def load_data(self, debug=False):
"""Loads train/valid/test data and sentence encoding"""
#num_supp_facts: not used (Strong supervised training)
#note: word_embedding returned by load_babi is not used - instead we define it using TF's functions
if self.config.train_mode:
self.train_or_test_data, self.valid_data, _, self.config.max_words_per_question, self.config.max_sentences_per_input, \
self.config.max_words_per_sentence, self.config.num_supp_facts, self.config.vocab_size \
= babi_input.load_babi(self.config, split_sentences=True)
else:
self.train_or_test_data, _, self.config.max_words_per_question, self.config.max_sentences_per_input, \
self.config.max_words_per_sentence, self.config.num_supp_facts, self.config.vocab_size \
= babi_input.load_babi(self.config, split_sentences=True)
def load_data(self, debug=False):
"""Loads train/valid/test data and sentence encoding"""
if self.config.train_mode:
self.train, self.valid, self.word_embedding, self.max_q_len, self.max_input_len, self.max_sen_len, self.num_supporting_facts, self.vocab_size = babi_input.load_babi(self.config, split_sentences=True)
else:
self.test, self.word_embedding, self.max_q_len, self.max_input_len, self.max_sen_len, self.num_supporting_facts, self.vocab_size = babi_input.load_babi(self.config, split_sentences=True)
self.encoding = _position_encoding(self.max_sen_len, self.config.embed_size)
def load_data(self):
if self.config.train_mode:
self.train, self.valid, self.word_embedding, self.max_q_len, self.max_sentences, self.max_sen_len, self.vocab_size = babi_input.load_babi(
self.config, split_sentences=True)
else:
self.test, self.word_embedding, self.max_q_len, self.max_sentences, self.max_sen_len, self.vocab_size = babi_input.load_babi(
self.config, split_sentences=True)
self.encoding = position_encoding(self.max_sen_len,
self.config.embed_size)
def load_data(self, debug=False):
"""Loads train/valid/test data and sentence encoding"""
if self.config.train_mode:
self.train, self.valid, self.word_embedding, self.max_q_len, self.max_input_len, self.max_sen_len, self.num_supporting_facts, self.vocab_size = babi_input.load_babi(self.config, split_sentences=True)
else:
self.test, self.word_embedding, self.max_q_len, self.max_input_len, self.max_sen_len, self.num_supporting_facts, self.vocab_size = babi_input.load_babi(self.config, split_sentences=True)
self.encoding = _position_encoding(self.max_sen_len, self.config.embed_size)
def load_data(self, debug=False):
"""Loads train/valid/test data and sentence encoding"""
#In DMN_test, ... it changes config.mode
if self.config.train_mode:
self.train, self.valid, self.word_embedding, self.max_q_len, self.max_sentences, self.max_sen_len, self.vocab_size = babi_input.load_babi(self.config, split_sentences=True)
else:
self.test, self.word_embedding, self.max_q_len, self.max_sentences, self.max_sen_len, self.vocab_size = babi_input.load_babi(self.config, split_sentences=True)
self.encoding = _position_encoding(self.max_sen_len, self.config.embed_size)
