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))
### 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)
