Merge pull request #4823 from luotao1/doc
remove duplicated doc/tutorials, and rename tutorials to v1_api_tutorialsrevert-4814-Add_sequence_project_op
Tao Luo
7 years ago
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Image Classification Tutorial
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==============================
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This tutorial will guide you through training a convolutional neural network to classify objects using the CIFAR-10 image classification dataset.
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As shown in the following figure, the convolutional neural network can recognize the main object in images, and output the classification result.
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<center></center>
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## Data Preparation
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First, download CIFAR-10 dataset. CIFAR-10 dataset can be downloaded from its official website.
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<https://www.cs.toronto.edu/~kriz/cifar.html>
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We have prepared a script to download and process CIFAR-10 dataset. The script will download CIFAR-10 dataset from the official dataset.
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It will convert it to jpeg images and organize them into a directory with the required structure for the tutorial. Make sure that you have installed pillow and its dependents.
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Consider the following commands:
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1. install pillow dependents
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```bash
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sudo apt-get install libjpeg-dev
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pip install pillow
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```
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2. download data and preparation
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```bash
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cd demo/image_classification/data/
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sh download_cifar.sh
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```
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The CIFAR-10 dataset consists of 60000 32x32 color images in 10 classes, with 6000 images per class. There are 50000 training images and 10000 test images.
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Here are the classes in the dataset, as well as 10 random images from each:
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<center></center>
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After downloading and converting, we should find a directory (cifar-out) containing the dataset in the following format:
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```
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train
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---airplane
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---automobile
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---bird
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---cat
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---deer
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---dog
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---frog
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---horse
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---ship
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---truck
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test
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---airplane
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---automobile
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---bird
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---cat
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---deer
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---dog
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---frog
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---horse
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---ship
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---truck
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```
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It has two directories:`train` and `test`. These two directories contain training data and testing data of CIFAR-10, respectively. Each of these two folders contains 10 sub-folders, ranging from `airplane` to `truck`. Each sub-folder contains images with the corresponding label. After the images are organized into this structure, we are ready to train an image classification model.
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## Preprocess
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After the data has been downloaded, it needs to be pre-processed into the Paddle format. We can run the following command for preprocessing.
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```
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cd demo/image_classification/
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sh preprocess.sh
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```
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`preprocess.sh` calls `./demo/image_classification/preprocess.py` to preprocess image data.
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```sh
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export PYTHONPATH=$PYTHONPATH:../../
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data_dir=./data/cifar-out
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python preprocess.py -i $data_dir -s 32 -c 1
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```
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`./demo/image_classification/preprocess.py` has the following arguments
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- `-i` or `--input` specifes the input data directory.
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- `-s` or `--size` specifies the processed size of images.
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- `-c` or `--color` specifes whether images are color images or gray images.
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## Model Training
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We need to create a model config file before training the model. An example of the config file (vgg_16_cifar.py) is listed below. **Note**, it is slightly different from the `vgg_16_cifar.py` which also applies to the prediction.
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```python
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from paddle.trainer_config_helpers import *
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data_dir='data/cifar-out/batches/'
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meta_path=data_dir+'batches.meta'
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args = {'meta':meta_path, 'mean_img_size': 32,
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'img_size': 32, 'num_classes': 10,
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'use_jpeg': 1, 'color': "color"}
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define_py_data_sources2(train_list=data_dir+"train.list",
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test_list=data_dir+'test.list',
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module='image_provider',
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obj='processData',
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args=args)
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settings(
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batch_size = 128,
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learning_rate = 0.1 / 128.0,
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learning_method = MomentumOptimizer(0.9),
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regularization = L2Regularization(0.0005 * 128))
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img = data_layer(name='image', size=3*32*32)
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lbl = data_layer(name="label", size=10)
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# small_vgg is predined in trainer_config_helpers.network
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predict = small_vgg(input_image=img, num_channels=3)
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outputs(classification_cost(input=predict, label=lbl))
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```
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The first line imports python functions for defining networks.
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```python
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from paddle.trainer_config_helpers import *
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```
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Then define an `define_py_data_sources2` which use python data provider
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interface. The arguments in `args` are used in `image_provider.py` which
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yeilds image data and transform them to Paddle.
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- `meta`: the mean value of training set.
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- `mean_img_size`: the size of mean feature map.
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- `img_size`: the height and width of input image.
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- `num_classes`: the number of classes.
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- `use_jpeg`: the data storage type when preprocessing.
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- `color`: specify color image.
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`settings` specifies the training algorithm. In the following example,
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it specifies learning rate as 0.1, but divided by batch size, and the weight decay
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is 0.0005 and multiplied by batch size.
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```python
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settings(
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batch_size = 128,
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learning_rate = 0.1 / 128.0,
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learning_method = MomentumOptimizer(0.9),
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regularization = L2Regularization(0.0005 * 128)
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)
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```
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The `small_vgg` specifies the network. We use a small version of VGG convolutional network as our network
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for classification. A description of VGG network can be found here [http://www.robots.ox.ac.uk/~vgg/research/very_deep/](http://www.robots.ox.ac.uk/~vgg/research/very_deep/).
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```python
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# small_vgg is predined in trainer_config_helpers.network
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predict = small_vgg(input_image=img, num_channels=3)
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```
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After writing the config, we can train the model by running the script train.sh.
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```bash
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config=vgg_16_cifar.py
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output=./cifar_vgg_model
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log=train.log
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paddle train \
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--config=$config \
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--dot_period=10 \
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--log_period=100 \
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--test_all_data_in_one_period=1 \
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--use_gpu=1 \
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--save_dir=$output \
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2>&1 | tee $log
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python -m paddle.utils.plotcurve -i $log > plot.png
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```
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- Here we use GPU mode to train. If you have no gpu environment, just set `use_gpu=0`.
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- `./demo/image_classification/vgg_16_cifar.py` is the network and data configuration file. The meaning of the other flags can be found in the documentation of the command line flags.
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- The script `plotcurve.py` requires the python module of `matplotlib`, so if it fails, maybe you need to install `matplotlib`.
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After training finishes, the training and testing error curves will be saved to `plot.png` using `plotcurve.py` script. An example of the plot is shown below:
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<center></center>
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## Prediction
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After we train the model, the model file as well as the model parameters are stored in path `./cifar_vgg_model/pass-%05d`. For example, the model of the 300-th pass is stored at `./cifar_vgg_model/pass-00299`.
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To make a prediction for an image, one can run `predict.sh` as follows. The script will output the label of the classfiication.
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```
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sh predict.sh
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```
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predict.sh:
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```
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model=cifar_vgg_model/pass-00299/
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image=data/cifar-out/test/airplane/seaplane_s_000978.png
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use_gpu=1
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python prediction.py $model $image $use_gpu
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```
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## Exercise
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Train a image classification of birds using VGG model and CUB-200 dataset. The birds dataset can be downloaded here. It contains an image dataset with photos of 200 bird species (mostly North American).
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<http://www.vision.caltech.edu/visipedia/CUB-200.html>
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## Delve into Details
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### Convolutional Neural Network
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A Convolutional Neural Network is a feedforward neural network that uses convolution layers. It is very suitable for building neural networks that process and understand images. A standard convolutional neural network is shown below:
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Convolutional Neural Network contains the following layers:
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- Convolutional layer: It uses convolution operation to extract features from an image or a feature map.
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- Pooling layer: It uses max-pooling to downsample feature maps.
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- Fully Connected layer: It uses fully connected connections to transform features.
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Convolutional Neural Network achieves amazing performance for image classification because it exploits two important characteristics of images: *local correlation* and *spatial invariance*. By iteratively applying convolution and max-pooing operations, convolutional neural network can well represent these two characteristics of images.
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For more details of how to define layers and their connections, please refer to the documentation of layers.
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# 完整教程
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* [快速入门](quick_start/index_cn.rst)
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* [个性化推荐](rec/ml_regression_cn.rst)
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* [图像分类](image_classification/index_cn.md)
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* [情感分析](sentiment_analysis/index_cn.md)
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* [语义角色标注](semantic_role_labeling/index_cn.md)
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* [机器翻译](text_generation/index_cn.md)
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## 常用模型
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* [ResNet模型](imagenet_model/resnet_model_cn.md)
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* [词向量模型](embedding_model/index_cn.md)
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@ -1,14 +0,0 @@
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# TUTORIALS
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There are several examples and demos here.
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* [Quick Start](quick_start/index_en.md)
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* [MovieLens Regression](rec/ml_regression_en.rst)
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* [Image Classification](image_classification/index_en.md)
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* [Sentiment Analysis](sentiment_analysis/index_en.md)
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* [Semantic Role Labeling](semantic_role_labeling/index_en.md)
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* [Text Generation](text_generation/index_en.md)
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* [Image Auto-Generation](gan/index_en.md)
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## Model Zoo
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* [ImageNet: ResNet](imagenet_model/resnet_model_en.md)
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* [Embedding: Chinese Word](embedding_model/index_en.md)
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@ -1,111 +0,0 @@
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```eval_rst
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.. _demo_ml_dataset:
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```
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# MovieLens Dataset
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The [MovieLens Dataset](http://grouplens.org/datasets/movielens/) was collected by GroupLens Research.
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The data set contains some user information, movie information, and many movie ratings from \[1-5\].
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The data sets have many version depending on the size of set.
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We use [MovieLens 1M Dataset](http://files.grouplens.org/datasets/movielens/ml-1m.zip) as a demo dataset, which contains
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1 million ratings from 6000 users on 4000 movies. Released 2/2003.
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## Dataset Features
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In [ml-1m Dataset](http://files.grouplens.org/datasets/movielens/ml-1m.zip), there are many features in these dataset.
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The data files (which have ".dat" extension) in [ml-1m Dataset](http://files.grouplens.org/datasets/movielens/ml-1m.zip)
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is basically CSV file that delimiter is "::". The description in README we quote here.
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### RATINGS FILE DESCRIPTION(ratings.dat)
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All ratings are contained in the file "ratings.dat" and are in the
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following format:
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UserID::MovieID::Rating::Timestamp
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- UserIDs range between 1 and 6040
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- MovieIDs range between 1 and 3952
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- Ratings are made on a 5-star scale (whole-star ratings only)
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- Timestamp is represented in seconds since the epoch as returned by time(2)
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- Each user has at least 20 ratings
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### USERS FILE DESCRIPTION(users.dat)
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User information is in the file "users.dat" and is in the following
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format:
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UserID::Gender::Age::Occupation::Zip-code
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All demographic information is provided voluntarily by the users and is
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not checked for accuracy. Only users who have provided some demographic
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information are included in this data set.
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- Gender is denoted by a "M" for male and "F" for female
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- Age is chosen from the following ranges:
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* 1: "Under 18"
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* 18: "18-24"
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* 25: "25-34"
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* 35: "35-44"
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* 45: "45-49"
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* 50: "50-55"
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* 56: "56+"
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- Occupation is chosen from the following choices:
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* 0: "other" or not specified
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* 1: "academic/educator"
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* 2: "artist"
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* 3: "clerical/admin"
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* 4: "college/grad student"
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* 5: "customer service"
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* 6: "doctor/health care"
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* 7: "executive/managerial"
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* 8: "farmer"
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* 9: "homemaker"
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* 10: "K-12 student"
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* 11: "lawyer"
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* 12: "programmer"
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* 13: "retired"
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* 14: "sales/marketing"
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* 15: "scientist"
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* 16: "self-employed"
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* 17: "technician/engineer"
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* 18: "tradesman/craftsman"
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* 19: "unemployed"
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* 20: "writer"
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### MOVIES FILE DESCRIPTION(movies.dat)
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Movie information is in the file "movies.dat" and is in the following
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format:
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MovieID::Title::Genres
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- Titles are identical to titles provided by the IMDB (including
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year of release)
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- Genres are pipe-separated and are selected from the following genres:
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* Action
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* Adventure
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* Animation
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* Children's
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* Comedy
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* Crime
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* Documentary
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* Drama
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* Fantasy
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* Film-Noir
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* Horror
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* Musical
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* Mystery
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* Romance
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* Sci-Fi
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* Thriller
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* War
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* Western
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- Some MovieIDs do not correspond to a movie due to accidental duplicate
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entries and/or test entries
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- Movies are mostly entered by hand, so errors and inconsistencies may exist
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