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.

The importNetworkFromPyTorch and importNetworkFromONNX function reference pages contain a Limitations section that describes the supported versions of PyTorch and ONNX respectively; see PyTorch Import Limitations and ONNX Import 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, 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.

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.

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.

modelFolder = "./digitsdlnetwork";
net = importNetworkFromTensorFlow(modelFolder)

Importing the saved model...
Translating the model, this may take a few minutes...
Finished translation. Assembling network...
Import finished.

net = 
  dlnetwork with properties:

         Layers: [12×1 nnet.cnn.layer.Layer]
    Connections: [12×2 table]
     Learnables: [6×3 table]
          State: [0×3 table]
     InputNames: {'input_1'}
    OutputNames: {'activation_1'}
    Initialized: 1

  View summary with summary.

classNames = ["0","1","2","3","4","5","6","7","8","9"];

digitDatasetPath = fullfile(toolboxdir("nnet"),"nndemos","nndatasets","DigitDataset","5","image4009.png");
I = imread(digitDatasetPath);
imshow(I)

I = single(I);
I = dlarray(I,"SSCB");

score = predict(net,I);
label = scores2label(score,classNames)

label = categorical
     5 

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 might contain automatically generated custom layers. For an example, see Classify Images in Simulink with Imported TensorFlow Network.

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 dlnetwork object, which you can use on a GPU. 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.

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.

The import functions return the network as a dlnetwork object, which supports 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.