To deploy MATLAB machine learning models to TensorFlow

To deploy MATLAB machine learning models to TensorFlow, follow these structured steps based on the model type (deep learning or traditional ML):

1. For Deep Learning Models (using ONNX conversion):

Step 1: Export the MATLAB Model to ONNX

Use MATLAB’s exportONNXNetwork function to convert a trained model to ONNX format.
matlab

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```
% Example: Export a trained network (net) to ONNX
exportONNXNetwork(net, 'model.onnx');
```

Step 2: Convert ONNX to TensorFlow

Install the ONNX-TensorFlow converter in Python:
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```
pip install onnx-tf
```

Convert the ONNX model to TensorFlow:

import onnx
from onnx_tf.backend import prepare

onnx_model = onnx.load('model.onnx')
tf_rep = prepare(onnx_model)
tf_rep.export_graph('tf_model')  # Saves as a TensorFlow SavedModel

Step 3: Load and Test in TensorFlow

Load the converted model in TensorFlow:

import tensorflow as tf

model = tf.saved_model.load('tf_model')
# Use the model for inference

2. For Traditional ML Models (e.g., SVM, Decision Trees):

Step 1: Extract Model Parameters from MATLAB

Save coefficients, intercepts, or tree structures to a file (e.g., CSV or JSON).

% Example: Save SVM coefficients
svm_model = fitcsvm(X, Y);
save('svm_params.mat', 'svm_model');

Step 2: Rebuild the Model in TensorFlow

Manually reconstruct the model using extracted parameters.
Example for Linear Regression:

import tensorflow as tf

# Load coefficients from MATLAB (e.g., coef = [0.5], intercept = 1.2)
class CustomLinearModel(tf.Module):
    def __init__(self, coef, intercept):
        self.coef = tf.constant(coef, dtype=tf.float32)
        self.intercept = tf.constant(intercept, dtype=tf.float32)
    
    @tf.function
    def predict(self, X):
        return tf.matmul(X, self.coef) + self.intercept

# Initialize with MATLAB parameters
model = CustomLinearModel(coef=[0.5], intercept=1.2)

3. MATLAB-Python Integration (Alternative Approach)

Use MATLAB Engine for Python to call MATLAB models directly:

import matlab.engine

eng = matlab.engine.start_matlab()
eng.eval("load('model.mat')", nargout=0)
prediction = eng.predict(matlab.double(X.tolist()))

Key Considerations:

Preprocessing: Replicate MATLAB’s data normalization/scaling in TensorFlow.
Validation: Test outputs from both frameworks with the same input data.
Limitations:
- ONNX conversion works best for deep learning models.
- Custom layers or unsupported operations may require manual reimplementation.
- Traditional ML models often need manual reconstruction.

Example Workflow for a Neural Network

MATLAB:

net = trainNetwork(X, Y, layers, options);
exportONNXNetwork(net, 'model.onnx');

Python:

import onnx
from onnx_tf.backend import prepare
onnx_model = onnx.load('model.onnx')
tf_rep = prepare(onnx_model)
tf_rep.export_graph('tf_model')
model = tf.saved_model.load('tf_model')

Troubleshooting Tips

Check for ONNX operator support: ONNX-TF Documentation.
Use MATLAB’s importONNXLayers to test compatibility by re-importing the ONNX model back into MATLAB