<s> """<STR_LIT>""" <EOL> from __future__ import division , print_function , absolute_import <EOL> import tflearn <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> import tflearn . datasets . oxflower17 as oxflower17 <EOL> X , Y = oxflower17 . load_data ( one_hot = True ) <EOL> network = input_data ( shape = [ None , <NUM_LIT> , <NUM_LIT> , <NUM_LIT:3> ] ) <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> , strides = <NUM_LIT:2> ) <EOL> network = conv_2d ( network , <NUM_LIT> , <NUM_LIT:3> , activation = '<STR_LIT:relu>' ) <EOL> network = conv_2d ( network , <NUM_LIT> , <NUM_LIT:3> , activation = '<STR_LIT:relu>' ) <EOL> network = max_pool_2d ( network , <NUM_LIT:2> , strides = <NUM_LIT:2> ) <EOL> network = conv_2d ( network , <NUM_LIT> , <NUM_LIT:3> , activation = '<STR_LIT:relu>' ) <EOL> network = conv_2d ( network , <NUM_LIT> , <NUM_LIT:3> , activation = '<STR_LIT:relu>' ) <EOL> network = conv_2d ( network , <NUM_LIT> , <NUM_LIT:3> , activation = '<STR_LIT:relu>' ) <EOL> network = max_pool_2d ( network , <NUM_LIT:2> , strides = <NUM_LIT:2> ) <EOL> network = conv_2d ( network , <NUM_LIT> , <NUM_LIT:3> , activation = '<STR_LIT:relu>' ) <EOL> network = conv_2d ( network , <NUM_LIT> , <NUM_LIT:3> , activation = '<STR_LIT:relu>' ) <EOL> network = conv_2d ( network , <NUM_LIT> , <NUM_LIT:3> , activation = '<STR_LIT:relu>' ) <EOL> network = max_pool_2d ( network , <NUM_LIT:2> , strides = <NUM_LIT:2> ) <EOL> network = conv_2d ( network , <NUM_LIT> , <NUM_LIT:3> , activation = '<STR_LIT:relu>' ) <EOL> network = conv_2d ( network , <NUM_LIT> , <NUM_LIT:3> , activation = '<STR_LIT:relu>' ) <EOL> network = conv_2d ( network , <NUM_LIT> , <NUM_LIT:3> , activation = '<STR_LIT:relu>' ) <EOL> network = max_pool_2d ( network , <NUM_LIT:2> , strides = <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> , activation = '<STR_LIT:relu>' ) <EOL> network = dropout ( network , <NUM_LIT:0.5> ) <EOL> network = fully_connected ( network , <NUM_LIT> , activation = '<STR_LIT>' ) <EOL> network = regression ( network , optimizer = '<STR_LIT>' , <EOL> loss = '<STR_LIT>' , <EOL> learning_rate = <NUM_LIT> ) <EOL> model = tflearn . DNN ( network , checkpoint_path = '<STR_LIT>' , <EOL> max_checkpoints = <NUM_LIT:1> , tensorboard_verbose = <NUM_LIT:0> ) <EOL> model . fit ( X , Y , n_epoch = <NUM_LIT> , shuffle = True , <EOL> show_metric = True , batch_size = <NUM_LIT:32> , snapshot_step = <NUM_LIT> , <EOL> snapshot_epoch = False , run_id = '<STR_LIT>' ) </s>
