自定义层

为深度学习定义自定义层

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

`functionLayer`	Function layer (自 R2021b 起)
`checkLayer`	Check validity of custom or function layer
`setLearnRateFactor`	Set learn rate factor of layer learnable parameter
`setL2Factor`	Set L₂ regularization factor of layer learnable parameter
`getLearnRateFactor`	Get learn rate factor of layer learnable parameter
`getL2Factor`	Get L₂ regularization factor of layer learnable parameter
`networkDataLayout`	Deep learning network data layout for learnable parameter initialization (自 R2022b 起)

`dlnetwork`	Deep learning neural network
`findPlaceholderLayers`	Find placeholder layers in network architecture imported from Keras or ONNX
`replaceLayer`	Replace layer in neural network
`PlaceholderLayer`	Layer replacing an unsupported Keras or ONNX layer

定义自定义深度学习层
了解如何定义自定义深度学习层。
Check Custom Layer Validity
Learn how to check the validity of custom deep learning layers.
View Autogenerated Custom Layers Using Deep Network Designer
This example shows how to import a pretrained TensorFlow™ network and view the autogenerated layers in Deep Network Designer.

Define Custom Deep Learning Layer with Learnable Parameters
This example shows how to define a SReLU layer and use it in a convolutional neural network.
Define Custom Deep Learning Layer with Multiple Inputs
This example shows how to define a custom weighted addition layer and use it in a convolutional neural network.
Define Custom Deep Learning Layer with Formatted Inputs
This example shows how to define a custom layer with formatted dlarray inputs.
Define Custom Recurrent Deep Learning Layer
This example shows how to define a peephole LSTM layer and use it in a neural network.
Specify Custom Layer Backward Function
This example shows how to define a SReLU layer and specify a custom backward function.
Custom Layer Function Acceleration
Accelerate custom layer forward and predict functions by caching and reusing traces.
Define Custom Deep Learning Layer for Code Generation
This example shows how to define a SReLU layer that supports code generation.

Deep Learning Network Composition
Define custom layers that contain neural networks.
Define Nested Deep Learning Layer Using Network Composition
This example shows how to define a nested custom deep learning layer.
Train Network with Custom Nested Layers
This example shows how to create and train a network with nested layers defined using network composition.
Weight Tying Using Nested Layer
This example shows how to implement weight tying using a nested layer.

Convert a conventional convolutional neural network (CNN) to a spiking neural network (SNN).

Train a Bayesian neural network (BNN) for image regression using Bayes by Backpropagation.

此示例说明如何训练深度学习变分自编码器 (VAE) 来生成图像。