<s> """<STR_LIT>""" <EOL> __author__ = '<STR_LIT>' <EOL> import tensorflow as tf <EOL> Data_PATH = '<STR_LIT>' <EOL> n_input = <NUM_LIT:32> * <NUM_LIT:32> * <NUM_LIT:3> <EOL> out_conv_1 = <NUM_LIT:64> <EOL> out_conv_2 = <NUM_LIT:64> <EOL> n_hidden_1 = <NUM_LIT> <EOL> n_hidden_2 = <NUM_LIT> <EOL> dropout = <NUM_LIT> <EOL> NUM_CLASSES = <NUM_LIT:10> <EOL> NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN = <NUM_LIT> <EOL> NUM_EXAMPLES_PER_EPOCH_FOR_EVAL = <NUM_LIT> <EOL> NUM_EPOCHS_PER_DECAY = <NUM_LIT> <EOL> LEARNING_RATE_DECAY_FACTOR = <NUM_LIT> <EOL> INITIAL_LEARNING_RATE = <NUM_LIT> <EOL> FLAGS = tf . app . flags . FLAGS <EOL> def conv2d ( img , w , b ) : <EOL> return tf . nn . relu ( tf . nn . bias_add ( tf . nn . conv2d ( img , w , strides = [ <NUM_LIT:1> , <NUM_LIT:1> , <NUM_LIT:1> , <NUM_LIT:1> ] , padding = '<STR_LIT>' ) , b ) ) <EOL> def max_pool ( img , k ) : <EOL> return tf . nn . max_pool ( img , ksize = [ <NUM_LIT:1> , k , k , <NUM_LIT:1> ] , strides = [ <NUM_LIT:1> , k , k , <NUM_LIT:1> ] , padding = '<STR_LIT>' ) <EOL> def inference ( images ) : <EOL> """<STR_LIT>""" <EOL> print ( '<STR_LIT>' , images . get_shape ( ) , type ( images ) ) <EOL> images = tf . reshape ( images , shape = [ - <NUM_LIT:1> , <NUM_LIT:32> , <NUM_LIT:32> , <NUM_LIT:3> ] ) <EOL> _dropout = tf . Variable ( dropout ) <EOL> _weights = { <EOL> '<STR_LIT>' : tf . Variable ( tf . random_normal ( [ <NUM_LIT:5> , <NUM_LIT:5> , <NUM_LIT:3> , out_conv_1 ] , stddev = <NUM_LIT> ) ) , <EOL> '<STR_LIT>' : tf . Variable ( tf . random_normal ( [ <NUM_LIT:5> , <NUM_LIT:5> , out_conv_1 , out_conv_2 ] , stddev = <NUM_LIT> ) ) , <EOL> '<STR_LIT>' : tf . Variable ( tf . random_normal ( [ out_conv_2 * <NUM_LIT:8> * <NUM_LIT:8> , n_hidden_1 ] , stddev = <NUM_LIT> ) ) , <EOL> '<STR_LIT>' : tf . Variable ( tf . random_normal ( [ n_hidden_1 , n_hidden_2 ] , stddev = <NUM_LIT> ) ) , <EOL> '<STR_LIT>' : tf . Variable ( tf . random_normal ( [ n_hidden_2 , NUM_CLASSES ] , stddev = <NUM_LIT> ) ) <EOL> } <EOL> _biases = { <EOL> '<STR_LIT>' : tf . Variable ( tf . random_normal ( [ out_conv_1 ] ) ) , <EOL> '<STR_LIT>' : tf . Variable ( tf . random_normal ( [ out_conv_2 ] ) ) , <EOL> '<STR_LIT>' : tf . Variable ( tf . random_normal ( [ n_hidden_1 ] ) ) , <EOL> '<STR_LIT>' : tf . Variable ( tf . random_normal ( [ n_hidden_2 ] ) ) , <EOL> '<STR_LIT>' : tf . Variable ( tf . random_normal ( [ NUM_CLASSES ] ) ) <EOL> } <EOL> with tf . name_scope ( '<STR_LIT>' ) : <EOL> conv1 = conv2d ( images , _weights [ '<STR_LIT>' ] , _biases [ '<STR_LIT>' ] ) <EOL> conv1 = max_pool ( conv1 , k = <NUM_LIT:2> ) <EOL> conv1 = tf . nn . lrn ( conv1 , <NUM_LIT:4> , bias = <NUM_LIT:1.0> , alpha = <NUM_LIT> / <NUM_LIT> , beta = <NUM_LIT> , <EOL> name = '<STR_LIT>' ) <EOL> conv1 = tf . nn . dropout ( conv1 , _dropout ) <EOL> with tf . name_scope ( '<STR_LIT>' ) : <EOL> conv2 = conv2d ( conv1 , _weights [ '<STR_LIT>' ] , _biases [ '<STR_LIT>' ] ) <EOL> conv2 = tf . nn . lrn ( conv2 , <NUM_LIT:4> , bias = <NUM_LIT:1.0> , alpha = <NUM_LIT> / <NUM_LIT> , beta = <NUM_LIT> , <EOL> name = '<STR_LIT>' ) <EOL> conv2 = max_pool ( conv2 , k = <NUM_LIT:2> ) <EOL> conv2 = tf . nn . dropout ( conv2 , _dropout ) <EOL> with tf . name_scope ( '<STR_LIT>' ) : <EOL> dense1 = tf . reshape ( conv2 , <EOL> [ - <NUM_LIT:1> , _weights [ '<STR_LIT>' ] . get_shape ( ) . as_list ( ) [ <NUM_LIT:0> ] ] ) <EOL> dense1 = tf . nn . relu_layer ( dense1 , _weights [ '<STR_LIT>' ] , _biases [ '<STR_LIT>' ] ) <EOL> dense1 = tf . nn . dropout ( dense1 , _dropout ) <EOL> with tf . name_scope ( '<STR_LIT>' ) : <EOL> dense2 = tf . nn . relu_layer ( dense1 , _weights [ '<STR_LIT>' ] , _biases [ '<STR_LIT>' ] ) <EOL> logits = tf . add ( tf . matmul ( dense2 , _weights [ '<STR_LIT>' ] ) , _biases [ '<STR_LIT>' ] ) <EOL> return logits <EOL> def loss ( logits , labels ) : <EOL> """<STR_LIT>""" <EOL> sparse_labels = tf . reshape ( labels , [ FLAGS . batch_size , <NUM_LIT:1> ] ) <EOL> indices = tf . reshape ( tf . range ( <NUM_LIT:0> , FLAGS . batch_size ) , [ FLAGS . batch_size , <NUM_LIT:1> ] ) <EOL> concated = tf . concat ( <NUM_LIT:1> , [ indices , sparse_labels ] ) <EOL> dense_labels = tf . sparse_to_dense ( concated , <EOL> [ FLAGS . batch_size , NUM_CLASSES ] , <EOL> <NUM_LIT:1.0> , <NUM_LIT:0.0> ) <EOL> cross_entropy = tf . nn . softmax_cross_entropy_with_logits ( <EOL> logits , dense_labels , name = '<STR_LIT>' ) <EOL> cross_entropy_mean = tf . reduce_mean ( cross_entropy , name = '<STR_LIT>' ) <EOL> tf . add_to_collection ( '<STR_LIT>' , cross_entropy_mean ) <EOL> return tf . add_n ( tf . get_collection ( '<STR_LIT>' ) , name = '<STR_LIT>' ) <EOL> def training ( loss , global_step ) : <EOL> """<STR_LIT>""" <EOL> num_batches_per_epoch = NUM_EXAMPLES_PER_EPOCH_FOR_TRAIN / FLAGS . batch_size <EOL> decay_steps = int ( num_batches_per_epoch * NUM_EPOCHS_PER_DECAY ) <EOL> print ( '<STR_LIT>' , decay_steps ) <EOL> lr = tf . train . exponential_decay ( INITIAL_LEARNING_RATE , <EOL> global_step , <EOL> decay_steps , <EOL> LEARNING_RATE_DECAY_FACTOR , <EOL> staircase = True ) <EOL> tf . scalar_summary ( '<STR_LIT>' , lr ) <EOL> tf . scalar_summary ( loss . op . name , loss ) <EOL> optimizer = tf . train . AdamOptimizer ( lr ) <EOL> train_op = optimizer . minimize ( loss , global_step = global_step ) <EOL> return train_op <EOL> def evaluation ( logits , labels ) : <EOL> """<STR_LIT>""" <EOL> print ( '<STR_LIT>' ) <EOL> correct = tf . nn . in_top_k ( logits , labels , <NUM_LIT:1> ) <EOL> num_correct = tf . reduce_sum ( tf . cast ( correct , tf . float32 ) ) <EOL> acc_percent = num_correct / FLAGS . batch_size <EOL> return acc_percent * <NUM_LIT> , num_correct <EOL> def main ( argv = None ) : <EOL> return <NUM_LIT:0> <EOL> if __name__ == '<STR_LIT:__main__>' : <EOL> tf . app . run ( ) </s>
