R2021b)

Normalization Layers

`batchNormalizationLayer`	Batch normalization layer
`groupNormalizationLayer`	Group normalization layer (Since R2020b)
`instanceNormalizationLayer`	Instance normalization layer (Since R2021a)
`layerNormalizationLayer`	Layer normalization layer (Since R2021a)
`crossChannelNormalizationLayer`	Channel-wise local response normalization layer

Utility Layers

`dropoutLayer`	Dropout layer
`crop2dLayer`	2-D crop layer
`crop3dLayer`	3-D crop layer (Since R2019b)

Pooling and Unpooling Layers

`averagePooling2dLayer`	Average pooling layer
`averagePooling3dLayer`	3-D average pooling layer (Since R2019a)
`globalAveragePooling2dLayer`	2-D global average pooling layer (Since R2019b)
`globalAveragePooling3dLayer`	3-D global average pooling layer (Since R2019b)
`globalMaxPooling2dLayer`	Global max pooling layer (Since R2020a)
`globalMaxPooling3dLayer`	3-D global max pooling layer (Since R2020a)
`maxPooling2dLayer`	Max pooling layer
`maxPooling3dLayer`	3-D max pooling layer (Since R2019a)
`maxUnpooling2dLayer`	Max unpooling layer

Combination Layers

`additionLayer`	Addition layer
`multiplicationLayer`	Multiplication layer (Since R2020b)
`concatenationLayer`	Concatenation layer (Since R2019a)
`depthConcatenationLayer`	Depth concatenation layer

Output Layers

`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 (Since R2021b)
`resnet3dLayers`	Create 3-D residual network (Since R2021b)
`isequal`	Check equality of deep learning layer graphs or networks (Since R2021a)
`isequaln`	Check equality of deep learning layer graphs or networks ignoring `NaN` values (Since R2021a)
`analyzeNetwork`	Analyze deep learning network architecture

Custom Networks

`dlnetwork`	Deep learning network for custom training loops (Since R2019b)
`addInputLayer`	Add input layer to network (Since R2022b)
`summary`	Print network summary (Since R2022b)
`initialize`	Initialize learnable and state parameters of a `dlnetwork` (Since R2021a)
`networkDataLayout`	Deep learning network data layout for learnable parameter initialization (Since R2022b)
`checkLayer`	Check validity of custom or function layer

Topics

Built-In Layers

Specify Layers of Convolutional Neural Network
Learn about the layers of a convolutional neural network (ConvNet), and the order they appear in a ConvNet.
Create Simple Deep Learning Neural 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.
Build Networks with Deep Network Designer
Interactively build and edit deep learning networks in Deep Network Designer.
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.
Multiple-Input and Multiple-Output Networks
Learn how to define and train deep learning networks with multiple inputs or multiple outputs.
Example Deep Learning Networks Architectures
This example shows how to define simple deep learning neural networks for classification and regression tasks.

Custom Layers

Define Custom Deep Learning Layers
Learn how to define custom deep learning layers.
Define Custom Deep Learning Intermediate Layers
Learn how to define custom deep learning intermediate layers.
Define Custom Deep Learning Output Layers
Learn how to define custom deep learning output layers.
Check Custom Layer Validity
Learn how to check the validity of custom deep learning layers.
Replace Unsupported Keras Layer with Function Layer
This example shows how to import the layers from a pretrained Keras network, replace the unsupported layers with function layers, and assemble the layers into a network ready for prediction.

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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