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Interoperability Between Deep Learning Toolbox, TensorFlow, PyTorch, and ONNX

This topic provides an overview of the Deep Learning Toolbox™ import and export functions and describes common deep learning workflows that you can perform in MATLAB® with an imported network from TensorFlow™, PyTorch®, or ONNX™. For more information about network import, see Tips on Importing Models from TensorFlow, PyTorch, and ONNX.

Many pretrained networks are available in Deep Learning Toolbox. For more information, see Pretrained Deep Neural Networks. However, MATLAB does not stand alone in the deep learning ecosystem. Use the import and export functions to access models available in open-source repositories and collaborate with colleagues who work in other deep learning frameworks.

Support Packages for Interoperability

You must have the relevant support packages to run the Deep Learning Toolbox import and export functions. If the support package is not installed, each function provides a download link to the corresponding support package in the Add-On Explorer. A recommended practice is to download the support package to the default location for the version of MATLAB you are running. You can also directly download the support packages from File Exchange.

This table lists the Deep Learning Toolbox support packages for import and export, the File Exchange links, and the functions each support package provides.

By using ONNX as an intermediate format, you can interoperate with other deep learning frameworks that support ONNX model export or import.

Functions that Import Deep Learning Networks

Import networks from TensorFlow and ONNX

External Deep Learning Platforms and Import Functions

This table describes the external deep learning platforms and model formats that the Deep Learning Toolbox functions can import.

External Deep Learning PlatformModel FormatImport Model as Network
TensorFlow 2 or TensorFlow-KerasSavedModel formatimportNetworkFromTensorFlow
PyTorchTraced model file with the .pt extensionimportNetworkFromPyTorch
ONNXONNX model formatimportNetworkFromONNX

The importNetworkFromPyTorch function reference page contains a Limitations section which describes the supported versions of PyTorch, see Limitations. The TensorFlow support package File Exchange page lists the supported versions of TensorFlow, see Deep Learning Toolbox Converter for TensorFlow Models.

Objects Returned by Import Functions

This table describes the Deep Learning Toolbox network object that the import functions return when you import a pretrained model from TensorFlow, PyTorch, or ONNX.

After you import a network or layer graph, the returned object is ready for all the workflows that Deep Learning Toolbox supports.

Automatic Generation of Custom Layers

The importNetworkFromTensorFlow, importNetworkFromPyTorch, and importNetworkFromONNX functions create automatically generated custom layers when you import a model with TensorFlow layers, PyTorch layers, or ONNX operators that the functions cannot convert to built-in MATLAB functions or layers. The functions save the automatically generated custom layers to a package in the current folder. For more information, see Autogenerated Custom Layers.

Visualize Imported Network

Use the plot function to plot a diagram of the imported network or layer graph. You cannot use the plot function with dlnetwork objects.

net = importTensorFlowNetwork("digitsNet");

Plot diagram of imported network

Use the analyzeNetwork function to visualize and understand the architecture of the imported network or layer graph, check that you have defined the architecture correctly, and detect problems before training. Problems that analyzeNetwork detects include missing or unconnected layers, incorrectly sized layer inputs, an incorrect number of layer inputs, and invalid graph structures.

net = importTensorFlowNetwork("NASNetMobile", ...

Analysis and visualization of imported network

Predict with Imported Model

Preprocess Input Data

Preprocessing data is a common first step in the deep learning workflow to prepare data in a format that the network can accept. The input data size must match the network input size. If the sizes do not match, you must resize the input data. For an example, see Import ONNX Network as DAGNetwork. In some cases, the input data requires further processing, such as normalization. For an example, see Import ONNX Network with Automatically Generated Custom Layers.

You must preprocess the input data in the same way as the training data. Often, open-source repositories provide information about the required input data preprocessing. To learn more about how to preprocess images and other types of data, see Preprocess Images for Deep Learning and Preprocess Data for Deep Neural Networks.

Import Model as DAGNetwork Object and Predict

Import a TensorFlow model in the SavedModel format. By default, importTensorFlowNetwork imports the network as a DAGNetwork object. Specify the output layer type for an image classification problem and the class names.

net = importTensorFlowNetwork("NASNetMobile", ...

Predict the label of an image.

label = classify(net,Im);

Import Model as dlnetwork Object and Predict

You must first convert the input data to a dlarray object. For image input data, specify the data format as "SSCB". For more information on how to specify the input data format, see Data Formats for Prediction with dlnetwork.

dlIm = dlarray(Im,"SSCB");

Import a TensorFlow model in the SavedModel format as a dlnetwork object. Specify the class names. A dlnetwork does not have output layers.

net = importTensorFlowNetwork("NASNetMobile", ...

Predict the label of an image.

prob = predict(net,dlIm);
[~,label] = max(prob);


The importNetworkFromPyTorch function imports the network as an uninitialized dlnetwork object. Before prediction, add an input layer or initialize the imported network. For examples, see Import Network from PyTorch and Add Input Layer and Import Network from PyTorch and Initialize.

Compare Prediction Results

To check whether the TensorFlow or ONNX model matches the imported network, you can compare inference results by using real or randomized inputs to the network. For examples that show how to compare inference results, see Inference Comparison Between TensorFlow and Imported Networks for Image Classification and Inference Comparison Between ONNX and Imported Networks for Image Classification.

Predict in Simulink

You can use the imported network with the Predict block of Deep Learning Toolbox to classify an image in Simulink®. The imported network can contain automatically generated custom layers. For an example, see Classify Images in Simulink with Imported TensorFlow Network.

Predict in Simulink with imported model

Predict on GPU

The import functions do not execute on a GPU. However, the functions import a pretrained neural network for deep learning as a DAGNetwork or dlnetwork object, which you can use on a GPU.

  • If you import the network as a DAGNetwork object, you can make predictions with the imported network on a CPU or GPU by using classify or predict. Specify the hardware requirements using the ExecutionEnvironment name-value argument.

    For networks with multiple outputs, use the predict function and specify the ReturnCategorical name-value argument as true.

  • If you import the network as a dlnetwork object, you can make predictions with the imported network on a CPU or GPU by using predict. The predict function executes on the GPU if the input data or network parameters are stored on the GPU.

    • If you use minibatchqueue to process and manage the mini-batches of input data, the minibatchqueue object converts the output to a GPU array by default if a GPU is available.

    • Use dlupdate to convert the learnable parameters of a dlnetwork object to GPU arrays.

      net = dlupdate(@gpuArray,net)

Transfer Learning with Imported Network

Transfer learning is common in deep learning applications. You can use a pretrained network as a starting point to learn a new task. Fine-tuning a network with transfer learning is usually much faster and easier than training a network with randomly initialized weights from scratch. You can quickly transfer learned features to a new task using a smaller quantity of training data. This section describes how to import a convolutional model from TensorFlow for transfer learning.

Import the NASNetMobile model as a layer graph and display its final layers.

lgraph = importTensorFlowLayers("NASNetMobile", ...
ans = 

  3×1 Layer array with layers:

     1   'predictions'                       Fully Connected         1000 fully connected layer
     2   'predictions_softmax'               Softmax                 softmax
     3   'ClassificationLayer_predictions'   Classification Output   crossentropyex

The last layer with learnable weights is a fully connected layer. Replace this fully connected layer with a new fully connected layer in which the number of outputs is equal to the number of classes in the new data set. To learn faster in the new layer than in the transferred layers, increase the learning rate factors of the layer.

learnableLayer = lgraph.Layers(end-2);

numClasses = numel(categories(imdsTrain.Labels));
newLearnableLayer = fullyConnectedLayer(numClasses, ...
        Name="new_fc", ...
        WeightLearnRateFactor=10, ...

lgraph = replaceLayer(lgraph,learnableLayer.Name,newLearnableLayer);

The classification layer specifies the output classes of the network. Replace the classification layer with a new one that does not have class labels. The trainNetwork function automatically sets the output classes of the layer at training time.

classLayer = lgraph.Layers(end);
newClassLayer = classificationLayer(Name="new_classoutput");
lgraph = replaceLayer(lgraph,classLayer.Name,newClassLayer);

For an example that shows the complete transfer learning workflow, see Train Deep Learning Network to Classify New Images. For an example that shows how to train a network imported as a dlnetwork object to classify new images, see Train Network Imported from PyTorch to Classify New Images.

Train Network on GPU

You can train the imported network on a CPU or GPU by using the trainnet or trainNetwork function. To specify training options, including options for the execution environment, use the trainingOptions function. Specify the hardware requirements using the ExecutionEnvironment name-value argument. For more information on how to accelerate training, see Scale Up Deep Learning in Parallel, on GPUs, and in the Cloud.

Deploy Imported Network

Deploy Imported Network with MATLAB Coder or GPU Coder

You can use MATLAB Coder™ or GPU Coder™ together with Deep Learning Toolbox to generate MEX, standalone CPU, CUDA® MEX, or standalone CUDA code for an imported network. For more information, see Code Generation.

  • Use MATLAB Coder with Deep Learning Toolbox to generate MEX or standalone CPU code that runs on desktop or embedded targets. You can deploy generated standalone code that uses the Intel® MKL-DNN library or the ARM® Compute library. Alternatively, you can generate generic C or C++ code that does not call third-party library functions. For more information, see Deep Learning with MATLAB Coder (MATLAB Coder).

  • Use GPU Coder with Deep Learning Toolbox to generate CUDA MEX or standalone CUDA code that runs on desktop or embedded targets. You can deploy generated standalone CUDA code that uses the CUDA deep neural network library (cuDNN), the TensorRT™ high performance inference library, or the ARM Compute library for Mali GPU. For more information, see Deep Learning with GPU Coder (GPU Coder).

The import functions return the network as a DAGNetwork or dlnetwork object. Both of these objects support code generation. For more information on MATLAB Coder and GPU Coder support for Deep Learning Toolbox objects, see Supported Classes (MATLAB Coder) and Supported Classes (GPU Coder), respectively.

You can generate code for any imported network whose layers support code generation. For lists of the layers that support code generation with MATLAB Coder and GPU Coder, see Supported Layers (MATLAB Coder) and Supported Layers (GPU Coder), respectively. For more information on the code generation capabilities and limitations of each built-in MATLAB layer, see the Extended Capabilities section of the layer. For example, see the Code Generation and GPU Code Generation sections of imageInputLayer.

Deploy Imported Network with MATLAB Compiler

An imported network might include layers that MATLAB Coder does not support for deployment. In this case, you can deploy the imported network as a standalone application using MATLAB Compiler™. The standalone executable you create with MATLAB Compiler is independent of MATLAB; therefore, you can deploy it to users who do not have access to MATLAB.

You can deploy only the imported network using MATLAB Compiler, either programmatically by using the mcc (MATLAB Compiler) function or interactively by using the Application Compiler (MATLAB Compiler) app. For an example, see Deploy Imported TensorFlow Model with MATLAB Compiler.


Automatically generated custom layers do not support code generation with MATLAB Coder, GPU Coder, or MATLAB Compiler.

Functions that Export Networks and Layer Graphs

When you complete your deep learning workflows in MATLAB, you can share the deep learning network or layer graph with colleagues who work in different deep learning platforms. By entering one line of code, you can export the network.


Export networks to TensorFlow and ONNX

You can export networks and layer graphs to TensorFlow and ONNX by using the exportNetworkToTensorFlow and exportONNXNetwork functions. The functions can export DAGNetwork, dlnetwork, and LayerGraph objects.

  • The exportONNXNetwork function exports to the ONNX model format.

  • The exportNetworkToTensorFlow function saves the exported TensorFlow model in a regular Python® package. You can load the exported model and use it for prediction or training. You can also share the exported model by saving it to SavedModel or HDF5 format. For more information on how to load the exported model and save it in a standard TensorFlow format, see Load Exported TensorFlow Model and Save Exported TensorFlow Model in Standard Format.

See Also

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