Redes preentrenadas desde plataformas externas
Importe redes y gráficas de capas de TensorFlow™ 2, TensorFlow-Keras, PyTorch®, el formato de modelos ONNX™ (Open Neural Network Exchange) y Caffe. 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 funciones
importONNXNetwork
,importONNXLayers
,importONNXFunction
requieren 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.Las funciones
importTensorFlowNetwork
,importTensorFlowLayers
,importKerasNetwork
yimportKerasLayers
requieren 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.La función
importNetworkFromPyTorch
requiere Deep Learning Toolbox Converter for PyTorch Models. Para descargar el paquete de soporte, vaya a https://www.mathworks.com/matlabcentral/fileexchange/111925.
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. - 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. - Assemble Network from Pretrained Keras Layers
This example shows how to import the layers from a pretrained Keras network, replace the unsupported layers with custom layers, and assemble the layers into a network ready for prediction. - Replace Unsupported Keras Layer with Function Layer
This example shows how to import the layers from a pretrained Keras network, replace the unsupported layers with function layers, and assemble the layers into a network ready for prediction. - 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™. - Select Function to Import ONNX Pretrained Network
Import an ONNX pretrained network usingimportONNXNetwork
,importONNXLayers
, orimportONNXFunction
. - 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.
Capas personalizadas
- Define Custom Deep Learning Layers
Learn how to define custom deep learning layers. - Define Custom Deep Learning Intermediate Layers
Learn how to define custom deep learning intermediate layers. - Define Custom Deep Learning Output Layers
Learn how to define custom deep learning output layers.
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
- https://www.mathworks.com/matlabcentral/fileexchange/61735-deep-learning-toolbox-importer-for-caffe-models