importNetworkFromTensorFlow

Import a pretrained TensorFlow network in the SavedModel format and use the imported network to predict class labels.

Specify the model folder.

if ~exist('digitsdlnetwork','dir')
    unzip('digitsdlnetwork.zip')
end
modelFolder = './digitsdlnetwork';

Specify the class names.

classNames = {'0','1','2','3','4','5','6','7','8','9'};

Import a TensorFlow network in the SavedModel format as an initialized dlnetwork object.

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: [12x1 nnet.cnn.layer.Layer]
    Connections: [12x2 table]
     Learnables: [6x3 table]
          State: [0x3 table]
     InputNames: {'input_1'}
    OutputNames: {'activation_1'}
    Initialized: 1

  View summary with summary.

Read the image you want to classify and display the size of the image. The image is a grayscale (one-channel) image with a size of 28-by-28 pixels.

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

ans = 1×2

    28    28

Display the input size of the network. In this case, the image size matches the network input size. If the sizes do not match, you must resize the image using this command: imresize(I,netInputSize(1:2)).

netInputSize = net.Layers(1).InputSize

netInputSize = 1×3

    28    28     1

Convert the image to a dlarray object. Format the images with the dimensions 'SSCB' (spatial, spatial, channel, batch). In this case, the batch size is 1 and you can omit it ('SSC').

I_dlarray = dlarray(single(I),'SSC');

Classify the sample image and find the predicted label.

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

Display the image and the classification result.

imshow(I)
title(['Classification result ' classNames{label}]) 

Import a pretrained TensorFlow network in the SavedModel format. The network contains layers that the software cannot convert to built-in MATLAB layers. The software automatically generates custom layers when you import this network.

This example uses the findCustomLayers helper function. To view the code for this function, see Helper Function.

Specify the model folder.

if ~exist('digitsdlnetworkwithnoise','dir')
    unzip('digitsdlnetworkwithnoise.zip')
end
modelFolder = './digitsdlnetworkwithnoise';

Import a TensorFlow network in the SavedModel format.

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: [14×1 nnet.cnn.layer.Layer]
    Connections: [14×2 table]
     Learnables: [6×3 table]
          State: [0×3 table]
     InputNames: {'input_1'}
    OutputNames: {'activation_1'}
    Initialized: 1

  View summary with summary.

The imported network contains layers the software cannot convert to built-in MATLAB layers, so importNetworkFromTensorFlow automatically generates custom layers in place of these layers. The function saves each generated custom layer to a separate M file in the +digitsdlnetworkwithnoise namespace in the current folder.

Find the indices of the automatically generated custom layers using the helper function findCustomLayers, and display the custom layers.

ind = findCustomLayers(net.Layers,'+digitsdlnetworkwithnoise');
net.Layers(ind)

ans = 
  3×1 Layer array with layers:

     1   'concatenate_1'      Concatenate     digitsdlnetworkwithnoise.kConcatenate1Layer3826
     2   'gaussian_noise_1'   GaussianNoise   digitsdlnetworkwithnoise.kGaussianNoise1Layer3766
     3   'gaussian_noise_2'   GaussianNoise   digitsdlnetworkwithnoise.kGaussianNoise2Layer3791

Plot the network architecture.

plot(net)
title('Network Architecture')

function indices = findCustomLayers(layers,Namespace)

s = what(['.' filesep Namespace]);

indices = zeros(1,length(s.m));
for i = 1:length(layers)
    for j = 1:length(s.m)
        if strcmpi(class(layers(i)),[Namespace(2:end) '.' s.m{j}(1:end-2)])
            indices(j) = i;
        end
    end
end

end

Import a pretrained TensorFlow network in the SavedModel format, which results in an uninitialized network. Then, initialize the network.

Specify the model folder.

if ~exist('imageClassificationNet','dir')
    unzip('imageClassificationNet.zip')
end
modelFolder = './imageClassificationNet';

Import the model using the importNetworkFromTensorFlow function. Because this model does not have a fixed input size, the function imports the model as an uninitialized dlnetwork object without an input layer. The software displays a warning that describes how to initialize the network.

net = importNetworkFromTensorFlow(modelFolder)

Importing the saved model...
Translating the model, this may take a few minutes...

Warning: Size of the input to "input_1" is unknown in TensorFlow.

Finished translation. Assembling network...

Warning: Network is imported as an uninitialized dlnetwork object. Before using the network, add input layer(s):

inputLayer1 = imageInputLayer(<inputSize1>, Normalization="none");
net = addInputLayer(net, inputLayer1, Initialize=true);

For more information on which format(s) to use, see documentation for dlarray.

Import finished.

net = 
  dlnetwork with properties:

         Layers: [7x1 nnet.cnn.layer.Layer]
    Connections: [6x2 table]
     Learnables: [6x3 table]
          State: [2x3 table]
     InputNames: {'conv2d'}
    OutputNames: {'softmax'}
    Initialized: 0

  View summary with summary.

Specify the input size of the imported network and create an image input layer. Then, add the image input layer to the imported network and initialize the network using the addInputLayer function.

inputLayer1 = imageInputLayer([28 28 3], Normalization="none");
net = addInputLayer(net,inputLayer1,Initialize=true)

net = 
  dlnetwork with properties:

         Layers: [8x1 nnet.cnn.layer.Layer]
    Connections: [7x2 table]
     Learnables: [6x3 table]
          State: [2x3 table]
     InputNames: {'imageinput'}
    OutputNames: {'softmax'}
    Initialized: 1

  View summary with summary.

The network contains the input layer and is ready to use for prediction. You can analyze the network further using the Deep Network Designer app.

deepNetworkDesigner(net)

Import a TensorFlow network in the SavedModel format as an initialized dlnetwork object. Analyze the network using the analyzeNetwork function.

net = importNetworkFromTensorFlow("CustomConvReluBN");

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

analyzeNetwork(net)

net is a dlnetwork object with seven layers. The second and third layers are networkLayer objects that each contain a nested neural network.

Expand the networkLayer layer using the expandLayers function and analyze the network.

netExpanded = expandLayers(net);
analyzeNetwork(netExpanded)

The app shows that the expanded network netExpanded has four more layers than net.

Import the TensorFlow network again, using custom layers to represent the nested neural network. Analyze the network.

netCustom = importNetworkFromTensorFlow("CustomConvReluBN",PreferredNestingType="customlayer");

Importing the saved model...
Translating the model, this may take a few minutes...

Warning: The namespace "CustomConvReluBN" already exists in the Current Folder, and will be overwritten.

Finished translation. Assembling network...
Import finished.

analyzeNetwork(netCustom)

netCustom contains seven layers. The second and third layers are custom layers that represent nested neural networks.