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))
### test the layer
if 1:
test = patches[:100]
recs = layer.compVHV(test)
rmse = np.sqrt(((recs - test)**2).mean())
print "rmse", rmse
plt.figure(1)
plt.clf()
deepnet.image_tools.compare([test, recs], vlims=(-2,2))
# 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)
sgd(trainer, images, nepochs=30, rate=0.05)
results = layer.compVHV(images)
plt.figure(1)
plt.clf()
deepnet.image_tools.compare([images, results], vlims=(-1,1))
