We are building a feed forward neural network and need to use the Levenberg–Marquardt algorithm as a starting point of optimization algorithm but this algorithm is not available as a part of default optimizers in Keras.
The Levenberg-Marquardt algorithm is a combination of the gradient descent and the Gauss-Newton algorithm. It is an iterative method that can be used to minimize a loss function in a neural network. It is not available as a built-in optimizer in Keras, but you can implement it yourself as a custom optimizer.
Here is an example of how you can implement the Levenberg-Marquardt algorithm as a custom optimizer in Keras:
https://github.com/fabiodimarco/tf-levenberg-marquardt
Keep in mind that the Levenberg-Marquardt algorithm may not always converge to the global minimum of the loss function and may get stuck in a local minimum. You may need to experiment with different values for the hyperparameters lr, beta, and epsilon to find the best performance for your specific problem.
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