<s> """<STR_LIT>""" <EOL> from __future__ import division , print_function , absolute_import <EOL> import tflearn <EOL> from tflearn . data_utils import shuffle , to_categorical <EOL> from tflearn . layers . core import input_data , dropout , fully_connected <EOL> from tflearn . layers . conv import conv_2d , max_pool_2d <EOL> from tflearn . layers . estimator import regression <EOL> from tflearn . datasets import cifar10 <EOL> ( X , Y ) , ( X_test , Y_test ) = cifar10 . load_data ( ) <EOL> X , Y = shuffle ( X , Y ) <EOL> print ( Y ) <EOL> Y = to_categorical ( Y , <NUM_LIT:10> ) <EOL> print ( Y ) <EOL> Y_test = to_categorical ( Y_test , <NUM_LIT:10> ) <EOL> network = input_data ( shape = [ None , <NUM_LIT:32> , <NUM_LIT:32> , <NUM_LIT:3> ] ) <EOL> network = conv_2d ( network , <NUM_LIT:32> , <NUM_LIT:3> , activation = '<STR_LIT:relu>' ) <EOL> network = max_pool_2d ( network , <NUM_LIT:2> ) <EOL> network = conv_2d ( network , <NUM_LIT:64> , <NUM_LIT:3> , activation = '<STR_LIT:relu>' ) <EOL> network = conv_2d ( network , <NUM_LIT:64> , <NUM_LIT:3> , activation = '<STR_LIT:relu>' ) <EOL> network = max_pool_2d ( network , <NUM_LIT:2> ) <EOL> network = fully_connected ( network , <NUM_LIT> , activation = '<STR_LIT:relu>' ) <EOL> network = dropout ( network , <NUM_LIT:0.5> ) <EOL> network = fully_connected ( network , <NUM_LIT:10> , activation = '<STR_LIT>' ) <EOL> network = regression ( network , optimizer = '<STR_LIT>' , <EOL> loss = '<STR_LIT>' , <EOL> learning_rate = <NUM_LIT> ) <EOL> model = tflearn . DNN ( network , tensorboard_verbose = <NUM_LIT:0> ) <EOL> model . fit ( X , Y , n_epoch = <NUM_LIT:50> , shuffle = True , validation_set = ( X_test , Y_test ) , <EOL> show_metric = True , batch_size = <NUM_LIT> , run_id = '<STR_LIT>' ) </s>
