2-Wat Net is a bi-directional neural network architecture for the task of matching vectors from two data sources. The model employs two tied neural network channels that project the two views into a common, maximally correlated space optimizing the Euclidean loss. The model contain: (i) a mid-way loss term that helps support the training of the hidden layers; (ii) a decorrelation regularization term that links the problem back to CCA; (iii) modified batch normalization layers; (iv) a regularization of the scale parameter that ensures that the variance does not diminish from one layer to the next; (v) a tied dropout method; and (vi) a method for dealing with high-dimensional data.
- Install Python 2.7 (it is recommended to use virtualenv, see https://virtualenv.pypa.io/en/stable/ for details)
- Clone this repository:
git clone https://github.com/aviveise/2WayNet - Install prequisites:
pip install -r /path/to/repo/requiremens.txt
See the requirements.txt file for required python modules
- Download the MNIST dataset from: http://yann.lecun.com/exdb/mnist/
- Edit MNIST.ini file and direct the dataset path to your local MNIST directory
- Modify the Params.py file to change the model's parameters
- Running training and testing execute the run_mode.py file:
python /path/to/repo/run_model.py /path/to/repo/MNIST.ini
The 2-Way Net project was build upon the following projects:
And other wonderful python modules