├── .dockerignore ├── .gitignore ├── .gitmodules ├── .pre-commit-config.yaml ├── .pre-commit-hooks ├── convert_markdown_into_html.py └── convert_markdown_into_ipynb.sh ├── .style.yapf ├── .tools ├── build_docker.sh ├── cache_dataset.py ├── convert-markdown-into-ipynb-and-test.sh ├── notedown.sh ├── templates │ ├── index.cn.html.json │ ├── index.cn.html.tmpl │ ├── index.html.json │ └── index.html.tmpl └── theme │ ├── PP_w.png │ ├── github-markdown.css │ └── marked.js ├── .travis.yml ├── .travis ├── deploy_docs.sh └── precommit.sh ├── 0.coding_guideline ├── README.MD └── appendix │ ├── README.MD │ ├── dir-1.png │ ├── dir-2.png │ ├── dir-3.png │ ├── dir-4.png │ ├── dir-5.png │ └── readme.png ├── 01.fit_a_line ├── .gitignore ├── .run_ce.sh ├── README.cn.md ├── README.md ├── _ce.py ├── fit_a_line.tar ├── fluid │ └── fit_a_line.fluid.tar ├── image │ ├── formula_fit_a_line_1.png │ ├── formula_fit_a_line_2.png │ ├── formula_fit_a_line_3.png │ ├── formula_fit_a_line_4.png │ ├── prediction_gt.png │ ├── predictions.png │ ├── predictions_en.png │ ├── ranges.png │ ├── ranges_en.png │ └── train_and_test.png ├── index.cn.html ├── index.html └── train.py ├── 02.recognize_digits ├── .gitignore ├── .run_ce.sh ├── README.cn.md ├── README.md ├── _ce.py ├── client │ └── client.py ├── image │ ├── 01.gif │ ├── 02.gif │ ├── 03.gif │ ├── 04.gif │ ├── 05.gif │ ├── cnn.png │ ├── cnn_en.png │ ├── cnn_train_log.png │ ├── cnn_train_log_en.png │ ├── conv_layer.png │ ├── infer_3.png │ ├── max_pooling.png │ ├── max_pooling_en.png │ ├── mlp.png │ ├── mlp_en.png │ ├── mlp_train_log.png │ ├── mlp_train_log_en.png │ ├── mnist_example_image.png │ ├── softmax_regression.png │ ├── softmax_regression_en.png │ ├── softmax_train_log.png │ ├── softmax_train_log_en.png │ └── train_and_test.png ├── index.cn.html ├── index.html └── train.py ├── 03.image_classification ├── .gitignore ├── .run_ce.sh ├── README.cn.md ├── README.md ├── _ce.py ├── image │ ├── cifar.png │ ├── dog.png │ ├── dog_cat.png │ ├── fea_conv0.png │ ├── flowers.png │ ├── googlenet.jpeg │ ├── ilsvrc.png │ ├── inception.png │ ├── inception_en.png │ ├── lenet.png │ ├── lenet_en.png │ ├── plot.png │ ├── plot_en.png │ ├── resnet.png │ ├── resnet_block.jpg │ ├── train_and_test.png │ ├── variations.png │ ├── variations_en.png │ └── vgg16.png ├── index.cn.html ├── index.html ├── resnet.py ├── train.py └── vgg.py ├── 04.word2vec ├── .gitignore ├── .run_ce.sh ├── README.cn.md ├── README.md ├── _ce.py ├── calculate_dis.py ├── format_convert.py ├── image │ ├── 2d_similarity.png │ ├── Eqn1.gif │ ├── Eqn2.gif │ ├── Eqn3.gif │ ├── Eqn4.gif │ ├── Eqn5.gif │ ├── Eqn6.gif │ ├── Eqn7.gif │ ├── Eqn8.gif │ ├── Eqn9.gif │ ├── cbow.png │ ├── cbow_en.png │ ├── ngram.en.png │ ├── ngram.png │ ├── nnlm.png │ ├── nnlm_en.png │ ├── sentence_emb.png │ ├── skipgram.png │ └── skipgram_en.png ├── index.cn.html ├── index.html └── train.py ├── 05.recommender_system ├── .gitignore ├── .run_ce.sh ├── README.cn.md ├── README.md ├── _ce.py ├── image │ ├── Deep_candidate_generation_model_architecture.en.png │ ├── Deep_candidate_generation_model_architecture.png │ ├── YouTube_Overview.en.png │ ├── YouTube_Overview.png │ ├── formula1.png │ ├── formula2.png │ ├── formula3.png │ ├── output_32_0.png │ ├── rec_regression_network.png │ ├── rec_regression_network_en.png │ ├── text_cnn.png │ └── text_cnn_en.png ├── index.cn.html ├── index.html └── train.py ├── 06.understand_sentiment ├── .gitignore ├── .run_ce.sh ├── README.cn.md ├── README.md ├── _ce.py ├── image │ ├── formula_lstm.png │ ├── formula_lstm_more.png │ ├── formula_recrurent.png │ ├── formula_rnn.png │ ├── lstm.png │ ├── lstm_en.png │ ├── rnn.png │ ├── stacked_lstm.jpg │ └── stacked_lstm_en.png ├── index.cn.html ├── index.html ├── train_conv.py ├── train_dyn_rnn.py └── train_stacked_lstm.py ├── 07.label_semantic_roles ├── .gitignore ├── .run_ce.sh ├── README.cn.md ├── README.md ├── _ce.py ├── image │ ├── Eqn1.png │ ├── Eqn2.gif │ ├── Eqn3.gif │ ├── Eqn4.png │ ├── bidirectional_stacked_lstm.png │ ├── bidirectional_stacked_lstm_en.png │ ├── bio_example.png │ ├── bio_example_en.png │ ├── db_lstm_network.png │ ├── db_lstm_network_en.png │ ├── dependency_parsing.png │ ├── dependency_parsing_en.png │ ├── linear_chain_crf.png │ ├── stacked_lstm.png │ └── stacked_lstm_en.png ├── index.cn.html ├── index.html └── train.py ├── 08.machine_translation ├── .gitignore ├── README.cn.md ├── README.md ├── image │ ├── attention_decoder_formula.png │ ├── bi_rnn.png │ ├── bi_rnn_en.png │ ├── decoder_attention.png │ ├── decoder_attention_en.png │ ├── decoder_formula.png │ ├── encoder_attention.png │ ├── encoder_attention_en.png │ ├── encoder_decoder.png │ ├── encoder_decoder_en.png │ ├── gru.png │ ├── gru_en.png │ ├── nmt.png │ ├── nmt_en.png │ ├── probability_formula.png │ ├── sum_formula.png │ └── weight_formula.png ├── index.cn.html ├── index.html └── seq2seq.py ├── 09.gan ├── .run_ce.sh ├── README.cn.md ├── _ce.py ├── dc_gan.py ├── image │ ├── 01.gif │ ├── dcgan_demo.png │ ├── dcgan_g.png │ └── process.png ├── index.cn.html ├── network.py └── utility.py ├── README.cn.md ├── README.md ├── index.cn.html ├── index.html ├── mnist-client ├── .eslintrc ├── .gitignore ├── Dockerfile ├── Procfile ├── README.md ├── app.json ├── gulpfile.js ├── main.py ├── package.json ├── requirements.txt ├── runtime.txt ├── src │ └── js │ │ └── main.js ├── static │ ├── css │ │ └── bootstrap.min.css │ └── js │ │ └── jquery.min.js └── templates │ └── index.html ├── paddle2.0_docs ├── Actor_Critic_Method │ └── Actor_Critic_Method.ipynb ├── Advantage Actor-Critic(A2C) │ └── Advantage Actor-Critic(A2C).ipynb ├── Autoencoder │ └── AutoEncoder.ipynb ├── Collaborative_filtering │ └── Collaborative_Filtering.ipynb ├── Deep_Deterministic_Policy_Gradient_(DDPG) │ └── Deep_Deterministic_Policy_Gradient_(DDPG).ipynb ├── README.md ├── addition_rnn │ └── addition_rnn.ipynb ├── convnet_image_classification │ └── convnet_image_classification.ipynb ├── cyclegan │ └── cyclegan.ipynb ├── dcgan_face │ ├── dcgan_face.ipynb │ └── images │ │ ├── face_image1.jpeg │ │ ├── face_image2.jpeg │ │ ├── loss.png │ │ └── models.png ├── dynamic_graph │ └── dynamic_graph.ipynb ├── hello_paddle │ └── hello_paddle.ipynb ├── high_level_api │ └── high_level_api.ipynb ├── image_classification │ └── image_classification.ipynb ├── image_ocr │ ├── image_ocr.ipynb │ ├── images │ │ ├── image1.png │ │ ├── image2.png │ │ └── image3.png │ └── sample_img │ │ ├── 9450.jpg │ │ ├── 9451.jpg │ │ └── 9452.jpg ├── image_search │ └── image_search.ipynb ├── image_segmentation │ └── image_segmentation.ipynb ├── imdb_bow_classification │ └── imdb_bow_classification.ipynb ├── landmark_detection │ └── landmark_detection.ipynb ├── linear_regression │ └── linear_regression.ipynb ├── n_gram_model │ └── n_gram_model.ipynb ├── pointnet │ └── pointnet.ipynb ├── pretrained_word_embeddings │ └── pretrained_word_embeddings.ipynb ├── save_model │ └── save_model.ipynb ├── seq2seq_with_attention │ └── seq2seq_with_attention.ipynb └── super_resolution_sub_pixel │ └── super_resolution_sub_pixel.ipynb ├── pending ├── gan │ ├── README.md │ └── index.html ├── image_caption │ ├── README.md │ └── index.html ├── image_detection │ ├── README.md │ └── index.html ├── image_qa │ ├── README.md │ └── index.html ├── query_relationship │ ├── README.md │ └── index.html ├── skip_thought │ ├── README.md │ └── index.html └── speech_recognition │ ├── README.md │ └── index.html └── serve ├── .gitignore ├── Dockerfile ├── Dockerfile.gpu ├── README.md ├── main.py └── requirements.txt /.dockerignore: -------------------------------------------------------------------------------- 1 | Dockerfile 2 | .git/ 3 | .gitignore 4 | -------------------------------------------------------------------------------- /.gitignore: -------------------------------------------------------------------------------- 1 | deprecated 2 | *~ 3 | pandoc.template 4 | .DS_Store 5 | .idea 6 | py_env* 7 | *.ipynb 8 | build 9 | .vscode 10 | -------------------------------------------------------------------------------- /.gitmodules: -------------------------------------------------------------------------------- 1 | [submodule "paddle"] 2 | path = paddle 3 | url = https://github.com/PaddlePaddle/Paddle.git 4 | branch = develop 5 | -------------------------------------------------------------------------------- /.pre-commit-config.yaml: -------------------------------------------------------------------------------- 1 | - repo: https://github.com/pre-commit/mirrors-yapf.git 2 | sha: v0.16.0 3 | hooks: 4 | - id: yapf 5 | files: \.py$ 6 | - repo: https://github.com/pre-commit/pre-commit-hooks 7 | sha: a11d9314b22d8f8c7556443875b731ef05965464 8 | hooks: 9 | - id: check-merge-conflict 10 | - id: check-symlinks 11 | - id: detect-private-key 12 | files: (?!.*paddle)^.*$ 13 | - id: end-of-file-fixer 14 | files: \.md$ 15 | - id: trailing-whitespace 16 | files: \.md$ 17 | - repo: https://github.com/Lucas-C/pre-commit-hooks 18 | sha: v1.0.1 19 | hooks: 20 | - id: forbid-crlf 21 | files: \.md$ 22 | - id: remove-crlf 23 | files: \.md$ 24 | - id: forbid-tabs 25 | files: \.md$ 26 | - id: remove-tabs 27 | files: \.md$ 28 | - repo: https://github.com/reyoung/pre-commit-hooks-jinja-compile.git 29 | sha: 4a369cc72a4a2b8d3813ab8cc17abb5f5b21ef6c 30 | hooks: 31 | - id: convert-jinja2-into-html 32 | # The argument means repleace filename from pattern `.*/([^/]*)\.tmpl` to `\1` 33 | args: ['--filename_pattern=.*/([^/]*)\.tmpl', '--filename_repl=\1'] 34 | - repo: local 35 | hooks: 36 | - id: convert-markdown-into-html 37 | name: convert-markdown-into-html 38 | description: Convert README.md into index.html and README.cn.md into index.cn.html 39 | entry: python .pre-commit-hooks/convert_markdown_into_html.py 40 | language: system 41 | files: .+README(\.cn)?\.md$ 42 | 43 | -------------------------------------------------------------------------------- /.pre-commit-hooks/convert_markdown_into_html.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import re 3 | import sys 4 | 5 | HEAD = """ 6 | 7 | 8 | 20 | 21 | 23 | 24 | 25 | 26 | 27 | 28 | 29 | 38 | 39 | 40 | 41 | 42 |
43 |
44 | 45 | 46 | 51 | 52 | 53 | 70 | 71 | """ 72 | 73 | 74 | def convert_markdown_into_html(argv=None): 75 | parser = argparse.ArgumentParser() 76 | parser.add_argument('filenames', nargs='*', help='Filenames to fix') 77 | args = parser.parse_args(argv) 78 | 79 | retv = 0 80 | 81 | for filename in args.filenames: 82 | with open( 83 | re.sub(r"README", "index", re.sub(r"\.md$", ".html", filename)), 84 | "w") as output: 85 | output.write(HEAD) 86 | with open(filename) as input: 87 | for line in input: 88 | output.write(line) 89 | output.write(TAIL) 90 | 91 | return retv 92 | 93 | 94 | if __name__ == '__main__': 95 | sys.exit(convert_markdown_into_html()) 96 | -------------------------------------------------------------------------------- /.pre-commit-hooks/convert_markdown_into_ipynb.sh: -------------------------------------------------------------------------------- 1 | #!/bin/sh 2 | for file in $@ ; do 3 | markdown-to-ipynb < $file > ${file%.*}".ipynb" 4 | if [ $? -ne 0 ]; then 5 | echo >&2 "markdown-to-ipynb $file error" 6 | exit 1 7 | fi 8 | done 9 | 10 | -------------------------------------------------------------------------------- /.style.yapf: -------------------------------------------------------------------------------- 1 | [style] 2 | based_on_style = pep8 3 | column_limit = 80 4 | -------------------------------------------------------------------------------- /.tools/build_docker.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -xe 3 | 4 | cur_path="$(cd "$(dirname "$0")" && pwd -P)" 5 | cd "$cur_path"/../ 6 | 7 | #paddle production image name 8 | if [ ! -n "$1" ]; then 9 | paddle_image=paddlepaddle/paddle 10 | else 11 | paddle_image=$1 12 | fi 13 | 14 | #paddle production image tag 15 | if [ ! -n "$2" ]; then 16 | paddle_tag=0.10.0 17 | else 18 | paddle_tag=$2 19 | fi 20 | 21 | #paddle book image name 22 | if [ ! -n "$3" ]; then 23 | book_image=paddlepaddle/book 24 | else 25 | book_image=$3 26 | fi 27 | 28 | #paddle book image tag 29 | if [ ! -n "$4" ]; then 30 | book_tag=latest 31 | else 32 | book_tag=$4 33 | fi 34 | 35 | #generate docker file 36 | if [ ${USE_UBUNTU_REPO_MIRROR} ]; then 37 | update_mirror_cmd="sed 's@http:\/\/archive.ubuntu.com\/ubuntu\/@mirror:\/\/mirrors.ubuntu.com\/mirrors.txt@' -i /etc/apt/sources.list && \\" 38 | else 39 | update_mirror_cmd="\\" 40 | fi 41 | 42 | #build docker image 43 | echo "paddle_tag:"$paddle_tag 44 | echo "book_tag:"$book_tag 45 | 46 | cat > Dockerfile < 49 | 50 | COPY . /book 51 | EOF 52 | 53 | if [ -n "${http_proxy}" ]; then 54 | cat >> Dockerfile <> Dockerfile </dev/null 2>&1 3 | if [ $? -ne 0 ]; then 4 | echo >&2 "Please install go https://golang.org/doc/install#install" 5 | exit 1 6 | fi 7 | 8 | export GOPATH=~/go; go get -u github.com/wangkuiyi/ipynb/markdown-to-ipynb 9 | 10 | cur_path="$(cd "$(dirname "$0")" && pwd -P)" 11 | cd $cur_path/../ 12 | 13 | #convert md to ipynb 14 | for file in */{README,README\.cn}.md ; do 15 | ~/go/bin/markdown-to-ipynb < $file > ${file%.*}".ipynb" 16 | if [ $? -ne 0 ]; then 17 | echo >&2 "markdown-to-ipynb $file error" 18 | exit 1 19 | fi 20 | done 21 | 22 | if [[ -z $TEST_EMBEDDED_PYTHON_SCRIPTS ]]; then 23 | exit 0 24 | fi 25 | 26 | #exec ipynb's py file 27 | for file in */{README,README\.cn}.ipynb ; do 28 | pushd $PWD > /dev/null 29 | cd $(dirname $file) > /dev/null 30 | 31 | echo "begin test $file" 32 | jupyter nbconvert --to python $(basename $file) --stdout | python 33 | 34 | popd > /dev/null 35 | #break 36 | done 37 | -------------------------------------------------------------------------------- /.tools/notedown.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | set -xe 3 | 4 | cd /book 5 | 6 | #convert md to ipynb 7 | for file in */{README,README\.cn}.md ; do 8 | notedown $file > ${file%.*}.ipynb 9 | done 10 | -------------------------------------------------------------------------------- /.tools/templates/index.cn.html.json: -------------------------------------------------------------------------------- 1 | { 2 | "is_en": false, 3 | "chapters": [ 4 | { 5 | "name": "线性回归", 6 | "link": "./01.fit_a_line/index.cn.html" 7 | }, 8 | { 9 | "name": "识别数字", 10 | "link": "./02.recognize_digits/index.cn.html" 11 | }, 12 | { 13 | "name": "图像分类", 14 | "link": "./03.image_classification/index.cn.html" 15 | }, 16 | { 17 | "name": "词向量", 18 | "link": "./04.word2vec/index.cn.html" 19 | }, 20 | { 21 | "name": "个性化推荐", 22 | "link": "./05.recommender_system/index.cn.html" 23 | }, 24 | { 25 | "name": "情感分析", 26 | "link": "./06.understand_sentiment/index.cn.html" 27 | }, 28 | { 29 | "name": "语义角色标注", 30 | "link": "./07.label_semantic_roles/index.cn.html" 31 | }, 32 | { 33 | "name": "机器翻译", 34 | "link": "./08.machine_translation/index.cn.html" 35 | } 36 | ] 37 | } 38 | -------------------------------------------------------------------------------- /.tools/templates/index.cn.html.tmpl: -------------------------------------------------------------------------------- 1 | index.html.tmpl -------------------------------------------------------------------------------- /.tools/templates/index.html.json: -------------------------------------------------------------------------------- 1 | { 2 | "is_en": true, 3 | "chapters": [ 4 | { 5 | "name": "Linear Regression", 6 | "link": "./01.fit_a_line/index.html" 7 | }, 8 | { 9 | "name": "Recognize Digits", 10 | "link": "./02.recognize_digits/index.html" 11 | }, 12 | { 13 | "name": "Image Classification", 14 | "link": "./03.image_classification/index.html" 15 | }, 16 | { 17 | "name": "Word2Vec", 18 | "link": "./04.word2vec/index.html" 19 | }, 20 | { 21 | "name": "Personalized Recommendation", 22 | "link": "./05.recommender_system/index.html" 23 | }, 24 | { 25 | "name": "Sentiment Analysis", 26 | "link": "./06.understand_sentiment/index.html" 27 | }, 28 | { 29 | "name": "Semantic Role Labeling", 30 | "link": "./07.label_semantic_roles/index.html" 31 | }, 32 | { 33 | "name": "Machine Translation", 34 | "link": "./08.machine_translation/index.html" 35 | } 36 | ] 37 | } 38 | -------------------------------------------------------------------------------- /.tools/templates/index.html.tmpl: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | {% if not is_en %}深度学习入门{%else%}Deep Learning 101{% endif %} 6 | 7 | 8 | 9 | 10 | 68 | 69 | 70 | 95 |
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110 | {% for chapter in chapters %} 111 | 112 | {{ chapter.name }} 113 | 114 | {% endfor %} 115 |
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127 | 144 | 145 | 146 | -------------------------------------------------------------------------------- /.tools/theme/PP_w.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/.tools/theme/PP_w.png -------------------------------------------------------------------------------- /.travis.yml: -------------------------------------------------------------------------------- 1 | language: cpp 2 | cache: ccache 3 | sudo: required 4 | dist: trusty 5 | os: 6 | - linux 7 | env: 8 | - JOB=PRE_COMMIT 9 | 10 | addons: 11 | apt: 12 | packages: 13 | - git 14 | - python 15 | - python-pip 16 | - python2.7-dev 17 | - golang 18 | ssh_known_hosts: 13.229.163.131 19 | before_install: 20 | - sudo pip install -U virtualenv pre-commit identify==1.5.9 -i https://pypi.tuna.tsinghua.edu.cn/simple 21 | - GOPATH=/tmp/go go get -u github.com/wangkuiyi/ipynb/markdown-to-ipynb 22 | script: 23 | - PATH=/tmp/go/bin:$PATH .travis/precommit.sh 24 | - | 25 | .travis/deploy_docs.sh 26 | notifications: 27 | email: 28 | on_success: change 29 | on_failure: always 30 | 31 | -------------------------------------------------------------------------------- /.travis/deploy_docs.sh: -------------------------------------------------------------------------------- 1 | exit_code=0 2 | 3 | if [[ "$TRAVIS_PULL_REQUEST" != "false" ]]; then exit $exit_code; fi; 4 | 5 | # Deploy to the the content server if its a "develop" or "release/version" branch 6 | # The "develop_doc" branch is reserved to test full deploy process without impacting the real content. 7 | if [ "$TRAVIS_BRANCH" == "develop_doc" ]; then 8 | PPO_SCRIPT_BRANCH=develop 9 | elif [[ "$TRAVIS_BRANCH" == "develop" || "$TRAVIS_BRANCH" =~ ^v|release/[[:digit:]]+\.[[:digit:]]+(\.[[:digit:]]+)?(-\S*)?$ ]]; then 10 | PPO_SCRIPT_BRANCH=master 11 | else 12 | # Early exit, this branch doesn't require documentation build 13 | exit $exit_code; 14 | fi 15 | 16 | export DEPLOY_DOCS_SH=https://raw.githubusercontent.com/PaddlePaddle/PaddlePaddle.org/$PPO_SCRIPT_BRANCH/scripts/deploy/deploy_docs.sh 17 | 18 | docker run -it \ 19 | -e CONTENT_DEC_PASSWD=$CONTENT_DEC_PASSWD \ 20 | -e TRAVIS_BRANCH=$TRAVIS_BRANCH \ 21 | -e DEPLOY_DOCS_SH=$DEPLOY_DOCS_SH \ 22 | -e TRAVIS_PULL_REQUEST=$TRAVIS_PULL_REQUEST \ 23 | -e PPO_SCRIPT_BRANCH=$PPO_SCRIPT_BRANCH \ 24 | -e PADDLE_ROOT=/book \ 25 | -v "$PWD:/book" \ 26 | -w /book \ 27 | paddlepaddle/paddle:latest-dev \ 28 | /bin/bash -c 'curl $DEPLOY_DOCS_SH | bash -s $CONTENT_DEC_PASSWD $TRAVIS_BRANCH /book /book/build/doc/ $PPO_SCRIPT_BRANCH' || exit_code=$(( exit_code | $? )) 29 | 30 | -------------------------------------------------------------------------------- /.travis/precommit.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | function abort(){ 3 | echo "Your commit not fit PaddlePaddle code style" 1>&2 4 | echo "Please use pre-commit scripts to auto-format your code" 1>&2 5 | exit 1 6 | } 7 | 8 | trap 'abort' 0 9 | set -e 10 | cd `dirname $0` 11 | cd .. 12 | export PATH=/usr/bin:$PATH 13 | export BRANCH=develop 14 | pre-commit install 15 | 16 | for file_name in `git diff --numstat upstream/$BRANCH |awk '{print $NF}'`;do 17 | if ! pre-commit run --files $file_name ; then 18 | commit_files=off 19 | fi 20 | done 21 | 22 | if [ $commit_files == 'off' ];then 23 | ls -lh 24 | git diff 2>&1 25 | exit 1 26 | fi 27 | 28 | trap : 0 29 | -------------------------------------------------------------------------------- /0.coding_guideline/appendix/README.MD: -------------------------------------------------------------------------------- 1 | Stores some images here. 2 | -------------------------------------------------------------------------------- /0.coding_guideline/appendix/dir-1.png: -------------------------------------------------------------------------------- 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data/housing* 2 | data/*.list 3 | *.pyc 4 | data/*.pyc 5 | output 6 | -------------------------------------------------------------------------------- /01.fit_a_line/.run_ce.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #This file is only used for continuous evaluation. 3 | python train.py --enable_ce | python _ce.py 4 | 5 | -------------------------------------------------------------------------------- /01.fit_a_line/_ce.py: -------------------------------------------------------------------------------- 1 | ### This file is only used for continuous evaluation test! 2 | from __future__ import print_function 3 | from __future__ import division 4 | from __future__ import absolute_import 5 | import os 6 | import sys 7 | sys.path.append(os.environ['ceroot']) 8 | from kpi import CostKpi 9 | 10 | train_cost_kpi = CostKpi('train_cost', 0.02, 0, actived=True, desc='train cost') 11 | test_cost_kpi = CostKpi('test_cost', 0.02, 0, actived=True, desc='test cost') 12 | tracking_kpis = [train_cost_kpi, test_cost_kpi] 13 | 14 | 15 | def parse_log(log): 16 | for line in log.split('\n'): 17 | fs = line.strip().split('\t') 18 | print(fs) 19 | if len(fs) == 3 and fs[0] == 'kpis': 20 | print("-----%s" % fs) 21 | kpi_name = fs[1] 22 | kpi_value = float(fs[2]) 23 | yield kpi_name, kpi_value 24 | 25 | 26 | def log_to_ce(log): 27 | kpi_tracker = {} 28 | for kpi in tracking_kpis: 29 | kpi_tracker[kpi.name] = kpi 30 | 31 | for (kpi_name, kpi_value) in parse_log(log): 32 | print(kpi_name, kpi_value) 33 | kpi_tracker[kpi_name].add_record(kpi_value) 34 | kpi_tracker[kpi_name].persist() 35 | 36 | 37 | if __name__ == '__main__': 38 | log = sys.stdin.read() 39 | log_to_ce(log) 40 | -------------------------------------------------------------------------------- /01.fit_a_line/fit_a_line.tar: -------------------------------------------------------------------------------- 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/01.fit_a_line/train.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from __future__ import print_function 16 | 17 | import sys 18 | import argparse 19 | 20 | import math 21 | import numpy 22 | 23 | import paddle 24 | import paddle.fluid as fluid 25 | 26 | 27 | def parse_args(): 28 | parser = argparse.ArgumentParser("fit_a_line") 29 | parser.add_argument( 30 | '--enable_ce', 31 | action='store_true', 32 | help="If set, run the task with continuous evaluation logs.") 33 | parser.add_argument( 34 | '--use_gpu', 35 | type=bool, 36 | default=False, 37 | help="Whether to use GPU or not.") 38 | parser.add_argument( 39 | '--num_epochs', type=int, default=100, help="number of epochs.") 40 | args = parser.parse_args() 41 | return args 42 | 43 | 44 | # For training test cost 45 | def train_test(executor, program, reader, feeder, fetch_list): 46 | accumulated = 1 * [0] 47 | count = 0 48 | for data_test in reader(): 49 | outs = executor.run( 50 | program=program, feed=feeder.feed(data_test), fetch_list=fetch_list) 51 | accumulated = [x_c[0] + x_c[1][0] for x_c in zip(accumulated, outs)] 52 | count += 1 53 | return [x_d / count for x_d in accumulated] 54 | 55 | 56 | def save_result(points1, points2): 57 | import matplotlib 58 | matplotlib.use('Agg') 59 | import matplotlib.pyplot as plt 60 | x1 = [idx for idx in range(len(points1))] 61 | y1 = points1 62 | y2 = points2 63 | l1 = plt.plot(x1, y1, 'r--', label='predictions') 64 | l2 = plt.plot(x1, y2, 'g--', label='GT') 65 | plt.plot(x1, y1, 'ro-', x1, y2, 'g+-') 66 | plt.title('predictions VS GT') 67 | plt.legend() 68 | plt.savefig('./image/prediction_gt.png') 69 | 70 | 71 | def main(): 72 | batch_size = 20 73 | 74 | if args.enable_ce: 75 | train_reader = paddle.batch( 76 | paddle.dataset.uci_housing.train(), batch_size=batch_size) 77 | test_reader = paddle.batch( 78 | paddle.dataset.uci_housing.test(), batch_size=batch_size) 79 | else: 80 | train_reader = paddle.batch( 81 | paddle.reader.shuffle( 82 | paddle.dataset.uci_housing.train(), buf_size=500), 83 | batch_size=batch_size) 84 | test_reader = paddle.batch( 85 | paddle.reader.shuffle( 86 | paddle.dataset.uci_housing.test(), buf_size=500), 87 | batch_size=batch_size) 88 | 89 | # feature vector of length 13 90 | x = fluid.data(name='x', shape=[None, 13], dtype='float32') 91 | y = fluid.data(name='y', shape=[None, 1], dtype='float32') 92 | 93 | main_program = fluid.default_main_program() 94 | startup_program = fluid.default_startup_program() 95 | 96 | if args.enable_ce: 97 | main_program.random_seed = 90 98 | startup_program.random_seed = 90 99 | 100 | y_predict = fluid.layers.fc(input=x, size=1, act=None) 101 | cost = fluid.layers.square_error_cost(input=y_predict, label=y) 102 | avg_loss = fluid.layers.mean(cost) 103 | 104 | test_program = main_program.clone(for_test=True) 105 | 106 | sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001) 107 | sgd_optimizer.minimize(avg_loss) 108 | 109 | # can use CPU or GPU 110 | use_cuda = args.use_gpu 111 | place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() 112 | exe = fluid.Executor(place) 113 | 114 | # Specify the directory to save the parameters 115 | params_dirname = "fit_a_line.inference.model" 116 | num_epochs = args.num_epochs 117 | 118 | # main train loop. 119 | feeder = fluid.DataFeeder(place=place, feed_list=[x, y]) 120 | exe.run(startup_program) 121 | 122 | train_prompt = "Train cost" 123 | test_prompt = "Test cost" 124 | step = 0 125 | 126 | exe_test = fluid.Executor(place) 127 | 128 | for pass_id in range(num_epochs): 129 | for data_train in train_reader(): 130 | avg_loss_value, = exe.run( 131 | main_program, 132 | feed=feeder.feed(data_train), 133 | fetch_list=[avg_loss]) 134 | if step % 10 == 0: # record a train cost every 10 batches 135 | print("%s, Step %d, Cost %f" % 136 | (train_prompt, step, avg_loss_value[0])) 137 | 138 | if step % 100 == 0: # record a test cost every 100 batches 139 | test_metics = train_test( 140 | executor=exe_test, 141 | program=test_program, 142 | reader=test_reader, 143 | fetch_list=[avg_loss], 144 | feeder=feeder) 145 | print("%s, Step %d, Cost %f" % 146 | (test_prompt, step, test_metics[0])) 147 | # If the accuracy is good enough, we can stop the training. 148 | if test_metics[0] < 10.0: 149 | break 150 | 151 | step += 1 152 | 153 | if math.isnan(float(avg_loss_value[0])): 154 | sys.exit("got NaN loss, training failed.") 155 | if params_dirname is not None: 156 | # We can save the trained parameters for the inferences later 157 | fluid.io.save_inference_model(params_dirname, ['x'], [y_predict], 158 | exe) 159 | 160 | if args.enable_ce and pass_id == args.num_epochs - 1: 161 | print("kpis\ttrain_cost\t%f" % avg_loss_value[0]) 162 | print("kpis\ttest_cost\t%f" % test_metics[0]) 163 | 164 | infer_exe = fluid.Executor(place) 165 | inference_scope = fluid.core.Scope() 166 | 167 | # infer 168 | with fluid.scope_guard(inference_scope): 169 | [inference_program, feed_target_names, fetch_targets 170 | ] = fluid.io.load_inference_model(params_dirname, infer_exe) 171 | batch_size = 10 172 | 173 | infer_reader = paddle.batch( 174 | paddle.dataset.uci_housing.test(), batch_size=batch_size) 175 | 176 | infer_data = next(infer_reader()) 177 | infer_feat = numpy.array( 178 | [data[0] for data in infer_data]).astype("float32") 179 | infer_label = numpy.array( 180 | [data[1] for data in infer_data]).astype("float32") 181 | 182 | assert feed_target_names[0] == 'x' 183 | results = infer_exe.run( 184 | inference_program, 185 | feed={feed_target_names[0]: numpy.array(infer_feat)}, 186 | fetch_list=fetch_targets) 187 | 188 | print("infer results: (House Price)") 189 | for idx, val in enumerate(results[0]): 190 | print("%d: %.2f" % (idx, val)) 191 | 192 | print("\nground truth:") 193 | for idx, val in enumerate(infer_label): 194 | print("%d: %.2f" % (idx, val)) 195 | 196 | save_result(results[0], infer_label) 197 | 198 | 199 | if __name__ == '__main__': 200 | args = parse_args() 201 | main() 202 | -------------------------------------------------------------------------------- /02.recognize_digits/.gitignore: -------------------------------------------------------------------------------- 1 | data/raw_data 2 | data/train.list 3 | data/test.list 4 | *.log 5 | *.pyc 6 | plot.png 7 | -------------------------------------------------------------------------------- /02.recognize_digits/.run_ce.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #This file is only used for continuous evaluation. 3 | python train.py --enable_ce | python _ce.py 4 | 5 | -------------------------------------------------------------------------------- /02.recognize_digits/_ce.py: -------------------------------------------------------------------------------- 1 | ### This file is only used for continuous evaluation test! 2 | from __future__ import print_function 3 | from __future__ import division 4 | from __future__ import absolute_import 5 | import os 6 | import sys 7 | sys.path.append(os.environ['ceroot']) 8 | from kpi import CostKpi 9 | from kpi import AccKpi 10 | 11 | train_cost_kpi = CostKpi('train_cost', 0.02, 0, actived=True, desc='train cost') 12 | test_cost_kpi = CostKpi('test_cost', 0.02, 0, actived=True, desc='test cost') 13 | test_acc_kpi = AccKpi('test_acc', 0.02, 0, actived=True, desc='test acc') 14 | tracking_kpis = [train_cost_kpi, test_cost_kpi, test_acc_kpi] 15 | 16 | 17 | def parse_log(log): 18 | for line in log.split('\n'): 19 | fs = line.strip().split('\t') 20 | print(fs) 21 | if len(fs) == 3 and fs[0] == 'kpis': 22 | kpi_name = fs[1] 23 | kpi_value = float(fs[2]) 24 | yield kpi_name, kpi_value 25 | 26 | 27 | def log_to_ce(log): 28 | kpi_tracker = {} 29 | for kpi in tracking_kpis: 30 | kpi_tracker[kpi.name] = kpi 31 | for (kpi_name, kpi_value) in parse_log(log): 32 | print(kpi_name, kpi_value) 33 | kpi_tracker[kpi_name].add_record(kpi_value) 34 | kpi_tracker[kpi_name].persist() 35 | 36 | 37 | if __name__ == '__main__': 38 | log = sys.stdin.read() 39 | log_to_ce(log) 40 | -------------------------------------------------------------------------------- /02.recognize_digits/client/client.py: -------------------------------------------------------------------------------- 1 | import requests 2 | from PIL import Image 3 | import numpy as np 4 | import os 5 | 6 | # this client is used by Paddle serve: https://github.com/PaddlePaddle/book/tree/develop/serve 7 | # please do not use it directly 8 | 9 | 10 | def load_image(file): 11 | im = Image.open(file).convert('L') 12 | im = im.resize((28, 28), Image.ANTIALIAS) 13 | im = np.array(im).astype(np.float32).flatten() 14 | im = im / 255.0 15 | return im 16 | 17 | 18 | cur_dir = os.path.dirname(os.path.realpath(__file__)) 19 | data = load_image(cur_dir + '/../image/infer_3.png') 20 | data = data.tolist() 21 | 22 | r = requests.post("http://0.0.0.0:8000", json={'img': data}) 23 | 24 | print(r.text) 25 | -------------------------------------------------------------------------------- /02.recognize_digits/image/01.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/02.recognize_digits/image/01.gif -------------------------------------------------------------------------------- /02.recognize_digits/image/02.gif: 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All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from __future__ import print_function 16 | 17 | import os 18 | import argparse 19 | from PIL import Image 20 | import numpy 21 | import paddle 22 | import paddle.fluid as fluid 23 | 24 | 25 | def parse_args(): 26 | parser = argparse.ArgumentParser("mnist") 27 | parser.add_argument( 28 | '--enable_ce', 29 | action='store_true', 30 | help="If set, run the task with continuous evaluation logs.") 31 | parser.add_argument( 32 | '--use_gpu', 33 | type=bool, 34 | default=False, 35 | help="Whether to use GPU or not.") 36 | parser.add_argument( 37 | '--num_epochs', type=int, default=5, help="number of epochs.") 38 | args = parser.parse_args() 39 | return args 40 | 41 | 42 | def loss_net(hidden, label): 43 | prediction = fluid.layers.fc(input=hidden, size=10, act='softmax') 44 | loss = fluid.layers.cross_entropy(input=prediction, label=label) 45 | avg_loss = fluid.layers.mean(loss) 46 | acc = fluid.layers.accuracy(input=prediction, label=label) 47 | return prediction, avg_loss, acc 48 | 49 | 50 | def multilayer_perceptron(img, label): 51 | img = fluid.layers.fc(input=img, size=200, act='tanh') 52 | hidden = fluid.layers.fc(input=img, size=200, act='tanh') 53 | return loss_net(hidden, label) 54 | 55 | 56 | def softmax_regression(img, label): 57 | return loss_net(img, label) 58 | 59 | 60 | def convolutional_neural_network(img, label): 61 | conv_pool_1 = fluid.nets.simple_img_conv_pool( 62 | input=img, 63 | filter_size=5, 64 | num_filters=20, 65 | pool_size=2, 66 | pool_stride=2, 67 | act="relu") 68 | conv_pool_1 = fluid.layers.batch_norm(conv_pool_1) 69 | conv_pool_2 = fluid.nets.simple_img_conv_pool( 70 | input=conv_pool_1, 71 | filter_size=5, 72 | num_filters=50, 73 | pool_size=2, 74 | pool_stride=2, 75 | act="relu") 76 | return loss_net(conv_pool_2, label) 77 | 78 | 79 | def train(nn_type, 80 | use_cuda, 81 | save_dirname=None, 82 | model_filename=None, 83 | params_filename=None): 84 | if use_cuda and not fluid.core.is_compiled_with_cuda(): 85 | return 86 | 87 | startup_program = fluid.default_startup_program() 88 | main_program = fluid.default_main_program() 89 | 90 | if args.enable_ce: 91 | train_reader = paddle.batch( 92 | paddle.dataset.mnist.train(), batch_size=BATCH_SIZE) 93 | test_reader = paddle.batch( 94 | paddle.dataset.mnist.test(), batch_size=BATCH_SIZE) 95 | startup_program.random_seed = 90 96 | main_program.random_seed = 90 97 | else: 98 | train_reader = paddle.batch( 99 | paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=500), 100 | batch_size=BATCH_SIZE) 101 | test_reader = paddle.batch( 102 | paddle.dataset.mnist.test(), batch_size=BATCH_SIZE) 103 | 104 | img = fluid.data(name='img', shape=[None, 1, 28, 28], dtype='float32') 105 | label = fluid.data(name='label', shape=[None, 1], dtype='int64') 106 | 107 | if nn_type == 'softmax_regression': 108 | net_conf = softmax_regression 109 | elif nn_type == 'multilayer_perceptron': 110 | net_conf = multilayer_perceptron 111 | else: 112 | net_conf = convolutional_neural_network 113 | 114 | prediction, avg_loss, acc = net_conf(img, label) 115 | 116 | test_program = main_program.clone(for_test=True) 117 | optimizer = fluid.optimizer.Adam(learning_rate=0.001) 118 | optimizer.minimize(avg_loss) 119 | 120 | def train_test(train_test_program, train_test_feed, train_test_reader): 121 | acc_set = [] 122 | avg_loss_set = [] 123 | for test_data in train_test_reader(): 124 | acc_np, avg_loss_np = exe.run( 125 | program=train_test_program, 126 | feed=train_test_feed.feed(test_data), 127 | fetch_list=[acc, avg_loss]) 128 | acc_set.append(float(acc_np)) 129 | avg_loss_set.append(float(avg_loss_np)) 130 | # get test acc and loss 131 | acc_val_mean = numpy.array(acc_set).mean() 132 | avg_loss_val_mean = numpy.array(avg_loss_set).mean() 133 | return avg_loss_val_mean, acc_val_mean 134 | 135 | place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() 136 | 137 | exe = fluid.Executor(place) 138 | 139 | feeder = fluid.DataFeeder(feed_list=[img, label], place=place) 140 | exe.run(startup_program) 141 | epochs = [epoch_id for epoch_id in range(PASS_NUM)] 142 | 143 | lists = [] 144 | step = 0 145 | for epoch_id in epochs: 146 | for step_id, data in enumerate(train_reader()): 147 | metrics = exe.run( 148 | main_program, 149 | feed=feeder.feed(data), 150 | fetch_list=[avg_loss, acc]) 151 | if step % 100 == 0: 152 | print("Pass %d, Epoch %d, Cost %f" % (step, epoch_id, 153 | metrics[0])) 154 | step += 1 155 | # test for epoch 156 | avg_loss_val, acc_val = train_test( 157 | train_test_program=test_program, 158 | train_test_reader=test_reader, 159 | train_test_feed=feeder) 160 | 161 | print("Test with Epoch %d, avg_cost: %s, acc: %s" % 162 | (epoch_id, avg_loss_val, acc_val)) 163 | lists.append((epoch_id, avg_loss_val, acc_val)) 164 | if save_dirname is not None: 165 | fluid.io.save_inference_model( 166 | save_dirname, ["img"], [prediction], 167 | exe, 168 | model_filename=model_filename, 169 | params_filename=params_filename) 170 | 171 | if args.enable_ce: 172 | print("kpis\ttrain_cost\t%f" % metrics[0]) 173 | print("kpis\ttest_cost\t%s" % avg_loss_val) 174 | print("kpis\ttest_acc\t%s" % acc_val) 175 | 176 | # find the best pass 177 | best = sorted(lists, key=lambda list: float(list[1]))[0] 178 | print('Best pass is %s, testing Avgcost is %s' % (best[0], best[1])) 179 | print('The classification accuracy is %.2f%%' % (float(best[2]) * 100)) 180 | 181 | 182 | def infer(use_cuda, 183 | save_dirname=None, 184 | model_filename=None, 185 | params_filename=None): 186 | if save_dirname is None: 187 | return 188 | 189 | place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() 190 | exe = fluid.Executor(place) 191 | 192 | def load_image(file): 193 | im = Image.open(file).convert('L') 194 | im = im.resize((28, 28), Image.ANTIALIAS) 195 | im = numpy.array(im).reshape(1, 1, 28, 28).astype(numpy.float32) 196 | im = im / 255.0 * 2.0 - 1.0 197 | return im 198 | 199 | cur_dir = os.path.dirname(os.path.realpath(__file__)) 200 | tensor_img = load_image(cur_dir + '/image/infer_3.png') 201 | 202 | inference_scope = fluid.core.Scope() 203 | with fluid.scope_guard(inference_scope): 204 | # Use fluid.io.load_inference_model to obtain the inference program desc, 205 | # the feed_target_names (the names of variables that will be feeded 206 | # data using feed operators), and the fetch_targets (variables that 207 | # we want to obtain data from using fetch operators). 208 | [inference_program, feed_target_names, 209 | fetch_targets] = fluid.io.load_inference_model( 210 | save_dirname, exe, model_filename, params_filename) 211 | 212 | # Construct feed as a dictionary of {feed_target_name: feed_target_data} 213 | # and results will contain a list of data corresponding to fetch_targets. 214 | results = exe.run( 215 | inference_program, 216 | feed={feed_target_names[0]: tensor_img}, 217 | fetch_list=fetch_targets) 218 | lab = numpy.argsort(results) 219 | print("Inference result of image/infer_3.png is: %d" % lab[0][0][-1]) 220 | 221 | 222 | def main(use_cuda, nn_type): 223 | model_filename = None 224 | params_filename = None 225 | save_dirname = "recognize_digits_" + nn_type + ".inference.model" 226 | 227 | # call train() with is_local argument to run distributed train 228 | train( 229 | nn_type=nn_type, 230 | use_cuda=use_cuda, 231 | save_dirname=save_dirname, 232 | model_filename=model_filename, 233 | params_filename=params_filename) 234 | infer( 235 | use_cuda=use_cuda, 236 | save_dirname=save_dirname, 237 | model_filename=model_filename, 238 | params_filename=params_filename) 239 | 240 | 241 | if __name__ == '__main__': 242 | args = parse_args() 243 | BATCH_SIZE = 64 244 | PASS_NUM = args.num_epochs 245 | use_cuda = args.use_gpu 246 | # predict = 'softmax_regression' # uncomment for Softmax 247 | # predict = 'multilayer_perceptron' # uncomment for MLP 248 | predict = 'convolutional_neural_network' # uncomment for LeNet5 249 | main(use_cuda=use_cuda, nn_type=predict) 250 | -------------------------------------------------------------------------------- /03.image_classification/.gitignore: -------------------------------------------------------------------------------- 1 | *.pyc 2 | train.log 3 | output 4 | data/cifar-10-batches-py/ 5 | data/cifar-10-python.tar.gz 6 | data/*.txt 7 | data/*.list 8 | data/mean.meta 9 | -------------------------------------------------------------------------------- /03.image_classification/.run_ce.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #This file is only used for continuous evaluation. 3 | export FLAGS_cudnn_deterministic=true 4 | export CUDA_VISIBLE_DEVICES=0 5 | python train.py --num_epochs 1 --use_gpu 1 --enable_ce | python _ce.py 6 | 7 | -------------------------------------------------------------------------------- /03.image_classification/_ce.py: -------------------------------------------------------------------------------- 1 | ### This file is only used for continuous evaluation test! 2 | from __future__ import print_function 3 | from __future__ import division 4 | from __future__ import absolute_import 5 | import os 6 | import sys 7 | sys.path.append(os.environ['ceroot']) 8 | from kpi import CostKpi 9 | from kpi import AccKpi 10 | 11 | train_cost_kpi = CostKpi('train_cost', 0.02, 0, actived=True, desc='train cost') 12 | train_acc_kpi = AccKpi('train_acc', 0.02, 0, actived=True, desc='train acc') 13 | test_cost_kpi = CostKpi('test_cost', 0.02, 0, actived=True, desc='test cost') 14 | test_acc_kpi = AccKpi('test_acc', 0.02, 0, actived=True, desc='test acc') 15 | 16 | tracking_kpis = [train_cost_kpi, train_acc_kpi, test_cost_kpi, test_acc_kpi] 17 | 18 | 19 | def parse_log(log): 20 | for line in log.split('\n'): 21 | fs = line.strip().split('\t') 22 | print(fs) 23 | if len(fs) == 3 and fs[0] == 'kpis': 24 | kpi_name = fs[1] 25 | kpi_value = float(fs[2]) 26 | yield kpi_name, kpi_value 27 | 28 | 29 | def log_to_ce(log): 30 | kpi_tracker = {} 31 | for kpi in tracking_kpis: 32 | kpi_tracker[kpi.name] = kpi 33 | for (kpi_name, kpi_value) in parse_log(log): 34 | print(kpi_name, kpi_value) 35 | kpi_tracker[kpi_name].add_record(kpi_value) 36 | kpi_tracker[kpi_name].persist() 37 | 38 | 39 | if __name__ == '__main__': 40 | log = sys.stdin.read() 41 | log_to_ce(log) 42 | 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/03.image_classification/resnet.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from __future__ import print_function 16 | 17 | import paddle.fluid as fluid 18 | 19 | __all__ = ['resnet_cifar10'] 20 | 21 | 22 | def conv_bn_layer(input, 23 | ch_out, 24 | filter_size, 25 | stride, 26 | padding, 27 | act='relu', 28 | bias_attr=False): 29 | tmp = fluid.layers.conv2d( 30 | input=input, 31 | filter_size=filter_size, 32 | num_filters=ch_out, 33 | stride=stride, 34 | padding=padding, 35 | act=None, 36 | bias_attr=bias_attr) 37 | return fluid.layers.batch_norm(input=tmp, act=act) 38 | 39 | 40 | def shortcut(input, ch_in, ch_out, stride): 41 | if ch_in != ch_out: 42 | return conv_bn_layer(input, ch_out, 1, stride, 0, None) 43 | else: 44 | return input 45 | 46 | 47 | def basicblock(input, ch_in, ch_out, stride): 48 | tmp = conv_bn_layer(input, ch_out, 3, stride, 1) 49 | tmp = conv_bn_layer(tmp, ch_out, 3, 1, 1, act=None, bias_attr=True) 50 | short = shortcut(input, ch_in, ch_out, stride) 51 | return fluid.layers.elementwise_add(x=tmp, y=short, act='relu') 52 | 53 | 54 | def layer_warp(block_func, input, ch_in, ch_out, count, stride): 55 | tmp = block_func(input, ch_in, ch_out, stride) 56 | for i in range(1, count): 57 | tmp = block_func(tmp, ch_out, ch_out, 1) 58 | return tmp 59 | 60 | 61 | def resnet_cifar10(ipt, depth=32): 62 | # depth should be one of 20, 32, 44, 56, 110, 1202 63 | assert (depth - 2) % 6 == 0 64 | n = (depth - 2) // 6 65 | nStages = {16, 64, 128} 66 | conv1 = conv_bn_layer(ipt, ch_out=16, filter_size=3, stride=1, padding=1) 67 | res1 = layer_warp(basicblock, conv1, 16, 16, n, 1) 68 | res2 = layer_warp(basicblock, res1, 16, 32, n, 2) 69 | res3 = layer_warp(basicblock, res2, 32, 64, n, 2) 70 | pool = fluid.layers.pool2d( 71 | input=res3, pool_size=8, pool_type='avg', pool_stride=1) 72 | predict = fluid.layers.fc(input=pool, size=10, act='softmax') 73 | return predict 74 | -------------------------------------------------------------------------------- /03.image_classification/train.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License 14 | 15 | from __future__ import print_function 16 | 17 | import os 18 | import argparse 19 | import paddle 20 | import paddle.fluid as fluid 21 | import numpy 22 | import sys 23 | from vgg import vgg_bn_drop 24 | from resnet import resnet_cifar10 25 | 26 | 27 | def parse_args(): 28 | parser = argparse.ArgumentParser("image_classification") 29 | parser.add_argument( 30 | '--enable_ce', 31 | action='store_true', 32 | help='If set, run the task with continuous evaluation logs.') 33 | parser.add_argument( 34 | '--use_gpu', type=bool, default=0, help='whether to use gpu') 35 | parser.add_argument( 36 | '--num_epochs', type=int, default=1, help='number of epoch') 37 | args = parser.parse_args() 38 | return args 39 | 40 | 41 | def inference_network(): 42 | # The image is 32 * 32 with RGB representation. 43 | data_shape = [3, 32, 32] 44 | images = fluid.layers.data(name='pixel', shape=data_shape, dtype='float32') 45 | 46 | predict = resnet_cifar10(images, 32) 47 | # predict = vgg_bn_drop(images) # un-comment to use vgg net 48 | return predict 49 | 50 | 51 | def train_network(predict): 52 | label = fluid.layers.data(name='label', shape=[1], dtype='int64') 53 | cost = fluid.layers.cross_entropy(input=predict, label=label) 54 | avg_cost = fluid.layers.mean(cost) 55 | accuracy = fluid.layers.accuracy(input=predict, label=label) 56 | return [avg_cost, accuracy] 57 | 58 | 59 | def optimizer_program(): 60 | return fluid.optimizer.Adam(learning_rate=0.001) 61 | 62 | 63 | def train(use_cuda, params_dirname): 64 | place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() 65 | BATCH_SIZE = 128 66 | 67 | if args.enable_ce: 68 | train_reader = paddle.batch( 69 | paddle.dataset.cifar.train10(), batch_size=BATCH_SIZE) 70 | test_reader = paddle.batch( 71 | paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE) 72 | else: 73 | test_reader = paddle.batch( 74 | paddle.dataset.cifar.test10(), batch_size=BATCH_SIZE) 75 | train_reader = paddle.batch( 76 | paddle.reader.shuffle( 77 | paddle.dataset.cifar.train10(), buf_size=128 * 100), 78 | batch_size=BATCH_SIZE) 79 | 80 | feed_order = ['pixel', 'label'] 81 | 82 | main_program = fluid.default_main_program() 83 | start_program = fluid.default_startup_program() 84 | 85 | if args.enable_ce: 86 | main_program.random_seed = 90 87 | start_program.random_seed = 90 88 | 89 | predict = inference_network() 90 | avg_cost, acc = train_network(predict) 91 | 92 | # Test program 93 | test_program = main_program.clone(for_test=True) 94 | optimizer = optimizer_program() 95 | optimizer.minimize(avg_cost) 96 | 97 | exe = fluid.Executor(place) 98 | 99 | EPOCH_NUM = args.num_epochs 100 | 101 | # For training test cost 102 | def train_test(program, reader): 103 | count = 0 104 | feed_var_list = [ 105 | program.global_block().var(var_name) for var_name in feed_order 106 | ] 107 | feeder_test = fluid.DataFeeder(feed_list=feed_var_list, place=place) 108 | test_exe = fluid.Executor(place) 109 | accumulated = len([avg_cost, acc]) * [0] 110 | for tid, test_data in enumerate(reader()): 111 | avg_cost_np = test_exe.run( 112 | program=program, 113 | feed=feeder_test.feed(test_data), 114 | fetch_list=[avg_cost, acc]) 115 | accumulated = [ 116 | x[0] + x[1][0] for x in zip(accumulated, avg_cost_np) 117 | ] 118 | count += 1 119 | return [x / count for x in accumulated] 120 | 121 | # main train loop. 122 | def train_loop(): 123 | feed_var_list_loop = [ 124 | main_program.global_block().var(var_name) for var_name in feed_order 125 | ] 126 | feeder = fluid.DataFeeder(feed_list=feed_var_list_loop, place=place) 127 | exe.run(start_program) 128 | 129 | step = 0 130 | for pass_id in range(EPOCH_NUM): 131 | for step_id, data_train in enumerate(train_reader()): 132 | avg_loss_value = exe.run( 133 | main_program, 134 | feed=feeder.feed(data_train), 135 | fetch_list=[avg_cost, acc]) 136 | if step_id % 100 == 0: 137 | print("\nPass %d, Batch %d, Cost %f, Acc %f" % ( 138 | step_id, pass_id, avg_loss_value[0], avg_loss_value[1])) 139 | else: 140 | sys.stdout.write('.') 141 | sys.stdout.flush() 142 | step += 1 143 | 144 | avg_cost_test, accuracy_test = train_test( 145 | test_program, reader=test_reader) 146 | print('\nTest with Pass {0}, Loss {1:2.2}, Acc {2:2.2}'.format( 147 | pass_id, avg_cost_test, accuracy_test)) 148 | 149 | if params_dirname is not None: 150 | fluid.io.save_inference_model(params_dirname, ["pixel"], 151 | [predict], exe) 152 | 153 | if args.enable_ce and pass_id == EPOCH_NUM - 1: 154 | print("kpis\ttrain_cost\t%f" % avg_loss_value[0]) 155 | print("kpis\ttrain_acc\t%f" % avg_loss_value[1]) 156 | print("kpis\ttest_cost\t%f" % avg_cost_test) 157 | print("kpis\ttest_acc\t%f" % accuracy_test) 158 | 159 | train_loop() 160 | 161 | 162 | def infer(use_cuda, params_dirname=None): 163 | from PIL import Image 164 | place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() 165 | exe = fluid.Executor(place) 166 | inference_scope = fluid.core.Scope() 167 | 168 | def load_image(infer_file): 169 | im = Image.open(infer_file) 170 | im = im.resize((32, 32), Image.ANTIALIAS) 171 | 172 | im = numpy.array(im).astype(numpy.float32) 173 | # The storage order of the loaded image is W(width), 174 | # H(height), C(channel). PaddlePaddle requires 175 | # the CHW order, so transpose them. 176 | im = im.transpose((2, 0, 1)) # CHW 177 | im = im / 255.0 178 | 179 | # Add one dimension to mimic the list format. 180 | im = numpy.expand_dims(im, axis=0) 181 | return im 182 | 183 | cur_dir = os.path.dirname(os.path.realpath(__file__)) 184 | img = load_image(cur_dir + '/image/dog.png') 185 | 186 | with fluid.scope_guard(inference_scope): 187 | # Use fluid.io.load_inference_model to obtain the inference program desc, 188 | # the feed_target_names (the names of variables that will be feeded 189 | # data using feed operators), and the fetch_targets (variables that 190 | # we want to obtain data from using fetch operators). 191 | [inference_program, feed_target_names, 192 | fetch_targets] = fluid.io.load_inference_model(params_dirname, exe) 193 | 194 | # Construct feed as a dictionary of {feed_target_name: feed_target_data} 195 | # and results will contain a list of data corresponding to fetch_targets. 196 | results = exe.run( 197 | inference_program, 198 | feed={feed_target_names[0]: img}, 199 | fetch_list=fetch_targets) 200 | 201 | # infer label 202 | label_list = [ 203 | "airplane", "automobile", "bird", "cat", "deer", "dog", "frog", 204 | "horse", "ship", "truck" 205 | ] 206 | 207 | print("infer results: %s" % label_list[numpy.argmax(results[0])]) 208 | 209 | 210 | def main(use_cuda): 211 | if use_cuda and not fluid.core.is_compiled_with_cuda(): 212 | return 213 | save_path = "image_classification_resnet.inference.model" 214 | 215 | train(use_cuda=use_cuda, params_dirname=save_path) 216 | 217 | infer(use_cuda=use_cuda, params_dirname=save_path) 218 | 219 | 220 | if __name__ == '__main__': 221 | # For demo purpose, the training runs on CPU 222 | # Please change accordingly. 223 | args = parse_args() 224 | use_cuda = args.use_gpu 225 | main(use_cuda) 226 | -------------------------------------------------------------------------------- /03.image_classification/vgg.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from __future__ import print_function 16 | 17 | import paddle.fluid as fluid 18 | 19 | 20 | def vgg_bn_drop(input): 21 | def conv_block(ipt, num_filter, groups, dropouts): 22 | return fluid.nets.img_conv_group( 23 | input=ipt, 24 | pool_size=2, 25 | pool_stride=2, 26 | conv_num_filter=[num_filter] * groups, 27 | conv_filter_size=3, 28 | conv_act='relu', 29 | conv_with_batchnorm=True, 30 | conv_batchnorm_drop_rate=dropouts, 31 | pool_type='max') 32 | 33 | conv1 = conv_block(input, 64, 2, [0.3, 0]) 34 | conv2 = conv_block(conv1, 128, 2, [0.4, 0]) 35 | conv3 = conv_block(conv2, 256, 3, [0.4, 0.4, 0]) 36 | conv4 = conv_block(conv3, 512, 3, [0.4, 0.4, 0]) 37 | conv5 = conv_block(conv4, 512, 3, [0.4, 0.4, 0]) 38 | 39 | drop = fluid.layers.dropout(x=conv5, dropout_prob=0.5) 40 | fc1 = fluid.layers.fc(input=drop, size=512, act=None) 41 | bn = fluid.layers.batch_norm(input=fc1, act='relu') 42 | drop2 = fluid.layers.dropout(x=bn, dropout_prob=0.5) 43 | fc2 = fluid.layers.fc(input=drop2, size=512, act=None) 44 | predict = fluid.layers.fc(input=fc2, size=10, act='softmax') 45 | return predict 46 | -------------------------------------------------------------------------------- /04.word2vec/.gitignore: -------------------------------------------------------------------------------- 1 | data/train.list 2 | data/test.list 3 | data/simple-examples* 4 | -------------------------------------------------------------------------------- /04.word2vec/.run_ce.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #This file is only used for continuous evaluation. 3 | python train.py --enable_ce | python _ce.py 4 | 5 | -------------------------------------------------------------------------------- /04.word2vec/_ce.py: -------------------------------------------------------------------------------- 1 | ### This file is only used for continuous evaluation test! 2 | from __future__ import print_function 3 | from __future__ import division 4 | from __future__ import absolute_import 5 | import os 6 | import sys 7 | sys.path.append(os.environ['ceroot']) 8 | from kpi import CostKpi 9 | 10 | train_cost_kpi = CostKpi('train_cost', 0.02, 0, actived=True, desc='train cost') 11 | tracking_kpis = [train_cost_kpi] 12 | 13 | 14 | def parse_log(log): 15 | for line in log.split('\n'): 16 | fs = line.strip().split('\t') 17 | print(fs) 18 | if len(fs) == 3 and fs[0] == 'kpis': 19 | kpi_name = fs[1] 20 | kpi_value = float(fs[2]) 21 | yield kpi_name, kpi_value 22 | 23 | 24 | def log_to_ce(log): 25 | kpi_tracker = {} 26 | for kpi in tracking_kpis: 27 | kpi_tracker[kpi.name] = kpi 28 | for (kpi_name, kpi_value) in parse_log(log): 29 | print(kpi_name, kpi_value) 30 | kpi_tracker[kpi_name].add_record(kpi_value) 31 | kpi_tracker[kpi_name].persist() 32 | 33 | 34 | if __name__ == '__main__': 35 | log = sys.stdin.read() 36 | log_to_ce(log) 37 | -------------------------------------------------------------------------------- /04.word2vec/calculate_dis.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | """ 15 | Example: 16 | python calculate_dis.py DICTIONARYTXT FEATURETXT 17 | 18 | Required arguments: 19 | DICTIONARYTXT the dictionary generated in dataprovider 20 | FEATURETXT the text format word feature, one line for one word 21 | """ 22 | 23 | import numpy as np 24 | from argparse import ArgumentParser 25 | 26 | 27 | def load_dict(fdict): 28 | words = [line.strip() for line in fdict.readlines()] 29 | dictionary = dict(zip(words, xrange(len(words)))) 30 | return dictionary 31 | 32 | 33 | def load_emb(femb): 34 | feaBank = [] 35 | flag_firstline = True 36 | for line in femb: 37 | if flag_firstline: 38 | flag_firstline = False 39 | continue 40 | fea = np.array([float(x) for x in line.strip().split(',')]) 41 | normfea = fea * 1.0 / np.linalg.norm(fea) 42 | feaBank.append(normfea) 43 | return feaBank 44 | 45 | 46 | def calcos(id1, id2, Fea): 47 | f1 = Fea[id1] 48 | f2 = Fea[id2] 49 | return np.dot(f1.transpose(), f2) 50 | 51 | 52 | def get_wordidx(w, Dict): 53 | if w not in Dict: 54 | print 'ERROR: %s not in the dictionary' % w 55 | return -1 56 | return Dict[w] 57 | 58 | 59 | if __name__ == '__main__': 60 | parser = ArgumentParser() 61 | parser.add_argument('dict', help='dictionary file') 62 | parser.add_argument('fea', help='feature file') 63 | args = parser.parse_args() 64 | 65 | with open(args.dict) as fdict: 66 | word_dict = load_dict(fdict) 67 | 68 | with open(args.fea) as ffea: 69 | word_fea = load_emb(ffea) 70 | 71 | while True: 72 | w1, w2 = raw_input("please input two words: ").split() 73 | w1_id = get_wordidx(w1, word_dict) 74 | w2_id = get_wordidx(w2, word_dict) 75 | if w1_id == -1 or w2_id == -1: 76 | continue 77 | print 'similarity: %s' % (calcos(w1_id, w2_id, word_fea)) 78 | -------------------------------------------------------------------------------- /04.word2vec/format_convert.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | """ 15 | Example: 16 | python format_convert.py --b2t -i INPUT -o OUTPUT -d DIM 17 | python format_convert.py --t2b -i INPUT -o OUTPUT 18 | 19 | Options: 20 | -h, --help show this help message and exit 21 | --b2t convert parameter file of embedding model from binary to text 22 | --t2b convert parameter file of embedding model from text to binary 23 | -i INPUT input parameter file name 24 | -o OUTPUT output parameter file name 25 | -d DIM dimension of parameter 26 | """ 27 | from optparse import OptionParser 28 | import struct 29 | 30 | 31 | def binary2text(input, output, paraDim): 32 | """ 33 | Convert a binary parameter file of embedding model to be a text file. 34 | input: the name of input binary parameter file, the format is: 35 | 1) the first 16 bytes is filehead: 36 | version(4 bytes): version of paddle, default = 0 37 | floatSize(4 bytes): sizeof(float) = 4 38 | paraCount(8 bytes): total number of parameter 39 | 2) the next (paraCount * 4) bytes is parameters, each has 4 bytes 40 | output: the name of output text parameter file, for example: 41 | 0,4,32156096 42 | -0.7845433,1.1937413,-0.1704215,... 43 | 0.0000909,0.0009465,-0.0008813,... 44 | ... 45 | the format is: 46 | 1) the first line is filehead: 47 | version=0, floatSize=4, paraCount=32156096 48 | 2) other lines print the paramters 49 | a) each line prints paraDim paramters splitted by ',' 50 | b) there is paraCount/paraDim lines (embedding words) 51 | paraDim: dimension of parameters 52 | """ 53 | fi = open(input, "rb") 54 | fo = open(output, "w") 55 | """ 56 | """ 57 | version, floatSize, paraCount = struct.unpack("iil", fi.read(16)) 58 | newHead = ','.join([str(version), str(floatSize), str(paraCount)]) 59 | print >> fo, newHead 60 | 61 | bytes = 4 * int(paraDim) 62 | format = "%df" % int(paraDim) 63 | context = fi.read(bytes) 64 | line = 0 65 | 66 | while context: 67 | numbers = struct.unpack(format, context) 68 | lst = [] 69 | for i in numbers: 70 | lst.append('%8.7f' % i) 71 | print >> fo, ','.join(lst) 72 | context = fi.read(bytes) 73 | line += 1 74 | fi.close() 75 | fo.close() 76 | print "binary2text finish, total", line, "lines" 77 | 78 | 79 | def get_para_count(input): 80 | """ 81 | Compute the total number of embedding parameters in input text file. 82 | input: the name of input text file 83 | """ 84 | numRows = 1 85 | paraDim = 0 86 | with open(input) as f: 87 | line = f.readline() 88 | paraDim = len(line.split(",")) 89 | for line in f: 90 | numRows += 1 91 | return numRows * paraDim 92 | 93 | 94 | def text2binary(input, output, paddle_head=True): 95 | """ 96 | Convert a text parameter file of embedding model to be a binary file. 97 | input: the name of input text parameter file, for example: 98 | -0.7845433,1.1937413,-0.1704215,... 99 | 0.0000909,0.0009465,-0.0008813,... 100 | ... 101 | the format is: 102 | 1) it doesn't have filehead 103 | 2) each line stores the same dimension of parameters, 104 | the separator is commas ',' 105 | output: the name of output binary parameter file, the format is: 106 | 1) the first 16 bytes is filehead: 107 | version(4 bytes), floatSize(4 bytes), paraCount(8 bytes) 108 | 2) the next (paraCount * 4) bytes is parameters, each has 4 bytes 109 | """ 110 | fi = open(input, "r") 111 | fo = open(output, "wb") 112 | 113 | newHead = struct.pack("iil", 0, 4, get_para_count(input)) 114 | fo.write(newHead) 115 | 116 | count = 0 117 | for line in fi: 118 | line = line.strip().split(",") 119 | for i in range(0, len(line)): 120 | binary_data = struct.pack("f", float(line[i])) 121 | fo.write(binary_data) 122 | count += 1 123 | fi.close() 124 | fo.close() 125 | print "text2binary finish, total", count, "lines" 126 | 127 | 128 | def main(): 129 | """ 130 | Main entry for running format_convert.py 131 | """ 132 | usage = "usage: \n" \ 133 | "python %prog --b2t -i INPUT -o OUTPUT -d DIM \n" \ 134 | "python %prog --t2b -i INPUT -o OUTPUT" 135 | parser = OptionParser(usage) 136 | parser.add_option( 137 | "--b2t", 138 | action="store_true", 139 | help="convert parameter file of embedding model from binary to text") 140 | parser.add_option( 141 | "--t2b", 142 | action="store_true", 143 | help="convert parameter file of embedding model from text to binary") 144 | parser.add_option( 145 | "-i", action="store", dest="input", help="input parameter file name") 146 | parser.add_option( 147 | "-o", action="store", dest="output", help="output parameter file name") 148 | parser.add_option( 149 | "-d", action="store", dest="dim", help="dimension of parameter") 150 | (options, args) = parser.parse_args() 151 | if options.b2t: 152 | binary2text(options.input, options.output, options.dim) 153 | if options.t2b: 154 | text2binary(options.input, options.output) 155 | 156 | 157 | if __name__ == '__main__': 158 | main() 159 | -------------------------------------------------------------------------------- /04.word2vec/image/2d_similarity.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/04.word2vec/image/2d_similarity.png -------------------------------------------------------------------------------- /04.word2vec/image/Eqn1.gif: -------------------------------------------------------------------------------- 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.ipynb_checkpoints 3 | -------------------------------------------------------------------------------- /05.recommender_system/.run_ce.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #This file is only used for continuous evaluation. 3 | python train.py --enable_ce | python _ce.py 4 | 5 | -------------------------------------------------------------------------------- /05.recommender_system/_ce.py: -------------------------------------------------------------------------------- 1 | ### This file is only used for continuous evaluation test! 2 | from __future__ import print_function 3 | from __future__ import division 4 | from __future__ import absolute_import 5 | import os 6 | import sys 7 | sys.path.append(os.environ['ceroot']) 8 | from kpi import CostKpi 9 | 10 | test_cost_kpi = CostKpi('test_cost', 0.02, 0, actived=True, desc='test cost') 11 | tracking_kpis = [test_cost_kpi] 12 | 13 | 14 | def parse_log(log): 15 | for line in log.split('\n'): 16 | fs = line.strip().split('\t') 17 | print(fs) 18 | if len(fs) == 3 and fs[0] == 'kpis': 19 | kpi_name = fs[1] 20 | kpi_value = float(fs[2]) 21 | yield kpi_name, kpi_value 22 | 23 | 24 | def log_to_ce(log): 25 | kpi_tracker = {} 26 | for kpi in tracking_kpis: 27 | kpi_tracker[kpi.name] = kpi 28 | for (kpi_name, kpi_value) in parse_log(log): 29 | print(kpi_name, kpi_value) 30 | kpi_tracker[kpi_name].add_record(kpi_value) 31 | kpi_tracker[kpi_name].persist() 32 | 33 | 34 | if __name__ == '__main__': 35 | log = sys.stdin.read() 36 | log_to_ce(log) 37 | -------------------------------------------------------------------------------- /05.recommender_system/image/Deep_candidate_generation_model_architecture.en.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/05.recommender_system/image/Deep_candidate_generation_model_architecture.en.png 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--enable_ce | python _ce.py 7 | python train_stacked_lstm.py --use_gpu 1 --num_epochs=1 --enable_ce | python _ce.py 8 | 9 | 10 | -------------------------------------------------------------------------------- /06.understand_sentiment/_ce.py: -------------------------------------------------------------------------------- 1 | ### This file is only used for continuous evaluation test! 2 | from __future__ import print_function 3 | from __future__ import division 4 | from __future__ import absolute_import 5 | import os 6 | import sys 7 | sys.path.append(os.environ['ceroot']) 8 | from kpi import CostKpi 9 | from kpi import AccKpi 10 | 11 | conv_train_cost_kpi = CostKpi( 12 | 'conv_train_cost', 0.02, 0, actived=True, desc='train cost') 13 | conv_train_acc_kpi = AccKpi( 14 | 'conv_train_acc', 0.02, 0, actived=True, desc='train acc') 15 | conv_test_cost_kpi = CostKpi( 16 | 'conv_test_cost', 0.02, 0, actived=True, desc='test cost') 17 | conv_test_acc_kpi = AccKpi( 18 | 'conv_test_acc', 0.02, 0, actived=True, desc='test acc') 19 | 20 | rnn_train_cost_kpi = CostKpi( 21 | 'rnn_train_cost', 0.02, 0, actived=True, desc='train cost') 22 | rnn_train_acc_kpi = AccKpi( 23 | 'rnn_train_acc', 0.02, 0, actived=True, desc='train acc') 24 | rnn_test_cost_kpi = CostKpi( 25 | 'rnn_test_cost', 0.02, 0, actived=True, desc='test cost') 26 | rnn_test_acc_kpi = AccKpi( 27 | 'rnn_test_acc', 0.02, 0, actived=True, desc='test acc') 28 | 29 | lstm_train_cost_kpi = CostKpi( 30 | 'lstm_train_cost', 0.02, 0, actived=True, desc='train cost') 31 | lstm_train_acc_kpi = AccKpi( 32 | 'lstm_train_acc', 0.02, 0, actived=True, desc='train acc') 33 | lstm_test_cost_kpi = CostKpi( 34 | 'lstm_test_cost', 0.02, 0, actived=True, desc='test cost') 35 | lstm_test_acc_kpi = AccKpi( 36 | 'lstm_test_acc', 0.02, 0, actived=True, desc='test acc') 37 | 38 | tracking_kpis = [ 39 | conv_train_cost_kpi, conv_train_acc_kpi, conv_test_cost_kpi, 40 | conv_test_acc_kpi, rnn_train_cost_kpi, rnn_train_acc_kpi, rnn_test_cost_kpi, 41 | rnn_test_acc_kpi, lstm_train_cost_kpi, lstm_train_acc_kpi, 42 | lstm_test_cost_kpi, lstm_test_acc_kpi 43 | ] 44 | 45 | 46 | def parse_log(log): 47 | for line in log.split('\n'): 48 | fs = line.strip().split('\t') 49 | print(fs) 50 | if len(fs) == 3 and fs[0] == 'kpis': 51 | kpi_name = fs[1] 52 | kpi_value = float(fs[2]) 53 | yield kpi_name, kpi_value 54 | 55 | 56 | def log_to_ce(log): 57 | kpi_tracker = {} 58 | for kpi in tracking_kpis: 59 | kpi_tracker[kpi.name] = kpi 60 | for (kpi_name, kpi_value) in parse_log(log): 61 | print(kpi_name, kpi_value) 62 | kpi_tracker[kpi_name].add_record(kpi_value) 63 | kpi_tracker[kpi_name].persist() 64 | 65 | 66 | if __name__ == '__main__': 67 | log = sys.stdin.read() 68 | log_to_ce(log) 69 | -------------------------------------------------------------------------------- /06.understand_sentiment/image/formula_lstm.png: -------------------------------------------------------------------------------- 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-------------------------------------------------------------------------------- /06.understand_sentiment/train_dyn_rnn.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from __future__ import print_function 16 | 17 | import paddle 18 | import paddle.fluid as fluid 19 | import numpy as np 20 | import sys 21 | import math 22 | import argparse 23 | 24 | CLASS_DIM = 2 25 | EMB_DIM = 128 26 | BATCH_SIZE = 128 27 | LSTM_SIZE = 128 28 | 29 | 30 | def parse_args(): 31 | parser = argparse.ArgumentParser("dyn_rnn") 32 | parser.add_argument( 33 | '--enable_ce', 34 | action='store_true', 35 | help="If set, run the task with continuous evaluation logs.") 36 | parser.add_argument( 37 | '--use_gpu', type=int, default=0, help="Whether to use GPU or not.") 38 | parser.add_argument( 39 | '--num_epochs', type=int, default=1, help="number of epochs.") 40 | args = parser.parse_args() 41 | return args 42 | 43 | 44 | def dynamic_rnn_lstm(data, input_dim, class_dim, emb_dim, lstm_size): 45 | emb = fluid.embedding(input=data, size=[input_dim, emb_dim], is_sparse=True) 46 | sentence = fluid.layers.fc(input=emb, size=lstm_size * 4, act='tanh') 47 | 48 | lstm, _ = fluid.layers.dynamic_lstm(sentence, size=lstm_size * 4) 49 | 50 | last = fluid.layers.sequence_last_step(lstm) 51 | prediction = fluid.layers.fc(input=last, size=class_dim, act="softmax") 52 | return prediction 53 | 54 | 55 | def inference_program(word_dict): 56 | data = fluid.data(name="words", shape=[None], dtype="int64", lod_level=1) 57 | dict_dim = len(word_dict) 58 | pred = dynamic_rnn_lstm(data, dict_dim, CLASS_DIM, EMB_DIM, LSTM_SIZE) 59 | return pred 60 | 61 | 62 | def train_program(prediction): 63 | label = fluid.data(name="label", shape=[None, 1], dtype="int64") 64 | cost = fluid.layers.cross_entropy(input=prediction, label=label) 65 | avg_cost = fluid.layers.mean(cost) 66 | accuracy = fluid.layers.accuracy(input=prediction, label=label) 67 | return [avg_cost, accuracy] 68 | 69 | 70 | def optimizer_func(): 71 | return fluid.optimizer.Adagrad(learning_rate=0.002) 72 | 73 | 74 | def train(use_cuda, params_dirname): 75 | place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() 76 | print("Loading IMDB word dict....") 77 | word_dict = paddle.dataset.imdb.word_dict() 78 | 79 | print("Reading training data....") 80 | if args.enable_ce: 81 | train_reader = paddle.batch( 82 | paddle.dataset.imdb.train(word_dict), batch_size=BATCH_SIZE) 83 | else: 84 | train_reader = paddle.batch( 85 | paddle.reader.shuffle( 86 | paddle.dataset.imdb.train(word_dict), buf_size=25000), 87 | batch_size=BATCH_SIZE) 88 | 89 | print("Reading testing data....") 90 | test_reader = paddle.batch( 91 | paddle.dataset.imdb.test(word_dict), batch_size=BATCH_SIZE) 92 | 93 | feed_order = ['words', 'label'] 94 | pass_num = args.num_epochs 95 | 96 | main_program = fluid.default_main_program() 97 | star_program = fluid.default_startup_program() 98 | 99 | if args.enable_ce: 100 | main_program.random_seed = 90 101 | star_program.random_seed = 90 102 | 103 | prediction = inference_program(word_dict) 104 | train_func_outputs = train_program(prediction) 105 | avg_cost = train_func_outputs[0] 106 | 107 | test_program = main_program.clone(for_test=True) 108 | 109 | sgd_optimizer = optimizer_func() 110 | sgd_optimizer.minimize(avg_cost) 111 | exe = fluid.Executor(place) 112 | 113 | def train_test(program, reader): 114 | count = 0 115 | feed_var_list = [ 116 | program.global_block().var(var_name) for var_name in feed_order 117 | ] 118 | feeder_test = fluid.DataFeeder(feed_list=feed_var_list, place=place) 119 | test_exe = fluid.Executor(place) 120 | accumulated = len(train_func_outputs) * [0] 121 | for test_data in reader(): 122 | avg_cost_np = test_exe.run( 123 | program=program, 124 | feed=feeder_test.feed(test_data), 125 | fetch_list=train_func_outputs) 126 | accumulated = [ 127 | x[0] + x[1][0] for x in zip(accumulated, avg_cost_np) 128 | ] 129 | count += 1 130 | return [x / count for x in accumulated] 131 | 132 | def train_loop(): 133 | 134 | feed_var_list_loop = [ 135 | main_program.global_block().var(var_name) for var_name in feed_order 136 | ] 137 | feeder = fluid.DataFeeder(feed_list=feed_var_list_loop, place=place) 138 | exe.run(fluid.default_startup_program()) 139 | 140 | for epoch_id in range(pass_num): 141 | for step_id, data in enumerate(train_reader()): 142 | metrics = exe.run( 143 | main_program, 144 | feed=feeder.feed(data), 145 | fetch_list=[var.name for var in train_func_outputs]) 146 | if (step_id + 1) % 10 == 0: 147 | 148 | avg_cost_test, acc_test = train_test(test_program, 149 | test_reader) 150 | print('Step {0}, Test Loss {1:0.2}, Acc {2:0.2}'.format( 151 | step_id, avg_cost_test, acc_test)) 152 | 153 | print("Step {0}, Epoch {1} Metrics {2}".format( 154 | step_id, epoch_id, list(map(np.array, metrics)))) 155 | if math.isnan(float(metrics[0])): 156 | sys.exit("got NaN loss, training failed.") 157 | if params_dirname is not None: 158 | fluid.io.save_inference_model(params_dirname, ["words"], 159 | prediction, exe) 160 | if args.enable_ce and epoch_id == pass_num - 1: 161 | print("kpis\trnn_train_cost\t%f" % metrics[0]) 162 | print("kpis\trnn_train_acc\t%f" % metrics[1]) 163 | print("kpis\trnn_test_cost\t%f" % avg_cost_test) 164 | print("kpis\trnn_test_acc\t%f" % acc_test) 165 | 166 | train_loop() 167 | 168 | 169 | def infer(use_cuda, params_dirname=None): 170 | place = fluid.CUDAPlace(0) if use_cuda else fluid.CPUPlace() 171 | word_dict = paddle.dataset.imdb.word_dict() 172 | 173 | exe = fluid.Executor(place) 174 | 175 | inference_scope = fluid.core.Scope() 176 | with fluid.scope_guard(inference_scope): 177 | # Use fluid.io.load_inference_model to obtain the inference program desc, 178 | # the feed_target_names (the names of variables that will be feeded 179 | # data using feed operators), and the fetch_targets (variables that 180 | # we want to obtain data from using fetch operators). 181 | [inferencer, feed_target_names, 182 | fetch_targets] = fluid.io.load_inference_model(params_dirname, exe) 183 | 184 | # Setup input by creating LoDTensor to represent sequence of words. 185 | # Here each word is the basic element of the LoDTensor and the shape of 186 | # each word (base_shape) should be [1] since it is simply an index to 187 | # look up for the corresponding word vector. 188 | # Suppose the length_based level of detail (lod) info is set to [[3, 4, 2]], 189 | # which has only one lod level. Then the created LoDTensor will have only 190 | # one higher level structure (sequence of words, or sentence) than the basic 191 | # element (word). Hence the LoDTensor will hold data for three sentences of 192 | # length 3, 4 and 2, respectively. 193 | # Note that lod info should be a list of lists. 194 | reviews_str = [ 195 | 'read the book forget the movie', 'this is a great movie', 196 | 'this is very bad' 197 | ] 198 | reviews = [c.split() for c in reviews_str] 199 | 200 | UNK = word_dict[''] 201 | lod = [] 202 | for c in reviews: 203 | lod.append([np.int64(word_dict.get(words, UNK)) for words in c]) 204 | 205 | base_shape = [[len(c) for c in lod]] 206 | 207 | tensor_words = fluid.create_lod_tensor(lod, base_shape, place) 208 | assert feed_target_names[0] == "words" 209 | results = exe.run( 210 | inferencer, 211 | feed={feed_target_names[0]: tensor_words}, 212 | fetch_list=fetch_targets, 213 | return_numpy=False) 214 | np_data = np.array(results[0]) 215 | for i, r in enumerate(np_data): 216 | print("Predict probability of ", r[0], " to be positive and ", r[1], 217 | " to be negative for review \'", reviews_str[i], "\'") 218 | 219 | 220 | def main(use_cuda): 221 | if use_cuda and not fluid.core.is_compiled_with_cuda(): 222 | return 223 | params_dirname = "understand_sentiment_conv.inference.model" 224 | train(use_cuda, params_dirname) 225 | infer(use_cuda, params_dirname) 226 | 227 | 228 | if __name__ == '__main__': 229 | args = parse_args() 230 | use_cuda = args.use_gpu # set to True if training with GPU 231 | main(use_cuda) 232 | -------------------------------------------------------------------------------- /07.label_semantic_roles/.gitignore: -------------------------------------------------------------------------------- 1 | data/train.list 2 | data/test.* 3 | data/conll05st-release.tar.gz 4 | data/conll05st-release 5 | data/predicate_dict 6 | data/label_dict 7 | data/word_dict 8 | data/emb 9 | data/feature 10 | output 11 | predict.res 12 | train.log 13 | -------------------------------------------------------------------------------- /07.label_semantic_roles/.run_ce.sh: -------------------------------------------------------------------------------- 1 | #!/bin/bash 2 | #This file is only used for continuous evaluation. 3 | python train.py --enable_ce | python _ce.py 4 | 5 | -------------------------------------------------------------------------------- /07.label_semantic_roles/_ce.py: -------------------------------------------------------------------------------- 1 | ### This file is only used for continuous evaluation test! 2 | from __future__ import print_function 3 | from __future__ import division 4 | from __future__ import absolute_import 5 | import os 6 | import sys 7 | sys.path.append(os.environ['ceroot']) 8 | from kpi import CostKpi 9 | 10 | train_cost_kpi = CostKpi('train_cost', 0.02, 0, actived=True, desc='train cost') 11 | tracking_kpis = [train_cost_kpi] 12 | 13 | 14 | def parse_log(log): 15 | for line in log.split('\n'): 16 | fs = line.strip().split('\t') 17 | print(fs) 18 | if len(fs) == 3 and fs[0] == 'kpis': 19 | kpi_name = fs[1] 20 | kpi_value = float(fs[2]) 21 | yield kpi_name, kpi_value 22 | 23 | 24 | def log_to_ce(log): 25 | kpi_tracker = {} 26 | for kpi in tracking_kpis: 27 | kpi_tracker[kpi.name] = kpi 28 | 29 | for (kpi_name, kpi_value) in parse_log(log): 30 | print(kpi_name, kpi_value) 31 | kpi_tracker[kpi_name].add_record(kpi_value) 32 | kpi_tracker[kpi_name].persist() 33 | 34 | 35 | if __name__ == '__main__': 36 | log = sys.stdin.read() 37 | log_to_ce(log) 38 | -------------------------------------------------------------------------------- /07.label_semantic_roles/image/Eqn1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/07.label_semantic_roles/image/Eqn1.png -------------------------------------------------------------------------------- /07.label_semantic_roles/image/Eqn2.gif: -------------------------------------------------------------------------------- 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--use_gpu True | python _ce.py 6 | 7 | -------------------------------------------------------------------------------- /09.gan/_ce.py: -------------------------------------------------------------------------------- 1 | ### This file is only used for continuous evaluation test! 2 | from __future__ import print_function 3 | from __future__ import division 4 | from __future__ import absolute_import 5 | import os 6 | import sys 7 | sys.path.append(os.environ['ceroot']) 8 | from kpi import CostKpi 9 | 10 | dcgan_d_train_cost_kpi = CostKpi( 11 | 'dcgan_d_train_cost', 12 | 0.02, 13 | 0, 14 | actived=True, 15 | desc='train cost of discriminator') 16 | dcgan_g_train_cost_kpi = CostKpi( 17 | 'dcgan_g_train_cost', 0.02, 0, actived=True, desc='train cost of generator') 18 | 19 | tracking_kpis = [dcgan_d_train_cost_kpi, dcgan_g_train_cost_kpi] 20 | 21 | 22 | def parse_log(log): 23 | for line in log.split('\n'): 24 | fs = line.strip().split('\t') 25 | print(fs) 26 | if len(fs) == 3 and fs[0] == 'kpis': 27 | kpi_name = fs[1] 28 | kpi_value = float(fs[2]) 29 | yield kpi_name, kpi_value 30 | 31 | 32 | def log_to_ce(log): 33 | kpi_tracker = {} 34 | for kpi in tracking_kpis: 35 | kpi_tracker[kpi.name] = kpi 36 | print(kpi.name) 37 | print(kpi) 38 | for (kpi_name, kpi_value) in parse_log(log): 39 | print(kpi_name, kpi_value) 40 | kpi_tracker[kpi_name].add_record(kpi_value) 41 | kpi_tracker[kpi_name].persist() 42 | 43 | 44 | if __name__ == '__main__': 45 | log = sys.stdin.read() 46 | log_to_ce(log) 47 | -------------------------------------------------------------------------------- /09.gan/dc_gan.py: -------------------------------------------------------------------------------- 1 | # Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. 2 | # 3 | # Licensed under the Apache License, Version 2.0 (the "License"); 4 | # you may not use this file except in compliance with the License. 5 | # You may obtain a copy of the License at 6 | # 7 | # http://www.apache.org/licenses/LICENSE-2.0 8 | # 9 | # Unless required by applicable law or agreed to in writing, software 10 | # distributed under the License is distributed on an "AS IS" BASIS, 11 | # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 | # See the License for the specific language governing permissions and 13 | # limitations under the License. 14 | 15 | from __future__ import absolute_import 16 | from __future__ import division 17 | from __future__ import print_function 18 | import sys 19 | import os 20 | import argparse 21 | import functools 22 | import matplotlib 23 | import six 24 | import numpy as np 25 | import paddle 26 | import time 27 | import paddle.fluid as fluid 28 | from utility import get_parent_function_name, plot, check, add_arguments, print_arguments 29 | from network import G, D 30 | matplotlib.use('agg') 31 | import matplotlib.pyplot as plt 32 | import matplotlib.gridspec as gridspec 33 | 34 | NOISE_SIZE = 100 35 | LEARNING_RATE = 2e-4 36 | 37 | parser = argparse.ArgumentParser(description=__doc__) 38 | add_arg = functools.partial(add_arguments, argparser=parser) 39 | # yapf: disable 40 | add_arg('batch_size', int, 128, "Minibatch size.") 41 | add_arg('epoch', int, 20, "The number of epoched to be trained.") 42 | add_arg('output', str, "./output_dcgan", "The directory the model and the test result to be saved to.") 43 | add_arg('use_gpu', bool, True, "Whether to use GPU to train.") 44 | add_arg('enable_ce', bool, False, "If set True, enable continuous evaluation job.") 45 | # yapf: enable 46 | 47 | 48 | def loss(x, label): 49 | return fluid.layers.mean( 50 | fluid.layers.sigmoid_cross_entropy_with_logits(x=x, label=label)) 51 | 52 | 53 | def train(args): 54 | 55 | if args.enable_ce: 56 | np.random.seed(10) 57 | fluid.default_startup_program().random_seed = 90 58 | 59 | d_program = fluid.Program() 60 | dg_program = fluid.Program() 61 | 62 | with fluid.program_guard(d_program): 63 | img = fluid.data(name='img', shape=[None, 784], dtype='float32') 64 | label = fluid.data(name='label', shape=[None, 1], dtype='float32') 65 | d_logit = D(img) 66 | d_loss = loss(d_logit, label) 67 | 68 | with fluid.program_guard(dg_program): 69 | noise = fluid.data( 70 | name='noise', shape=[None, NOISE_SIZE], dtype='float32') 71 | g_img = G(x=noise) 72 | 73 | g_program = dg_program.clone() 74 | g_program_test = dg_program.clone(for_test=True) 75 | 76 | dg_logit = D(g_img) 77 | dg_loss = loss(dg_logit, 78 | fluid.layers.fill_constant_batch_size_like( 79 | input=noise, 80 | dtype='float32', 81 | shape=[-1, 1], 82 | value=1.0)) 83 | 84 | opt = fluid.optimizer.Adam(learning_rate=LEARNING_RATE) 85 | 86 | opt.minimize(loss=d_loss) 87 | parameters = [p.name for p in g_program.global_block().all_parameters()] 88 | 89 | opt.minimize(loss=dg_loss, parameter_list=parameters) 90 | 91 | exe = fluid.Executor(fluid.CPUPlace()) 92 | if args.use_gpu: 93 | exe = fluid.Executor(fluid.CUDAPlace(0)) 94 | exe.run(fluid.default_startup_program()) 95 | if args.enable_ce: 96 | train_reader = paddle.batch( 97 | paddle.dataset.mnist.train(), batch_size=args.batch_size) 98 | else: 99 | 100 | train_reader = paddle.batch( 101 | paddle.reader.shuffle(paddle.dataset.mnist.train(), buf_size=60000), 102 | batch_size=args.batch_size) 103 | 104 | NUM_TRAIN_TIMES_OF_DG = 2 105 | const_n = np.random.uniform( 106 | low=-1.0, high=1.0, 107 | size=[args.batch_size, NOISE_SIZE]).astype('float32') 108 | 109 | t_time = 0 110 | losses = [[], []] 111 | for pass_id in range(args.epoch): 112 | for batch_id, data in enumerate(train_reader()): 113 | if len(data) != args.batch_size: 114 | continue 115 | noise_data = np.random.uniform( 116 | low=-1.0, high=1.0, 117 | size=[args.batch_size, NOISE_SIZE]).astype('float32') 118 | real_image = np.array(list(map(lambda x: x[0], data))).reshape( 119 | -1, 784).astype('float32') 120 | real_labels = np.ones( 121 | shape=[real_image.shape[0], 1], dtype='float32') 122 | fake_labels = np.zeros( 123 | shape=[real_image.shape[0], 1], dtype='float32') 124 | total_label = np.concatenate([real_labels, fake_labels]) 125 | s_time = time.time() 126 | generated_image = exe.run( 127 | g_program, feed={'noise': noise_data}, fetch_list=[g_img])[0] 128 | 129 | total_images = np.concatenate([real_image, generated_image]) 130 | 131 | d_loss_1 = exe.run( 132 | d_program, 133 | feed={ 134 | 'img': generated_image, 135 | 'label': fake_labels, 136 | }, 137 | fetch_list=[d_loss])[0][0] 138 | 139 | d_loss_2 = exe.run( 140 | d_program, 141 | feed={ 142 | 'img': real_image, 143 | 'label': real_labels, 144 | }, 145 | fetch_list=[d_loss])[0][0] 146 | 147 | d_loss_n = d_loss_1 + d_loss_2 148 | losses[0].append(d_loss_n) 149 | for _ in six.moves.xrange(NUM_TRAIN_TIMES_OF_DG): 150 | noise_data = np.random.uniform( 151 | low=-1.0, high=1.0, 152 | size=[args.batch_size, NOISE_SIZE]).astype('float32') 153 | dg_loss_n = exe.run( 154 | dg_program, 155 | feed={'noise': noise_data}, 156 | fetch_list=[dg_loss])[0][0] 157 | losses[1].append(dg_loss_n) 158 | t_time += (time.time() - s_time) 159 | if batch_id % 10 == 0: 160 | if not os.path.exists(args.output): 161 | os.makedirs(args.output) 162 | # generate image each batch 163 | generated_images = exe.run( 164 | g_program_test, feed={'noise': const_n}, 165 | fetch_list=[g_img])[0] 166 | total_images = np.concatenate([real_image, generated_images]) 167 | fig = plot(total_images) 168 | msg = "Epoch ID={0} Batch ID={1} D-Loss={2} DG-Loss={3}\n gen={4}".format( 169 | pass_id, batch_id, d_loss_n, dg_loss_n, 170 | check(generated_images)) 171 | print(msg) 172 | plt.title(msg) 173 | plt.savefig( 174 | '{}/{:04d}_{:04d}.png'.format(args.output, pass_id, 175 | batch_id), 176 | bbox_inches='tight') 177 | plt.close(fig) 178 | if args.enable_ce and pass_id == args.epoch - 1: 179 | print("kpis\tdcgan_d_train_cost\t%f" % np.mean(losses[0])) 180 | print("kpis\tdcgan_g_train_cost\t%f" % np.mean(losses[1])) 181 | 182 | 183 | if __name__ == "__main__": 184 | args = parser.parse_args() 185 | print_arguments(args) 186 | train(args) 187 | -------------------------------------------------------------------------------- /09.gan/image/01.gif: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/09.gan/image/01.gif -------------------------------------------------------------------------------- /09.gan/image/dcgan_demo.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/09.gan/image/dcgan_demo.png -------------------------------------------------------------------------------- /09.gan/image/dcgan_g.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/09.gan/image/dcgan_g.png -------------------------------------------------------------------------------- /09.gan/image/process.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/09.gan/image/process.png -------------------------------------------------------------------------------- /09.gan/network.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | import paddle 5 | import paddle.fluid as fluid 6 | from utility import get_parent_function_name 7 | import os 8 | 9 | gf_dim = 64 10 | df_dim = 64 11 | gfc_dim = 1024 * 2 12 | dfc_dim = 1024 13 | img_dim = 28 14 | 15 | c_dim = 3 16 | y_dim = 1 17 | output_height = 28 18 | output_width = 28 19 | 20 | use_cudnn = True 21 | if 'ce_mode' in os.environ: 22 | use_cudnn = False 23 | 24 | 25 | def bn(x, name=None, act='relu'): 26 | if name is None: 27 | name = get_parent_function_name() 28 | #return fluid.layers.leaky_relu(x) 29 | return fluid.layers.batch_norm( 30 | x, 31 | param_attr=name + '1', 32 | bias_attr=name + '2', 33 | moving_mean_name=name + '3', 34 | moving_variance_name=name + '4', 35 | name=name, 36 | act=act) 37 | 38 | 39 | def conv(x, num_filters, name=None, act=None): 40 | if name is None: 41 | name = get_parent_function_name() 42 | return fluid.nets.simple_img_conv_pool( 43 | input=x, 44 | filter_size=5, 45 | num_filters=num_filters, 46 | pool_size=2, 47 | pool_stride=2, 48 | param_attr=name + 'w', 49 | bias_attr=name + 'b', 50 | use_cudnn=use_cudnn, 51 | act=act) 52 | 53 | 54 | def fc(x, num_filters, name=None, act=None): 55 | if name is None: 56 | name = get_parent_function_name() 57 | return fluid.layers.fc( 58 | input=x, 59 | size=num_filters, 60 | act=act, 61 | param_attr=name + 'w', 62 | bias_attr=name + 'b') 63 | 64 | 65 | def deconv(x, 66 | num_filters, 67 | name=None, 68 | filter_size=5, 69 | stride=2, 70 | dilation=1, 71 | padding=2, 72 | output_size=None, 73 | act=None): 74 | if name is None: 75 | name = get_parent_function_name() 76 | return fluid.layers.conv2d_transpose( 77 | input=x, 78 | param_attr=name + 'w', 79 | bias_attr=name + 'b', 80 | num_filters=num_filters, 81 | output_size=output_size, 82 | filter_size=filter_size, 83 | stride=stride, 84 | dilation=dilation, 85 | padding=padding, 86 | use_cudnn=use_cudnn, 87 | act=act) 88 | 89 | 90 | def conv_cond_concat(x, y): 91 | """Concatenate conditioning vector on feature map axis.""" 92 | ones = fluid.layers.fill_constant_batch_size_like( 93 | x, [-1, y.shape[1], x.shape[2], x.shape[3]], "float32", 1.0) 94 | return fluid.layers.concat([x, ones * y], 1) 95 | 96 | 97 | def D_cond(image, y): 98 | image = fluid.layers.reshape(x=image, shape=[-1, 1, 28, 28]) 99 | yb = fluid.layers.reshape(y, [-1, y_dim, 1, 1]) 100 | x = conv_cond_concat(image, yb) 101 | 102 | h0 = conv(x, c_dim + y_dim, act="leaky_relu") 103 | h0 = conv_cond_concat(h0, yb) 104 | h1 = bn(conv(h0, df_dim + y_dim), act="leaky_relu") 105 | h1 = fluid.layers.flatten(h1, axis=1) 106 | 107 | h1 = fluid.layers.concat([h1, y], 1) 108 | 109 | h2 = bn(fc(h1, dfc_dim), act='leaky_relu') 110 | h2 = fluid.layers.concat([h2, y], 1) 111 | 112 | h3 = fc(h2, 1, act='sigmoid') 113 | return h3 114 | 115 | 116 | def G_cond(z, y): 117 | s_h, s_w = output_height, output_width 118 | s_h2, s_h4 = int(s_h // 2), int(s_h // 4) 119 | s_w2, s_w4 = int(s_w // 2), int(s_w // 4) 120 | 121 | yb = fluid.layers.reshape(y, [-1, y_dim, 1, 1]) #NCHW 122 | 123 | z = fluid.layers.concat([z, y], 1) 124 | h0 = bn(fc(z, gfc_dim // 2), act='relu') 125 | h0 = fluid.layers.concat([h0, y], 1) 126 | 127 | h1 = bn(fc(h0, gf_dim * 2 * s_h4 * s_w4), act='relu') 128 | h1 = fluid.layers.reshape(h1, [-1, gf_dim * 2, s_h4, s_w4]) 129 | 130 | h1 = conv_cond_concat(h1, yb) 131 | h2 = bn(deconv(h1, gf_dim * 2, output_size=[s_h2, s_w2]), act='relu') 132 | h2 = conv_cond_concat(h2, yb) 133 | h3 = deconv(h2, 1, output_size=[s_h, s_w], act='tanh') 134 | return fluid.layers.reshape(h3, shape=[-1, s_h * s_w]) 135 | 136 | 137 | def D(x): 138 | x = fluid.layers.reshape(x=x, shape=[-1, 1, 28, 28]) 139 | x = conv(x, df_dim, act='leaky_relu') 140 | x = bn(conv(x, df_dim * 2), act='leaky_relu') 141 | x = bn(fc(x, dfc_dim), act='leaky_relu') 142 | x = fc(x, 1, act='sigmoid') 143 | return x 144 | 145 | 146 | def G(x): 147 | x = bn(fc(x, gfc_dim)) 148 | x = bn(fc(x, gf_dim * 2 * img_dim // 4 * img_dim // 4)) 149 | x = fluid.layers.reshape(x, [-1, gf_dim * 2, img_dim // 4, img_dim // 4]) 150 | x = deconv(x, gf_dim * 2, act='relu', output_size=[14, 14]) 151 | x = deconv(x, 1, filter_size=5, padding=2, act='tanh', output_size=[28, 28]) 152 | x = fluid.layers.reshape(x, shape=[-1, 28 * 28]) 153 | return x 154 | -------------------------------------------------------------------------------- /09.gan/utility.py: -------------------------------------------------------------------------------- 1 | from __future__ import absolute_import 2 | from __future__ import division 3 | from __future__ import print_function 4 | import math 5 | import distutils.util 6 | import numpy as np 7 | import inspect 8 | import matplotlib 9 | import six 10 | matplotlib.use('agg') 11 | import matplotlib.pyplot as plt 12 | import matplotlib.gridspec as gridspec 13 | 14 | img_dim = 28 15 | 16 | 17 | def get_parent_function_name(): 18 | return inspect.stack()[2][3] + '.' + inspect.stack()[1][3] + '.' + str( 19 | inspect.stack()[2][2]) + '.' 20 | 21 | 22 | def plot(gen_data): 23 | pad_dim = 1 24 | paded = pad_dim + img_dim 25 | gen_data = gen_data.reshape(gen_data.shape[0], img_dim, img_dim) 26 | n = int(math.ceil(math.sqrt(gen_data.shape[0]))) 27 | gen_data = (np.pad( 28 | gen_data, [[0, n * n - gen_data.shape[0]], [pad_dim, 0], [pad_dim, 0]], 29 | 'constant').reshape((n, n, paded, paded)).transpose((0, 2, 1, 3)) 30 | .reshape((n * paded, n * paded))) 31 | fig = plt.figure(figsize=(8, 8)) 32 | plt.axis('off') 33 | plt.imshow(gen_data, cmap='Greys_r', vmin=-1, vmax=1) 34 | return fig 35 | 36 | 37 | def check(a): 38 | a = np.sort(np.array(a).flatten()) 39 | return [ 40 | np.average(a), np.min(a), np.max(a), a[int(len(a) * 0.25)], 41 | a[int(len(a) * 0.75)] 42 | ] 43 | 44 | 45 | def print_arguments(args): 46 | """Print argparse's arguments. 47 | 48 | Usage: 49 | 50 | .. code-block:: python 51 | 52 | parser = argparse.ArgumentParser() 53 | parser.add_argument("name", default="Jonh", type=str, help="User name.") 54 | args = parser.parse_args() 55 | print_arguments(args) 56 | 57 | :param args: Input argparse.Namespace for printing. 58 | :type args: argparse.Namespace 59 | """ 60 | print("----------- Configuration Arguments -----------") 61 | for arg, value in sorted(six.iteritems(vars(args))): 62 | print("%s: %s" % (arg, value)) 63 | print("------------------------------------------------") 64 | 65 | 66 | def add_arguments(argname, type, default, help, argparser, **kwargs): 67 | """Add argparse's argument. 68 | 69 | Usage: 70 | 71 | .. code-block:: python 72 | 73 | parser = argparse.ArgumentParser() 74 | add_argument("name", str, "Jonh", "User name.", parser) 75 | args = parser.parse_args() 76 | """ 77 | type = distutils.util.strtobool if type == bool else type 78 | argparser.add_argument( 79 | "--" + argname, 80 | default=default, 81 | type=type, 82 | help=help + ' Default: %(default)s.', 83 | **kwargs) 84 | -------------------------------------------------------------------------------- /README.cn.md: -------------------------------------------------------------------------------- 1 | # 深度学习入门 2 | 3 | [![Build Status](https://travis-ci.org/PaddlePaddle/book.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/book) 4 | [![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](https://github.com/PaddlePaddle/book/blob/develop/README.md) 5 | [![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](https://github.com/PaddlePaddle/book/blob/develop/README.cn.md) 6 | 7 | 1. [线性回归](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/beginners_guide/basics/fit_a_line/README.cn.html) 8 | 1. [识别数字](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/beginners_guide/basics/recognize_digits/README.cn.html) 9 | 1. [图像分类](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/beginners_guide/basics/image_classification/index.html) 10 | 1. [词向量](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/beginners_guide/basics/word2vec/index.html) 11 | 1. [个性化推荐](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/beginners_guide/basics/recommender_system/index.html) 12 | 1. [情感分析](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/beginners_guide/basics/understand_sentiment/index.html) 13 | 1. [语义角色标注](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/beginners_guide/basics/label_semantic_roles/index.html) 14 | 1. [机器翻译](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/beginners_guide/basics/machine_translation/index.html) 15 | 1. [生成对抗网络](https://www.paddlepaddle.org.cn/documentation/docs/zh/develop/beginners_guide/basics/gan/index.html) 16 | 17 | 更多学习内容请访问PaddlePaddle[视频课堂](http://bit.baidu.com/Course/datalist/column/117.html)。 18 | 19 | ## 运行这本书 20 | 21 | 您现在在看的这本书是一本“交互式”电子书 —— 每一章都可以运行在一个Jupyter Notebook里。 22 | 23 | 我们把Jupyter、PaddlePaddle、以及各种被依赖的软件都打包进一个Docker image了。所以您不需要自己来安装各种软件，只需要安装Docker即可。对于各种Linux发行版，请参考 https://www.docker.com 。如果您使用[Windows](https://www.docker.com/docker-windows)或者[Mac](https://www.docker.com/docker-mac)，可以考虑[给Docker更多内存和CPU资源](http://stackoverflow.com/a/39720010/724872)。 24 | 25 | 只需要在命令行窗口里运行： 26 | 27 | ```bash 28 | docker run -d -p 8888:8888 paddlepaddle/book 29 | ``` 30 | 31 | 会从DockerHub.com下载和运行本书的Docker image。阅读和在线编辑本书请在浏览器里访问 http://localhost:8888 。 32 | 33 | 如果您访问DockerHub.com很慢，可以试试我们的另一个镜像hub.baidubce.com： 34 | 35 | ```bash 36 | docker run -d -p 8888:8888 hub.baidubce.com/paddlepaddle/book 37 | ``` 38 | 39 | ### 使用GPU训练 40 | 41 | 本书默认使用CPU训练，若是要使用GPU训练，使用步骤会稍有变化。为了保证GPU驱动能够在镜像里面正常运行，我们推荐使用[nvidia-docker](https://github.com/NVIDIA/nvidia-docker)来运行镜像。请先安装nvidia-docker，之后请运行： 42 | 43 | ```bash 44 | nvidia-docker run -d -p 8888:8888 paddlepaddle/book:latest-gpu 45 | ``` 46 | 47 | 或者使用国内的镜像请运行： 48 | 49 | ```bash 50 | nvidia-docker run -d -p 8888:8888 hub.baidubce.com/paddlepaddle/book:latest-gpu 51 | ``` 52 | 53 | 还需要将以下代码 54 | ```python 55 | use_cuda = False 56 | ``` 57 | 58 | 改成： 59 | ```python 60 | use_cuda = True 61 | ``` 62 | 63 | 64 | ## 贡献内容 65 | 66 | 您要是能贡献新的章节那就太好了！请发Pull Requests把您写的章节加入到`/pending`下面的一个子目录里。当这一章稳定下来，我们一起把您的目录挪到根目录。 67 | 68 | 为了写作、运行、调试，您需要安装Python 2.x和Go >1.5, 并可以用[脚本程序](https://github.com/PaddlePaddle/book/blob/develop/.tools/convert-markdown-into-ipynb-and-test.sh)来生成新的Docker image。 69 | 70 | **Note:** We also provide [English Readme](https://github.com/PaddlePaddle/book/blob/develop/README.md) for PaddlePaddle book. 71 | 72 | 73 | 知识共享许可协议
本教程 由 PaddlePaddle 创作，采用 知识共享 署名-相同方式共享 4.0 国际 许可协议进行许可。 74 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # Deep Learning with PaddlePaddle 2 | 3 | [![Build Status](https://travis-ci.org/PaddlePaddle/book.svg?branch=develop)](https://travis-ci.org/PaddlePaddle/book) 4 | [![Documentation Status](https://img.shields.io/badge/docs-latest-brightgreen.svg?style=flat)](https://github.com/PaddlePaddle/book/blob/develop/README.md) 5 | [![Documentation Status](https://img.shields.io/badge/中文文档-最新-brightgreen.svg)](https://github.com/PaddlePaddle/book/blob/develop/README.cn.md) 6 | 7 | 1. [Fit a Line](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/beginners_guide/basics/fit_a_line/README.html) 8 | 1. [Recognize Digits](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/beginners_guide/basics/recognize_digits/README.html) 9 | 1. [Image Classification](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/beginners_guide/basics/image_classification/index_en.html) 10 | 1. [Word to Vector](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/beginners_guide/basics/word2vec/index_en.html) 11 | 1. [Recommender System](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/beginners_guide/basics/recommender_system/index_en.html) 12 | 1. [Understand Sentiment](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/beginners_guide/basics/understand_sentiment/index_en.html) 13 | 1. [Label Semantic Roles](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/beginners_guide/basics/label_semantic_roles/index_en.html) 14 | 1. [Machine Translation](https://www.paddlepaddle.org.cn/documentation/docs/en/develop/beginners_guide/basics/machine_translation/index_en.html) 15 | 16 | ## Running the Book 17 | 18 | This book you are reading is interactive -- each chapter can run as a Jupyter Notebook. 19 | 20 | We packed this book, Jupyter, PaddlePaddle, and all dependencies into a Docker image. So you don't need to install anything except Docker. If you are using Windows, please follow [this installation guide](https://www.docker.com/docker-windows). If you are running Mac, please follow [this](https://www.docker.com/docker-mac). For various Linux distros, please refer to https://www.docker.com. If you are using Windows or Mac, you might want to give Docker [more memory and CPUs/cores](http://stackoverflow.com/a/39720010/724872). 21 | 22 | Just type 23 | 24 | ```bash 25 | docker run -d -p 8888:8888 paddlepaddle/book 26 | 27 | ``` 28 | 29 | This command will download the pre-built Docker image from DockerHub.com and run it in a container. Please direct your Web browser to http://localhost:8888 to read the book. 30 | 31 | If you are living in somewhere slow to access DockerHub.com, you might try our mirror server hub.baidubce.com: 32 | 33 | ```bash 34 | docker run -d -p 8888:8888 hub.baidubce.com/paddlepaddle/book 35 | 36 | ``` 37 | 38 | ### Training with GPU 39 | 40 | By default we are using CPU for training, if you want to train with GPU, the steps are a little different. 41 | 42 | To make sure GPU can be successfully used from inside container, please install [nvidia-docker](https://github.com/NVIDIA/nvidia-docker). Then run: 43 | 44 | ```bash 45 | nvidia-docker run -d -p 8888:8888 paddlepaddle/book:latest-gpu 46 | 47 | ``` 48 | 49 | Or you can use the image registry mirror in China: 50 | 51 | ```bash 52 | nvidia-docker run -d -p 8888:8888 hub.baidubce.com/paddlepaddle/book:latest-gpu 53 | 54 | ``` 55 | 56 | Change the code in the chapter that you are reading from 57 | ```python 58 | use_cuda = False 59 | ``` 60 | 61 | to: 62 | ```python 63 | use_cuda = True 64 | ``` 65 | 66 | 67 | ## Contribute 68 | 69 | Your contribution is welcome! Please feel free to file Pull Requests to add your chapter as a directory under `/pending`. Once it is going stable, the community would like to move it to `/`. 70 | 71 | To write, run, and debug your chapters, you will need Python 2.x, Go >1.5. You can build the Docker image using [this script](https://github.com/PaddlePaddle/book/blob/develop/.tools/convert-markdown-into-ipynb-and-test.sh). 72 | This tutorial is contributed by PaddlePaddle, and licensed under a Creative Commons Attribution-ShareAlike 4.0 International License. 73 | -------------------------------------------------------------------------------- /index.cn.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 深度学习入门 6 | 7 | 8 | 9 | 10 | 68 | 69 | 70 | 95 |
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151 | 168 | 169 | -------------------------------------------------------------------------------- /mnist-client/.eslintrc: -------------------------------------------------------------------------------- 1 | { 2 | "rules": { 3 | "indent": [ 4 | 2, 5 | 4 6 | ], 7 | "quotes": [ 8 | 2, 9 | "single" 10 | ], 11 | "linebreak-style": [ 12 | 2, 13 | "unix" 14 | ], 15 | "semi": [ 16 | 2, 17 | "always" 18 | ] 19 | }, 20 | "env": { 21 | "es6": true, 22 | "node": true, 23 | "browser": true 24 | }, 25 | "extends": "eslint:recommended" 26 | } -------------------------------------------------------------------------------- /mnist-client/.gitignore: -------------------------------------------------------------------------------- 1 | venv 2 | *.pyc 3 | node_modules 4 | static/js/main.js 5 | index.html 6 | -------------------------------------------------------------------------------- /mnist-client/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM mhart/alpine-node:6.11.3 2 | 3 | RUN mkdir /workspace 4 | WORKDIR /workspace/ 5 | ADD * /workspace/ 6 | RUN apk add --no-cache python py-pip 7 | RUN pip install -r /workspace/requirements.txt 8 | RUN cd /workspace && npm install && mkdir templates && mv index.html templates && mkdir static && mv js static && mv css static 9 | CMD ["python", "main.py"] 10 | -------------------------------------------------------------------------------- /mnist-client/Procfile: -------------------------------------------------------------------------------- 1 | web: gunicorn main:app --log-file=- 2 | -------------------------------------------------------------------------------- /mnist-client/README.md: -------------------------------------------------------------------------------- 1 | # MNIST classification by PaddlePaddle 2 | 3 | ![screencast](https://cloud.githubusercontent.com/assets/80381/11339453/f04f885e-923c-11e5-8845-33c16978c54d.gif) 4 | 5 | ## Usage 6 | 7 | This MNIST classification demo consists of two parts: a PaddlePaddle 8 | inference server and a Javascript front end. We will start them 9 | separately. 10 | 11 | We will use Docker to run the demo, if you are not familiar with 12 | Docker, please checkout 13 | this 14 | [tutorial](https://github.com/PaddlePaddle/Paddle/wiki/TLDR-for-new-docker-user). 15 | 16 | ### Start the Inference Server 17 | 18 | The inference server can be used to inference any model trained by 19 | PaddlePaddle. Please see [here](../serve/README.md) for more details. 20 | 21 | 1. Download the MNIST inference model topylogy and parameters to the 22 | current working directory. 23 | 24 | ```bash 25 | wget https://s3.us-east-2.amazonaws.com/models.paddlepaddle/end-to-end-mnist/inference_topology.pkl 26 | wget https://s3.us-east-2.amazonaws.com/models.paddlepaddle/end-to-end-mnist/param.tar 27 | ``` 28 | 29 | 1. Run following command to start the inference server: 30 | 31 | ```bash 32 | docker run --name paddle_serve -v `pwd`:/data -d -p 8000:80 -e WITH_GPU=0 paddlepaddle/book:serve 33 | ``` 34 | 35 | The above command will mount the current working directory to the 36 | `/data` directory inside the docker container. The inference 37 | server will load the model topology and parameters that we just 38 | downloaded from there. 39 | 40 | After you are done with the demo, you can run `docker stop 41 | paddle_serve` to stop this docker container. 42 | 43 | ### Start the Front End 44 | 45 | 1. Run the following command 46 | ```bash 47 | docker run -it -p 5000:5000 -e BACKEND_URL=http://localhost:8000/ paddlepaddle/book:mnist 48 | ``` 49 | 50 | `BACKEND_URL` in the above command specifies the inference server 51 | endpoint. If you started the inference server on another machine, 52 | or want to visit the front end remotely, you may want to change its 53 | value. 54 | 55 | 1. Visit http://localhost:5000 and you will see the PaddlePaddle MNIST demo. 56 | 57 | 58 | ## Build 59 | 60 | We have already prepared the pre-built docker image 61 | `paddlepaddle/book:mnist`, here is the command if you want to build 62 | the docker image again. 63 | 64 | ```bash 65 | docker build -t paddlepaddle/book:mnist . 66 | ``` 67 | 68 | 69 | ## Acknowledgement 70 | 71 | Thanks to the great project https://github.com/sugyan/tensorflow-mnist 72 | . Most of the code in this project comes from there. 73 | -------------------------------------------------------------------------------- /mnist-client/app.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "paddlepaddle-mnist", 3 | "buildpacks": [ 4 | { "url": "https://github.com/heroku/heroku-buildpack-nodejs" }, 5 | { "url": "https://github.com/heroku/heroku-buildpack-python" } 6 | ] 7 | } 8 | -------------------------------------------------------------------------------- /mnist-client/gulpfile.js: -------------------------------------------------------------------------------- 1 | var gulp = require('gulp'); 2 | var babel = require('gulp-babel'); 3 | var sourcemaps = require('gulp-sourcemaps'); 4 | var uglify = require('gulp-uglify'); 5 | 6 | gulp.task('build', function() { 7 | return gulp.src('src/js/*.js') 8 | .pipe(babel({ presets: ['es2015'] })) 9 | .pipe(sourcemaps.init({ loadMaps: true })) 10 | .pipe(uglify()) 11 | .pipe(sourcemaps.write()) 12 | .pipe(gulp.dest('static/js')); 13 | }); 14 | 15 | gulp.task('watch', function() { 16 | gulp.watch('src/js/*.js', ['build']); 17 | }); 18 | 19 | gulp.task('default', ['build']); 20 | -------------------------------------------------------------------------------- /mnist-client/main.py: -------------------------------------------------------------------------------- 1 | from flask import Flask, jsonify, render_template, request 2 | import os 3 | 4 | # webapp 5 | app = Flask(__name__) 6 | 7 | 8 | @app.route('/') 9 | def main(): 10 | backend_url = os.getenv('BACKEND_URL', 'http://localhost:8000/') 11 | return render_template('index.html', backend_url=backend_url) 12 | 13 | 14 | if __name__ == '__main__': 15 | app.run(host='0.0.0.0', port=5000, threaded=True) 16 | -------------------------------------------------------------------------------- /mnist-client/package.json: -------------------------------------------------------------------------------- 1 | { 2 | "name": "paddlepaddle-mnist", 3 | "version": "1.0.0", 4 | "description": "", 5 | "main": "index.js", 6 | "scripts": { 7 | "test": "echo \"Error: no test specified\" && exit 1", 8 | "postinstall": "gulp" 9 | }, 10 | "keywords": [], 11 | "author": "", 12 | "license": "ISC", 13 | "repository": { 14 | "type": "git", 15 | "url": "https://github.com/sugyan/tensorflow-mnist.git" 16 | }, 17 | "engines": { 18 | "node": "6.x" 19 | }, 20 | "dependencies": { 21 | "babel-preset-es2015": "^6.1.18", 22 | "bootstrap": "^3.3.5", 23 | "gulp": "^3.9.0", 24 | "gulp-babel": "^6.1.0", 25 | "gulp-sourcemaps": "^1.6.0", 26 | "gulp-uglify": "^1.5.1", 27 | "jquery": "^3.0.0" 28 | } 29 | } 30 | -------------------------------------------------------------------------------- /mnist-client/requirements.txt: -------------------------------------------------------------------------------- 1 | flask>=1.0.0 2 | -------------------------------------------------------------------------------- /mnist-client/runtime.txt: -------------------------------------------------------------------------------- 1 | python-3.6.0 -------------------------------------------------------------------------------- /mnist-client/src/js/main.js: -------------------------------------------------------------------------------- 1 | /* global $ */ 2 | class Main { 3 | constructor() { 4 | this.canvas = document.getElementById('main'); 5 | this.input = document.getElementById('input'); 6 | this.canvas.width = 449; // 16 * 28 + 1 7 | this.canvas.height = 449; // 16 * 28 + 1 8 | this.ctx = this.canvas.getContext('2d'); 9 | this.canvas.addEventListener('mousedown', this.onMouseDown.bind(this)); 10 | this.canvas.addEventListener('mouseup', this.onMouseUp.bind(this)); 11 | this.canvas.addEventListener('mousemove', this.onMouseMove.bind(this)); 12 | this.initialize(); 13 | } 14 | initialize() { 15 | this.ctx.fillStyle = '#FFFFFF'; 16 | this.ctx.fillRect(0, 0, 449, 449); 17 | this.ctx.lineWidth = 1; 18 | this.ctx.strokeRect(0, 0, 449, 449); 19 | this.ctx.lineWidth = 0.05; 20 | for (var i = 0; i < 27; i++) { 21 | this.ctx.beginPath(); 22 | this.ctx.moveTo((i + 1) * 16, 0); 23 | this.ctx.lineTo((i + 1) * 16, 449); 24 | this.ctx.closePath(); 25 | this.ctx.stroke(); 26 | 27 | this.ctx.beginPath(); 28 | this.ctx.moveTo( 0, (i + 1) * 16); 29 | this.ctx.lineTo(449, (i + 1) * 16); 30 | this.ctx.closePath(); 31 | this.ctx.stroke(); 32 | } 33 | this.drawInput(); 34 | $('#output td').text('').removeClass('success'); 35 | } 36 | onMouseDown(e) { 37 | this.canvas.style.cursor = 'default'; 38 | this.drawing = true; 39 | this.prev = this.getPosition(e.clientX, e.clientY); 40 | } 41 | onMouseUp() { 42 | this.drawing = false; 43 | this.drawInput(); 44 | } 45 | onMouseMove(e) { 46 | if (this.drawing) { 47 | var curr = this.getPosition(e.clientX, e.clientY); 48 | this.ctx.lineWidth = 16; 49 | this.ctx.lineCap = 'round'; 50 | this.ctx.beginPath(); 51 | this.ctx.moveTo(this.prev.x, this.prev.y); 52 | this.ctx.lineTo(curr.x, curr.y); 53 | this.ctx.stroke(); 54 | this.ctx.closePath(); 55 | this.prev = curr; 56 | } 57 | } 58 | getPosition(clientX, clientY) { 59 | var rect = this.canvas.getBoundingClientRect(); 60 | return { 61 | x: clientX - rect.left, 62 | y: clientY - rect.top 63 | }; 64 | } 65 | drawInput() { 66 | var ctx = this.input.getContext('2d'); 67 | var img = new Image(); 68 | img.onload = () => { 69 | var inputs = []; 70 | var small = document.createElement('canvas').getContext('2d'); 71 | small.drawImage(img, 0, 0, img.width, img.height, 0, 0, 28, 28); 72 | var data = small.getImageData(0, 0, 28, 28).data; 73 | for (var i = 0; i < 28; i++) { 74 | for (var j = 0; j < 28; j++) { 75 | var n = 4 * (i * 28 + j); 76 | inputs[i * 28 + j] = (data[n + 0] + data[n + 1] + data[n + 2]) / 3; 77 | ctx.fillStyle = 'rgb(' + [data[n + 0], data[n + 1], data[n + 2]].join(',') + ')'; 78 | ctx.fillRect(j * 5, i * 5, 5, 5); 79 | } 80 | } 81 | if (Math.min(...inputs) === 255) { 82 | return; 83 | } 84 | for (var i = 0; i < 784; i++) { 85 | if (inputs[i] == 255) { 86 | // background 87 | inputs[i] = -1.0 88 | } else { 89 | inputs[i] = 1.0 90 | } 91 | } 92 | $.ajax({ 93 | url: BACKEND_URL, 94 | method: 'POST', 95 | contentType: 'application/json', 96 | data: JSON.stringify({"img":inputs}), 97 | success: (data) => { 98 | data = data["data"][0] 99 | var max = 0; 100 | var max_index = 0; 101 | for (let j = 0; j < 10; j++) { 102 | var value = Math.round(data[j] * 1000); 103 | if (value > max) { 104 | max = value; 105 | max_index = j; 106 | } 107 | var digits = String(value).length; 108 | for (var k = 0; k < 3 - digits; k++) { 109 | value = '0' + value; 110 | } 111 | var text = '0.' + value; 112 | if (value > 999) { 113 | text = '1.000'; 114 | } 115 | $('#output tr').eq(j + 1).find('td').text(text); 116 | } 117 | for (let j = 0; j < 10; j++) { 118 | if (j === max_index) { 119 | $('#output tr').eq(j + 1).find('td').addClass('success'); 120 | } else { 121 | $('#output tr').eq(j + 1).find('td').removeClass('success'); 122 | } 123 | } 124 | } 125 | }); 126 | }; 127 | img.src = this.canvas.toDataURL(); 128 | } 129 | } 130 | 131 | $(() => { 132 | var main = new Main(); 133 | $('#clear').click(() => { 134 | main.initialize(); 135 | }); 136 | }); 137 | -------------------------------------------------------------------------------- /mnist-client/static/css/bootstrap.min.css: -------------------------------------------------------------------------------- 1 | ../../node_modules/bootstrap/dist/css/bootstrap.min.css -------------------------------------------------------------------------------- /mnist-client/static/js/jquery.min.js: -------------------------------------------------------------------------------- 1 | ../../node_modules/jquery/dist/jquery.min.js -------------------------------------------------------------------------------- /mnist-client/templates/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | MNIST 5 | 6 | 7 | 10 | 11 | 12 | 13 | Fork me on GitHub 14 |
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78 | 79 | 80 | -------------------------------------------------------------------------------- /paddle2.0_docs/README.md: -------------------------------------------------------------------------------- 1 | # 基于Paddle2.0的示例教程 2 | 3 | 本目录存放进展中的Paddle2.0版本的文档、教程和示例。目前处于work in progress状态，待2.0正式版发布之后会做相应的调整，并最终完成。 4 | 5 | - 用中文来写。 6 | - 当中的示例代码基于paddle2.0-alpha，随着paddle2.0的研发的进展而调整。 7 | - 整体上用Notebook来完成，每一篇教程可以解决一个具体的示例任务，这样用户可以直接运行(未来考虑与AIStudio联动) 8 | - 只用来向用户说明飞桨框架的使用，不涵盖深度学习的理论知识。(但可以通过链接，提供其他的优质的深度学习理论资料) 9 | 10 | 可以用来参考和借鉴的资料 11 | 12 | - 本repo下的，现存的教程和示例。 13 | - 飞桨官网上的系统性的教程: https://www.paddlepaddle.org.cn/tutorials/projectdetail/590324 14 | - 其他的深度学习框架的教程资料。 15 | -------------------------------------------------------------------------------- /paddle2.0_docs/dcgan_face/images/face_image1.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/paddle2.0_docs/dcgan_face/images/face_image1.jpeg -------------------------------------------------------------------------------- /paddle2.0_docs/dcgan_face/images/face_image2.jpeg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/paddle2.0_docs/dcgan_face/images/face_image2.jpeg -------------------------------------------------------------------------------- /paddle2.0_docs/dcgan_face/images/loss.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/paddle2.0_docs/dcgan_face/images/loss.png -------------------------------------------------------------------------------- /paddle2.0_docs/dcgan_face/images/models.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/paddle2.0_docs/dcgan_face/images/models.png -------------------------------------------------------------------------------- /paddle2.0_docs/image_ocr/images/image1.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/paddle2.0_docs/image_ocr/images/image1.png -------------------------------------------------------------------------------- /paddle2.0_docs/image_ocr/images/image2.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/paddle2.0_docs/image_ocr/images/image2.png -------------------------------------------------------------------------------- /paddle2.0_docs/image_ocr/images/image3.png: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/paddle2.0_docs/image_ocr/images/image3.png -------------------------------------------------------------------------------- /paddle2.0_docs/image_ocr/sample_img/9450.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/paddle2.0_docs/image_ocr/sample_img/9450.jpg -------------------------------------------------------------------------------- /paddle2.0_docs/image_ocr/sample_img/9451.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/paddle2.0_docs/image_ocr/sample_img/9451.jpg -------------------------------------------------------------------------------- /paddle2.0_docs/image_ocr/sample_img/9452.jpg: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/PaddlePaddle/book/ffaa2d315f1009887ffce26df961f943ba1ffc3f/paddle2.0_docs/image_ocr/sample_img/9452.jpg -------------------------------------------------------------------------------- /pending/gan/README.md: -------------------------------------------------------------------------------- 1 | TODO: Write about https://github.com/PaddlePaddle/Paddle/tree/develop/demo/gan 2 | -------------------------------------------------------------------------------- /pending/gan/index.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 16 | 17 | 19 | 20 | 21 | 22 | 23 | 24 | 25 | 34 | 35 | 36 | 37 | 38 |
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40 | 41 | 42 | 45 | 46 | 47 | 64 | 65 | -------------------------------------------------------------------------------- /serve/.gitignore: -------------------------------------------------------------------------------- 1 | *~ 2 | .idea 3 | index.html 4 | -------------------------------------------------------------------------------- /serve/Dockerfile: -------------------------------------------------------------------------------- 1 | FROM paddlepaddle/paddle 2 | 3 | ENV PARAMETER_TAR_PATH=/data/param.tar \ 4 | TOPOLOGY_FILE_PATH=/data/inference_topology.pkl 5 | ADD requirements.txt /root 6 | ADD main.py /root 7 | RUN pip install -r /root/requirements.txt 8 | CMD ["python", "/root/main.py"] 9 | -------------------------------------------------------------------------------- /serve/Dockerfile.gpu: -------------------------------------------------------------------------------- 1 | FROM paddlepaddle/paddle:latest-gpu 2 | 3 | ENV PARAMETER_TAR_PATH=/data/param.tar \ 4 | TOPOLOGY_FILE_PATH=/data/inference_topology.pkl 5 | ADD requirements.txt /root 6 | ADD main.py /root 7 | RUN pip install -r /root/requirements.txt 8 | CMD ["python", "/root/main.py"] 9 | -------------------------------------------------------------------------------- /serve/README.md: -------------------------------------------------------------------------------- 1 | # Inference Server Example 2 | 3 | The inference server can be used to perform inference on any model trained on 4 | PaddlePaddle. It provides an HTTP endpoint. 5 | 6 | ## Run 7 | 8 | The inference server reads a trained model (a topology file and a 9 | parameter file) and serves HTTP request at port `8000`. Because models 10 | differ in the numbers and types of inputs, **the HTTP API will differ 11 | slightly for each model,** please see [HTTP API](#http-api) for the 12 | API spec, 13 | and 14 | [here](https://github.com/PaddlePaddle/book/wiki/Using-Pre-trained-Models) for 15 | the request examples of different models that illustrate the 16 | difference. 17 | 18 | We will first show how to obtain the PaddlePaddle model, and then how 19 | to start the server. 20 | 21 | We will use Docker to run the demo, if you are not familiar with 22 | Docker, please checkout 23 | this 24 | [TLDR](https://github.com/PaddlePaddle/Paddle/wiki/Docker-for-Beginners). 25 | 26 | ### Obtain the PaddlePaddle Model 27 | 28 | A neural network model in PaddlePaddle contains two parts: the 29 | **parameter** and the **topology**. 30 | 31 | A PaddlePaddle training script contains the neural network topology, 32 | which is represented by layers. For example, 33 | 34 | ```python 35 | img = paddle.layer.data(name="img", type=paddle.data_type.dense_vector(784)) 36 | hidden = fc_layer(input=type, size=200) 37 | prediction = fc_layer(input=hidden, size=10, act=paddle.activation.Softmax()) 38 | ``` 39 | 40 | The parameter instance is created by the topology and updated by the 41 | `train` method. 42 | 43 | ```python 44 | ... 45 | params = paddle.parameters.create(cost) 46 | ... 47 | trainer = paddle.trainer.SGD(cost=cost, parameters=params) 48 | ... 49 | ``` 50 | 51 | PaddlePaddle stores the topology and parameter separately. 52 | 53 | 1. To serialize a topology, we need to create a topology instance 54 | explicitly by the outputs of the neural network. Then, invoke 55 | `serialize_for_inference` method. 56 | 57 | ```python 58 | # Save the inference topology to protobuf. 59 | inference_topology = paddle.topology.Topology(layers=prediction) 60 | with open("inference_topology.pkl", 'wb') as f: 61 | inference_topology.serialize_for_inference(f) 62 | ``` 63 | 64 | 2. To save a parameter, we need to invoke `save_parameter_to_tar` method of 65 | `trainer`. 66 | 67 | ```python 68 | with open('param.tar', 'w') as f: 69 | trainer.save_parameter_to_tar(f) 70 | ``` 71 | 72 | After serializing the parameter and topology into two files, we could 73 | use them to set up an inference server. 74 | 75 | For a working example, please see [train.py](https://github.com/reyoung/paddle_mnist_v2_demo/blob/master/train.py). 76 | 77 | 78 | ### Start the Server 79 | 80 | Make sure the `inference_topology.pkl` and `param.tar` mentioned in 81 | the last section are in your current working directory, and run the 82 | command: 83 | 84 | ```bash 85 | docker run --name paddle_serve -v `pwd`:/data -d -p 8000:80 -e WITH_GPU=0 paddlepaddle/book:serve 86 | ``` 87 | 88 | The above command will mount the current working directory to the 89 | `/data/` directory inside the docker container. The inference server 90 | will load the model topology and parameters that we just created from 91 | there. 92 | 93 | To run the inference server with GPU support, please make sure you have 94 | [nvidia-docker](https://github.com/NVIDIA/nvidia-docker) 95 | first, and run: 96 | 97 | ```bash 98 | nvidia-docker run --name paddle_serve -v `pwd`:/data -d -p 8000:80 -e WITH_GPU=1 paddlepaddle/book:serve-gpu 99 | ``` 100 | 101 | this command will start a server on port `8000`. 102 | 103 | After you are done with the demo, you can run `docker stop 104 | paddle_serve` to stop this docker container. 105 | 106 | ## HTTP API 107 | 108 | The inference server will handle HTTP POST request on path `/`. The 109 | content type of the request and response is json. You need to manually 110 | add `Content-Type` request header as `Content-Type: application/json`. 111 | 112 | The request json object is a single json dictionay object, whose key 113 | is the layer name of input data. The type of the corresponding value 114 | is decided by the data type. For most cases the corresponding value 115 | will be a list of floats. For completeness, we will list all data types 116 | below: 117 | 118 | There are twelve data types supported by PaddePaddle: 119 | 120 | | | plain | a sequence | a sequence of sequence | 121 | | --- | --- | --- | ---| 122 | | dense | [ f, f, f, f, ... ] | [ [f, f, f, ...], [f, f, f, ...]] | [[[f, f, ...], [f, f, ...]], [[f, f, ...], [f, f, ...]], ...] | 123 | | integer | i | [i, i, ...] | [[i, i, ...], [i, i, ...], ...] | 124 | | sparse | [i, i, ...] | [[i, i, ...], [i, i, ...], ...] | [[[i, i, ...], [i, i, ...], ...], [[i, i, ...], [i, i, ...], ...], ...] | 125 | | sparse | [[i, f], [i, f], ... ] | [[[i, f], [i, f], ... ], ...] | [[[[i, f], [i, f], ... ], ...], ...] 126 | 127 | In the table, `i` stands for a `int` value and `f` stands for a 128 | `float` value. 129 | 130 | What `data_type` should be used is decided by the training 131 | topology. For example, 132 | 133 | * For image data, they are usually a plain dense vector, we flatten 134 | the image into a vector. The pixel values of that image are usually 135 | normalized in `[-1.0, 1.0]` or `[0.0, 1.0]`(depends on each neural 136 | network). 137 | 138 | ```text 139 | +-------+ 140 | |243 241| 141 | |139 211| +---->[0.95, 0.95, 0.54, 0.82] 142 | +-------+ 143 | ``` 144 | 145 | * For text data, each word of that text is represented by an 146 | integer. The association map between word and integer is decided by 147 | the training process. A sentence is represented by a list of 148 | integer. 149 | 150 | ```text 151 | I am good . 152 | + 153 | | 154 | v 155 | 23 942 402 19 +-----> [23, 942, 402, 19] 156 | ``` 157 | 158 | A sample request data of a `4x4` image and a sentence could be 159 | 160 | ```json 161 | { 162 | "img": [ 163 | 0.95, 164 | 0.95, 165 | 0.54, 166 | 0.82 167 | ], 168 | "sentence": [ 169 | 23, 170 | 942, 171 | 402, 172 | 19 173 | ] 174 | } 175 | ``` 176 | 177 | The response is a json object, too. The example of return data are: 178 | 179 | ```json 180 | { 181 | "code": 0, 182 | "data": [ 183 | [ 184 | 0.10060056298971176, 185 | 0.057179879397153854, 186 | 0.1453431099653244, 187 | 0.15825574100017548, 188 | 0.04464773088693619, 189 | 0.1566203236579895, 190 | 0.05657859891653061, 191 | 0.12077419459819794, 192 | 0.08073269575834274, 193 | 0.07926714420318604 194 | ] 195 | ], 196 | "message": "success" 197 | } 198 | ``` 199 | 200 | Here, `code` and `message` represent the status of the request. 201 | `data` corresponds to the outputs of the neural network; they could be a 202 | probability of each class, could be the IDs of output sentence, and so 203 | on. 204 | 205 | ## MNIST Demo Client 206 | 207 | If you have trained an model with [train.py](https://github.com/reyoung/paddle_mnist_v2_demo/blob/master/train.py) and 208 | start a inference server. Then you can use this [client](https://github.com/PaddlePaddle/book/tree/develop/02.recognize_digits/client/client.py) to test if it works right. 209 | 210 | ## Build 211 | 212 | We have already prepared the pre-built docker image 213 | `paddlepaddle/book:serve`, here is the command if you want to build 214 | the docker image again. 215 | 216 | ```bash 217 | docker build -t paddlepaddle/book:serve . 218 | docker build -t paddlepaddle/book:serve-gpu -f Dockerfile.gpu . 219 | ``` 220 | -------------------------------------------------------------------------------- /serve/main.py: -------------------------------------------------------------------------------- 1 | import os 2 | import traceback 3 | 4 | import paddle as paddle 5 | from flask import Flask, jsonify, request 6 | from flask_cors import CORS 7 | from Queue import Queue 8 | import threading 9 | 10 | tarfn = os.getenv('PARAMETER_TAR_PATH', None) 11 | 12 | if tarfn is None: 13 | raise ValueError( 14 | "please specify parameter tar file path with environment variable PARAMETER_TAR_PATH" 15 | ) 16 | 17 | topology_filepath = os.getenv('TOPOLOGY_FILE_PATH', None) 18 | 19 | if topology_filepath is None: 20 | raise ValueError( 21 | "please specify topology file path with environment variable TOPOLOGY_FILE_PATH" 22 | ) 23 | 24 | with_gpu = os.getenv('WITH_GPU', '0') != '0' 25 | output_field = os.getenv('OUTPUT_FIELD', 'value') 26 | port = int(os.getenv('PORT', '80')) 27 | 28 | app = Flask(__name__) 29 | CORS(app) 30 | 31 | 32 | def errorResp(msg): 33 | return jsonify(code=-1, message=msg) 34 | 35 | 36 | def successResp(data): 37 | return jsonify(code=0, message="success", data=data) 38 | 39 | 40 | sendQ = Queue() 41 | 42 | 43 | @app.route('/', methods=['POST']) 44 | def infer(): 45 | recv_queue = Queue() 46 | sendQ.put((request.json, recv_queue)) 47 | success, resp = recv_queue.get() 48 | if success: 49 | return successResp(resp) 50 | else: 51 | return errorResp(resp) 52 | 53 | 54 | # PaddlePaddle v0.10.0 does not support inference from different 55 | # threads, so we create a single worker thread. 56 | def worker(): 57 | paddle.init(use_gpu=with_gpu) 58 | 59 | fields = filter(lambda x: len(x) != 0, output_field.split(",")) 60 | 61 | with open(tarfn) as param_f, open(topology_filepath) as topo_f: 62 | params = paddle.parameters.Parameters.from_tar(param_f) 63 | inferer = paddle.inference.Inference(parameters=params, fileobj=topo_f) 64 | 65 | while True: 66 | j, recv_queue = sendQ.get() 67 | try: 68 | feeding = {} 69 | d = [] 70 | for i, key in enumerate(j): 71 | d.append(j[key]) 72 | feeding[key] = i 73 | r = inferer.infer([d], feeding=feeding, field=fields) 74 | except: 75 | trace = traceback.format_exc() 76 | recv_queue.put((False, trace)) 77 | continue 78 | if isinstance(r, list): 79 | recv_queue.put((True, [elem.tolist() for elem in r])) 80 | else: 81 | recv_queue.put((True, r.tolist())) 82 | 83 | 84 | if __name__ == '__main__': 85 | t = threading.Thread(target=worker) 86 | t.daemon = True 87 | t.start() 88 | print 'serving on port', port 89 | app.run(host='0.0.0.0', port=port, threaded=True) 90 | -------------------------------------------------------------------------------- /serve/requirements.txt: -------------------------------------------------------------------------------- 1 | flask>=1.0.0 2 | Flask-CORS==3.0.3 3 | --------------------------------------------------------------------------------