<s> import DeepFried2 as df <EOL> from examples . utils import make_progressbar <EOL> from train import train <EOL> from test import test <EOL> from model import net , lenet2 <EOL> if __name__ == "<STR_LIT:__main__>" : <EOL> print ( "<STR_LIT>" ) <EOL> print ( "<STR_LIT>" ) <EOL> print ( "<STR_LIT>" ) <EOL> batch_size = <NUM_LIT:64> <EOL> ( Xtrain , ytrain ) , ( Xval , yval ) , ( Xtest , ytest ) = df . datasets . mnist . data ( ) <EOL> criterion = df . ClassNLLCriterion ( ) <EOL> def run ( optim ) : <EOL> progress = make_progressbar ( '<STR_LIT>' + str ( optim ) , <NUM_LIT:5> ) <EOL> progress . start ( ) <EOL> model = net ( ) <EOL> model . training ( ) <EOL> for epoch in range ( <NUM_LIT:5> ) : <EOL> train ( Xtrain , ytrain , model , optim , criterion , batch_size , '<STR_LIT:train>' ) <EOL> train ( Xtrain , ytrain , model , optim , criterion , batch_size , '<STR_LIT>' ) <EOL> progress . update ( epoch + <NUM_LIT:1> ) <EOL> progress . finish ( ) <EOL> model . evaluate ( ) <EOL> nll , _ = test ( Xtrain , ytrain , model , batch_size ) <EOL> _ , nerr = test ( Xval , yval , model , batch_size ) <EOL> print ( "<STR_LIT>" . format ( nll ) ) <EOL> print ( "<STR_LIT>" . format ( nerr ) ) <EOL> run ( df . SGD ( lr = <NUM_LIT> ) ) <EOL> run ( df . Momentum ( lr = <NUM_LIT> , momentum = <NUM_LIT> ) ) <EOL> run ( df . Nesterov ( lr = <NUM_LIT> , momentum = <NUM_LIT> ) ) <EOL> run ( df . AdaGrad ( lr = <NUM_LIT> , eps = <NUM_LIT> ) ) <EOL> run ( df . RMSProp ( lr = <NUM_LIT> , rho = <NUM_LIT> , eps = <NUM_LIT> ) ) <EOL> run ( df . DQNProp ( lr = <NUM_LIT> , rho = <NUM_LIT> , eps = <NUM_LIT> ) ) <EOL> run ( df . AdaDelta ( rho = <NUM_LIT> , lr = <NUM_LIT> , eps = <NUM_LIT> ) ) <EOL> run ( df . Adam ( alpha = <NUM_LIT> , beta1 = <NUM_LIT> , beta2 = <NUM_LIT> , eps = <NUM_LIT> ) ) </s>
