Skip to content
master
Go to file
Code

Latest commit

 

Git stats

Files

Permalink
Failed to load latest commit information.
Type
Name
Latest commit message
Commit time
 
 
 
 
 
 
 
 
 
 
 
 
 
 

README.md

Implementation of the trust-region limited-memory BFGS quasi-Newton optimization in Deep Learning

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 LBFGS_TR.py -m=10 -minibatch=1000

args:
-m=10             # the L-BFGS memory storage
-num-batch=4 # number of overlapped samples --> refer to the paper 
-minibatch=1000   # minibatch size
-use-whole-data # uses whole data to calculate gradients.

About

Limited Memory BFGS with Trust Region

Resources

Releases

No releases published

Packages

No packages published

Languages

You can’t perform that action at this time.