Reti preaddestrate da piattaforme esterne

Importare reti preaddestrate da piattaforme esterne di Deep Learning

Importare reti neurali da TensorFlow™ 2, TensorFlow-Keras, PyTorch^®, il formato di modello ONNX™ (Open Neural Network Exchange) e Caffe. 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 importNetworkFromONNX richiede 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 importNetworkFromPyTorch richiede 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.
La funzione importNetworkFromTensorFlow richiede il convertitore Deep Learning Toolbox per i modelli TensorFlow. Per scaricare il pacchetto di supporto, andare a https://www.mathworks.com/matlabcentral/fileexchange/64649-deep-learning-toolbox-converter-for-tensorflow-models.

Diagram showing the interoperability between Deep Learning Toolbox, TensorFlow, ONNX, and PyTorch.

Funzioni

espandi tutto

Importazione della rete

Importazione TensorFlow

importNetworkFromTensorFlow Import TensorFlow network as MATLAB network (Da R2023b)

Importazione PyTorch

importNetworkFromPyTorch Import PyTorch network as MATLAB network (Da R2022b)

Importazione Caffe

`importCaffeNetwork`	Import pretrained convolutional neural network models from Caffe
`importCaffeLayers`	Import convolutional neural network layers from Caffe

Importazione ONNX

importNetworkFromONNX Import ONNX network as MATLAB network (Da R2023b)

Parametri importati da `importONNXFunction`

`ONNXParameters`	Parameters of imported ONNX network for deep learning (Da R2020b)
`freezeParameters`	Convert learnable network parameters in `ONNXParameters` to nonlearnable (Da R2020b)
`unfreezeParameters`	Convert nonlearnable network parameters in `ONNXParameters` to learnable (Da R2020b)
`addParameter`	Add parameter to `ONNXParameters` object (Da R2020b)
`removeParameter`	Remove parameter from `ONNXParameters` object (Da R2020b)

Assemblaggio della rete

`dlnetwork`	Deep learning neural network (Da R2019b)
`functionLayer`	Function layer (Da R2021b)
`replaceLayer`	Replace layer in neural network
`addLayers`	Add layers to neural network
`removeLayers`	Remove layers from neural network

Argomenti

Importazione

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. (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 using importTensorFlowNetwork, 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 using importONNXNetwork, and then compare inference results between ONNX and MATLAB networks.
Classify Images in Simulink with Imported TensorFlow Network
Import a pretrained TensorFlow network using importTensorFlowNetwork, 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. (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.