Pruning
Prune filters from convolution layers by using first-order Taylor approximation. You can then generate C/C++ or CUDA® code from the pruned network.
For a detailed overview of the compression techniques available in Deep Learning Toolbox™ Model Compression Library, see Reduce Memory Footprint of Deep Neural Networks.
Functions
taylorPrunableNetwork | Neural network suitable for compression using Taylor pruning (Since R2022a) |
forward | Compute deep learning network output for training |
predict | Compute deep learning network output for inference |
updatePrunables | Remove filters from prunable layers based on importance scores (Since R2022a) |
updateScore | Compute and accumulate Taylor-based importance scores for pruning (Since R2022a) |
Topics
- 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.
Featured Examples
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.
