Looking closer, it seems like the model is being tested on a small amount of the data it was trained on. Providing the additional arguments on line 9:--count=$COUNT_TEST --offset=$COUNT_TRAIN fixes that and yields a more reasonable assessment of accuracy:
I ran this script, and the generated stats from evaluate.py were unbelievably good:
Looking closer, it seems like the model is being tested on a small amount of the data it was trained on. Providing the additional arguments on line 9:
--count=$COUNT_TEST --offset=$COUNT_TRAIN
fixes that and yields a more reasonable assessment of accuracy: