def write_socres(model_path, checkpoint_file, meta_file):
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
config = CNNConfig()
cnn = TextCNN(config)
cnn.prepare_data()
# 读取模型
checkpoint_dir = os.path.abspath(model_path)
checkpoint_file = os.path.join(checkpoint_dir, checkpoint_file)
saver = tf.train.import_meta_graph(
os.path.join(checkpoint_dir, meta_file))
saver.restore(sess, checkpoint_file)
graph = tf.get_default_graph()
# 从图中读取变量
input_x = graph.get_operation_by_name("input_x").outputs[0]
dropout_keep_prob = graph.get_operation_by_name(
"dropout_keep_prob").outputs[0]
# prediction = graph.get_operation_by_name("output/prediction").outputs[0]
score = graph.get_operation_by_name("output/score").outputs[0]
training = graph.get_operation_by_name("training").outputs[0]
train_init_op, valid_init_op, next_train_element, next_valid_element = cnn.shuffle_datset(
)
sess.run(train_init_op)
label = preprocess.read_label(
os.path.join('data', preprocess.LABEL_ID_PATH))
while True:
try:
lines = sess.run(next_train_element)
titles, batch_y = cnn.convert_input(lines)
batch_x = []
if cnn.train_mode == 'CHAR-RANDOM' or cnn.train_mode == 'WORD-NON-STATIC':
for title in titles:
batch_x.append(
preprocess.to_id(title, cnn.vocab, cnn.train_mode))
batch_x = np.stack(batch_x)
feed_dict = {
input_x: batch_x,
dropout_keep_prob: 1.0,
training: False
}
scr = sess.run(score, feed_dict)
print(scr)
break
except tf.errors.OutOfRangeError:
# 初始化验证集迭代器
sess.run(valid_init_op)
# 计算验证集准确率
# valid_step(next_valid_element)
break
def predict(self, titles):
# 自定义批量查询
# ================================================================================
batch_x = []
# 1.id
for title in titles:
batch_x.append(preprocess.to_id(title, self.vocab, self.pred_mode))
batch_x = np.stack(batch_x)
pre = self.predictStep(batch_x)
results = [self.label[x] for x in pre]
final_results = list(zip(titles, results))
return final_results
def convert_test_input(self, titles):
"""
将测试集tsv数据转为id或词向量表示
:param titles:
:return:
"""
batch_x = []
# 1.id
for title in titles:
valid_title = title.decode('gb18030').strip('\t')
batch_x.append(preprocess.to_id(valid_title, self.vocab, self.train_mode))
batch_x = np.stack(batch_x)
return batch_x
def convert_input(self, lines):
"""
将训练集数据转换为id或词向量表示
"""
batch_x = []
batch_y = []
title = ""
# 1.id
for line in lines:
line_ = line.decode("gbk").strip().split(',')
title = ''.join(line_[0:-1]) # 逗号前段为标题
label = ''.join(line_[-1]) # 最后一项为标签
batch_x.append(preprocess.to_id(title, self.vocab, self.train_mode))
batch_y.append(label)
batch_x = np.stack(batch_x)
return batch_x, batch_y
def get_score(model_path, checkpoint_file, meta_file, titles):
'''
根据给定模型和标题，返回模型softmax层得分[None, 1258]
'''
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
with tf.Session(config=config) as sess:
checkpoint_dir = os.path.abspath(model_path)
checkpoint_file = os.path.join(checkpoint_dir, checkpoint_file)
saver = tf.train.import_meta_graph(
os.path.join(checkpoint_dir, meta_file))
saver.restore(sess, checkpoint_file)
graph = tf.get_default_graph()
config = CNNConfig()
model = cnn(config)
# 读取测试集及词汇表数据
dataset, next_element = model.prepare_test_data()
# 从图中读取变量
input_x = graph.get_operation_by_name("input_x").outputs[0]
dropout_keep_prob = graph.get_operation_by_name(
"dropout_keep_prob").outputs[0]
# prediction = graph.get_operation_by_name("output/prediction").outputs[0]
score = graph.get_operation_by_name("output/score").outputs[0]
training = graph.get_operation_by_name("training").outputs[0]
label = preprocess.read_label(
os.path.join('data', preprocess.LABEL_ID_PATH))
batch_x = []
if model.train_mode == 'CHAR-RANDOM' or model.train_mode == 'WORD-NON-STATIC':
# 1.id
for title in titles:
batch_x.append(
preprocess.to_id(title, model.vocab, model.train_mode))
batch_x = np.stack(batch_x)
feed_dict = {input_x: batch_x, dropout_keep_prob: 1.0, training: False}
scr = sess.run(score, feed_dict)
# pre = sess.run(prediction, feed_dict)
return scr
def predict_train(self):
# 给训练集打标签
rf = open('data/train.tsv', mode='r', encoding='gbk', errors='ignore')
wf = open('data/train_predict.tsv', mode='w', encoding='gbk')
# 跳过标题
wf.write(rf.readline().strip() + '\n')
while True:
line = rf.readline()
if line == '':
break
title, label = line.strip().split('\t')
batch_x = np.asarray(
[preprocess.to_id(title, self.vocab, self.pred_mode)],
dtype=np.int32)
pre = self.predictStep(batch_x)
result = self.label[pre[0]]
wf.write(title + '\t' + label + '\t' + result + '\n')
rf.close()
wf.close()
