Reti preaddestrate da piattaforme esterne
Importare reti neurali da TensorFlow™ 2, TensorFlow-Keras, Keras 3, PyTorch®, il formato di modello ONNX™ (Open Neural Network Exchange) e Caffe. È possibile importare le reti in modo interattivo utilizzando l'app Deep Network Designer o le funzioni dalla riga di comando. L'app genera un report di importazione che include informazioni sull'importazione e su eventuali azioni richieste. Per ulteriori informazioni, vedere Reti neurali profonde preaddestrate e Interoperability Between Deep Learning Toolbox, TensorFlow, PyTorch, and ONNX.
Per eseguire le funzioni di importazione in Deep Learning Toolbox™ è necessario disporre dei pacchetti di supporto. Se il pacchetto di supporto non è installato, ciascuna funzione fornisce un link per il download del pacchetto di supporto corrispondente nell'Add-On Explorer. Si consiglia di scaricare il pacchetto di supporto nella posizione predefinita per la versione MATLAB® in uso. È inoltre possibile scaricare direttamente i pacchetti di supporto dai seguenti link.
La funzione
importNetworkFromONNXrichiede Deep Learning Toolbox Converter for ONNX Model Format. Per scaricare il pacchetto di supporto, andare a https://www.mathworks.com/matlabcentral/fileexchange/67296-deep-learning-toolbox-converter-for-onnx-model-format.La funzione
importNetworkFromPyTorchrichiede il convertitore Deep Learning Toolbox per i modelli PyTorch. Per scaricare il pacchetto di supporto, andare a https://www.mathworks.com/matlabcentral/fileexchange/111925-deep-learning-toolbox-converter-for-pytorch-models.Le funzioni
importNetworkFromTensorFloweimportNetworkFromKerasrichiedono Deep Learning Toolbox Converter for TensorFlow Models. Per scaricare il pacchetto di supporto, andare a https://www.mathworks.com/matlabcentral/fileexchange/64649-deep-learning-toolbox-converter-for-tensorflow-models.
App
| Deep Network Designer | Progettare e visualizzare reti di Deep Learning |
Funzioni
Argomenti
Importazione
- Interoperability Between Deep Learning Toolbox, TensorFlow, PyTorch, and ONNX
Learn how to import networks from TensorFlow, Keras 3, PyTorch, and ONNX and use the imported networks for common Deep Learning Toolbox workflows. Learn how to export networks to TensorFlow and ONNX. - Tips on Importing Models from TensorFlow, PyTorch, and ONNX
Tips on importing Deep Learning Toolbox networks from TensorFlow, Keras, PyTorch, and ONNX. - Import PyTorch Model Using Deep Network Designer
This example shows how to import a PyTorch® model interactively by using the Deep Network Designer app. (Da R2023b) - Reti neurali profonde preaddestrate
Apprendere come scaricare e utilizzare le reti neurali convoluzionali preaddestrate per la classificazione, il transfer learning e l’estrazione di feature. - Inference Comparison Between TensorFlow and Imported Networks for Image Classification
Perform prediction in TensorFlow with a pretrained network, import the network into MATLAB usingimportTensorFlowNetwork, and then compare inference results between TensorFlow and MATLAB networks. - Inference Comparison Between ONNX and Imported Networks for Image Classification
Perform prediction in ONNX with a pretrained network, import the network into MATLAB usingimportONNXNetwork, and then compare inference results between ONNX and MATLAB networks. - Classify Images in Simulink with Imported TensorFlow Network
Import a pretrained TensorFlow network usingimportTensorFlowNetwork, and then use the Predict block for image classification in Simulink®. - Deploy Imported TensorFlow Model with MATLAB Compiler
Import third-party pretrained networks and deploy the networks using MATLAB Compiler™. - View Autogenerated Custom Layers Using Deep Network Designer
This example shows how to import a pretrained TensorFlow™ network and view the autogenerated layers in Deep Network Designer. - Verify Robustness of Imported ONNX Network
This example shows how to verify the adversarial robustness of an imported ONNX™ deep neural network. (Da R2024a)
Esecuzione simultanea di Python
- Classify Images Using TensorFlow Model Predict Block
Classify images using TensorFlow Model Predict block. - Classify Images Using ONNX Model Predict Block
Classify images using ONNX Model Predict block. - Classify Images Using PyTorch Model Predict Block
Classify images using PyTorch Model Predict block. - Predict Responses Using TensorFlow Model Predict Block
Predict Responses Using TensorFlow Model Predict block. - Predict Responses Using ONNX Model Predict Block
Predict Responses Using ONNX Model Predict block. - Predict Responses Using PyTorch Model Predict Block
Predict Responses Using PyTorch Model Predict block. - Predict Responses Using Custom Python Model in Simulink (Statistics and Machine Learning Toolbox)
This example shows how to use the Custom Python Model Predict (Statistics and Machine Learning Toolbox) block for prediction in Simulink®.
Livelli personalizzati
- Define Custom Deep Learning Layers
Learn how to define custom deep learning layers.
Informazioni complementari
- https://www.mathworks.com/matlabcentral/fileexchange/67296-deep-learning-toolbox-converter-for-onnx-model-format
- https://www.mathworks.com/matlabcentral/fileexchange/64649-deep-learning-toolbox-converter-for-tensorflow-models
- https://www.mathworks.com/matlabcentral/fileexchange/111925-deep-learning-toolbox-converter-for-pytorch-models
- https://www.mathworks.com/matlabcentral/fileexchange/61735-deep-learning-toolbox-importer-for-caffe-models
