<s> from training . train import TrainModel <EOL> from lasagne_extensions . nonlinearities import rectify <EOL> from data_preparation import mnist <EOL> from models import ADGMSSL <EOL> import numpy as np <EOL> def run_adgmssl_mnist ( ) : <EOL> """<STR_LIT>""" <EOL> n_labeled = <NUM_LIT:100> <EOL> n_samples = <NUM_LIT:100> <EOL> n_batches = <NUM_LIT> <EOL> mnist_data = mnist . load_semi_supervised ( n_batches = n_batches , n_labeled = n_labeled , n_samples = n_samples , <EOL> filter_std = <NUM_LIT:0.0> , seed = <NUM_LIT> , train_valid_combine = True ) <EOL> n , n_x = mnist_data [ <NUM_LIT:0> ] [ <NUM_LIT:0> ] . shape <EOL> bs = n / n_batches <EOL> model = ADGMSSL ( n_x = n_x , n_a = <NUM_LIT:100> , n_z = <NUM_LIT:100> , n_y = <NUM_LIT:10> , a_hidden = [ <NUM_LIT> , <NUM_LIT> ] , <EOL> z_hidden = [ <NUM_LIT> , <NUM_LIT> ] , xhat_hidden = [ <NUM_LIT> , <NUM_LIT> ] , y_hidden = [ <NUM_LIT> , <NUM_LIT> ] , <EOL> trans_func = rectify , 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> * n <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:10> <EOL> test_args [ '<STR_LIT>' ] [ '<STR_LIT>' ] = <NUM_LIT:1> <EOL> validate_args [ '<STR_LIT>' ] [ '<STR_LIT>' ] = <NUM_LIT:1> <EOL> def error_evaluation ( * args ) : <EOL> mean_evals = model . get_output ( mnist_data [ <NUM_LIT:1> ] [ <NUM_LIT:0> ] , <NUM_LIT:100> ) <EOL> t_class = np . argmax ( mnist_data [ <NUM_LIT:1> ] [ <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 , anneal_lr = <NUM_LIT> , anneal_lr_freq = <NUM_LIT:200> , output_freq = <NUM_LIT:1> , <EOL> pickle_f_custom_freq = <NUM_LIT:10> , f_custom_eval = error_evaluation ) <EOL> train . add_initial_training_notes ( "<STR_LIT>" % n_labeled ) <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> ) <EOL> if __name__ == "<STR_LIT:__main__>" : <EOL> run_adgmssl_mnist ( ) </s>
