def inference_step(self, X_data):
"""
利用GAN去计算每个分类的类别，X——data会自动的拓展到合适的num数目
:param X_data:
:return:
"""
num_class = self.config.num_class
Y_data = [[i for i in range(num_class)] for x in X_data]
# print(np.shape(Y_data))
X_data = [[x for i in range(num_class)] for x in X_data]
# print(np.shape(X_data))
Y_data = np.reshape(Y_data, [-1])
X_data = np.reshape(X_data, [-1, self.config.input_dim])
Y_data = one_hot(Y_data, num_class)
probs = self.sess.run([self.infer_discriminator],
feed_dict={
self.X_input: X_data,
self.Y: Y_data
})
# batch_size * num_class
probs = np.reshape(probs, [-1, num_class])
# print(probs)
predict_label = np.argmax(probs, axis=-1)
return predict_label
def next_train_data(self):
batch_instances = self.next_batch()
pos_tag, y, x, t, c, pos_c, pos_t = [list() for _ in range(7)]
for instance in batch_instances:
words = instance['words']
pos_taggings = instance['pos_taggings']
marks = instance['marks']
label = instance['label']
index_candidates = find_candidates(marks, ['B'])
assert (len(index_candidates)) == 1
index_triggers = find_candidates(marks, ['T'])
# assert (len(index_triggers)) == 1
y.append(label)
marks = marks + ['A'] * (self.max_sequence_length - len(marks))
words = words + ['<eos>'] * (self.max_sequence_length - len(words))
pos_taggings = pos_taggings + ['*'] * (self.max_sequence_length -
len(pos_taggings))
pos_taggings = list(
map(lambda x: self.pos_taggings_id[x], pos_taggings))
pos_tag.append(pos_taggings)
index_words = list(map(lambda x: self.word_id[x], words))
x.append(index_words)
pos_candidate = [i for i in range(-index_candidates[0], 0)] + [
i
for i in range(0, self.max_sequence_length -
index_candidates[0])
]
pos_c.append(pos_candidate)
pos_trigger = [i for i in range(-index_triggers[0], 0)] + [
i
for i in range(0, self.max_sequence_length - index_triggers[0])
]
pos_t.append(pos_trigger)
t.append([index_words[index_triggers[0]]] *
self.max_sequence_length)
c.append([index_words[index_candidates[0]]] *
self.max_sequence_length)
# print(len(words), len(marks), len(pos_taggings), len(index_words), len(pos_candidate), len(pos_trigger))
assert len(words) == len(marks) == len(pos_taggings) == len(
index_words) == len(pos_candidate) == len(pos_trigger)
assert len(y) == len(x) == len(t) == len(c) == len(pos_c) == len(
pos_t) == len(pos_tag)
return x, t, c, one_hot(y, self.label_id,
len(self.all_labels)), pos_c, pos_t, pos_tag