def babi_handler(data_dir, task_number):
"""
Handle for bAbI task.
Args:
data_dir (string) : Path to bAbI data directory.
task_number (int) : The task ID from the bAbI dataset (1-20).
Returns:
BABI : Handler for bAbI task.
"""
task = task_list[task_number - 1]
return BABI(path=data_dir, task=task, subset=subset)
print ex_answer
while True:
# ask user for story and question
story_lines = []
line = raw_input("\nPlease enter a story:\n")
while line != "":
story_lines.append(line)
line = raw_input()
story = ("\n".join(story_lines)).strip()
question = raw_input("Please enter a question:\n")
# convert user input into a suitable network input
vectorize = lambda words, max_len: \
be.array(Text.pad_sentences([babi.words_to_vector(BABI.tokenize(words))], max_len))
s = vectorize(story, babi.story_maxlen)
q = vectorize(question, babi.query_maxlen)
# get prediction probabilities with forward propagation
probs = model_inference.fprop(x=(s, q), inference=True).get()
# get top k answers
top_k = -min(5, babi.vocab_size)
max_indices = np.argpartition(probs, top_k, axis=0)[top_k:]
max_probs = probs[max_indices]
sorted_idx = max_indices[np.argsort(max_probs, axis=0)]
print "\nAnswer:"
for idx in reversed(sorted_idx):
idx = int(idx)
print ex_answer
while True:
# ask user for story and question
story_lines = []
line = raw_input("\nPlease enter a story:\n")
while line != "":
story_lines.append(line)
line = raw_input()
story = ("\n".join(story_lines)).strip()
question = raw_input("Please enter a question:\n")
# convert user input into a suitable network input
vectorize = lambda words, max_len: \
be.array(Text.pad_sentences([babi.words_to_vector(BABI.tokenize(words))], max_len))
s = vectorize(story, babi.story_maxlen)
q = vectorize(question, babi.query_maxlen)
# get prediction probabilities with forward propagation
probs = model_inference.fprop(x=(s, q), inference=True).get()
# get top k answers
top_k = -min(5, babi.vocab_size)
max_indices = np.argpartition(probs, top_k, axis=0)[top_k:]
max_probs = probs[max_indices]
sorted_idx = max_indices[np.argsort(max_probs, axis=0)]
print "\nAnswer:"
for idx in reversed(sorted_idx):
idx = int(idx)
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)')
args = parser.parse_args(gen_be=False)
args.batch_size = 32
task = task_list[args.task - 1]
# setup backend
be = gen_backend(**extract_valid_args(args, gen_backend))
# load the bAbI dataset
babi = BABI(path=args.data_dir, task=task, subset=subset)
train_set = QA(*babi.train)
valid_set = QA(*babi.test)
# recurrent layer parameters (default gru)
rlayer_obj = GRU if args.rlayer_type == 'gru' else LSTM
rlayer_params = dict(output_size=100,
reset_cells=True,
init=GlorotUniform(),
init_inner=Orthonormal(0.5),
activation=Tanh(),
gate_activation=Logistic())
# if using lstm, swap the activation functions
if args.rlayer_type == 'lstm':
rlayer_params.update(dict(activation=Logistic(), gate_activation=Tanh()))
def vectorize(words, max_len):
return be.array(pad_sentences([babi.words_to_vector(BABI.tokenize(words))], max_len))
def vectorize(words, max_len):
return be.array(
pad_sentences([babi.words_to_vector(BABI.tokenize(words))], max_len))
