<s> """<STR_LIT>""" <EOL> from neon . backends import gen_backend <EOL> from neon . data . text import Text <EOL> from neon . data . dataloaders import load_ptb_train , load_ptb_test <EOL> from neon . initializers import Uniform <EOL> from neon . layers import GeneralizedCost , LSTM , Affine , GRU <EOL> from neon . models import Model <EOL> from neon . optimizers import RMSProp , Schedule <EOL> from neon . transforms import Logistic , Tanh , Softmax , CrossEntropyMulti <EOL> from neon . callbacks . callbacks import Callbacks <EOL> from neon . util . argparser import NeonArgparser , extract_valid_args <EOL> parser = NeonArgparser ( __doc__ ) <EOL> parser . add_argument ( '<STR_LIT>' , default = '<STR_LIT>' , choices = [ '<STR_LIT>' , '<STR_LIT>' ] , <EOL> help = '<STR_LIT>' ) <EOL> args = parser . parse_args ( gen_be = False ) <EOL> args . batch_size = <NUM_LIT:64> <EOL> time_steps = <NUM_LIT:50> <EOL> hidden_size = <NUM_LIT> <EOL> gradient_clip_value = <NUM_LIT:5> <EOL> be = gen_backend ( ** extract_valid_args ( args , gen_backend ) ) <EOL> train_path = load_ptb_train ( path = args . data_dir ) <EOL> valid_path = load_ptb_test ( path = args . data_dir ) <EOL> train_set = Text ( time_steps , train_path ) <EOL> valid_set = Text ( time_steps , valid_path , vocab = train_set . vocab ) <EOL> init = Uniform ( low = - <NUM_LIT> , high = <NUM_LIT> ) <EOL> if args . rlayer_type == '<STR_LIT>' : <EOL> rlayer1 = LSTM ( hidden_size , init , activation = Tanh ( ) , gate_activation = Logistic ( ) ) <EOL> rlayer2 = LSTM ( hidden_size , init , activation = Tanh ( ) , gate_activation = Logistic ( ) ) <EOL> else : <EOL> rlayer1 = GRU ( hidden_size , init , activation = Tanh ( ) , gate_activation = Logistic ( ) ) <EOL> rlayer2 = GRU ( hidden_size , init , activation = Tanh ( ) , gate_activation = Logistic ( ) ) <EOL> layers = [ rlayer1 , <EOL> rlayer2 , <EOL> Affine ( len ( train_set . vocab ) , init , bias = init , activation = Softmax ( ) ) ] <EOL> cost = GeneralizedCost ( costfunc = CrossEntropyMulti ( usebits = True ) ) <EOL> model = Model ( layers = layers ) <EOL> learning_rate_sched = Schedule ( range ( <NUM_LIT:10> , args . epochs ) , <NUM_LIT> ) <EOL> optimizer = RMSProp ( gradient_clip_value = gradient_clip_value , <EOL> stochastic_round = args . rounding , <EOL> schedule = learning_rate_sched ) <EOL> callbacks = Callbacks ( model , eval_set = valid_set , ** args . callback_args ) <EOL> model . fit ( train_set , <EOL> optimizer = optimizer , <EOL> num_epochs = args . epochs , <EOL> cost = cost , callbacks = callbacks ) <EOL> ypred = model . get_outputs ( valid_set ) <EOL> shape = ( valid_set . nbatches , args . batch_size , time_steps ) <EOL> prediction = ypred . argmax ( <NUM_LIT:2> ) . reshape ( shape ) . transpose ( <NUM_LIT:1> , <NUM_LIT:0> , <NUM_LIT:2> ) <EOL> fraction_correct = ( prediction == valid_set . y ) . mean ( ) <EOL> print '<STR_LIT>' % ( ( <NUM_LIT:1> - fraction_correct ) * <NUM_LIT:100> ) </s>
