剪枝
使用一阶泰勒逼近从卷积层中对滤波器进行剪枝。然后，您可以从剪枝过的网络生成 C/C++ 或 CUDA® 代码。
有关 Deep Learning Toolbox™ Model Compression Library 中提供的压缩技术的详细概述，请参阅Reduce Memory Footprint of Deep Neural Networks。
函数
taylorPrunableNetwork | Neural network suitable for compression using Taylor pruning (自 R2022a 起) |
forward | Compute deep learning network output for training |
predict | 计算深度学习网络输出以进行推断 |
updatePrunables | Remove filters from prunable layers based on importance scores (自 R2022a 起) |
updateScore | Compute and accumulate Taylor-based importance scores for pruning (自 R2022a 起) |
主题
- 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.
精选示例
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.
