# loadfile = None
loadfile = 'mnist_layer.pkl'
if loadfile is None or not os.path.exists(loadfile):
linear = Linear(slope=1.0)
noisylif = NoisyLIFApprox(
tRef=0.02, tauRC=0.06, alpha=10.0, xint=-0.5, amp=1./41, sigma=0.05)
# layer = SparseAutoencoder(visshape=imshape, hidshape=(50,50),
# rfshape=(9,9), f=noisylif, g=linear)
layer = SparseAutoencoder(visshape=imshape, hidshape=(40,40),
rfshape=(9,9), f=noisylif, g=linear)
if loadfile is not None:
layer.tofile(loadfile)
else:
layer = deepnet.CacheObject.fromfile(loadfile)
################################################################################
train_params = {'rho': 0.01, 'lamb': 25, 'noise_std': 0.2}
trainer = SparseTrainer(layer, **train_params)
sgd(trainer, images, nepochs=30, rate=0.05)
if 0:
### untied training
sgd(trainer, images, nepochs=1, rate=0.05)
layer.untie()
trainer = SparseTrainer(layer, **train_params)
