I understand you are looking to use the .h5 weights of your pretrained model in matlab. But As far as I know weight sharing is not supported in matlab.
As a workarround you can do the following steps:
1.Export Weights from Keras
model.save_weights('model_weights.h5')
2. Recreate Architecture in MATLAB
You will need to manually code the architecture. Here's a simplified example of defining a layer in MATLAB:
layers = [
imageInputLayer([28 28 1], 'Name', 'input')
convolution2dLayer(3, 16, 'Padding', 'same', 'Name', 'conv1')
% Add more layers according to your Requirement.
];
3.Load the Weights into MATLAB Model:
- Load the weights into MATLAB. If you saved them in .npz format, you can use MATLAB's "load" function for .mat files or a library like numpy in Python to extract the weights and then load them into MATLAB.
- Assign the weights to the corresponding layers in your MATLAB model. This step can be a bit tedious, as you'll need to ensure that the weights are correctly matched to each layer, especially considering the shared weights aspect of your Siamese network
- For .h5 files, you might need a custom function to parse and load the weights into MATLAB, as MATLAB does not directly support loading Keras weights into its layers. If you choose to convert the weights into .mat format, you can easily load them using MATLAB's "load" function but would still need to manually assign each weight to the corresponding layer in your MATLAB model.
You can additionally refer "importNetworkFromTensorFlow" Documentation: Import TensorFlow network as MATLAB network - MATLAB importNetworkFromTensorFlow - MathWorks India
I hope this workarround will help you!
