Redes preentrenadas desde plataformas externas
Importe redes neuronales de TensorFlow™ 2, TensorFlow-Keras, PyTorch®, del formato de modelos ONNX™ (Open Neural Network Exchange) y de Caffe. Puede importar redes de forma interactiva utilizando la app Deep Network Designer o utilizando funciones de la línea de comandos. La app genera un informe sobre la importación que también indica si es necesario realizar alguna acción. Para obtener más información, consulte Redes neuronales profundas preentrenadas y Interoperability Between Deep Learning Toolbox, TensorFlow, PyTorch, and ONNX.
Debe disponer de paquetes de soporte para ejecutar las funciones de importación en Deep Learning Toolbox™. Si el paquete de soporte no está instalado, cada función proporciona un enlace de descarga al paquete de soporte correspondiente en Add-On Explorer. La práctica recomendada es descargar el paquete de soporte en la ubicación predeterminada para la versión de MATLAB® que está ejecutando. También puede descargar directamente los paquetes de soporte desde los siguientes enlaces.
La función
importNetworkFromONNXrequiere Deep Learning Toolbox Converter for ONNX Model Format. Para descargar el paquete de soporte, vaya a https://www.mathworks.com/matlabcentral/fileexchange/67296-deep-learning-toolbox-converter-for-onnx-model-format.La función
importNetworkFromPyTorchrequiere Deep Learning Toolbox Converter for PyTorch Models. Para descargar el paquete de soporte, vaya a https://www.mathworks.com/matlabcentral/fileexchange/111925-deep-learning-toolbox-converter-for-pytorch-models.La función
importNetworkFromTensorFlowrequiere Deep Learning Toolbox Converter for TensorFlow Models. Para descargar el paquete de soporte, vaya a https://www.mathworks.com/matlabcentral/fileexchange/64649-deep-learning-toolbox-converter-for-tensorflow-models.
Apps
| Deep Network Designer | Diseñar y visualizar redes de deep learning |
Funciones
Temas
Importación
- 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. (Desde R2023b) - Redes neuronales profundas preentrenadas
Aprenda a descargar y utilizar redes neuronales convolucionales preentrenadas para clasificación, transferencia del aprendizaje y extracción de características. - 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. (Desde R2024a)
Coejecución de 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®.
Capas personalizadas
- Definir capas de deep learning personalizadas
Aprenda a definir capas de deep learning personalizadas.
Información relacionada
- 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
