<s> import numpy as np <EOL> import tensorflow as tf <EOL> from tensorflow . python . framework import ops <EOL> from models . rbm_models import dbn <EOL> from utils import datasets <EOL> flags = tf . app . flags <EOL> FLAGS = flags . FLAGS <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_boolean ( '<STR_LIT>' , True , '<STR_LIT>' ) <EOL> flags . DEFINE_integer ( '<STR_LIT>' , <NUM_LIT:0> , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_boolean ( '<STR_LIT>' , False , '<STR_LIT>' ) <EOL> flags . DEFINE_float ( '<STR_LIT>' , <NUM_LIT:0.1> , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT:relu>' , '<STR_LIT>' ) <EOL> flags . DEFINE_float ( '<STR_LIT>' , <NUM_LIT> , '<STR_LIT>' ) <EOL> flags . DEFINE_float ( '<STR_LIT>' , <NUM_LIT> , '<STR_LIT>' ) <EOL> flags . DEFINE_integer ( '<STR_LIT>' , <NUM_LIT:10> , '<STR_LIT>' ) <EOL> flags . DEFINE_integer ( '<STR_LIT>' , <NUM_LIT:10> , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_string ( '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ) <EOL> flags . DEFINE_float ( '<STR_LIT>' , <NUM_LIT:1> , '<STR_LIT>' ) <EOL> rbm_names = [ _ for _ in FLAGS . rbm_names . split ( '<STR_LIT:U+002C>' ) if _ ] <EOL> layers = [ int ( _ ) for _ in FLAGS . layers . split ( '<STR_LIT:U+002C>' ) if _ ] <EOL> rbm_learning_rate = [ float ( _ ) for _ in FLAGS . rbm_learning_rate . split ( '<STR_LIT:U+002C>' ) if _ ] <EOL> rbm_num_epochs = [ int ( _ ) for _ in FLAGS . rbm_num_epochs . split ( '<STR_LIT:U+002C>' ) if _ ] <EOL> rbm_batch_size = [ int ( _ ) for _ in FLAGS . rbm_batch_size . split ( '<STR_LIT:U+002C>' ) if _ ] <EOL> rbm_gibbs_k = [ float ( _ ) for _ in FLAGS . rbm_gibbs_k . split ( '<STR_LIT:U+002C>' ) if _ ] <EOL> dae_params = { '<STR_LIT>' : layers , '<STR_LIT>' : rbm_learning_rate , '<STR_LIT>' : rbm_num_epochs , <EOL> '<STR_LIT>' : rbm_batch_size , '<STR_LIT>' : rbm_gibbs_k , '<STR_LIT>' : rbm_names } <EOL> for p in dae_params : <EOL> if len ( dae_params [ p ] ) != len ( layers ) : <EOL> dae_params [ p ] = [ dae_params [ p ] [ <NUM_LIT:0> ] for _ in layers ] <EOL> assert FLAGS . dataset in [ '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT>' ] <EOL> assert FLAGS . act_func in [ '<STR_LIT>' , '<STR_LIT>' , '<STR_LIT:relu>' ] <EOL> assert len ( layers ) > <NUM_LIT:0> <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> if FLAGS . dataset == '<STR_LIT>' : <EOL> trX , trY , vlX , vlY , teX , teY = datasets . load_mnist_dataset ( mode = '<STR_LIT>' ) <EOL> elif FLAGS . dataset == '<STR_LIT>' : <EOL> trX , trY , teX , teY = datasets . load_cifar10_dataset ( FLAGS . cifar_dir , mode = '<STR_LIT>' ) <EOL> vlX = teX [ : <NUM_LIT> ] <EOL> vlY = teY [ : <NUM_LIT> ] <EOL> elif FLAGS . dataset == '<STR_LIT>' : <EOL> def load_from_np ( dataset_path ) : <EOL> if dataset_path != '<STR_LIT>' : <EOL> return np . load ( dataset_path ) <EOL> else : <EOL> return None <EOL> trX , trY = load_from_np ( FLAGS . train_dataset ) , load_from_np ( FLAGS . train_labels ) <EOL> vlX , vlY = load_from_np ( FLAGS . valid_dataset ) , load_from_np ( FLAGS . valid_labels ) <EOL> teX , teY = load_from_np ( FLAGS . test_dataset ) , load_from_np ( FLAGS . test_labels ) <EOL> else : <EOL> trX , trY , vlX , vlY , teX , teY = None , None , None , None , None , None <EOL> srbm = dbn . DBN ( <EOL> model_name = FLAGS . model_name , rbm_names = dae_params [ '<STR_LIT>' ] , do_pretrain = FLAGS . do_pretrain , <EOL> layers = dae_params [ '<STR_LIT>' ] , dataset = FLAGS . dataset , main_dir = FLAGS . main_dir , act_func = FLAGS . act_func , <EOL> rbm_learning_rate = dae_params [ '<STR_LIT>' ] , rbm_gibbs_k = dae_params [ '<STR_LIT>' ] , <EOL> verbose = FLAGS . verbose , rbm_num_epochs = dae_params [ '<STR_LIT>' ] , momentum = FLAGS . momentum , <EOL> rbm_batch_size = dae_params [ '<STR_LIT>' ] , learning_rate = FLAGS . learning_rate , num_epochs = FLAGS . num_epochs , <EOL> batch_size = FLAGS . batch_size , opt = FLAGS . opt , loss_func = FLAGS . loss_func , dropout = FLAGS . dropout , <EOL> gauss_visible = FLAGS . gauss_visible , stddev = FLAGS . stddev ) <EOL> if FLAGS . do_pretrain : <EOL> srbm . pretrain ( trX , vlX ) <EOL> ops . reset_default_graph ( ) <EOL> print ( '<STR_LIT>' ) <EOL> srbm . build_model ( trX . shape [ <NUM_LIT:1> ] , trY . shape [ <NUM_LIT:1> ] ) <EOL> srbm . fit ( trX , trY , vlX , vlY ) <EOL> print ( '<STR_LIT>' . format ( srbm . predict ( teX , teY ) ) ) </s>
