사용자 지정 계층

사용자 지정 딥러닝 계층 정의하기

사용자 지정 딥러닝 계층을 정의하는 방법을 알아봅니다.

Check Custom Layer Validity

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

학습 가능한 파라미터를 갖는 사용자 지정 딥러닝 계층 정의하기

이 예제에서는 PReLU 계층을 정의하고 이를 컨벌루션 신경망에서 사용하는 방법을 보여줍니다.

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.

사용자 지정 회귀 출력 계층 정의하기

이 예제에서는 절대 오차 평균(MAE) 손실이 있는 사용자 지정 회귀 출력 계층을 정의하고 이를 컨벌루션 신경망에서 사용하는 방법을 보여줍니다.

네트워크 훈련 및 조합

Train Generative Adversarial Network (GAN)

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

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.

Make Predictions Using Model Function

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

Specify Custom Weight Initialization Function

This example shows how to create a custom He weight initialization function for convolution layers followed by leaky ReLU layers.

사전 훈련된 Keras 계층에서 네트워크 조합하기

이 예제에서는 사전 훈련된 Keras 네트워크에서 계층을 가져오고 지원되지 않는 계층을 사용자 지정 계층으로 바꾼 다음 이러한 계층을 예측을 실행할 준비가 된 네트워크로 조합하는 방법을 보여줍니다.

MIMO(다중 입력 다중 출력) 네트워크

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

Instead of using the model function for prediction, you can assemble the network into a DAGNetwork ready for prediction using the functionToLayerGraph and assembleNetwork functions.

자동 미분

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.