classification - How to add new custom category in imagenet tutorial sample in Tensorflow? -


i new tensorflow. have tried mnist example , trying out imagenet tutorial examples. have run imagenet classification example , able classification sample panda image , several others. want add new category (say traffic signs) in pretrained inception_v3 model. don't know input train image formats nor how properly. far have learned save variables checkpoint directory using saver:

  saver = tf.train.saver(max_to_keep=20, keep_checkpoint_every_n_hours=1)   saver.save(s, ssavepathfilename)   ...   path = tf.train.get_checkpoint_state(checkpoint_dir)   saver.restore(sess, path.model_checkpoint_path)

i used above snippet save , restore mnist model. inception_v3 pretrained, i'm little confused on how use in sample code given in classify_image.py understand far that, in order train pre-trained inception_v3 custom category need create checkoint first train on images. need save images this:

ls {image_path}/traffic-signs speed-breaker-sign.jpg right-turn-sign.jpg left-turn-sign.jpg ...

i have checked related question new machine learning , tf library 'm not able understand i'm supposed in case. have understood --fine-tune flag not sure flag create checkpoint. there list available possible flags can use in model? how tell model use images "{image_path}/traffic-signs" training ? flags need provided or set training custom category while retaining existing trained data ? need change images in specific format?


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