操作
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。要了解详细信息，请参阅定义自定义深度学习层。要查看支持的层的列表，请参阅深度学习层列表。
使用深度学习运算为自定义层、训练循环和模型函数开发 MATLAB® 代码。
函数
主题
自动微分
- Automatic Differentiation Background
Learn how automatic differentiation works. - Use Automatic Differentiation In Deep Learning Toolbox
How to use automatic differentiation in deep learning. - List of Functions with dlarray Support
View the list of functions that supportdlarrayobjects. - Define Custom Deep Learning Operations
Learn how to define custom deep learning operation. (自 R2024a 起) - Specify Custom Operation Backward Function
This example shows how to define the SReLU operation as a differentiable function and specify a custom backward function. (自 R2024a 起) - Train Model Using Custom Backward Function
This example shows how to train a deep learning model that contains an operation with a custom backward function. - Create Bidirectional LSTM (BiLSTM) Function
This example shows how to create a bidirectional long-short term memory (BiLSTM) function for custom deep learning functions. (自 R2023b 起)
模型函数
- Train Network Using Model Function
This example shows how to create and train a deep learning network by using functions rather than a layer graph or adlnetwork. - Update Batch Normalization Statistics Using Model Function
This example shows how to update the network state in a network defined as a function. - Make Predictions Using Model Function
This example shows how to make predictions using a model function by splitting data into mini-batches. - Initialize Learnable Parameters for Model Function
Learn how to initialize learnable parameters for custom training loops using a model function.
深度学习函数加速
- Deep Learning Function Acceleration
Accelerate model functions and model loss functions by caching and reusing traces. - Accelerate Custom Training Loop Functions
This example shows how to accelerate deep learning custom training loop and prediction functions. - 检查加速深度学习函数输出
此示例说明如何检查加速函数的输出是否与底层函数的输出匹配。 - Evaluate Performance of Accelerated Deep Learning Function
This example shows how to evaluate the performance gains of using an accelerated function.
相关信息
精选示例
Train a Twin Network for Dimensionality Reduction
Train a twin neural network with shared weights to compare handwritten digits using dimensionality reduction.
Train a Twin Neural Network to Compare Images
Train a twin neural network with shared weights to identify similar images of handwritten characters.
Image Captioning Using Attention
Train a deep learning model for image captioning using attention.
Sequence-to-Sequence Translation Using Attention
Convert decimal strings to Roman numerals using a recurrent sequence-to-sequence encoder-decoder model with attention.
使用物理信息神经网络求解 PDE
此示例说明如何训练物理信息神经网络 (PINN) 来预测偏微分方程 (PDE) 的解。
Dynamical System Modeling Using Neural ODE
Train a neural network with neural ordinary differential equations (ODEs) to learn the dynamics of a physical system.
Node Classification Using Graph Convolutional Network
Classify nodes in a graph using a graph convolutional network (GCN).
Multilabel Graph Classification Using Graph Attention Networks
Classify graphs that have multiple independent labels using graph attention networks (GATs).
Train Latent ODE Network with Irregularly Sampled Time-Series Data
Train a latent ordinary differential equation (ODE) autoencoder with time-series data that is sampled at irregular time intervals.
Multivariate Time Series Anomaly Detection Using Graph Neural Network
Detect anomalies in multivariate time series data using a graph neural network (GNN).
