czczup / Urbanregionfunctionclassification
第五届百度西安交大大数据竞赛 城市区域功能分类 Baseline
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城市区域功能分类
简介
使用遥感图像和访问数据两个模态,在特征层进行融合,大概能拿到0.57的准确率。
快速起步
1.1 依赖环境
tensorflow-gpu==1.8
opencv-python
pandas
1.2 数据准备
将数据放在data文件夹下,如下所示:
- data/test_image/test/xxxxxx.jpg
- data/test_visit/test/xxxxxx.txt
- data/train_image/train/00x/xxxxxx_00x.jpg
- data/train_visit/xxxxxx_00x.txt
把压缩文件放在data文件夹里直接解压应该就是上面这样。
我把给的训练集划分了一部分当验证集,具体过程看check_data.ipynb。
划分后的文件名记录在data/train.txt和data/valid.txt中。
1.3 数据转换
把visit数据转换为7x26x24的矩阵,这一步耗时比较长,大概要一个小时。
python visit2array.py
转换后的数据存储在:
- data/npy/train_visit
- data/npy/test_visit
1.4 生成tfrecord
python tfrecord.py
生成的tfrecord存储在:
- data/tfrecord/train.tfrecord
- data/tfrecord/valid.tfrecord
备注:由于这里直接加载了所有数据,大约要占用5G内存。
1.5 训练
python train.py
为了调参方便,每组实验存在不同的文件夹里。 需要输入显卡的编号和文件夹名称,比如:
device id: 0
dir id: 1001
查看tensorboard:
cd model/
tensorboard --logdir=./
1.6 测试
python test.py
测试完成后在result文件夹中生成结果。
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