가지치기
R2026b가지치기는 컨벌루션 계층에서 중요도가 가장 낮은 필터를 제거하여 신경망의 학습 가능한 파라미터 개수를 줄입니다.
trainnet 함수를 사용하여 신경망을 훈련시킬 수 있으면 compressNetworkUsingTaylorPruning 함수를 사용하여 신경망을 가지치기하십시오. trainnet 함수를 사용하여 신경망을 훈련시킬 수 없는 경우에는 대신 taylorPrunableNetwork 객체를 사용하여 사용자 지정 가지치기 루프를 만드십시오.
Deep Learning Toolbox™ Model Compression Library에서 사용 가능한 압축 기법에 대한 자세한 개요는 Reduce Memory Footprint of Deep Neural Networks 항목을 참조하십시오.
함수
도움말 항목
- Prune Neural Network with Memory Requirement
This example shows how to compress a neural network to a specific size using Taylor pruning. (R2026a 이후)
- Prune Neural Network with Accuracy Requirement
This example shows how to compress a neural network with a minimum accuracy requirement using Taylor pruning. (R2026a 이후)
- Prune Image Classification Network Using Taylor Scores
Reduce the size of a deep neural network using Taylor pruning.
- Prune Filters in a Detection Network Using Taylor Scores
Reduce network size and increase inference speed by pruning convolutional filters in a you only look once (YOLO) v3 object detection network.
- Prune and Quantize Convolutional Neural Network for Speech Recognition
Compress a convolutional neural network (CNN) to prepare it for deployment on an embedded system.
- Parameter Pruning and Quantization of Image Classification Network
Use parameter pruning and quantization to reduce network size.
추천 예제
Prune Neural Network with Memory Requirement
Compress a neural network to a specific size using Taylor pruning.
Prune Neural Network with Accuracy Requirement
Compress a neural network with a minimum accuracy requirement using Taylor pruning.
Analyze and Compress 1-D Convolutional Neural Network
Analyze 1-D convolutional network for compression and compress it using Taylor pruning and projection.
