#NEWLINE# def validate( X , y_c , y_f , model , epoch , batch_size ) : #NEWLINE# #INDENT# progress = make_progressbar ( 'Testing epoch #{}' . format ( epoch ) , len ( X ) ) #NEWLINE# progress . start ( ) #NEWLINE# ( nerrors_c , nerrors_f ) = ( 0 , 0 ) #NEWLINE# for ibatch in range ( ( ( ( len ( X ) + batch_size ) - 1 ) // batch_size ) ) : #NEWLINE# #INDENT# Xbatch = X [ ( ibatch * batch_size ) : ( ( ibatch + 1 ) * batch_size ) ] . astype ( df . floatX ) #NEWLINE# ybatch_c = y_c [ ( ibatch * batch_size ) : ( ( ibatch + 1 ) * batch_size ) ] . astype ( df . floatX ) #NEWLINE# ybatch_f = y_f [ ( ibatch * batch_size ) : ( ( ibatch + 1 ) * batch_size ) ] . astype ( df . floatX ) #NEWLINE# ( preds_c , preds_f ) = map ( ( lambda py : np . argmax ( py , axis = 1 ) ) , model . forward ( Xbatch ) ) #NEWLINE# nerrors_c += sum ( ( ybatch_c != preds_c ) ) #NEWLINE# nerrors_f += sum ( ( ybatch_f != preds_f ) ) #NEWLINE# progress . update ( ( ( ibatch * batch_size ) + len ( Xbatch ) ) ) #NEWLINE# #UNINDENT# progress . finish ( ) #NEWLINE# accuracy_c = ( 1 - ( float ( nerrors_c ) / len ( X ) ) ) #NEWLINE# accuracy_f = ( 1 - ( float ( nerrors_f ) / len ( X ) ) ) #NEWLINE# print ( 'Epoch #{}, Coarse accuracy: {:.2%} ({} errors)' . format ( epoch , accuracy_c , nerrors_c ) ) #NEWLINE# print ( 'Epoch #{}, Fine   accuracy: {:.2%} ({} errors)' . format ( epoch , accuracy_f , nerrors_f ) )