가지치기
1차 테일러 근사를 사용하여 컨벌루션 계층에서 필터를 가지치기합니다. 그런 다음 가지치기된 신경망에서 C/C++ 또는 CUDA® 코드를 생성할 수 있습니다.
Deep Learning Toolbox™ Model Compression Library에서 사용 가능한 압축 기법에 대한 자세한 개요는 Reduce Memory Footprint of Deep Neural Networks 항목을 참조하십시오.
함수
taylorPrunableNetwork | Neural network suitable for compression using Taylor pruning (R2022a 이후) |
forward | Compute deep learning network output for training |
predict | 추론에 사용할 딥러닝 신경망 출력값 계산 |
updatePrunables | Remove filters from prunable layers based on importance scores (R2022a 이후) |
updateScore | Compute and accumulate Taylor-based importance scores for pruning (R2022a 이후) |
도움말 항목
- 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.
추천 예제
Analyze and Compress 1-D Convolutional Neural Network
Analyze 1-D convolutional network for compression and compress it using Taylor pruning and projection.
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.
Prune and Quantize Semantic Segmentation Network
Reduce the memory footprint of a semantic segmentation network and speed-up inference by compressing the network using pruning and quantization.
