Repository for Improving L-BFGS Initialization For Trust-Region Methods In Deep Learning
Switch branches/tags
Nothing to show
Clone or download
Fetching latest commit…
Cannot retrieve the latest commit at this time.
Failed to load latest commit information.

Improving L-BFGS Initialization For Trust Region Methods in Deep Learning

You can choose different method of initialization for L-BFGS matrices in form of B0 = gamma * I and run the trust region algorithm. The example here is using the classification task of MNIST dataset.

TensorFlow is used to compute the gradients. Numpy and Scipy is used for the matrix computations.

Run the Python program

$ python -m=10 -minibatch=1000 -use-init-methods -B0-method=3

-m=10             # the L-BFGS memory storage
-minibatch=1000   # minibatch size
-use-init-methods # default: False
-B0-method        # method 1, 2, 3 if -use-init-methods