def vectorize_stories(self, data):
"""
Convert (story, query, answer) word data into vectors.
Args:
data (tuple) : Tuple of story, query, answer word data.
Returns:
tuple : Tuple of story, query, answer vectors.
"""
s, q, a = [], [], []
for story, query, answer in data:
s.append(self.words_to_vector(story))
q.append(self.words_to_vector(query))
a.append(self.one_hot_vector(answer))
s = Text.pad_sentences(s, self.story_maxlen)
q = Text.pad_sentences(q, self.query_maxlen)
a = np.array(a)
return (s, q, a)
def vectorize_stories(self, data):
"""
Convert (story, query, answer) word data into vectors.
Args:
data (tuple) : Tuple of story, query, answer word data.
Returns:
tuple : Tuple of story, query, answer vectors.
"""
s, q, a = [], [], []
for story, query, answer in data:
s.append(self.words_to_vector(story))
q.append(self.words_to_vector(query))
a.append(self.one_hot_vector(answer))
s = Text.pad_sentences(s, self.story_maxlen)
q = Text.pad_sentences(q, self.query_maxlen)
a = np.array(a)
return (s, q, a)
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)
