added vector_from_image_multilabel.py

from argparse import ArgumentParser

import tensorflow as tf

import numpy as np

import pickle

def main():

    parser = ArgumentParser(

        description=''

    )

    parser.add_argument(

        '-g', '--graph', required=True, help='path to graph'

        )

    parser.add_argument(

        '-i', '--image', required=True, help='path to image')

    parser.add_argument(

        '-l', '--label', required=True, help='path to labels')

    args = parser.parse_args()

    graph = args.graph

    img = args.image

    interface = []

    with tf.gfile.FastGFile(graph, 'rb') as f:

        graph_def = tf.GraphDef()

        graph_def.ParseFromString(f.read())

        _ = tf.import_graph_def(graph_def, name='')

    image_data = tf.gfile.FastGFile(img, 'rb').read()

    label_lines = [line.rstrip() for line 

                    in tf.gfile.GFile(args.label)]

    with tf.Session() as sess:

        # Feed the image_data as input to the graph and get first prediction

        feature_tensor = sess.graph.get_tensor_by_name('pool_3/_reshape:0')

        results = sess.run(feature_tensor, \

                {'DecodeJpeg/contents:0': image_data})[0]

        d = dict()

        d['file_name'] = img

        d['vector'] = np.squeeze(results)

        interface.append(d)

    output_file = 'single_picture_multilabel.pickle'

    with open(output_file, 'wb') as handle:

        pickle.dump(interface, handle, protocol=pickle.HIGHEST_PROTOCOL)

if __name__ == '__main__':

    main()