构建深度神经网络

使用 MATLAB^® 代码或以交互方式使用深度网络设计器为图像数据构建神经网络

通过从头开始定义网络架构，为图像分类和回归等任务创建新的深度网络。使用 MATLAB 或以交互方式使用深度网络设计器构建网络。

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。您可以使用自定义输出层指定自定义损失函数，并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。

对于层图不支持的模型，您可以将自定义模型定义为函数。要了解详细信息，请参阅定义自定义训练循环、损失函数和网络。

App

深度网络设计器

设计、可视化和训练深度学习网络

函数

全部展开

内置层

输入层

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

卷积和全连接层

`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

激活层

`reluLayer`	修正线性单元 (ReLU) 层
`leakyReluLayer`	Leaky Rectified Linear Unit (ReLU) layer
`clippedReluLayer`	Clipped Rectified Linear Unit (ReLU) layer
`eluLayer`	Exponential linear unit (ELU) layer
`tanhLayer`	双曲正切 (tanh) 层
`swishLayer`	Swish layer
`geluLayer`	Gaussian error linear unit (GELU) layer
`sigmoidLayer`	Sigmoid layer
`softmaxLayer`	Softmax 层
`functionLayer`	Function layer

归一化层

`batchNormalizationLayer`	Batch normalization layer
`groupNormalizationLayer`	Group normalization layer
`instanceNormalizationLayer`	Instance normalization layer
`layerNormalizationLayer`	Layer normalization layer
`crossChannelNormalizationLayer`	Channel-wise local response normalization layer

实用工具层

`dropoutLayer`	Dropout layer
`crop2dLayer`	2-D crop layer
`crop3dLayer`	3-D crop layer

池化和去池化层

`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

组合层

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

输出层

`classificationLayer`	分类输出层
`regressionLayer`	回归输出层

构建网络

`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
`analyzeNetwork`	Analyze deep learning network architecture

自定义网络

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

主题

内置层

指定卷积神经网络的层
了解卷积神经网络 (ConvNet) 的层，以及它们在 ConvNet 中出现的顺序。
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。卷积神经网络是深度学习的基本工具，尤其适用于图像识别。
针对回归训练卷积神经网络
此示例说明如何使用卷积神经网络拟合回归模型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。
在 MATLAB 中进行深度学习
通过使用卷积神经网络进行分类和回归来探索 MATLAB 的深度学习能力，包括预训练网络和迁移学习，以及在 GPU、CPU、集群和云上进行训练。
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.

自定义层

定义自定义深度学习层
了解如何定义自定义深度学习层。
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.