To train https://github.com/karpathy/neuraltalk2 on my own data. Just run from a folder with tagged JPEG images. I mean the keywords in the file properties.

neuraltalk_data_from_exif_keywords.py

# pip install exifread

import exifread
import array
import string
import glob
import json

KEYWORDS = 'Image XPKeywords'
OUTPUT = 'neuraltalk.json'

def filter_str(str):
  s = ''.join(filter(lambda x: (x in string.printable) and (x not in '\t\n\r'), str))
  return s.strip()

def tags(filename):
  try:
    f = open(filename, 'rb')
    exif = exifread.process_file(f)
    tags = array.array('B', exif[KEYWORDS].values).tostring().decode('utf-16').split(';')
    tags = [filter_str(t) for t in tags]
    return tags
  except Exception as e:
    print(filename, 'has no keywords.')
    return []

def save(arr):
  f = open(OUTPUT, 'w')
  json.dump(arr, f, sort_keys=True, indent=2, separators=(',', ': '))
  f.close()
  print('Finished.')

def main():
  files = glob.glob('*.[jJ][pP][gG]') + glob.glob('*.[jJ][pP][eE][gG]')
  result = []
  for filename in files:
    image_tags = tags(filename)
    if len(image_tags) > 0:
      result.append({'file_path': filename, 'captions': image_tags})
  save(result)

if __name__ == "__main__":
  main()

neuraltalk_train.sh

NEUROTALK=../neuraltalk2
JSON_OUTPUT=neuraltalk_output.json
H5_OUTPUT=neuraltalk.h5
CHECKPOINTS_OUTPUT=./checkpoints

# Making the paths absolute.
JSON_OUTPUT=`readlink -f $JSON_OUTPUT`
H5_OUTPUT=`readlink -f $H5_OUTPUT`
CHECKPOINTS_OUTPUT=`readlink -f $CHECKPOINTS_OUTPUT`
START_FOLDER=`readlink -f .`
CNN_MODEL=`readlink -f $NEUROTALK/model/VGG_ILSVRC_16_layers.caffemodel`
CNN_PROTO=`readlink -f $NEUROTALK/model/VGG_ILSVRC_16_layers_deploy.prototxt`

if [ ! -f $H5_OUTPUT ] || [ ! -f $JSON_OUTPUT ]; then
  rm -f $H5_OUTPUT
  rm -f $JSON_OUTPUT
  python $NEUROTALK/prepro.py \
      --input_json neuraltalk.json \
      --num_val 5000 --num_test 5000 \
      --images_root . \
      --word_count_threshold 5 \
      --output_json $JSON_OUTPUT \
      --output_h5 $H5_OUTPUT
fi

mkdir -p $CHECKPOINTS_OUTPUT
cd $NEUROTALK
th train.lua -input_h5 $H5_OUTPUT -input_json $JSON_OUTPUT -checkpoint_path $CHECKPOINTS_OUTPUT \
    -gpuid -1 \
    -cnn_model $CNN_MODEL \
    -cnn_proto $CNN_PROTO
cd $START_FOLDER