<s> import theano <EOL> from theano import tensor <EOL> import pylearn2 <EOL> from pylearn2 . corruption import BinomialCorruptor <EOL> import pylearn2 . models . autoencoder <EOL> from udl . model import UDLModel <EOL> from udl . pylearni . datasets . VectorDataset import VectorDataset <EOL> from udl . pylearni . trainers . SGDTrainer import SGDTrainer <EOL> __author__ = '<STR_LIT>' <EOL> def abstractfit ( model , X_train ) : <EOL> nfeat = X_train . shape [ <NUM_LIT:1> ] <EOL> model . configs [ '<STR_LIT>' ] = nfeat <EOL> dataset = model . dataset_adaptor . fit_transform ( X_train ) <EOL> super ( type ( model ) , model ) . __init__ ( ** model . configs ) <EOL> trainer = model . trainer . get_trainer ( model , dataset ) <EOL> trainer . main_loop ( ) <EOL> X = tensor . matrix ( ) <EOL> ff = theano . function ( [ X ] , model . encode ( X ) ) <EOL> model . estimator = ff <EOL> return model <EOL> class Autoencoder ( pylearn2 . models . autoencoder . Autoencoder , UDLModel ) : <EOL> def __init__ ( self , nvis = <NUM_LIT:0> , nhid = <NUM_LIT:10> , act_enc = '<STR_LIT>' , act_dec = '<STR_LIT>' , tied_weights = False , irange = <NUM_LIT> , rng = <NUM_LIT> , <EOL> dataset_adaptor = VectorDataset ( ) , <EOL> trainer = SGDTrainer ( ) ) : <EOL> self . configs = { '<STR_LIT>' : nvis , <EOL> '<STR_LIT>' : nhid , <EOL> '<STR_LIT>' : act_enc , <EOL> '<STR_LIT>' : act_dec , <EOL> '<STR_LIT>' : tied_weights , <EOL> '<STR_LIT>' : irange , <EOL> '<STR_LIT>' : rng , <EOL> } <EOL> self . dataset_adaptor = dataset_adaptor <EOL> self . trainer = trainer <EOL> def fit ( self , X_train ) : <EOL> return abstractfit ( self , X_train ) <EOL> class DenoisingAutoencoder ( pylearn2 . models . autoencoder . DenoisingAutoencoder , UDLModel ) : <EOL> def __init__ ( self , <EOL> corruptor = BinomialCorruptor ( <NUM_LIT:0.5> ) , <EOL> nvis = <NUM_LIT:0> , <EOL> nhid = <NUM_LIT:10> , <EOL> act_enc = '<STR_LIT>' , <EOL> act_dec = '<STR_LIT>' , <EOL> tied_weights = False , <EOL> irange = <NUM_LIT> , <EOL> rng = <NUM_LIT> , <EOL> dataset_adaptor = VectorDataset ( ) , <EOL> trainer = SGDTrainer ( ) <EOL> ) : <EOL> self . configs = { '<STR_LIT>' : corruptor , <EOL> '<STR_LIT>' : nvis , <EOL> '<STR_LIT>' : nhid , <EOL> '<STR_LIT>' : act_enc , <EOL> '<STR_LIT>' : act_dec , <EOL> '<STR_LIT>' : tied_weights , <EOL> '<STR_LIT>' : irange , <EOL> '<STR_LIT>' : rng , <EOL> } <EOL> self . dataset_adaptor = dataset_adaptor <EOL> self . trainer = trainer <EOL> def fit ( self , X_train ) : <EOL> return abstractfit ( self , X_train ) <EOL> class ContractiveAutoencoder ( pylearn2 . models . autoencoder . ContractiveAutoencoder , UDLModel ) : <EOL> def __init__ ( self , <EOL> nvis = <NUM_LIT:0> , <EOL> nhid = <NUM_LIT:10> , <EOL> act_enc = '<STR_LIT>' , <EOL> act_dec = '<STR_LIT>' , <EOL> tied_weights = False , <EOL> irange = <NUM_LIT> , <EOL> rng = <NUM_LIT> , <EOL> dataset_adaptor = VectorDataset ( ) , <EOL> trainer = SGDTrainer ( ) ) : <EOL> self . configs = { <EOL> '<STR_LIT>' : nvis , <EOL> '<STR_LIT>' : nhid , <EOL> '<STR_LIT>' : act_enc , <EOL> '<STR_LIT>' : act_dec , <EOL> '<STR_LIT>' : tied_weights , <EOL> '<STR_LIT>' : irange , <EOL> '<STR_LIT>' : rng , <EOL> } <EOL> self . dataset_adaptor = dataset_adaptor <EOL> self . trainer = trainer <EOL> def fit ( self , X_train ) : <EOL> return abstractfit ( self , X_train ) <EOL> class HigherOrderContractiveAutoencoder ( pylearn2 . models . autoencoder . HigherOrderContractiveAutoencoder , UDLModel ) : <EOL> def __init__ ( self , corruptor = BinomialCorruptor ( <NUM_LIT:0.5> ) , <EOL> num_corruptions = <NUM_LIT:2> , <EOL> nvis = <NUM_LIT:0> , <EOL> nhid = <NUM_LIT:10> , <EOL> act_enc = '<STR_LIT>' , <EOL> act_dec = '<STR_LIT>' , <EOL> tied_weights = False , <EOL> irange = <NUM_LIT> , <EOL> rng = <NUM_LIT> , <EOL> dataset_adaptor = VectorDataset ( ) , <EOL> trainer = SGDTrainer ( ) ) : <EOL> self . configs = { '<STR_LIT>' : corruptor , <EOL> '<STR_LIT>' : num_corruptions , <EOL> '<STR_LIT>' : nvis , <EOL> '<STR_LIT>' : nhid , <EOL> '<STR_LIT>' : act_enc , <EOL> '<STR_LIT>' : act_dec , <EOL> '<STR_LIT>' : tied_weights , <EOL> '<STR_LIT>' : irange , <EOL> '<STR_LIT>' : rng , <EOL> } <EOL> self . dataset_adaptor = dataset_adaptor <EOL> self . trainer = trainer <EOL> def fit ( self , X_train ) : <EOL> return abstractfit ( self , X_train ) </s>
