default='1',
choices=xrange(1, 21),
help='the task ID to train/test on from bAbI dataset (1-20)')
parser.add_argument('--rlayer_type',
default='gru',
choices=['gru', 'lstm'],
help='type of recurrent layer to use (gru or lstm)')
parser.add_argument('--model_weights', help='pickle file of trained weights')
args = parser.parse_args(gen_be=False)
# setup backend
be = gen_backend(**extract_valid_args(args, gen_backend))
be.bsz = 1
# load the bAbI dataset
babi = babi_handler(args.data_dir, args.task)
valid_set = QA(*babi.test)
# create model
model_inference = create_model(babi.vocab_size, args.rlayer_type)
model_inference.load_params(args.model_weights)
model_inference.initialize(dataset=valid_set)
ex_story, ex_question, ex_answer = babi.test_parsed[0]
stitch_sentence = lambda words: \
" ".join(words).replace(" ?", "?").replace(" .", ".\n").replace("\n ", "\n")
print "\nThe vocabulary set from this task has {} words:".format(
babi.vocab_size)
print stitch_sentence(babi.vocab)
print "\nExample from test set:"
print "\nStory"
# parse the command line arguments
parser = NeonArgparser(__doc__)
parser.add_argument('-t', '--task', type=int, default='1', choices=xrange(1, 21),
help='the task ID to train/test on from bAbI dataset (1-20)')
parser.add_argument('--rlayer_type', default='gru', choices=['gru', 'lstm'],
help='type of recurrent layer to use (gru or lstm)')
parser.add_argument('--model_weights',
help='pickle file of trained weights')
args = parser.parse_args(gen_be=False)
# setup backend
be = gen_backend(**extract_valid_args(args, gen_backend))
be.bsz = 1
# load the bAbI dataset
babi = babi_handler(args.data_dir, args.task)
valid_set = QA(*babi.test)
# create model
model_inference = create_model(babi.vocab_size, args.rlayer_type)
model_inference.load_params(args.model_weights)
model_inference.initialize(dataset=valid_set)
ex_story, ex_question, ex_answer = babi.test_parsed[0]
stitch_sentence = lambda words: \
" ".join(words).replace(" ?", "?").replace(" .", ".\n").replace("\n ", "\n")
print "\nThe vocabulary set from this task has {} words:".format(babi.vocab_size)
print stitch_sentence(babi.vocab)
print "\nExample from test set:"
print "\nStory"
print stitch_sentence(ex_story)