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 cannot be specified as networks of layers, you can define the model as a function. To learn more, see Define Custom Training Loops, Loss Functions, and Networks.

Apps

Deep Network Designer

Design and visualize deep learning networks

Funktionen

alle erweitern

Built-In 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

Transformer Layers

`selfAttentionLayer`	Self-attention layer (Seit R2023a)
`attentionLayer`	Dot-product attention layer (Seit R2024a)
`positionEmbeddingLayer`	Position embedding layer (Seit R2023b)
`sinusoidalPositionEncodingLayer`	Sinusoidal position encoding layer (Seit R2023b)
`embeddingConcatenationLayer`	Embedding concatenation layer (Seit R2023b)
`indexing1dLayer`	1-D indexing layer (Seit R2023b)

Neural ODE Layers

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

Normalization Layers

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

Utility Layers

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

Pooling and Unpooling Layers

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

Combination Layers

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

Network Building

`dlnetwork`	Deep learning neural network (Seit R2019b)
`imagePretrainedNetwork`	Pretrained neural network for images (Seit R2024a)
`resnetNetwork`	2-D residual neural network (Seit R2024a)
`resnet3dNetwork`	3-D residual neural network (Seit R2024a)
`addLayers`	Add layers to neural network
`removeLayers`	Remove layers from neural network
`replaceLayer`	Replace layer in neural network
`connectLayers`	Connect layers in neural network
`disconnectLayers`	Disconnect layers in neural network
`addInputLayer`	Add input layer to network (Seit R2022b)
`initialize`	Initialize learnable and state parameters of a `dlnetwork` (Seit R2021a)
`networkDataLayout`	Deep learning network data layout for learnable parameter initialization (Seit R2022b)
`setL2Factor`	Set L₂ regularization factor of layer learnable parameter
`getL2Factor`	Get L₂ regularization factor of layer learnable parameter
`setLearnRateFactor`	Set learn rate factor of layer learnable parameter
`getLearnRateFactor`	Get learn rate factor of layer learnable parameter

Network Information

`plot`	Plot neural network architecture
`summary`	Print network summary (Seit R2022b)
`analyzeNetwork`	Analyze deep learning network architecture
`checkLayer`	Check validity of custom or function layer
`isequal`	Check equality of neural networks (Seit R2021a)
`isequaln`	Check equality of neural networks ignoring `NaN` values (Seit R2021a)

Themen

Built-In Layers

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 train a convolutional neural network 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.
Check Custom Layer Validity
Learn how to check the validity of custom deep learning layers.

Enthaltene Beispiele

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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