<s> import theano <EOL> from training . train import TrainModel <EOL> from lasagne_extensions . nonlinearities import rectify <EOL> from data_loaders import mnist <EOL> from models . sdgmssl import SDGMSSL <EOL> import numpy as np <EOL> def run_sdgmssl_mnist ( ) : <EOL> """<STR_LIT>""" <EOL> seed = np . random . randint ( <NUM_LIT:1> , <NUM_LIT> ) <EOL> n_labeled = <NUM_LIT:100> <EOL> mnist_data = mnist . load_semi_supervised ( n_labeled = n_labeled , filter_std = <NUM_LIT:0.0> , seed = seed , train_valid_combine = True ) <EOL> n , n_x = mnist_data [ <NUM_LIT:0> ] [ <NUM_LIT:0> ] . shape <EOL> n_samples = <NUM_LIT:100> <EOL> n_batches = n / <NUM_LIT:100> <EOL> bs = n / n_batches <EOL> model = SDGMSSL ( n_x = n_x , n_a = <NUM_LIT:100> , n_z = <NUM_LIT:100> , n_y = <NUM_LIT:10> , qa_hid = [ <NUM_LIT> , <NUM_LIT> ] , <EOL> qz_hid = [ <NUM_LIT> , <NUM_LIT> ] , qy_hid = [ <NUM_LIT> , <NUM_LIT> ] , px_hid = [ <NUM_LIT> , <NUM_LIT> ] , pa_hid = [ <NUM_LIT> , <NUM_LIT> ] , <EOL> nonlinearity = rectify , batchnorm = True , x_dist = '<STR_LIT>' ) <EOL> f_train , f_test , f_validate , train_args , test_args , validate_args = model . build_model ( * mnist_data ) <EOL> train_args [ '<STR_LIT>' ] [ '<STR_LIT>' ] = bs <EOL> train_args [ '<STR_LIT>' ] [ '<STR_LIT>' ] = n_samples <EOL> train_args [ '<STR_LIT>' ] [ '<STR_LIT>' ] = <NUM_LIT> <EOL> train_args [ '<STR_LIT>' ] [ '<STR_LIT>' ] = <NUM_LIT> <EOL> train_args [ '<STR_LIT>' ] [ '<STR_LIT>' ] = <NUM_LIT> <EOL> train_args [ '<STR_LIT>' ] [ '<STR_LIT>' ] = <NUM_LIT> <EOL> train_args [ '<STR_LIT>' ] [ '<STR_LIT>' ] = <NUM_LIT:5> <EOL> test_args [ '<STR_LIT>' ] [ '<STR_LIT>' ] = <NUM_LIT:5> <EOL> validate_args [ '<STR_LIT>' ] [ '<STR_LIT>' ] = <NUM_LIT:5> <EOL> def custom_evaluation ( model , path ) : <EOL> mean_evals = model . get_output ( mnist_data [ <NUM_LIT:2> ] [ <NUM_LIT:0> ] , <NUM_LIT:100> ) <EOL> t_class = np . argmax ( mnist_data [ <NUM_LIT:2> ] [ <NUM_LIT:1> ] , axis = <NUM_LIT:1> ) <EOL> y_class = np . argmax ( mean_evals , axis = <NUM_LIT:1> ) <EOL> missclass = ( np . sum ( y_class != t_class , dtype = '<STR_LIT>' ) / len ( y_class ) ) * <NUM_LIT> <EOL> train . write_to_logger ( "<STR_LIT>" % missclass ) <EOL> train = TrainModel ( model = model , output_freq = <NUM_LIT:1> , pickle_f_custom_freq = <NUM_LIT:10> , f_custom_eval = custom_evaluation ) <EOL> train . add_initial_training_notes ( "<STR_LIT>" % ( <EOL> n_labeled , str ( model . batchnorm ) , seed ) ) <EOL> train . train_model ( f_train , train_args , <EOL> f_test , test_args , <EOL> f_validate , validate_args , <EOL> n_train_batches = n_batches , <EOL> n_epochs = <NUM_LIT:1000> , <EOL> anneal = [ ( "<STR_LIT>" , <NUM_LIT:200> , <NUM_LIT> , <NUM_LIT> ) ] ) <EOL> if __name__ == "<STR_LIT:__main__>" : <EOL> run_sdgmssl_mnist ( ) </s>
