I have a Keras model that trains find when eager mode is on (TF 2.1.0). One of my features is a string that I need to map to its corresponding vocabulary index. However, with eager execution disabled, I cannot find a neat way to do this.
I was initially using tft.apply_vocabulary, which used to work fine but fails without eager execution. I also tried tf.lookup.StaticVocabularyTable:
table = tf.lookup.StaticVocabularyTable(TextFileIdTableInitializer('item_vocab.txt'), 1)
out = table.lookup(input_strings)
which (with eager mode off) fails with:
tensorflow.python.framework.errors_impl.FailedPreconditionError: Table not initialized. [[{{node transformer/hash_table_Lookup_1/hash_table_Lookup/LookupTableFindV2}}]]
I am able to run the table's _initialize method in a tf.Session, but that feels like too much work for such a common task and is not TF2.0 compatible.
So, how do you map strings to integer indexes from a vocab file without eager execution?
Why not eager?
I have the impression that graph mode training has wider support (e.g. multi-gpu training) and better performance and I'm trying to make sure my code works with eager mode disabled, so that I can eventually tunr it off when I'm done developing. Is that a sensible goal?