Deep Learning Toolbox Deep Learning Toolbox Model Quantization Library Fixed-Point Designer This example shows how to create a target agnostic, simulatable quantized deep neural network in MATLAB. Target agnostic quantization allows you to see the effect quantization has on your neural network without...
Generate int8 Code for Deep Learning Networks Prune Image Classification Network Using Taylor Scores(Deep Learning Toolbox) To perform quantization, you must install theDeep Learning Toolbox Model Quantization Librarysupport package. Related Topics ...
These functions require the Deep Learning Toolbox Model Quantization Library support package. This support package is a free add-on that you can download using the Add-On Explorer. Alternatively, see Deep Learning Toolbox Model Quantization Library. For an example that shows how to compress a neu...
This app requiresDeep Learning ToolboxModel Quantization Library. To learn about the products required to quantize a deep neural network, seeQuantization Workflow Prerequisites. Open the Deep Network Quantizer App MATLAB command prompt: EnterdeepNetworkQuantizer. ...
Deep Learning Toolbox Model Quantization Library This example shows how to improve the performance of a quantized deep learning model by equalizing layer parameters in the network. Use theequalizeLayersfunction to adjust the compatible network parameters of compute layers in order to make the layers ...
Deep Learning Toolbox Model Quantization Library Parallel Computing Toolbox This example shows how to quantize learnable parameters in the convolution layers of a neural network for GPU and explore the behavior of the quantized network. In this example, you quantize the squeezenet neural network after...
Deep Learning Toolbox Model Quantization Library Load a trained and prunedtaylorPrunableNetworkobject. load("prunedDigitsCustom.mat"); Analyze the network.analyzeNetworkdisplays an interactive plot of the network architecture and a table containing information about the network layers. The table shows th...
(Computer Vision Toolbox) 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. Activity Recognition from Video and Optical Flow Data Using Deep Learning First shows...
2.2. Quantization of deep convolutional neural networks Recent advances in deep learning led to designing more sophisticated models for solving increasingly challenging pattern-recognition problems. Although these models can generalize fairly well and provide high-quality performance, their memory requirements ...
the network size was 40 × 5. The fully-connected network was trained by the MATLAB Deep-learning Toolbox, utilizing the Softmax output function and the logistic regression to supervise the learning. The stochastic noise was made by the dot product of the MATLAB randn matrix and the gray...