Generative Adverserial Networks for MNIST
This is an example of generating adversarial examples to exploit the deep MNIST Convolution network. It is inspired from Intriguing Properties of Neural Networks, Explaining and Harnessing Adversarial Examples and Breaking Convnets.
- Clone the repository.
git clone https://github.com/divyam3897/adversarial-examples.git
- Make sure tha you have Jupyter Notebook installed. You can install Anaconda (which installs Python, Jupyter Notebook, and a bunch of other useful computing libraries) or use pip.
To install Anaconda.
If you want to install using pip, update pip with the following code (Replace pip with pip3 if using Python 3).
On Linux/Mac OS:
pip install -r requirements.txt
You should be able to run the following.
pip install jupyter
- Run the following command to open the jupyter notebook in the browser.
For more resources on Jupyter Notebooks, check out the following: