/stats_hw3

CNN and Residual block & Deep_Q_learning

Primary LanguagePython

In this project you will practice writing backpropagation code, and training fully-connected Neural Networks. The goals of this project are as follows:

  • understand Neural Networks and how they are arranged in layered architectures
  • understand and be able to implement (vectorized) backpropagation
  • implement various update rules used to optimize Neural Networks
  • effectively cross-validate and find the best hyperparameters for Neural Network architecture

Setup

Here's how you install the necessary dependencies:

Installing Python 3.5+: To use python3, make sure to install version 3.5 or 3.6 on your local machine. If you are on Mac OS X, you can do this using Homebrew with brew install python3. You can find instructions for Ubuntu here.

Virtual environment: We recommend using 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 project2
sudo pip3 install virtualenv      # This may already be installed
virtualenv -p python3 .env       # Create a virtual environment (python3)
source .env/bin/activate         # Activate the virtual environment
pip3 install -r requirements.txt  # Install dependencies

# Work on the project for a while ...
# ... and when you're done:
deactivate                       # Exit the virtual environment

Note that every time you want to work on the project, you should run source .env/bin/activate (from within your project2 folder) to re-activate the virtual environment, and deactivate again whenever you are done.

Download data:

Once you have the starter code (regardless of which method you choose above), you will need to download the MNIST and CIFAR-10 datasets. Run the following from the project2 directory:

cd stats232a/datasets
./get_datasets.sh

Start IPython:

After you have datasets, you should start the IPython notebook server from the project2 directory, with the jupyter notebook command.

Submitting your work:

Please zip your working folder (project2), but don’t include folder ./stats232a/datasets. Submit this zip file to CCLE.

To do this project, follow the instruction in the IPython notebook FullyConnectedNets.ipynb. It will introduce you to the 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.