Vortrainierte Netze von externen Plattformen
Importieren Sie neuronale Netze von TensorFlow™ 2, TensorFlow-Keras, PyTorch®, dem ONNX™-Modellformat (Open Neural Network Exchange) und Caffe. Sie können Netzwerke interaktiv über die Anwendung Deep Network Designer oder mithilfe der Befehlszeilenfunktionen importieren. Die App generiert einen Importbericht, der Informationen zum Import und etwaigen erforderlichen Aktionen enthält. Weitere Informationen finden Sie unter Pretrained Deep Neural Networks und Interoperability Between Deep Learning Toolbox, TensorFlow, PyTorch, and ONNX.
Sie müssen über Support-Paket verfügen, um die Importfunktionen in Deep Learning Toolbox™ ausführen zu können. Wenn das Support-Paket nicht installiert ist, bietet jede Funktion einen Download-Link zum entsprechenden Support Package im Add-On Explorer. Es wird empfohlen, das Support-Paket an den Standardspeicherort der von Ihnen verwendete MATLAB®-Version herunterzuladen. Sie können die Support-Pakete auch direkt über die folgenden Links herunterladen.
Die Funktion
importNetworkFromONNXerfordert den Deep Learning Toolbox-Konverter für das ONNX-Modellformat. Sie können das Support-Paket unter https://www.mathworks.com/matlabcentral/fileexchange/67296-deep-learning-toolbox-converter-for-onnx-model-format herunterladen.Die Funktion
importNetworkFromPyTorcherfordert den Deep Learning Toolbox-Konverter für PyTorch-Modelle. Sie können das Support-Paket unter https://www.mathworks.com/matlabcentral/fileexchange/111925-deep-learning-toolbox-converter-for-pytorch-models herunterladen.Die Funktion
importNetworkFromTensorFlowerfordert den Deep Learning Toolbox Konverter für TensorFlow-Modelle. Sie können das Support-Paket unter https://www.mathworks.com/matlabcentral/fileexchange/64649-deep-learning-toolbox-converter-for-tensorflow-models herunterladen.
Apps
| Deep Network Designer | Entwurf und Visualisierung von Deep-Learning-Netzen |
Funktionen
Themen
Importieren
- 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. (Seit 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. (Seit R2024a)
Gemeinsame Ausführung mit 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®.
Benutzerdefinierte Schichten
- Define Custom Deep Learning Layers
Learn how to define custom deep learning layers.
Verwandte Informationen
- 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
