def pattern2neuralnet(pattern, verbose=0):
"""
Apply this to a pattern object, and it will behave similar to a
nolearn.lasagne.NeuralNet object.
"""
pattern.layers = []
pattern.layers_ = Layers()
pattern.verbose = verbose
for fn_name in ['phi', 'psi', 'beta']:
if fn_name not in pattern.__dict__ or pattern.__dict__[fn_name] is None:
continue
fn = pattern.__dict__[fn_name]
for i, l in enumerate(pattern._get_all_function_layers(fn)):
name = l.name
dct = l.__dict__
if name is None:
name = ""
prefix = "%s%d" % (fn_name, i)
if prefix not in name:
name = prefix + ("_" + name if name != "" else name)
dct['name'] = name
pattern.layers_[name] = l
pattern.layers.append((l.__class__, dct))
return pattern
def layers(self):
from nolearn.lasagne.base import Layers
return Layers([('one', 1), ('two', 2), ('three', 3)])