<s> import tensorflow as tf <EOL> import argparse <EOL> import numpy as np <EOL> import os <EOL> from tensorpack . train import TrainConfig , QueueInputTrainer <EOL> from tensorpack . models import * <EOL> from tensorpack . callbacks import * <EOL> from tensorpack . utils import * <EOL> from tensorpack . tfutils import * <EOL> from tensorpack . tfutils . symbolic_functions import * <EOL> from tensorpack . tfutils . summary import * <EOL> from tensorpack . dataflow import * <EOL> from tensorpack . dataflow import imgaug <EOL> """<STR_LIT>""" <EOL> class Model ( ModelDesc ) : <EOL> def _get_input_vars ( self ) : <EOL> return [ InputVar ( tf . float32 , [ None , <NUM_LIT> , <NUM_LIT> , <NUM_LIT:3> ] , '<STR_LIT:input>' ) , <EOL> InputVar ( tf . int32 , [ None ] , '<STR_LIT:label>' ) ] <EOL> def _get_cost ( self , input_vars , is_training ) : <EOL> image , label = input_vars <EOL> keep_prob = tf . constant ( <NUM_LIT:0.5> if is_training else <NUM_LIT:1.0> ) <EOL> image = image / <NUM_LIT> - <NUM_LIT:1> <EOL> l = Conv2D ( '<STR_LIT>' , image , <NUM_LIT> , <NUM_LIT:5> , padding = '<STR_LIT>' ) <EOL> l = MaxPooling ( '<STR_LIT>' , l , <NUM_LIT:2> , padding = '<STR_LIT>' ) <EOL> l = Conv2D ( '<STR_LIT>' , l , <NUM_LIT:32> , <NUM_LIT:3> , padding = '<STR_LIT>' ) <EOL> l = Conv2D ( '<STR_LIT>' , l , <NUM_LIT:32> , <NUM_LIT:3> , padding = '<STR_LIT>' ) <EOL> l = MaxPooling ( '<STR_LIT>' , l , <NUM_LIT:2> , padding = '<STR_LIT>' ) <EOL> l = Conv2D ( '<STR_LIT>' , l , <NUM_LIT:64> , <NUM_LIT:3> , padding = '<STR_LIT>' ) <EOL> l = tf . nn . dropout ( l , keep_prob ) <EOL> l = FullyConnected ( '<STR_LIT>' , l , <NUM_LIT> , <EOL> b_init = tf . constant_initializer ( <NUM_LIT:0.1> ) ) <EOL> logits = FullyConnected ( '<STR_LIT>' , l , out_dim = <NUM_LIT:10> , nl = tf . identity ) <EOL> prob = tf . nn . softmax ( logits , name = '<STR_LIT>' ) <EOL> cost = tf . nn . sparse_softmax_cross_entropy_with_logits ( logits , label ) <EOL> cost = tf . reduce_mean ( cost , name = '<STR_LIT>' ) <EOL> tf . add_to_collection ( MOVING_SUMMARY_VARS_KEY , cost ) <EOL> wrong = prediction_incorrect ( logits , label ) <EOL> nr_wrong = tf . reduce_sum ( wrong , name = '<STR_LIT>' ) <EOL> tf . add_to_collection ( <EOL> MOVING_SUMMARY_VARS_KEY , tf . reduce_mean ( wrong , name = '<STR_LIT>' ) ) <EOL> wd_cost = regularize_cost ( '<STR_LIT>' , l2_regularizer ( <NUM_LIT> ) ) <EOL> tf . add_to_collection ( MOVING_SUMMARY_VARS_KEY , wd_cost ) <EOL> add_param_summary ( [ ( '<STR_LIT>' , [ '<STR_LIT>' , '<STR_LIT>' ] ) ] ) <EOL> return tf . add_n ( [ cost , wd_cost ] , name = '<STR_LIT>' ) <EOL> def get_config ( ) : <EOL> d1 = dataset . SVHNDigit ( '<STR_LIT:train>' ) <EOL> d2 = dataset . SVHNDigit ( '<STR_LIT>' ) <EOL> train = RandomMixData ( [ d1 , d2 ] ) <EOL> test = dataset . SVHNDigit ( '<STR_LIT:test>' ) <EOL> augmentors = [ <EOL> imgaug . Resize ( ( <NUM_LIT> , <NUM_LIT> ) ) , <EOL> imgaug . Brightness ( <NUM_LIT:30> ) , <EOL> imgaug . Contrast ( ( <NUM_LIT:0.5> , <NUM_LIT> ) ) , <EOL> imgaug . GaussianDeform ( <EOL> [ ( <NUM_LIT> , <NUM_LIT> ) , ( <NUM_LIT> , <NUM_LIT> ) , ( <NUM_LIT> , <NUM_LIT> ) , ( <NUM_LIT> , <NUM_LIT> ) ] , <EOL> ( <NUM_LIT> , <NUM_LIT> ) , <NUM_LIT> , <NUM_LIT:3> ) , <EOL> ] <EOL> train = AugmentImageComponent ( train , augmentors ) <EOL> train = BatchData ( train , <NUM_LIT> ) <EOL> nr_proc = <NUM_LIT:5> <EOL> train = PrefetchData ( train , <NUM_LIT:5> , nr_proc ) <EOL> step_per_epoch = train . size ( ) <EOL> augmentors = [ <EOL> imgaug . Resize ( ( <NUM_LIT> , <NUM_LIT> ) ) , <EOL> ] <EOL> test = AugmentImageComponent ( test , augmentors ) <EOL> test = BatchData ( test , <NUM_LIT> , remainder = True ) <EOL> sess_config = get_default_sess_config ( <NUM_LIT> ) <EOL> lr = tf . train . exponential_decay ( <EOL> learning_rate = <NUM_LIT> , <EOL> global_step = get_global_step_var ( ) , <EOL> decay_steps = train . size ( ) * <NUM_LIT> , <EOL> decay_rate = <NUM_LIT> , staircase = True , name = '<STR_LIT>' ) <EOL> tf . scalar_summary ( '<STR_LIT>' , lr ) <EOL> return TrainConfig ( <EOL> dataset = train , <EOL> optimizer = tf . train . AdamOptimizer ( lr ) , <EOL> callbacks = Callbacks ( [ <EOL> StatPrinter ( ) , <EOL> ModelSaver ( ) , <EOL> InferenceRunner ( dataset_test , <EOL> [ ScalarStats ( '<STR_LIT>' ) , ClassificationError ( ) ] ) <EOL> ] ) , <EOL> session_config = sess_config , <EOL> model = Model ( ) , <EOL> step_per_epoch = step_per_epoch , <EOL> max_epoch = <NUM_LIT> , <EOL> ) <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> parser = argparse . ArgumentParser ( ) <EOL> parser . add_argument ( '<STR_LIT>' , help = '<STR_LIT>' ) <EOL> parser . add_argument ( '<STR_LIT>' , help = '<STR_LIT>' ) <EOL> args = parser . parse_args ( ) <EOL> basename = os . path . basename ( __file__ ) <EOL> logger . set_logger_dir ( <EOL> os . path . join ( '<STR_LIT>' , basename [ : basename . rfind ( '<STR_LIT:.>' ) ] ) ) <EOL> if args . gpu : <EOL> os . environ [ '<STR_LIT>' ] = args . gpu <EOL> else : <EOL> os . environ [ '<STR_LIT>' ] = '<STR_LIT:0>' <EOL> with tf . Graph ( ) . as_default ( ) : <EOL> config = get_config ( ) <EOL> if args . load : <EOL> config . session_init = SaverRestore ( args . load ) <EOL> if args . gpu : <EOL> config . nr_tower = len ( args . gpu . split ( '<STR_LIT:U+002C>' ) ) <EOL> QueueInputTrainer ( config ) . train ( ) </s>
