# subtract completed epochs
sgd_param['n_epochs'] -= algo_epochs(layer, 'sgd')
if sgd_param['n_epochs'] > 0:
trainer = auto.SparseTrainer(layer, **train_param)
test_fn = net.propVHV_fn(maxlayer=i)
# save_fn = lambda: net.to_file(loadfile)
save_fn = None
auto.sgd(trainer, images, timages, test_fn=test_fn,
vlims=(-2,2), save_fn=save_fn,
**sgd_param)
if save_fn is None:
# net.to_file(loadfile)
layer_file = "%s.layer_%d_%s.npz" % (
loadfile, i,
datetime.datetime.now().strftime("%Y-%m-%d_%H:%M:%S"))
layer.to_file(layer_file)
images = layer.compup(images)
net.to_file(loadfile)
if 1:
results = net.compVHV(patches)
rmses = imtools.rmse(patches, results)
print "rmse", rmses.mean(), rmses.std()
if imtools.display_available():
plt.figure(figsize=(11.925, 12.425))
imtools.compare([patches, results], rows=8, cols=12, vlims=(-2,2))
# 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 = auto.SparseTrainer(layer, **train_params)
plt.figure(101)
raw_input("Please place the figure...")
auto.sgd(trainer, images, n_epochs=30, rate=0.05, vlims=(-2,2))
if 'filename' in locals():
layer.to_file(filename)
else:
layer = deepnet.CacheObject.from_file(filename)
### test the layer
if 1:
# test = timages[:200]
test = timages
recs = layer.compVHV(test)
rmse = np.sqrt(((recs - test)**2).mean())
print "rmse", rmse
plt.figure(1)
plt.clf()
imtools.compare([test, recs], vlims=(-2,2))
