<s> import argparse <EOL> import numpy as np <EOL> import os <EOL> import warnings <EOL> import theano <EOL> import theano . tensor as T <EOL> from theano . tensor . shared_randomstreams import RandomStreams <EOL> from blocks . algorithms import ( RMSProp , GradientDescent , CompositeRule , <EOL> RemoveNotFinite ) <EOL> from blocks . extensions import FinishAfter , Timing , Printing <EOL> from blocks . extensions . monitoring import ( TrainingDataMonitoring , <EOL> DataStreamMonitoring ) <EOL> from blocks . extensions . saveload import Checkpoint <EOL> from blocks . extensions . training import SharedVariableModifier <EOL> from blocks . filter import VariableFilter <EOL> from blocks . graph import ComputationGraph , apply_dropout <EOL> from blocks . main_loop import MainLoop <EOL> import blocks . model <EOL> from blocks . roles import INPUT , PARAMETER <EOL> from fuel . streams import DataStream <EOL> from fuel . schemes import ShuffledScheme <EOL> from fuel . transformers import Flatten , ScaleAndShift <EOL> import extensions <EOL> import model <EOL> import util <EOL> def parse_args ( ) : <EOL> parser = argparse . ArgumentParser ( ) <EOL> parser . add_argument ( '<STR_LIT>' , default = <NUM_LIT> , type = int , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , default = <NUM_LIT> , type = float , <EOL> help = '<STR_LIT>' + '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , default = None , type = str , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , default = '<STR_LIT>' , type = str , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = str , default = '<STR_LIT>' , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , default = <NUM_LIT> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = str , default = '<STR_LIT>' , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = float , default = <NUM_LIT:0.> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = str , default = '<STR_LIT>' , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , type = bool , default = False , <EOL> help = '<STR_LIT>' ) <EOL> args = parser . parse_args ( ) <EOL> model_args = eval ( '<STR_LIT>' + args . model_args + '<STR_LIT:)>' ) <EOL> print model_args <EOL> if not os . path . exists ( args . output_dir ) : <EOL> raise IOError ( "<STR_LIT>" % args . output_dir ) <EOL> return args , model_args <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> batches_per_epoch = <NUM_LIT> <EOL> import sys <EOL> sys . setrecursionlimit ( <NUM_LIT> ) <EOL> args , model_args = parse_args ( ) <EOL> if args . resume_file is not None : <EOL> print "<STR_LIT>" + args . resume_file <EOL> from blocks . scripts import continue_training <EOL> continue_training ( args . resume_file ) <EOL> if args . dataset == '<STR_LIT>' : <EOL> from fuel . datasets import MNIST <EOL> dataset_train = MNIST ( [ '<STR_LIT:train>' ] , sources = ( '<STR_LIT>' , ) ) <EOL> dataset_test = MNIST ( [ '<STR_LIT:test>' ] , sources = ( '<STR_LIT>' , ) ) <EOL> n_colors = <NUM_LIT:1> <EOL> spatial_width = <NUM_LIT> <EOL> elif args . dataset == '<STR_LIT>' : <EOL> from fuel . datasets import CIFAR10 <EOL> dataset_train = CIFAR10 ( [ '<STR_LIT:train>' ] , sources = ( '<STR_LIT>' , ) ) <EOL> dataset_test = CIFAR10 ( [ '<STR_LIT:test>' ] , sources = ( '<STR_LIT>' , ) ) <EOL> n_colors = <NUM_LIT:3> <EOL> spatial_width = <NUM_LIT:32> <EOL> elif args . dataset == '<STR_LIT>' : <EOL> from imagenet_data import IMAGENET <EOL> spatial_width = <NUM_LIT> <EOL> dataset_train = IMAGENET ( [ '<STR_LIT:train>' ] , width = spatial_width ) <EOL> dataset_test = IMAGENET ( [ '<STR_LIT:test>' ] , width = spatial_width ) <EOL> n_colors = <NUM_LIT:3> <EOL> else : <EOL> raise ValueError ( "<STR_LIT>" % args . dataset ) <EOL> train_stream = Flatten ( DataStream . default_stream ( dataset_train , <EOL> iteration_scheme = ShuffledScheme ( <EOL> examples = dataset_train . num_examples , <EOL> batch_size = args . batch_size ) ) ) <EOL> test_stream = Flatten ( DataStream . default_stream ( dataset_test , <EOL> iteration_scheme = ShuffledScheme ( <EOL> examples = dataset_test . num_examples , <EOL> batch_size = args . batch_size ) ) <EOL> ) <EOL> shp = next ( train_stream . get_epoch_iterator ( ) ) [ <NUM_LIT:0> ] . shape <EOL> Xbatch = next ( train_stream . get_epoch_iterator ( ) ) [ <NUM_LIT:0> ] <EOL> scl = <NUM_LIT:1.> / np . sqrt ( np . mean ( ( Xbatch - np . mean ( Xbatch ) ) ** <NUM_LIT:2> ) ) <EOL> shft = - np . mean ( Xbatch * scl ) <EOL> train_stream = ScaleAndShift ( train_stream , scl , shft ) <EOL> test_stream = ScaleAndShift ( test_stream , scl , shft ) <EOL> baseline_uniform_noise = <NUM_LIT:1.> / <NUM_LIT> <EOL> uniform_noise = baseline_uniform_noise / scl <EOL> dpm = model . DiffusionModel ( spatial_width , n_colors , uniform_noise = uniform_noise , ** model_args ) <EOL> dpm . initialize ( ) <EOL> features = T . matrix ( '<STR_LIT>' , dtype = theano . config . floatX ) <EOL> cost = dpm . cost ( features ) <EOL> blocks_model = blocks . model . Model ( cost ) <EOL> cg_nodropout = ComputationGraph ( cost ) <EOL> if args . dropout_rate > <NUM_LIT:0> : <EOL> inputs = VariableFilter ( roles = [ INPUT ] ) ( cg_nodropout . variables ) <EOL> cg = apply_dropout ( cg_nodropout , inputs , args . dropout_rate ) <EOL> else : <EOL> cg = cg_nodropout <EOL> step_compute = RMSProp ( learning_rate = args . lr , max_scaling = <NUM_LIT> ) <EOL> algorithm = GradientDescent ( step_rule = CompositeRule ( [ RemoveNotFinite ( ) , <EOL> step_compute ] ) , <EOL> parameters = cg . parameters , cost = cost ) <EOL> extension_list = [ ] <EOL> extension_list . append ( <EOL> SharedVariableModifier ( step_compute . learning_rate , <EOL> extensions . decay_learning_rate , <EOL> after_batch = False , <EOL> every_n_batches = batches_per_epoch , ) ) <EOL> extension_list . append ( FinishAfter ( after_n_epochs = <NUM_LIT> ) ) <EOL> extension_list . append ( extensions . LogLikelihood ( dpm , test_stream , scl , <EOL> every_n_batches = args . ext_every_n * batches_per_epoch , before_training = False ) ) <EOL> extension_list . extend ( [ Timing ( ) , Printing ( ) ] ) <EOL> model_dir = util . create_log_dir ( args , dpm . name + '<STR_LIT:_>' + args . dataset ) <EOL> model_save_name = os . path . join ( model_dir , '<STR_LIT>' ) <EOL> extension_list . append ( <EOL> Checkpoint ( model_save_name , every_n_batches = args . ext_every_n * batches_per_epoch , save_separately = [ '<STR_LIT>' ] ) ) <EOL> extension_list . append ( extensions . PlotMonitors ( model_dir , <EOL> every_n_batches = args . ext_every_n * batches_per_epoch , before_training = args . plot_before_training ) ) <EOL> test_batch = next ( test_stream . get_epoch_iterator ( ) ) [ <NUM_LIT:0> ] <EOL> extension_list . append ( extensions . PlotSamples ( dpm , algorithm , test_batch , model_dir , <EOL> every_n_batches = args . ext_every_n * batches_per_epoch , before_training = args . plot_before_training ) ) <EOL> internal_state = dpm . internal_state ( features ) <EOL> train_batch = next ( train_stream . get_epoch_iterator ( ) ) [ <NUM_LIT:0> ] <EOL> extension_list . append ( <EOL> extensions . PlotParameters ( dpm , blocks_model , model_dir , <EOL> every_n_batches = args . ext_every_n * batches_per_epoch , before_training = args . plot_before_training ) ) <EOL> sys . setrecursionlimit ( <NUM_LIT> ) <EOL> main_loop = MainLoop ( model = blocks_model , algorithm = algorithm , <EOL> data_stream = train_stream , <EOL> extensions = extension_list ) <EOL> main_loop . run ( ) </s>
