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。

Check Custom Layer Validity

Learn how to check the validity of custom deep learning layers.

Define Custom Deep Learning Layer with Learnable Parameters

This example shows how to define a PReLU 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 Classification Output Layer

This example shows how to define a custom classification output layer with sum of squares error (SSE) loss and use it in a convolutional neural network.

Define Custom Weighted Classification Layer

This example shows how to define and create a custom weighted classification output layer with weighted cross entropy loss.

Define Custom Regression Output Layer

This example shows how to define a custom regression output layer with mean absolute error (MAE) loss and use it in a convolutional neural network.

Specify Custom Layer Backward Function

This example shows how to define a PReLU layer and specify a custom backward function.

Specify Custom Output Layer Backward Loss Function

This example shows how to define a weighted classification layer and specify a custom backward loss function.

网络训练与整合

Train Generative Adversarial Network (GAN)

This example shows how to train a generative adversarial network (GAN) to generate images.

Train Conditional Generative Adversarial Network (CGAN)

This example shows how to train a conditional generative adversarial network (CGAN) to generate images.

Train a Siamese Network for Dimensionality Reduction

This example shows how to train a Siamese network to compare handwritten digits using dimensionality reduction.

Train a Siamese Network to Compare Images

This example shows how to train a Siamese network to identify similar images of handwritten characters.

Define Custom Training Loops, Loss Functions, and Networks

Learn how to define and customize deep learning training loops, loss functions, and networks using automatic differentiation.

Specify Training Options in Custom Training Loop

Learn how to specify common training options in a custom training loop.

Train Network Using Custom Training Loop

This example shows how to train a network that classifies handwritten digits with a custom learning rate schedule.

Update Batch Normalization Statistics in Custom Training Loop

This example shows how to update the network state in a custom training loop.

Make Predictions Using dlnetwork Object

This example shows how to make predictions using a dlnetwork object by splitting data into mini-batches.

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 a dlnetwork.

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.

比较层权重初始化函数

此示例说明如何使用不同权重初始化函数来训练深度学习网络。

指定自定义权重初始化函数

此示例说明如何为后跟泄漏 ReLU 层的卷积层创建自定义 He 权重初始化函数。

基于预训练的 Keras 层组合网络

此示例说明如何从预训练的 Keras 网络中导入层、用自定义层替换不支持的层，以及将各层组合成可以进行预测的网络。

多输入和多输出网络

Multiple-Input and Multiple-Output Networks

Learn how to define and train deep learning networks with multiple inputs or multiple outputs.

Train Network with Multiple Outputs

This example shows how to train a deep learning network with multiple outputs that predict both labels and angles of rotations of handwritten digits.

Assemble Multiple-Output Network for Prediction

This example shows how to assemble a multiple output network for prediction.

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 support dlarray objects.

Grad-CAM Reveals the Why Behind Deep Learning Decisions

This example shows how to use the gradient-weighted class activation mapping (Grad-CAM) technique to understand why a deep learning network makes its classification decisions.