内置层

使用内置层构建深度神经网络

对于大多数任务，您可以使用内置层。如果没有您的任务所需的内置层，则可以定义您自己的自定义层。您可以定义具有可学习参数和状态参数的自定义层。定义自定义层后，您可以检查该层是否有效，是否与 GPU 兼容，以及是否输出正确定义的梯度。要查看支持的层的列表，请参阅深度学习层列表。

App

设计和可视化深度学习网络

`convolution1dLayer`	1-D convolutional layer (自 R2021b 起)
`convolution2dLayer`	2-D convolutional layer
`convolution3dLayer`	3-D convolutional layer
`groupedConvolution2dLayer`	2-D grouped convolutional layer
`transposedConv1dLayer`	Transposed 1-D convolution layer (自 R2022a 起)
`transposedConv2dLayer`	Transposed 2-D convolution layer
`transposedConv3dLayer`	Transposed 3-D convolution layer
`fullyConnectedLayer`	Fully connected layer

`lstmLayer`	Long short-term memory (LSTM) layer for recurrent neural network (RNN)
`bilstmLayer`	Bidirectional long short-term memory (BiLSTM) layer for recurrent neural network (RNN)
`gruLayer`	Gated recurrent unit (GRU) layer for recurrent neural network (RNN)
`lstmProjectedLayer`	Long short-term memory (LSTM) projected layer for recurrent neural network (RNN) (自 R2022b 起)
`gruProjectedLayer`	Gated recurrent unit (GRU) projected layer for recurrent neural network (RNN) (自 R2023b 起)

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

`neuralODELayer`	Neural ODE layer (自 R2023b 起)
`deep.ode.options.ODE1`	Neural ODE solver options for nonstiff differential equations using Euler method (自 R2025a 起)
`deep.ode.options.ODE45`	Neural ODE solver options for nonstiff differential equations (自 R2025a 起)

`reluLayer`	修正线性单元 (ReLU) 层
`leakyReluLayer`	Leaky Rectified Linear Unit (ReLU) layer
`preluLayer`	Parametrized Rectified Linear Unit (PReLU) layer (自 R2024a 起)
`clippedReluLayer`	Clipped Rectified Linear Unit (ReLU) layer
`eluLayer`	Exponential linear unit (ELU) layer
`tanhLayer`	双曲正切 (tanh) 层
`swishLayer`	Swish layer (自 R2021a 起)
`geluLayer`	Gaussian error linear unit (GELU) layer (自 R2022b 起)
`softmaxLayer`	Softmax 层
`sigmoidLayer`	sigmoid 层
`softplusLayer`	Softplus layer
`complexReluLayer`	Complex rectified linear unit (ReLU) layer (自 R2025a 起)
`functionLayer`	Function layer (自 R2021b 起)

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

`averagePooling1dLayer`	1-D average pooling layer (自 R2021b 起)
`averagePooling2dLayer`	Average pooling layer
`averagePooling3dLayer`	3-D average pooling layer
`adaptiveAveragePooling2dLayer`	Adaptive average pooling 2-D layer (自 R2024a 起)
`globalAveragePooling1dLayer`	1-D global average pooling layer (自 R2021b 起)
`globalAveragePooling2dLayer`	2-D global average pooling layer
`globalAveragePooling3dLayer`	3-D global average pooling layer
`globalMaxPooling1dLayer`	1-D global max pooling layer (自 R2021b 起)
`globalMaxPooling2dLayer`	Global max pooling layer
`globalMaxPooling3dLayer`	3-D global max pooling layer
`maxPooling1dLayer`	1-D max pooling layer (自 R2021b 起)
`maxPooling2dLayer`	Max pooling layer
`maxPooling3dLayer`	3-D max pooling layer
`maxUnpooling2dLayer`	Max unpooling layer

`dlnetwork`	Deep learning neural network
`imagePretrainedNetwork`	适用于图像的预训练神经网络 (自 R2024a 起)
`resnetNetwork`	2-D residual neural network (自 R2024a 起)
`resnet3dNetwork`	3-D residual neural network (自 R2024a 起)
`dag2dlnetwork`	Convert `SeriesNetwork` and `DAGNetwork` to `dlnetwork` (自 R2024a 起)

`addLayers`	向神经网络添加层
`removeLayers`	Remove layers from neural network
`replaceLayer`	Replace layer in neural network
`getLayer`	Look up a layer by name or path (自 R2024a 起)
`connectLayers`	在神经网络中连接各层
`disconnectLayers`	Disconnect layers in neural network
`expandLayers`	Expand network layers (自 R2024a 起)
`groupLayers`	Group layers into network layers (自 R2024a 起)
`addInputLayer`	Add input layer to network (自 R2022b 起)

`initialize`	Initialize learnable and state parameters of neural network (自 R2021a 起)
`networkDataLayout`	Deep learning network data layout for learnable parameter initialization (自 R2022b 起)
`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

`plot`	绘制神经网络架构
`summary`	打印网络摘要 (自 R2022b 起)
`analyzeNetwork`	Analyze deep learning network architecture
`checkLayer`	Check validity of custom or function layer
`isequal`	Check equality of neural networks (自 R2021a 起)
`isequaln`	Check equality of neural networks ignoring `NaN` values (自 R2021a 起)

长短期记忆神经网络
了解长短期记忆 (LSTM) 神经网络。
创建简单的深度学习神经网络以用于分类
此示例说明如何创建和训练简单的卷积神经网络来进行深度学习分类。
针对回归训练卷积神经网络
此示例说明如何训练卷积神经网络来预测手写数字的旋转角度。
深度学习层列表
探索 MATLAB^® 中的所有深度学习层。
使用深度网络设计器构建网络
在深度网络设计器中以交互方式构建和编辑深度学习网络。
Create and Train Network with Nested Layers
This example shows how to create and train a network with nested layers using network layers. (自 R2024a 起)
Example Deep Learning Networks Architectures
This example shows how to define simple deep learning neural networks for classification and regression tasks.
Choose an AI Model
Explore options for choosing an AI model.
从深度网络设计器生成 MATLAB 代码
生成 MATLAB 代码，以便在深度网络设计器中重新创建设计网络。
Multiple-Input and Multiple-Output Networks
Learn how to define and train deep learning networks with multiple inputs or multiple outputs.