def data_training():
"""
仅用样本集进行训练
"""
sentences = []
reader = DataReader(TRAIN_DATA_TYPE)
reader.set_pos()
start_id = reader.get_next_pic_id()
qa = reader.get_pic_qa(start_id)
for q in qa:
question = q['question']
question = question.replace('?', ' ?')
question = question.replace(',', ' ,')
question = question.replace('.', ' .')
sentence = question.split(' ')
sentences.append(sentence)
now_id = reader.get_next_pic_id()
i = 0
while now_id != start_id:
qa = reader.get_pic_qa(now_id)
for q in qa:
question = q['question']
question = question.replace('?', ' ?')
question = question.replace(',', ' ,')
question = question.replace('.', ' .')
sentence = question.split(' ')
sentences.append(sentence)
now_id = reader.get_next_pic_id()
i = i + 1
if i % 1000 == 0:
print('*', end='')
print('load data over!')
model = gensim.models.Word2Vec(sentences, size=300, min_count=1)
model.save(GENSIM_DATA_PATH)
def createQuestionsDict():
"""
创建问题字典（包含回答字典）
"""
reader = DataReader()
reader.set_pos()
dealer = DataDealer(ANSWERS_DICT_PATH)
start_id = reader.get_next_pic_id()
qa = reader.get_pic_qa(start_id)
for q in qa:
question = q['question']
dealer.deal(question)
now_id = reader.get_next_pic_id()
i = 0
while now_id != start_id:
qa = reader.get_pic_qa(now_id)
for q in qa:
question = q['question']
dealer.deal(question)
now_id = reader.get_next_pic_id()
i = i + 1
if i % 1000 == 0:
print('*', end='')
dealer.saveData(QUESTIONS_DICT_PATH)
print('over!')
def createAnswersDict():
"""
创建回答字典
"""
reader = DataReader()
reader.set_pos()
dealer = DataDealer(ANSWERS_DICT_PATH)
start_id = reader.get_next_pic_id()
qa = reader.get_pic_qa(start_id)
for q in qa:
answers = dict()
for a in q['answers']:
answer = a['answer']
weight = 0
if a['answer_confidence'] == 'yes':
weight = 1
elif a['answer_confidence'] == 'maybe':
weight = 0.5
if not answer in answers.keys():
answers[answer] = 0
answers[answer] = answers[answer] + weight
answers_list = []
for key in answers.keys():
if answers[key] >= 3:
dealer.deal(key)
now_id = reader.get_next_pic_id()
i = 0
while now_id != start_id:
qa = reader.get_pic_qa(now_id)
for q in qa:
answers = dict()
for a in q['answers']:
answer = a['answer']
weight = 0
if a['answer_confidence'] == 'yes':
weight = 1
elif a['answer_confidence'] == 'maybe':
weight = 0.5
if not answer in answers.keys():
answers[answer] = 0
answers[answer] = answers[answer] + weight
answers_list = []
for key in answers.keys():
if answers[key] >= 3:
dealer.deal(key)
now_id = reader.get_next_pic_id()
i = i + 1
if i % 1000 == 0:
print('*', end='')
dealer.saveData()
print('over!')
from data_reader import DataReader
if __name__ == '__main__':
count = 0
reader = DataReader()
reader.set_pos()
start_id = reader.get_next_pic_id()
qa = reader.get_pic_qa(start_id)
max_len = 0
for q in qa:
question = q['question']
len_ = len(question)
if len_ > max_len:
max_len = len_
if len_ < 32:
count = count + 1
now_id = reader.get_next_pic_id()
i = 0
while now_id != start_id:
qa = reader.get_pic_qa(now_id)
for q in qa:
question = q['question']
len_ = len(question)
if len_ > max_len:
max_len = len_
if len_ < 32:
count = count + 1
now_id = reader.get_next_pic_id()
i = i + 1
if i % 1000 == 0:
print('*', end='')
class BatchMaker:
def __init__(self):
# 获取字典
self.dealer = DataDealer(ANSWERS_DICT_PATH)
# 获取样本集信息
self.reader = DataReader(TRAIN_DATA_TYPE)
self.reader.set_pos()
self.weight_vgg = None
self.biase_vgg = None
self.model_word2vec = gensim.models.KeyedVectors.load_word2vec_format(
GLOVE_WIKI_GENSIM_DATA_PATH)
def __getOnehot(self, pos, value, len_):
"""
获取onehot标签
位置为pos[i]的值为value[i]
其他为0
pos和value的长度必须相等
返回列表，长度为len_
"""
if type(pos) != list:
pos = [pos]
result = []
for i in range(len_):
if i in pos:
result.append(value)
else:
result.append(0)
return result
def __getVGG19Result(self, img_batch):
"""
获取VGG19网络的结果
img_batch:tf.Variable->[1,224,224,3]
返回最后一层隐藏层的一维列表
"""
# 导入VGG19模型
model = VGG19model()
if self.weight_vgg == None and self.biase_vgg == None:
self.weight_vgg, self.biase_vgg = model.loadWeightsAndBiases(
VGG19_WEIGHTS_AND_BIASE_PATH, False)
# 这里输出的是最后一个隐藏层
out = model.getNet(img_batch, self.weight_vgg, self.biase_vgg, 0.2,
True)
init = tf.global_variables_initializer()
with tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True))) as sess:
sess.run(init)
data = sess.run(out)
return data[0]
def makeTrainBatch(self, start_pos=0, end_pos=0):
"""
制作训练集
"""
# 保存位置TRAIN_BATCH_PATH
# 速度很慢...
if os.path.exists(os.path.dirname(TRAIN_BATCH_PATH)) == False:
os.mkdir(os.path.dirname(TRAIN_BATCH_PATH))
path = TRAIN_BATCH_PATH.split('.')
writer = tf.python_io.TFRecordWriter(path[0] + str(start_pos) + '_' +
str(end_pos) + '.' + path[1])
self.reader.set_pos(start_pos)
# 导入VGG19模型
model = VGG19model()
# img_batch为占位符
img_batch = tf.placeholder(dtype=tf.float32,
shape=[224, 224, 3],
name='IMG')
img_batch_1 = tf.reshape(img_batch, [1, 224, 224, 3])
weight_vgg, biase_vgg = model.loadWeightsAndBiases(
VGG19_WEIGHTS_AND_BIASE_PATH, False)
# 这里输出的是最后的池化层
out = model.getNet(img_batch_1, weight_vgg, biase_vgg, 0, True)
out = tf.reshape(out, [1, 7 * 7 * 512])
init = tf.global_variables_initializer()
with tf.Session(config=tf.ConfigProto(gpu_options=tf.GPUOptions(
allow_growth=True))) as sess:
sess.run(init)
print('running...')
while self.reader.get_pos() < end_pos:
now_id = self.reader.get_next_pic_id()
img = self.reader.get_pic_data(now_id)
# 是否为黑白图判定
if len(img.shape) == 2:
continue
img_data = sess.run(out, feed_dict={img_batch: img})
img_data = img_data.tolist()[0]
qa = self.reader.get_pic_qa(now_id)
for q in qa:
question = q['question']
question = question.replace('?', '')
question = question.replace(',', ' ,')
question = question.replace('.', ' .')
question = question.split(' ')
answers = dict()
confidences = []
for a in q['answers']:
# 判断条件->对该回答的信心程度，'yes'加权1，'maybe'加权0.5，三分以上为yes
answer = a['answer']
weight = 0
if a['answer_confidence'] == 'yes':
weight = 1
elif a['answer_confidence'] == 'maybe':
weight = 0.5
if not answer in answers.keys():
answers[answer] = 0
answers[answer] = answers[answer] + weight
answers_list = []
for key in answers.keys():
if answers[key] >= 3:
answers_list.append(self.dealer.deal(key)[1])
# 若这个问题没有正确回答，则跳过
if len(answers_list) == 0:
continue
label = self.__getOnehot(answers_list, 1,
self.dealer.getWordNum())
data = []
for word in question:
# data.shape = (len(question)*300)
try:
data = data + list(self.model_word2vec[word])
except:
# 识别不出的填0
# 例如问号
data = data + [0] * 300
else:
pass
data = data + [0] * (
(QUESTION_MAX_LEN - len(question)) * 300)
example = tf.train.Example(features=tf.train.Features(
feature={
"answer":
tf.train.Feature(
int64_list=tf.train.Int64List(value=label)
), # len = self.dealer.getWordNum()
"question":
tf.train.Feature(float_list=tf.train.FloatList(
value=data)), # len = len(question)*300
'img':
tf.train.Feature(float_list=tf.train.FloatList(
value=img_data)), # len = 7*7*512
'question_len':
tf.train.Feature(int64_list=tf.train.Int64List(
value=[len(question)])) # len = 1
}))
writer.write(example.SerializeToString())
writer.close()
print('over!')
