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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.


Deep Network DesignerDesign, visualize, and train deep learning networks


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Input Layers

imageInputLayerImage input layer
image3dInputLayer3-D image input layer

Convolution and Fully Connected Layers

convolution2dLayer2-D convolutional layer
convolution3dLayer3-D convolutional layer
groupedConvolution2dLayer2-D grouped convolutional layer
transposedConv2dLayerTransposed 2-D convolution layer
transposedConv3dLayerTransposed 3-D convolution layer
fullyConnectedLayerFully connected layer

Activation Layers

reluLayerRectified Linear Unit (ReLU) layer
leakyReluLayerLeaky Rectified Linear Unit (ReLU) layer
clippedReluLayerClipped Rectified Linear Unit (ReLU) layer
eluLayerExponential linear unit (ELU) layer
tanhLayerHyperbolic tangent (tanh) layer
swishLayerSwish layer
geluLayerGaussian error linear unit (GELU) layer
sigmoidLayerSigmoid layer
softmaxLayerSoftmax layer
functionLayerFunction layer

Normalization Layers

batchNormalizationLayerBatch normalization layer
groupNormalizationLayerGroup normalization layer
instanceNormalizationLayerInstance normalization layer
layerNormalizationLayerLayer normalization layer
crossChannelNormalizationLayer Channel-wise local response normalization layer

Utility Layers

dropoutLayerDropout layer
crop2dLayer2-D crop layer
crop3dLayer3-D crop layer

Pooling and Unpooling Layers

averagePooling2dLayerAverage pooling layer
averagePooling3dLayer3-D average pooling layer
globalAveragePooling2dLayer2-D global average pooling layer
globalAveragePooling3dLayer3-D global average pooling layer
globalMaxPooling2dLayerGlobal max pooling layer
globalMaxPooling3dLayer3-D global max pooling layer
maxPooling2dLayerMax pooling layer
maxPooling3dLayer3-D max pooling layer
maxUnpooling2dLayerMax unpooling layer

Combination Layers

additionLayerAddition layer
multiplicationLayerMultiplication layer
concatenationLayerConcatenation layer
depthConcatenationLayerDepth concatenation layer

Output Layers

classificationLayerClassification output layer
regressionLayerRegression output layer
layerGraphGraph of network layers for deep learning
plotPlot neural network architecture
addLayersAdd layers to layer graph or network
removeLayersRemove layers from layer graph or network
replaceLayerReplace layer in layer graph or network
connectLayersConnect layers in layer graph or network
disconnectLayersDisconnect layers in layer graph or network
DAGNetworkDirected acyclic graph (DAG) network for deep learning
resnetLayersCreate 2-D residual network
resnet3dLayersCreate 3-D residual network
isequalCheck equality of deep learning layer graphs or networks
isequalnCheck equality of deep learning layer graphs or networks ignoring NaN values
analyzeNetworkAnalyze deep learning network architecture
dlnetworkDeep learning network for custom training loops
addInputLayerAdd input layer to network
summaryPrint network summary
initializeInitialize learnable and state parameters of a dlnetwork
networkDataLayoutDeep learning network data layout for learnable parameter initialization
checkLayerCheck validity of custom or function layer


Built-In Layers

Custom Layers