Using string labels in Tensorflow

Question

i\'am still trying to run Tensorflow with own image data. I was able to create a .tfrecords file with the conevert_to() function from this example link

Now i i\'d l

Answer 1

To add to @mrry 's answer, supposing your string is ascii, you can:

def _bytes_feature(value):
    return tf.train.Feature(bytes_list=tf.train.BytesList(value=[value]))

def write_proto(cls, filepath, ..., item_id): # itemid is an ascii encodable string
    # ...
    with tf.python_io.TFRecordWriter(filepath) as writer:
        example = tf.train.Example(features=tf.train.Features(feature={
             # write it as a bytes array, supposing your string is `ascii`
            'item_id': _bytes_feature(bytes(item_id, encoding='ascii')), # python 3
            # ...
        }))
        writer.write(example.SerializeToString())

Then:

def parse_single_example(cls, example_proto, graph=None):
    features_dict = tf.parse_single_example(example_proto,
        features={'item_id': tf.FixedLenFeature([], tf.string),
        # ...
        })
    # decode as uint8 aka bytes
    instance.item_id = tf.decode_raw(features_dict['item_id'], tf.uint8)

and then when you get it back in your session, transform back to string:

item_id, ... = session.run(your_tfrecords_iterator.get_next())
print(str(item_id.flatten(), 'ascii')) # python 3

I took the uint8 trick from this related so answer. Works for me but comments/improvements welcome.