├── saved_models ├── .gitignore ├── weights.best.VGG16.hdf5 ├── weights.best.Resnet50.hdf5 └── weights.best.from_scratch.hdf5 ├── .gitignore ├── bottleneck_features └── .gitignore ├── images ├── sample_cnn.png ├── Brittany_02625.jpg ├── sample_dog_output.png ├── sample_human_output.png ├── Labrador_retriever_06449.jpg ├── Labrador_retriever_06455.jpg ├── Labrador_retriever_06457.jpg ├── American_water_spaniel_00648.jpg ├── Curly-coated_retriever_03896.jpg └── Welsh_springer_spaniel_08203.jpg ├── predict_images ├── 001_husky.jpg ├── 004_samo_dog.jpg ├── 005_black_people.jpg ├── 006_white_people.jpg ├── 002_tibetan_mastiff.jpg └── 003_german_shepherd.jpg ├── __pycache__ └── extract_bottleneck_features.cpython-36.pyc ├── CODEOWNERS ├── requirements ├── requirements.txt ├── requirements-gpu.txt ├── dog-linux.yml ├── dog-linux-gpu.yml ├── dog-windows-gpu.yml ├── dog-windows.yml ├── dog-mac.yml └── dog-mac-gpu.yml ├── extract_bottleneck_features.py ├── README.md └── tabelle1.txt 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-------------------------------------------------------------------------------- https://raw.githubusercontent.com/rongmax-gufei/udacity-dlnd-cn-deep-learning/HEAD/__pycache__/extract_bottleneck_features.cpython-36.pyc -------------------------------------------------------------------------------- /CODEOWNERS: -------------------------------------------------------------------------------- 1 | s is a comment. 2 | # Each line is a file pattern followed by one or more owners. 3 | 4 | # These owners will be the default owners for everything in 5 | # the repo. 6 | * @cgearhart @luisguiserrano 7 | 8 | -------------------------------------------------------------------------------- /requirements/requirements.txt: -------------------------------------------------------------------------------- 1 | opencv-python==3.2.0.6 2 | h5py==2.6.0 3 | matplotlib==2.0.0 4 | numpy==1.12.0 5 | scipy==0.18.1 6 | tqdm==4.11.2 7 | keras==2.0.2 8 | scikit-learn==0.18.1 9 | pillow==4.0.0 10 | ipykernel==4.6.1 11 | tensorflow==1.0.0 -------------------------------------------------------------------------------- /requirements/requirements-gpu.txt: -------------------------------------------------------------------------------- 1 | opencv-python==3.2.0.6 2 | h5py==2.6.0 3 | matplotlib==2.0.0 4 | numpy==1.12.0 5 | scipy==0.18.1 6 | tqdm==4.11.2 7 | keras==2.0.2 8 | scikit-learn==0.18.1 9 | pillow==4.0.0 10 | ipykernel==4.6.1 11 | tensorflow-gpu==1.0.0 -------------------------------------------------------------------------------- /extract_bottleneck_features.py: -------------------------------------------------------------------------------- 1 | def extract_VGG16(tensor): 2 | from keras.applications.vgg16 import VGG16, preprocess_input 3 | return VGG16(weights='imagenet', include_top=False).predict(preprocess_input(tensor)) 4 | 5 | def extract_VGG19(tensor): 6 | from keras.applications.vgg19 import VGG19, preprocess_input 7 | return VGG19(weights='imagenet', include_top=False).predict(preprocess_input(tensor)) 8 | 9 | def extract_Resnet50(tensor): 10 | from keras.applications.resnet50 import ResNet50, preprocess_input 11 | return ResNet50(weights='imagenet', include_top=False).predict(preprocess_input(tensor)) 12 | 13 | def extract_Xception(tensor): 14 | from keras.applications.xception import Xception, preprocess_input 15 | return Xception(weights='imagenet', include_top=False).predict(preprocess_input(tensor)) 16 | 17 | def extract_InceptionV3(tensor): 18 | from keras.applications.inception_v3 import InceptionV3, preprocess_input 19 | return InceptionV3(weights='imagenet', include_top=False).predict(preprocess_input(tensor)) -------------------------------------------------------------------------------- /requirements/dog-linux.yml: -------------------------------------------------------------------------------- 1 | name: dog-project 2 | channels: 3 | - defaults 4 | dependencies: 5 | - openssl=1.0.2l=0 6 | - pip=9.0.1=py36_1 7 | - python=3.6.1=2 8 | - readline=6.2=2 9 | - setuptools=27.2.0=py36_0 10 | - sqlite=3.13.0=0 11 | - tk=8.5.18=0 12 | - wheel=0.29.0=py36_0 13 | - xz=5.2.2=1 14 | - zlib=1.2.8=3 15 | - pip: 16 | - bleach==2.0.0 17 | - cycler==0.10.0 18 | - decorator==4.0.11 19 | - entrypoints==0.2.3 20 | - h5py==2.6.0 21 | - html5lib==0.999999999 22 | - ipykernel==4.6.1 23 | - ipython==6.1.0 24 | - ipython-genutils==0.2.0 25 | - ipywidgets==6.0.0 26 | - jedi==0.10.2 27 | - jinja2==2.9.6 28 | - jsonschema==2.6.0 29 | - jupyter==1.0.0 30 | - jupyter-client==5.0.1 31 | - jupyter-console==5.1.0 32 | - jupyter-core==4.3.0 33 | - keras==2.0.2 34 | - markupsafe==1.0 35 | - matplotlib==2.0.0 36 | - mistune==0.7.4 37 | - nbconvert==5.2.1 38 | - nbformat==4.3.0 39 | - notebook==5.0.0 40 | - numpy==1.12.0 41 | - olefile==0.44 42 | - opencv-python==3.2.0.6 43 | - pandocfilters==1.4.1 44 | - pexpect==4.2.1 45 | - pickleshare==0.7.4 46 | - pillow==4.0.0 47 | - prompt-toolkit==1.0.14 48 | - protobuf==3.3.0 49 | - ptyprocess==0.5.1 50 | - pygments==2.2.0 51 | - pyparsing==2.2.0 52 | - python-dateutil==2.6.0 53 | - pytz==2017.2 54 | - pyyaml==3.12 55 | - pyzmq==16.0.2 56 | - qtconsole==4.3.0 57 | - scikit-learn==0.18.1 58 | - scipy==0.18.1 59 | - simplegeneric==0.8.1 60 | - six==1.10.0 61 | - tensorflow==1.0.0 62 | - terminado==0.6 63 | - testpath==0.3.1 64 | - theano==0.9.0 65 | - tornado==4.5.1 66 | - tqdm==4.11.2 67 | - traitlets==4.3.2 68 | - wcwidth==0.1.7 69 | - webencodings==0.5.1 70 | - widgetsnbextension==2.0.0 71 | -------------------------------------------------------------------------------- /requirements/dog-linux-gpu.yml: -------------------------------------------------------------------------------- 1 | name: dog-project 2 | channels: 3 | - defaults 4 | dependencies: 5 | - openssl=1.0.2l=0 6 | - pip=9.0.1=py36_1 7 | - python=3.6.1=2 8 | - readline=6.2=2 9 | - setuptools=27.2.0=py36_0 10 | - sqlite=3.13.0=0 11 | - tk=8.5.18=0 12 | - wheel=0.29.0=py36_0 13 | - xz=5.2.2=1 14 | - zlib=1.2.8=3 15 | - pip: 16 | - bleach==2.0.0 17 | - cycler==0.10.0 18 | - decorator==4.0.11 19 | - entrypoints==0.2.3 20 | - h5py==2.6.0 21 | - html5lib==0.999999999 22 | - ipykernel==4.6.1 23 | - ipython==6.1.0 24 | - ipython-genutils==0.2.0 25 | - ipywidgets==6.0.0 26 | - jedi==0.10.2 27 | - jinja2==2.9.6 28 | - jsonschema==2.6.0 29 | - jupyter==1.0.0 30 | - jupyter-client==5.0.1 31 | - jupyter-console==5.1.0 32 | - jupyter-core==4.3.0 33 | - keras==2.0.2 34 | - markupsafe==1.0 35 | - matplotlib==2.0.0 36 | - mistune==0.7.4 37 | - nbconvert==5.2.1 38 | - nbformat==4.3.0 39 | - notebook==5.0.0 40 | - numpy==1.12.0 41 | - olefile==0.44 42 | - opencv-python==3.2.0.6 43 | - pandocfilters==1.4.1 44 | - pexpect==4.2.1 45 | - pickleshare==0.7.4 46 | - pillow==4.0.0 47 | - prompt-toolkit==1.0.14 48 | - protobuf==3.3.0 49 | - ptyprocess==0.5.1 50 | - pygments==2.2.0 51 | - pyparsing==2.2.0 52 | - python-dateutil==2.6.0 53 | - pytz==2017.2 54 | - pyyaml==3.12 55 | - pyzmq==16.0.2 56 | - qtconsole==4.3.0 57 | - scikit-learn==0.18.1 58 | - scipy==0.18.1 59 | - simplegeneric==0.8.1 60 | - six==1.10.0 61 | - tensorflow-gpu==1.0.0 62 | - terminado==0.6 63 | - testpath==0.3.1 64 | - theano==0.9.0 65 | - tornado==4.5.1 66 | - tqdm==4.11.2 67 | - traitlets==4.3.2 68 | - wcwidth==0.1.7 69 | - webencodings==0.5.1 70 | - widgetsnbextension==2.0.0 71 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | - 广告：Udacity课程优惠券：邀请码: 67D6DA2E，立减 300 元 2 | 3 | [//]: # (Image References) 4 | 5 | [image1]: ./images/sample_dog_output.png "Sample Output" 6 | [image2]: ./images/vgg16_model.png "VGG-16 Model Keras Layers" 7 | [image3]: ./images/vgg16_model_draw.png "VGG16 Model Figure" 8 | 9 | 10 | ## 项目概述 11 | 12 | 欢迎来到卷积神经网络（CNN）项目！在这一项目中，你将学到如何建立一个处理现实生活中的，用户提供的图像的算法。给你一个狗的图像，你的算法将会识别并估计狗的品种，如果提供的图像是人，代码将会识别最相近的狗的品种。 13 | 14 | ![Sample Output][image1] 15 | 16 | 在学习用于分类的最先进的 CNN 模型的同时，你将会为用户体验做出重要的设计与决定。我们的目标是，当你完成这一项目时，你将可以理解，通过将一系列模型拼接在一起，设计数据处理管道完成各式各样的任务所面临的挑战。每个模型都有它的优点与缺点，并且设计实际应用时，经常会面对解决许多没有最优解的问题。尽管你的解答不是最优的，但你的设计将带来愉快的用户体验！ 17 | 18 | 19 | ## 项目指南 20 | 21 | ### 步骤 22 | 23 | 1. 克隆存储库并打开下载的文件夹。 24 | 25 | ``` 26 | git clone https://github.com/udacity/cn-deep-learning.git 27 | cd cn-deep-learning/dog-project 28 | ``` 29 | 30 | 2. 下载[狗狗数据集](https://s3.cn-north-1.amazonaws.com.cn/static-documents/nd101/v4-dataset/dogImages.zip) ，并将数据集解压大存储库中，地点为`项目路径/dogImages`. 31 | 32 | 3. 下载[人类数据集](https://s3.cn-north-1.amazonaws.com.cn/static-documents/nd101/v4-dataset/lfw.zip)。并将数据集解压大存储库中，位置为`项目路径/lfw `。 33 | 34 | 4. 为狗狗数据集下载 [VGG-16关键特征](https://s3.cn-north-1.amazonaws.com.cn/static-documents/nd101/v4-dataset/DogVGG16Data.npz) 并将其放置于存储库中，位置为`项目路径/bottleneck_features `。 35 | 36 | 5. 安装必要的 Python 依赖包 37 | 38 | 39 | 对于 __Mac/OSX__： 40 | 41 | ```bash 42 | conda env create -f requirements/dog-mac.yml 43 | source activate dog-project 44 | KERAS_BACKEND=tensorflow python -c "from keras import backend" 45 | ``` 46 | 47 | 对于 __Windows__： 48 | 49 | ```bash 50 | conda env create -f requirements/dog-windows.yml 51 | activate dog-project 52 | set KERAS_BACKEND=tensorflow 53 | python -c "from keras import backend" 54 | ``` 55 | 6. 打开 notebook 56 | 57 | ``` 58 | jupyter notebook dog_app.ipynb 59 | ``` 60 | 61 | __注意：__ 我们虽然已经实现了一些代码，让你更快地开始工作，你仍需要实现额外的功能，以回答 notebook 中所有的问题。 62 | __除非有要求，否则不要修改任何已经包含的代码。__ 63 | 64 | ## 项目评审 65 | 66 | 你的项目将会由优达学城的审阅者依据次项目的[评审标准](https://review.udacity.com/#!/rubrics/1080/view)进行审阅。请仔细阅读，并在提交之前自我评估你的项目。你必须通过了规则中的所有要求，才会审核通过。 67 | 68 | ## 项目提交 69 | 70 | 当你准备好提交你的项目时，将下列文件整理并压缩成一个文件，以便上传。 71 | 72 | - 代码完整可运行的文件 `dog_app.ipynb`，所有的代码块都要执行并展示结果，并要求回答所有问题 73 | - 将你的 notebook 导出为 HTML 或 PDF 格式，并以 `report.html` 或是 `report.pdf` 命名 74 | - 任何用于项目中，并且并非由我们为这一项目提供的额外数据图片。 75 | __请不要将 `dogImages/` 或是 `lfw/` 文件夹中的项目数据包含在内，同样的，请不要将 `bottleneck_features/` 文件夹包含在内。__ 76 | 77 | 此外，你也可以通过 GitHub 连接提交项目。 78 | -------------------------------------------------------------------------------- /requirements/dog-windows-gpu.yml: -------------------------------------------------------------------------------- 1 | name: dog-project 2 | channels: 3 | - defaults 4 | dependencies: 5 | - _nb_ext_conf=0.3.0=py35_0 6 | - anaconda-client=1.6.2=py35_0 7 | - bleach=1.5.0=py35_0 8 | - bzip2=1.0.6=vc14_3 9 | - clyent=1.2.2=py35_0 10 | - colorama=0.3.7=py35_0 11 | - cycler=0.10.0=py35_0 12 | - decorator=4.0.11=py35_0 13 | - entrypoints=0.2.2=py35_1 14 | - freetype=2.5.5=vc14_2 15 | - h5py=2.7.0=np112py35_0 16 | - hdf5=1.8.15.1=vc14_4 17 | - html5lib=0.999=py35_0 18 | - icu=57.1=vc14_0 19 | - ipykernel=4.5.2=py35_0 20 | - ipython=5.3.0=py35_0 21 | - ipython_genutils=0.1.0=py35_0 22 | - ipywidgets=6.0.0=py35_0 23 | - jinja2=2.9.5=py35_0 24 | - jpeg=9b=vc14_0 25 | - jsonschema=2.5.1=py35_0 26 | - jupyter=1.0.0=py35_3 27 | - jupyter_client=5.0.0=py35_0 28 | - jupyter_console=5.1.0=py35_0 29 | - jupyter_core=4.3.0=py35_0 30 | - libpng=1.6.27=vc14_0 31 | - libtiff=4.0.6=vc14_3 32 | - markupsafe=0.23=py35_2 33 | - matplotlib=2.0.0=np112py35_0 34 | - mistune=0.7.4=py35_0 35 | - mkl=2017.0.1=0 36 | - nb_anacondacloud=1.2.0=py35_0 37 | - nb_conda=2.0.0=py35_0 38 | - nb_conda_kernels=2.0.0=py35_0 39 | - nbconvert=5.1.1=py35_0 40 | - nbformat=4.3.0=py35_0 41 | - nbpresent=3.0.2=py35_0 42 | - notebook=4.4.1=py35_0 43 | - numpy=1.12.1=py35_0 44 | - olefile=0.44=py35_0 45 | - openssl=1.0.2k=vc14_0 46 | - pandocfilters=1.4.1=py35_0 47 | - path.py=10.1=py35_0 48 | - pickleshare=0.7.4=py35_0 49 | - pillow=4.0.0=py35_1 50 | - pip=9.0.1=py35_1 51 | - prompt_toolkit=1.0.13=py35_0 52 | - pygments=2.2.0=py35_0 53 | - pyparsing=2.1.4=py35_0 54 | - pyqt=5.6.0=py35_2 55 | - python=3.5.3=0 56 | - python-dateutil=2.6.0=py35_0 57 | - pytz=2016.10=py35_0 58 | - pyyaml=3.12=py35_0 59 | - pyzmq=16.0.2=py35_0 60 | - qt=5.6.2=vc14_3 61 | - qtconsole=4.2.1=py35_2 62 | - requests=2.13.0=py35_0 63 | - scikit-learn=0.18.1=np112py35_1 64 | - scipy=0.19.0=np112py35_0 65 | - setuptools=27.2.0=py35_1 66 | - simplegeneric=0.8.1=py35_1 67 | - sip=4.18=py35_0 68 | - six=1.10.0=py35_0 69 | - testpath=0.3=py35_0 70 | - tk=8.5.18=vc14_0 71 | - tornado=4.4.2=py35_0 72 | - traitlets=4.3.2=py35_0 73 | - vs2015_runtime=14.0.25123=0 74 | - wcwidth=0.1.7=py35_0 75 | - wheel=0.29.0=py35_0 76 | - widgetsnbextension=2.0.0=py35_0 77 | - win_unicode_console=0.5=py35_0 78 | - zlib=1.2.8=vc14_3 79 | - pip: 80 | - ipython-genutils==0.1.0 81 | - jupyter-client==5.0.0 82 | - jupyter-console==5.1.0 83 | - jupyter-core==4.3.0 84 | - keras==2.0.2 85 | - nb-anacondacloud==1.2.0 86 | - nb-conda==2.0.0 87 | - nb-conda-kernels==2.0.0 88 | - opencv-python==3.1.0.0 89 | - prompt-toolkit==1.0.13 90 | - protobuf==3.2.0 91 | - tensorflow-gpu==1.0.1 92 | - theano==0.9.0 93 | - tqdm==4.11.2 94 | - win-unicode-console==0.5 95 | 96 | 97 | -------------------------------------------------------------------------------- /requirements/dog-windows.yml: -------------------------------------------------------------------------------- 1 | name: dog-project 2 | channels: 3 | - defaults 4 | dependencies: 5 | - _nb_ext_conf=0.3.0=py35_0 6 | - anaconda-client=1.6.2=py35_0 7 | - bleach=1.5.0=py35_0 8 | - bzip2=1.0.6=vc14_3 9 | - clyent=1.2.2=py35_0 10 | - colorama=0.3.7=py35_0 11 | - cycler=0.10.0=py35_0 12 | - decorator=4.0.11=py35_0 13 | - entrypoints=0.2.2=py35_1 14 | - freetype=2.5.5=vc14_2 15 | - h5py=2.7.0=np112py35_0 16 | - hdf5=1.8.15.1=vc14_4 17 | - html5lib=0.999=py35_0 18 | - icu=57.1=vc14_0 19 | - ipykernel=4.5.2=py35_0 20 | - ipython=5.3.0=py35_0 21 | - ipython_genutils=0.1.0=py35_0 22 | - ipywidgets=6.0.0=py35_0 23 | - jinja2=2.9.5=py35_0 24 | - jpeg=9b=vc14_0 25 | - jsonschema=2.5.1=py35_0 26 | - jupyter=1.0.0=py35_3 27 | - jupyter_client=5.0.0=py35_0 28 | - jupyter_console=5.1.0=py35_0 29 | - jupyter_core=4.3.0=py35_0 30 | - libpng=1.6.27=vc14_0 31 | - libtiff=4.0.6=vc14_3 32 | - 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9.090909090909089940e+00 1.126855805078008773e-03 93 | 9.191919191919192045e+00 1.018591896650204720e-03 94 | 9.292929292929292373e+00 9.207295620664066689e-04 95 | 9.393939393939394478e+00 8.322694587016918203e-04 96 | 9.494949494949494806e+00 7.523082568708162107e-04 97 | 9.595959595959595134e+00 6.800294152796305253e-04 98 | 9.696969696969697239e+00 6.146948427351419178e-04 99 | 9.797979797979797567e+00 5.556373609659349781e-04 100 | 9.898989898989899672e+00 5.022538915853797751e-04 101 | 1.000000000000000000e+01 4.539992976248485553e-04 102 | -------------------------------------------------------------------------------- /requirements/dog-mac.yml: -------------------------------------------------------------------------------- 1 | name: dog-project 2 | channels: 3 | - damianavila82 4 | - defaults 5 | dependencies: 6 | - rise=4.0.0b1=py35_0 7 | - _license=1.1=py35_1 8 | - alabaster=0.7.10=py35_0 9 | - anaconda-client=1.6.2=py35_0 10 | - anaconda=custom=py35_0 11 | - 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| - rope=0.9.4=py35_1 151 | - ruamel_yaml=0.11.14=py35_1 152 | - scikit-image=0.12.3=np112py35_1 153 | - scikit-learn=0.18.1=np112py35_1 154 | - scipy=0.19.0=np112py35_0 155 | - seaborn=0.7.1=py35_0 156 | - setuptools=27.2.0=py35_0 157 | - simplegeneric=0.8.1=py35_1 158 | - singledispatch=3.4.0.3=py35_0 159 | - sip=4.18=py35_0 160 | - six=1.10.0=py35_0 161 | - snowballstemmer=1.2.1=py35_0 162 | - sockjs-tornado=1.0.3=py35_0 163 | - sphinx=1.5.1=py35_0 164 | - spyder=3.1.3=py35_0 165 | - sqlalchemy=1.1.6=py35_0 166 | - sqlite=3.13.0=0 167 | - statsmodels=0.8.0=np112py35_0 168 | - sympy=1.0=py35_0 169 | - terminado=0.6=py35_0 170 | - testpath=0.3=py35_0 171 | - tk=8.5.18=0 172 | - toolz=0.8.2=py35_0 173 | - tornado=4.4.2=py35_0 174 | - traitlets=4.3.2=py35_0 175 | - unicodecsv=0.14.1=py35_0 176 | - wcwidth=0.1.7=py35_0 177 | - werkzeug=0.12=py35_0 178 | - wheel=0.29.0=py35_0 179 | - widgetsnbextension=2.0.0=py35_0 180 | - wrapt=1.10.8=py35_0 181 | - xlrd=1.0.0=py35_0 182 | - xlsxwriter=0.9.6=py35_0 183 | - xlwings=0.10.2=py35_0 184 | - xlwt=1.2.0=py35_0 185 | - xz=5.2.2=1 186 | - yaml=0.1.6=0 187 | - zlib=1.2.8=3 188 | - pip: 189 | - backports.shutil-get-terminal-size==1.0.0 190 | - cvxopt==1.1.9 191 | - et-xmlfile==1.0.1 192 | - ipython-genutils==0.1.0 193 | - jupyter-client==5.0.0 194 | - jupyter-console==5.1.0 195 | - jupyter-core==4.3.0 196 | - keras==2.0.0 197 | - opencv-python==3.2.0.6 198 | - prompt-toolkit==1.0.13 199 | - protobuf==3.2.0 200 | - rope-py3k==0.9.4.post1 201 | - tables==3.3.0 202 | - tensorflow==1.0.0 203 | - theano==0.8.2 204 | - tqdm==4.11.2 -------------------------------------------------------------------------------- /requirements/dog-mac-gpu.yml: -------------------------------------------------------------------------------- 1 | name: dog-project 2 | channels: 3 | - damianavila82 4 | - defaults 5 | dependencies: 6 | - rise=4.0.0b1=py35_0 7 | - _license=1.1=py35_1 8 | - alabaster=0.7.10=py35_0 9 | - anaconda-client=1.6.2=py35_0 10 | - anaconda=custom=py35_0 11 | - anaconda-navigator=1.5.0=py35_0 12 | - anaconda-project=0.4.1=py35_0 13 | - appnope=0.1.0=py35_0 14 | - appscript=1.0.1=py35_0 15 | - astroid=1.4.9=py35_0 16 | - astropy=1.3=np112py35_0 17 | - babel=2.3.4=py35_0 18 | - backports=1.0=py35_0 19 | - beautifulsoup4=4.5.3=py35_0 20 | - bitarray=0.8.1=py35_0 21 | - blaze=0.10.1=py35_0 22 | - bleach=1.5.0=py35_0 23 | - bokeh=0.12.4=py35_0 24 | - boto=2.46.1=py35_0 25 | - bottleneck=1.2.0=np112py35_0 26 | - cffi=1.9.1=py35_0 27 | - chardet=2.3.0=py35_0 28 | - chest=0.2.3=py35_0 29 | - click=6.7=py35_0 30 | - cloudpickle=0.2.2=py35_0 31 | - clyent=1.2.2=py35_0 32 | - colorama=0.3.7=py35_0 33 | - configobj=5.0.6=py35_0 34 | - contextlib2=0.5.4=py35_0 35 | - cryptography=1.7.1=py35_0 36 | - curl=7.52.1=0 37 | - cycler=0.10.0=py35_0 38 | - cython=0.25.2=py35_0 39 | - cytoolz=0.8.2=py35_0 40 | - dask=0.14.0=py35_0 41 | - datashape=0.5.4=py35_0 42 | - decorator=4.0.11=py35_0 43 | - dill=0.2.5=py35_0 44 | - docutils=0.13.1=py35_0 45 | - entrypoints=0.2.2=py35_1 46 | - et_xmlfile=1.0.1=py35_0 47 | - fastcache=1.0.2=py35_1 48 | - flask=0.12=py35_0 49 | - flask-cors=3.0.2=py35_0 50 | - freetype=2.5.5=2 51 | - get_terminal_size=1.0.0=py35_0 52 | - gevent=1.2.1=py35_0 53 | - greenlet=0.4.12=py35_0 54 | - h5py=2.6.0=np112py35_2 55 | - hdf5=1.8.17=1 56 | - heapdict=1.0.0=py35_1 57 | - html5lib=0.999=py35_0 58 | - icu=54.1=0 59 | - idna=2.2=py35_0 60 | - imagesize=0.7.1=py35_0 61 | - ipykernel=4.5.2=py35_0 62 | - ipython=5.3.0=py35_0 63 | - ipython_genutils=0.1.0=py35_0 64 | - ipywidgets=6.0.0=py35_0 65 | - isort=4.2.5=py35_0 66 | - itsdangerous=0.24=py35_0 67 | - jbig=2.1=0 68 | - jdcal=1.3=py35_0 69 | - jedi=0.9.0=py35_1 70 | - jinja2=2.9.5=py35_0 71 | - jpeg=9b=0 72 | - jsonschema=2.5.1=py35_0 73 | - jupyter=1.0.0=py35_3 74 | - jupyter_client=5.0.0=py35_0 75 | - jupyter_console=5.1.0=py35_0 76 | - jupyter_core=4.3.0=py35_0 77 | - lazy-object-proxy=1.2.2=py35_0 78 | - libiconv=1.14=0 79 | - libpng=1.6.27=0 80 | - libtiff=4.0.6=3 81 | - libxml2=2.9.4=0 82 | - libxslt=1.1.29=0 83 | - llvmlite=0.16.0=py35_0 84 | - locket=0.2.0=py35_1 85 | - lxml=3.7.3=py35_0 86 | - markupsafe=0.23=py35_2 87 | - matplotlib=2.0.0=np112py35_0 88 | - mistune=0.7.4=py35_0 89 | - mkl=2017.0.1=0 90 | - mkl-service=1.1.2=py35_3 91 | - mpmath=0.19=py35_1 92 | - multipledispatch=0.4.9=py35_0 93 | - nbconvert=5.1.1=py35_0 94 | - nbformat=4.3.0=py35_0 95 | - networkx=1.11=py35_0 96 | - nltk=3.2.2=py35_0 97 | - nose=1.3.7=py35_1 98 | - notebook=4.4.1=py35_0 99 | - numba=0.31.0=np112py35_0 100 | - numexpr=2.6.2=np112py35_0 101 | - numpy=1.12.0=py35_0 102 | - numpydoc=0.6.0=py35_0 103 | - odo=0.5.0=py35_1 104 | - olefile=0.44=py35_0 105 | - openpyxl=2.4.1=py35_0 106 | - openssl=1.0.2k=0 107 | - pandas=0.19.2=np112py35_1 108 | - pandocfilters=1.4.1=py35_0 109 | - partd=0.3.7=py35_0 110 | - path.py=10.1=py35_0 111 | - pathlib2=2.2.0=py35_0 112 | - patsy=0.4.1=py35_0 113 | - pep8=1.7.0=py35_0 114 | - pexpect=4.2.1=py35_0 115 | - pickleshare=0.7.4=py35_0 116 | - pillow=4.0.0=py35_1 117 | - pip=9.0.1=py35_1 118 | - ply=3.10=py35_0 119 | - prompt_toolkit=1.0.13=py35_0 120 | - psutil=5.2.0=py35_0 121 | - ptyprocess=0.5.1=py35_0 122 | - py=1.4.32=py35_0 123 | - pyasn1=0.2.3=py35_0 124 | - pycosat=0.6.1=py35_1 125 | - pycparser=2.17=py35_0 126 | - pycrypto=2.6.1=py35_4 127 | - pycurl=7.43.0=py35_2 128 | - pyflakes=1.5.0=py35_0 129 | - pygments=2.2.0=py35_0 130 | - pylint=1.6.4=py35_1 131 | - pyopenssl=16.2.0=py35_0 132 | - pyparsing=2.1.4=py35_0 133 | - pyqt=5.6.0=py35_2 134 | - pytables=3.3.0=np112py35_0 135 | - pytest=3.0.6=py35_0 136 | - python=3.5.3=1 137 | - python-dateutil=2.6.0=py35_0 138 | - python.app=1.2=py35_4 139 | - pytz=2016.10=py35_0 140 | - pyyaml=3.12=py35_0 141 | - pyzmq=16.0.2=py35_0 142 | - qt=5.6.2=0 143 | - qtawesome=0.4.4=py35_0 144 | - qtconsole=4.2.1=py35_1 145 | - qtpy=1.2.1=py35_0 146 | - readline=6.2=2 147 | - redis=3.2.0=0 148 | - redis-py=2.10.5=py35_0 149 | - requests=2.13.0=py35_0 150 | - rope=0.9.4=py35_1 151 | - ruamel_yaml=0.11.14=py35_1 152 | - scikit-image=0.12.3=np112py35_1 153 | - scikit-learn=0.18.1=np112py35_1 154 | - scipy=0.19.0=np112py35_0 155 | - seaborn=0.7.1=py35_0 156 | - setuptools=27.2.0=py35_0 157 | - simplegeneric=0.8.1=py35_1 158 | - singledispatch=3.4.0.3=py35_0 159 | - sip=4.18=py35_0 160 | - six=1.10.0=py35_0 161 | - snowballstemmer=1.2.1=py35_0 162 | - sockjs-tornado=1.0.3=py35_0 163 | - sphinx=1.5.1=py35_0 164 | - spyder=3.1.3=py35_0 165 | - sqlalchemy=1.1.6=py35_0 166 | - sqlite=3.13.0=0 167 | - statsmodels=0.8.0=np112py35_0 168 | - sympy=1.0=py35_0 169 | - terminado=0.6=py35_0 170 | - testpath=0.3=py35_0 171 | - tk=8.5.18=0 172 | - toolz=0.8.2=py35_0 173 | - tornado=4.4.2=py35_0 174 | - traitlets=4.3.2=py35_0 175 | - unicodecsv=0.14.1=py35_0 176 | - wcwidth=0.1.7=py35_0 177 | - werkzeug=0.12=py35_0 178 | - wheel=0.29.0=py35_0 179 | - widgetsnbextension=2.0.0=py35_0 180 | - wrapt=1.10.8=py35_0 181 | - xlrd=1.0.0=py35_0 182 | - xlsxwriter=0.9.6=py35_0 183 | - xlwings=0.10.2=py35_0 184 | - xlwt=1.2.0=py35_0 185 | - xz=5.2.2=1 186 | - yaml=0.1.6=0 187 | - zlib=1.2.8=3 188 | - pip: 189 | - backports.shutil-get-terminal-size==1.0.0 190 | - cvxopt==1.1.9 191 | - et-xmlfile==1.0.1 192 | - ipython-genutils==0.1.0 193 | - jupyter-client==5.0.0 194 | - jupyter-console==5.1.0 195 | - jupyter-core==4.3.0 196 | - keras==2.0.0 197 | - opencv-python==3.2.0.6 198 | - prompt-toolkit==1.0.13 199 | - protobuf==3.2.0 200 | - rope-py3k==0.9.4.post1 201 | - tables==3.3.0 202 | - tensorflow-gpu==1.0.0 203 | - theano==0.8.2 204 | - tqdm==4.11.2 --------------------------------------------------------------------------------