Trains autoencoder on MNIST.
AE models are tied-weight (weights are shared between encoder and decoder).
Chainer, OpenCV
$ pip install chainer opencv-python
PyTorch, OpenCV
$ python ae.py [options]
You can read help with -h
option.
$ python ae.py -h
usage: ae.py [-h] [-b BATCHSIZE] [-e EPOCH] [-g GPU] [--graph GRAPH] [--cnn]
[--lam LAM] [-m MODEL] [-r RESULT]
optional arguments:
-h, --help show this help message and exit
-b BATCHSIZE, --batchsize BATCHSIZE
batchsize
-e EPOCH, --epoch EPOCH
iteration
-g GPU, --gpu GPU GPU ID
--graph GRAPH computational graph
--cnn CNN
--lam LAM weight decay
-m MODEL, --model MODEL
model file name
-r RESULT, --result RESULT
result directory