提交 9eae4f59 编写于 作者: Q qingqing01 提交者: GitHub

Merge pull request #894 from maurise-yq-hu/develop

Create index_cn.md on image classification
上级 16d16b49 f41339f9
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2 合并请求!11636[IMPORTANT] MKLDNN layout: Support for sum operator,!2081Release/0.10.0
图像分类教程
==========
在本教程中,我们将使用CIFAR-10数据集训练一个卷积神经网络,并使用这个神经网络来对图片进行分类。如下图所示,卷积神经网络可以辨识图片中的主体,并给出分类结果。
<center>![Image Classification](./image_classification.png)</center>
## 数据准备
首先下载CIFAR-10数据集。下面是CIFAR-10数据集的官方网址:
<https://www.cs.toronto.edu/~kriz/cifar.html>
我们准备了一个脚本,可以用于从官方网站上下载CIFAR-10数据集,转为jpeg文件并存入特定的目录。使用这个脚本前请确认已经安装了pillow及相关依赖模块。可以参照下面的命令进行安装:
1. 安装pillow
```bash
sudo apt-get install libjpeg-dev
pip install pillow
```
2. 下载数据集
```bash
cd demo/image_classification/data/
sh download_cifar.sh
```
CIFAR-10数据集包含60000张32x32的彩色图片。图片分为10类,每个类包含6000张。其中50000张图片作为训练集,10000张作为测试集。
下图展示了所有的图片类别,每个类别中随机抽取了10张图片。
<center>![Image Classification](./cifar.png)</center>
脚本运行完成后,我们应当会得到一个名为cifar-out的文件夹,其下子文件夹的结构如下
```
train
---airplane
---automobile
---bird
---cat
---deer
---dog
---frog
---horse
---ship
---truck
test
---airplane
---automobile
---bird
---cat
---deer
---dog
---frog
---horse
---ship
---truck
```
cifar-out下包含`train``test`两个文件夹,其中分别包含了CIFAR-10中的训练集和测试集。这两个文件夹下各自有10个子文件夹,每个子文件夹下存储相应分类的图片。将图片按照上述结构存储好之后,我们就可以着手对分类模型进行训练了。
## 预处理
数据下载之后,还需要进行预处理,将数据转换为Paddle的格式。我们可以通过如下命令进行预处理工作:
```
cd demo/image_classification/
sh preprocess.sh
```
其中`preprocess.sh` 调用 `./demo/image_classification/preprocess.py` 对图片进行预处理
```sh
export PYTHONPATH=$PYTHONPATH:../../
data_dir=./data/cifar-out
python preprocess.py -i $data_dir -s 32 -c 1
```
`./demo/image_classification/preprocess.py` 使用如下参数:
- `-i``--input` 给出输入数据所在路径;
- `-s``--size` 给出图片尺寸;
- `-c``--color` 标示图片是彩色图或灰度图
## 模型训练
在开始训练之前,我们需要先创建一个模型配置文件。下面我们给出了一个配置示例。**注意**,这里的列出的和`vgg_16_cifar.py`文件稍有差别,因为该文件可适用于预测。
```python
from paddle.trainer_config_helpers import *
data_dir='data/cifar-out/batches/'
meta_path=data_dir+'batches.meta'
args = {'meta':meta_path, 'mean_img_size': 32,
'img_size': 32, 'num_classes': 10,
'use_jpeg': 1, 'color': "color"}
define_py_data_sources2(train_list=data_dir+"train.list",
test_list=data_dir+'test.list',
module='image_provider',
obj='processData',
args=args)
settings(
batch_size = 128,
learning_rate = 0.1 / 128.0,
learning_method = MomentumOptimizer(0.9),
regularization = L2Regularization(0.0005 * 128))
img = data_layer(name='image', size=3*32*32)
lbl = data_layer(name="label", size=10)
# small_vgg is predined in trainer_config_helpers.network
predict = small_vgg(input_image=img, num_channels=3)
outputs(classification_cost(input=predict, label=lbl))
```
在第一行中我们载入用于定义网络的函数。
```python
from paddle.trainer_config_helpers import *
```
之后定义的`define_py_data_sources2`使用Python数据提供器,其中 `args`将在`image_provider.py`进行使用,该文件负责产生图片数据并传递给Paddle系统
- `meta`: 训练集平均值。
- `mean_img_size`: 平均特征图的高度及宽度。
- `img_size`:输入图片的高度及宽度。
- `num_classes`:类别个数。
- `use_jpeg`:处理过程中数据存储格式。
- `color`:标示是否为彩色图片。
`settings`用于设置训练算法。在下面的例子中,learning rate被设置为0.1除以batch size,而weight decay则为0.0005乘以batch size。
```python
settings(
batch_size = 128,
learning_rate = 0.1 / 128.0,
learning_method = MomentumOptimizer(0.9),
regularization = L2Regularization(0.0005 * 128)
)
```
`small_vgg`定义了网络结构。这里我们使用的是一个小的VGG网络。关于VGG卷积神经网络的描述可以参考:[http://www.robots.ox.ac.uk/~vgg/research/very_deep/](http://www.robots.ox.ac.uk/~vgg/research/very_deep/)
```python
# small_vgg is predined in trainer_config_helpers.network
predict = small_vgg(input_image=img, num_channels=3)
```
配置创建完毕后,可以运行脚本train.sh来训练模型。
```bash
config=vgg_16_cifar.py
output=./cifar_vgg_model
log=train.log
paddle train \
--config=$config \
--dot_period=10 \
--log_period=100 \
--test_all_data_in_one_period=1 \
--use_gpu=1 \
--save_dir=$output \
2>&1 | tee $log
python -m paddle.utils.plotcurve -i $log > plot.png
```
- 这里我们使用的是GPU模式进行训练。如果你没有GPU环境,可以设置`use_gpu=0`
- `./demo/image_classification/vgg_16_cifar.py`是网络和数据配置文件。各项参数的详细说明可以在命令行参数相关文档中找到。
- 脚本`plotcurve.py`依赖于python的`matplotlib`模块。因此如果这个脚本运行失败,也许是因为需要安装`matplotlib`
在训练完成后,训练及测试误差曲线图会被`plotcurve.py`脚本保存在 `plot.png`中。下面是一个误差曲线图的示例:
<center>![Training and testing curves.](./plot.png)</center>
## 预测
在训练完成后,模型及参数会被保存在路径`./cifar_vgg_model/pass-%05d`下。例如第300个pass的模型会被保存在`./cifar_vgg_model/pass-00299`
要对一个图片的进行分类预测,我们可以使用`predict.sh`,该脚本将输出预测分类的标签:
```
sh predict.sh
```
predict.sh:
```
model=cifar_vgg_model/pass-00299/
image=data/cifar-out/test/airplane/seaplane_s_000978.png
use_gpu=1
python prediction.py $model $image $use_gpu
```
## 练习
在CUB-200数据集上使用VGG模型训练一个鸟类图片分类模型。相关的鸟类数据集可以从如下地址下载,其中包含了200种鸟类的照片(主要来自北美洲)。
<http://www.vision.caltech.edu/visipedia/CUB-200.html>
## 细节探究
### 卷积神经网络
卷积神经网络是一种使用卷积层的前向神经网络,很适合构建用于理解图片内容的模型。一个典型的神经网络如下图所示:
![Convolutional Neural Network](./lenet.png)
一个卷积神经网络包含如下层:
- 卷积层:通过卷积操作从图片或特征图中提取特征
- 池化层:使用max-pooling对特征图下采样
- 全连接层:使输入层到隐藏层的神经元是全部连接的。
卷积神经网络在图片分类上有着惊人的性能,这是因为它发掘出了图片的两类重要信息:局部关联性质和空间不变性质。通过交替使用卷积和池化处理, 卷积神经网络能够很好的表示这两类信息。
关于如何定义网络中的层,以及如何在层之间进行连接,请参考Layer文档。
......@@ -147,7 +147,7 @@ for classification. A description of VGG network can be found here [http://www.r
# small_vgg is predined in trainer_config_helpers.network
predict = small_vgg(input_image=img, num_channels=3)
```
After writing the config, we can train the model by running the script train.sh. Notice that the following script assumes the you run the script in the `./demo/image_classification` folder. If you run the script in a different folder, you need to change the paths of the scripts and the configuration files accordingly.
After writing the config, we can train the model by running the script train.sh.
```bash
config=vgg_16_cifar.py
......
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