Deep Learning Toolbox Importer for TensorFlow-Keras Models

Import pretrained Keras model for prediction and transfer learning

The importer for the TensorFlow-Keras models would enable you to import a pretrained Keras model and weights. You can then use this model for prediction or transfer learning. Alternatively, you can import layer architecture as a Layer array or a LayerGraph object. You can then train this model.
Opening the kerasimporter.mlpkginstall file from your operating system or from within MATLAB will initiate the installation process for the release you have.
This mlpkginstall file is functional for R2017b and beyond.
Usage Example:
1. Import Keras Layers

% Import the Layers

layers = importKerasLayers('digitsDAGnet.h5');

%Load a dataset for training a classifier to recognize the digits
digitDatasetPath = fullfile(toolboxdir('nnet'), 'nndemos', ...
'nndatasets', 'DigitDataset');

digitData = imageDatastore(digitDatasetPath, ...
'IncludeSubfolders',true,'LabelSource','foldernames');

%Partition the dataset into training & test images

rng(1) % For reproducibility
trainingFileSplitRatio = 0.6;
[trainDigitData,testDigitData] = splitEachLabel(digitData,...
trainingFileSplitRatio,'randomize');

%Set some training options

options = trainingOptions('sgdm','MaxEpochs',20,...
'InitialLearnRate',0.001);

%Train network

convnet = trainNetwork(trainDigitData,layers,options);

%Read image for classification

I = imread(fullfile(digitDatasetPath,'5','image4009.png'));
figure
imshow(I)

%Classify the image using the network
label = classify(convnet, I);
title(['Classification result ' char(label)])

2. Import a pretrained Keras Network
netfile = 'digitsDAGnet.h5';
classNames = {'0','1','2','3','4','5','6','7','8','9'};
network = importKerasNetwork(netfile, 'Classes', classNames);

%Read the image to classify
digitDatasetPath = fullfile(toolboxdir('nnet'), 'nndemos','nndatasets',...
'DigitDataset');
I = imread(fullfile(digitDatasetPath,'5','image4009.png'));

%Classify the image using the network
label = classify(network, I);

%Show the image and classification result
figure
imshow(I)
title(['Classification result ' char(label)])