Basic network(s) for classifying MNIST digits in Nengo
To train a new network, run
python train.py
This will train a network and save it to a .npz file starting with params.
The optional --gpu flag runs on the GPU, and the save file can be specified.
To run a trained network in spiking neurons, do
python run.py params_file.npz output_file.npz
where params_file.npz is the name of your trained network params file
(or one of the pretrained files lif-126-error.npz or lif-111-error.npz),
and output_file.npz is an optional location to save the output.
If you do choose to save your output, you can view it again with
python view.py output_file.npz
You can also get some information about static trained networks with
python view.py params_file.npz
You can also run any of the above scripts with the --help argument to get
a full list of arguments.
This project requires Nengo, and additionally Theano and Scipy if you
want to train your own networks. Both should be installable from pip,
but using Theano on the GPU requires CUDA to also be installed
(details).