<s> '''<STR_LIT>''' <EOL> import sys , time <EOL> from DataSet import DataSet , DataSets <EOL> import tensorflow as tf <EOL> import tensorflow . python . platform <EOL> import numpy <EOL> def train_neural_net ( bands_to_use , <EOL> image_size , <EOL> train_images , <EOL> train_labels , <EOL> test_images , <EOL> test_labels , <EOL> convolution_patch_size , <EOL> number_of_batches , <EOL> batch_size ) : <EOL> on_band_count = <NUM_LIT:0> <EOL> for b in bands_to_use : <EOL> if b == <NUM_LIT:1> : <EOL> on_band_count += <NUM_LIT:1> <EOL> data_sets = DataSets ( ) <EOL> data_sets . train = DataSet ( on_band_count , train_images , train_labels , dtype = tf . float32 ) <EOL> data_sets . test = DataSet ( on_band_count , test_images , test_labels , dtype = tf . float32 ) <EOL> print ( "<STR_LIT>" . format ( len ( train_images ) , len ( test_images ) , len ( train_labels ) , len ( test_labels ) ) ) <EOL> sess = tf . InteractiveSession ( ) <EOL> def weight_variable ( shape ) : <EOL> initial = tf . truncated_normal ( shape , stddev = <NUM_LIT:0.1> ) <EOL> return tf . Variable ( initial ) <EOL> def bias_variable ( shape ) : <EOL> initial = tf . constant ( <NUM_LIT:0.1> , shape = shape ) <EOL> return tf . Variable ( initial ) <EOL> def conv2d ( x , W ) : <EOL> return tf . nn . conv2d ( x , W , strides = [ <NUM_LIT:1> , <NUM_LIT:1> , <NUM_LIT:1> , <NUM_LIT:1> ] , padding = '<STR_LIT>' ) <EOL> def max_pool_2x2 ( x ) : <EOL> return tf . nn . max_pool ( x , ksize = [ <NUM_LIT:1> , <NUM_LIT:2> , <NUM_LIT:2> , <NUM_LIT:1> ] , <EOL> strides = [ <NUM_LIT:1> , <NUM_LIT:2> , <NUM_LIT:2> , <NUM_LIT:1> ] , padding = '<STR_LIT>' ) <EOL> x = tf . placeholder ( "<STR_LIT:float>" , shape = [ None , image_size * image_size * on_band_count ] ) <EOL> y_ = tf . placeholder ( tf . float32 , [ None , <NUM_LIT:2> ] ) <EOL> patch_size = convolution_patch_size <EOL> W_conv1 = weight_variable ( [ patch_size , patch_size , <NUM_LIT:1> , <NUM_LIT:32> ] ) <EOL> b_conv1 = bias_variable ( [ <NUM_LIT:32> ] ) <EOL> x_image = tf . reshape ( x , [ - <NUM_LIT:1> , image_size , image_size , <NUM_LIT:1> ] ) <EOL> h_conv1 = tf . nn . relu ( conv2d ( x_image , W_conv1 ) + b_conv1 ) <EOL> h_pool1 = max_pool_2x2 ( h_conv1 ) <EOL> W_conv2 = weight_variable ( [ patch_size , patch_size , <NUM_LIT:32> , <NUM_LIT:64> ] ) <EOL> b_conv2 = bias_variable ( [ <NUM_LIT:64> ] ) <EOL> h_conv2 = tf . nn . relu ( conv2d ( h_pool1 , W_conv2 ) + b_conv2 ) <EOL> h_pool2 = max_pool_2x2 ( h_conv2 ) <EOL> W_fc1 = weight_variable ( [ image_size / <NUM_LIT:4> * image_size / <NUM_LIT:4> * <NUM_LIT:64> * on_band_count , <NUM_LIT> ] ) <EOL> b_fc1 = bias_variable ( [ <NUM_LIT> ] ) <EOL> h_pool2_flat = tf . reshape ( h_pool2 , [ - <NUM_LIT:1> , image_size / <NUM_LIT:4> * image_size / <NUM_LIT:4> * <NUM_LIT:64> * on_band_count ] ) <EOL> h_fc1 = tf . nn . relu ( tf . matmul ( h_pool2_flat , W_fc1 ) + b_fc1 ) <EOL> W_fc2 = weight_variable ( [ <NUM_LIT> , <NUM_LIT:2> ] ) <EOL> b_fc2 = bias_variable ( [ <NUM_LIT:2> ] ) <EOL> y_conv = tf . nn . softmax ( tf . matmul ( h_fc1 , W_fc2 ) + b_fc2 ) <EOL> cross_entropy = - tf . reduce_sum ( y_ * tf . log ( y_conv ) ) <EOL> loss = tf . reduce_mean ( cross_entropy , name = '<STR_LIT>' ) <EOL> train_step = tf . train . AdamOptimizer ( <NUM_LIT> ) . minimize ( cross_entropy ) <EOL> correct_prediction = tf . equal ( tf . argmax ( y_conv , <NUM_LIT:1> ) , tf . argmax ( y_ , <NUM_LIT:1> ) ) <EOL> accuracy = tf . reduce_mean ( tf . cast ( correct_prediction , "<STR_LIT:float>" ) ) <EOL> sess . run ( tf . initialize_all_variables ( ) ) <EOL> loss_total = <NUM_LIT:0> <EOL> print ( "<STR_LIT>" ) <EOL> t0 = time . time ( ) <EOL> for i in range ( number_of_batches ) : <EOL> batch = data_sets . train . next_batch ( batch_size ) <EOL> _ , loss_val = sess . run ( [ train_step , cross_entropy ] , <EOL> feed_dict = { x : batch [ <NUM_LIT:0> ] , y_ : batch [ <NUM_LIT:1> ] } ) <EOL> loss_total += loss_val <EOL> print ( '<STR_LIT>' . format ( i , loss_val , loss_total / ( i + <NUM_LIT:1> ) ) ) <EOL> print ( "<STR_LIT>" . format ( time . time ( ) - t0 ) ) <EOL> print ( "<STR_LIT>" % accuracy . eval ( feed_dict = { <EOL> x : data_sets . test . images , y_ : data_sets . test . labels , } ) ) <EOL> prediction = tf . argmax ( y_conv , <NUM_LIT:1> ) <EOL> index = <NUM_LIT:0> <EOL> '''<STR_LIT>''' <EOL> return prediction . eval ( feed_dict = { x : data_sets . test . images } , session = sess ) </s>
