In[3]
# 创建数据集和数据字典
data_root_path='/home/aistudio/data/'
def create_data_list(data_root_path):
with open(data_root_path + 'test_list.txt', 'w') as f:
pass
with open(data_root_path + 'train_list.txt', 'w') as f:
pass
with open(os.path.join(data_root_path, 'dict_txt.txt'), 'r', encoding='utf-8') as f_data:
dict_txt = eval(f_data.readlines()[0])
with open(os.path.join(data_root_path, 'news_classify_data.txt'), 'r', encoding='utf-8') as f_data:
lines = f_data.readlines()
i = 0
for line in lines:
title = line.split('_!_')[-1].replace('\n', '')
l = line.split('_!_')[1]
labs = ""
if i % 10 == 0:
with open(os.path.join(data_root_path, 'test_list.txt'), 'a', encoding='utf-8') as f_test:
for s in title:
lab = str(dict_txt[s])
labs = labs + lab + ','
labs = labs[:-1]
labs = labs + '\t' + l + '\n'
f_test.write(labs)
else:
with open(os.path.join(data_root_path, 'train_list.txt'), 'a', encoding='utf-8') as f_train:
for s in title:
lab = str(dict_txt[s])
labs = labs + lab + ','
labs = labs[:-1]
labs = labs + '\t' + l + '\n'
f_train.write(labs)
i += 1
print("数据列表生成完成!")
# 把下载得数据生成一个字典
def create_dict(data_path, dict_path):
dict_set = set()
# 读取已经下载得数据
with open(data_path, 'r', encoding='utf-8') as f:
lines = f.readlines()
# 把数据生成一个元组
for line in lines:
title = line.split('_!_')[-1].replace('\n', '')
for s in title:
dict_set.add(s)
# 把元组转换成字典,一个字对应一个数字
dict_list = []
i = 0
for s in dict_set:
dict_list.append([s, i])
i += 1
# 添加未知字符
dict_txt = dict(dict_list)
end_dict = {"": i}
dict_txt.update(end_dict)
# 把这些字典保存到本地中
with open(dict_path, 'w', encoding='utf-8') as f:
f.write(str(dict_txt))
print("数据字典生成完成!")
# 获取字典的长度
def get_dict_len(dict_path):
with open(dict_path, 'r', encoding='utf-8') as f:
line = eval(f.readlines()[0])
return len(line.keys())
if __name__ == '__main__':
# 把生产的数据列表都放在自己的总类别文件夹中
data_root_path = "/home/aistudio/data/"
data_path = os.path.join(data_root_path, 'news_classify_data.txt')
dict_path = os.path.join(data_root_path, "dict_txt.txt")
# 创建数据字典
create_dict(data_path, dict_path)
# 创建数据列表
create_data_list(data_root_path)
数据字典生成完成!
数据列表生成完成!
paddle.reader.xmap_readers():通过多线程方式,通过用户自定义的映射器mapper来映射reader返回的样本(到输出队列)。
In[4]
# 创建数据读取器train_reader 和test_reader
# 训练/测试数据的预处理
def data_mapper(sample):
data, label = sample
data = [int(data) for data in data.split(',')]
return data, int(label)
# 创建数据读取器train_reader
def train_reader(train_list_path):
def reader():
with open(train_list_path, 'r') as f:
lines = f.readlines()
# 打乱数据
np.random.shuffle(lines)
# 开始获取每张图像和标签
for line in lines:
data, label = line.split('\t')
yield data, label
return paddle.reader.xmap_readers(data_mapper, reader, cpu_count(), 1024)
# 创建数据读取器test_reader
def test_reader(test_list_path):
def reader():
with open(test_list_path, 'r') as f:
lines = f.readlines()
for line in lines:
data, label = line.split('\t')
yield data, label
return paddle.reader.xmap_readers(data_mapper, reader, cpu_count(), 1024)
输入词向量序列,产生一个特征图(feature map),对特征图采用时间维度上的最大池化(max pooling over time)操作得到此卷积核对应的整句话的特征,最后,将所有卷积核得到的特征拼接起来即为文本的定长向量表示,对于文本分类问题,将其连接至softmax即构建出完整的模型。
在实际应用中,我们会使用多个卷积核来处理句子,窗口大小相同的卷积核堆叠起来形成一个矩阵,这样可以更高效的完成运算。
另外,我们也可使用窗口大小不同的卷积核来处理句子.
In[5]
# 创建CNN网络
def CNN_net(data,dict_dim, class_dim=10, emb_dim=128, hid_dim=128,hid_dim2=98):
emb = fluid.layers.embedding(input=data,
size=[dict_dim, emb_dim])
conv_3 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim,
filter_size=3,
act="tanh",
pool_type="sqrt")
conv_4 = fluid.nets.sequence_conv_pool(
input=emb,
num_filters=hid_dim2,
filter_size=4,
act="tanh",
pool_type="sqrt")
output = fluid.layers.fc(
input=[conv_3, conv_4], size=class_dim, act='softmax')
return output
In[6]
# 定义输入数据, lod_level不为0指定输入数据为序列数据
words = fluid.layers.data(name='words', shape=[1], dtype='int64', lod_level=1)
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# 获取数据字典长度
dict_dim = get_dict_len('/home/aistudio/data/dict_txt.txt')
# 获取卷积神经网络
# model = CNN_net(words, dict_dim, 15)
# 获取分类器
model = CNN_net(words, dict_dim)
# 获取损失函数和准确率
cost = fluid.layers.cross_entropy(input=model, label=label)
avg_cost = fluid.layers.mean(cost)
acc = fluid.layers.accuracy(input=model, label=label)
# 获取预测程序
test_program = fluid.default_main_program().clone(for_test=True)
# 定义优化方法
optimizer = fluid.optimizer.AdagradOptimizer(learning_rate=0.002)
opt = optimizer.minimize(avg_cost)
# 创建一个执行器,CPU训练速度比较慢
place = fluid.CPUPlace()
# place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
# 进行参数初始化
exe.run(fluid.default_startup_program())
[]
In[7]
# 获取训练数据读取器和测试数据读取器
train_reader = paddle.batch(reader=train_reader('/home/aistudio/data/train_list.txt'), batch_size=128)
test_reader = paddle.batch(reader=test_reader('/home/aistudio/data/test_list.txt'), batch_size=128)
In[8]
# 定义数据映射器
feeder = fluid.DataFeeder(place=place, feed_list=[words, label])
In[9]
EPOCH_NUM=10
model_save_dir = '/home/aistudio/work/infer_model/'
# 开始训练
for pass_id in range(EPOCH_NUM):
# 进行训练
for batch_id, data in enumerate(train_reader()):
train_cost, train_acc = exe.run(program=fluid.default_main_program(),
feed=feeder.feed(data),
fetch_list=[avg_cost, acc])
if batch_id % 100 == 0:
print('Pass:%d, Batch:%d, Cost:%0.5f, Acc:%0.5f' % (pass_id, batch_id, train_cost[0], train_acc[0]))
# 进行测试
test_costs = []
test_accs = []
for batch_id, data in enumerate(test_reader()):
test_cost, test_acc = exe.run(program=test_program,
feed=feeder.feed(data),
fetch_list=[avg_cost, acc])
test_costs.append(test_cost[0])
test_accs.append(test_acc[0])
# 计算平均预测损失在和准确率
test_cost = (sum(test_costs) / len(test_costs))
test_acc = (sum(test_accs) / len(test_accs))
print('Test:%d, Cost:%0.5f, ACC:%0.5f' % (pass_id, test_cost, test_acc))
# 保存预测模型
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
fluid.io.save_inference_model(model_save_dir,
feeded_var_names=[words.name],
target_vars=[model],
executor=exe)
print('训练模型保存完成!')
Pass:0, Batch:0, Cost:2.30787, Acc:0.09375
Pass:0, Batch:100, Cost:0.99437, Acc:0.75781
Pass:0, Batch:200, Cost:0.86369, Acc:0.75781
Pass:0, Batch:300, Cost:0.91219, Acc:0.66406
Test:0, Cost:0.81688, ACC:0.73704
Pass:1, Batch:0, Cost:0.64618, Acc:0.78906
Pass:1, Batch:100, Cost:0.86004, Acc:0.75781
Pass:1, Batch:200, Cost:0.75906, Acc:0.74219
Pass:1, Batch:300, Cost:0.61665, Acc:0.79688
Test:1, Cost:0.75453, ACC:0.75476
Pass:2, Batch:0, Cost:0.73743, Acc:0.76562
Pass:2, Batch:100, Cost:0.72537, Acc:0.77344
Pass:2, Batch:200, Cost:0.64703, Acc:0.78125
Pass:2, Batch:300, Cost:0.89957, Acc:0.66406
Test:2, Cost:0.73184, ACC:0.76048
Pass:3, Batch:0, Cost:0.52136, Acc:0.85156
Pass:3, Batch:100, Cost:0.66709, Acc:0.82031
Pass:3, Batch:200, Cost:0.82518, Acc:0.76562
Pass:3, Batch:300, Cost:0.71064, Acc:0.79688
Test:3, Cost:0.72042, ACC:0.76213
Pass:4, Batch:0, Cost:0.63134, Acc:0.77344
Pass:4, Batch:100, Cost:0.67031, Acc:0.76562
Pass:4, Batch:200, Cost:0.57762, Acc:0.82031
Pass:4, Batch:300, Cost:0.66310, Acc:0.74219
Test:4, Cost:0.71157, ACC:0.76518
Pass:5, Batch:0, Cost:0.74650, Acc:0.82031
Pass:5, Batch:100, Cost:0.60415, Acc:0.78906
Pass:5, Batch:200, Cost:0.47689, Acc:0.85156
Pass:5, Batch:300, Cost:0.47061, Acc:0.86719
Test:5, Cost:0.71292, ACC:0.76282
Pass:6, Batch:0, Cost:0.47714, Acc:0.87500
Pass:6, Batch:100, Cost:0.61330, Acc:0.79688
Pass:6, Batch:200, Cost:0.46030, Acc:0.86719
Pass:6, Batch:300, Cost:0.62100, Acc:0.79688
Test:6, Cost:0.70767, ACC:0.76743
Pass:7, Batch:0, Cost:0.59985, Acc:0.81250
Pass:7, Batch:100, Cost:0.56416, Acc:0.83594
Pass:7, Batch:200, Cost:0.63928, Acc:0.82031
Pass:7, Batch:300, Cost:0.63925, Acc:0.82031
Test:7, Cost:0.70949, ACC:0.76560
Pass:8, Batch:0, Cost:0.53597, Acc:0.84375
Pass:8, Batch:100, Cost:0.70637, Acc:0.77344
Pass:8, Batch:200, Cost:0.56455, Acc:0.82812
Pass:8, Batch:300, Cost:0.53035, Acc:0.81250
Test:8, Cost:0.70518, ACC:0.76821
Pass:9, Batch:0, Cost:0.54741, Acc:0.79688
Pass:9, Batch:100, Cost:0.47959, Acc:0.85938
Pass:9, Batch:200, Cost:0.55342, Acc:0.82812
Pass:9, Batch:300, Cost:0.50934, Acc:0.85938
Test:9, Cost:0.70361, ACC:0.76977
训练模型保存完成!
In[10]
# 用训练好的模型进行预测并输出预测结果
# 创建执行器
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
save_path = '/home/aistudio/work/infer_model/'
# 从模型中获取预测程序、输入数据名称列表、分类器
[infer_program, feeded_var_names, target_var] = fluid.io.load_inference_model(dirname=save_path, executor=exe)
# 获取数据
def get_data(sentence):
# 读取数据字典
with open('/home/aistudio/data/dict_txt.txt', 'r', encoding='utf-8') as f_data:
dict_txt = eval(f_data.readlines()[0])
dict_txt = dict(dict_txt)
# 把字符串数据转换成列表数据
keys = dict_txt.keys()
data = []
for s in sentence:
# 判断是否存在未知字符
if not s in keys:
s = ''
data.append(int(dict_txt[s]))
return data
data = []
# 获取图片数据
data1 = get_data('在获得诺贝尔文学奖7年之后,莫言15日晚间在山西汾阳贾家庄如是说')
data2 = get_data('综合“今日美国”、《世界日报》等当地媒体报道,芝加哥河滨警察局表示,')
data.append(data1)
data.append(data2)
# 获取每句话的单词数量
base_shape = [[len(c) for c in data]]
# 生成预测数据
tensor_words = fluid.create_lod_tensor(data, base_shape, place)
# 执行预测
result = exe.run(program=infer_program,
feed={feeded_var_names[0]: tensor_words},
fetch_list=target_var)
# 分类名称
names = [ '文化', '娱乐', '体育', '财经','房产', '汽车', '教育', '科技', '国际', '证券']
# 获取结果概率最大的label
for i in range(len(data)):
lab = np.argsort(result)[0][i][-1]
print('预测结果标签为:%d, 名称为:%s, 概率为:%f' % (lab, names[lab], result[0][i][lab]))
预测结果标签为:0, 名称为:文化, 概率为:0.900590
预测结果标签为:8, 名称为:国际, 概率为:0.620146