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Add common.h and remove DisableCopy and Typedefs #1004
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图像分类教程 | ||
========== | ||
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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> | ||
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## 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: | ||
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<https://www.cs.toronto.edu/~kriz/cifar.html> | ||
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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: | ||
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1. 安装pillow | ||
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```bash | ||
sudo apt-get install libjpeg-dev | ||
pip install pillow | ||
``` | ||
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2. 下载数据集 | ||
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```bash | ||
cd demo/image_classification/data/ | ||
sh download_cifar.sh | ||
``` | ||
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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。 | ||
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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> | ||
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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 | ||
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``` | ||
train | ||
---airplane | ||
---automobile | ||
---bird | ||
---cat | ||
---deer | ||
---dog | ||
---frog | ||
---horse | ||
---ship | ||
---truck | ||
test | ||
---airplane | ||
---automobile | ||
---bird | ||
---cat | ||
---deer | ||
---dog | ||
---frog | ||
---horse | ||
---ship | ||
---truck | ||
``` | ||
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cifar-out下包含`train`和`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。 | ||
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## 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: | ||
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``` | ||
cd demo/image_classification/ | ||
sh preprocess.sh | ||
``` | ||
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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 | ||
``` | ||
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`./demo/image_classification/preprocess.py` 使用如下参数: | ||
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- `-i` 或 `--input` 给出输入数据所在路径; | ||
- `-s` 或 `--size` 给出图片尺寸; | ||
- `-c` 或 `--color` 标示图片是彩色图或灰度图 | ||
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## 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。 | ||
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```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)) | ||
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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)) | ||
``` | ||
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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 * | ||
``` | ||
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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。 | ||
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`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。 | ||
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```python | ||
settings( | ||
batch_size = 128, | ||
learning_rate = 0.1 / 128.0, | ||
learning_method = MomentumOptimizer(0.9), | ||
regularization = L2Regularization(0.0005 * 128) | ||
) | ||
``` | ||
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`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。 | ||
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```bash | ||
config=vgg_16_cifar.py | ||
output=./cifar_vgg_model | ||
log=train.log | ||
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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 | ||
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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: | ||
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<center>![Training and testing curves.](./plot.png)</center> | ||
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## 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`。 | ||
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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: | ||
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``` | ||
sh predict.sh | ||
``` | ||
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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 | ||
``` | ||
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## 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)。 | ||
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<http://www.vision.caltech.edu/visipedia/CUB-200.html> | ||
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## 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: | ||
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![Convolutional Neural Network](./lenet.png) | ||
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一个卷积神经网络包含如下层: | ||
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- 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。 | ||
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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。 | ||
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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。 |
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|
@@ -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. | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. 忽略 |
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```bash | ||
config=vgg_16_cifar.py | ||
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忽略这两个文档文件。