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
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:
layer = deepnet.CacheObject.from_file(filename)
print "loaded layer from file: %s" % filename
### untied training
if 1:
if layer.tied:
layer.untie()
train_params = {'rho': 0.05, 'lamb': 5, 'noise_std': 0}
trainer = SparseTrainer(layer, **train_params)
lbfgs(trainer, patches, n_evals=30, vlims=(-2,2))
