<s> """<STR_LIT>""" <EOL> from neon . util . argparser import NeonArgparser <EOL> from neon . initializers import Kaiming <EOL> from neon . layers import Conv , Pooling , GeneralizedCost , Activation <EOL> from neon . layers import MergeSum , SkipNode <EOL> from neon . optimizers import GradientDescentMomentum , Schedule <EOL> from neon . transforms import Rectlin , Softmax , CrossEntropyMulti , TopKMisclassification <EOL> from neon . models import Model <EOL> from neon . data import ImageLoader <EOL> from neon . callbacks . callbacks import Callbacks <EOL> import itertools as itt <EOL> import sys <EOL> parser = NeonArgparser ( __doc__ ) <EOL> parser . add_argument ( '<STR_LIT>' , type = int , default = <NUM_LIT:0> , <EOL> help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , action = "<STR_LIT:store_true>" , <EOL> help = "<STR_LIT>" ) <EOL> parser . add_argument ( '<STR_LIT>' , type = float , default = <NUM_LIT:100> , <EOL> help = '<STR_LIT>' ) <EOL> args = parser . parse_args ( ) <EOL> if args . depth in ( <NUM_LIT:0> , <NUM_LIT> ) : <EOL> stages = ( <NUM_LIT:2> , <NUM_LIT:2> , <NUM_LIT:2> , <NUM_LIT:2> ) <EOL> elif args . depth in ( <NUM_LIT:1> , <NUM_LIT> , <NUM_LIT:50> ) : <EOL> stages = ( <NUM_LIT:3> , <NUM_LIT:4> , <NUM_LIT:6> , <NUM_LIT:3> ) <EOL> elif args . depth in ( <NUM_LIT:2> , <NUM_LIT> , <NUM_LIT> ) : <EOL> stages = ( <NUM_LIT:3> , <NUM_LIT:4> , <NUM_LIT> , <NUM_LIT:3> ) <EOL> elif args . depth in ( <NUM_LIT:3> , <NUM_LIT> , <NUM_LIT> ) : <EOL> stages = ( <NUM_LIT:3> , <NUM_LIT:8> , <NUM_LIT> , <NUM_LIT:3> ) <EOL> elif args . depth in ( <NUM_LIT:4> , <NUM_LIT> , <NUM_LIT> ) : <EOL> stages = ( <NUM_LIT:3> , <NUM_LIT:7> , <NUM_LIT> , <NUM_LIT:3> ) <EOL> else : <EOL> print "<STR_LIT>" <EOL> sys . exit ( ) <EOL> img_set_options = dict ( repo_dir = args . data_dir , <EOL> inner_size = <NUM_LIT> , <EOL> subset_pct = args . subset_pct ) <EOL> train = ImageLoader ( set_name = '<STR_LIT:train>' , scale_range = ( <NUM_LIT> , <NUM_LIT> ) , shuffle = True , ** img_set_options ) <EOL> test = ImageLoader ( set_name = '<STR_LIT>' , scale_range = <NUM_LIT:0> , do_transforms = False , ** img_set_options ) <EOL> def conv_params ( fsize , nfm , strides = <NUM_LIT:1> , relu = True , batch_norm = True ) : <EOL> return dict ( fshape = ( fsize , fsize , nfm ) , <EOL> strides = strides , <EOL> activation = ( Rectlin ( ) if relu else None ) , <EOL> padding = ( <NUM_LIT:1> if fsize > <NUM_LIT:1> else <NUM_LIT:0> ) , <EOL> batch_norm = batch_norm , <EOL> init = Kaiming ( local = True ) ) <EOL> def module_factory ( nfm , stride = <NUM_LIT:1> ) : <EOL> nfm_out = nfm * <NUM_LIT:4> if args . bottleneck else nfm <EOL> use_skip = True if stride == <NUM_LIT:1> else False <EOL> stride = abs ( stride ) <EOL> sidepath = [ SkipNode ( ) if use_skip else Conv ( ** conv_params ( <NUM_LIT:1> , nfm_out , stride , False ) ) ] <EOL> if args . bottleneck : <EOL> mainpath = [ Conv ( ** conv_params ( <NUM_LIT:1> , nfm ) ) , <EOL> Conv ( ** conv_params ( <NUM_LIT:3> , nfm , stride ) ) , <EOL> Conv ( ** conv_params ( <NUM_LIT:1> , nfm_out , relu = False ) ) ] <EOL> else : <EOL> mainpath = [ Conv ( ** conv_params ( <NUM_LIT:3> , nfm , stride ) ) , <EOL> Conv ( ** conv_params ( <NUM_LIT:3> , nfm , relu = False ) ) ] <EOL> return [ MergeSum ( [ mainpath , sidepath ] ) , <EOL> Activation ( Rectlin ( ) ) ] <EOL> layers = [ Conv ( ** conv_params ( <NUM_LIT:7> , <NUM_LIT:64> , strides = <NUM_LIT:2> ) ) , <EOL> Pooling ( <NUM_LIT:3> , strides = <NUM_LIT:2> ) ] <EOL> nfms = list ( itt . chain . from_iterable ( [ itt . repeat ( <NUM_LIT:2> ** ( x + <NUM_LIT:6> ) , r ) for x , r in enumerate ( stages ) ] ) ) <EOL> strides = [ - <NUM_LIT:1> ] + [ <NUM_LIT:1> if cur == prev else <NUM_LIT:2> for cur , prev in zip ( nfms [ <NUM_LIT:1> : ] , nfms [ : - <NUM_LIT:1> ] ) ] <EOL> for nfm , stride in zip ( nfms , strides ) : <EOL> layers . append ( module_factory ( nfm , stride ) ) <EOL> layers . append ( Pooling ( '<STR_LIT:all>' , op = '<STR_LIT>' ) ) <EOL> layers . append ( Conv ( ** conv_params ( <NUM_LIT:1> , train . nclass , relu = False , batch_norm = False ) ) ) <EOL> layers . append ( Activation ( Softmax ( ) ) ) <EOL> model = Model ( layers = layers ) <EOL> weight_sched = Schedule ( [ <NUM_LIT:30> , <NUM_LIT> ] , <NUM_LIT:0.1> ) <EOL> opt = GradientDescentMomentum ( <NUM_LIT:0.1> , <NUM_LIT> , wdecay = <NUM_LIT> , schedule = weight_sched ) <EOL> valmetric = TopKMisclassification ( k = <NUM_LIT:5> ) <EOL> callbacks = Callbacks ( model , eval_set = test , metric = valmetric , ** args . callback_args ) <EOL> cost = GeneralizedCost ( costfunc = CrossEntropyMulti ( ) ) <EOL> model . fit ( train , optimizer = opt , num_epochs = args . epochs , cost = cost , callbacks = callbacks ) </s>
