csalcedo001/cs182_hw1

In this assignment you will practice writing backpropagation code and training Neural Networks. The goals of this assignment are as follows:

• understand Neural Networks and how they are arranged in layered architectures
• understand and be able to implement (vectorized) backpropagation

Setup

Make sure your machine is set up with the assignment dependencies.

[Option 1] Use Anaconda: The preferred approach for installing all the assignment dependencies is to use Anaconda, which is a Python distribution that includes many of the most popular Python packages for science, math, engineering and data analysis. Once you install it you can skip all mentions of requirements and you are ready to go directly to working on the assignment.

[Option 2] Manual install, virtual environment: If you do not want to use Anaconda and want to go with a more manual and risky installation route you will likely want to create a virtual environment for the project. If you choose not to use a virtual environment, it is up to you to make sure that all dependencies for the code are installed globally on your machine. To set up a virtual environment, run the following:

cd hw1
sudo pip install virtualenv      # This may already be installed
virtualenv .env                  # Create a virtual environment
source .env/bin/activate         # Activate the virtual environment
pip install -r requirements.txt  # Install dependencies
# Work on the assignment for a while ...
deactivate                       # Exit the virtual environment

Download data: Once you have the starter code, you will need to download the CIFAR-10 dataset. Run the following from the hw1 directory:

cd deeplearning/datasets
./get_datasets.sh

If you are on Mac, this script may not work if you do not have the wget command installed, but you can use curl instead with the alternative script.

cd deeplearning/datasets
./get_datasets_curl.sh

Start IPython: After you have the CIFAR-10 data, you should start the IPython notebook server from the hw1 directory.

NOTE: If you are working in a virtual environment on OSX, you may encounter errors with matplotlib due to the issues described here. You can work around this issue by starting the IPython server using the start_ipython_osx.sh script from the hw1 directory; the script assumes that your virtual environment is named .env.

Fully-connected Neural Network

The IPython notebook FullyConnectedNets.ipynb will introduce you to our modular layer design, and then use those layers to implement fully-connected networks of arbitrary depth. To optimize these models you will implement several popular update rules.

If you use Colab for this notebook, make sure to manually download the completed notebook and place it in the assignment directory before submitting. Also remember to download required output file and place it into submission_logs/ directory.