pratikac / cs268-dl

Introduction to deep learning lecture for CS 268

Introductory lecture on deep learning in CS 268 at UCLA.

Install Torch from http://torch.ch/docs/getting-started.html.

In addition to this, you will need two packages:

• Install the "mnist" package using luarocks install mnist
• Install the fakecuda package from https://github.com/soumith/fakecuda

Run the code in this directory as

th mnist.lua -h

This is a LeNet-style network for MNIST, it should train to about ~1% testing error in 10 epochs and should get to ~0.75% error around 50 epochs.

Instructions for PyTorch

The file mnist.py contains code to train a CNN on MNIST using PyTorch.

1. Installing Python on Mac is easiest with conda: https://www.continuum.io/downloads.

2. Install PyTorch for your computer with the appropriate command. For instance, for training with CPU and a Mac with Python 2.7 (should be default for most)

   pip install https://s3.amazonaws.com/pytorch/whl/torch-0.1.9.post2-cp27-none-macosx_10_7_x86_64.whl 
   pip install torchvision
   

3. You can now run the code in mnist.py by doing python mnist.py. It has a few parameters which you can find out by python mnist.py -h.

4. It will download the MNIST dataset and train a convolutional neural network on it. You should expect a test error of about 0.55% after 50 epochs using the parameters in the code (learning rate = 0.1). You can also see an example of learning rate annealing in this code.