def objective(args):
n_epochs = 10
rho = 0.01
lamb = 5
noise_std = 0.2
hidshape = (50, 50)
rflen, rate, clip = args
rfshape = (rflen, rflen)
clip = (-clip, clip)
layer = SparseAutoencoder(
visshape=imshape, hidshape=hidshape, rfshape=rfshape,
f=noisylif, g=linear)
train_params = {'rho': rho, 'lamb': lamb, 'noise_std': noise_std}
trainer = SparseTrainer(layer, **train_params)
stats = sgd(trainer, patches,
n_epochs=n_epochs, rate=rate, clip=clip, show=False)
cost = stats['cost'][-1]
filename = 'results/layer_opt_vh_cost=%0.3e.npz' % cost
layer.to_file(filename)
return cost
std0 = patches.std()
mean, std = p.mean(axis=(1,2)), p.std(axis=(1,2))
return ((p - mean[:,None,None]) / np.maximum(std, 0.01*std0)[:,None,None])
patches = normalize(patches)
patches = patches.clip(-3, 3)
################################################################################
### train one layer
filename = 'results/vh_layer.npz'
if 'filename' not in locals() or not os.path.exists(filename):
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=(11,11), f=noisylif, g=linear)
# train_params = {'rho': 0.01, 'lamb': 25, 'noise_std': 0.2}
# train_params = {'rho': 0.01, 'lamb': 5, 'noise_std': 0.2}
train_params = {'rho': 0.05, 'lamb': 5, 'noise_std': 0.2}
trainer = SparseTrainer(layer, **train_params)
plt.figure(101)
raw_input("Please place the figure...")
sgd(trainer, patches, n_epochs=30, rate=0.05, vlims=(-2,2))
if 'filename' in locals():
layer.to_file(filename)
else:
################################################################################
### train one layer
# 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)
