def initModel(self):
print('Loading model:', self.model, 'pred_mode:', self.pred_mode)
checkpoint_file = self.__getCkptfile(self.model, self.pred_mode)
if not checkpoint_file:
return
# 加载模型，这要加.meta后缀
with self.graph.as_default():
saver = tf.train.import_meta_graph(checkpoint_file + '.meta')
saver.restore(self.sess, checkpoint_file)
# self.graph = tf.get_default_graph()
# 从图中读取变量
self.input_x = self.graph.get_operation_by_name("input_x").outputs[0]
self.input_y = self.graph.get_operation_by_name("input_y").outputs[0]
self.dropout_keep_prob = self.graph.get_operation_by_name(
"dropout_keep_prob").outputs[0]
self.prediction = self.graph.get_operation_by_name(
"output/prediction").outputs[0]
self.training = self.graph.get_operation_by_name("training").outputs[0]
# 加载词向量
if self.pred_mode == 'CHAR-RANDOM':
self.vocab = preprocess.read_vocab(
os.path.join('data', preprocess.CHAR_VOCAB_PATH))
elif self.pred_mode == 'WORD-NON-STATIC' or self.pred_mode == 'MULTI':
self.vocab = preprocess.read_vocab(
os.path.join('data', preprocess.WORD_VOCAB_PATH))
# 加载标签
self.label = preprocess.read_label(
os.path.join('data', preprocess.LABEL_ID_PATH))
def prepare_data(self):
# Data preparation.
# =======================================================
if self.train_mode == 'CHAR-RANDOM':
# 1.字符级
# 读取词汇表
self.vocab = preprocess.read_vocab(
os.path.join('data', preprocess.CHAR_VOCAB_PATH))
elif self.train_mode == 'WORD-NON-STATIC' or self.train_mode == 'MULTI':
# 把预训练词向量的值读到变量中
self.vocab = preprocess.read_vocab(
os.path.join('data', preprocess.WORD_VOCAB_PATH))
self.vecs_dict = preprocess.load_vecs(
os.path.join('data', preprocess.SGNS_WORD_PATH))
self.embedding_W = np.ndarray(
shape=[self.vocab_size, self.embedding_dim], dtype=np.float32)
for word in self.vocab:
# 第n行对应id为n的词的词向量
if word not in self.vecs_dict:
preprocess.add_word(word, self.vecs_dict)
self.embedding_W[self.vocab[word]] = self.vecs_dict[word]
self.dataset = TextLineDataset(
os.path.join('data', preprocess.TRAIN_WITH_ID_PATH))
return
def prepare_data(self):
# Data preparation.
# =======================================================
if self.train_mode == 'CHAR-RANDOM':
# 1.字符级
# 读取词汇表
self.vocab = preprocess.read_vocab(
os.path.join('data', preprocess.CHAR_VOCAB_PATH))
elif self.train_mode == 'WORD-NON-STATIC' or self.train_mode == 'MULTI':
# 把预训练词向量的值读到变量中
self.vocab = preprocess.read_vocab(
os.path.join('data', preprocess.WORD_VOCAB_PATH))
self.vecs_dict = preprocess.load_vecs(
os.path.join('data', preprocess.SGNS_WORD_PATH))
self.embedding_W = np.ndarray(
shape=[self.vocab_size, self.embedding_dim], dtype=np.float32)
for word in self.vocab:
# 第n行对应id为n的词的词向量
if word not in self.vecs_dict:
preprocess.add_word(word, self.vecs_dict)
self.embedding_W[self.vocab[word]] = self.vecs_dict[word]
self.dataset = TextLineDataset(
os.path.join('data', preprocess.TRAIN_WITH_ID_PATH))
print('Shuffling dataset...')
self.dataset = self.dataset.shuffle(preprocess.TOTAL_TRAIN_SIZE)
# 分割数据集
# 取前VALID_SIZE个样本给验证集
valid_dataset = self.dataset.take(preprocess.VALID_SIZE).batch(
self.valid_batch_size)
# 剩下的给训练集
train_dataset = self.dataset.skip(preprocess.VALID_SIZE).batch(
self.train_batch_size)
# Create a reinitializable iterator
train_iterator = train_dataset.make_initializable_iterator()
valid_iterator = valid_dataset.make_initializable_iterator()
train_init_op = train_iterator.initializer
valid_init_op = valid_iterator.initializer
# 要获取元素，先sess.run(train_init_op)初始化迭代器
# 再sess.run(next_train_element)
next_train_element = train_iterator.get_next()
next_valid_element = valid_iterator.get_next()
return train_init_op, valid_init_op, next_train_element, next_valid_element
def prepare_test_data(self):
# 读取词汇表
if self.train_mode == 'CHAR-RANDOM':
# 1.字符级
self.vocab = preprocess.read_vocab(os.path.join('data',preprocess.CHAR_VOCAB_PATH))
elif self.train_mode == 'WORD-NON-STATIC':
self.vocab = preprocess.read_vocab(os.path.join('data', preprocess.WORD_VOCAB_PATH))
# 测试集有标题，读取时注意跳过第一行
dataset = TextLineDataset(os.path.join('data',preprocess.TEST_PATH))
dataset = dataset.shuffle(preprocess.TOTAL_TEST_SIZE).batch(self.test_batch_size)
iterator = dataset.make_one_shot_iterator()
next_element = iterator.get_next()
return dataset, next_element
