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Réseaux préentrainés provenant de plateformes externes
Importez des réseaux de neurones à partir de TensorFlow™ 2, de TensorFlow-Keras, de PyTorch®, du format de modèle ONNX™ (Open Neural Network Exchange) et de Caffe. Vous pouvez importer des réseaux de manière interactive avec l’application Deep Network Designer ou en utilisant les fonctions en ligne de commande. L’application génère un rapport d’importation qui fournit des informations à propos de l’importation et indique si certaines mesures doivent être prises. Pour plus d’informations, veuillez consulter Pretrained Deep Neural Networks et Interoperability Between Deep Learning Toolbox, TensorFlow, PyTorch, and ONNX.
Vous devez avoir des support packages pour exécuter les fonctions d’importation dans Deep Learning Toolbox™. Si le support package n’est pas installé, chaque fonction offre un lien de téléchargement vers le support package correspondant dans l’Add-On Explorer. Il est recommandé de télécharger le support package à l’emplacement par défaut de la version de MATLAB® que vous exécutez. Vous pouvez également télécharger directement les support packages à partir des liens suivants.
La fonction
importNetworkFromONNXnécessite Deep Learning Toolbox Converter for ONNX Model Format. Pour télécharger le support package, allez sur https://www.mathworks.com/matlabcentral/fileexchange/67296-deep-learning-toolbox-converter-for-onnx-model-format.La fonction
importNetworkFromPyTorchnécessite Deep Learning Toolbox Converter for PyTorch Models. Pour télécharger le support package, allez sur https://www.mathworks.com/matlabcentral/fileexchange/111925-deep-learning-toolbox-converter-for-pytorch-models.La fonction
importNetworkFromTensorFlownécessite Deep Learning Toolbox Converter for TensorFlow Models. Pour télécharger le support package, allez sur https://www.mathworks.com/matlabcentral/fileexchange/64649-deep-learning-toolbox-converter-for-tensorflow-models.
Applications
| Deep Network Designer | Concevoir et visualiser des réseaux de Deep Learning |
Fonctions
Rubriques
Importation
- Interoperability Between Deep Learning Toolbox, TensorFlow, PyTorch, and ONNX
Learn how to import networks from TensorFlow, 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, 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. (depuis R2023b) - Pretrained Deep Neural Networks
Learn how to download and use pretrained convolutional neural networks for classification, transfer learning and feature extraction. - 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 ONNX Network
This example shows how to verify the adversarial robustness of an imported ONNX™ deep neural network. (depuis R2024a)
Coexécution avec 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®.
Couches personnalisées
- Define Custom Deep Learning Layers
Learn how to define custom deep learning layers.
Informations connexes
- 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
