From 29867c44642d31e65c941fa8cb1f8dbd7c21efa4 Mon Sep 17 00:00:00 2001 From: ArthurJahn <stutrzbecher@gmail.com> Date: Sat, 6 Jul 2019 00:32:49 -0300 Subject: [PATCH] submition --- .gitignore | 7 + CODEOWNERS | 7 + LICENSE.txt | 19 + README.md | 116 + bottleneck_features/.gitignore | 1 + dog_app.html | 15307 ++++++++++ dog_app.ipynb | 1041 + dog_app_zh.ipynb | 1122 + extract_bottleneck_features.py | 19 + haarcascades/haarcascade_frontalface_alt.xml | 24350 ++++++++++++++++ haarcascades/haarcascade_frontalface_alt2.xml | 20719 +++++++++++++ images/American_water_spaniel_00648.jpg | Bin 0 -> 41784 bytes images/Brittany_02625.jpg | Bin 0 -> 77473 bytes images/Curly-coated_retriever_03896.jpg | Bin 0 -> 61998 bytes images/Labrador_retriever_06449.jpg | Bin 0 -> 58710 bytes images/Labrador_retriever_06455.jpg | Bin 0 -> 35774 bytes images/Labrador_retriever_06457.jpg | Bin 0 -> 52100 bytes images/Welsh_springer_spaniel_08203.jpg | Bin 0 -> 24063 bytes images/dogs/114.jpg | Bin 0 -> 26765 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bottleneck_features/.gitignore create mode 100644 dog_app.html create mode 100644 dog_app.ipynb create mode 100644 dog_app_zh.ipynb create mode 100644 extract_bottleneck_features.py create mode 100644 haarcascades/haarcascade_frontalface_alt.xml create mode 100644 haarcascades/haarcascade_frontalface_alt2.xml create mode 100644 images/American_water_spaniel_00648.jpg create mode 100644 images/Brittany_02625.jpg create mode 100644 images/Curly-coated_retriever_03896.jpg create mode 100644 images/Labrador_retriever_06449.jpg create mode 100644 images/Labrador_retriever_06455.jpg create mode 100644 images/Labrador_retriever_06457.jpg create mode 100644 images/Welsh_springer_spaniel_08203.jpg create mode 100644 images/dogs/114.jpg create mode 100644 images/dogs/115.jpg create mode 100644 images/dogs/23.jpg create mode 100644 images/dogs/25.jpg create mode 100644 images/dogs/31.jpg create mode 100644 images/dogs/64.jpg create mode 100644 images/sample_cnn.png create mode 100644 images/sample_dog_output.png create mode 100644 images/sample_human_output.png create mode 100644 requirements/dog-linux-gpu.yml create mode 100644 requirements/dog-linux.yml create mode 100644 requirements/dog-mac-gpu.yml create mode 100644 requirements/dog-mac.yml create mode 100644 requirements/dog-windows-gpu.yml create mode 100644 requirements/dog-windows.yml create mode 100644 requirements/requirements-gpu.txt create mode 100644 requirements/requirements.txt create mode 100644 saved_models/.gitignore create mode 100644 saved_models/weights.best.RESNET.hdf5 create mode 100644 saved_models/weights.best.my_model.hdf5 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..94b0107 --- /dev/null +++ b/.gitignore @@ -0,0 +1,7 @@ +.DS_Store +dogImages/ +lfw/ +saved_models/weights.best.from_scratch.hdf5 +saved_models/weights.best.vgg16.hdf5 +.ipynb_checkpoints/ +bottleneck_features/DogVGG16Data.npz diff --git a/CODEOWNERS b/CODEOWNERS new file mode 100644 index 0000000..284dca1 --- /dev/null +++ b/CODEOWNERS @@ -0,0 +1,7 @@ +s is a comment. +# Each line is a file pattern followed by one or more owners. + +# These owners will be the default owners for everything in +# the repo. +* @cgearhart @luisguiserrano + diff --git a/LICENSE.txt b/LICENSE.txt new file mode 100644 index 0000000..1862e57 --- /dev/null +++ b/LICENSE.txt @@ -0,0 +1,19 @@ +Copyright (c) 2017 Udacity, Inc. + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..3ac0204 --- /dev/null +++ b/README.md @@ -0,0 +1,116 @@ +[//]: # (Image References) + +[image1]: ./images/sample_dog_output.png "Sample Output" +[image2]: ./images/vgg16_model.png "VGG-16 Model Keras Layers" +[image3]: ./images/vgg16_model_draw.png "VGG16 Model Figure" + + +## Project Overview + +Welcome to the Convolutional Neural Networks (CNN) project in the AI Nanodegree! In this project, you will learn how to build a pipeline that can be used within a web or mobile app to process real-world, user-supplied images. Given an image of a dog, your algorithm will identify an estimate of the canine’s breed. If supplied an image of a human, the code will identify the resembling dog breed. + +![Sample Output][image1] + +Along with exploring state-of-the-art CNN models for classification, you will make important design decisions about the user experience for your app. Our goal is that by completing this lab, you understand the challenges involved in piecing together a series of models designed to perform various tasks in a data processing pipeline. Each model has its strengths and weaknesses, and engineering a real-world application often involves solving many problems without a perfect answer. Your imperfect solution will nonetheless create a fun user experience! + +## Project Instructions + +### Instructions + +1. Clone the repository and navigate to the downloaded folder. +``` +git clone https://github.com/udacity/dog-project.git +cd dog-project +``` + +2. Download the [dog dataset](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/dogImages.zip). Unzip the folder and place it in the repo, at location `path/to/dog-project/dogImages`. + +3. Download the [human dataset](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/lfw.zip). Unzip the folder and place it in the repo, at location `path/to/dog-project/lfw`. If you are using a Windows machine, you are encouraged to use [7zip](http://www.7-zip.org/) to extract the folder. + +4. Download the [VGG-16 bottleneck features](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG16Data.npz) for the dog dataset. Place it in the repo, at location `path/to/dog-project/bottleneck_features`. + +5. (Optional) __If you plan to install TensorFlow with GPU support on your local machine__, follow [the guide](https://www.tensorflow.org/install/) to install the necessary NVIDIA software on your system. If you are using an EC2 GPU instance, you can skip this step. + +6. (Optional) **If you are running the project on your local machine (and not using AWS)**, create (and activate) a new environment. + + - __Linux__ (to install with __GPU support__, change `requirements/dog-linux.yml` to `requirements/dog-linux-gpu.yml`): + ``` + conda env create -f requirements/dog-linux.yml + source activate dog-project + ``` + - __Mac__ (to install with __GPU support__, change `requirements/dog-mac.yml` to `requirements/dog-mac-gpu.yml`): + ``` + conda env create -f requirements/dog-mac.yml + source activate dog-project + ``` + **NOTE:** Some Mac users may need to install a different version of OpenCV + ``` + conda install --channel https://conda.anaconda.org/menpo opencv3 + ``` + - __Windows__ (to install with __GPU support__, change `requirements/dog-windows.yml` to `requirements/dog-windows-gpu.yml`): + ``` + conda env create -f requirements/dog-windows.yml + activate dog-project + ``` + +7. (Optional) **If you are running the project on your local machine (and not using AWS)** and Step 6 throws errors, try this __alternative__ step to create your environment. + + - __Linux__ or __Mac__ (to install with __GPU support__, change `requirements/requirements.txt` to `requirements/requirements-gpu.txt`): + ``` + conda create --name dog-project python=3.5 + source activate dog-project + pip install -r requirements/requirements.txt + ``` + **NOTE:** Some Mac users may need to install a different version of OpenCV + ``` + conda install --channel https://conda.anaconda.org/menpo opencv3 + ``` + - __Windows__ (to install with __GPU support__, change `requirements/requirements.txt` to `requirements/requirements-gpu.txt`): + ``` + conda create --name dog-project python=3.5 + activate dog-project + pip install -r requirements/requirements.txt + ``` + +8. (Optional) **If you are using AWS**, install Tensorflow. +``` +sudo python3 -m pip install -r requirements/requirements-gpu.txt +``` + +9. Switch [Keras backend](https://keras.io/backend/) to TensorFlow. + - __Linux__ or __Mac__: + ``` + KERAS_BACKEND=tensorflow python -c "from keras import backend" + ``` + - __Windows__: + ``` + set KERAS_BACKEND=tensorflow + python -c "from keras import backend" + ``` + +10. (Optional) **If you are running the project on your local machine (and not using AWS)**, create an [IPython kernel](http://ipython.readthedocs.io/en/stable/install/kernel_install.html) for the `dog-project` environment. +``` +python -m ipykernel install --user --name dog-project --display-name "dog-project" +``` + +11. Open the notebook. +``` +jupyter notebook dog_app.ipynb +``` + +12. (Optional) **If you are running the project on your local machine (and not using AWS)**, before running code, change the kernel to match the dog-project environment by using the drop-down menu (**Kernel > Change kernel > dog-project**). Then, follow the instructions in the notebook. + +__NOTE:__ While some code has already been implemented to get you started, you will need to implement additional functionality to successfully answer all of the questions included in the notebook. __Unless requested, do not modify code that has already been included.__ + +## Evaluation + +Your project will be reviewed by a Udacity reviewer against the CNN project [rubric](https://review.udacity.com/#!/rubrics/810/view). Review this rubric thoroughly, and self-evaluate your project before submission. All criteria found in the rubric must meet specifications for you to pass. + +## Project Submission + +When you are ready to submit your project, collect the following files and compress them into a single archive for upload: +- The `dog_app.ipynb` file with fully functional code, all code cells executed and displaying output, and all questions answered. +- An HTML or PDF export of the project notebook with the name `report.html` or `report.pdf`. +- Any additional images used for the project that were not supplied to you for the project. __Please do not include the project data sets in the `dogImages/` or `lfw/` folders. Likewise, please do not include the `bottleneck_features/` folder.__ + +Alternatively, your submission could consist of the GitHub link to your repository. diff --git a/bottleneck_features/.gitignore b/bottleneck_features/.gitignore new file mode 100644 index 0000000..c6c85ba --- /dev/null +++ b/bottleneck_features/.gitignore @@ -0,0 +1 @@ +DogVGG16Data.npz \ No newline at end of file diff --git a/dog_app.html b/dog_app.html new file mode 100644 index 0000000..0b22576 --- /dev/null +++ b/dog_app.html @@ -0,0 +1,15307 @@ +<!DOCTYPE html> +<html> +<head><meta charset="utf-8" /> + +<title>dog_app</title> + +<script src="https://cdnjs.cloudflare.com/ajax/libs/require.js/2.1.10/require.min.js"></script> +<script src="https://cdnjs.cloudflare.com/ajax/libs/jquery/2.0.3/jquery.min.js"></script> + + + +<style type="text/css"> + /*! +* +* Twitter Bootstrap +* +*/ +/*! + * Bootstrap v3.3.7 (http://getbootstrap.com) + * Copyright 2011-2016 Twitter, Inc. + * Licensed under MIT (https://github.com/twbs/bootstrap/blob/master/LICENSE) + */ +/*! normalize.css v3.0.3 | MIT License | github.com/necolas/normalize.css */ +html { + font-family: sans-serif; + -ms-text-size-adjust: 100%; + -webkit-text-size-adjust: 100%; +} +body { + margin: 0; +} +article, +aside, +details, +figcaption, +figure, +footer, +header, +hgroup, +main, +menu, +nav, +section, +summary { + display: block; +} +audio, +canvas, +progress, +video { + display: inline-block; + vertical-align: baseline; +} +audio:not([controls]) { + display: none; + height: 0; +} +[hidden], +template { + display: none; +} +a { + background-color: transparent; +} +a:active, +a:hover { + outline: 0; +} +abbr[title] { + border-bottom: 1px dotted; +} +b, +strong { + font-weight: bold; +} +dfn { + font-style: italic; +} +h1 { + font-size: 2em; + margin: 0.67em 0; +} +mark { + background: #ff0; + color: #000; +} +small { + font-size: 80%; +} +sub, +sup { + font-size: 75%; + line-height: 0; + position: relative; + vertical-align: baseline; +} +sup { + top: -0.5em; +} +sub { + bottom: -0.25em; +} +img { + border: 0; +} +svg:not(:root) { + overflow: hidden; +} +figure { + margin: 1em 40px; +} +hr { + box-sizing: content-box; + height: 0; +} +pre { + overflow: auto; +} +code, +kbd, +pre, +samp { + font-family: monospace, monospace; + font-size: 1em; +} +button, +input, +optgroup, +select, +textarea { + color: inherit; + font: inherit; + margin: 0; +} +button { + overflow: visible; +} +button, +select { + text-transform: none; +} +button, +html input[type="button"], +input[type="reset"], +input[type="submit"] { + -webkit-appearance: button; + cursor: pointer; +} +button[disabled], +html input[disabled] { + cursor: default; +} +button::-moz-focus-inner, +input::-moz-focus-inner { + border: 0; + padding: 0; +} +input { + line-height: normal; +} +input[type="checkbox"], +input[type="radio"] { + box-sizing: border-box; + padding: 0; +} +input[type="number"]::-webkit-inner-spin-button, +input[type="number"]::-webkit-outer-spin-button { + height: auto; +} +input[type="search"] { + -webkit-appearance: textfield; + box-sizing: content-box; +} +input[type="search"]::-webkit-search-cancel-button, +input[type="search"]::-webkit-search-decoration { + -webkit-appearance: none; +} +fieldset { + border: 1px solid #c0c0c0; + margin: 0 2px; + padding: 0.35em 0.625em 0.75em; +} +legend { + border: 0; + padding: 0; +} +textarea { + overflow: auto; +} +optgroup { + font-weight: bold; +} +table { + border-collapse: collapse; + border-spacing: 0; +} +td, +th { + padding: 0; +} +/*! 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+} +.glyphicon-wrench:before { + content: "\e136"; +} +.glyphicon-tasks:before { + content: "\e137"; +} +.glyphicon-filter:before { + content: "\e138"; +} +.glyphicon-briefcase:before { + content: "\e139"; +} +.glyphicon-fullscreen:before { + content: "\e140"; +} +.glyphicon-dashboard:before { + content: "\e141"; +} +.glyphicon-paperclip:before { + content: "\e142"; +} +.glyphicon-heart-empty:before { + content: "\e143"; +} +.glyphicon-link:before { + content: "\e144"; +} +.glyphicon-phone:before { + content: "\e145"; +} +.glyphicon-pushpin:before { + content: "\e146"; +} +.glyphicon-usd:before { + content: "\e148"; +} +.glyphicon-gbp:before { + content: "\e149"; +} +.glyphicon-sort:before { + content: "\e150"; +} +.glyphicon-sort-by-alphabet:before { + content: "\e151"; +} +.glyphicon-sort-by-alphabet-alt:before { + content: "\e152"; +} +.glyphicon-sort-by-order:before { + content: "\e153"; +} +.glyphicon-sort-by-order-alt:before { + content: "\e154"; +} +.glyphicon-sort-by-attributes:before { + content: "\e155"; +} +.glyphicon-sort-by-attributes-alt:before { + content: "\e156"; +} +.glyphicon-unchecked:before { + content: "\e157"; +} +.glyphicon-expand:before { + content: "\e158"; +} +.glyphicon-collapse-down:before { + content: "\e159"; +} +.glyphicon-collapse-up:before { + content: "\e160"; +} +.glyphicon-log-in:before { + content: "\e161"; +} +.glyphicon-flash:before { + content: "\e162"; +} +.glyphicon-log-out:before { + content: "\e163"; +} +.glyphicon-new-window:before { + content: "\e164"; +} +.glyphicon-record:before { + content: "\e165"; +} +.glyphicon-save:before { + content: "\e166"; +} +.glyphicon-open:before { + content: "\e167"; +} +.glyphicon-saved:before { + content: "\e168"; +} +.glyphicon-import:before { + content: "\e169"; +} +.glyphicon-export:before { + content: "\e170"; +} +.glyphicon-send:before { + content: "\e171"; +} +.glyphicon-floppy-disk:before { + content: "\e172"; +} +.glyphicon-floppy-saved:before { + content: "\e173"; +} +.glyphicon-floppy-remove:before { + content: "\e174"; +} +.glyphicon-floppy-save:before { + content: "\e175"; +} +.glyphicon-floppy-open:before { + content: "\e176"; +} +.glyphicon-credit-card:before { + content: "\e177"; +} +.glyphicon-transfer:before { + content: "\e178"; +} +.glyphicon-cutlery:before { + content: "\e179"; +} +.glyphicon-header:before { + content: "\e180"; +} +.glyphicon-compressed:before { + content: "\e181"; +} +.glyphicon-earphone:before { + content: "\e182"; +} +.glyphicon-phone-alt:before { + content: "\e183"; +} +.glyphicon-tower:before { + content: "\e184"; +} +.glyphicon-stats:before { + content: "\e185"; +} +.glyphicon-sd-video:before { + content: "\e186"; +} +.glyphicon-hd-video:before { + content: "\e187"; +} +.glyphicon-subtitles:before { + content: "\e188"; +} +.glyphicon-sound-stereo:before { + content: "\e189"; +} +.glyphicon-sound-dolby:before { + content: "\e190"; +} +.glyphicon-sound-5-1:before { + content: "\e191"; +} +.glyphicon-sound-6-1:before { + content: "\e192"; +} +.glyphicon-sound-7-1:before { + content: "\e193"; +} +.glyphicon-copyright-mark:before { + content: "\e194"; +} +.glyphicon-registration-mark:before { + content: "\e195"; +} +.glyphicon-cloud-download:before { + content: "\e197"; +} +.glyphicon-cloud-upload:before { + content: "\e198"; +} +.glyphicon-tree-conifer:before { + content: "\e199"; +} +.glyphicon-tree-deciduous:before { + content: "\e200"; +} +.glyphicon-cd:before { + content: "\e201"; +} +.glyphicon-save-file:before { + content: "\e202"; +} +.glyphicon-open-file:before { + content: "\e203"; +} +.glyphicon-level-up:before { + content: "\e204"; +} +.glyphicon-copy:before { + content: "\e205"; +} +.glyphicon-paste:before { + content: "\e206"; +} +.glyphicon-alert:before { + content: "\e209"; +} +.glyphicon-equalizer:before { + content: "\e210"; +} +.glyphicon-king:before { + content: "\e211"; +} +.glyphicon-queen:before { + content: "\e212"; +} +.glyphicon-pawn:before { + content: "\e213"; +} +.glyphicon-bishop:before { + content: "\e214"; +} +.glyphicon-knight:before { + content: "\e215"; +} +.glyphicon-baby-formula:before { + content: "\e216"; +} +.glyphicon-tent:before { + content: "\26fa"; +} +.glyphicon-blackboard:before { + content: "\e218"; +} +.glyphicon-bed:before { + content: "\e219"; +} +.glyphicon-apple:before { + content: "\f8ff"; +} +.glyphicon-erase:before { + content: "\e221"; +} +.glyphicon-hourglass:before { + content: "\231b"; +} +.glyphicon-lamp:before { + content: "\e223"; +} +.glyphicon-duplicate:before { + content: "\e224"; +} +.glyphicon-piggy-bank:before { + content: "\e225"; +} +.glyphicon-scissors:before { + content: "\e226"; +} +.glyphicon-bitcoin:before { + content: "\e227"; +} +.glyphicon-btc:before { + content: "\e227"; +} +.glyphicon-xbt:before { + content: "\e227"; +} +.glyphicon-yen:before { + content: "\00a5"; +} +.glyphicon-jpy:before { + content: "\00a5"; +} +.glyphicon-ruble:before { + content: "\20bd"; +} +.glyphicon-rub:before { + content: "\20bd"; +} +.glyphicon-scale:before { + content: "\e230"; +} +.glyphicon-ice-lolly:before { + content: "\e231"; +} +.glyphicon-ice-lolly-tasted:before { + content: "\e232"; +} +.glyphicon-education:before { + content: "\e233"; +} +.glyphicon-option-horizontal:before { + content: "\e234"; +} +.glyphicon-option-vertical:before { + content: "\e235"; +} +.glyphicon-menu-hamburger:before { + content: "\e236"; +} +.glyphicon-modal-window:before { + content: "\e237"; +} +.glyphicon-oil:before { + content: "\e238"; +} +.glyphicon-grain:before { + content: "\e239"; +} +.glyphicon-sunglasses:before { + content: "\e240"; +} +.glyphicon-text-size:before { + content: "\e241"; +} +.glyphicon-text-color:before { + content: "\e242"; +} +.glyphicon-text-background:before { + content: "\e243"; +} +.glyphicon-object-align-top:before { + content: "\e244"; +} +.glyphicon-object-align-bottom:before { + content: "\e245"; +} +.glyphicon-object-align-horizontal:before { + content: "\e246"; +} +.glyphicon-object-align-left:before { + content: "\e247"; +} +.glyphicon-object-align-vertical:before { + content: "\e248"; +} +.glyphicon-object-align-right:before { + content: "\e249"; +} +.glyphicon-triangle-right:before { + content: "\e250"; +} +.glyphicon-triangle-left:before { + content: "\e251"; +} +.glyphicon-triangle-bottom:before { + content: "\e252"; +} +.glyphicon-triangle-top:before { + content: "\e253"; +} +.glyphicon-console:before { + content: "\e254"; +} +.glyphicon-superscript:before { + content: "\e255"; +} +.glyphicon-subscript:before { + content: "\e256"; +} +.glyphicon-menu-left:before { + content: "\e257"; +} +.glyphicon-menu-right:before { + content: "\e258"; +} +.glyphicon-menu-down:before { + content: "\e259"; +} +.glyphicon-menu-up:before { + content: "\e260"; +} +* { + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} +*:before, +*:after { + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} +html { + font-size: 10px; + -webkit-tap-highlight-color: rgba(0, 0, 0, 0); +} +body { + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; + font-size: 13px; + line-height: 1.42857143; + color: #000; + background-color: #fff; +} +input, +button, +select, +textarea { + font-family: inherit; + font-size: inherit; + line-height: inherit; +} +a { + color: #337ab7; + text-decoration: none; +} +a:hover, +a:focus { + color: #23527c; + text-decoration: underline; +} +a:focus { + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} +figure { + margin: 0; +} +img { + vertical-align: middle; +} +.img-responsive, +.thumbnail > img, +.thumbnail a > img, +.carousel-inner > .item > img, +.carousel-inner > .item > a > img { + display: block; + max-width: 100%; + height: auto; +} +.img-rounded { + border-radius: 3px; +} +.img-thumbnail { + padding: 4px; + line-height: 1.42857143; + background-color: #fff; + border: 1px solid #ddd; + border-radius: 2px; + -webkit-transition: all 0.2s ease-in-out; + -o-transition: all 0.2s ease-in-out; + transition: all 0.2s ease-in-out; + display: inline-block; + max-width: 100%; + height: auto; +} +.img-circle { + border-radius: 50%; +} +hr { + margin-top: 18px; + margin-bottom: 18px; + border: 0; + border-top: 1px solid #eeeeee; +} +.sr-only { + position: absolute; + width: 1px; + height: 1px; + margin: -1px; + padding: 0; + overflow: hidden; + clip: rect(0, 0, 0, 0); + border: 0; +} +.sr-only-focusable:active, +.sr-only-focusable:focus { + position: static; + width: auto; + height: auto; + margin: 0; + overflow: visible; + clip: auto; +} +[role="button"] { + cursor: pointer; +} +h1, +h2, +h3, +h4, +h5, +h6, +.h1, +.h2, +.h3, +.h4, +.h5, +.h6 { + font-family: inherit; + font-weight: 500; + line-height: 1.1; + color: inherit; +} +h1 small, +h2 small, +h3 small, +h4 small, +h5 small, +h6 small, +.h1 small, +.h2 small, +.h3 small, +.h4 small, +.h5 small, +.h6 small, +h1 .small, +h2 .small, +h3 .small, +h4 .small, +h5 .small, +h6 .small, +.h1 .small, +.h2 .small, +.h3 .small, +.h4 .small, +.h5 .small, +.h6 .small { + font-weight: normal; + line-height: 1; + color: #777777; +} +h1, +.h1, +h2, +.h2, +h3, +.h3 { + margin-top: 18px; + margin-bottom: 9px; +} +h1 small, +.h1 small, +h2 small, +.h2 small, +h3 small, +.h3 small, +h1 .small, +.h1 .small, +h2 .small, +.h2 .small, +h3 .small, +.h3 .small { + font-size: 65%; +} +h4, +.h4, +h5, +.h5, +h6, +.h6 { + margin-top: 9px; + margin-bottom: 9px; +} +h4 small, +.h4 small, +h5 small, +.h5 small, +h6 small, +.h6 small, +h4 .small, +.h4 .small, +h5 .small, +.h5 .small, +h6 .small, +.h6 .small { + font-size: 75%; +} +h1, +.h1 { + font-size: 33px; +} +h2, +.h2 { + font-size: 27px; +} +h3, +.h3 { + font-size: 23px; +} +h4, +.h4 { + font-size: 17px; +} +h5, +.h5 { + font-size: 13px; +} +h6, +.h6 { + font-size: 12px; +} +p { + margin: 0 0 9px; +} +.lead { + margin-bottom: 18px; + font-size: 14px; + font-weight: 300; + line-height: 1.4; +} +@media (min-width: 768px) { + .lead { + font-size: 19.5px; + } +} +small, +.small { + font-size: 92%; +} +mark, +.mark { + background-color: #fcf8e3; + padding: .2em; +} +.text-left { + text-align: left; +} +.text-right { + text-align: right; +} +.text-center { + text-align: center; +} +.text-justify { + text-align: justify; +} +.text-nowrap { + white-space: nowrap; +} +.text-lowercase { + text-transform: lowercase; +} +.text-uppercase { + text-transform: uppercase; +} +.text-capitalize { + text-transform: capitalize; +} +.text-muted { + color: #777777; +} +.text-primary { + color: #337ab7; +} +a.text-primary:hover, +a.text-primary:focus { + color: #286090; +} +.text-success { + color: #3c763d; +} +a.text-success:hover, +a.text-success:focus { + color: #2b542c; +} +.text-info { + color: #31708f; +} +a.text-info:hover, +a.text-info:focus { + color: #245269; +} +.text-warning { + color: #8a6d3b; +} +a.text-warning:hover, +a.text-warning:focus { + color: #66512c; +} +.text-danger { + color: #a94442; +} +a.text-danger:hover, +a.text-danger:focus { + color: #843534; +} +.bg-primary { + color: #fff; + background-color: #337ab7; +} +a.bg-primary:hover, +a.bg-primary:focus { + background-color: #286090; +} +.bg-success { + background-color: #dff0d8; +} +a.bg-success:hover, +a.bg-success:focus { + background-color: #c1e2b3; +} +.bg-info { + background-color: #d9edf7; +} +a.bg-info:hover, +a.bg-info:focus { + background-color: #afd9ee; +} +.bg-warning { + background-color: #fcf8e3; +} +a.bg-warning:hover, +a.bg-warning:focus { + background-color: #f7ecb5; +} +.bg-danger { + background-color: #f2dede; +} +a.bg-danger:hover, +a.bg-danger:focus { + background-color: #e4b9b9; +} +.page-header { + padding-bottom: 8px; + margin: 36px 0 18px; + border-bottom: 1px solid #eeeeee; +} +ul, +ol { + margin-top: 0; + margin-bottom: 9px; +} +ul ul, +ol ul, +ul ol, +ol ol { + margin-bottom: 0; +} +.list-unstyled { + padding-left: 0; + list-style: none; +} +.list-inline { + padding-left: 0; + list-style: none; + margin-left: -5px; +} +.list-inline > li { + display: inline-block; + padding-left: 5px; + padding-right: 5px; +} +dl { + margin-top: 0; + margin-bottom: 18px; +} +dt, +dd { + line-height: 1.42857143; +} +dt { + font-weight: bold; +} +dd { + margin-left: 0; +} +@media (min-width: 541px) { + .dl-horizontal dt { + float: left; + width: 160px; + clear: left; + text-align: right; + overflow: hidden; + text-overflow: ellipsis; + white-space: nowrap; + } + .dl-horizontal dd { + margin-left: 180px; + } +} +abbr[title], +abbr[data-original-title] { + cursor: help; + border-bottom: 1px dotted #777777; +} +.initialism { + font-size: 90%; + text-transform: uppercase; +} +blockquote { + padding: 9px 18px; + margin: 0 0 18px; + font-size: inherit; + border-left: 5px solid #eeeeee; +} +blockquote p:last-child, +blockquote ul:last-child, +blockquote ol:last-child { + margin-bottom: 0; +} +blockquote footer, +blockquote small, +blockquote .small { + display: block; + font-size: 80%; + line-height: 1.42857143; + color: #777777; +} +blockquote footer:before, +blockquote small:before, +blockquote .small:before { + content: '\2014 \00A0'; +} +.blockquote-reverse, +blockquote.pull-right { + padding-right: 15px; + padding-left: 0; + border-right: 5px solid #eeeeee; + border-left: 0; + text-align: right; +} +.blockquote-reverse footer:before, +blockquote.pull-right footer:before, +.blockquote-reverse small:before, +blockquote.pull-right small:before, +.blockquote-reverse .small:before, +blockquote.pull-right .small:before { + content: ''; +} +.blockquote-reverse footer:after, +blockquote.pull-right footer:after, +.blockquote-reverse small:after, +blockquote.pull-right small:after, +.blockquote-reverse .small:after, +blockquote.pull-right .small:after { + content: '\00A0 \2014'; +} +address { + margin-bottom: 18px; + font-style: normal; + line-height: 1.42857143; +} +code, +kbd, +pre, +samp { + font-family: monospace; +} +code { + padding: 2px 4px; + font-size: 90%; + color: #c7254e; + background-color: #f9f2f4; + border-radius: 2px; +} +kbd { + padding: 2px 4px; + font-size: 90%; + color: #888; + background-color: transparent; + border-radius: 1px; + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.25); +} +kbd kbd { + padding: 0; + font-size: 100%; + font-weight: bold; + box-shadow: none; +} +pre { + display: block; + padding: 8.5px; + margin: 0 0 9px; + font-size: 12px; + line-height: 1.42857143; + word-break: break-all; + word-wrap: break-word; + color: #333333; + background-color: #f5f5f5; + border: 1px solid #ccc; + border-radius: 2px; +} +pre code { + padding: 0; + font-size: inherit; + color: inherit; + white-space: pre-wrap; + background-color: transparent; + border-radius: 0; +} +.pre-scrollable { + max-height: 340px; + overflow-y: scroll; +} +.container { + margin-right: auto; + margin-left: auto; + padding-left: 0px; + padding-right: 0px; +} +@media (min-width: 768px) { + .container { + width: 768px; + } +} +@media (min-width: 992px) { + .container { + width: 940px; + } +} +@media (min-width: 1200px) { + .container { + width: 1140px; + } +} +.container-fluid { + margin-right: auto; + margin-left: auto; + padding-left: 0px; + padding-right: 0px; +} +.row { + margin-left: 0px; + margin-right: 0px; +} +.col-xs-1, .col-sm-1, .col-md-1, .col-lg-1, .col-xs-2, .col-sm-2, .col-md-2, .col-lg-2, .col-xs-3, .col-sm-3, .col-md-3, .col-lg-3, .col-xs-4, .col-sm-4, .col-md-4, .col-lg-4, .col-xs-5, .col-sm-5, .col-md-5, .col-lg-5, .col-xs-6, .col-sm-6, .col-md-6, .col-lg-6, .col-xs-7, .col-sm-7, .col-md-7, .col-lg-7, .col-xs-8, .col-sm-8, .col-md-8, .col-lg-8, .col-xs-9, .col-sm-9, .col-md-9, .col-lg-9, .col-xs-10, .col-sm-10, .col-md-10, .col-lg-10, .col-xs-11, .col-sm-11, .col-md-11, .col-lg-11, .col-xs-12, .col-sm-12, .col-md-12, .col-lg-12 { + position: relative; + min-height: 1px; + padding-left: 0px; + padding-right: 0px; +} +.col-xs-1, .col-xs-2, .col-xs-3, .col-xs-4, .col-xs-5, .col-xs-6, .col-xs-7, .col-xs-8, .col-xs-9, .col-xs-10, .col-xs-11, .col-xs-12 { + float: left; +} +.col-xs-12 { + width: 100%; +} +.col-xs-11 { + width: 91.66666667%; +} +.col-xs-10 { + width: 83.33333333%; +} +.col-xs-9 { + width: 75%; +} +.col-xs-8 { + width: 66.66666667%; +} +.col-xs-7 { + width: 58.33333333%; +} +.col-xs-6 { + width: 50%; +} +.col-xs-5 { + width: 41.66666667%; +} +.col-xs-4 { + width: 33.33333333%; +} +.col-xs-3 { + width: 25%; +} +.col-xs-2 { + width: 16.66666667%; +} +.col-xs-1 { + width: 8.33333333%; +} +.col-xs-pull-12 { + right: 100%; +} +.col-xs-pull-11 { + right: 91.66666667%; +} +.col-xs-pull-10 { + right: 83.33333333%; +} +.col-xs-pull-9 { + right: 75%; +} +.col-xs-pull-8 { + right: 66.66666667%; +} +.col-xs-pull-7 { + right: 58.33333333%; +} +.col-xs-pull-6 { + right: 50%; +} +.col-xs-pull-5 { + right: 41.66666667%; +} +.col-xs-pull-4 { + right: 33.33333333%; +} +.col-xs-pull-3 { + right: 25%; +} +.col-xs-pull-2 { + right: 16.66666667%; +} +.col-xs-pull-1 { + right: 8.33333333%; +} +.col-xs-pull-0 { + right: auto; +} +.col-xs-push-12 { + left: 100%; +} +.col-xs-push-11 { + left: 91.66666667%; +} +.col-xs-push-10 { + left: 83.33333333%; +} +.col-xs-push-9 { + left: 75%; +} +.col-xs-push-8 { + left: 66.66666667%; +} +.col-xs-push-7 { + left: 58.33333333%; +} +.col-xs-push-6 { + left: 50%; +} +.col-xs-push-5 { + left: 41.66666667%; +} +.col-xs-push-4 { + left: 33.33333333%; +} +.col-xs-push-3 { + left: 25%; +} +.col-xs-push-2 { + left: 16.66666667%; +} +.col-xs-push-1 { + left: 8.33333333%; +} +.col-xs-push-0 { + left: auto; +} +.col-xs-offset-12 { + margin-left: 100%; +} +.col-xs-offset-11 { + margin-left: 91.66666667%; +} +.col-xs-offset-10 { + margin-left: 83.33333333%; +} +.col-xs-offset-9 { + margin-left: 75%; +} +.col-xs-offset-8 { + margin-left: 66.66666667%; +} +.col-xs-offset-7 { + margin-left: 58.33333333%; +} +.col-xs-offset-6 { + margin-left: 50%; +} +.col-xs-offset-5 { + margin-left: 41.66666667%; +} +.col-xs-offset-4 { + margin-left: 33.33333333%; +} +.col-xs-offset-3 { + margin-left: 25%; +} +.col-xs-offset-2 { + margin-left: 16.66666667%; +} +.col-xs-offset-1 { + margin-left: 8.33333333%; +} +.col-xs-offset-0 { + margin-left: 0%; +} +@media (min-width: 768px) { + .col-sm-1, .col-sm-2, .col-sm-3, .col-sm-4, .col-sm-5, .col-sm-6, .col-sm-7, .col-sm-8, .col-sm-9, .col-sm-10, .col-sm-11, .col-sm-12 { + float: left; + } + .col-sm-12 { + width: 100%; + } + .col-sm-11 { + width: 91.66666667%; + } + .col-sm-10 { + width: 83.33333333%; + } + .col-sm-9 { + width: 75%; + } + .col-sm-8 { + width: 66.66666667%; + } + .col-sm-7 { + width: 58.33333333%; + } + .col-sm-6 { + width: 50%; + } + .col-sm-5 { + width: 41.66666667%; + } + .col-sm-4 { + width: 33.33333333%; + } + .col-sm-3 { + width: 25%; + } + .col-sm-2 { + width: 16.66666667%; + } + .col-sm-1 { + width: 8.33333333%; + } + .col-sm-pull-12 { + right: 100%; + } + .col-sm-pull-11 { + right: 91.66666667%; + } + .col-sm-pull-10 { + right: 83.33333333%; + } + .col-sm-pull-9 { + right: 75%; + } + .col-sm-pull-8 { + right: 66.66666667%; + } + .col-sm-pull-7 { + right: 58.33333333%; + } + .col-sm-pull-6 { + right: 50%; + } + .col-sm-pull-5 { + right: 41.66666667%; + } + .col-sm-pull-4 { + right: 33.33333333%; + } + .col-sm-pull-3 { + right: 25%; + } + .col-sm-pull-2 { + right: 16.66666667%; + } + .col-sm-pull-1 { + right: 8.33333333%; + } + .col-sm-pull-0 { + right: auto; + } + .col-sm-push-12 { + left: 100%; + } + .col-sm-push-11 { + left: 91.66666667%; + } + .col-sm-push-10 { + left: 83.33333333%; + } + .col-sm-push-9 { + left: 75%; + } + .col-sm-push-8 { + left: 66.66666667%; + } + .col-sm-push-7 { + left: 58.33333333%; + } + .col-sm-push-6 { + left: 50%; + } + .col-sm-push-5 { + left: 41.66666667%; + } + .col-sm-push-4 { + left: 33.33333333%; + } + .col-sm-push-3 { + left: 25%; + } + .col-sm-push-2 { + left: 16.66666667%; + } + .col-sm-push-1 { + left: 8.33333333%; + } + .col-sm-push-0 { + left: auto; + } + .col-sm-offset-12 { + margin-left: 100%; + } + .col-sm-offset-11 { + margin-left: 91.66666667%; + } + .col-sm-offset-10 { + margin-left: 83.33333333%; + } + .col-sm-offset-9 { + margin-left: 75%; + } + .col-sm-offset-8 { + margin-left: 66.66666667%; + } + .col-sm-offset-7 { + margin-left: 58.33333333%; + } + .col-sm-offset-6 { + margin-left: 50%; + } + .col-sm-offset-5 { + margin-left: 41.66666667%; + } + .col-sm-offset-4 { + margin-left: 33.33333333%; + } + .col-sm-offset-3 { + margin-left: 25%; + } + .col-sm-offset-2 { + margin-left: 16.66666667%; + } + .col-sm-offset-1 { + margin-left: 8.33333333%; + } + .col-sm-offset-0 { + margin-left: 0%; + } +} +@media (min-width: 992px) { + .col-md-1, .col-md-2, .col-md-3, .col-md-4, .col-md-5, .col-md-6, .col-md-7, .col-md-8, .col-md-9, .col-md-10, .col-md-11, .col-md-12 { + float: left; + } + .col-md-12 { + width: 100%; + } + .col-md-11 { + width: 91.66666667%; + } + .col-md-10 { + width: 83.33333333%; + } + .col-md-9 { + width: 75%; + } + .col-md-8 { + width: 66.66666667%; + } + .col-md-7 { + width: 58.33333333%; + } + .col-md-6 { + width: 50%; + } + .col-md-5 { + width: 41.66666667%; + } + .col-md-4 { + width: 33.33333333%; + } + .col-md-3 { + width: 25%; + } + .col-md-2 { + width: 16.66666667%; + } + .col-md-1 { + width: 8.33333333%; + } + .col-md-pull-12 { + right: 100%; + } + .col-md-pull-11 { + right: 91.66666667%; + } + .col-md-pull-10 { + right: 83.33333333%; + } + .col-md-pull-9 { + right: 75%; + } + .col-md-pull-8 { + right: 66.66666667%; + } + .col-md-pull-7 { + right: 58.33333333%; + } + .col-md-pull-6 { + right: 50%; + } + .col-md-pull-5 { + right: 41.66666667%; + } + .col-md-pull-4 { + right: 33.33333333%; + } + .col-md-pull-3 { + right: 25%; + } + .col-md-pull-2 { + right: 16.66666667%; + } + .col-md-pull-1 { + right: 8.33333333%; + } + .col-md-pull-0 { + right: auto; + } + .col-md-push-12 { + left: 100%; + } + .col-md-push-11 { + left: 91.66666667%; + } + .col-md-push-10 { + left: 83.33333333%; + } + .col-md-push-9 { + left: 75%; + } + .col-md-push-8 { + left: 66.66666667%; + } + .col-md-push-7 { + left: 58.33333333%; + } + .col-md-push-6 { + left: 50%; + } + .col-md-push-5 { + left: 41.66666667%; + } + .col-md-push-4 { + left: 33.33333333%; + } + .col-md-push-3 { + left: 25%; + } + .col-md-push-2 { + left: 16.66666667%; + } + .col-md-push-1 { + left: 8.33333333%; + } + .col-md-push-0 { + left: auto; + } + .col-md-offset-12 { + margin-left: 100%; + } + .col-md-offset-11 { + margin-left: 91.66666667%; + } + .col-md-offset-10 { + margin-left: 83.33333333%; + } + .col-md-offset-9 { + margin-left: 75%; + } + .col-md-offset-8 { + margin-left: 66.66666667%; + } + .col-md-offset-7 { + margin-left: 58.33333333%; + } + .col-md-offset-6 { + margin-left: 50%; + } + .col-md-offset-5 { + margin-left: 41.66666667%; + } + .col-md-offset-4 { + margin-left: 33.33333333%; + } + .col-md-offset-3 { + margin-left: 25%; + } + .col-md-offset-2 { + margin-left: 16.66666667%; + } + .col-md-offset-1 { + margin-left: 8.33333333%; + } + .col-md-offset-0 { + margin-left: 0%; + } +} +@media (min-width: 1200px) { + .col-lg-1, .col-lg-2, .col-lg-3, .col-lg-4, .col-lg-5, .col-lg-6, .col-lg-7, .col-lg-8, .col-lg-9, .col-lg-10, .col-lg-11, .col-lg-12 { + float: left; + } + .col-lg-12 { + width: 100%; + } + .col-lg-11 { + width: 91.66666667%; + } + .col-lg-10 { + width: 83.33333333%; + } + .col-lg-9 { + width: 75%; + } + .col-lg-8 { + width: 66.66666667%; + } + .col-lg-7 { + width: 58.33333333%; + } + .col-lg-6 { + width: 50%; + } + .col-lg-5 { + width: 41.66666667%; + } + .col-lg-4 { + width: 33.33333333%; + } + .col-lg-3 { + width: 25%; + } + .col-lg-2 { + width: 16.66666667%; + } + .col-lg-1 { + width: 8.33333333%; + } + .col-lg-pull-12 { + right: 100%; + } + .col-lg-pull-11 { + right: 91.66666667%; + } + .col-lg-pull-10 { + right: 83.33333333%; + } + .col-lg-pull-9 { + right: 75%; + } + .col-lg-pull-8 { + right: 66.66666667%; + } + .col-lg-pull-7 { + right: 58.33333333%; + } + .col-lg-pull-6 { + right: 50%; + } + .col-lg-pull-5 { + right: 41.66666667%; + } + .col-lg-pull-4 { + right: 33.33333333%; + } + .col-lg-pull-3 { + right: 25%; + } + .col-lg-pull-2 { + right: 16.66666667%; + } + .col-lg-pull-1 { + right: 8.33333333%; + } + .col-lg-pull-0 { + right: auto; + } + .col-lg-push-12 { + left: 100%; + } + .col-lg-push-11 { + left: 91.66666667%; + } + .col-lg-push-10 { + left: 83.33333333%; + } + .col-lg-push-9 { + left: 75%; + } + .col-lg-push-8 { + left: 66.66666667%; + } + .col-lg-push-7 { + left: 58.33333333%; + } + .col-lg-push-6 { + left: 50%; + } + .col-lg-push-5 { + left: 41.66666667%; + } + .col-lg-push-4 { + left: 33.33333333%; + } + .col-lg-push-3 { + left: 25%; + } + .col-lg-push-2 { + left: 16.66666667%; + } + .col-lg-push-1 { + left: 8.33333333%; + } + .col-lg-push-0 { + left: auto; + } + .col-lg-offset-12 { + margin-left: 100%; + } + .col-lg-offset-11 { + margin-left: 91.66666667%; + } + .col-lg-offset-10 { + margin-left: 83.33333333%; + } + .col-lg-offset-9 { + margin-left: 75%; + } + .col-lg-offset-8 { + margin-left: 66.66666667%; + } + .col-lg-offset-7 { + margin-left: 58.33333333%; + } + .col-lg-offset-6 { + margin-left: 50%; + } + .col-lg-offset-5 { + margin-left: 41.66666667%; + } + .col-lg-offset-4 { + margin-left: 33.33333333%; + } + .col-lg-offset-3 { + margin-left: 25%; + } + .col-lg-offset-2 { + margin-left: 16.66666667%; + } + .col-lg-offset-1 { + margin-left: 8.33333333%; + } + .col-lg-offset-0 { + margin-left: 0%; + } +} +table { + background-color: transparent; +} +caption { + padding-top: 8px; + padding-bottom: 8px; + color: #777777; + text-align: left; +} +th { + text-align: left; +} +.table { + width: 100%; + max-width: 100%; + margin-bottom: 18px; +} +.table > thead > tr > th, +.table > tbody > tr > th, +.table > tfoot > tr > th, +.table > thead > tr > td, +.table > tbody > tr > td, +.table > tfoot > tr > td { + padding: 8px; + line-height: 1.42857143; + vertical-align: top; + border-top: 1px solid #ddd; +} +.table > thead > tr > th { + vertical-align: bottom; + border-bottom: 2px solid #ddd; +} +.table > caption + thead > tr:first-child > th, +.table > colgroup + thead > tr:first-child > th, +.table > thead:first-child > tr:first-child > th, +.table > caption + thead > tr:first-child > td, +.table > colgroup + thead > tr:first-child > td, +.table > thead:first-child > tr:first-child > td { + border-top: 0; +} +.table > tbody + tbody { + border-top: 2px solid #ddd; +} +.table .table { + background-color: #fff; +} +.table-condensed > thead > tr > th, +.table-condensed > tbody > tr > th, +.table-condensed > tfoot > tr > th, +.table-condensed > thead > tr > td, +.table-condensed > tbody > tr > td, +.table-condensed > tfoot > tr > td { + padding: 5px; +} +.table-bordered { + border: 1px solid #ddd; +} +.table-bordered > thead > tr > th, +.table-bordered > tbody > tr > th, +.table-bordered > tfoot > tr > th, +.table-bordered > thead > tr > td, +.table-bordered > tbody > tr > td, +.table-bordered > tfoot > tr > td { + border: 1px solid #ddd; +} +.table-bordered > thead > tr > th, +.table-bordered > thead > tr > td { + border-bottom-width: 2px; +} +.table-striped > tbody > tr:nth-of-type(odd) { + background-color: #f9f9f9; +} +.table-hover > tbody > tr:hover { + background-color: #f5f5f5; +} +table col[class*="col-"] { + position: static; + float: none; + display: table-column; +} +table td[class*="col-"], +table th[class*="col-"] { + position: static; + float: none; + display: table-cell; +} +.table > thead > tr > td.active, +.table > tbody > tr > td.active, +.table > tfoot > tr > td.active, +.table > thead > tr > th.active, +.table > tbody > tr > th.active, +.table > tfoot > tr > th.active, +.table > thead > tr.active > td, +.table > tbody > tr.active > td, +.table > tfoot > tr.active > td, +.table > thead > tr.active > th, +.table > tbody > tr.active > th, +.table > tfoot > tr.active > th { + background-color: #f5f5f5; +} +.table-hover > tbody > tr > td.active:hover, +.table-hover > tbody > tr > th.active:hover, +.table-hover > tbody > tr.active:hover > td, +.table-hover > tbody > tr:hover > .active, +.table-hover > tbody > tr.active:hover > th { + background-color: #e8e8e8; +} +.table > thead > tr > td.success, +.table > tbody > tr > td.success, +.table > tfoot > tr > td.success, +.table > thead > tr > th.success, +.table > tbody > tr > th.success, +.table > tfoot > tr > th.success, +.table > thead > tr.success > td, +.table > tbody > tr.success > td, +.table > tfoot > tr.success > td, +.table > thead > tr.success > th, +.table > tbody > tr.success > th, +.table > tfoot > tr.success > th { + background-color: #dff0d8; +} +.table-hover > tbody > tr > td.success:hover, +.table-hover > tbody > tr > th.success:hover, +.table-hover > tbody > tr.success:hover > td, +.table-hover > tbody > tr:hover > .success, +.table-hover > tbody > tr.success:hover > th { + background-color: #d0e9c6; +} +.table > thead > tr > td.info, +.table > tbody > tr > td.info, +.table > tfoot > tr > td.info, +.table > thead > tr > th.info, +.table > tbody > tr > th.info, +.table > tfoot > tr > th.info, +.table > thead > tr.info > td, +.table > tbody > tr.info > td, +.table > tfoot > tr.info > td, +.table > thead > tr.info > th, +.table > tbody > tr.info > th, +.table > tfoot > tr.info > th { + background-color: #d9edf7; +} +.table-hover > tbody > tr > td.info:hover, +.table-hover > tbody > tr > th.info:hover, +.table-hover > tbody > tr.info:hover > td, +.table-hover > tbody > tr:hover > .info, +.table-hover > tbody > tr.info:hover > th { + background-color: #c4e3f3; +} +.table > thead > tr > td.warning, +.table > tbody > tr > td.warning, +.table > tfoot > tr > td.warning, +.table > thead > tr > th.warning, +.table > tbody > tr > th.warning, +.table > tfoot > tr > th.warning, +.table > thead > tr.warning > td, +.table > tbody > tr.warning > td, +.table > tfoot > tr.warning > td, +.table > thead > tr.warning > th, +.table > tbody > tr.warning > th, +.table > tfoot > tr.warning > th { + background-color: #fcf8e3; +} +.table-hover > tbody > tr > td.warning:hover, +.table-hover > tbody > tr > th.warning:hover, +.table-hover > tbody > tr.warning:hover > td, +.table-hover > tbody > tr:hover > .warning, +.table-hover > tbody > tr.warning:hover > th { + background-color: #faf2cc; +} +.table > thead > tr > td.danger, +.table > tbody > tr > td.danger, +.table > tfoot > tr > td.danger, +.table > thead > tr > th.danger, +.table > tbody > tr > th.danger, +.table > tfoot > tr > th.danger, +.table > thead > tr.danger > td, +.table > tbody > tr.danger > td, +.table > tfoot > tr.danger > td, +.table > thead > tr.danger > th, +.table > tbody > tr.danger > th, +.table > tfoot > tr.danger > th { + background-color: #f2dede; +} +.table-hover > tbody > tr > td.danger:hover, +.table-hover > tbody > tr > th.danger:hover, +.table-hover > tbody > tr.danger:hover > td, +.table-hover > tbody > tr:hover > .danger, +.table-hover > tbody > tr.danger:hover > th { + background-color: #ebcccc; +} +.table-responsive { + overflow-x: auto; + min-height: 0.01%; +} +@media screen and (max-width: 767px) { + .table-responsive { + width: 100%; + margin-bottom: 13.5px; + overflow-y: hidden; + -ms-overflow-style: -ms-autohiding-scrollbar; + border: 1px solid #ddd; + } + .table-responsive > .table { + margin-bottom: 0; + } + .table-responsive > .table > thead > tr > th, + .table-responsive > .table > tbody > tr > th, + .table-responsive > .table > tfoot > tr > th, + .table-responsive > .table > thead > tr > td, + .table-responsive > .table > tbody > tr > td, + .table-responsive > .table > tfoot > tr > td { + white-space: nowrap; + } + .table-responsive > .table-bordered { + border: 0; + } + .table-responsive > .table-bordered > thead > tr > th:first-child, + .table-responsive > .table-bordered > tbody > tr > th:first-child, + .table-responsive > .table-bordered > tfoot > tr > th:first-child, + .table-responsive > .table-bordered > thead > tr > td:first-child, + .table-responsive > .table-bordered > tbody > tr > td:first-child, + .table-responsive > .table-bordered > tfoot > tr > td:first-child { + border-left: 0; + } + .table-responsive > .table-bordered > thead > tr > th:last-child, + .table-responsive > .table-bordered > tbody > tr > th:last-child, + .table-responsive > .table-bordered > tfoot > tr > th:last-child, + .table-responsive > .table-bordered > thead > tr > td:last-child, + .table-responsive > .table-bordered > tbody > tr > td:last-child, + .table-responsive > .table-bordered > tfoot > tr > td:last-child { + border-right: 0; + } + .table-responsive > .table-bordered > tbody > tr:last-child > th, + .table-responsive > .table-bordered > tfoot > tr:last-child > th, + .table-responsive > .table-bordered > tbody > tr:last-child > td, + .table-responsive > .table-bordered > tfoot > tr:last-child > td { + border-bottom: 0; + } +} +fieldset { + padding: 0; + margin: 0; + border: 0; + min-width: 0; +} +legend { + display: block; + width: 100%; + padding: 0; + margin-bottom: 18px; + font-size: 19.5px; + line-height: inherit; + color: #333333; + border: 0; + border-bottom: 1px solid #e5e5e5; +} +label { + display: inline-block; + max-width: 100%; + margin-bottom: 5px; + font-weight: bold; +} +input[type="search"] { + -webkit-box-sizing: border-box; + -moz-box-sizing: border-box; + box-sizing: border-box; +} +input[type="radio"], +input[type="checkbox"] { + margin: 4px 0 0; + margin-top: 1px \9; + line-height: normal; +} +input[type="file"] { + display: block; +} +input[type="range"] { + display: block; + width: 100%; +} +select[multiple], +select[size] { + height: auto; +} +input[type="file"]:focus, +input[type="radio"]:focus, +input[type="checkbox"]:focus { + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} +output { + display: block; + padding-top: 7px; + font-size: 13px; + line-height: 1.42857143; + color: #555555; +} +.form-control { + display: block; + width: 100%; + height: 32px; + padding: 6px 12px; + font-size: 13px; + line-height: 1.42857143; + color: #555555; + background-color: #fff; + background-image: none; + border: 1px solid #ccc; + border-radius: 2px; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; + -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; + transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; +} +.form-control:focus { + border-color: #66afe9; + outline: 0; + -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); + box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); +} +.form-control::-moz-placeholder { + color: #999; + opacity: 1; +} +.form-control:-ms-input-placeholder { + color: #999; +} +.form-control::-webkit-input-placeholder { + color: #999; +} +.form-control::-ms-expand { + border: 0; + background-color: transparent; +} +.form-control[disabled], +.form-control[readonly], +fieldset[disabled] .form-control { + background-color: #eeeeee; + opacity: 1; +} +.form-control[disabled], +fieldset[disabled] .form-control { + cursor: not-allowed; +} +textarea.form-control { + height: auto; +} +input[type="search"] { + -webkit-appearance: none; +} +@media screen and (-webkit-min-device-pixel-ratio: 0) { + input[type="date"].form-control, + input[type="time"].form-control, + input[type="datetime-local"].form-control, + input[type="month"].form-control { + line-height: 32px; + } + input[type="date"].input-sm, + input[type="time"].input-sm, + input[type="datetime-local"].input-sm, + input[type="month"].input-sm, + .input-group-sm input[type="date"], + .input-group-sm input[type="time"], + .input-group-sm input[type="datetime-local"], + .input-group-sm input[type="month"] { + line-height: 30px; + } + input[type="date"].input-lg, + input[type="time"].input-lg, + input[type="datetime-local"].input-lg, + input[type="month"].input-lg, + .input-group-lg input[type="date"], + .input-group-lg input[type="time"], + .input-group-lg input[type="datetime-local"], + .input-group-lg input[type="month"] { + line-height: 45px; + } +} +.form-group { + margin-bottom: 15px; +} +.radio, +.checkbox { + position: relative; + display: block; + margin-top: 10px; + margin-bottom: 10px; +} +.radio label, +.checkbox label { + min-height: 18px; + padding-left: 20px; + margin-bottom: 0; + font-weight: normal; + cursor: pointer; +} +.radio input[type="radio"], +.radio-inline input[type="radio"], +.checkbox input[type="checkbox"], +.checkbox-inline input[type="checkbox"] { + position: absolute; + margin-left: -20px; + margin-top: 4px \9; +} +.radio + .radio, +.checkbox + .checkbox { + margin-top: -5px; +} +.radio-inline, +.checkbox-inline { + position: relative; + display: inline-block; + padding-left: 20px; + margin-bottom: 0; + vertical-align: middle; + font-weight: normal; + cursor: pointer; +} +.radio-inline + .radio-inline, +.checkbox-inline + .checkbox-inline { + margin-top: 0; + margin-left: 10px; +} +input[type="radio"][disabled], +input[type="checkbox"][disabled], +input[type="radio"].disabled, +input[type="checkbox"].disabled, +fieldset[disabled] input[type="radio"], +fieldset[disabled] input[type="checkbox"] { + cursor: not-allowed; +} +.radio-inline.disabled, +.checkbox-inline.disabled, +fieldset[disabled] .radio-inline, +fieldset[disabled] .checkbox-inline { + cursor: not-allowed; +} +.radio.disabled label, +.checkbox.disabled label, +fieldset[disabled] .radio label, +fieldset[disabled] .checkbox label { + cursor: not-allowed; +} +.form-control-static { + padding-top: 7px; + padding-bottom: 7px; + margin-bottom: 0; + min-height: 31px; +} +.form-control-static.input-lg, +.form-control-static.input-sm { + padding-left: 0; + padding-right: 0; +} +.input-sm { + height: 30px; + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 1px; +} +select.input-sm { + height: 30px; + line-height: 30px; +} +textarea.input-sm, +select[multiple].input-sm { + height: auto; +} +.form-group-sm .form-control { + height: 30px; + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 1px; +} +.form-group-sm select.form-control { + height: 30px; + line-height: 30px; +} +.form-group-sm textarea.form-control, +.form-group-sm select[multiple].form-control { + height: auto; +} +.form-group-sm .form-control-static { + height: 30px; + min-height: 30px; + padding: 6px 10px; + font-size: 12px; + line-height: 1.5; +} +.input-lg { + height: 45px; + padding: 10px 16px; + font-size: 17px; + line-height: 1.3333333; + border-radius: 3px; +} +select.input-lg { + height: 45px; + line-height: 45px; +} +textarea.input-lg, +select[multiple].input-lg { + height: auto; +} +.form-group-lg .form-control { + height: 45px; + padding: 10px 16px; + font-size: 17px; + line-height: 1.3333333; + border-radius: 3px; +} +.form-group-lg select.form-control { + height: 45px; + line-height: 45px; +} +.form-group-lg textarea.form-control, +.form-group-lg select[multiple].form-control { + height: auto; +} +.form-group-lg .form-control-static { + height: 45px; + min-height: 35px; + padding: 11px 16px; + font-size: 17px; + line-height: 1.3333333; +} +.has-feedback { + position: relative; +} +.has-feedback .form-control { + padding-right: 40px; +} +.form-control-feedback { + position: absolute; + top: 0; + right: 0; + z-index: 2; + display: block; + width: 32px; + height: 32px; + line-height: 32px; + text-align: center; + pointer-events: none; +} +.input-lg + .form-control-feedback, +.input-group-lg + .form-control-feedback, +.form-group-lg .form-control + .form-control-feedback { + width: 45px; + height: 45px; + line-height: 45px; +} +.input-sm + .form-control-feedback, +.input-group-sm + .form-control-feedback, +.form-group-sm .form-control + .form-control-feedback { + width: 30px; + height: 30px; + line-height: 30px; +} +.has-success .help-block, +.has-success .control-label, +.has-success .radio, +.has-success .checkbox, +.has-success .radio-inline, +.has-success .checkbox-inline, +.has-success.radio label, +.has-success.checkbox label, +.has-success.radio-inline label, +.has-success.checkbox-inline label { + color: #3c763d; +} +.has-success .form-control { + border-color: #3c763d; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} +.has-success .form-control:focus { + border-color: #2b542c; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #67b168; +} +.has-success .input-group-addon { + color: #3c763d; + border-color: #3c763d; + background-color: #dff0d8; +} +.has-success .form-control-feedback { + color: #3c763d; +} +.has-warning .help-block, +.has-warning .control-label, +.has-warning .radio, +.has-warning .checkbox, +.has-warning .radio-inline, +.has-warning .checkbox-inline, +.has-warning.radio label, +.has-warning.checkbox label, +.has-warning.radio-inline label, +.has-warning.checkbox-inline label { + color: #8a6d3b; +} +.has-warning .form-control { + border-color: #8a6d3b; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} +.has-warning .form-control:focus { + border-color: #66512c; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #c0a16b; +} +.has-warning .input-group-addon { + color: #8a6d3b; + border-color: #8a6d3b; + background-color: #fcf8e3; +} +.has-warning .form-control-feedback { + color: #8a6d3b; +} +.has-error .help-block, +.has-error .control-label, +.has-error .radio, +.has-error .checkbox, +.has-error .radio-inline, +.has-error .checkbox-inline, +.has-error.radio label, +.has-error.checkbox label, +.has-error.radio-inline label, +.has-error.checkbox-inline label { + color: #a94442; +} +.has-error .form-control { + border-color: #a94442; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); +} +.has-error .form-control:focus { + border-color: #843534; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075), 0 0 6px #ce8483; +} +.has-error .input-group-addon { + color: #a94442; + border-color: #a94442; + background-color: #f2dede; +} +.has-error .form-control-feedback { + color: #a94442; +} +.has-feedback label ~ .form-control-feedback { + top: 23px; +} +.has-feedback label.sr-only ~ .form-control-feedback { + top: 0; +} +.help-block { + display: block; + margin-top: 5px; + margin-bottom: 10px; + color: #404040; +} +@media (min-width: 768px) { + .form-inline .form-group { + display: inline-block; + margin-bottom: 0; + vertical-align: middle; + } + .form-inline .form-control { + display: inline-block; + width: auto; + vertical-align: middle; + } + .form-inline .form-control-static { + display: inline-block; + } + .form-inline .input-group { + display: inline-table; + vertical-align: middle; + } + .form-inline .input-group .input-group-addon, + .form-inline .input-group .input-group-btn, + .form-inline .input-group .form-control { + width: auto; + } + .form-inline .input-group > .form-control { + width: 100%; + } + .form-inline .control-label { + margin-bottom: 0; + vertical-align: middle; + } + .form-inline .radio, + .form-inline .checkbox { + display: inline-block; + margin-top: 0; + margin-bottom: 0; + vertical-align: middle; + } + .form-inline .radio label, + .form-inline .checkbox label { + padding-left: 0; + } + .form-inline .radio input[type="radio"], + .form-inline .checkbox input[type="checkbox"] { + position: relative; + margin-left: 0; + } + .form-inline .has-feedback .form-control-feedback { + top: 0; + } +} +.form-horizontal .radio, +.form-horizontal .checkbox, +.form-horizontal .radio-inline, +.form-horizontal .checkbox-inline { + margin-top: 0; + margin-bottom: 0; + padding-top: 7px; +} +.form-horizontal .radio, +.form-horizontal .checkbox { + min-height: 25px; +} +.form-horizontal .form-group { + margin-left: 0px; + margin-right: 0px; +} +@media (min-width: 768px) { + .form-horizontal .control-label { + text-align: right; + margin-bottom: 0; + padding-top: 7px; + } +} +.form-horizontal .has-feedback .form-control-feedback { + right: 0px; +} +@media (min-width: 768px) { + .form-horizontal .form-group-lg .control-label { + padding-top: 11px; + font-size: 17px; + } +} +@media (min-width: 768px) { + .form-horizontal .form-group-sm .control-label { + padding-top: 6px; + font-size: 12px; + } +} +.btn { + display: inline-block; + margin-bottom: 0; + font-weight: normal; + text-align: center; + vertical-align: middle; + touch-action: manipulation; + cursor: pointer; + background-image: none; + border: 1px solid transparent; + white-space: nowrap; + padding: 6px 12px; + font-size: 13px; + line-height: 1.42857143; + border-radius: 2px; + -webkit-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; +} +.btn:focus, +.btn:active:focus, +.btn.active:focus, +.btn.focus, +.btn:active.focus, +.btn.active.focus { + outline: 5px auto -webkit-focus-ring-color; + outline-offset: -2px; +} +.btn:hover, +.btn:focus, +.btn.focus { + color: #333; + text-decoration: none; +} +.btn:active, +.btn.active { + outline: 0; + background-image: none; + -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); + box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); +} +.btn.disabled, +.btn[disabled], +fieldset[disabled] .btn { + cursor: not-allowed; + opacity: 0.65; + filter: alpha(opacity=65); + -webkit-box-shadow: none; + box-shadow: none; +} +a.btn.disabled, +fieldset[disabled] a.btn { + pointer-events: none; +} +.btn-default { + color: #333; + background-color: #fff; + border-color: #ccc; +} +.btn-default:focus, +.btn-default.focus { + color: #333; + background-color: #e6e6e6; + border-color: #8c8c8c; +} +.btn-default:hover { + color: #333; + background-color: #e6e6e6; + border-color: #adadad; +} +.btn-default:active, +.btn-default.active, +.open > .dropdown-toggle.btn-default { + color: #333; + background-color: #e6e6e6; + border-color: #adadad; +} +.btn-default:active:hover, +.btn-default.active:hover, +.open > .dropdown-toggle.btn-default:hover, +.btn-default:active:focus, +.btn-default.active:focus, +.open > .dropdown-toggle.btn-default:focus, +.btn-default:active.focus, +.btn-default.active.focus, +.open > .dropdown-toggle.btn-default.focus { + color: #333; + background-color: #d4d4d4; + border-color: #8c8c8c; +} +.btn-default:active, +.btn-default.active, +.open > .dropdown-toggle.btn-default { + background-image: none; +} +.btn-default.disabled:hover, +.btn-default[disabled]:hover, +fieldset[disabled] .btn-default:hover, +.btn-default.disabled:focus, +.btn-default[disabled]:focus, +fieldset[disabled] .btn-default:focus, +.btn-default.disabled.focus, +.btn-default[disabled].focus, +fieldset[disabled] .btn-default.focus { + background-color: #fff; + border-color: #ccc; +} +.btn-default .badge { + color: #fff; + background-color: #333; +} +.btn-primary { + color: #fff; + background-color: #337ab7; + border-color: #2e6da4; +} +.btn-primary:focus, +.btn-primary.focus { + color: #fff; + background-color: #286090; + border-color: #122b40; +} +.btn-primary:hover { + color: #fff; + background-color: #286090; + border-color: #204d74; +} +.btn-primary:active, +.btn-primary.active, +.open > .dropdown-toggle.btn-primary { + color: #fff; + background-color: #286090; + border-color: #204d74; +} +.btn-primary:active:hover, +.btn-primary.active:hover, +.open > .dropdown-toggle.btn-primary:hover, +.btn-primary:active:focus, +.btn-primary.active:focus, +.open > .dropdown-toggle.btn-primary:focus, +.btn-primary:active.focus, +.btn-primary.active.focus, +.open > .dropdown-toggle.btn-primary.focus { + color: #fff; + background-color: #204d74; + border-color: #122b40; +} +.btn-primary:active, +.btn-primary.active, +.open > .dropdown-toggle.btn-primary { + background-image: none; +} +.btn-primary.disabled:hover, +.btn-primary[disabled]:hover, +fieldset[disabled] .btn-primary:hover, +.btn-primary.disabled:focus, +.btn-primary[disabled]:focus, +fieldset[disabled] .btn-primary:focus, +.btn-primary.disabled.focus, +.btn-primary[disabled].focus, +fieldset[disabled] .btn-primary.focus { + background-color: #337ab7; + border-color: #2e6da4; +} +.btn-primary .badge { + color: #337ab7; + background-color: #fff; +} +.btn-success { + color: #fff; + background-color: #5cb85c; + border-color: #4cae4c; +} +.btn-success:focus, +.btn-success.focus { + color: #fff; + background-color: #449d44; + border-color: #255625; +} +.btn-success:hover { + color: #fff; + background-color: #449d44; + border-color: #398439; +} +.btn-success:active, +.btn-success.active, +.open > .dropdown-toggle.btn-success { + color: #fff; + background-color: #449d44; + border-color: #398439; +} +.btn-success:active:hover, +.btn-success.active:hover, +.open > .dropdown-toggle.btn-success:hover, +.btn-success:active:focus, +.btn-success.active:focus, +.open > .dropdown-toggle.btn-success:focus, +.btn-success:active.focus, +.btn-success.active.focus, +.open > .dropdown-toggle.btn-success.focus { + color: #fff; + background-color: #398439; + border-color: #255625; +} +.btn-success:active, +.btn-success.active, +.open > .dropdown-toggle.btn-success { + background-image: none; +} +.btn-success.disabled:hover, +.btn-success[disabled]:hover, +fieldset[disabled] .btn-success:hover, +.btn-success.disabled:focus, +.btn-success[disabled]:focus, +fieldset[disabled] .btn-success:focus, +.btn-success.disabled.focus, +.btn-success[disabled].focus, +fieldset[disabled] .btn-success.focus { + background-color: #5cb85c; + border-color: #4cae4c; +} +.btn-success .badge { + color: #5cb85c; + background-color: #fff; +} +.btn-info { + color: #fff; + background-color: #5bc0de; + border-color: #46b8da; +} +.btn-info:focus, +.btn-info.focus { + color: #fff; + background-color: #31b0d5; + border-color: #1b6d85; +} +.btn-info:hover { + color: #fff; + background-color: #31b0d5; + border-color: #269abc; +} +.btn-info:active, +.btn-info.active, +.open > .dropdown-toggle.btn-info { + color: #fff; + background-color: #31b0d5; + border-color: #269abc; +} +.btn-info:active:hover, +.btn-info.active:hover, +.open > .dropdown-toggle.btn-info:hover, +.btn-info:active:focus, +.btn-info.active:focus, +.open > .dropdown-toggle.btn-info:focus, +.btn-info:active.focus, +.btn-info.active.focus, +.open > .dropdown-toggle.btn-info.focus { + color: #fff; + background-color: #269abc; + border-color: #1b6d85; +} +.btn-info:active, +.btn-info.active, +.open > .dropdown-toggle.btn-info { + background-image: none; +} +.btn-info.disabled:hover, +.btn-info[disabled]:hover, +fieldset[disabled] .btn-info:hover, +.btn-info.disabled:focus, +.btn-info[disabled]:focus, +fieldset[disabled] .btn-info:focus, +.btn-info.disabled.focus, +.btn-info[disabled].focus, +fieldset[disabled] .btn-info.focus { + background-color: #5bc0de; + border-color: #46b8da; +} +.btn-info .badge { + color: #5bc0de; + background-color: #fff; +} +.btn-warning { + color: #fff; + background-color: #f0ad4e; + border-color: #eea236; +} +.btn-warning:focus, +.btn-warning.focus { + color: #fff; + background-color: #ec971f; + border-color: #985f0d; +} +.btn-warning:hover { + color: #fff; + background-color: #ec971f; + border-color: #d58512; +} +.btn-warning:active, +.btn-warning.active, +.open > .dropdown-toggle.btn-warning { + color: #fff; + background-color: #ec971f; + border-color: #d58512; +} +.btn-warning:active:hover, +.btn-warning.active:hover, +.open > .dropdown-toggle.btn-warning:hover, +.btn-warning:active:focus, +.btn-warning.active:focus, +.open > .dropdown-toggle.btn-warning:focus, +.btn-warning:active.focus, +.btn-warning.active.focus, +.open > .dropdown-toggle.btn-warning.focus { + color: #fff; + background-color: #d58512; + border-color: #985f0d; +} +.btn-warning:active, +.btn-warning.active, +.open > .dropdown-toggle.btn-warning { + background-image: none; +} +.btn-warning.disabled:hover, +.btn-warning[disabled]:hover, +fieldset[disabled] .btn-warning:hover, +.btn-warning.disabled:focus, +.btn-warning[disabled]:focus, +fieldset[disabled] .btn-warning:focus, +.btn-warning.disabled.focus, +.btn-warning[disabled].focus, +fieldset[disabled] .btn-warning.focus { + background-color: #f0ad4e; + border-color: #eea236; +} +.btn-warning .badge { + color: #f0ad4e; + background-color: #fff; +} +.btn-danger { + color: #fff; + background-color: #d9534f; + border-color: #d43f3a; +} +.btn-danger:focus, +.btn-danger.focus { + color: #fff; + background-color: #c9302c; + border-color: #761c19; +} +.btn-danger:hover { + color: #fff; + background-color: #c9302c; + border-color: #ac2925; +} +.btn-danger:active, +.btn-danger.active, +.open > .dropdown-toggle.btn-danger { + color: #fff; + background-color: #c9302c; + border-color: #ac2925; +} +.btn-danger:active:hover, +.btn-danger.active:hover, +.open > .dropdown-toggle.btn-danger:hover, +.btn-danger:active:focus, +.btn-danger.active:focus, +.open > .dropdown-toggle.btn-danger:focus, +.btn-danger:active.focus, +.btn-danger.active.focus, +.open > .dropdown-toggle.btn-danger.focus { + color: #fff; + background-color: #ac2925; + border-color: #761c19; +} +.btn-danger:active, +.btn-danger.active, +.open > .dropdown-toggle.btn-danger { + background-image: none; +} +.btn-danger.disabled:hover, +.btn-danger[disabled]:hover, +fieldset[disabled] .btn-danger:hover, +.btn-danger.disabled:focus, +.btn-danger[disabled]:focus, +fieldset[disabled] .btn-danger:focus, +.btn-danger.disabled.focus, +.btn-danger[disabled].focus, +fieldset[disabled] .btn-danger.focus { + background-color: #d9534f; + border-color: #d43f3a; +} +.btn-danger .badge { + color: #d9534f; + background-color: #fff; +} +.btn-link { + color: #337ab7; + font-weight: normal; + border-radius: 0; +} +.btn-link, +.btn-link:active, +.btn-link.active, +.btn-link[disabled], +fieldset[disabled] .btn-link { + background-color: transparent; + -webkit-box-shadow: none; + box-shadow: none; +} +.btn-link, +.btn-link:hover, +.btn-link:focus, +.btn-link:active { + border-color: transparent; +} +.btn-link:hover, +.btn-link:focus { + color: #23527c; + text-decoration: underline; + background-color: transparent; +} +.btn-link[disabled]:hover, +fieldset[disabled] .btn-link:hover, +.btn-link[disabled]:focus, +fieldset[disabled] .btn-link:focus { + color: #777777; + text-decoration: none; +} +.btn-lg, +.btn-group-lg > .btn { + padding: 10px 16px; + font-size: 17px; + line-height: 1.3333333; + border-radius: 3px; +} +.btn-sm, +.btn-group-sm > .btn { + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 1px; +} +.btn-xs, +.btn-group-xs > .btn { + padding: 1px 5px; + font-size: 12px; + line-height: 1.5; + border-radius: 1px; +} +.btn-block { + display: block; + width: 100%; +} +.btn-block + .btn-block { + margin-top: 5px; +} +input[type="submit"].btn-block, +input[type="reset"].btn-block, +input[type="button"].btn-block { + width: 100%; +} +.fade { + opacity: 0; + -webkit-transition: opacity 0.15s linear; + -o-transition: opacity 0.15s linear; + transition: opacity 0.15s linear; +} +.fade.in { + opacity: 1; +} +.collapse { + display: none; +} +.collapse.in { + display: block; +} +tr.collapse.in { + display: table-row; +} +tbody.collapse.in { + display: table-row-group; +} +.collapsing { + position: relative; + height: 0; + overflow: hidden; + -webkit-transition-property: height, visibility; + transition-property: height, visibility; + -webkit-transition-duration: 0.35s; + transition-duration: 0.35s; + -webkit-transition-timing-function: ease; + transition-timing-function: ease; +} +.caret { + display: inline-block; + width: 0; + height: 0; + margin-left: 2px; + vertical-align: middle; + border-top: 4px dashed; + border-top: 4px solid \9; + border-right: 4px solid transparent; + border-left: 4px solid transparent; +} +.dropup, +.dropdown { + position: relative; +} +.dropdown-toggle:focus { + outline: 0; +} +.dropdown-menu { + position: absolute; + top: 100%; + left: 0; + z-index: 1000; + display: none; + float: left; + min-width: 160px; + padding: 5px 0; + margin: 2px 0 0; + list-style: none; + font-size: 13px; + text-align: left; + background-color: #fff; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, 0.15); + border-radius: 2px; + -webkit-box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); + box-shadow: 0 6px 12px rgba(0, 0, 0, 0.175); + background-clip: padding-box; +} +.dropdown-menu.pull-right { + right: 0; + left: auto; +} +.dropdown-menu .divider { + height: 1px; + margin: 8px 0; + overflow: hidden; + background-color: #e5e5e5; +} +.dropdown-menu > li > a { + display: block; + padding: 3px 20px; + clear: both; + font-weight: normal; + line-height: 1.42857143; + color: #333333; + white-space: nowrap; +} +.dropdown-menu > li > a:hover, +.dropdown-menu > li > a:focus { + text-decoration: none; + color: #262626; + background-color: #f5f5f5; +} +.dropdown-menu > .active > a, +.dropdown-menu > .active > a:hover, +.dropdown-menu > .active > a:focus { + color: #fff; + text-decoration: none; + outline: 0; + background-color: #337ab7; +} +.dropdown-menu > .disabled > a, +.dropdown-menu > .disabled > a:hover, +.dropdown-menu > .disabled > a:focus { + color: #777777; +} +.dropdown-menu > .disabled > a:hover, +.dropdown-menu > .disabled > a:focus { + text-decoration: none; + background-color: transparent; + background-image: none; + filter: progid:DXImageTransform.Microsoft.gradient(enabled = false); + cursor: not-allowed; +} +.open > .dropdown-menu { + display: block; +} +.open > a { + outline: 0; +} +.dropdown-menu-right { + left: auto; + right: 0; +} +.dropdown-menu-left { + left: 0; + right: auto; +} +.dropdown-header { + display: block; + padding: 3px 20px; + font-size: 12px; + line-height: 1.42857143; + color: #777777; + white-space: nowrap; +} +.dropdown-backdrop { + position: fixed; + left: 0; + right: 0; + bottom: 0; + top: 0; + z-index: 990; +} +.pull-right > .dropdown-menu { + right: 0; + left: auto; +} +.dropup .caret, +.navbar-fixed-bottom .dropdown .caret { + border-top: 0; + border-bottom: 4px dashed; + border-bottom: 4px solid \9; + content: ""; +} +.dropup .dropdown-menu, +.navbar-fixed-bottom .dropdown .dropdown-menu { + top: auto; + bottom: 100%; + margin-bottom: 2px; +} +@media (min-width: 541px) { + .navbar-right .dropdown-menu { + left: auto; + right: 0; + } + .navbar-right .dropdown-menu-left { + left: 0; + right: auto; + } +} +.btn-group, +.btn-group-vertical { + position: relative; + display: inline-block; + vertical-align: middle; +} +.btn-group > .btn, +.btn-group-vertical > .btn { + position: relative; + float: left; +} +.btn-group > .btn:hover, +.btn-group-vertical > .btn:hover, +.btn-group > .btn:focus, +.btn-group-vertical > .btn:focus, +.btn-group > .btn:active, +.btn-group-vertical > .btn:active, +.btn-group > .btn.active, +.btn-group-vertical > .btn.active { + z-index: 2; +} +.btn-group .btn + .btn, +.btn-group .btn + .btn-group, +.btn-group .btn-group + .btn, +.btn-group .btn-group + .btn-group { + margin-left: -1px; +} +.btn-toolbar { + margin-left: -5px; +} +.btn-toolbar .btn, +.btn-toolbar .btn-group, +.btn-toolbar .input-group { + float: left; +} +.btn-toolbar > .btn, +.btn-toolbar > .btn-group, +.btn-toolbar > .input-group { + margin-left: 5px; +} +.btn-group > .btn:not(:first-child):not(:last-child):not(.dropdown-toggle) { + border-radius: 0; +} +.btn-group > .btn:first-child { + margin-left: 0; +} +.btn-group > .btn:first-child:not(:last-child):not(.dropdown-toggle) { + border-bottom-right-radius: 0; + border-top-right-radius: 0; +} +.btn-group > .btn:last-child:not(:first-child), +.btn-group > .dropdown-toggle:not(:first-child) { + border-bottom-left-radius: 0; + border-top-left-radius: 0; +} +.btn-group > .btn-group { + float: left; +} +.btn-group > .btn-group:not(:first-child):not(:last-child) > .btn { + border-radius: 0; +} +.btn-group > .btn-group:first-child:not(:last-child) > .btn:last-child, +.btn-group > .btn-group:first-child:not(:last-child) > .dropdown-toggle { + border-bottom-right-radius: 0; + border-top-right-radius: 0; +} +.btn-group > .btn-group:last-child:not(:first-child) > .btn:first-child { + border-bottom-left-radius: 0; + border-top-left-radius: 0; +} +.btn-group .dropdown-toggle:active, +.btn-group.open .dropdown-toggle { + outline: 0; +} +.btn-group > .btn + .dropdown-toggle { + padding-left: 8px; + padding-right: 8px; +} +.btn-group > .btn-lg + .dropdown-toggle { + padding-left: 12px; + padding-right: 12px; +} +.btn-group.open .dropdown-toggle { + -webkit-box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); + box-shadow: inset 0 3px 5px rgba(0, 0, 0, 0.125); +} +.btn-group.open .dropdown-toggle.btn-link { + -webkit-box-shadow: none; + box-shadow: none; +} +.btn .caret { + margin-left: 0; +} +.btn-lg .caret { + border-width: 5px 5px 0; + border-bottom-width: 0; +} +.dropup .btn-lg .caret { + border-width: 0 5px 5px; +} +.btn-group-vertical > .btn, +.btn-group-vertical > .btn-group, +.btn-group-vertical > .btn-group > .btn { + display: block; + float: none; + width: 100%; + max-width: 100%; +} +.btn-group-vertical > .btn-group > .btn { + float: none; +} +.btn-group-vertical > .btn + .btn, +.btn-group-vertical > .btn + .btn-group, +.btn-group-vertical > .btn-group + .btn, +.btn-group-vertical > .btn-group + .btn-group { + margin-top: -1px; + margin-left: 0; +} +.btn-group-vertical > .btn:not(:first-child):not(:last-child) { + border-radius: 0; +} +.btn-group-vertical > .btn:first-child:not(:last-child) { + border-top-right-radius: 2px; + border-top-left-radius: 2px; + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.btn-group-vertical > .btn:last-child:not(:first-child) { + border-top-right-radius: 0; + border-top-left-radius: 0; + border-bottom-right-radius: 2px; + border-bottom-left-radius: 2px; +} +.btn-group-vertical > .btn-group:not(:first-child):not(:last-child) > .btn { + border-radius: 0; +} +.btn-group-vertical > .btn-group:first-child:not(:last-child) > .btn:last-child, +.btn-group-vertical > .btn-group:first-child:not(:last-child) > .dropdown-toggle { + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.btn-group-vertical > .btn-group:last-child:not(:first-child) > .btn:first-child { + border-top-right-radius: 0; + border-top-left-radius: 0; +} +.btn-group-justified { + display: table; + width: 100%; + table-layout: fixed; + border-collapse: separate; +} +.btn-group-justified > .btn, +.btn-group-justified > .btn-group { + float: none; + display: table-cell; + width: 1%; +} +.btn-group-justified > .btn-group .btn { + width: 100%; +} +.btn-group-justified > .btn-group .dropdown-menu { + left: auto; +} +[data-toggle="buttons"] > .btn input[type="radio"], +[data-toggle="buttons"] > .btn-group > .btn input[type="radio"], +[data-toggle="buttons"] > .btn input[type="checkbox"], +[data-toggle="buttons"] > .btn-group > .btn input[type="checkbox"] { + position: absolute; + clip: rect(0, 0, 0, 0); + pointer-events: none; +} +.input-group { + position: relative; + display: table; + border-collapse: separate; +} +.input-group[class*="col-"] { + float: none; + padding-left: 0; + padding-right: 0; +} +.input-group .form-control { + position: relative; + z-index: 2; + float: left; + width: 100%; + margin-bottom: 0; +} +.input-group .form-control:focus { + z-index: 3; +} +.input-group-lg > .form-control, +.input-group-lg > .input-group-addon, +.input-group-lg > .input-group-btn > .btn { + height: 45px; + padding: 10px 16px; + font-size: 17px; + line-height: 1.3333333; + border-radius: 3px; +} +select.input-group-lg > .form-control, +select.input-group-lg > .input-group-addon, +select.input-group-lg > .input-group-btn > .btn { + height: 45px; + line-height: 45px; +} +textarea.input-group-lg > .form-control, +textarea.input-group-lg > .input-group-addon, +textarea.input-group-lg > .input-group-btn > .btn, +select[multiple].input-group-lg > .form-control, +select[multiple].input-group-lg > .input-group-addon, +select[multiple].input-group-lg > .input-group-btn > .btn { + height: auto; +} +.input-group-sm > .form-control, +.input-group-sm > .input-group-addon, +.input-group-sm > .input-group-btn > .btn { + height: 30px; + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 1px; +} +select.input-group-sm > .form-control, +select.input-group-sm > .input-group-addon, +select.input-group-sm > .input-group-btn > .btn { + height: 30px; + line-height: 30px; +} +textarea.input-group-sm > .form-control, +textarea.input-group-sm > .input-group-addon, +textarea.input-group-sm > .input-group-btn > .btn, +select[multiple].input-group-sm > .form-control, +select[multiple].input-group-sm > .input-group-addon, +select[multiple].input-group-sm > .input-group-btn > .btn { + height: auto; +} +.input-group-addon, +.input-group-btn, +.input-group .form-control { + display: table-cell; +} +.input-group-addon:not(:first-child):not(:last-child), +.input-group-btn:not(:first-child):not(:last-child), +.input-group .form-control:not(:first-child):not(:last-child) { + border-radius: 0; +} +.input-group-addon, +.input-group-btn { + width: 1%; + white-space: nowrap; + vertical-align: middle; +} +.input-group-addon { + padding: 6px 12px; + font-size: 13px; + font-weight: normal; + line-height: 1; + color: #555555; + text-align: center; + background-color: #eeeeee; + border: 1px solid #ccc; + border-radius: 2px; +} +.input-group-addon.input-sm { + padding: 5px 10px; + font-size: 12px; + border-radius: 1px; +} +.input-group-addon.input-lg { + padding: 10px 16px; + font-size: 17px; + border-radius: 3px; +} +.input-group-addon input[type="radio"], +.input-group-addon input[type="checkbox"] { + margin-top: 0; +} +.input-group .form-control:first-child, +.input-group-addon:first-child, +.input-group-btn:first-child > .btn, +.input-group-btn:first-child > .btn-group > .btn, +.input-group-btn:first-child > .dropdown-toggle, +.input-group-btn:last-child > .btn:not(:last-child):not(.dropdown-toggle), +.input-group-btn:last-child > .btn-group:not(:last-child) > .btn { + border-bottom-right-radius: 0; + border-top-right-radius: 0; +} +.input-group-addon:first-child { + border-right: 0; +} +.input-group .form-control:last-child, +.input-group-addon:last-child, +.input-group-btn:last-child > .btn, +.input-group-btn:last-child > .btn-group > .btn, +.input-group-btn:last-child > .dropdown-toggle, +.input-group-btn:first-child > .btn:not(:first-child), +.input-group-btn:first-child > .btn-group:not(:first-child) > .btn { + border-bottom-left-radius: 0; + border-top-left-radius: 0; +} +.input-group-addon:last-child { + border-left: 0; +} +.input-group-btn { + position: relative; + font-size: 0; + white-space: nowrap; +} +.input-group-btn > .btn { + position: relative; +} +.input-group-btn > .btn + .btn { + margin-left: -1px; +} +.input-group-btn > .btn:hover, +.input-group-btn > .btn:focus, +.input-group-btn > .btn:active { + z-index: 2; +} +.input-group-btn:first-child > .btn, +.input-group-btn:first-child > .btn-group { + margin-right: -1px; +} +.input-group-btn:last-child > .btn, +.input-group-btn:last-child > .btn-group { + z-index: 2; + margin-left: -1px; +} +.nav { + margin-bottom: 0; + padding-left: 0; + list-style: none; +} +.nav > li { + position: relative; + display: block; +} +.nav > li > a { + position: relative; + display: block; + padding: 10px 15px; +} +.nav > li > a:hover, +.nav > li > a:focus { + text-decoration: none; + background-color: #eeeeee; +} +.nav > li.disabled > a { + color: #777777; +} +.nav > li.disabled > a:hover, +.nav > li.disabled > a:focus { + color: #777777; + text-decoration: none; + background-color: transparent; + cursor: not-allowed; +} +.nav .open > a, +.nav .open > a:hover, +.nav .open > a:focus { + background-color: #eeeeee; + border-color: #337ab7; +} +.nav .nav-divider { + height: 1px; + margin: 8px 0; + overflow: hidden; + background-color: #e5e5e5; +} +.nav > li > a > img { + max-width: none; +} +.nav-tabs { + border-bottom: 1px solid #ddd; +} +.nav-tabs > li { + float: left; + margin-bottom: -1px; +} +.nav-tabs > li > a { + margin-right: 2px; + line-height: 1.42857143; + border: 1px solid transparent; + border-radius: 2px 2px 0 0; +} +.nav-tabs > li > a:hover { + border-color: #eeeeee #eeeeee #ddd; +} +.nav-tabs > li.active > a, +.nav-tabs > li.active > a:hover, +.nav-tabs > li.active > a:focus { + color: #555555; + background-color: #fff; + border: 1px solid #ddd; + border-bottom-color: transparent; + cursor: default; +} +.nav-tabs.nav-justified { + width: 100%; + border-bottom: 0; +} +.nav-tabs.nav-justified > li { + float: none; +} +.nav-tabs.nav-justified > li > a { + text-align: center; + margin-bottom: 5px; +} +.nav-tabs.nav-justified > .dropdown .dropdown-menu { + top: auto; + left: auto; +} +@media (min-width: 768px) { + .nav-tabs.nav-justified > li { + display: table-cell; + width: 1%; + } + .nav-tabs.nav-justified > li > a { + margin-bottom: 0; + } +} +.nav-tabs.nav-justified > li > a { + margin-right: 0; + border-radius: 2px; +} +.nav-tabs.nav-justified > .active > a, +.nav-tabs.nav-justified > .active > a:hover, +.nav-tabs.nav-justified > .active > a:focus { + border: 1px solid #ddd; +} +@media (min-width: 768px) { + .nav-tabs.nav-justified > li > a { + border-bottom: 1px solid #ddd; + border-radius: 2px 2px 0 0; + } + .nav-tabs.nav-justified > .active > a, + .nav-tabs.nav-justified > .active > a:hover, + .nav-tabs.nav-justified > .active > a:focus { + border-bottom-color: #fff; + } +} +.nav-pills > li { + float: left; +} +.nav-pills > li > a { + border-radius: 2px; +} +.nav-pills > li + li { + margin-left: 2px; +} +.nav-pills > li.active > a, +.nav-pills > li.active > a:hover, +.nav-pills > li.active > a:focus { + color: #fff; + background-color: #337ab7; +} +.nav-stacked > li { + float: none; +} +.nav-stacked > li + li { + margin-top: 2px; + margin-left: 0; +} +.nav-justified { + width: 100%; +} +.nav-justified > li { + float: none; +} +.nav-justified > li > a { + text-align: center; + margin-bottom: 5px; +} +.nav-justified > .dropdown .dropdown-menu { + top: auto; + left: auto; +} +@media (min-width: 768px) { + .nav-justified > li { + display: table-cell; + width: 1%; + } + .nav-justified > li > a { + margin-bottom: 0; + } +} +.nav-tabs-justified { + border-bottom: 0; +} +.nav-tabs-justified > li > a { + margin-right: 0; + border-radius: 2px; +} +.nav-tabs-justified > .active > a, +.nav-tabs-justified > .active > a:hover, +.nav-tabs-justified > .active > a:focus { + border: 1px solid #ddd; +} +@media (min-width: 768px) { + .nav-tabs-justified > li > a { + border-bottom: 1px solid #ddd; + border-radius: 2px 2px 0 0; + } + .nav-tabs-justified > .active > a, + .nav-tabs-justified > .active > a:hover, + .nav-tabs-justified > .active > a:focus { + border-bottom-color: #fff; + } +} +.tab-content > .tab-pane { + display: none; +} +.tab-content > .active { + display: block; +} +.nav-tabs .dropdown-menu { + margin-top: -1px; + border-top-right-radius: 0; + border-top-left-radius: 0; +} +.navbar { + position: relative; + min-height: 30px; + margin-bottom: 18px; + border: 1px solid transparent; +} +@media (min-width: 541px) { + .navbar { + border-radius: 2px; + } +} +@media (min-width: 541px) { + .navbar-header { + float: left; + } +} +.navbar-collapse { + overflow-x: visible; + padding-right: 0px; + padding-left: 0px; + border-top: 1px solid transparent; + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1); + -webkit-overflow-scrolling: touch; +} +.navbar-collapse.in { + overflow-y: auto; +} +@media (min-width: 541px) { + .navbar-collapse { + width: auto; + border-top: 0; + box-shadow: none; + } + .navbar-collapse.collapse { + display: block !important; + height: auto !important; + padding-bottom: 0; + overflow: visible !important; + } + .navbar-collapse.in { + overflow-y: visible; + } + .navbar-fixed-top .navbar-collapse, + .navbar-static-top .navbar-collapse, + .navbar-fixed-bottom .navbar-collapse { + padding-left: 0; + padding-right: 0; + } +} +.navbar-fixed-top .navbar-collapse, +.navbar-fixed-bottom .navbar-collapse { + max-height: 340px; +} +@media (max-device-width: 540px) and (orientation: landscape) { + .navbar-fixed-top .navbar-collapse, + .navbar-fixed-bottom .navbar-collapse { + max-height: 200px; + } +} +.container > .navbar-header, +.container-fluid > .navbar-header, +.container > .navbar-collapse, +.container-fluid > .navbar-collapse { + margin-right: 0px; + margin-left: 0px; +} +@media (min-width: 541px) { + .container > .navbar-header, + .container-fluid > .navbar-header, + .container > .navbar-collapse, + .container-fluid > .navbar-collapse { + margin-right: 0; + margin-left: 0; + } +} +.navbar-static-top { + z-index: 1000; + border-width: 0 0 1px; +} +@media (min-width: 541px) { + .navbar-static-top { + border-radius: 0; + } +} +.navbar-fixed-top, +.navbar-fixed-bottom { + position: fixed; + right: 0; + left: 0; + z-index: 1030; +} +@media (min-width: 541px) { + .navbar-fixed-top, + .navbar-fixed-bottom { + border-radius: 0; + } +} +.navbar-fixed-top { + top: 0; + border-width: 0 0 1px; +} +.navbar-fixed-bottom { + bottom: 0; + margin-bottom: 0; + border-width: 1px 0 0; +} +.navbar-brand { + float: left; + padding: 6px 0px; + font-size: 17px; + line-height: 18px; + height: 30px; +} +.navbar-brand:hover, +.navbar-brand:focus { + text-decoration: none; +} +.navbar-brand > img { + display: block; +} +@media (min-width: 541px) { + .navbar > .container .navbar-brand, + .navbar > .container-fluid .navbar-brand { + margin-left: 0px; + } +} +.navbar-toggle { + position: relative; + float: right; + margin-right: 0px; + padding: 9px 10px; + margin-top: -2px; + margin-bottom: -2px; + background-color: transparent; + background-image: none; + border: 1px solid transparent; + border-radius: 2px; +} +.navbar-toggle:focus { + outline: 0; +} +.navbar-toggle .icon-bar { + display: block; + width: 22px; + height: 2px; + border-radius: 1px; +} +.navbar-toggle .icon-bar + .icon-bar { + margin-top: 4px; +} +@media (min-width: 541px) { + .navbar-toggle { + display: none; + } +} +.navbar-nav { + margin: 3px 0px; +} +.navbar-nav > li > a { + padding-top: 10px; + padding-bottom: 10px; + line-height: 18px; +} +@media (max-width: 540px) { + .navbar-nav .open .dropdown-menu { + position: static; + float: none; + width: auto; + margin-top: 0; + background-color: transparent; + border: 0; + box-shadow: none; + } + .navbar-nav .open .dropdown-menu > li > a, + .navbar-nav .open .dropdown-menu .dropdown-header { + padding: 5px 15px 5px 25px; + } + .navbar-nav .open .dropdown-menu > li > a { + line-height: 18px; + } + .navbar-nav .open .dropdown-menu > li > a:hover, + .navbar-nav .open .dropdown-menu > li > a:focus { + background-image: none; + } +} +@media (min-width: 541px) { + .navbar-nav { + float: left; + margin: 0; + } + .navbar-nav > li { + float: left; + } + .navbar-nav > li > a { + padding-top: 6px; + padding-bottom: 6px; + } +} +.navbar-form { + margin-left: 0px; + margin-right: 0px; + padding: 10px 0px; + border-top: 1px solid transparent; + border-bottom: 1px solid transparent; + -webkit-box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); + box-shadow: inset 0 1px 0 rgba(255, 255, 255, 0.1), 0 1px 0 rgba(255, 255, 255, 0.1); + margin-top: -1px; + margin-bottom: -1px; +} +@media (min-width: 768px) { + .navbar-form .form-group { + display: inline-block; + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .form-control { + display: inline-block; + width: auto; + vertical-align: middle; + } + .navbar-form .form-control-static { + display: inline-block; + } + .navbar-form .input-group { + display: inline-table; + vertical-align: middle; + } + .navbar-form .input-group .input-group-addon, + .navbar-form .input-group .input-group-btn, + .navbar-form .input-group .form-control { + width: auto; + } + .navbar-form .input-group > .form-control { + width: 100%; + } + .navbar-form .control-label { + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .radio, + .navbar-form .checkbox { + display: inline-block; + margin-top: 0; + margin-bottom: 0; + vertical-align: middle; + } + .navbar-form .radio label, + .navbar-form .checkbox label { + padding-left: 0; + } + .navbar-form .radio input[type="radio"], + .navbar-form .checkbox input[type="checkbox"] { + position: relative; + margin-left: 0; + } + .navbar-form .has-feedback .form-control-feedback { + top: 0; + } +} +@media (max-width: 540px) { + .navbar-form .form-group { + margin-bottom: 5px; + } + .navbar-form .form-group:last-child { + margin-bottom: 0; + } +} +@media (min-width: 541px) { + .navbar-form { + width: auto; + border: 0; + margin-left: 0; + margin-right: 0; + padding-top: 0; + padding-bottom: 0; + -webkit-box-shadow: none; + box-shadow: none; + } +} +.navbar-nav > li > .dropdown-menu { + margin-top: 0; + border-top-right-radius: 0; + border-top-left-radius: 0; +} +.navbar-fixed-bottom .navbar-nav > li > .dropdown-menu { + margin-bottom: 0; + border-top-right-radius: 2px; + border-top-left-radius: 2px; + border-bottom-right-radius: 0; + border-bottom-left-radius: 0; +} +.navbar-btn { + margin-top: -1px; + margin-bottom: -1px; +} +.navbar-btn.btn-sm { + margin-top: 0px; + margin-bottom: 0px; +} +.navbar-btn.btn-xs { + margin-top: 4px; + margin-bottom: 4px; +} +.navbar-text { + margin-top: 6px; + margin-bottom: 6px; +} +@media (min-width: 541px) { + .navbar-text { + float: left; + margin-left: 0px; + margin-right: 0px; + } +} +@media (min-width: 541px) { + .navbar-left { + float: left !important; + float: left; + } + .navbar-right { + float: right !important; + float: right; + margin-right: 0px; + } + .navbar-right ~ .navbar-right { + margin-right: 0; + } +} +.navbar-default { + background-color: #f8f8f8; + border-color: #e7e7e7; +} +.navbar-default .navbar-brand { + color: #777; +} +.navbar-default .navbar-brand:hover, +.navbar-default .navbar-brand:focus { + color: #5e5e5e; + background-color: transparent; +} +.navbar-default .navbar-text { + color: #777; +} +.navbar-default .navbar-nav > li > a { + color: #777; +} +.navbar-default .navbar-nav > li > a:hover, +.navbar-default .navbar-nav > li > a:focus { + color: #333; + background-color: transparent; +} +.navbar-default .navbar-nav > .active > a, +.navbar-default .navbar-nav > .active > a:hover, +.navbar-default .navbar-nav > .active > a:focus { + color: #555; + background-color: #e7e7e7; +} +.navbar-default .navbar-nav > .disabled > a, +.navbar-default .navbar-nav > .disabled > a:hover, +.navbar-default .navbar-nav > .disabled > a:focus { + color: #ccc; + background-color: transparent; +} +.navbar-default .navbar-toggle { + border-color: #ddd; +} +.navbar-default .navbar-toggle:hover, +.navbar-default .navbar-toggle:focus { + background-color: #ddd; +} +.navbar-default .navbar-toggle .icon-bar { + background-color: #888; +} +.navbar-default .navbar-collapse, +.navbar-default .navbar-form { + border-color: #e7e7e7; +} +.navbar-default .navbar-nav > .open > a, +.navbar-default .navbar-nav > .open > a:hover, +.navbar-default .navbar-nav > .open > a:focus { + background-color: #e7e7e7; + color: #555; +} +@media (max-width: 540px) { + .navbar-default .navbar-nav .open .dropdown-menu > li > a { + color: #777; + } + .navbar-default .navbar-nav .open .dropdown-menu > li > a:hover, + .navbar-default .navbar-nav .open .dropdown-menu > li > a:focus { + color: #333; + background-color: transparent; + } + .navbar-default .navbar-nav .open .dropdown-menu > .active > a, + .navbar-default .navbar-nav .open .dropdown-menu > .active > a:hover, + .navbar-default .navbar-nav .open .dropdown-menu > .active > a:focus { + color: #555; + background-color: #e7e7e7; + } + .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a, + .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:hover, + .navbar-default .navbar-nav .open .dropdown-menu > .disabled > a:focus { + color: #ccc; + background-color: transparent; + } +} +.navbar-default .navbar-link { + color: #777; +} +.navbar-default .navbar-link:hover { + color: #333; +} +.navbar-default .btn-link { + color: #777; +} +.navbar-default .btn-link:hover, +.navbar-default .btn-link:focus { + color: #333; +} +.navbar-default .btn-link[disabled]:hover, +fieldset[disabled] .navbar-default .btn-link:hover, +.navbar-default .btn-link[disabled]:focus, +fieldset[disabled] .navbar-default .btn-link:focus { + color: #ccc; +} +.navbar-inverse { + background-color: #222; + border-color: #080808; +} +.navbar-inverse .navbar-brand { + color: #9d9d9d; +} +.navbar-inverse .navbar-brand:hover, +.navbar-inverse .navbar-brand:focus { + color: #fff; + background-color: transparent; +} +.navbar-inverse .navbar-text { + color: #9d9d9d; +} +.navbar-inverse .navbar-nav > li > a { + color: #9d9d9d; +} +.navbar-inverse .navbar-nav > li > a:hover, +.navbar-inverse .navbar-nav > li > a:focus { + color: #fff; + background-color: transparent; +} +.navbar-inverse .navbar-nav > .active > a, +.navbar-inverse .navbar-nav > .active > a:hover, +.navbar-inverse .navbar-nav > .active > a:focus { + color: #fff; + background-color: #080808; +} +.navbar-inverse .navbar-nav > .disabled > a, +.navbar-inverse .navbar-nav > .disabled > a:hover, +.navbar-inverse .navbar-nav > .disabled > a:focus { + color: #444; + background-color: transparent; +} +.navbar-inverse .navbar-toggle { + border-color: #333; +} +.navbar-inverse .navbar-toggle:hover, +.navbar-inverse .navbar-toggle:focus { + background-color: #333; +} +.navbar-inverse .navbar-toggle .icon-bar { + background-color: #fff; +} +.navbar-inverse .navbar-collapse, +.navbar-inverse .navbar-form { + border-color: #101010; +} +.navbar-inverse .navbar-nav > .open > a, +.navbar-inverse .navbar-nav > .open > a:hover, +.navbar-inverse .navbar-nav > .open > a:focus { + background-color: #080808; + color: #fff; +} +@media (max-width: 540px) { + .navbar-inverse .navbar-nav .open .dropdown-menu > .dropdown-header { + border-color: #080808; + } + .navbar-inverse .navbar-nav .open .dropdown-menu .divider { + background-color: #080808; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > li > a { + color: #9d9d9d; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:hover, + .navbar-inverse .navbar-nav .open .dropdown-menu > li > a:focus { + color: #fff; + background-color: transparent; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a, + .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:hover, + .navbar-inverse .navbar-nav .open .dropdown-menu > .active > a:focus { + color: #fff; + background-color: #080808; + } + .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a, + .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:hover, + .navbar-inverse .navbar-nav .open .dropdown-menu > .disabled > a:focus { + color: #444; + background-color: transparent; + } +} +.navbar-inverse .navbar-link { + color: #9d9d9d; +} +.navbar-inverse .navbar-link:hover { + color: #fff; +} +.navbar-inverse .btn-link { + color: #9d9d9d; +} +.navbar-inverse .btn-link:hover, +.navbar-inverse .btn-link:focus { + color: #fff; +} +.navbar-inverse .btn-link[disabled]:hover, +fieldset[disabled] .navbar-inverse .btn-link:hover, +.navbar-inverse .btn-link[disabled]:focus, +fieldset[disabled] .navbar-inverse .btn-link:focus { + color: #444; +} +.breadcrumb { + padding: 8px 15px; + margin-bottom: 18px; + list-style: none; + background-color: #f5f5f5; + border-radius: 2px; +} +.breadcrumb > li { + display: inline-block; +} +.breadcrumb > li + li:before { + content: "/\00a0"; + padding: 0 5px; + color: #5e5e5e; +} +.breadcrumb > .active { + color: #777777; +} +.pagination { + display: inline-block; + padding-left: 0; + margin: 18px 0; + border-radius: 2px; +} +.pagination > li { + display: inline; +} +.pagination > li > a, +.pagination > li > span { + position: relative; + float: left; + padding: 6px 12px; + line-height: 1.42857143; + text-decoration: none; + color: #337ab7; + background-color: #fff; + border: 1px solid #ddd; + margin-left: -1px; +} +.pagination > li:first-child > a, +.pagination > li:first-child > span { + margin-left: 0; + border-bottom-left-radius: 2px; + border-top-left-radius: 2px; +} +.pagination > li:last-child > a, +.pagination > li:last-child > span { + border-bottom-right-radius: 2px; + border-top-right-radius: 2px; +} +.pagination > li > a:hover, +.pagination > li > span:hover, +.pagination > li > a:focus, +.pagination > li > span:focus { + z-index: 2; + color: #23527c; + background-color: #eeeeee; + border-color: #ddd; +} +.pagination > .active > a, +.pagination > .active > span, +.pagination > .active > a:hover, +.pagination > .active > span:hover, +.pagination > .active > a:focus, +.pagination > .active > span:focus { + z-index: 3; + color: #fff; + background-color: #337ab7; + border-color: #337ab7; + cursor: default; +} +.pagination > .disabled > span, +.pagination > .disabled > span:hover, +.pagination > .disabled > span:focus, +.pagination > .disabled > a, +.pagination > .disabled > a:hover, +.pagination > .disabled > a:focus { + color: #777777; + background-color: #fff; + border-color: #ddd; + cursor: not-allowed; +} +.pagination-lg > li > a, +.pagination-lg > li > span { + padding: 10px 16px; + font-size: 17px; + line-height: 1.3333333; +} +.pagination-lg > li:first-child > a, +.pagination-lg > li:first-child > span { + border-bottom-left-radius: 3px; + border-top-left-radius: 3px; +} +.pagination-lg > li:last-child > a, +.pagination-lg > li:last-child > span { + border-bottom-right-radius: 3px; + border-top-right-radius: 3px; +} +.pagination-sm > li > a, +.pagination-sm > li > span { + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; +} +.pagination-sm > li:first-child > a, +.pagination-sm > li:first-child > span { + border-bottom-left-radius: 1px; + border-top-left-radius: 1px; +} +.pagination-sm > li:last-child > a, +.pagination-sm > li:last-child > span { + border-bottom-right-radius: 1px; + border-top-right-radius: 1px; +} +.pager { + padding-left: 0; + margin: 18px 0; + list-style: none; + text-align: center; +} +.pager li { + display: inline; +} +.pager li > a, +.pager li > span { + display: inline-block; + padding: 5px 14px; + background-color: #fff; + border: 1px solid #ddd; + border-radius: 15px; +} +.pager li > a:hover, +.pager li > a:focus { + text-decoration: none; + background-color: #eeeeee; +} +.pager .next > a, +.pager .next > span { + float: right; +} +.pager .previous > a, +.pager .previous > span { + float: left; +} +.pager .disabled > a, +.pager .disabled > a:hover, +.pager .disabled > a:focus, +.pager .disabled > span { + color: #777777; + background-color: #fff; + cursor: not-allowed; +} +.label { + display: inline; + padding: .2em .6em .3em; + font-size: 75%; + font-weight: bold; + line-height: 1; + color: #fff; + text-align: center; + white-space: nowrap; + vertical-align: baseline; + border-radius: .25em; +} +a.label:hover, +a.label:focus { + color: #fff; + text-decoration: none; + cursor: pointer; +} +.label:empty { + display: none; +} +.btn .label { + position: relative; + top: -1px; +} +.label-default { + background-color: #777777; +} +.label-default[href]:hover, +.label-default[href]:focus { + background-color: #5e5e5e; +} +.label-primary { + background-color: #337ab7; +} +.label-primary[href]:hover, +.label-primary[href]:focus { + background-color: #286090; +} +.label-success { + background-color: #5cb85c; +} +.label-success[href]:hover, +.label-success[href]:focus { + background-color: #449d44; +} +.label-info { + background-color: #5bc0de; +} +.label-info[href]:hover, +.label-info[href]:focus { + background-color: #31b0d5; +} +.label-warning { + background-color: #f0ad4e; +} +.label-warning[href]:hover, +.label-warning[href]:focus { + background-color: #ec971f; +} +.label-danger { + background-color: #d9534f; +} +.label-danger[href]:hover, +.label-danger[href]:focus { + background-color: #c9302c; +} +.badge { + display: inline-block; + min-width: 10px; + padding: 3px 7px; + font-size: 12px; + font-weight: bold; + color: #fff; + line-height: 1; + vertical-align: middle; + white-space: nowrap; + text-align: center; + background-color: #777777; + border-radius: 10px; +} +.badge:empty { + display: none; +} +.btn .badge { + position: relative; + top: -1px; +} +.btn-xs .badge, +.btn-group-xs > .btn .badge { + top: 0; + padding: 1px 5px; +} +a.badge:hover, +a.badge:focus { + color: #fff; + text-decoration: none; + cursor: pointer; +} +.list-group-item.active > .badge, +.nav-pills > .active > a > .badge { + color: #337ab7; + background-color: #fff; +} +.list-group-item > .badge { + float: right; +} +.list-group-item > .badge + .badge { + margin-right: 5px; +} +.nav-pills > li > a > .badge { + margin-left: 3px; +} +.jumbotron { + padding-top: 30px; + padding-bottom: 30px; + margin-bottom: 30px; + color: inherit; + background-color: #eeeeee; +} +.jumbotron h1, +.jumbotron .h1 { + color: inherit; +} +.jumbotron p { + margin-bottom: 15px; + font-size: 20px; + font-weight: 200; +} +.jumbotron > hr { + border-top-color: #d5d5d5; +} +.container .jumbotron, +.container-fluid .jumbotron { + border-radius: 3px; + padding-left: 0px; + padding-right: 0px; +} +.jumbotron .container { + max-width: 100%; +} +@media screen and (min-width: 768px) { + .jumbotron { + padding-top: 48px; + padding-bottom: 48px; + } + .container .jumbotron, + .container-fluid .jumbotron { + padding-left: 60px; + padding-right: 60px; + } + .jumbotron h1, + .jumbotron .h1 { + font-size: 59px; + } +} +.thumbnail { + display: block; + padding: 4px; + margin-bottom: 18px; + line-height: 1.42857143; + background-color: #fff; + border: 1px solid #ddd; + border-radius: 2px; + -webkit-transition: border 0.2s ease-in-out; + -o-transition: border 0.2s ease-in-out; + transition: border 0.2s ease-in-out; +} +.thumbnail > img, +.thumbnail a > img { + margin-left: auto; + margin-right: auto; +} +a.thumbnail:hover, +a.thumbnail:focus, +a.thumbnail.active { + border-color: #337ab7; +} +.thumbnail .caption { + padding: 9px; + color: #000; +} +.alert { + padding: 15px; + margin-bottom: 18px; + border: 1px solid transparent; + border-radius: 2px; +} +.alert h4 { + margin-top: 0; + color: inherit; +} +.alert .alert-link { + font-weight: bold; +} +.alert > p, +.alert > ul { + margin-bottom: 0; +} +.alert > p + p { + margin-top: 5px; +} +.alert-dismissable, +.alert-dismissible { + padding-right: 35px; +} +.alert-dismissable .close, +.alert-dismissible .close { + position: relative; + top: -2px; + right: -21px; + color: inherit; +} +.alert-success { + background-color: #dff0d8; + border-color: #d6e9c6; + color: #3c763d; +} +.alert-success hr { + border-top-color: #c9e2b3; +} +.alert-success .alert-link { + color: #2b542c; +} +.alert-info { + background-color: #d9edf7; + border-color: #bce8f1; + color: #31708f; +} +.alert-info hr { + border-top-color: #a6e1ec; +} +.alert-info .alert-link { + color: #245269; +} +.alert-warning { + background-color: #fcf8e3; + border-color: #faebcc; + color: #8a6d3b; +} +.alert-warning hr { + border-top-color: #f7e1b5; +} +.alert-warning .alert-link { + color: #66512c; +} +.alert-danger { + background-color: #f2dede; + border-color: #ebccd1; + color: #a94442; +} +.alert-danger hr { + border-top-color: #e4b9c0; +} +.alert-danger .alert-link { + color: #843534; +} +@-webkit-keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} +@keyframes progress-bar-stripes { + from { + background-position: 40px 0; + } + to { + background-position: 0 0; + } +} +.progress { + overflow: hidden; + height: 18px; + margin-bottom: 18px; + background-color: #f5f5f5; + border-radius: 2px; + -webkit-box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); + box-shadow: inset 0 1px 2px rgba(0, 0, 0, 0.1); +} +.progress-bar { + float: left; + width: 0%; + height: 100%; + font-size: 12px; + line-height: 18px; + color: #fff; + text-align: center; + background-color: #337ab7; + -webkit-box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + box-shadow: inset 0 -1px 0 rgba(0, 0, 0, 0.15); + -webkit-transition: width 0.6s ease; + -o-transition: width 0.6s ease; + transition: width 0.6s ease; +} +.progress-striped .progress-bar, +.progress-bar-striped { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-size: 40px 40px; +} +.progress.active .progress-bar, +.progress-bar.active { + -webkit-animation: progress-bar-stripes 2s linear infinite; + -o-animation: progress-bar-stripes 2s linear infinite; + animation: progress-bar-stripes 2s linear infinite; +} +.progress-bar-success { + background-color: #5cb85c; +} +.progress-striped .progress-bar-success { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} +.progress-bar-info { + background-color: #5bc0de; +} +.progress-striped .progress-bar-info { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} +.progress-bar-warning { + background-color: #f0ad4e; +} +.progress-striped .progress-bar-warning { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} +.progress-bar-danger { + background-color: #d9534f; +} +.progress-striped .progress-bar-danger { + background-image: -webkit-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: -o-linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); + background-image: linear-gradient(45deg, rgba(255, 255, 255, 0.15) 25%, transparent 25%, transparent 50%, rgba(255, 255, 255, 0.15) 50%, rgba(255, 255, 255, 0.15) 75%, transparent 75%, transparent); +} +.media { + margin-top: 15px; +} +.media:first-child { + margin-top: 0; +} +.media, +.media-body { + zoom: 1; + overflow: hidden; +} +.media-body { + width: 10000px; +} +.media-object { + display: block; +} +.media-object.img-thumbnail { + max-width: none; +} +.media-right, +.media > .pull-right { + padding-left: 10px; +} +.media-left, +.media > .pull-left { + padding-right: 10px; +} +.media-left, +.media-right, +.media-body { + display: table-cell; + vertical-align: top; +} +.media-middle { + vertical-align: middle; +} +.media-bottom { + vertical-align: bottom; +} +.media-heading { + margin-top: 0; + margin-bottom: 5px; +} +.media-list { + padding-left: 0; + list-style: none; +} +.list-group { + margin-bottom: 20px; + padding-left: 0; +} +.list-group-item { + position: relative; + display: block; + padding: 10px 15px; + margin-bottom: -1px; + background-color: #fff; + border: 1px solid #ddd; +} +.list-group-item:first-child { + border-top-right-radius: 2px; + border-top-left-radius: 2px; +} +.list-group-item:last-child { + margin-bottom: 0; + border-bottom-right-radius: 2px; + border-bottom-left-radius: 2px; +} +a.list-group-item, +button.list-group-item { + color: #555; +} +a.list-group-item .list-group-item-heading, +button.list-group-item .list-group-item-heading { + color: #333; +} +a.list-group-item:hover, +button.list-group-item:hover, +a.list-group-item:focus, +button.list-group-item:focus { + text-decoration: none; + color: #555; + background-color: #f5f5f5; +} +button.list-group-item { + width: 100%; + text-align: left; +} +.list-group-item.disabled, +.list-group-item.disabled:hover, +.list-group-item.disabled:focus { + background-color: #eeeeee; + color: #777777; + cursor: not-allowed; +} +.list-group-item.disabled .list-group-item-heading, +.list-group-item.disabled:hover .list-group-item-heading, +.list-group-item.disabled:focus .list-group-item-heading { + color: inherit; +} +.list-group-item.disabled .list-group-item-text, +.list-group-item.disabled:hover .list-group-item-text, +.list-group-item.disabled:focus .list-group-item-text { + color: #777777; +} +.list-group-item.active, +.list-group-item.active:hover, +.list-group-item.active:focus { + z-index: 2; + color: #fff; + background-color: #337ab7; + border-color: #337ab7; +} +.list-group-item.active .list-group-item-heading, +.list-group-item.active:hover .list-group-item-heading, +.list-group-item.active:focus .list-group-item-heading, +.list-group-item.active .list-group-item-heading > small, +.list-group-item.active:hover .list-group-item-heading > small, +.list-group-item.active:focus .list-group-item-heading > small, +.list-group-item.active .list-group-item-heading > .small, +.list-group-item.active:hover .list-group-item-heading > .small, +.list-group-item.active:focus .list-group-item-heading > .small { + color: inherit; +} +.list-group-item.active .list-group-item-text, +.list-group-item.active:hover .list-group-item-text, +.list-group-item.active:focus .list-group-item-text { + color: #c7ddef; +} +.list-group-item-success { + color: #3c763d; + background-color: #dff0d8; +} +a.list-group-item-success, +button.list-group-item-success { + color: #3c763d; +} +a.list-group-item-success .list-group-item-heading, +button.list-group-item-success .list-group-item-heading { + color: inherit; +} +a.list-group-item-success:hover, +button.list-group-item-success:hover, +a.list-group-item-success:focus, +button.list-group-item-success:focus { + color: #3c763d; + background-color: #d0e9c6; +} +a.list-group-item-success.active, +button.list-group-item-success.active, +a.list-group-item-success.active:hover, +button.list-group-item-success.active:hover, +a.list-group-item-success.active:focus, +button.list-group-item-success.active:focus { + color: #fff; + background-color: #3c763d; + border-color: #3c763d; +} +.list-group-item-info { + color: #31708f; + background-color: #d9edf7; +} +a.list-group-item-info, +button.list-group-item-info { + color: #31708f; +} +a.list-group-item-info .list-group-item-heading, +button.list-group-item-info .list-group-item-heading { + color: inherit; +} +a.list-group-item-info:hover, +button.list-group-item-info:hover, +a.list-group-item-info:focus, +button.list-group-item-info:focus { + color: #31708f; + background-color: #c4e3f3; +} +a.list-group-item-info.active, +button.list-group-item-info.active, +a.list-group-item-info.active:hover, +button.list-group-item-info.active:hover, +a.list-group-item-info.active:focus, +button.list-group-item-info.active:focus { + color: #fff; + background-color: #31708f; + border-color: #31708f; +} +.list-group-item-warning { + color: #8a6d3b; + background-color: #fcf8e3; +} +a.list-group-item-warning, +button.list-group-item-warning { + color: #8a6d3b; +} +a.list-group-item-warning .list-group-item-heading, +button.list-group-item-warning .list-group-item-heading { + color: inherit; +} +a.list-group-item-warning:hover, +button.list-group-item-warning:hover, +a.list-group-item-warning:focus, +button.list-group-item-warning:focus { + color: #8a6d3b; + background-color: #faf2cc; +} +a.list-group-item-warning.active, +button.list-group-item-warning.active, +a.list-group-item-warning.active:hover, +button.list-group-item-warning.active:hover, +a.list-group-item-warning.active:focus, +button.list-group-item-warning.active:focus { + color: #fff; + background-color: #8a6d3b; + border-color: #8a6d3b; +} +.list-group-item-danger { + color: #a94442; + background-color: #f2dede; +} +a.list-group-item-danger, +button.list-group-item-danger { + color: #a94442; +} +a.list-group-item-danger .list-group-item-heading, +button.list-group-item-danger .list-group-item-heading { + color: inherit; +} +a.list-group-item-danger:hover, +button.list-group-item-danger:hover, +a.list-group-item-danger:focus, +button.list-group-item-danger:focus { + color: #a94442; + background-color: #ebcccc; +} +a.list-group-item-danger.active, +button.list-group-item-danger.active, +a.list-group-item-danger.active:hover, +button.list-group-item-danger.active:hover, +a.list-group-item-danger.active:focus, +button.list-group-item-danger.active:focus { + color: #fff; + background-color: #a94442; + border-color: #a94442; +} +.list-group-item-heading { + margin-top: 0; + margin-bottom: 5px; +} +.list-group-item-text { + margin-bottom: 0; + line-height: 1.3; +} +.panel { + margin-bottom: 18px; + background-color: #fff; + border: 1px solid transparent; + border-radius: 2px; + -webkit-box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); + box-shadow: 0 1px 1px rgba(0, 0, 0, 0.05); +} +.panel-body { + padding: 15px; +} +.panel-heading { + padding: 10px 15px; + border-bottom: 1px solid transparent; + border-top-right-radius: 1px; + border-top-left-radius: 1px; +} +.panel-heading > .dropdown .dropdown-toggle { + color: inherit; +} +.panel-title { + margin-top: 0; + margin-bottom: 0; + font-size: 15px; + color: inherit; +} +.panel-title > a, +.panel-title > small, +.panel-title > .small, +.panel-title > small > a, +.panel-title > .small > a { + color: inherit; +} +.panel-footer { + padding: 10px 15px; + background-color: #f5f5f5; + border-top: 1px solid #ddd; + border-bottom-right-radius: 1px; + border-bottom-left-radius: 1px; +} +.panel > .list-group, +.panel > .panel-collapse > .list-group { + margin-bottom: 0; +} +.panel > .list-group .list-group-item, +.panel > .panel-collapse > .list-group .list-group-item { + border-width: 1px 0; + border-radius: 0; +} +.panel > .list-group:first-child .list-group-item:first-child, +.panel > .panel-collapse > .list-group:first-child .list-group-item:first-child { + border-top: 0; + border-top-right-radius: 1px; + border-top-left-radius: 1px; +} +.panel > .list-group:last-child .list-group-item:last-child, +.panel > .panel-collapse > .list-group:last-child .list-group-item:last-child { + border-bottom: 0; + border-bottom-right-radius: 1px; + border-bottom-left-radius: 1px; +} +.panel > .panel-heading + .panel-collapse > .list-group .list-group-item:first-child { + border-top-right-radius: 0; + border-top-left-radius: 0; +} +.panel-heading + .list-group .list-group-item:first-child { + border-top-width: 0; +} +.list-group + .panel-footer { + border-top-width: 0; +} +.panel > .table, +.panel > .table-responsive > .table, +.panel > .panel-collapse > .table { + margin-bottom: 0; +} +.panel > .table caption, +.panel > .table-responsive > .table caption, +.panel > .panel-collapse > .table caption { + padding-left: 15px; + padding-right: 15px; +} +.panel > .table:first-child, +.panel > .table-responsive:first-child > .table:first-child { + border-top-right-radius: 1px; + border-top-left-radius: 1px; +} +.panel > .table:first-child > thead:first-child > tr:first-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child, +.panel > .table:first-child > tbody:first-child > tr:first-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child { + border-top-left-radius: 1px; + border-top-right-radius: 1px; +} +.panel > .table:first-child > thead:first-child > tr:first-child td:first-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:first-child, +.panel > .table:first-child > tbody:first-child > tr:first-child td:first-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:first-child, +.panel > .table:first-child > thead:first-child > tr:first-child th:first-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:first-child, +.panel > .table:first-child > tbody:first-child > tr:first-child th:first-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:first-child { + border-top-left-radius: 1px; +} +.panel > .table:first-child > thead:first-child > tr:first-child td:last-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child td:last-child, +.panel > .table:first-child > tbody:first-child > tr:first-child td:last-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child td:last-child, +.panel > .table:first-child > thead:first-child > tr:first-child th:last-child, +.panel > .table-responsive:first-child > .table:first-child > thead:first-child > tr:first-child th:last-child, +.panel > .table:first-child > tbody:first-child > tr:first-child th:last-child, +.panel > .table-responsive:first-child > .table:first-child > tbody:first-child > tr:first-child th:last-child { + border-top-right-radius: 1px; +} +.panel > .table:last-child, +.panel > .table-responsive:last-child > .table:last-child { + border-bottom-right-radius: 1px; + border-bottom-left-radius: 1px; +} +.panel > .table:last-child > tbody:last-child > tr:last-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child { + border-bottom-left-radius: 1px; + border-bottom-right-radius: 1px; +} +.panel > .table:last-child > tbody:last-child > tr:last-child td:first-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:first-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child td:first-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:first-child, +.panel > .table:last-child > tbody:last-child > tr:last-child th:first-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:first-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child th:first-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:first-child { + border-bottom-left-radius: 1px; +} +.panel > .table:last-child > tbody:last-child > tr:last-child td:last-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child td:last-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child td:last-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child td:last-child, +.panel > .table:last-child > tbody:last-child > tr:last-child th:last-child, +.panel > .table-responsive:last-child > .table:last-child > tbody:last-child > tr:last-child th:last-child, +.panel > .table:last-child > tfoot:last-child > tr:last-child th:last-child, +.panel > .table-responsive:last-child > .table:last-child > tfoot:last-child > tr:last-child th:last-child { + border-bottom-right-radius: 1px; +} +.panel > .panel-body + .table, +.panel > .panel-body + .table-responsive, +.panel > .table + .panel-body, +.panel > .table-responsive + .panel-body { + border-top: 1px solid #ddd; +} +.panel > .table > tbody:first-child > tr:first-child th, +.panel > .table > tbody:first-child > tr:first-child td { + border-top: 0; +} +.panel > .table-bordered, +.panel > .table-responsive > .table-bordered { + border: 0; +} +.panel > .table-bordered > thead > tr > th:first-child, +.panel > .table-responsive > .table-bordered > thead > tr > th:first-child, +.panel > .table-bordered > tbody > tr > th:first-child, +.panel > .table-responsive > .table-bordered > tbody > tr > th:first-child, +.panel > .table-bordered > tfoot > tr > th:first-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > th:first-child, +.panel > .table-bordered > thead > tr > td:first-child, +.panel > .table-responsive > .table-bordered > thead > tr > td:first-child, +.panel > .table-bordered > tbody > tr > td:first-child, +.panel > .table-responsive > .table-bordered > tbody > tr > td:first-child, +.panel > .table-bordered > tfoot > tr > td:first-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > td:first-child { + border-left: 0; +} +.panel > .table-bordered > thead > tr > th:last-child, +.panel > .table-responsive > .table-bordered > thead > tr > th:last-child, +.panel > .table-bordered > tbody > tr > th:last-child, +.panel > .table-responsive > .table-bordered > tbody > tr > th:last-child, +.panel > .table-bordered > tfoot > tr > th:last-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > th:last-child, +.panel > .table-bordered > thead > tr > td:last-child, +.panel > .table-responsive > .table-bordered > thead > tr > td:last-child, +.panel > .table-bordered > tbody > tr > td:last-child, +.panel > .table-responsive > .table-bordered > tbody > tr > td:last-child, +.panel > .table-bordered > tfoot > tr > td:last-child, +.panel > .table-responsive > .table-bordered > tfoot > tr > td:last-child { + border-right: 0; +} +.panel > .table-bordered > thead > tr:first-child > td, +.panel > .table-responsive > .table-bordered > thead > tr:first-child > td, +.panel > .table-bordered > tbody > tr:first-child > td, +.panel > .table-responsive > .table-bordered > tbody > tr:first-child > td, +.panel > .table-bordered > thead > tr:first-child > th, +.panel > .table-responsive > .table-bordered > thead > tr:first-child > th, +.panel > .table-bordered > tbody > tr:first-child > th, +.panel > .table-responsive > .table-bordered > tbody > tr:first-child > th { + border-bottom: 0; +} +.panel > .table-bordered > tbody > tr:last-child > td, +.panel > .table-responsive > .table-bordered > tbody > tr:last-child > td, +.panel > .table-bordered > tfoot > tr:last-child > td, +.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > td, +.panel > .table-bordered > tbody > tr:last-child > th, +.panel > .table-responsive > .table-bordered > tbody > tr:last-child > th, +.panel > .table-bordered > tfoot > tr:last-child > th, +.panel > .table-responsive > .table-bordered > tfoot > tr:last-child > th { + border-bottom: 0; +} +.panel > .table-responsive { + border: 0; + margin-bottom: 0; +} +.panel-group { + margin-bottom: 18px; +} +.panel-group .panel { + margin-bottom: 0; + border-radius: 2px; +} +.panel-group .panel + .panel { + margin-top: 5px; +} +.panel-group .panel-heading { + border-bottom: 0; +} +.panel-group .panel-heading + .panel-collapse > .panel-body, +.panel-group .panel-heading + .panel-collapse > .list-group { + border-top: 1px solid #ddd; +} +.panel-group .panel-footer { + border-top: 0; +} +.panel-group .panel-footer + .panel-collapse .panel-body { + border-bottom: 1px solid #ddd; +} +.panel-default { + border-color: #ddd; +} +.panel-default > .panel-heading { + color: #333333; + background-color: #f5f5f5; + border-color: #ddd; +} +.panel-default > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #ddd; +} +.panel-default > .panel-heading .badge { + color: #f5f5f5; + background-color: #333333; +} +.panel-default > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #ddd; +} +.panel-primary { + border-color: #337ab7; +} +.panel-primary > .panel-heading { + color: #fff; + background-color: #337ab7; + border-color: #337ab7; +} +.panel-primary > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #337ab7; +} +.panel-primary > .panel-heading .badge { + color: #337ab7; + background-color: #fff; +} +.panel-primary > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #337ab7; +} +.panel-success { + border-color: #d6e9c6; +} +.panel-success > .panel-heading { + color: #3c763d; + background-color: #dff0d8; + border-color: #d6e9c6; +} +.panel-success > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #d6e9c6; +} +.panel-success > .panel-heading .badge { + color: #dff0d8; + background-color: #3c763d; +} +.panel-success > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #d6e9c6; +} +.panel-info { + border-color: #bce8f1; +} +.panel-info > .panel-heading { + color: #31708f; + background-color: #d9edf7; + border-color: #bce8f1; +} +.panel-info > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #bce8f1; +} +.panel-info > .panel-heading .badge { + color: #d9edf7; + background-color: #31708f; +} +.panel-info > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #bce8f1; +} +.panel-warning { + border-color: #faebcc; +} +.panel-warning > .panel-heading { + color: #8a6d3b; + background-color: #fcf8e3; + border-color: #faebcc; +} +.panel-warning > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #faebcc; +} +.panel-warning > .panel-heading .badge { + color: #fcf8e3; + background-color: #8a6d3b; +} +.panel-warning > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #faebcc; +} +.panel-danger { + border-color: #ebccd1; +} +.panel-danger > .panel-heading { + color: #a94442; + background-color: #f2dede; + border-color: #ebccd1; +} +.panel-danger > .panel-heading + .panel-collapse > .panel-body { + border-top-color: #ebccd1; +} +.panel-danger > .panel-heading .badge { + color: #f2dede; + background-color: #a94442; +} +.panel-danger > .panel-footer + .panel-collapse > .panel-body { + border-bottom-color: #ebccd1; +} +.embed-responsive { + position: relative; + display: block; + height: 0; + padding: 0; + overflow: hidden; +} +.embed-responsive .embed-responsive-item, +.embed-responsive iframe, +.embed-responsive embed, +.embed-responsive object, +.embed-responsive video { + position: absolute; + top: 0; + left: 0; + bottom: 0; + height: 100%; + width: 100%; + border: 0; +} +.embed-responsive-16by9 { + padding-bottom: 56.25%; +} +.embed-responsive-4by3 { + padding-bottom: 75%; +} +.well { + min-height: 20px; + padding: 19px; + margin-bottom: 20px; + background-color: #f5f5f5; + border: 1px solid #e3e3e3; + border-radius: 2px; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.05); +} +.well blockquote { + border-color: #ddd; + border-color: rgba(0, 0, 0, 0.15); +} +.well-lg { + padding: 24px; + border-radius: 3px; +} +.well-sm { + padding: 9px; + border-radius: 1px; +} +.close { + float: right; + font-size: 19.5px; + font-weight: bold; + line-height: 1; + color: #000; + text-shadow: 0 1px 0 #fff; + opacity: 0.2; + filter: alpha(opacity=20); +} +.close:hover, +.close:focus { + color: #000; + text-decoration: none; + cursor: pointer; + opacity: 0.5; + filter: alpha(opacity=50); +} +button.close { + padding: 0; + cursor: pointer; + background: transparent; + border: 0; + -webkit-appearance: none; +} +.modal-open { + overflow: hidden; +} +.modal { + display: none; + overflow: hidden; + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1050; + -webkit-overflow-scrolling: touch; + outline: 0; +} +.modal.fade .modal-dialog { + -webkit-transform: translate(0, -25%); + -ms-transform: translate(0, -25%); + -o-transform: translate(0, -25%); + transform: translate(0, -25%); + -webkit-transition: -webkit-transform 0.3s ease-out; + -moz-transition: -moz-transform 0.3s ease-out; + -o-transition: -o-transform 0.3s ease-out; + transition: transform 0.3s ease-out; +} +.modal.in .modal-dialog { + -webkit-transform: translate(0, 0); + -ms-transform: translate(0, 0); + -o-transform: translate(0, 0); + transform: translate(0, 0); +} +.modal-open .modal { + overflow-x: hidden; + overflow-y: auto; +} +.modal-dialog { + position: relative; + width: auto; + margin: 10px; +} +.modal-content { + position: relative; + background-color: #fff; + border: 1px solid #999; + border: 1px solid rgba(0, 0, 0, 0.2); + border-radius: 3px; + -webkit-box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); + box-shadow: 0 3px 9px rgba(0, 0, 0, 0.5); + background-clip: padding-box; + outline: 0; +} +.modal-backdrop { + position: fixed; + top: 0; + right: 0; + bottom: 0; + left: 0; + z-index: 1040; + background-color: #000; +} +.modal-backdrop.fade { + opacity: 0; + filter: alpha(opacity=0); +} +.modal-backdrop.in { + opacity: 0.5; + filter: alpha(opacity=50); +} +.modal-header { + padding: 15px; + border-bottom: 1px solid #e5e5e5; +} +.modal-header .close { + margin-top: -2px; +} +.modal-title { + margin: 0; + line-height: 1.42857143; +} +.modal-body { + position: relative; + padding: 15px; +} +.modal-footer { + padding: 15px; + text-align: right; + border-top: 1px solid #e5e5e5; +} +.modal-footer .btn + .btn { + margin-left: 5px; + margin-bottom: 0; +} +.modal-footer .btn-group .btn + .btn { + margin-left: -1px; +} +.modal-footer .btn-block + .btn-block { + margin-left: 0; +} +.modal-scrollbar-measure { + position: absolute; + top: -9999px; + width: 50px; + height: 50px; + overflow: scroll; +} +@media (min-width: 768px) { + .modal-dialog { + width: 600px; + margin: 30px auto; + } + .modal-content { + -webkit-box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); + box-shadow: 0 5px 15px rgba(0, 0, 0, 0.5); + } + .modal-sm { + width: 300px; + } +} +@media (min-width: 992px) { + .modal-lg { + width: 900px; + } +} +.tooltip { + position: absolute; + z-index: 1070; + display: block; + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; + font-style: normal; + font-weight: normal; + letter-spacing: normal; + line-break: auto; + line-height: 1.42857143; + text-align: left; + text-align: start; + text-decoration: none; + text-shadow: none; + text-transform: none; + white-space: normal; + word-break: normal; + word-spacing: normal; + word-wrap: normal; + font-size: 12px; + opacity: 0; + filter: alpha(opacity=0); +} +.tooltip.in { + opacity: 0.9; + filter: alpha(opacity=90); +} +.tooltip.top { + margin-top: -3px; + padding: 5px 0; +} +.tooltip.right { + margin-left: 3px; + padding: 0 5px; +} +.tooltip.bottom { + margin-top: 3px; + padding: 5px 0; +} +.tooltip.left { + margin-left: -3px; + padding: 0 5px; +} +.tooltip-inner { + max-width: 200px; + padding: 3px 8px; + color: #fff; + text-align: center; + background-color: #000; + border-radius: 2px; +} +.tooltip-arrow { + position: absolute; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; +} +.tooltip.top .tooltip-arrow { + bottom: 0; + left: 50%; + margin-left: -5px; + border-width: 5px 5px 0; + border-top-color: #000; +} +.tooltip.top-left .tooltip-arrow { + bottom: 0; + right: 5px; + margin-bottom: -5px; + border-width: 5px 5px 0; + border-top-color: #000; +} +.tooltip.top-right .tooltip-arrow { + bottom: 0; + left: 5px; + margin-bottom: -5px; + border-width: 5px 5px 0; + border-top-color: #000; +} +.tooltip.right .tooltip-arrow { + top: 50%; + left: 0; + margin-top: -5px; + border-width: 5px 5px 5px 0; + border-right-color: #000; +} +.tooltip.left .tooltip-arrow { + top: 50%; + right: 0; + margin-top: -5px; + border-width: 5px 0 5px 5px; + border-left-color: #000; +} +.tooltip.bottom .tooltip-arrow { + top: 0; + left: 50%; + margin-left: -5px; + border-width: 0 5px 5px; + border-bottom-color: #000; +} +.tooltip.bottom-left .tooltip-arrow { + top: 0; + right: 5px; + margin-top: -5px; + border-width: 0 5px 5px; + border-bottom-color: #000; +} +.tooltip.bottom-right .tooltip-arrow { + top: 0; + left: 5px; + margin-top: -5px; + border-width: 0 5px 5px; + border-bottom-color: #000; +} +.popover { + position: absolute; + top: 0; + left: 0; + z-index: 1060; + display: none; + max-width: 276px; + padding: 1px; + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; + font-style: normal; + font-weight: normal; + letter-spacing: normal; + line-break: auto; + line-height: 1.42857143; + text-align: left; + text-align: start; + text-decoration: none; + text-shadow: none; + text-transform: none; + white-space: normal; + word-break: normal; + word-spacing: normal; + word-wrap: normal; + font-size: 13px; + background-color: #fff; + background-clip: padding-box; + border: 1px solid #ccc; + border: 1px solid rgba(0, 0, 0, 0.2); + border-radius: 3px; + -webkit-box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); + box-shadow: 0 5px 10px rgba(0, 0, 0, 0.2); +} +.popover.top { + margin-top: -10px; +} +.popover.right { + margin-left: 10px; +} +.popover.bottom { + margin-top: 10px; +} +.popover.left { + margin-left: -10px; +} +.popover-title { + margin: 0; + padding: 8px 14px; + font-size: 13px; + background-color: #f7f7f7; + border-bottom: 1px solid #ebebeb; + border-radius: 2px 2px 0 0; +} +.popover-content { + padding: 9px 14px; +} +.popover > .arrow, +.popover > .arrow:after { + position: absolute; + display: block; + width: 0; + height: 0; + border-color: transparent; + border-style: solid; +} +.popover > .arrow { + border-width: 11px; +} +.popover > .arrow:after { + border-width: 10px; + content: ""; +} +.popover.top > .arrow { + left: 50%; + margin-left: -11px; + border-bottom-width: 0; + border-top-color: #999999; + border-top-color: rgba(0, 0, 0, 0.25); + bottom: -11px; +} +.popover.top > .arrow:after { + content: " "; + bottom: 1px; + margin-left: -10px; + border-bottom-width: 0; + border-top-color: #fff; +} +.popover.right > .arrow { + top: 50%; + left: -11px; + margin-top: -11px; + border-left-width: 0; + border-right-color: #999999; + border-right-color: rgba(0, 0, 0, 0.25); +} +.popover.right > .arrow:after { + content: " "; + left: 1px; + bottom: -10px; + border-left-width: 0; + border-right-color: #fff; +} +.popover.bottom > .arrow { + left: 50%; + margin-left: -11px; + border-top-width: 0; + border-bottom-color: #999999; + border-bottom-color: rgba(0, 0, 0, 0.25); + top: -11px; +} +.popover.bottom > .arrow:after { + content: " "; + top: 1px; + margin-left: -10px; + border-top-width: 0; + border-bottom-color: #fff; +} +.popover.left > .arrow { + top: 50%; + right: -11px; + margin-top: -11px; + border-right-width: 0; + border-left-color: #999999; + border-left-color: rgba(0, 0, 0, 0.25); +} +.popover.left > .arrow:after { + content: " "; + right: 1px; + border-right-width: 0; + border-left-color: #fff; + bottom: -10px; +} +.carousel { + position: relative; +} +.carousel-inner { + position: relative; + overflow: hidden; + width: 100%; +} +.carousel-inner > .item { + display: none; + position: relative; + -webkit-transition: 0.6s ease-in-out left; + -o-transition: 0.6s ease-in-out left; + transition: 0.6s ease-in-out left; +} +.carousel-inner > .item > img, +.carousel-inner > .item > a > img { + line-height: 1; +} +@media all and (transform-3d), (-webkit-transform-3d) { + .carousel-inner > .item { + -webkit-transition: -webkit-transform 0.6s ease-in-out; + -moz-transition: -moz-transform 0.6s ease-in-out; + -o-transition: -o-transform 0.6s ease-in-out; + transition: transform 0.6s ease-in-out; + -webkit-backface-visibility: hidden; + -moz-backface-visibility: hidden; + backface-visibility: hidden; + -webkit-perspective: 1000px; + -moz-perspective: 1000px; + perspective: 1000px; + } + .carousel-inner > .item.next, + .carousel-inner > .item.active.right { + -webkit-transform: translate3d(100%, 0, 0); + transform: translate3d(100%, 0, 0); + left: 0; + } + .carousel-inner > .item.prev, + .carousel-inner > .item.active.left { + -webkit-transform: translate3d(-100%, 0, 0); + transform: translate3d(-100%, 0, 0); + left: 0; + } + .carousel-inner > .item.next.left, + .carousel-inner > .item.prev.right, + .carousel-inner > .item.active { + -webkit-transform: translate3d(0, 0, 0); + transform: translate3d(0, 0, 0); + left: 0; + } +} +.carousel-inner > .active, +.carousel-inner > .next, +.carousel-inner > .prev { + display: block; +} +.carousel-inner > .active { + left: 0; +} +.carousel-inner > .next, +.carousel-inner > .prev { + position: absolute; + top: 0; + width: 100%; +} +.carousel-inner > .next { + left: 100%; +} +.carousel-inner > .prev { + left: -100%; +} +.carousel-inner > .next.left, +.carousel-inner > .prev.right { + left: 0; +} +.carousel-inner > .active.left { + left: -100%; +} +.carousel-inner > .active.right { + left: 100%; +} +.carousel-control { + position: absolute; + top: 0; + left: 0; + bottom: 0; + width: 15%; + opacity: 0.5; + filter: alpha(opacity=50); + font-size: 20px; + color: #fff; + text-align: center; + text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); + background-color: rgba(0, 0, 0, 0); +} +.carousel-control.left { + background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); + background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); + background-image: linear-gradient(to right, rgba(0, 0, 0, 0.5) 0%, rgba(0, 0, 0, 0.0001) 100%); + background-repeat: repeat-x; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#80000000', endColorstr='#00000000', GradientType=1); +} +.carousel-control.right { + left: auto; + right: 0; + background-image: -webkit-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); + background-image: -o-linear-gradient(left, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); + background-image: linear-gradient(to right, rgba(0, 0, 0, 0.0001) 0%, rgba(0, 0, 0, 0.5) 100%); + background-repeat: repeat-x; + filter: progid:DXImageTransform.Microsoft.gradient(startColorstr='#00000000', endColorstr='#80000000', GradientType=1); +} +.carousel-control:hover, +.carousel-control:focus { + outline: 0; + color: #fff; + text-decoration: none; + opacity: 0.9; + filter: alpha(opacity=90); +} +.carousel-control .icon-prev, +.carousel-control .icon-next, +.carousel-control .glyphicon-chevron-left, +.carousel-control .glyphicon-chevron-right { + position: absolute; + top: 50%; + margin-top: -10px; + z-index: 5; + display: inline-block; +} +.carousel-control .icon-prev, +.carousel-control .glyphicon-chevron-left { + left: 50%; + margin-left: -10px; +} +.carousel-control .icon-next, +.carousel-control .glyphicon-chevron-right { + right: 50%; + margin-right: -10px; +} +.carousel-control .icon-prev, +.carousel-control .icon-next { + width: 20px; + height: 20px; + line-height: 1; + font-family: serif; +} +.carousel-control .icon-prev:before { + content: '\2039'; +} +.carousel-control .icon-next:before { + content: '\203a'; +} +.carousel-indicators { + position: absolute; + bottom: 10px; + left: 50%; + z-index: 15; + width: 60%; + margin-left: -30%; + padding-left: 0; + list-style: none; + text-align: center; +} +.carousel-indicators li { + display: inline-block; + width: 10px; + height: 10px; + margin: 1px; + text-indent: -999px; + border: 1px solid #fff; + border-radius: 10px; + cursor: pointer; + background-color: #000 \9; + background-color: rgba(0, 0, 0, 0); +} +.carousel-indicators .active { + margin: 0; + width: 12px; + height: 12px; + background-color: #fff; +} +.carousel-caption { + position: absolute; + left: 15%; + right: 15%; + bottom: 20px; + z-index: 10; + padding-top: 20px; + padding-bottom: 20px; + color: #fff; + text-align: center; + text-shadow: 0 1px 2px rgba(0, 0, 0, 0.6); +} +.carousel-caption .btn { + text-shadow: none; +} +@media screen and (min-width: 768px) { + .carousel-control .glyphicon-chevron-left, + .carousel-control .glyphicon-chevron-right, + .carousel-control .icon-prev, + .carousel-control .icon-next { + width: 30px; + height: 30px; + margin-top: -10px; + font-size: 30px; + } + .carousel-control .glyphicon-chevron-left, + .carousel-control .icon-prev { + margin-left: -10px; + } + .carousel-control .glyphicon-chevron-right, + .carousel-control .icon-next { + margin-right: -10px; + } + .carousel-caption { + left: 20%; + right: 20%; + padding-bottom: 30px; + } + .carousel-indicators { + bottom: 20px; + } +} +.clearfix:before, +.clearfix:after, +.dl-horizontal dd:before, +.dl-horizontal dd:after, +.container:before, +.container:after, +.container-fluid:before, +.container-fluid:after, +.row:before, +.row:after, +.form-horizontal .form-group:before, +.form-horizontal .form-group:after, +.btn-toolbar:before, +.btn-toolbar:after, +.btn-group-vertical > .btn-group:before, +.btn-group-vertical > .btn-group:after, +.nav:before, +.nav:after, +.navbar:before, +.navbar:after, +.navbar-header:before, +.navbar-header:after, +.navbar-collapse:before, +.navbar-collapse:after, +.pager:before, +.pager:after, +.panel-body:before, +.panel-body:after, +.modal-header:before, +.modal-header:after, +.modal-footer:before, +.modal-footer:after, +.item_buttons:before, +.item_buttons:after { + content: " "; + display: table; +} +.clearfix:after, +.dl-horizontal dd:after, +.container:after, +.container-fluid:after, +.row:after, +.form-horizontal .form-group:after, +.btn-toolbar:after, +.btn-group-vertical > .btn-group:after, +.nav:after, +.navbar:after, +.navbar-header:after, +.navbar-collapse:after, +.pager:after, +.panel-body:after, +.modal-header:after, +.modal-footer:after, +.item_buttons:after { + clear: both; +} +.center-block { + display: block; + margin-left: auto; + margin-right: auto; +} +.pull-right { + float: right !important; +} +.pull-left { + float: left !important; +} +.hide { + display: none !important; +} +.show { + display: block !important; +} +.invisible { + visibility: hidden; +} +.text-hide { + font: 0/0 a; + color: transparent; + text-shadow: none; + background-color: transparent; + border: 0; +} +.hidden { + display: none !important; +} +.affix { + position: fixed; +} +@-ms-viewport { + width: device-width; +} +.visible-xs, +.visible-sm, +.visible-md, +.visible-lg { + display: none !important; +} +.visible-xs-block, +.visible-xs-inline, +.visible-xs-inline-block, +.visible-sm-block, +.visible-sm-inline, +.visible-sm-inline-block, +.visible-md-block, +.visible-md-inline, +.visible-md-inline-block, +.visible-lg-block, +.visible-lg-inline, +.visible-lg-inline-block { + display: none !important; +} +@media (max-width: 767px) { + .visible-xs { + display: block !important; + } + table.visible-xs { + display: table !important; + } + tr.visible-xs { + display: table-row !important; + } + th.visible-xs, + td.visible-xs { + display: table-cell !important; + } +} +@media (max-width: 767px) { + .visible-xs-block { + display: block !important; + } +} +@media (max-width: 767px) { + .visible-xs-inline { + display: inline !important; + } +} +@media (max-width: 767px) { + .visible-xs-inline-block { + display: inline-block !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm { + display: block !important; + } + table.visible-sm { + display: table !important; + } + tr.visible-sm { + display: table-row !important; + } + th.visible-sm, + td.visible-sm { + display: table-cell !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm-block { + display: block !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm-inline { + display: inline !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .visible-sm-inline-block { + display: inline-block !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md { + display: block !important; + } + table.visible-md { + display: table !important; + } + tr.visible-md { + display: table-row !important; + } + th.visible-md, + td.visible-md { + display: table-cell !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md-block { + display: block !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md-inline { + display: inline !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .visible-md-inline-block { + display: inline-block !important; + } +} +@media (min-width: 1200px) { + .visible-lg { + display: block !important; + } + table.visible-lg { + display: table !important; + } + tr.visible-lg { + display: table-row !important; + } + th.visible-lg, + td.visible-lg { + display: table-cell !important; + } +} +@media (min-width: 1200px) { + .visible-lg-block { + display: block !important; + } +} +@media (min-width: 1200px) { + .visible-lg-inline { + display: inline !important; + } +} +@media (min-width: 1200px) { + .visible-lg-inline-block { + display: inline-block !important; + } +} +@media (max-width: 767px) { + .hidden-xs { + display: none !important; + } +} +@media (min-width: 768px) and (max-width: 991px) { + .hidden-sm { + display: none !important; + } +} +@media (min-width: 992px) and (max-width: 1199px) { + .hidden-md { + display: none !important; + } +} +@media (min-width: 1200px) { + .hidden-lg { + display: none !important; + } +} +.visible-print { + display: none !important; +} +@media print { + .visible-print { + display: block !important; + } + table.visible-print { + display: table !important; + } + tr.visible-print { + display: table-row !important; + } + th.visible-print, + td.visible-print { + display: table-cell !important; + } +} +.visible-print-block { + display: none !important; +} +@media print { + .visible-print-block { + display: block !important; + } +} +.visible-print-inline { + display: none !important; +} +@media print { + .visible-print-inline { + display: inline !important; + } +} +.visible-print-inline-block { + display: none !important; +} +@media print { + .visible-print-inline-block { + display: inline-block !important; + } +} +@media print { + .hidden-print { + display: none !important; + } +} +/*! +* +* Font Awesome +* +*/ +/*! + * Font Awesome 4.7.0 by @davegandy - http://fontawesome.io - @fontawesome + * License - http://fontawesome.io/license (Font: SIL OFL 1.1, CSS: MIT License) + */ +/* FONT PATH + * -------------------------- */ +@font-face { + font-family: 'FontAwesome'; + src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?v=4.7.0'); + src: url('../components/font-awesome/fonts/fontawesome-webfont.eot?#iefix&v=4.7.0') format('embedded-opentype'), url('../components/font-awesome/fonts/fontawesome-webfont.woff2?v=4.7.0') format('woff2'), url('../components/font-awesome/fonts/fontawesome-webfont.woff?v=4.7.0') format('woff'), url('../components/font-awesome/fonts/fontawesome-webfont.ttf?v=4.7.0') format('truetype'), url('../components/font-awesome/fonts/fontawesome-webfont.svg?v=4.7.0#fontawesomeregular') format('svg'); + font-weight: normal; + font-style: normal; +} +.fa { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; +} +/* makes the font 33% larger relative to the icon container */ +.fa-lg { + font-size: 1.33333333em; + line-height: 0.75em; + vertical-align: -15%; +} +.fa-2x { + font-size: 2em; +} +.fa-3x { + font-size: 3em; +} +.fa-4x { + font-size: 4em; +} +.fa-5x { + font-size: 5em; +} +.fa-fw { + width: 1.28571429em; + text-align: center; +} +.fa-ul { + padding-left: 0; + margin-left: 2.14285714em; + list-style-type: none; +} +.fa-ul > li { + position: relative; +} +.fa-li { + position: absolute; + left: -2.14285714em; + width: 2.14285714em; + top: 0.14285714em; + text-align: center; +} +.fa-li.fa-lg { + left: -1.85714286em; +} +.fa-border { + padding: .2em .25em .15em; + border: solid 0.08em #eee; + border-radius: .1em; +} +.fa-pull-left { + float: left; +} +.fa-pull-right { + float: right; +} +.fa.fa-pull-left { + margin-right: .3em; +} +.fa.fa-pull-right { + margin-left: .3em; +} +/* Deprecated as of 4.4.0 */ +.pull-right { + float: right; +} +.pull-left { + float: left; +} +.fa.pull-left { + margin-right: .3em; +} +.fa.pull-right { + margin-left: .3em; +} +.fa-spin { + -webkit-animation: fa-spin 2s infinite linear; + animation: fa-spin 2s infinite linear; +} +.fa-pulse { + -webkit-animation: fa-spin 1s infinite steps(8); + animation: fa-spin 1s infinite steps(8); +} +@-webkit-keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + transform: rotate(359deg); + } +} +@keyframes fa-spin { + 0% { + -webkit-transform: rotate(0deg); + transform: rotate(0deg); + } + 100% { + -webkit-transform: rotate(359deg); + transform: rotate(359deg); + } +} +.fa-rotate-90 { + -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=1)"; + -webkit-transform: rotate(90deg); + -ms-transform: rotate(90deg); + transform: rotate(90deg); +} +.fa-rotate-180 { + -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2)"; + -webkit-transform: rotate(180deg); + -ms-transform: rotate(180deg); + transform: rotate(180deg); +} +.fa-rotate-270 { + -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=3)"; + -webkit-transform: rotate(270deg); + -ms-transform: rotate(270deg); + transform: rotate(270deg); +} +.fa-flip-horizontal { + -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=0, mirror=1)"; + -webkit-transform: scale(-1, 1); + -ms-transform: scale(-1, 1); + transform: scale(-1, 1); +} +.fa-flip-vertical { + -ms-filter: "progid:DXImageTransform.Microsoft.BasicImage(rotation=2, mirror=1)"; + -webkit-transform: scale(1, -1); + -ms-transform: scale(1, -1); + transform: scale(1, -1); +} +:root .fa-rotate-90, +:root .fa-rotate-180, +:root .fa-rotate-270, +:root .fa-flip-horizontal, +:root .fa-flip-vertical { + filter: none; +} +.fa-stack { + position: relative; + display: inline-block; + width: 2em; + height: 2em; + line-height: 2em; + vertical-align: middle; +} +.fa-stack-1x, +.fa-stack-2x { + position: absolute; + left: 0; + width: 100%; + text-align: center; +} +.fa-stack-1x { + line-height: inherit; +} +.fa-stack-2x { + font-size: 2em; +} +.fa-inverse { + color: #fff; +} +/* Font Awesome uses the Unicode Private Use Area (PUA) to ensure screen + readers do not read off random characters that represent icons */ +.fa-glass:before { + content: "\f000"; +} +.fa-music:before { + content: "\f001"; +} +.fa-search:before { + content: "\f002"; +} +.fa-envelope-o:before { + content: "\f003"; +} +.fa-heart:before { + content: "\f004"; +} +.fa-star:before { + content: "\f005"; +} +.fa-star-o:before { + content: "\f006"; +} +.fa-user:before { + content: "\f007"; +} +.fa-film:before { + content: "\f008"; +} +.fa-th-large:before { + content: "\f009"; +} +.fa-th:before { + content: "\f00a"; +} +.fa-th-list:before { + content: "\f00b"; +} +.fa-check:before { + content: "\f00c"; +} +.fa-remove:before, +.fa-close:before, +.fa-times:before { + content: "\f00d"; +} +.fa-search-plus:before { + content: "\f00e"; +} +.fa-search-minus:before { + content: "\f010"; +} +.fa-power-off:before { + content: "\f011"; +} +.fa-signal:before { + content: "\f012"; +} +.fa-gear:before, +.fa-cog:before { + content: "\f013"; +} +.fa-trash-o:before { + content: "\f014"; +} +.fa-home:before { + content: "\f015"; +} +.fa-file-o:before { + content: "\f016"; +} +.fa-clock-o:before { + content: "\f017"; +} +.fa-road:before { + content: "\f018"; +} +.fa-download:before { + content: "\f019"; +} +.fa-arrow-circle-o-down:before { + content: "\f01a"; +} +.fa-arrow-circle-o-up:before { + content: "\f01b"; +} +.fa-inbox:before { + content: "\f01c"; +} +.fa-play-circle-o:before { + content: "\f01d"; +} +.fa-rotate-right:before, +.fa-repeat:before { + content: "\f01e"; +} +.fa-refresh:before { + content: "\f021"; +} +.fa-list-alt:before { + content: "\f022"; +} +.fa-lock:before { + content: "\f023"; +} +.fa-flag:before { + content: "\f024"; +} +.fa-headphones:before { + content: "\f025"; +} +.fa-volume-off:before { + content: "\f026"; +} +.fa-volume-down:before { + content: "\f027"; +} +.fa-volume-up:before { + content: "\f028"; +} +.fa-qrcode:before { + content: "\f029"; +} +.fa-barcode:before { + content: "\f02a"; +} +.fa-tag:before { + content: "\f02b"; +} +.fa-tags:before { + content: "\f02c"; +} +.fa-book:before { + content: "\f02d"; +} +.fa-bookmark:before { + content: "\f02e"; +} +.fa-print:before { + content: "\f02f"; +} +.fa-camera:before { + content: "\f030"; +} +.fa-font:before { + content: "\f031"; +} +.fa-bold:before { + content: "\f032"; +} +.fa-italic:before { + content: "\f033"; +} +.fa-text-height:before { + content: "\f034"; +} +.fa-text-width:before { + content: "\f035"; +} +.fa-align-left:before { + content: "\f036"; +} +.fa-align-center:before { + content: "\f037"; +} +.fa-align-right:before { + content: "\f038"; +} +.fa-align-justify:before { + content: "\f039"; +} +.fa-list:before { + content: "\f03a"; +} +.fa-dedent:before, +.fa-outdent:before { + content: "\f03b"; +} +.fa-indent:before { + content: "\f03c"; +} +.fa-video-camera:before { + content: "\f03d"; +} +.fa-photo:before, +.fa-image:before, +.fa-picture-o:before { + content: "\f03e"; +} +.fa-pencil:before { + content: "\f040"; +} +.fa-map-marker:before { + content: "\f041"; +} +.fa-adjust:before { + content: "\f042"; +} +.fa-tint:before { + content: "\f043"; +} +.fa-edit:before, +.fa-pencil-square-o:before { + content: "\f044"; +} +.fa-share-square-o:before { + content: "\f045"; +} +.fa-check-square-o:before { + content: "\f046"; +} +.fa-arrows:before { + content: "\f047"; +} +.fa-step-backward:before { + content: "\f048"; +} +.fa-fast-backward:before { + content: "\f049"; +} +.fa-backward:before { + content: "\f04a"; +} +.fa-play:before { + content: "\f04b"; +} +.fa-pause:before { + content: "\f04c"; +} +.fa-stop:before { + content: "\f04d"; +} +.fa-forward:before { + content: "\f04e"; +} +.fa-fast-forward:before { + content: "\f050"; +} +.fa-step-forward:before { + content: "\f051"; +} +.fa-eject:before { + content: "\f052"; +} +.fa-chevron-left:before { + content: "\f053"; +} +.fa-chevron-right:before { + content: "\f054"; +} +.fa-plus-circle:before { + content: "\f055"; +} +.fa-minus-circle:before { + content: "\f056"; +} +.fa-times-circle:before { + content: "\f057"; +} +.fa-check-circle:before { + content: "\f058"; +} +.fa-question-circle:before { + content: "\f059"; +} +.fa-info-circle:before { + content: "\f05a"; +} +.fa-crosshairs:before { + content: "\f05b"; +} +.fa-times-circle-o:before { + content: "\f05c"; +} +.fa-check-circle-o:before { + content: "\f05d"; +} +.fa-ban:before { + content: "\f05e"; +} +.fa-arrow-left:before { + content: "\f060"; +} +.fa-arrow-right:before { + content: "\f061"; +} +.fa-arrow-up:before { + content: "\f062"; +} +.fa-arrow-down:before { + content: "\f063"; +} +.fa-mail-forward:before, +.fa-share:before { + content: "\f064"; +} +.fa-expand:before { + content: "\f065"; +} +.fa-compress:before { + content: "\f066"; +} +.fa-plus:before { + content: "\f067"; +} +.fa-minus:before { + content: "\f068"; +} +.fa-asterisk:before { + content: "\f069"; +} +.fa-exclamation-circle:before { + content: "\f06a"; +} +.fa-gift:before { + content: "\f06b"; +} +.fa-leaf:before { + content: "\f06c"; +} +.fa-fire:before { + content: "\f06d"; +} +.fa-eye:before { + content: "\f06e"; +} +.fa-eye-slash:before { + content: "\f070"; +} +.fa-warning:before, +.fa-exclamation-triangle:before { + content: "\f071"; +} +.fa-plane:before { + content: "\f072"; +} +.fa-calendar:before { + content: "\f073"; +} +.fa-random:before { + content: "\f074"; +} +.fa-comment:before { + content: "\f075"; +} +.fa-magnet:before { + content: "\f076"; +} +.fa-chevron-up:before { + content: "\f077"; +} +.fa-chevron-down:before { + content: "\f078"; +} +.fa-retweet:before { + content: "\f079"; +} +.fa-shopping-cart:before { + content: "\f07a"; +} +.fa-folder:before { + content: "\f07b"; +} +.fa-folder-open:before { + content: "\f07c"; +} +.fa-arrows-v:before { + content: "\f07d"; +} +.fa-arrows-h:before { + content: "\f07e"; +} +.fa-bar-chart-o:before, +.fa-bar-chart:before { + content: "\f080"; +} +.fa-twitter-square:before { + content: "\f081"; +} +.fa-facebook-square:before { + content: "\f082"; +} +.fa-camera-retro:before { + content: "\f083"; +} +.fa-key:before { + content: "\f084"; +} +.fa-gears:before, +.fa-cogs:before { + content: "\f085"; +} +.fa-comments:before { + content: "\f086"; +} +.fa-thumbs-o-up:before { + content: "\f087"; +} +.fa-thumbs-o-down:before { + content: "\f088"; +} +.fa-star-half:before { + content: "\f089"; +} +.fa-heart-o:before { + content: "\f08a"; +} +.fa-sign-out:before { + content: "\f08b"; +} +.fa-linkedin-square:before { + content: "\f08c"; +} +.fa-thumb-tack:before { + content: "\f08d"; +} +.fa-external-link:before { + content: "\f08e"; +} +.fa-sign-in:before { + content: "\f090"; +} +.fa-trophy:before { + content: "\f091"; +} +.fa-github-square:before { + content: "\f092"; +} +.fa-upload:before { + content: "\f093"; +} +.fa-lemon-o:before { + content: "\f094"; +} +.fa-phone:before { + content: "\f095"; +} +.fa-square-o:before { + content: "\f096"; +} +.fa-bookmark-o:before { + content: "\f097"; +} +.fa-phone-square:before { + content: "\f098"; +} +.fa-twitter:before { + content: "\f099"; +} +.fa-facebook-f:before, +.fa-facebook:before { + content: "\f09a"; +} +.fa-github:before { + content: "\f09b"; +} +.fa-unlock:before { + content: "\f09c"; +} +.fa-credit-card:before { + content: "\f09d"; +} +.fa-feed:before, +.fa-rss:before { + content: "\f09e"; +} +.fa-hdd-o:before { + content: "\f0a0"; +} +.fa-bullhorn:before { + content: "\f0a1"; +} +.fa-bell:before { + content: "\f0f3"; +} +.fa-certificate:before { + content: "\f0a3"; +} +.fa-hand-o-right:before { + content: "\f0a4"; +} +.fa-hand-o-left:before { + content: "\f0a5"; +} +.fa-hand-o-up:before { + content: "\f0a6"; +} +.fa-hand-o-down:before { + content: "\f0a7"; +} +.fa-arrow-circle-left:before { + content: "\f0a8"; +} +.fa-arrow-circle-right:before { + content: "\f0a9"; +} +.fa-arrow-circle-up:before { + content: "\f0aa"; +} +.fa-arrow-circle-down:before { + content: "\f0ab"; +} +.fa-globe:before { + content: "\f0ac"; +} +.fa-wrench:before { + content: "\f0ad"; +} +.fa-tasks:before { + content: "\f0ae"; +} +.fa-filter:before { + content: "\f0b0"; +} +.fa-briefcase:before { + content: "\f0b1"; +} +.fa-arrows-alt:before { + content: "\f0b2"; +} +.fa-group:before, +.fa-users:before { + content: "\f0c0"; +} +.fa-chain:before, +.fa-link:before { + content: "\f0c1"; +} +.fa-cloud:before { + content: "\f0c2"; +} +.fa-flask:before { + content: "\f0c3"; +} +.fa-cut:before, +.fa-scissors:before { + content: "\f0c4"; +} +.fa-copy:before, +.fa-files-o:before { + content: "\f0c5"; +} +.fa-paperclip:before { + content: "\f0c6"; +} +.fa-save:before, +.fa-floppy-o:before { + content: "\f0c7"; +} +.fa-square:before { + content: "\f0c8"; +} +.fa-navicon:before, +.fa-reorder:before, +.fa-bars:before { + content: "\f0c9"; +} +.fa-list-ul:before { + content: "\f0ca"; +} +.fa-list-ol:before { + content: "\f0cb"; +} +.fa-strikethrough:before { + content: "\f0cc"; +} +.fa-underline:before { + content: "\f0cd"; +} +.fa-table:before { + content: "\f0ce"; +} +.fa-magic:before { + content: "\f0d0"; +} +.fa-truck:before { + content: "\f0d1"; +} +.fa-pinterest:before { + content: "\f0d2"; +} +.fa-pinterest-square:before { + content: "\f0d3"; +} +.fa-google-plus-square:before { + content: "\f0d4"; +} +.fa-google-plus:before { + content: "\f0d5"; +} +.fa-money:before { + content: "\f0d6"; +} +.fa-caret-down:before { + content: "\f0d7"; +} +.fa-caret-up:before { + content: "\f0d8"; +} +.fa-caret-left:before { + content: "\f0d9"; +} +.fa-caret-right:before { + content: "\f0da"; +} +.fa-columns:before { + content: "\f0db"; +} +.fa-unsorted:before, +.fa-sort:before { + content: "\f0dc"; +} +.fa-sort-down:before, +.fa-sort-desc:before { + content: "\f0dd"; +} +.fa-sort-up:before, +.fa-sort-asc:before { + content: "\f0de"; +} +.fa-envelope:before { + content: "\f0e0"; +} +.fa-linkedin:before { + content: "\f0e1"; +} +.fa-rotate-left:before, +.fa-undo:before { + content: "\f0e2"; +} +.fa-legal:before, +.fa-gavel:before { + content: "\f0e3"; +} +.fa-dashboard:before, +.fa-tachometer:before { + content: "\f0e4"; +} +.fa-comment-o:before { + content: "\f0e5"; +} +.fa-comments-o:before { + content: "\f0e6"; +} +.fa-flash:before, +.fa-bolt:before { + content: "\f0e7"; +} +.fa-sitemap:before { + content: "\f0e8"; +} +.fa-umbrella:before { + content: "\f0e9"; +} +.fa-paste:before, +.fa-clipboard:before { + content: "\f0ea"; +} +.fa-lightbulb-o:before { + content: "\f0eb"; +} +.fa-exchange:before { + content: "\f0ec"; +} +.fa-cloud-download:before { + content: "\f0ed"; +} +.fa-cloud-upload:before { + content: "\f0ee"; +} +.fa-user-md:before { + content: "\f0f0"; +} +.fa-stethoscope:before { + content: "\f0f1"; +} +.fa-suitcase:before { + content: "\f0f2"; +} +.fa-bell-o:before { + content: "\f0a2"; +} +.fa-coffee:before { + content: "\f0f4"; +} +.fa-cutlery:before { + content: "\f0f5"; +} +.fa-file-text-o:before { + content: "\f0f6"; +} +.fa-building-o:before { + content: "\f0f7"; +} +.fa-hospital-o:before { + content: "\f0f8"; +} +.fa-ambulance:before { + content: "\f0f9"; +} +.fa-medkit:before { + content: "\f0fa"; +} +.fa-fighter-jet:before { + content: "\f0fb"; +} +.fa-beer:before { + content: "\f0fc"; +} +.fa-h-square:before { + content: "\f0fd"; +} +.fa-plus-square:before { + content: "\f0fe"; +} +.fa-angle-double-left:before { + content: "\f100"; +} +.fa-angle-double-right:before { + content: "\f101"; +} +.fa-angle-double-up:before { + content: "\f102"; +} +.fa-angle-double-down:before { + content: "\f103"; +} +.fa-angle-left:before { + content: "\f104"; +} +.fa-angle-right:before { + content: "\f105"; +} +.fa-angle-up:before { + content: "\f106"; +} +.fa-angle-down:before { + content: "\f107"; +} +.fa-desktop:before { + content: "\f108"; +} +.fa-laptop:before { + content: "\f109"; +} +.fa-tablet:before { + content: "\f10a"; +} +.fa-mobile-phone:before, +.fa-mobile:before { + content: "\f10b"; +} +.fa-circle-o:before { + content: "\f10c"; +} +.fa-quote-left:before { + content: "\f10d"; +} +.fa-quote-right:before { + content: "\f10e"; +} +.fa-spinner:before { + content: "\f110"; +} +.fa-circle:before { + content: "\f111"; +} +.fa-mail-reply:before, +.fa-reply:before { + content: "\f112"; +} +.fa-github-alt:before { + content: "\f113"; +} +.fa-folder-o:before { + content: "\f114"; +} +.fa-folder-open-o:before { + content: "\f115"; +} +.fa-smile-o:before { + content: "\f118"; +} +.fa-frown-o:before { + content: "\f119"; +} +.fa-meh-o:before { + content: "\f11a"; +} +.fa-gamepad:before { + content: "\f11b"; +} +.fa-keyboard-o:before { + content: "\f11c"; +} +.fa-flag-o:before { + content: "\f11d"; +} +.fa-flag-checkered:before { + content: "\f11e"; +} +.fa-terminal:before { + content: "\f120"; +} +.fa-code:before { + content: "\f121"; +} +.fa-mail-reply-all:before, +.fa-reply-all:before { + content: "\f122"; +} +.fa-star-half-empty:before, +.fa-star-half-full:before, +.fa-star-half-o:before { + content: "\f123"; +} +.fa-location-arrow:before { + content: "\f124"; +} +.fa-crop:before { + content: "\f125"; +} +.fa-code-fork:before { + content: "\f126"; +} +.fa-unlink:before, +.fa-chain-broken:before { + content: "\f127"; +} +.fa-question:before { + content: "\f128"; +} +.fa-info:before { + content: "\f129"; +} +.fa-exclamation:before { + content: "\f12a"; +} +.fa-superscript:before { + content: "\f12b"; +} +.fa-subscript:before { + content: "\f12c"; +} +.fa-eraser:before { + content: "\f12d"; +} +.fa-puzzle-piece:before { + content: "\f12e"; +} +.fa-microphone:before { + content: "\f130"; +} +.fa-microphone-slash:before { + content: "\f131"; +} +.fa-shield:before { + content: "\f132"; +} +.fa-calendar-o:before { + content: "\f133"; +} +.fa-fire-extinguisher:before { + content: "\f134"; +} +.fa-rocket:before { + content: "\f135"; +} +.fa-maxcdn:before { + content: "\f136"; +} +.fa-chevron-circle-left:before { + content: "\f137"; +} +.fa-chevron-circle-right:before { + content: "\f138"; +} +.fa-chevron-circle-up:before { + content: "\f139"; +} +.fa-chevron-circle-down:before { + content: "\f13a"; +} +.fa-html5:before { + content: "\f13b"; +} +.fa-css3:before { + content: "\f13c"; +} +.fa-anchor:before { + content: "\f13d"; +} +.fa-unlock-alt:before { + content: "\f13e"; +} +.fa-bullseye:before { + content: "\f140"; +} +.fa-ellipsis-h:before { + content: "\f141"; +} +.fa-ellipsis-v:before { + content: "\f142"; +} +.fa-rss-square:before { + content: "\f143"; +} +.fa-play-circle:before { + content: "\f144"; +} +.fa-ticket:before { + content: "\f145"; +} +.fa-minus-square:before { + content: "\f146"; +} +.fa-minus-square-o:before { + content: "\f147"; +} +.fa-level-up:before { + content: "\f148"; +} +.fa-level-down:before { + content: "\f149"; +} +.fa-check-square:before { + content: "\f14a"; +} +.fa-pencil-square:before { + content: "\f14b"; +} +.fa-external-link-square:before { + content: "\f14c"; +} +.fa-share-square:before { + content: "\f14d"; +} +.fa-compass:before { + content: "\f14e"; +} +.fa-toggle-down:before, +.fa-caret-square-o-down:before { + content: "\f150"; +} +.fa-toggle-up:before, +.fa-caret-square-o-up:before { + content: "\f151"; +} +.fa-toggle-right:before, +.fa-caret-square-o-right:before { + content: "\f152"; +} +.fa-euro:before, +.fa-eur:before { + content: "\f153"; +} +.fa-gbp:before { + content: "\f154"; +} +.fa-dollar:before, +.fa-usd:before { + content: "\f155"; +} +.fa-rupee:before, +.fa-inr:before { + content: "\f156"; +} +.fa-cny:before, +.fa-rmb:before, +.fa-yen:before, +.fa-jpy:before { + content: "\f157"; +} +.fa-ruble:before, +.fa-rouble:before, +.fa-rub:before { + content: "\f158"; +} +.fa-won:before, +.fa-krw:before { + content: "\f159"; +} +.fa-bitcoin:before, +.fa-btc:before { + content: "\f15a"; +} +.fa-file:before { + content: "\f15b"; +} +.fa-file-text:before { + content: "\f15c"; +} +.fa-sort-alpha-asc:before { + content: "\f15d"; +} +.fa-sort-alpha-desc:before { + content: "\f15e"; +} +.fa-sort-amount-asc:before { + content: "\f160"; +} +.fa-sort-amount-desc:before { + content: "\f161"; +} +.fa-sort-numeric-asc:before { + content: "\f162"; +} +.fa-sort-numeric-desc:before { + content: "\f163"; +} +.fa-thumbs-up:before { + content: "\f164"; +} +.fa-thumbs-down:before { + content: "\f165"; +} +.fa-youtube-square:before { + content: "\f166"; +} +.fa-youtube:before { + content: "\f167"; +} +.fa-xing:before { + content: "\f168"; +} +.fa-xing-square:before { + content: "\f169"; +} +.fa-youtube-play:before { + content: "\f16a"; +} +.fa-dropbox:before { + content: "\f16b"; +} +.fa-stack-overflow:before { + content: "\f16c"; +} +.fa-instagram:before { + content: "\f16d"; +} +.fa-flickr:before { + content: "\f16e"; +} +.fa-adn:before { + content: "\f170"; +} +.fa-bitbucket:before { + content: "\f171"; +} +.fa-bitbucket-square:before { + content: "\f172"; +} +.fa-tumblr:before { + content: "\f173"; +} +.fa-tumblr-square:before { + content: "\f174"; +} +.fa-long-arrow-down:before { + content: "\f175"; +} +.fa-long-arrow-up:before { + content: "\f176"; +} +.fa-long-arrow-left:before { + content: "\f177"; +} +.fa-long-arrow-right:before { + content: "\f178"; +} +.fa-apple:before { + content: "\f179"; +} +.fa-windows:before { + content: "\f17a"; +} +.fa-android:before { + content: "\f17b"; +} +.fa-linux:before { + content: "\f17c"; +} +.fa-dribbble:before { + content: "\f17d"; +} +.fa-skype:before { + content: "\f17e"; +} +.fa-foursquare:before { + content: "\f180"; +} +.fa-trello:before { + content: "\f181"; +} +.fa-female:before { + content: "\f182"; +} +.fa-male:before { + content: "\f183"; +} +.fa-gittip:before, +.fa-gratipay:before { + content: "\f184"; +} +.fa-sun-o:before { + content: "\f185"; +} +.fa-moon-o:before { + content: "\f186"; +} +.fa-archive:before { + content: "\f187"; +} +.fa-bug:before { + content: "\f188"; +} +.fa-vk:before { + content: "\f189"; +} +.fa-weibo:before { + content: "\f18a"; +} +.fa-renren:before { + content: "\f18b"; +} +.fa-pagelines:before { + content: "\f18c"; +} +.fa-stack-exchange:before { + content: "\f18d"; +} +.fa-arrow-circle-o-right:before { + content: "\f18e"; +} +.fa-arrow-circle-o-left:before { + content: "\f190"; +} +.fa-toggle-left:before, +.fa-caret-square-o-left:before { + content: "\f191"; +} +.fa-dot-circle-o:before { + content: "\f192"; +} +.fa-wheelchair:before { + content: "\f193"; +} +.fa-vimeo-square:before { + content: "\f194"; +} +.fa-turkish-lira:before, +.fa-try:before { + content: "\f195"; +} +.fa-plus-square-o:before { + content: "\f196"; +} +.fa-space-shuttle:before { + content: "\f197"; +} +.fa-slack:before { + content: "\f198"; +} +.fa-envelope-square:before { + content: "\f199"; +} +.fa-wordpress:before { + content: "\f19a"; +} +.fa-openid:before { + content: "\f19b"; +} +.fa-institution:before, +.fa-bank:before, +.fa-university:before { + content: "\f19c"; +} +.fa-mortar-board:before, +.fa-graduation-cap:before { + content: "\f19d"; +} +.fa-yahoo:before { + content: "\f19e"; +} +.fa-google:before { + content: "\f1a0"; +} +.fa-reddit:before { + content: "\f1a1"; +} +.fa-reddit-square:before { + content: "\f1a2"; +} +.fa-stumbleupon-circle:before { + content: "\f1a3"; +} +.fa-stumbleupon:before { + content: "\f1a4"; +} +.fa-delicious:before { + content: "\f1a5"; +} +.fa-digg:before { + content: "\f1a6"; +} +.fa-pied-piper-pp:before { + content: "\f1a7"; +} +.fa-pied-piper-alt:before { + content: "\f1a8"; +} +.fa-drupal:before { + content: "\f1a9"; +} +.fa-joomla:before { + content: "\f1aa"; +} +.fa-language:before { + content: "\f1ab"; +} +.fa-fax:before { + content: "\f1ac"; +} +.fa-building:before { + content: "\f1ad"; +} +.fa-child:before { + content: "\f1ae"; +} +.fa-paw:before { + content: "\f1b0"; +} +.fa-spoon:before { + content: "\f1b1"; +} +.fa-cube:before { + content: "\f1b2"; +} +.fa-cubes:before { + content: "\f1b3"; +} +.fa-behance:before { + content: "\f1b4"; +} +.fa-behance-square:before { + content: "\f1b5"; +} +.fa-steam:before { + content: "\f1b6"; +} +.fa-steam-square:before { + content: "\f1b7"; +} +.fa-recycle:before { + content: "\f1b8"; +} +.fa-automobile:before, +.fa-car:before { + content: "\f1b9"; +} +.fa-cab:before, +.fa-taxi:before { + content: "\f1ba"; +} +.fa-tree:before { + content: "\f1bb"; +} +.fa-spotify:before { + content: "\f1bc"; +} +.fa-deviantart:before { + content: "\f1bd"; +} +.fa-soundcloud:before { + content: "\f1be"; +} +.fa-database:before { + content: "\f1c0"; +} +.fa-file-pdf-o:before { + content: "\f1c1"; +} +.fa-file-word-o:before { + content: "\f1c2"; +} +.fa-file-excel-o:before { + content: "\f1c3"; +} +.fa-file-powerpoint-o:before { + content: "\f1c4"; +} +.fa-file-photo-o:before, +.fa-file-picture-o:before, +.fa-file-image-o:before { + content: "\f1c5"; +} +.fa-file-zip-o:before, +.fa-file-archive-o:before { + content: "\f1c6"; +} +.fa-file-sound-o:before, +.fa-file-audio-o:before { + content: "\f1c7"; +} +.fa-file-movie-o:before, +.fa-file-video-o:before { + content: "\f1c8"; +} +.fa-file-code-o:before { + content: "\f1c9"; +} +.fa-vine:before { + content: "\f1ca"; +} +.fa-codepen:before { + content: "\f1cb"; +} +.fa-jsfiddle:before { + content: "\f1cc"; +} +.fa-life-bouy:before, +.fa-life-buoy:before, +.fa-life-saver:before, +.fa-support:before, +.fa-life-ring:before { + content: "\f1cd"; +} +.fa-circle-o-notch:before { + content: "\f1ce"; +} +.fa-ra:before, +.fa-resistance:before, +.fa-rebel:before { + content: "\f1d0"; +} +.fa-ge:before, +.fa-empire:before { + content: "\f1d1"; +} +.fa-git-square:before { + content: "\f1d2"; +} +.fa-git:before { + content: "\f1d3"; +} +.fa-y-combinator-square:before, +.fa-yc-square:before, +.fa-hacker-news:before { + content: "\f1d4"; +} +.fa-tencent-weibo:before { + content: "\f1d5"; +} +.fa-qq:before { + content: "\f1d6"; +} +.fa-wechat:before, +.fa-weixin:before { + content: "\f1d7"; +} +.fa-send:before, +.fa-paper-plane:before { + content: "\f1d8"; +} +.fa-send-o:before, +.fa-paper-plane-o:before { + content: "\f1d9"; +} +.fa-history:before { + content: "\f1da"; +} +.fa-circle-thin:before { + content: "\f1db"; +} +.fa-header:before { + content: "\f1dc"; +} +.fa-paragraph:before { + content: "\f1dd"; +} +.fa-sliders:before { + content: "\f1de"; +} +.fa-share-alt:before { + content: "\f1e0"; +} +.fa-share-alt-square:before { + content: "\f1e1"; +} +.fa-bomb:before { + content: "\f1e2"; +} +.fa-soccer-ball-o:before, +.fa-futbol-o:before { + content: "\f1e3"; +} +.fa-tty:before { + content: "\f1e4"; +} +.fa-binoculars:before { + content: "\f1e5"; +} +.fa-plug:before { + content: "\f1e6"; +} +.fa-slideshare:before { + content: "\f1e7"; +} +.fa-twitch:before { + content: "\f1e8"; +} +.fa-yelp:before { + content: "\f1e9"; +} +.fa-newspaper-o:before { + content: "\f1ea"; +} +.fa-wifi:before { + content: "\f1eb"; +} +.fa-calculator:before { + content: "\f1ec"; +} +.fa-paypal:before { + content: "\f1ed"; +} +.fa-google-wallet:before { + content: "\f1ee"; +} +.fa-cc-visa:before { + content: "\f1f0"; +} +.fa-cc-mastercard:before { + content: "\f1f1"; +} +.fa-cc-discover:before { + content: "\f1f2"; +} +.fa-cc-amex:before { + content: "\f1f3"; +} +.fa-cc-paypal:before { + content: "\f1f4"; +} +.fa-cc-stripe:before { + content: "\f1f5"; +} +.fa-bell-slash:before { + content: "\f1f6"; +} +.fa-bell-slash-o:before { + content: "\f1f7"; +} +.fa-trash:before { + content: "\f1f8"; +} +.fa-copyright:before { + content: "\f1f9"; +} +.fa-at:before { + content: "\f1fa"; +} +.fa-eyedropper:before { + content: "\f1fb"; +} +.fa-paint-brush:before { + content: "\f1fc"; +} +.fa-birthday-cake:before { + content: "\f1fd"; +} +.fa-area-chart:before { + content: "\f1fe"; +} +.fa-pie-chart:before { + content: "\f200"; +} +.fa-line-chart:before { + content: "\f201"; +} +.fa-lastfm:before { + content: "\f202"; +} +.fa-lastfm-square:before { + content: "\f203"; +} +.fa-toggle-off:before { + content: "\f204"; +} +.fa-toggle-on:before { + content: "\f205"; +} +.fa-bicycle:before { + content: "\f206"; +} +.fa-bus:before { + content: "\f207"; +} +.fa-ioxhost:before { + content: "\f208"; +} +.fa-angellist:before { + content: "\f209"; +} +.fa-cc:before { + content: "\f20a"; +} +.fa-shekel:before, +.fa-sheqel:before, +.fa-ils:before { + content: "\f20b"; +} +.fa-meanpath:before { + content: "\f20c"; +} +.fa-buysellads:before { + content: "\f20d"; +} +.fa-connectdevelop:before { + content: "\f20e"; +} +.fa-dashcube:before { + content: "\f210"; +} +.fa-forumbee:before { + content: "\f211"; +} +.fa-leanpub:before { + content: "\f212"; +} +.fa-sellsy:before { + content: "\f213"; +} +.fa-shirtsinbulk:before { + content: "\f214"; +} +.fa-simplybuilt:before { + content: "\f215"; +} +.fa-skyatlas:before { + content: "\f216"; +} +.fa-cart-plus:before { + content: "\f217"; +} +.fa-cart-arrow-down:before { + content: "\f218"; +} +.fa-diamond:before { + content: "\f219"; +} +.fa-ship:before { + content: "\f21a"; +} +.fa-user-secret:before { + content: "\f21b"; +} +.fa-motorcycle:before { + content: "\f21c"; +} +.fa-street-view:before { + content: "\f21d"; +} +.fa-heartbeat:before { + content: "\f21e"; +} +.fa-venus:before { + content: "\f221"; +} +.fa-mars:before { + content: "\f222"; +} +.fa-mercury:before { + content: "\f223"; +} +.fa-intersex:before, +.fa-transgender:before { + content: "\f224"; +} +.fa-transgender-alt:before { + content: "\f225"; +} +.fa-venus-double:before { + content: "\f226"; +} +.fa-mars-double:before { + content: "\f227"; +} +.fa-venus-mars:before { + content: "\f228"; +} +.fa-mars-stroke:before { + content: "\f229"; +} +.fa-mars-stroke-v:before { + content: "\f22a"; +} +.fa-mars-stroke-h:before { + content: "\f22b"; +} +.fa-neuter:before { + content: "\f22c"; +} +.fa-genderless:before { + content: "\f22d"; +} +.fa-facebook-official:before { + content: "\f230"; +} +.fa-pinterest-p:before { + content: "\f231"; +} +.fa-whatsapp:before { + content: "\f232"; +} +.fa-server:before { + content: "\f233"; +} +.fa-user-plus:before { + content: "\f234"; +} +.fa-user-times:before { + content: "\f235"; +} +.fa-hotel:before, +.fa-bed:before { + content: "\f236"; +} +.fa-viacoin:before { + content: "\f237"; +} +.fa-train:before { + content: "\f238"; +} +.fa-subway:before { + content: "\f239"; +} +.fa-medium:before { + content: "\f23a"; +} +.fa-yc:before, +.fa-y-combinator:before { + content: "\f23b"; +} +.fa-optin-monster:before { + content: "\f23c"; +} +.fa-opencart:before { + content: "\f23d"; +} +.fa-expeditedssl:before { + content: "\f23e"; +} +.fa-battery-4:before, +.fa-battery:before, +.fa-battery-full:before { + content: "\f240"; +} +.fa-battery-3:before, +.fa-battery-three-quarters:before { + content: "\f241"; +} +.fa-battery-2:before, +.fa-battery-half:before { + content: "\f242"; +} +.fa-battery-1:before, +.fa-battery-quarter:before { + content: "\f243"; +} +.fa-battery-0:before, +.fa-battery-empty:before { + content: "\f244"; +} +.fa-mouse-pointer:before { + content: "\f245"; +} +.fa-i-cursor:before { + content: "\f246"; +} +.fa-object-group:before { + content: "\f247"; +} +.fa-object-ungroup:before { + content: "\f248"; +} +.fa-sticky-note:before { + content: "\f249"; +} +.fa-sticky-note-o:before { + content: "\f24a"; +} +.fa-cc-jcb:before { + content: "\f24b"; +} +.fa-cc-diners-club:before { + content: "\f24c"; +} +.fa-clone:before { + content: "\f24d"; +} +.fa-balance-scale:before { + content: "\f24e"; +} +.fa-hourglass-o:before { + content: "\f250"; +} +.fa-hourglass-1:before, +.fa-hourglass-start:before { + content: "\f251"; +} +.fa-hourglass-2:before, +.fa-hourglass-half:before { + content: "\f252"; +} +.fa-hourglass-3:before, +.fa-hourglass-end:before { + content: "\f253"; +} +.fa-hourglass:before { + content: "\f254"; +} +.fa-hand-grab-o:before, +.fa-hand-rock-o:before { + content: "\f255"; +} +.fa-hand-stop-o:before, +.fa-hand-paper-o:before { + content: "\f256"; +} +.fa-hand-scissors-o:before { + content: "\f257"; +} +.fa-hand-lizard-o:before { + content: "\f258"; +} +.fa-hand-spock-o:before { + content: "\f259"; +} +.fa-hand-pointer-o:before { + content: "\f25a"; +} +.fa-hand-peace-o:before { + content: "\f25b"; +} +.fa-trademark:before { + content: "\f25c"; +} +.fa-registered:before { + content: "\f25d"; +} +.fa-creative-commons:before { + content: "\f25e"; +} +.fa-gg:before { + content: "\f260"; +} +.fa-gg-circle:before { + content: "\f261"; +} +.fa-tripadvisor:before { + content: "\f262"; +} +.fa-odnoklassniki:before { + content: "\f263"; +} +.fa-odnoklassniki-square:before { + content: "\f264"; +} +.fa-get-pocket:before { + content: "\f265"; +} +.fa-wikipedia-w:before { + content: "\f266"; +} +.fa-safari:before { + content: "\f267"; +} +.fa-chrome:before { + content: "\f268"; +} +.fa-firefox:before { + content: "\f269"; +} +.fa-opera:before { + content: "\f26a"; +} +.fa-internet-explorer:before { + content: "\f26b"; +} +.fa-tv:before, +.fa-television:before { + content: "\f26c"; +} +.fa-contao:before { + content: "\f26d"; +} +.fa-500px:before { + content: "\f26e"; +} +.fa-amazon:before { + content: "\f270"; +} +.fa-calendar-plus-o:before { + content: "\f271"; +} +.fa-calendar-minus-o:before { + content: "\f272"; +} +.fa-calendar-times-o:before { + content: "\f273"; +} +.fa-calendar-check-o:before { + content: "\f274"; +} +.fa-industry:before { + content: "\f275"; +} +.fa-map-pin:before { + content: "\f276"; +} +.fa-map-signs:before { + content: "\f277"; +} +.fa-map-o:before { + content: "\f278"; +} +.fa-map:before { + content: "\f279"; +} +.fa-commenting:before { + content: "\f27a"; +} +.fa-commenting-o:before { + content: "\f27b"; +} +.fa-houzz:before { + content: "\f27c"; +} +.fa-vimeo:before { + content: "\f27d"; +} +.fa-black-tie:before { + content: "\f27e"; +} +.fa-fonticons:before { + content: "\f280"; +} +.fa-reddit-alien:before { + content: "\f281"; +} +.fa-edge:before { + content: "\f282"; +} +.fa-credit-card-alt:before { + content: "\f283"; +} +.fa-codiepie:before { + content: "\f284"; +} +.fa-modx:before { + content: "\f285"; +} +.fa-fort-awesome:before { + content: "\f286"; +} +.fa-usb:before { + content: "\f287"; +} +.fa-product-hunt:before { + content: "\f288"; +} +.fa-mixcloud:before { + content: "\f289"; +} +.fa-scribd:before { + content: "\f28a"; +} +.fa-pause-circle:before { + content: "\f28b"; +} +.fa-pause-circle-o:before { + content: "\f28c"; +} +.fa-stop-circle:before { + content: "\f28d"; +} +.fa-stop-circle-o:before { + content: "\f28e"; +} +.fa-shopping-bag:before { + content: "\f290"; +} +.fa-shopping-basket:before { + content: "\f291"; +} +.fa-hashtag:before { + content: "\f292"; +} +.fa-bluetooth:before { + content: "\f293"; +} +.fa-bluetooth-b:before { + content: "\f294"; +} +.fa-percent:before { + content: "\f295"; +} +.fa-gitlab:before { + content: "\f296"; +} +.fa-wpbeginner:before { + content: "\f297"; +} +.fa-wpforms:before { + content: "\f298"; +} +.fa-envira:before { + content: "\f299"; +} +.fa-universal-access:before { + content: "\f29a"; +} +.fa-wheelchair-alt:before { + content: "\f29b"; +} +.fa-question-circle-o:before { + content: "\f29c"; +} +.fa-blind:before { + content: "\f29d"; +} +.fa-audio-description:before { + content: "\f29e"; +} +.fa-volume-control-phone:before { + content: "\f2a0"; +} +.fa-braille:before { + content: "\f2a1"; +} +.fa-assistive-listening-systems:before { + content: "\f2a2"; +} +.fa-asl-interpreting:before, +.fa-american-sign-language-interpreting:before { + content: "\f2a3"; +} +.fa-deafness:before, +.fa-hard-of-hearing:before, +.fa-deaf:before { + content: "\f2a4"; +} +.fa-glide:before { + content: "\f2a5"; +} +.fa-glide-g:before { + content: "\f2a6"; +} +.fa-signing:before, +.fa-sign-language:before { + content: "\f2a7"; +} +.fa-low-vision:before { + content: "\f2a8"; +} +.fa-viadeo:before { + content: "\f2a9"; +} +.fa-viadeo-square:before { + content: "\f2aa"; +} +.fa-snapchat:before { + content: "\f2ab"; +} +.fa-snapchat-ghost:before { + content: "\f2ac"; +} +.fa-snapchat-square:before { + content: "\f2ad"; +} +.fa-pied-piper:before { + content: "\f2ae"; +} +.fa-first-order:before { + content: "\f2b0"; +} +.fa-yoast:before { + content: "\f2b1"; +} +.fa-themeisle:before { + content: "\f2b2"; +} +.fa-google-plus-circle:before, +.fa-google-plus-official:before { + content: "\f2b3"; +} +.fa-fa:before, +.fa-font-awesome:before { + content: "\f2b4"; +} +.fa-handshake-o:before { + content: "\f2b5"; +} +.fa-envelope-open:before { + content: "\f2b6"; +} +.fa-envelope-open-o:before { + content: "\f2b7"; +} +.fa-linode:before { + content: "\f2b8"; +} +.fa-address-book:before { + content: "\f2b9"; +} +.fa-address-book-o:before { + content: "\f2ba"; +} +.fa-vcard:before, +.fa-address-card:before { + content: "\f2bb"; +} +.fa-vcard-o:before, +.fa-address-card-o:before { + content: "\f2bc"; +} +.fa-user-circle:before { + content: "\f2bd"; +} +.fa-user-circle-o:before { + content: "\f2be"; +} +.fa-user-o:before { + content: "\f2c0"; +} +.fa-id-badge:before { + content: "\f2c1"; +} +.fa-drivers-license:before, +.fa-id-card:before { + content: "\f2c2"; +} +.fa-drivers-license-o:before, +.fa-id-card-o:before { + content: "\f2c3"; +} +.fa-quora:before { + content: "\f2c4"; +} +.fa-free-code-camp:before { + content: "\f2c5"; +} +.fa-telegram:before { + content: "\f2c6"; +} +.fa-thermometer-4:before, +.fa-thermometer:before, +.fa-thermometer-full:before { + content: "\f2c7"; +} +.fa-thermometer-3:before, +.fa-thermometer-three-quarters:before { + content: "\f2c8"; +} +.fa-thermometer-2:before, +.fa-thermometer-half:before { + content: "\f2c9"; +} +.fa-thermometer-1:before, +.fa-thermometer-quarter:before { + content: "\f2ca"; +} +.fa-thermometer-0:before, +.fa-thermometer-empty:before { + content: "\f2cb"; +} +.fa-shower:before { + content: "\f2cc"; +} +.fa-bathtub:before, +.fa-s15:before, +.fa-bath:before { + content: "\f2cd"; +} +.fa-podcast:before { + content: "\f2ce"; +} +.fa-window-maximize:before { + content: "\f2d0"; +} +.fa-window-minimize:before { + content: "\f2d1"; +} +.fa-window-restore:before { + content: "\f2d2"; +} +.fa-times-rectangle:before, +.fa-window-close:before { + content: "\f2d3"; +} +.fa-times-rectangle-o:before, +.fa-window-close-o:before { + content: "\f2d4"; +} +.fa-bandcamp:before { + content: "\f2d5"; +} +.fa-grav:before { + content: "\f2d6"; +} +.fa-etsy:before { + content: "\f2d7"; +} +.fa-imdb:before { + content: "\f2d8"; +} +.fa-ravelry:before { + content: "\f2d9"; +} +.fa-eercast:before { + content: "\f2da"; +} +.fa-microchip:before { + content: "\f2db"; +} +.fa-snowflake-o:before { + content: "\f2dc"; +} +.fa-superpowers:before { + content: "\f2dd"; +} +.fa-wpexplorer:before { + content: "\f2de"; +} +.fa-meetup:before { + content: "\f2e0"; +} +.sr-only { + position: absolute; + width: 1px; + height: 1px; + padding: 0; + margin: -1px; + overflow: hidden; + clip: rect(0, 0, 0, 0); + border: 0; +} +.sr-only-focusable:active, +.sr-only-focusable:focus { + position: static; + width: auto; + height: auto; + margin: 0; + overflow: visible; + clip: auto; +} +.sr-only-focusable:active, +.sr-only-focusable:focus { + position: static; + width: auto; + height: auto; + margin: 0; + overflow: visible; + clip: auto; +} +/*! +* +* IPython base +* +*/ +.modal.fade .modal-dialog { + -webkit-transform: translate(0, 0); + -ms-transform: translate(0, 0); + -o-transform: translate(0, 0); + transform: translate(0, 0); +} +code { + color: #000; +} +pre { + font-size: inherit; + line-height: inherit; +} +label { + font-weight: normal; +} +/* Make the page background atleast 100% the height of the view port */ +/* Make the page itself atleast 70% the height of the view port */ +.border-box-sizing { + box-sizing: border-box; + -moz-box-sizing: border-box; + -webkit-box-sizing: border-box; +} +.corner-all { + border-radius: 2px; +} +.no-padding { + padding: 0px; +} +/* Flexible box model classes */ +/* Taken from Alex Russell http://infrequently.org/2009/08/css-3-progress/ */ +/* This file is a compatability layer. It allows the usage of flexible box +model layouts accross multiple browsers, including older browsers. The newest, +universal implementation of the flexible box model is used when available (see +`Modern browsers` comments below). Browsers that are known to implement this +new spec completely include: + + Firefox 28.0+ + Chrome 29.0+ + Internet Explorer 11+ + Opera 17.0+ + +Browsers not listed, including Safari, are supported via the styling under the +`Old browsers` comments below. +*/ +.hbox { + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: horizontal; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: horizontal; + -moz-box-align: stretch; + display: box; + box-orient: horizontal; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: row; + align-items: stretch; +} +.hbox > * { + /* Old browsers */ + -webkit-box-flex: 0; + -moz-box-flex: 0; + box-flex: 0; + /* Modern browsers */ + flex: none; +} +.vbox { + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: vertical; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: vertical; + -moz-box-align: stretch; + display: box; + box-orient: vertical; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: column; + align-items: stretch; +} +.vbox > * { + /* Old browsers */ + -webkit-box-flex: 0; + -moz-box-flex: 0; + box-flex: 0; + /* Modern browsers */ + flex: none; +} +.hbox.reverse, +.vbox.reverse, +.reverse { + /* Old browsers */ + -webkit-box-direction: reverse; + -moz-box-direction: reverse; + box-direction: reverse; + /* Modern browsers */ + flex-direction: row-reverse; +} +.hbox.box-flex0, +.vbox.box-flex0, +.box-flex0 { + /* Old browsers */ + -webkit-box-flex: 0; + -moz-box-flex: 0; + box-flex: 0; + /* Modern browsers */ + flex: none; + width: auto; +} +.hbox.box-flex1, +.vbox.box-flex1, +.box-flex1 { + /* Old browsers */ + -webkit-box-flex: 1; + -moz-box-flex: 1; + box-flex: 1; + /* Modern browsers */ + flex: 1; +} +.hbox.box-flex, +.vbox.box-flex, +.box-flex { + /* Old browsers */ + /* Old browsers */ + -webkit-box-flex: 1; + -moz-box-flex: 1; + box-flex: 1; + /* Modern browsers */ + flex: 1; +} +.hbox.box-flex2, +.vbox.box-flex2, +.box-flex2 { + /* Old browsers */ + -webkit-box-flex: 2; + -moz-box-flex: 2; + box-flex: 2; + /* Modern browsers */ + flex: 2; +} +.box-group1 { + /* Deprecated */ + -webkit-box-flex-group: 1; + -moz-box-flex-group: 1; + box-flex-group: 1; +} +.box-group2 { + /* Deprecated */ + -webkit-box-flex-group: 2; + -moz-box-flex-group: 2; + box-flex-group: 2; +} +.hbox.start, +.vbox.start, +.start { + /* Old browsers */ + -webkit-box-pack: start; + -moz-box-pack: start; + box-pack: start; + /* Modern browsers */ + justify-content: flex-start; +} +.hbox.end, +.vbox.end, +.end { + /* Old browsers */ + -webkit-box-pack: end; + -moz-box-pack: end; + box-pack: end; + /* Modern browsers */ + justify-content: flex-end; +} +.hbox.center, +.vbox.center, +.center { + /* Old browsers */ + -webkit-box-pack: center; + -moz-box-pack: center; + box-pack: center; + /* Modern browsers */ + justify-content: center; +} +.hbox.baseline, +.vbox.baseline, +.baseline { + /* Old browsers */ + -webkit-box-pack: baseline; + -moz-box-pack: baseline; + box-pack: baseline; + /* Modern browsers */ + justify-content: baseline; +} +.hbox.stretch, +.vbox.stretch, +.stretch { + /* Old browsers */ + -webkit-box-pack: stretch; + -moz-box-pack: stretch; + box-pack: stretch; + /* Modern browsers */ + justify-content: stretch; +} +.hbox.align-start, +.vbox.align-start, +.align-start { + /* Old browsers */ + -webkit-box-align: start; + -moz-box-align: start; + box-align: start; + /* Modern browsers */ + align-items: flex-start; +} +.hbox.align-end, +.vbox.align-end, +.align-end { + /* Old browsers */ + -webkit-box-align: end; + -moz-box-align: end; + box-align: end; + /* Modern browsers */ + align-items: flex-end; +} +.hbox.align-center, +.vbox.align-center, +.align-center { + /* Old browsers */ + -webkit-box-align: center; + -moz-box-align: center; + box-align: center; + /* Modern browsers */ + align-items: center; +} +.hbox.align-baseline, +.vbox.align-baseline, +.align-baseline { + /* Old browsers */ + -webkit-box-align: baseline; + -moz-box-align: baseline; + box-align: baseline; + /* Modern browsers */ + align-items: baseline; +} +.hbox.align-stretch, +.vbox.align-stretch, +.align-stretch { + /* Old browsers */ + -webkit-box-align: stretch; + -moz-box-align: stretch; + box-align: stretch; + /* Modern browsers */ + align-items: stretch; +} +div.error { + margin: 2em; + text-align: center; +} +div.error > h1 { + font-size: 500%; + line-height: normal; +} +div.error > p { + font-size: 200%; + line-height: normal; +} +div.traceback-wrapper { + text-align: left; + max-width: 800px; + margin: auto; +} +div.traceback-wrapper pre.traceback { + max-height: 600px; + overflow: auto; +} +/** + * Primary styles + * + * Author: Jupyter Development Team + */ +body { + background-color: #fff; + /* This makes sure that the body covers the entire window and needs to + be in a different element than the display: box in wrapper below */ + position: absolute; + left: 0px; + right: 0px; + top: 0px; + bottom: 0px; + overflow: visible; +} +body > #header { + /* Initially hidden to prevent FLOUC */ + display: none; + background-color: #fff; + /* Display over codemirror */ + position: relative; + z-index: 100; +} +body > #header #header-container { + display: flex; + flex-direction: row; + justify-content: space-between; + padding: 5px; + padding-bottom: 5px; + padding-top: 5px; + box-sizing: border-box; + -moz-box-sizing: border-box; + -webkit-box-sizing: border-box; +} +body > #header .header-bar { + width: 100%; + height: 1px; + background: #e7e7e7; + margin-bottom: -1px; +} +@media print { + body > #header { + display: none !important; + } +} +#header-spacer { + width: 100%; + visibility: hidden; +} +@media print { + #header-spacer { + display: none; + } +} +#ipython_notebook { + padding-left: 0px; + padding-top: 1px; + padding-bottom: 1px; +} +[dir="rtl"] #ipython_notebook { + margin-right: 10px; + margin-left: 0; +} +[dir="rtl"] #ipython_notebook.pull-left { + float: right !important; + float: right; +} +.flex-spacer { + flex: 1; +} +#noscript { + width: auto; + padding-top: 16px; + padding-bottom: 16px; + text-align: center; + font-size: 22px; + color: red; + font-weight: bold; +} +#ipython_notebook img { + height: 28px; +} +#site { + width: 100%; + display: none; + box-sizing: border-box; + -moz-box-sizing: border-box; + -webkit-box-sizing: border-box; + overflow: auto; +} +@media print { + #site { + height: auto !important; + } +} +/* Smaller buttons */ +.ui-button .ui-button-text { + padding: 0.2em 0.8em; + font-size: 77%; +} +input.ui-button { + padding: 0.3em 0.9em; +} +span#kernel_logo_widget { + margin: 0 10px; +} +span#login_widget { + float: right; +} +[dir="rtl"] span#login_widget { + float: left; +} +span#login_widget > .button, +#logout { + color: #333; + background-color: #fff; + border-color: #ccc; +} +span#login_widget > .button:focus, +#logout:focus, +span#login_widget > .button.focus, +#logout.focus { + color: #333; + background-color: #e6e6e6; + border-color: #8c8c8c; +} +span#login_widget > .button:hover, +#logout:hover { + color: #333; + background-color: #e6e6e6; + border-color: #adadad; +} +span#login_widget > .button:active, +#logout:active, +span#login_widget > .button.active, +#logout.active, +.open > .dropdown-togglespan#login_widget > .button, +.open > .dropdown-toggle#logout { + color: #333; + background-color: #e6e6e6; + border-color: #adadad; +} +span#login_widget > .button:active:hover, +#logout:active:hover, +span#login_widget > .button.active:hover, +#logout.active:hover, +.open > .dropdown-togglespan#login_widget > .button:hover, +.open > .dropdown-toggle#logout:hover, +span#login_widget > .button:active:focus, +#logout:active:focus, +span#login_widget > .button.active:focus, +#logout.active:focus, +.open > .dropdown-togglespan#login_widget > .button:focus, +.open > .dropdown-toggle#logout:focus, +span#login_widget > .button:active.focus, +#logout:active.focus, +span#login_widget > .button.active.focus, +#logout.active.focus, +.open > .dropdown-togglespan#login_widget > .button.focus, +.open > .dropdown-toggle#logout.focus { + color: #333; + background-color: #d4d4d4; + border-color: #8c8c8c; +} +span#login_widget > .button:active, +#logout:active, +span#login_widget > .button.active, +#logout.active, +.open > .dropdown-togglespan#login_widget > .button, +.open > .dropdown-toggle#logout { + background-image: none; +} +span#login_widget > .button.disabled:hover, +#logout.disabled:hover, +span#login_widget > .button[disabled]:hover, +#logout[disabled]:hover, +fieldset[disabled] span#login_widget > .button:hover, +fieldset[disabled] #logout:hover, +span#login_widget > .button.disabled:focus, +#logout.disabled:focus, +span#login_widget > .button[disabled]:focus, +#logout[disabled]:focus, +fieldset[disabled] span#login_widget > .button:focus, +fieldset[disabled] #logout:focus, +span#login_widget > .button.disabled.focus, +#logout.disabled.focus, +span#login_widget > .button[disabled].focus, +#logout[disabled].focus, +fieldset[disabled] span#login_widget > .button.focus, +fieldset[disabled] #logout.focus { + background-color: #fff; + border-color: #ccc; +} +span#login_widget > .button .badge, +#logout .badge { + color: #fff; + background-color: #333; +} +.nav-header { + text-transform: none; +} +#header > span { + margin-top: 10px; +} +.modal_stretch .modal-dialog { + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: vertical; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: vertical; + -moz-box-align: stretch; + display: box; + box-orient: vertical; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: column; + align-items: stretch; + min-height: 80vh; +} +.modal_stretch .modal-dialog .modal-body { + max-height: calc(100vh - 200px); + overflow: auto; + flex: 1; +} +.modal-header { + cursor: move; +} +@media (min-width: 768px) { + .modal .modal-dialog { + width: 700px; + } +} +@media (min-width: 768px) { + select.form-control { + margin-left: 12px; + margin-right: 12px; + } +} +/*! +* +* IPython auth +* +*/ +.center-nav { + display: inline-block; + margin-bottom: -4px; +} +[dir="rtl"] .center-nav form.pull-left { + float: right !important; + float: right; +} +[dir="rtl"] .center-nav .navbar-text { + float: right; +} +[dir="rtl"] .navbar-inner { + text-align: right; +} +[dir="rtl"] div.text-left { + text-align: right; +} +/*! +* +* IPython tree view +* +*/ +/* We need an invisible input field on top of the sentense*/ +/* "Drag file onto the list ..." */ +.alternate_upload { + background-color: none; + display: inline; +} +.alternate_upload.form { + padding: 0; + margin: 0; +} +.alternate_upload input.fileinput { + position: absolute; + display: block; + width: 100%; + height: 100%; + overflow: hidden; + cursor: pointer; + opacity: 0; + z-index: 2; +} +.alternate_upload .btn-xs > input.fileinput { + margin: -1px -5px; +} +.alternate_upload .btn-upload { + position: relative; + height: 22px; +} +::-webkit-file-upload-button { + cursor: pointer; +} +/** + * Primary styles + * + * Author: Jupyter Development Team + */ +ul#tabs { + margin-bottom: 4px; +} +ul#tabs a { + padding-top: 6px; + padding-bottom: 4px; +} +[dir="rtl"] ul#tabs.nav-tabs > li { + float: right; +} +[dir="rtl"] ul#tabs.nav.nav-tabs { + padding-right: 0; +} +ul.breadcrumb a:focus, +ul.breadcrumb a:hover { + text-decoration: none; +} +ul.breadcrumb i.icon-home { + font-size: 16px; + margin-right: 4px; +} +ul.breadcrumb span { + color: #5e5e5e; +} +.list_toolbar { + padding: 4px 0 4px 0; + vertical-align: middle; +} +.list_toolbar .tree-buttons { + padding-top: 1px; +} +[dir="rtl"] .list_toolbar .tree-buttons .pull-right { + float: left !important; + float: left; +} +[dir="rtl"] .list_toolbar .col-sm-4, +[dir="rtl"] .list_toolbar .col-sm-8 { + float: right; +} +.dynamic-buttons { + padding-top: 3px; + display: inline-block; +} +.list_toolbar [class*="span"] { + min-height: 24px; +} +.list_header { + font-weight: bold; + background-color: #EEE; +} +.list_placeholder { + font-weight: bold; + padding-top: 4px; + padding-bottom: 4px; + padding-left: 7px; + padding-right: 7px; +} +.list_container { + margin-top: 4px; + margin-bottom: 20px; + border: 1px solid #ddd; + border-radius: 2px; +} +.list_container > div { + border-bottom: 1px solid #ddd; +} +.list_container > div:hover .list-item { + background-color: red; +} +.list_container > div:last-child { + border: none; +} +.list_item:hover .list_item { + background-color: #ddd; +} +.list_item a { + text-decoration: none; +} +.list_item:hover { + background-color: #fafafa; +} +.list_header > div, +.list_item > div { + padding-top: 4px; + padding-bottom: 4px; + padding-left: 7px; + padding-right: 7px; + line-height: 22px; +} +.list_header > div input, +.list_item > div input { + margin-right: 7px; + margin-left: 14px; + vertical-align: text-bottom; + line-height: 22px; + position: relative; + top: -1px; +} +.list_header > div .item_link, +.list_item > div .item_link { + margin-left: -1px; + vertical-align: baseline; + line-height: 22px; +} +[dir="rtl"] .list_item > div input { + margin-right: 0; +} +.new-file input[type=checkbox] { + visibility: hidden; +} +.item_name { + line-height: 22px; + height: 24px; +} +.item_icon { + font-size: 14px; + color: #5e5e5e; + margin-right: 7px; + margin-left: 7px; + line-height: 22px; + vertical-align: baseline; +} +.item_modified { + margin-right: 7px; + margin-left: 7px; +} +[dir="rtl"] .item_modified.pull-right { + float: left !important; + float: left; +} +.item_buttons { + line-height: 1em; + margin-left: -5px; +} +.item_buttons .btn, +.item_buttons .btn-group, +.item_buttons .input-group { + float: left; +} +.item_buttons > .btn, +.item_buttons > .btn-group, +.item_buttons > .input-group { + margin-left: 5px; +} +.item_buttons .btn { + min-width: 13ex; +} +.item_buttons .running-indicator { + padding-top: 4px; + color: #5cb85c; +} +.item_buttons .kernel-name { + padding-top: 4px; + color: #5bc0de; + margin-right: 7px; + float: left; +} +[dir="rtl"] .item_buttons.pull-right { + float: left !important; + float: left; +} +[dir="rtl"] .item_buttons .kernel-name { + margin-left: 7px; + float: right; +} +.toolbar_info { + height: 24px; + line-height: 24px; +} +.list_item input:not([type=checkbox]) { + padding-top: 3px; + padding-bottom: 3px; + height: 22px; + line-height: 14px; + margin: 0px; +} +.highlight_text { + color: blue; +} +#project_name { + display: inline-block; + padding-left: 7px; + margin-left: -2px; +} +#project_name > .breadcrumb { + padding: 0px; + margin-bottom: 0px; + background-color: transparent; + font-weight: bold; +} +.sort_button { + display: inline-block; + padding-left: 7px; +} +[dir="rtl"] .sort_button.pull-right { + float: left !important; + float: left; +} +#tree-selector { + padding-right: 0px; +} +#button-select-all { + min-width: 50px; +} +[dir="rtl"] #button-select-all.btn { + float: right ; +} +#select-all { + margin-left: 7px; + margin-right: 2px; + margin-top: 2px; + height: 16px; +} +[dir="rtl"] #select-all.pull-left { + float: right !important; + float: right; +} +.menu_icon { + margin-right: 2px; +} +.tab-content .row { + margin-left: 0px; + margin-right: 0px; +} +.folder_icon:before { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + content: "\f114"; +} +.folder_icon:before.fa-pull-left { + margin-right: .3em; +} +.folder_icon:before.fa-pull-right { + margin-left: .3em; +} +.folder_icon:before.pull-left { + margin-right: .3em; +} +.folder_icon:before.pull-right { + margin-left: .3em; +} +.notebook_icon:before { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + content: "\f02d"; + position: relative; + top: -1px; +} +.notebook_icon:before.fa-pull-left { + margin-right: .3em; +} +.notebook_icon:before.fa-pull-right { + margin-left: .3em; +} +.notebook_icon:before.pull-left { + margin-right: .3em; +} +.notebook_icon:before.pull-right { + margin-left: .3em; +} +.running_notebook_icon:before { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + content: "\f02d"; + position: relative; + top: -1px; + color: #5cb85c; +} +.running_notebook_icon:before.fa-pull-left { + margin-right: .3em; +} +.running_notebook_icon:before.fa-pull-right { + margin-left: .3em; +} +.running_notebook_icon:before.pull-left { + margin-right: .3em; +} +.running_notebook_icon:before.pull-right { + margin-left: .3em; +} +.file_icon:before { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + content: "\f016"; + position: relative; + top: -2px; +} +.file_icon:before.fa-pull-left { + margin-right: .3em; +} +.file_icon:before.fa-pull-right { + margin-left: .3em; +} +.file_icon:before.pull-left { + margin-right: .3em; +} +.file_icon:before.pull-right { + margin-left: .3em; +} +#notebook_toolbar .pull-right { + padding-top: 0px; + margin-right: -1px; +} +ul#new-menu { + left: auto; + right: 0; +} +#new-menu .dropdown-header { + font-size: 10px; + border-bottom: 1px solid #e5e5e5; + padding: 0 0 3px; + margin: -3px 20px 0; +} +.kernel-menu-icon { + padding-right: 12px; + width: 24px; + content: "\f096"; +} +.kernel-menu-icon:before { + content: "\f096"; +} +.kernel-menu-icon-current:before { + content: "\f00c"; +} +#tab_content { + padding-top: 20px; +} +#running .panel-group .panel { + margin-top: 3px; + margin-bottom: 1em; +} +#running .panel-group .panel .panel-heading { + background-color: #EEE; + padding-top: 4px; + padding-bottom: 4px; + padding-left: 7px; + padding-right: 7px; + line-height: 22px; +} +#running .panel-group .panel .panel-heading a:focus, +#running .panel-group .panel .panel-heading a:hover { + text-decoration: none; +} +#running .panel-group .panel .panel-body { + padding: 0px; +} +#running .panel-group .panel .panel-body .list_container { + margin-top: 0px; + margin-bottom: 0px; + border: 0px; + border-radius: 0px; +} +#running .panel-group .panel .panel-body .list_container .list_item { + border-bottom: 1px solid #ddd; +} +#running .panel-group .panel .panel-body .list_container .list_item:last-child { + border-bottom: 0px; +} +.delete-button { + display: none; +} +.duplicate-button { + display: none; +} +.rename-button { + display: none; +} +.move-button { + display: none; +} +.download-button { + display: none; +} +.shutdown-button { + display: none; +} +.dynamic-instructions { + display: inline-block; + padding-top: 4px; +} +/*! +* +* IPython text editor webapp +* +*/ +.selected-keymap i.fa { + padding: 0px 5px; +} +.selected-keymap i.fa:before { + content: "\f00c"; +} +#mode-menu { + overflow: auto; + max-height: 20em; +} +.edit_app #header { + -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); + box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); +} +.edit_app #menubar .navbar { + /* Use a negative 1 bottom margin, so the border overlaps the border of the + header */ + margin-bottom: -1px; +} +.dirty-indicator { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + width: 20px; +} +.dirty-indicator.fa-pull-left { + margin-right: .3em; +} +.dirty-indicator.fa-pull-right { + margin-left: .3em; +} +.dirty-indicator.pull-left { + margin-right: .3em; +} +.dirty-indicator.pull-right { + margin-left: .3em; +} +.dirty-indicator-dirty { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + width: 20px; +} +.dirty-indicator-dirty.fa-pull-left { + margin-right: .3em; +} +.dirty-indicator-dirty.fa-pull-right { + margin-left: .3em; +} +.dirty-indicator-dirty.pull-left { + margin-right: .3em; +} +.dirty-indicator-dirty.pull-right { + margin-left: .3em; +} +.dirty-indicator-clean { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + width: 20px; +} +.dirty-indicator-clean.fa-pull-left { + margin-right: .3em; +} +.dirty-indicator-clean.fa-pull-right { + margin-left: .3em; +} +.dirty-indicator-clean.pull-left { + margin-right: .3em; +} +.dirty-indicator-clean.pull-right { + margin-left: .3em; +} +.dirty-indicator-clean:before { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + content: "\f00c"; +} +.dirty-indicator-clean:before.fa-pull-left { + margin-right: .3em; +} +.dirty-indicator-clean:before.fa-pull-right { + margin-left: .3em; +} +.dirty-indicator-clean:before.pull-left { + margin-right: .3em; +} +.dirty-indicator-clean:before.pull-right { + margin-left: .3em; +} +#filename { + font-size: 16pt; + display: table; + padding: 0px 5px; +} +#current-mode { + padding-left: 5px; + padding-right: 5px; +} +#texteditor-backdrop { + padding-top: 20px; + padding-bottom: 20px; +} +@media not print { + #texteditor-backdrop { + background-color: #EEE; + } +} +@media print { + #texteditor-backdrop #texteditor-container .CodeMirror-gutter, + #texteditor-backdrop #texteditor-container .CodeMirror-gutters { + background-color: #fff; + } +} +@media not print { + #texteditor-backdrop #texteditor-container .CodeMirror-gutter, + #texteditor-backdrop #texteditor-container .CodeMirror-gutters { + background-color: #fff; + } +} +@media not print { + #texteditor-backdrop #texteditor-container { + padding: 0px; + background-color: #fff; + -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); + box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); + } +} +.CodeMirror-dialog { + background-color: #fff; +} +/*! +* +* IPython notebook +* +*/ +/* CSS font colors for translated ANSI escape sequences */ +/* The color values are a mix of + http://www.xcolors.net/dl/baskerville-ivorylight and + http://www.xcolors.net/dl/euphrasia */ +.ansi-black-fg { + color: #3E424D; +} +.ansi-black-bg { + background-color: #3E424D; +} +.ansi-black-intense-fg { + color: #282C36; +} +.ansi-black-intense-bg { + background-color: #282C36; +} +.ansi-red-fg { + color: #E75C58; +} +.ansi-red-bg { + background-color: #E75C58; +} +.ansi-red-intense-fg { + color: #B22B31; +} +.ansi-red-intense-bg { + background-color: #B22B31; +} +.ansi-green-fg { + color: #00A250; +} +.ansi-green-bg { + background-color: #00A250; +} +.ansi-green-intense-fg { + color: #007427; +} +.ansi-green-intense-bg { + background-color: #007427; +} +.ansi-yellow-fg { + color: #DDB62B; +} +.ansi-yellow-bg { + background-color: #DDB62B; +} +.ansi-yellow-intense-fg { + color: #B27D12; +} +.ansi-yellow-intense-bg { + background-color: #B27D12; +} +.ansi-blue-fg { + color: #208FFB; +} +.ansi-blue-bg { + background-color: #208FFB; +} +.ansi-blue-intense-fg { + color: #0065CA; +} +.ansi-blue-intense-bg { + background-color: #0065CA; +} +.ansi-magenta-fg { + color: #D160C4; +} +.ansi-magenta-bg { + background-color: #D160C4; +} +.ansi-magenta-intense-fg { + color: #A03196; +} +.ansi-magenta-intense-bg { + background-color: #A03196; +} +.ansi-cyan-fg { + color: #60C6C8; +} +.ansi-cyan-bg { + background-color: #60C6C8; +} +.ansi-cyan-intense-fg { + color: #258F8F; +} +.ansi-cyan-intense-bg { + background-color: #258F8F; +} +.ansi-white-fg { + color: #C5C1B4; +} +.ansi-white-bg { + background-color: #C5C1B4; +} +.ansi-white-intense-fg { + color: #A1A6B2; +} +.ansi-white-intense-bg { + background-color: #A1A6B2; +} +.ansi-default-inverse-fg { + color: #FFFFFF; +} +.ansi-default-inverse-bg { + background-color: #000000; +} +.ansi-bold { + font-weight: bold; +} +.ansi-underline { + text-decoration: underline; +} +/* The following styles are deprecated an will be removed in a future version */ +.ansibold { + font-weight: bold; +} +.ansi-inverse { + outline: 0.5px dotted; +} +/* use dark versions for foreground, to improve visibility */ +.ansiblack { + color: black; +} +.ansired { + color: darkred; +} +.ansigreen { + color: darkgreen; +} +.ansiyellow { + color: #c4a000; +} +.ansiblue { + color: darkblue; +} +.ansipurple { + color: darkviolet; +} +.ansicyan { + color: steelblue; +} +.ansigray { + color: gray; +} +/* and light for background, for the same reason */ +.ansibgblack { + background-color: black; +} +.ansibgred { + background-color: red; +} +.ansibggreen { + background-color: green; +} +.ansibgyellow { + background-color: yellow; +} +.ansibgblue { + background-color: blue; +} +.ansibgpurple { + background-color: magenta; +} +.ansibgcyan { + background-color: cyan; +} +.ansibggray { + background-color: gray; +} +div.cell { + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: vertical; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: vertical; + -moz-box-align: stretch; + display: box; + box-orient: vertical; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: column; + align-items: stretch; + border-radius: 2px; + box-sizing: border-box; + -moz-box-sizing: border-box; + -webkit-box-sizing: border-box; + border-width: 1px; + border-style: solid; + border-color: transparent; + width: 100%; + padding: 5px; + /* This acts as a spacer between cells, that is outside the border */ + margin: 0px; + outline: none; + position: relative; + overflow: visible; +} +div.cell:before { + position: absolute; + display: block; + top: -1px; + left: -1px; + width: 5px; + height: calc(100% + 2px); + content: ''; + background: transparent; +} +div.cell.jupyter-soft-selected { + border-left-color: #E3F2FD; + border-left-width: 1px; + padding-left: 5px; + border-right-color: #E3F2FD; + border-right-width: 1px; + background: #E3F2FD; +} +@media print { + div.cell.jupyter-soft-selected { + border-color: transparent; + } +} +div.cell.selected, +div.cell.selected.jupyter-soft-selected { + border-color: #ababab; +} +div.cell.selected:before, +div.cell.selected.jupyter-soft-selected:before { + position: absolute; + display: block; + top: -1px; + left: -1px; + width: 5px; + height: calc(100% + 2px); + content: ''; + background: #42A5F5; +} +@media print { + div.cell.selected, + div.cell.selected.jupyter-soft-selected { + border-color: transparent; + } +} +.edit_mode div.cell.selected { + border-color: #66BB6A; +} +.edit_mode div.cell.selected:before { + position: absolute; + display: block; + top: -1px; + left: -1px; + width: 5px; + height: calc(100% + 2px); + content: ''; + background: #66BB6A; +} +@media print { + .edit_mode div.cell.selected { + border-color: transparent; + } +} +.prompt { + /* This needs to be wide enough for 3 digit prompt numbers: In[100]: */ + min-width: 14ex; + /* This padding is tuned to match the padding on the CodeMirror editor. */ + padding: 0.4em; + margin: 0px; + font-family: monospace; + text-align: right; + /* This has to match that of the the CodeMirror class line-height below */ + line-height: 1.21429em; + /* Don't highlight prompt number selection */ + -webkit-touch-callout: none; + -webkit-user-select: none; + -khtml-user-select: none; + -moz-user-select: none; + -ms-user-select: none; + user-select: none; + /* Use default cursor */ + cursor: default; +} +@media (max-width: 540px) { + .prompt { + text-align: left; + } +} +div.inner_cell { + min-width: 0; + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: vertical; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: vertical; + -moz-box-align: stretch; + display: box; + box-orient: vertical; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: column; + align-items: stretch; + /* Old browsers */ + -webkit-box-flex: 1; + -moz-box-flex: 1; + box-flex: 1; + /* Modern browsers */ + flex: 1; +} +/* input_area and input_prompt must match in top border and margin for alignment */ +div.input_area { + border: 1px solid #cfcfcf; + border-radius: 2px; + background: #f7f7f7; + line-height: 1.21429em; +} +/* This is needed so that empty prompt areas can collapse to zero height when there + is no content in the output_subarea and the prompt. The main purpose of this is + to make sure that empty JavaScript output_subareas have no height. */ +div.prompt:empty { + padding-top: 0; + padding-bottom: 0; +} +div.unrecognized_cell { + padding: 5px 5px 5px 0px; + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: horizontal; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: horizontal; + -moz-box-align: stretch; + display: box; + box-orient: horizontal; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: row; + align-items: stretch; +} +div.unrecognized_cell .inner_cell { + border-radius: 2px; + padding: 5px; + font-weight: bold; + color: red; + border: 1px solid #cfcfcf; + background: #eaeaea; +} +div.unrecognized_cell .inner_cell a { + color: inherit; + text-decoration: none; +} +div.unrecognized_cell .inner_cell a:hover { + color: inherit; + text-decoration: none; +} +@media (max-width: 540px) { + div.unrecognized_cell > div.prompt { + display: none; + } +} +div.code_cell { + /* avoid page breaking on code cells when printing */ +} +@media print { + div.code_cell { + page-break-inside: avoid; + } +} +/* any special styling for code cells that are currently running goes here */ +div.input { + page-break-inside: avoid; + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: horizontal; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: horizontal; + -moz-box-align: stretch; + display: box; + box-orient: horizontal; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: row; + align-items: stretch; +} +@media (max-width: 540px) { + div.input { + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: vertical; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: vertical; + -moz-box-align: stretch; + display: box; + box-orient: vertical; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: column; + align-items: stretch; + } +} +/* input_area and input_prompt must match in top border and margin for alignment */ +div.input_prompt { + color: #303F9F; + border-top: 1px solid transparent; +} +div.input_area > div.highlight { + margin: 0.4em; + border: none; + padding: 0px; + background-color: transparent; +} +div.input_area > div.highlight > pre { + margin: 0px; + border: none; + padding: 0px; + background-color: transparent; +} +/* The following gets added to the <head> if it is detected that the user has a + * monospace font with inconsistent normal/bold/italic height. See + * notebookmain.js. Such fonts will have keywords vertically offset with + * respect to the rest of the text. The user should select a better font. + * See: https://github.com/ipython/ipython/issues/1503 + * + * .CodeMirror span { + * vertical-align: bottom; + * } + */ +.CodeMirror { + line-height: 1.21429em; + /* Changed from 1em to our global default */ + font-size: 14px; + height: auto; + /* Changed to auto to autogrow */ + background: none; + /* Changed from white to allow our bg to show through */ +} +.CodeMirror-scroll { + /* The CodeMirror docs are a bit fuzzy on if overflow-y should be hidden or visible.*/ + /* We have found that if it is visible, vertical scrollbars appear with font size changes.*/ + overflow-y: hidden; + overflow-x: auto; +} +.CodeMirror-lines { + /* In CM2, this used to be 0.4em, but in CM3 it went to 4px. We need the em value because */ + /* we have set a different line-height and want this to scale with that. */ + /* Note that this should set vertical padding only, since CodeMirror assumes + that horizontal padding will be set on CodeMirror pre */ + padding: 0.4em 0; +} +.CodeMirror-linenumber { + padding: 0 8px 0 4px; +} +.CodeMirror-gutters { + border-bottom-left-radius: 2px; + border-top-left-radius: 2px; +} +.CodeMirror pre { + /* In CM3 this went to 4px from 0 in CM2. This sets horizontal padding only, + use .CodeMirror-lines for vertical */ + padding: 0 0.4em; + border: 0; + border-radius: 0; +} +.CodeMirror-cursor { + border-left: 1.4px solid black; +} +@media screen and (min-width: 2138px) and (max-width: 4319px) { + .CodeMirror-cursor { + border-left: 2px solid black; + } +} +@media screen and (min-width: 4320px) { + .CodeMirror-cursor { + border-left: 4px solid black; + } +} +/* + +Original style from softwaremaniacs.org (c) Ivan Sagalaev <Maniac@SoftwareManiacs.Org> +Adapted from GitHub theme + +*/ +.highlight-base { + color: #000; +} +.highlight-variable { + color: #000; +} +.highlight-variable-2 { + color: #1a1a1a; +} +.highlight-variable-3 { + color: #333333; +} +.highlight-string { + color: #BA2121; +} +.highlight-comment { + color: #408080; + font-style: italic; +} +.highlight-number { + color: #080; +} +.highlight-atom { + color: #88F; +} +.highlight-keyword { + color: #008000; + font-weight: bold; +} +.highlight-builtin { + color: #008000; +} +.highlight-error { + color: #f00; +} +.highlight-operator { + color: #AA22FF; + font-weight: bold; +} +.highlight-meta { + color: #AA22FF; +} +/* previously not defined, copying from default codemirror */ +.highlight-def { + color: #00f; +} +.highlight-string-2 { + color: #f50; +} +.highlight-qualifier { + color: #555; +} +.highlight-bracket { + color: #997; +} +.highlight-tag { + color: #170; +} +.highlight-attribute { + color: #00c; +} +.highlight-header { + color: blue; +} +.highlight-quote { + color: #090; +} +.highlight-link { + color: #00c; +} +/* apply the same style to codemirror */ +.cm-s-ipython span.cm-keyword { + color: #008000; + font-weight: bold; +} +.cm-s-ipython span.cm-atom { + color: #88F; +} +.cm-s-ipython span.cm-number { + color: #080; +} +.cm-s-ipython span.cm-def { + color: #00f; +} +.cm-s-ipython span.cm-variable { + color: #000; +} +.cm-s-ipython span.cm-operator { + color: #AA22FF; + font-weight: bold; +} +.cm-s-ipython span.cm-variable-2 { + color: #1a1a1a; +} +.cm-s-ipython span.cm-variable-3 { + color: #333333; +} +.cm-s-ipython span.cm-comment { + color: #408080; + font-style: italic; +} +.cm-s-ipython span.cm-string { + color: #BA2121; +} +.cm-s-ipython span.cm-string-2 { + color: #f50; +} +.cm-s-ipython span.cm-meta { + color: #AA22FF; +} +.cm-s-ipython span.cm-qualifier { + color: #555; +} +.cm-s-ipython span.cm-builtin { + color: #008000; +} +.cm-s-ipython span.cm-bracket { + color: #997; +} +.cm-s-ipython span.cm-tag { + color: #170; +} +.cm-s-ipython span.cm-attribute { + color: #00c; +} +.cm-s-ipython span.cm-header { + color: blue; +} +.cm-s-ipython span.cm-quote { + color: #090; +} +.cm-s-ipython span.cm-link { + color: #00c; +} +.cm-s-ipython span.cm-error { + color: #f00; +} +.cm-s-ipython span.cm-tab { + background: url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAADAAAAAMCAYAAAAkuj5RAAAAAXNSR0IArs4c6QAAAGFJREFUSMft1LsRQFAQheHPowAKoACx3IgEKtaEHujDjORSgWTH/ZOdnZOcM/sgk/kFFWY0qV8foQwS4MKBCS3qR6ixBJvElOobYAtivseIE120FaowJPN75GMu8j/LfMwNjh4HUpwg4LUAAAAASUVORK5CYII=); + background-position: right; + background-repeat: no-repeat; +} +div.output_wrapper { + /* this position must be relative to enable descendents to be absolute within it */ + position: relative; + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: vertical; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: vertical; + -moz-box-align: stretch; + display: box; + box-orient: vertical; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: column; + align-items: stretch; + z-index: 1; +} +/* class for the output area when it should be height-limited */ +div.output_scroll { + /* ideally, this would be max-height, but FF barfs all over that */ + height: 24em; + /* FF needs this *and the wrapper* to specify full width, or it will shrinkwrap */ + width: 100%; + overflow: auto; + border-radius: 2px; + -webkit-box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8); + box-shadow: inset 0 2px 8px rgba(0, 0, 0, 0.8); + display: block; +} +/* output div while it is collapsed */ +div.output_collapsed { + margin: 0px; + padding: 0px; + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: vertical; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: vertical; + -moz-box-align: stretch; + display: box; + box-orient: vertical; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: column; + align-items: stretch; +} +div.out_prompt_overlay { + height: 100%; + padding: 0px 0.4em; + position: absolute; + border-radius: 2px; +} +div.out_prompt_overlay:hover { + /* use inner shadow to get border that is computed the same on WebKit/FF */ + -webkit-box-shadow: inset 0 0 1px #000; + box-shadow: inset 0 0 1px #000; + background: rgba(240, 240, 240, 0.5); +} +div.output_prompt { + color: #D84315; +} +/* This class is the outer container of all output sections. */ +div.output_area { + padding: 0px; + page-break-inside: avoid; + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: horizontal; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: horizontal; + -moz-box-align: stretch; + display: box; + box-orient: horizontal; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: row; + align-items: stretch; +} +div.output_area .MathJax_Display { + text-align: left !important; +} +div.output_area .rendered_html table { + margin-left: 0; + margin-right: 0; +} +div.output_area .rendered_html img { + margin-left: 0; + margin-right: 0; +} +div.output_area img, +div.output_area svg { + max-width: 100%; + height: auto; +} +div.output_area img.unconfined, +div.output_area svg.unconfined { + max-width: none; +} +div.output_area .mglyph > img { + max-width: none; +} +/* This is needed to protect the pre formating from global settings such + as that of bootstrap */ +.output { + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: vertical; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: vertical; + -moz-box-align: stretch; + display: box; + box-orient: vertical; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: column; + align-items: stretch; +} +@media (max-width: 540px) { + div.output_area { + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: vertical; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: vertical; + -moz-box-align: stretch; + display: box; + box-orient: vertical; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: column; + align-items: stretch; + } +} +div.output_area pre { + margin: 0; + padding: 1px 0 1px 0; + border: 0; + vertical-align: baseline; + color: black; + background-color: transparent; + border-radius: 0; +} +/* This class is for the output subarea inside the output_area and after + the prompt div. */ +div.output_subarea { + overflow-x: auto; + padding: 0.4em; + /* Old browsers */ + -webkit-box-flex: 1; + -moz-box-flex: 1; + box-flex: 1; + /* Modern browsers */ + flex: 1; + max-width: calc(100% - 14ex); +} +div.output_scroll div.output_subarea { + overflow-x: visible; +} +/* The rest of the output_* classes are for special styling of the different + output types */ +/* all text output has this class: */ +div.output_text { + text-align: left; + color: #000; + /* This has to match that of the the CodeMirror class line-height below */ + line-height: 1.21429em; +} +/* stdout/stderr are 'text' as well as 'stream', but execute_result/error are *not* streams */ +div.output_stderr { + background: #fdd; + /* very light red background for stderr */ +} +div.output_latex { + text-align: left; +} +/* Empty output_javascript divs should have no height */ +div.output_javascript:empty { + padding: 0; +} +.js-error { + color: darkred; +} +/* raw_input styles */ +div.raw_input_container { + line-height: 1.21429em; + padding-top: 5px; +} +pre.raw_input_prompt { + /* nothing needed here. */ +} +input.raw_input { + font-family: monospace; + font-size: inherit; + color: inherit; + width: auto; + /* make sure input baseline aligns with prompt */ + vertical-align: baseline; + /* padding + margin = 0.5em between prompt and cursor */ + padding: 0em 0.25em; + margin: 0em 0.25em; +} +input.raw_input:focus { + box-shadow: none; +} +p.p-space { + margin-bottom: 10px; +} +div.output_unrecognized { + padding: 5px; + font-weight: bold; + color: red; +} +div.output_unrecognized a { + color: inherit; + text-decoration: none; +} +div.output_unrecognized a:hover { + color: inherit; + text-decoration: none; +} +.rendered_html { + color: #000; + /* any extras will just be numbers: */ +} +.rendered_html em { + font-style: italic; +} +.rendered_html strong { + font-weight: bold; +} +.rendered_html u { + text-decoration: underline; +} +.rendered_html :link { + text-decoration: underline; +} +.rendered_html :visited { + text-decoration: underline; +} +.rendered_html h1 { + font-size: 185.7%; + margin: 1.08em 0 0 0; + font-weight: bold; + line-height: 1.0; +} +.rendered_html h2 { + font-size: 157.1%; + margin: 1.27em 0 0 0; + font-weight: bold; + line-height: 1.0; +} +.rendered_html h3 { + font-size: 128.6%; + margin: 1.55em 0 0 0; + font-weight: bold; + line-height: 1.0; +} +.rendered_html h4 { + font-size: 100%; + margin: 2em 0 0 0; + font-weight: bold; + line-height: 1.0; +} +.rendered_html h5 { + font-size: 100%; + margin: 2em 0 0 0; + font-weight: bold; + line-height: 1.0; + font-style: italic; +} +.rendered_html h6 { + font-size: 100%; + margin: 2em 0 0 0; + font-weight: bold; + line-height: 1.0; + font-style: italic; +} +.rendered_html h1:first-child { + margin-top: 0.538em; +} +.rendered_html h2:first-child { + margin-top: 0.636em; +} +.rendered_html h3:first-child { + margin-top: 0.777em; +} +.rendered_html h4:first-child { + margin-top: 1em; +} +.rendered_html h5:first-child { + margin-top: 1em; +} +.rendered_html h6:first-child { + margin-top: 1em; +} +.rendered_html ul:not(.list-inline), +.rendered_html ol:not(.list-inline) { + padding-left: 2em; +} +.rendered_html ul { + list-style: disc; +} +.rendered_html ul ul { + list-style: square; + margin-top: 0; +} +.rendered_html ul ul ul { + list-style: circle; +} +.rendered_html ol { + list-style: decimal; +} +.rendered_html ol ol { + list-style: upper-alpha; + margin-top: 0; +} +.rendered_html ol ol ol { + list-style: lower-alpha; +} +.rendered_html ol ol ol ol { + list-style: lower-roman; +} +.rendered_html ol ol ol ol ol { + list-style: decimal; +} +.rendered_html * + ul { + margin-top: 1em; +} +.rendered_html * + ol { + margin-top: 1em; +} +.rendered_html hr { + color: black; + background-color: black; +} +.rendered_html pre { + margin: 1em 2em; + padding: 0px; + background-color: #fff; +} +.rendered_html code { + background-color: #eff0f1; +} +.rendered_html p code { + padding: 1px 5px; +} +.rendered_html pre code { + background-color: #fff; +} +.rendered_html pre, +.rendered_html code { + border: 0; + color: #000; + font-size: 100%; +} +.rendered_html blockquote { + margin: 1em 2em; +} +.rendered_html table { + margin-left: auto; + margin-right: auto; + border: none; + border-collapse: collapse; + border-spacing: 0; + color: black; + font-size: 12px; + table-layout: fixed; +} +.rendered_html thead { + border-bottom: 1px solid black; + vertical-align: bottom; +} +.rendered_html tr, +.rendered_html th, +.rendered_html td { + text-align: right; + vertical-align: middle; + padding: 0.5em 0.5em; + line-height: normal; + white-space: normal; + max-width: none; + border: none; +} +.rendered_html th { + font-weight: bold; +} +.rendered_html tbody tr:nth-child(odd) { + background: #f5f5f5; +} +.rendered_html tbody tr:hover { + background: rgba(66, 165, 245, 0.2); +} +.rendered_html * + table { + margin-top: 1em; +} +.rendered_html p { + text-align: left; +} +.rendered_html * + p { + margin-top: 1em; +} +.rendered_html img { + display: block; + margin-left: auto; + margin-right: auto; +} +.rendered_html * + img { + margin-top: 1em; +} +.rendered_html img, +.rendered_html svg { + max-width: 100%; + height: auto; +} +.rendered_html img.unconfined, +.rendered_html svg.unconfined { + max-width: none; +} +.rendered_html .alert { + margin-bottom: initial; +} +.rendered_html * + .alert { + margin-top: 1em; +} +[dir="rtl"] .rendered_html p { + text-align: right; +} +div.text_cell { + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: horizontal; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: horizontal; + -moz-box-align: stretch; + display: box; + box-orient: horizontal; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: row; + align-items: stretch; +} +@media (max-width: 540px) { + div.text_cell > div.prompt { + display: none; + } +} +div.text_cell_render { + /*font-family: "Helvetica Neue", Arial, Helvetica, Geneva, sans-serif;*/ + outline: none; + resize: none; + width: inherit; + border-style: none; + padding: 0.5em 0.5em 0.5em 0.4em; + color: #000; + box-sizing: border-box; + -moz-box-sizing: border-box; + -webkit-box-sizing: border-box; +} +a.anchor-link:link { + text-decoration: none; + padding: 0px 20px; + visibility: hidden; +} +h1:hover .anchor-link, +h2:hover .anchor-link, +h3:hover .anchor-link, +h4:hover .anchor-link, +h5:hover .anchor-link, +h6:hover .anchor-link { + visibility: visible; +} +.text_cell.rendered .input_area { + display: none; +} +.text_cell.rendered .rendered_html { + overflow-x: auto; + overflow-y: hidden; +} +.text_cell.rendered .rendered_html tr, +.text_cell.rendered .rendered_html th, +.text_cell.rendered .rendered_html td { + max-width: none; +} +.text_cell.unrendered .text_cell_render { + display: none; +} +.text_cell .dropzone .input_area { + border: 2px dashed #bababa; + margin: -1px; +} +.cm-header-1, +.cm-header-2, +.cm-header-3, +.cm-header-4, +.cm-header-5, +.cm-header-6 { + font-weight: bold; + font-family: "Helvetica Neue", Helvetica, Arial, sans-serif; +} +.cm-header-1 { + font-size: 185.7%; +} +.cm-header-2 { + font-size: 157.1%; +} +.cm-header-3 { + font-size: 128.6%; +} +.cm-header-4 { + font-size: 110%; +} +.cm-header-5 { + font-size: 100%; + font-style: italic; +} +.cm-header-6 { + font-size: 100%; + font-style: italic; +} +/*! +* +* IPython notebook webapp +* +*/ +@media (max-width: 767px) { + .notebook_app { + padding-left: 0px; + padding-right: 0px; + } +} +#ipython-main-app { + box-sizing: border-box; + -moz-box-sizing: border-box; + -webkit-box-sizing: border-box; + height: 100%; +} +div#notebook_panel { + margin: 0px; + padding: 0px; + box-sizing: border-box; + -moz-box-sizing: border-box; + -webkit-box-sizing: border-box; + height: 100%; +} +div#notebook { + font-size: 14px; + line-height: 20px; + overflow-y: hidden; + overflow-x: auto; + width: 100%; + /* This spaces the page away from the edge of the notebook area */ + padding-top: 20px; + margin: 0px; + outline: none; + box-sizing: border-box; + -moz-box-sizing: border-box; + -webkit-box-sizing: border-box; + min-height: 100%; +} +@media not print { + #notebook-container { + padding: 15px; + background-color: #fff; + min-height: 0; + -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); + box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); + } +} +@media print { + #notebook-container { + width: 100%; + } +} +div.ui-widget-content { + border: 1px solid #ababab; + outline: none; +} +pre.dialog { + background-color: #f7f7f7; + border: 1px solid #ddd; + border-radius: 2px; + padding: 0.4em; + padding-left: 2em; +} +p.dialog { + padding: 0.2em; +} +/* Word-wrap output correctly. This is the CSS3 spelling, though Firefox seems + to not honor it correctly. Webkit browsers (Chrome, rekonq, Safari) do. + */ +pre, +code, +kbd, +samp { + white-space: pre-wrap; +} +#fonttest { + font-family: monospace; +} +p { + margin-bottom: 0; +} +.end_space { + min-height: 100px; + transition: height .2s ease; +} +.notebook_app > #header { + -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); + box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); +} +@media not print { + .notebook_app { + background-color: #EEE; + } +} +kbd { + border-style: solid; + border-width: 1px; + box-shadow: none; + margin: 2px; + padding-left: 2px; + padding-right: 2px; + padding-top: 1px; + padding-bottom: 1px; +} +.jupyter-keybindings { + padding: 1px; + line-height: 24px; + border-bottom: 1px solid gray; +} +.jupyter-keybindings input { + margin: 0; + padding: 0; + border: none; +} +.jupyter-keybindings i { + padding: 6px; +} +.well code { + background-color: #ffffff; + border-color: #ababab; + border-width: 1px; + border-style: solid; + padding: 2px; + padding-top: 1px; + padding-bottom: 1px; +} +/* CSS for the cell toolbar */ +.celltoolbar { + border: thin solid #CFCFCF; + border-bottom: none; + background: #EEE; + border-radius: 2px 2px 0px 0px; + width: 100%; + height: 29px; + padding-right: 4px; + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: horizontal; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: horizontal; + -moz-box-align: stretch; + display: box; + box-orient: horizontal; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: row; + align-items: stretch; + /* Old browsers */ + -webkit-box-pack: end; + -moz-box-pack: end; + box-pack: end; + /* Modern browsers */ + justify-content: flex-end; + display: -webkit-flex; +} +@media print { + .celltoolbar { + display: none; + } +} +.ctb_hideshow { + display: none; + vertical-align: bottom; +} +/* ctb_show is added to the ctb_hideshow div to show the cell toolbar. + Cell toolbars are only shown when the ctb_global_show class is also set. +*/ +.ctb_global_show .ctb_show.ctb_hideshow { + display: block; +} +.ctb_global_show .ctb_show + .input_area, +.ctb_global_show .ctb_show + div.text_cell_input, +.ctb_global_show .ctb_show ~ div.text_cell_render { + border-top-right-radius: 0px; + border-top-left-radius: 0px; +} +.ctb_global_show .ctb_show ~ div.text_cell_render { + border: 1px solid #cfcfcf; +} +.celltoolbar { + font-size: 87%; + padding-top: 3px; +} +.celltoolbar select { + display: block; + width: 100%; + height: 32px; + padding: 6px 12px; + font-size: 13px; + line-height: 1.42857143; + color: #555555; + background-color: #fff; + background-image: none; + border: 1px solid #ccc; + border-radius: 2px; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; + -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; + transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; + height: 30px; + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 1px; + width: inherit; + font-size: inherit; + height: 22px; + padding: 0px; + display: inline-block; +} +.celltoolbar select:focus { + border-color: #66afe9; + outline: 0; + -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); + box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); +} +.celltoolbar select::-moz-placeholder { + color: #999; + opacity: 1; +} +.celltoolbar select:-ms-input-placeholder { + color: #999; +} +.celltoolbar select::-webkit-input-placeholder { + color: #999; +} +.celltoolbar select::-ms-expand { + border: 0; + background-color: transparent; +} +.celltoolbar select[disabled], +.celltoolbar select[readonly], +fieldset[disabled] .celltoolbar select { + background-color: #eeeeee; + opacity: 1; +} +.celltoolbar select[disabled], +fieldset[disabled] .celltoolbar select { + cursor: not-allowed; +} +textarea.celltoolbar select { + height: auto; +} +select.celltoolbar select { + height: 30px; + line-height: 30px; +} +textarea.celltoolbar select, +select[multiple].celltoolbar select { + height: auto; +} +.celltoolbar label { + margin-left: 5px; + margin-right: 5px; +} +.tags_button_container { + width: 100%; + display: flex; +} +.tag-container { + display: flex; + flex-direction: row; + flex-grow: 1; + overflow: hidden; + position: relative; +} +.tag-container > * { + margin: 0 4px; +} +.remove-tag-btn { + margin-left: 4px; +} +.tags-input { + display: flex; +} +.cell-tag:last-child:after { + content: ""; + position: absolute; + right: 0; + width: 40px; + height: 100%; + /* Fade to background color of cell toolbar */ + background: linear-gradient(to right, rgba(0, 0, 0, 0), #EEE); +} +.tags-input > * { + margin-left: 4px; +} +.cell-tag, +.tags-input input, +.tags-input button { + display: block; + width: 100%; + height: 32px; + padding: 6px 12px; + font-size: 13px; + line-height: 1.42857143; + color: #555555; + background-color: #fff; + background-image: none; + border: 1px solid #ccc; + border-radius: 2px; + -webkit-box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + box-shadow: inset 0 1px 1px rgba(0, 0, 0, 0.075); + -webkit-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; + -o-transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; + transition: border-color ease-in-out .15s, box-shadow ease-in-out .15s; + height: 30px; + padding: 5px 10px; + font-size: 12px; + line-height: 1.5; + border-radius: 1px; + box-shadow: none; + width: inherit; + font-size: inherit; + height: 22px; + line-height: 22px; + padding: 0px 4px; + display: inline-block; +} +.cell-tag:focus, +.tags-input input:focus, +.tags-input button:focus { + border-color: #66afe9; + outline: 0; + -webkit-box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); + box-shadow: inset 0 1px 1px rgba(0,0,0,.075), 0 0 8px rgba(102, 175, 233, 0.6); +} +.cell-tag::-moz-placeholder, +.tags-input input::-moz-placeholder, +.tags-input button::-moz-placeholder { + color: #999; + opacity: 1; +} +.cell-tag:-ms-input-placeholder, +.tags-input input:-ms-input-placeholder, +.tags-input button:-ms-input-placeholder { + color: #999; +} +.cell-tag::-webkit-input-placeholder, +.tags-input input::-webkit-input-placeholder, +.tags-input button::-webkit-input-placeholder { + color: #999; +} +.cell-tag::-ms-expand, +.tags-input input::-ms-expand, +.tags-input button::-ms-expand { + border: 0; + background-color: transparent; +} +.cell-tag[disabled], +.tags-input input[disabled], +.tags-input button[disabled], +.cell-tag[readonly], +.tags-input input[readonly], +.tags-input button[readonly], +fieldset[disabled] .cell-tag, +fieldset[disabled] .tags-input input, +fieldset[disabled] .tags-input button { + background-color: #eeeeee; + opacity: 1; +} +.cell-tag[disabled], +.tags-input input[disabled], +.tags-input button[disabled], +fieldset[disabled] .cell-tag, +fieldset[disabled] .tags-input input, +fieldset[disabled] .tags-input button { + cursor: not-allowed; +} +textarea.cell-tag, +textarea.tags-input input, +textarea.tags-input button { + height: auto; +} +select.cell-tag, +select.tags-input input, +select.tags-input button { + height: 30px; + line-height: 30px; +} +textarea.cell-tag, +textarea.tags-input input, +textarea.tags-input button, +select[multiple].cell-tag, +select[multiple].tags-input input, +select[multiple].tags-input button { + height: auto; +} +.cell-tag, +.tags-input button { + padding: 0px 4px; +} +.cell-tag { + background-color: #fff; + white-space: nowrap; +} +.tags-input input[type=text]:focus { + outline: none; + box-shadow: none; + border-color: #ccc; +} +.completions { + position: absolute; + z-index: 110; + overflow: hidden; + border: 1px solid #ababab; + border-radius: 2px; + -webkit-box-shadow: 0px 6px 10px -1px #adadad; + box-shadow: 0px 6px 10px -1px #adadad; + line-height: 1; +} +.completions select { + background: white; + outline: none; + border: none; + padding: 0px; + margin: 0px; + overflow: auto; + font-family: monospace; + font-size: 110%; + color: #000; + width: auto; +} +.completions select option.context { + color: #286090; +} +#kernel_logo_widget .current_kernel_logo { + display: none; + margin-top: -1px; + margin-bottom: -1px; + width: 32px; + height: 32px; +} +[dir="rtl"] #kernel_logo_widget { + float: left !important; + float: left; +} +.modal .modal-body .move-path { + display: flex; + flex-direction: row; + justify-content: space; + align-items: center; +} +.modal .modal-body .move-path .server-root { + padding-right: 20px; +} +.modal .modal-body .move-path .path-input { + flex: 1; +} +#menubar { + box-sizing: border-box; + -moz-box-sizing: border-box; + -webkit-box-sizing: border-box; + margin-top: 1px; +} +#menubar .navbar { + border-top: 1px; + border-radius: 0px 0px 2px 2px; + margin-bottom: 0px; +} +#menubar .navbar-toggle { + float: left; + padding-top: 7px; + padding-bottom: 7px; + border: none; +} +#menubar .navbar-collapse { + clear: left; +} +[dir="rtl"] #menubar .navbar-toggle { + float: right; +} +[dir="rtl"] #menubar .navbar-collapse { + clear: right; +} +[dir="rtl"] #menubar .navbar-nav { + float: right; +} +[dir="rtl"] #menubar .nav { + padding-right: 0px; +} +[dir="rtl"] #menubar .navbar-nav > li { + float: right; +} +[dir="rtl"] #menubar .navbar-right { + float: left !important; +} +[dir="rtl"] ul.dropdown-menu { + text-align: right; + left: auto; +} +[dir="rtl"] ul#new-menu.dropdown-menu { + right: auto; + left: 0; +} +.nav-wrapper { + border-bottom: 1px solid #e7e7e7; +} +i.menu-icon { + padding-top: 4px; +} +[dir="rtl"] i.menu-icon.pull-right { + float: left !important; + float: left; +} +ul#help_menu li a { + overflow: hidden; + padding-right: 2.2em; +} +ul#help_menu li a i { + margin-right: -1.2em; +} +[dir="rtl"] ul#help_menu li a { + padding-left: 2.2em; +} +[dir="rtl"] ul#help_menu li a i { + margin-right: 0; + margin-left: -1.2em; +} +[dir="rtl"] ul#help_menu li a i.pull-right { + float: left !important; + float: left; +} +.dropdown-submenu { + position: relative; +} +.dropdown-submenu > .dropdown-menu { + top: 0; + left: 100%; + margin-top: -6px; + margin-left: -1px; +} +[dir="rtl"] .dropdown-submenu > .dropdown-menu { + right: 100%; + margin-right: -1px; +} +.dropdown-submenu:hover > .dropdown-menu { + display: block; +} +.dropdown-submenu > a:after { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + display: block; + content: "\f0da"; + float: right; + color: #333333; + margin-top: 2px; + margin-right: -10px; +} +.dropdown-submenu > a:after.fa-pull-left { + margin-right: .3em; +} +.dropdown-submenu > a:after.fa-pull-right { + margin-left: .3em; +} +.dropdown-submenu > a:after.pull-left { + margin-right: .3em; +} +.dropdown-submenu > a:after.pull-right { + margin-left: .3em; +} +[dir="rtl"] .dropdown-submenu > a:after { + float: left; + content: "\f0d9"; + margin-right: 0; + margin-left: -10px; +} +.dropdown-submenu:hover > a:after { + color: #262626; +} +.dropdown-submenu.pull-left { + float: none; +} +.dropdown-submenu.pull-left > .dropdown-menu { + left: -100%; + margin-left: 10px; +} +#notification_area { + float: right !important; + float: right; + z-index: 10; +} +[dir="rtl"] #notification_area { + float: left !important; + float: left; +} +.indicator_area { + float: right !important; + float: right; + color: #777; + margin-left: 5px; + margin-right: 5px; + width: 11px; + z-index: 10; + text-align: center; + width: auto; +} +[dir="rtl"] .indicator_area { + float: left !important; + float: left; +} +#kernel_indicator { + float: right !important; + float: right; + color: #777; + margin-left: 5px; + margin-right: 5px; + width: 11px; + z-index: 10; + text-align: center; + width: auto; + border-left: 1px solid; +} +#kernel_indicator .kernel_indicator_name { + padding-left: 5px; + padding-right: 5px; +} +[dir="rtl"] #kernel_indicator { + float: left !important; + float: left; + border-left: 0; + border-right: 1px solid; +} +#modal_indicator { + float: right !important; + float: right; + color: #777; + margin-left: 5px; + margin-right: 5px; + width: 11px; + z-index: 10; + text-align: center; + width: auto; +} +[dir="rtl"] #modal_indicator { + float: left !important; + float: left; +} +#readonly-indicator { + float: right !important; + float: right; + color: #777; + margin-left: 5px; + margin-right: 5px; + width: 11px; + z-index: 10; + text-align: center; + width: auto; + margin-top: 2px; + margin-bottom: 0px; + margin-left: 0px; + margin-right: 0px; + display: none; +} +.modal_indicator:before { + width: 1.28571429em; + text-align: center; +} +.edit_mode .modal_indicator:before { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + content: "\f040"; +} +.edit_mode .modal_indicator:before.fa-pull-left { + margin-right: .3em; +} +.edit_mode .modal_indicator:before.fa-pull-right { + margin-left: .3em; +} +.edit_mode .modal_indicator:before.pull-left { + margin-right: .3em; +} +.edit_mode .modal_indicator:before.pull-right { + margin-left: .3em; +} +.command_mode .modal_indicator:before { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + content: ' '; +} +.command_mode .modal_indicator:before.fa-pull-left { + margin-right: .3em; +} +.command_mode .modal_indicator:before.fa-pull-right { + margin-left: .3em; +} +.command_mode .modal_indicator:before.pull-left { + margin-right: .3em; +} +.command_mode .modal_indicator:before.pull-right { + margin-left: .3em; +} +.kernel_idle_icon:before { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + content: "\f10c"; +} +.kernel_idle_icon:before.fa-pull-left { + margin-right: .3em; +} +.kernel_idle_icon:before.fa-pull-right { + margin-left: .3em; +} +.kernel_idle_icon:before.pull-left { + margin-right: .3em; +} +.kernel_idle_icon:before.pull-right { + margin-left: .3em; +} +.kernel_busy_icon:before { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + content: "\f111"; +} +.kernel_busy_icon:before.fa-pull-left { + margin-right: .3em; +} +.kernel_busy_icon:before.fa-pull-right { + margin-left: .3em; +} +.kernel_busy_icon:before.pull-left { + margin-right: .3em; +} +.kernel_busy_icon:before.pull-right { + margin-left: .3em; +} +.kernel_dead_icon:before { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + content: "\f1e2"; +} +.kernel_dead_icon:before.fa-pull-left { + margin-right: .3em; +} +.kernel_dead_icon:before.fa-pull-right { + margin-left: .3em; +} +.kernel_dead_icon:before.pull-left { + margin-right: .3em; +} +.kernel_dead_icon:before.pull-right { + margin-left: .3em; +} +.kernel_disconnected_icon:before { + display: inline-block; + font: normal normal normal 14px/1 FontAwesome; + font-size: inherit; + text-rendering: auto; + -webkit-font-smoothing: antialiased; + -moz-osx-font-smoothing: grayscale; + content: "\f127"; +} +.kernel_disconnected_icon:before.fa-pull-left { + margin-right: .3em; +} +.kernel_disconnected_icon:before.fa-pull-right { + margin-left: .3em; +} +.kernel_disconnected_icon:before.pull-left { + margin-right: .3em; +} +.kernel_disconnected_icon:before.pull-right { + margin-left: .3em; +} +.notification_widget { + color: #777; + z-index: 10; + background: rgba(240, 240, 240, 0.5); + margin-right: 4px; + color: #333; + background-color: #fff; + border-color: #ccc; +} +.notification_widget:focus, +.notification_widget.focus { + color: #333; + background-color: #e6e6e6; + border-color: #8c8c8c; +} +.notification_widget:hover { + color: #333; + background-color: #e6e6e6; + border-color: #adadad; +} +.notification_widget:active, +.notification_widget.active, +.open > .dropdown-toggle.notification_widget { + color: #333; + background-color: #e6e6e6; + border-color: #adadad; +} +.notification_widget:active:hover, +.notification_widget.active:hover, +.open > .dropdown-toggle.notification_widget:hover, +.notification_widget:active:focus, +.notification_widget.active:focus, +.open > .dropdown-toggle.notification_widget:focus, +.notification_widget:active.focus, +.notification_widget.active.focus, +.open > .dropdown-toggle.notification_widget.focus { + color: #333; + background-color: #d4d4d4; + border-color: #8c8c8c; +} +.notification_widget:active, +.notification_widget.active, +.open > .dropdown-toggle.notification_widget { + background-image: none; +} +.notification_widget.disabled:hover, +.notification_widget[disabled]:hover, +fieldset[disabled] .notification_widget:hover, +.notification_widget.disabled:focus, +.notification_widget[disabled]:focus, +fieldset[disabled] .notification_widget:focus, +.notification_widget.disabled.focus, +.notification_widget[disabled].focus, +fieldset[disabled] .notification_widget.focus { + background-color: #fff; + border-color: #ccc; +} +.notification_widget .badge { + color: #fff; + background-color: #333; +} +.notification_widget.warning { + color: #fff; + background-color: #f0ad4e; + border-color: #eea236; +} +.notification_widget.warning:focus, +.notification_widget.warning.focus { + color: #fff; + background-color: #ec971f; + border-color: #985f0d; +} +.notification_widget.warning:hover { + color: #fff; + background-color: #ec971f; + border-color: #d58512; +} +.notification_widget.warning:active, +.notification_widget.warning.active, +.open > .dropdown-toggle.notification_widget.warning { + color: #fff; + background-color: #ec971f; + border-color: #d58512; +} +.notification_widget.warning:active:hover, +.notification_widget.warning.active:hover, +.open > .dropdown-toggle.notification_widget.warning:hover, +.notification_widget.warning:active:focus, +.notification_widget.warning.active:focus, +.open > .dropdown-toggle.notification_widget.warning:focus, +.notification_widget.warning:active.focus, +.notification_widget.warning.active.focus, +.open > .dropdown-toggle.notification_widget.warning.focus { + color: #fff; + background-color: #d58512; + border-color: #985f0d; +} +.notification_widget.warning:active, +.notification_widget.warning.active, +.open > .dropdown-toggle.notification_widget.warning { + background-image: none; +} +.notification_widget.warning.disabled:hover, +.notification_widget.warning[disabled]:hover, +fieldset[disabled] .notification_widget.warning:hover, +.notification_widget.warning.disabled:focus, +.notification_widget.warning[disabled]:focus, +fieldset[disabled] .notification_widget.warning:focus, +.notification_widget.warning.disabled.focus, +.notification_widget.warning[disabled].focus, +fieldset[disabled] .notification_widget.warning.focus { + background-color: #f0ad4e; + border-color: #eea236; +} +.notification_widget.warning .badge { + color: #f0ad4e; + background-color: #fff; +} +.notification_widget.success { + color: #fff; + background-color: #5cb85c; + border-color: #4cae4c; +} +.notification_widget.success:focus, +.notification_widget.success.focus { + color: #fff; + background-color: #449d44; + border-color: #255625; +} +.notification_widget.success:hover { + color: #fff; + background-color: #449d44; + border-color: #398439; +} +.notification_widget.success:active, +.notification_widget.success.active, +.open > .dropdown-toggle.notification_widget.success { + color: #fff; + background-color: #449d44; + border-color: #398439; +} +.notification_widget.success:active:hover, +.notification_widget.success.active:hover, +.open > .dropdown-toggle.notification_widget.success:hover, +.notification_widget.success:active:focus, +.notification_widget.success.active:focus, +.open > .dropdown-toggle.notification_widget.success:focus, +.notification_widget.success:active.focus, +.notification_widget.success.active.focus, +.open > .dropdown-toggle.notification_widget.success.focus { + color: #fff; + background-color: #398439; + border-color: #255625; +} +.notification_widget.success:active, +.notification_widget.success.active, +.open > .dropdown-toggle.notification_widget.success { + background-image: none; +} +.notification_widget.success.disabled:hover, +.notification_widget.success[disabled]:hover, +fieldset[disabled] .notification_widget.success:hover, +.notification_widget.success.disabled:focus, +.notification_widget.success[disabled]:focus, +fieldset[disabled] .notification_widget.success:focus, +.notification_widget.success.disabled.focus, +.notification_widget.success[disabled].focus, +fieldset[disabled] .notification_widget.success.focus { + background-color: #5cb85c; + border-color: #4cae4c; +} +.notification_widget.success .badge { + color: #5cb85c; + background-color: #fff; +} +.notification_widget.info { + color: #fff; + background-color: #5bc0de; + border-color: #46b8da; +} +.notification_widget.info:focus, +.notification_widget.info.focus { + color: #fff; + background-color: #31b0d5; + border-color: #1b6d85; +} +.notification_widget.info:hover { + color: #fff; + background-color: #31b0d5; + border-color: #269abc; +} +.notification_widget.info:active, +.notification_widget.info.active, +.open > .dropdown-toggle.notification_widget.info { + color: #fff; + background-color: #31b0d5; + border-color: #269abc; +} +.notification_widget.info:active:hover, +.notification_widget.info.active:hover, +.open > .dropdown-toggle.notification_widget.info:hover, +.notification_widget.info:active:focus, +.notification_widget.info.active:focus, +.open > .dropdown-toggle.notification_widget.info:focus, +.notification_widget.info:active.focus, +.notification_widget.info.active.focus, +.open > .dropdown-toggle.notification_widget.info.focus { + color: #fff; + background-color: #269abc; + border-color: #1b6d85; +} +.notification_widget.info:active, +.notification_widget.info.active, +.open > .dropdown-toggle.notification_widget.info { + background-image: none; +} +.notification_widget.info.disabled:hover, +.notification_widget.info[disabled]:hover, +fieldset[disabled] .notification_widget.info:hover, +.notification_widget.info.disabled:focus, +.notification_widget.info[disabled]:focus, +fieldset[disabled] .notification_widget.info:focus, +.notification_widget.info.disabled.focus, +.notification_widget.info[disabled].focus, +fieldset[disabled] .notification_widget.info.focus { + background-color: #5bc0de; + border-color: #46b8da; +} +.notification_widget.info .badge { + color: #5bc0de; + background-color: #fff; +} +.notification_widget.danger { + color: #fff; + background-color: #d9534f; + border-color: #d43f3a; +} +.notification_widget.danger:focus, +.notification_widget.danger.focus { + color: #fff; + background-color: #c9302c; + border-color: #761c19; +} +.notification_widget.danger:hover { + color: #fff; + background-color: #c9302c; + border-color: #ac2925; +} +.notification_widget.danger:active, +.notification_widget.danger.active, +.open > .dropdown-toggle.notification_widget.danger { + color: #fff; + background-color: #c9302c; + border-color: #ac2925; +} +.notification_widget.danger:active:hover, +.notification_widget.danger.active:hover, +.open > .dropdown-toggle.notification_widget.danger:hover, +.notification_widget.danger:active:focus, +.notification_widget.danger.active:focus, +.open > .dropdown-toggle.notification_widget.danger:focus, +.notification_widget.danger:active.focus, +.notification_widget.danger.active.focus, +.open > .dropdown-toggle.notification_widget.danger.focus { + color: #fff; + background-color: #ac2925; + border-color: #761c19; +} +.notification_widget.danger:active, +.notification_widget.danger.active, +.open > .dropdown-toggle.notification_widget.danger { + background-image: none; +} +.notification_widget.danger.disabled:hover, +.notification_widget.danger[disabled]:hover, +fieldset[disabled] .notification_widget.danger:hover, +.notification_widget.danger.disabled:focus, +.notification_widget.danger[disabled]:focus, +fieldset[disabled] .notification_widget.danger:focus, +.notification_widget.danger.disabled.focus, +.notification_widget.danger[disabled].focus, +fieldset[disabled] .notification_widget.danger.focus { + background-color: #d9534f; + border-color: #d43f3a; +} +.notification_widget.danger .badge { + color: #d9534f; + background-color: #fff; +} +div#pager { + background-color: #fff; + font-size: 14px; + line-height: 20px; + overflow: hidden; + display: none; + position: fixed; + bottom: 0px; + width: 100%; + max-height: 50%; + padding-top: 8px; + -webkit-box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); + box-shadow: 0px 0px 12px 1px rgba(87, 87, 87, 0.2); + /* Display over codemirror */ + z-index: 100; + /* Hack which prevents jquery ui resizable from changing top. */ + top: auto !important; +} +div#pager pre { + line-height: 1.21429em; + color: #000; + background-color: #f7f7f7; + padding: 0.4em; +} +div#pager #pager-button-area { + position: absolute; + top: 8px; + right: 20px; +} +div#pager #pager-contents { + position: relative; + overflow: auto; + width: 100%; + height: 100%; +} +div#pager #pager-contents #pager-container { + position: relative; + padding: 15px 0px; + box-sizing: border-box; + -moz-box-sizing: border-box; + -webkit-box-sizing: border-box; +} +div#pager .ui-resizable-handle { + top: 0px; + height: 8px; + background: #f7f7f7; + border-top: 1px solid #cfcfcf; + border-bottom: 1px solid #cfcfcf; + /* This injects handle bars (a short, wide = symbol) for + the resize handle. */ +} +div#pager .ui-resizable-handle::after { + content: ''; + top: 2px; + left: 50%; + height: 3px; + width: 30px; + margin-left: -15px; + position: absolute; + border-top: 1px solid #cfcfcf; +} +.quickhelp { + /* Old browsers */ + display: -webkit-box; + -webkit-box-orient: horizontal; + -webkit-box-align: stretch; + display: -moz-box; + -moz-box-orient: horizontal; + -moz-box-align: stretch; + display: box; + box-orient: horizontal; + box-align: stretch; + /* Modern browsers */ + display: flex; + flex-direction: row; + align-items: stretch; + line-height: 1.8em; +} +.shortcut_key { + display: inline-block; + width: 21ex; + text-align: right; + font-family: monospace; +} +.shortcut_descr { + display: inline-block; + /* Old browsers */ + -webkit-box-flex: 1; + -moz-box-flex: 1; + box-flex: 1; + /* Modern browsers */ + flex: 1; +} +span.save_widget { + height: 30px; + margin-top: 4px; + display: flex; + justify-content: flex-start; + align-items: baseline; + width: 50%; + flex: 1; +} +span.save_widget span.filename { + height: 100%; + line-height: 1em; 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+ padding: 8px; +} + +div#notebook { + overflow: visible; + border-top: none; +}@media print { + div.cell { + display: block; + page-break-inside: avoid; + } + div.output_wrapper { + display: block; + page-break-inside: avoid; + } + div.output { + display: block; + page-break-inside: avoid; + } +} +</style> + +<!-- Custom stylesheet, it must be in the same directory as the html file --> +<link rel="stylesheet" href="custom.css"> + +<!-- Loading mathjax macro --> +<!-- Load mathjax --> + <script src="https://cdnjs.cloudflare.com/ajax/libs/mathjax/2.7.1/MathJax.js?config=TeX-AMS_HTML"></script> + <!-- MathJax configuration --> + <script type="text/x-mathjax-config"> + MathJax.Hub.Config({ + tex2jax: { + inlineMath: [ ['$','$'], ["\\(","\\)"] ], + displayMath: [ ['$$','$$'], ["\\[","\\]"] ], + processEscapes: true, + processEnvironments: true + }, + // Center justify equations in code and markdown cells. Elsewhere + // we use CSS to left justify single line equations in code cells. + displayAlign: 'center', + "HTML-CSS": { + styles: {'.MathJax_Display': {"margin": 0}}, + linebreaks: { automatic: true } + } + }); + </script> + <!-- End of mathjax configuration --></head> +<body> + <div tabindex="-1" id="notebook" class="border-box-sizing"> + <div class="container" id="notebook-container"> + +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h2 id="Convolutional-Neural-Networks">Convolutional Neural Networks<a class="anchor-link" href="#Convolutional-Neural-Networks">¶</a></h2><h2 id="Project:-Write-an-Algorithm-for-a-Dog-Identification-App">Project: Write an Algorithm for a Dog Identification App<a class="anchor-link" href="#Project:-Write-an-Algorithm-for-a-Dog-Identification-App">¶</a></h2><hr> +<p>In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with <strong>'(IMPLEMENTATION)'</strong> in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully!</p> +<blockquote><p><strong>Note</strong>: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \n", + "<strong>File -> Download as -> HTML (.html)</strong>. Include the finished document along with this notebook as your submission.</p> +</blockquote> +<p>In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a <strong>'Question X'</strong> header. Carefully read each question and provide thorough answers in the following text boxes that begin with <strong>'Answer:'</strong>. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.</p> +<blockquote><p><strong>Note:</strong> Code and Markdown cells can be executed using the <strong>Shift + Enter</strong> keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.</p> +</blockquote> +<p>The rubric contains <em>optional</em> "Stand Out Suggestions" for enhancing the project beyond the minimum requirements. If you decide to pursue the "Stand Out Suggestions", you should include the code in this IPython notebook.</p> +<hr> +<h3 id="Why-We're-Here">Why We're Here<a class="anchor-link" href="#Why-We're-Here">¶</a></h3><p>In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!).</p> +<p><img src="images/sample_dog_output.png" alt="Sample Dog Output"></p> +<p>In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!</p> +<h3 id="The-Road-Ahead">The Road Ahead<a class="anchor-link" href="#The-Road-Ahead">¶</a></h3><p>We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.</p> +<ul> +<li><a href="#step0">Step 0</a>: Import Datasets</li> +<li><a href="#step1">Step 1</a>: Detect Humans</li> +<li><a href="#step2">Step 2</a>: Detect Dogs</li> +<li><a href="#step3">Step 3</a>: Create a CNN to Classify Dog Breeds (from Scratch)</li> +<li><a href="#step4">Step 4</a>: Use a CNN to Classify Dog Breeds (using Transfer Learning)</li> +<li><a href="#step5">Step 5</a>: Create a CNN to Classify Dog Breeds (using Transfer Learning)</li> +<li><a href="#step6">Step 6</a>: Write your Algorithm</li> +<li><a href="#step7">Step 7</a>: Test Your Algorithm</li> +</ul> +<hr> +<p><a id='step0'></a></p> +<h2 id="Step-0:-Import-Datasets">Step 0: Import Datasets<a class="anchor-link" href="#Step-0:-Import-Datasets">¶</a></h2><h3 id="Import-Dog-Dataset">Import Dog Dataset<a class="anchor-link" href="#Import-Dog-Dataset">¶</a></h3><p>In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the <code>load_files</code> function from the scikit-learn library:</p> +<ul> +<li><code>train_files</code>, <code>valid_files</code>, <code>test_files</code> - numpy arrays containing file paths to images</li> +<li><code>train_targets</code>, <code>valid_targets</code>, <code>test_targets</code> - numpy arrays containing onehot-encoded classification labels </li> +<li><code>dog_names</code> - list of string-valued dog breed names for translating labels</li> +</ul> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [57]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">sklearn.datasets</span> <span class="k">import</span> <span class="n">load_files</span> +<span class="kn">from</span> <span class="nn">keras.utils</span> <span class="k">import</span> <span class="n">np_utils</span> +<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span> +<span class="kn">from</span> <span class="nn">glob</span> <span class="k">import</span> <span class="n">glob</span> + +<span class="c1"># define function to load train, test, and validation datasets</span> +<span class="k">def</span> <span class="nf">load_dataset</span><span class="p">(</span><span class="n">path</span><span class="p">):</span> + <span class="n">data</span> <span class="o">=</span> <span class="n">load_files</span><span class="p">(</span><span class="n">path</span><span class="p">)</span> + <span class="n">dog_files</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">'filenames'</span><span class="p">])</span> + <span class="n">dog_targets</span> <span class="o">=</span> <span class="n">np_utils</span><span class="o">.</span><span class="n">to_categorical</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">data</span><span class="p">[</span><span class="s1">'target'</span><span class="p">]),</span> <span class="mi">133</span><span class="p">)</span> + <span class="k">return</span> <span class="n">dog_files</span><span class="p">,</span> <span class="n">dog_targets</span> + +<span class="c1"># load train, test, and validation datasets</span> +<span class="n">train_files</span><span class="p">,</span> <span class="n">train_targets</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s1">'/data/dog_images/train'</span><span class="p">)</span> +<span class="n">valid_files</span><span class="p">,</span> <span class="n">valid_targets</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s1">'/data/dog_images/valid'</span><span class="p">)</span> +<span class="n">test_files</span><span class="p">,</span> <span class="n">test_targets</span> <span class="o">=</span> <span class="n">load_dataset</span><span class="p">(</span><span class="s1">'/data/dog_images/test'</span><span class="p">)</span> + +<span class="c1"># load list of dog names</span> +<span class="n">dog_names</span> <span class="o">=</span> <span class="p">[</span><span class="n">item</span><span class="p">[</span><span class="mi">20</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">item</span> <span class="ow">in</span> <span class="nb">sorted</span><span class="p">(</span><span class="n">glob</span><span class="p">(</span><span class="s2">"/data/dog_images/train/*/"</span><span class="p">))]</span> + +<span class="c1"># print statistics about the dataset</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'There are </span><span class="si">%d</span><span class="s1"> total dog categories.'</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">dog_names</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'There are </span><span class="si">%s</span><span class="s1"> total dog images.</span><span class="se">\n</span><span class="s1">'</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">hstack</span><span class="p">([</span><span class="n">train_files</span><span class="p">,</span> <span class="n">valid_files</span><span class="p">,</span> <span class="n">test_files</span><span class="p">])))</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'There are </span><span class="si">%d</span><span class="s1"> training dog images.'</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">train_files</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'There are </span><span class="si">%d</span><span class="s1"> validation dog images.'</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">valid_files</span><span class="p">))</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'There are </span><span class="si">%d</span><span class="s1"> test dog images.'</span><span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">test_files</span><span class="p">))</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>There are 133 total dog categories. +There are 8351 total dog images. + +There are 6680 training dog images. +There are 835 validation dog images. +There are 836 test dog images. +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="Import-Human-Dataset">Import Human Dataset<a class="anchor-link" href="#Import-Human-Dataset">¶</a></h3><p>In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array <code>human_files</code>.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [2]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">random</span> +<span class="n">random</span><span class="o">.</span><span class="n">seed</span><span class="p">(</span><span class="mi">8675309</span><span class="p">)</span> + +<span class="c1"># load filenames in shuffled human dataset</span> +<span class="n">human_files</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">glob</span><span class="p">(</span><span class="s2">"/data/lfw/*/*"</span><span class="p">))</span> +<span class="n">random</span><span class="o">.</span><span class="n">shuffle</span><span class="p">(</span><span class="n">human_files</span><span class="p">)</span> + +<span class="c1"># print statistics about the dataset</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'There are </span><span class="si">%d</span><span class="s1"> total human images.'</span> <span class="o">%</span> <span class="nb">len</span><span class="p">(</span><span class="n">human_files</span><span class="p">))</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>There are 13233 total human images. +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<hr> +<p><a id='step1'></a></p> +<h2 id="Step-1:-Detect-Humans">Step 1: Detect Humans<a class="anchor-link" href="#Step-1:-Detect-Humans">¶</a></h2><p>We use OpenCV's implementation of <a href="http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html">Haar feature-based cascade classifiers</a> to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on <a href="https://github.com/opencv/opencv/tree/master/data/haarcascades">github</a>. We have downloaded one of these detectors and stored it in the <code>haarcascades</code> directory.</p> +<p>In the next code cell, we demonstrate how to use this detector to find human faces in a sample image.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [3]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">import</span> <span class="nn">cv2</span> +<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span> +<span class="o">%</span><span class="k">matplotlib</span> inline + +<span class="c1"># extract pre-trained face detector</span> +<span class="n">face_cascade</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">CascadeClassifier</span><span class="p">(</span><span class="s1">'haarcascades/haarcascade_frontalface_alt.xml'</span><span class="p">)</span> + +<span class="c1"># load color (BGR) image</span> +<span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">human_files</span><span class="p">[</span><span class="mi">3</span><span class="p">])</span> +<span class="c1"># convert BGR image to grayscale</span> +<span class="n">gray</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2GRAY</span><span class="p">)</span> + +<span class="c1"># find faces in image</span> +<span class="n">faces</span> <span class="o">=</span> <span class="n">face_cascade</span><span class="o">.</span><span class="n">detectMultiScale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span> + +<span class="c1"># print number of faces detected in the image</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'Number of faces detected:'</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">faces</span><span class="p">))</span> + +<span class="c1"># get bounding box for each detected face</span> +<span class="k">for</span> <span class="p">(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">,</span><span class="n">w</span><span class="p">,</span><span class="n">h</span><span class="p">)</span> <span class="ow">in</span> <span class="n">faces</span><span class="p">:</span> + <span class="c1"># add bounding box to color image</span> + <span class="n">cv2</span><span class="o">.</span><span class="n">rectangle</span><span class="p">(</span><span class="n">img</span><span class="p">,(</span><span class="n">x</span><span class="p">,</span><span class="n">y</span><span class="p">),(</span><span class="n">x</span><span class="o">+</span><span class="n">w</span><span class="p">,</span><span class="n">y</span><span class="o">+</span><span class="n">h</span><span class="p">),(</span><span class="mi">255</span><span class="p">,</span><span class="mi">0</span><span class="p">,</span><span class="mi">0</span><span class="p">),</span><span class="mi">2</span><span class="p">)</span> + +<span class="c1"># convert BGR image to RGB for plotting</span> +<span class="n">cv_rgb</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2RGB</span><span class="p">)</span> + +<span class="c1"># display the image, along with bounding box</span> +<span class="n">plt</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">cv_rgb</span><span class="p">)</span> +<span class="n">plt</span><span class="o">.</span><span class="n">show</span><span class="p">()</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>Number of faces detected: 1 +</pre> +</div> +</div> + +<div class="output_area"> + + <div class="prompt"></div> + + + + +<div class="output_png output_subarea "> +<img src="data:image/png;base64,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 +" +> +</div> + +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<p>Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The <code>detectMultiScale</code> function executes the classifier stored in <code>face_cascade</code> and takes the grayscale image as a parameter.</p> +<p>In the above code, <code>faces</code> is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as <code>x</code> and <code>y</code>) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as <code>w</code> and <code>h</code>) specify the width and height of the box.</p> +<h3 id="Write-a-Human-Face-Detector">Write a Human Face Detector<a class="anchor-link" href="#Write-a-Human-Face-Detector">¶</a></h3><p>We can use this procedure to write a function that returns <code>True</code> if a human face is detected in an image and <code>False</code> otherwise. This function, aptly named <code>face_detector</code>, takes a string-valued file path to an image as input and appears in the code block below.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [4]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># returns "True" if face is detected in image stored at img_path</span> +<span class="k">def</span> <span class="nf">face_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span> + <span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span> + <span class="n">gray</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2GRAY</span><span class="p">)</span> + <span class="n">faces</span> <span class="o">=</span> <span class="n">face_cascade</span><span class="o">.</span><span class="n">detectMultiScale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span> + <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">faces</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="(IMPLEMENTATION)-Assess-the-Human-Face-Detector">(IMPLEMENTATION) Assess the Human Face Detector<a class="anchor-link" href="#(IMPLEMENTATION)-Assess-the-Human-Face-Detector">¶</a></h3><p><strong>Question 1:</strong> Use the code cell below to test the performance of the <code>face_detector</code> function.</p> +<ul> +<li>What percentage of the first 100 images in <code>human_files</code> have a detected human face? </li> +<li>What percentage of the first 100 images in <code>dog_files</code> have a detected human face? </li> +</ul> +<p>Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays <code>human_files_short</code> and <code>dog_files_short</code>.</p> +<p><strong>Answer:</strong> Percent of faces detected: 100 % +Percent of dogs detected: 11 %</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [5]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="n">human_files_short</span> <span class="o">=</span> <span class="n">human_files</span><span class="p">[:</span><span class="mi">100</span><span class="p">]</span> +<span class="n">dog_files_short</span> <span class="o">=</span> <span class="n">train_files</span><span class="p">[:</span><span class="mi">100</span><span class="p">]</span> +<span class="c1"># Do NOT modify the code above this line.</span> + +<span class="c1">## TODO: Test the performance of the face_detector algorithm </span> +<span class="c1">## on the images in human_files_short and dog_files_short.</span> +<span class="n">detected_faces</span> <span class="o">=</span> <span class="p">[</span><span class="n">f</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">human_files_short</span> <span class="k">if</span> <span class="n">face_detector</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span><span class="p">]</span> +<span class="n">detected_dogs</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">dog_files_short</span> <span class="k">if</span> <span class="n">face_detector</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span><span class="p">]</span> + +<span class="nb">print</span><span class="p">(</span><span class="s1">'Percent of faces detected:'</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">detected_faces</span><span class="p">),</span> <span class="s1">'%'</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'Percent of dogs detected:'</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">detected_dogs</span><span class="p">),</span> <span class="s1">'%'</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>Percent of faces detected: 100 % +Percent of dogs detected: 11 % +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<p><strong>Question 2:</strong> This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?</p> +<p><strong>Answer:</strong> Maybe to train a network to identify also human-only objects like shoes, clothes, and so. This would make much easier to identify images with humans.</p> +<p>We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this <em>optional</em> task, report performance on each of the datasets.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [6]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## (Optional) TODO: Report the performance of another </span> +<span class="c1">## face detection algorithm on the LFW dataset</span> +<span class="c1">### Feel free to use as many code cells as needed.</span> +<span class="n">ext_face_cascade</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">CascadeClassifier</span><span class="p">(</span><span class="s1">'haarcascades/haarcascade_frontalface_alt2.xml'</span><span class="p">)</span> + +<span class="k">def</span> <span class="nf">ext_face_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span> + <span class="n">img</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">imread</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span> + <span class="n">gray</span> <span class="o">=</span> <span class="n">cv2</span><span class="o">.</span><span class="n">cvtColor</span><span class="p">(</span><span class="n">img</span><span class="p">,</span> <span class="n">cv2</span><span class="o">.</span><span class="n">COLOR_BGR2GRAY</span><span class="p">)</span> + <span class="n">faces</span> <span class="o">=</span> <span class="n">ext_face_cascade</span><span class="o">.</span><span class="n">detectMultiScale</span><span class="p">(</span><span class="n">gray</span><span class="p">)</span> + <span class="k">return</span> <span class="nb">len</span><span class="p">(</span><span class="n">faces</span><span class="p">)</span> <span class="o">></span> <span class="mi">0</span> + +<span class="c1">## on the images in human_files_short and dog_files_short.</span> +<span class="n">detected_ext_faces</span> <span class="o">=</span> <span class="p">[</span><span class="n">f</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">human_files_short</span> <span class="k">if</span> <span class="n">ext_face_detector</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span><span class="p">]</span> +<span class="n">detected_ext_dogs</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">dog_files_short</span> <span class="k">if</span> <span class="n">ext_face_detector</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span><span class="p">]</span> + +<span class="nb">print</span><span class="p">(</span><span class="s1">'Percent of faces detected:'</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">detected_ext_faces</span><span class="p">),</span> <span class="s1">'%'</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'Percent of dogs detected:'</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">detected_ext_dogs</span><span class="p">),</span> <span class="s1">'%'</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>Percent of faces detected: 100 % +Percent of dogs detected: 20 % +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<hr> +<p><a id='step2'></a></p> +<h2 id="Step-2:-Detect-Dogs">Step 2: Detect Dogs<a class="anchor-link" href="#Step-2:-Detect-Dogs">¶</a></h2><p>In this section, we use a pre-trained <a href="http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006">ResNet-50</a> model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on <a href="http://www.image-net.org/">ImageNet</a>, a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of <a href="https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a">1000 categories</a>. Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [7]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.applications.resnet50</span> <span class="k">import</span> <span class="n">ResNet50</span> + +<span class="c1"># define ResNet50 model</span> +<span class="n">ResNet50_model</span> <span class="o">=</span> <span class="n">ResNet50</span><span class="p">(</span><span class="n">weights</span><span class="o">=</span><span class="s1">'imagenet'</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>Downloading data from https://github.com/fchollet/deep-learning-models/releases/download/v0.2/resnet50_weights_tf_dim_ordering_tf_kernels.h5 +102858752/102853048 [==============================] - 2s 0us/step +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="Pre-process-the-Data">Pre-process the Data<a class="anchor-link" href="#Pre-process-the-Data">¶</a></h3><p>When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape</p> +$$ +(\text{nb_samples}, \text{rows}, \text{columns}, \text{channels}), +$$<p>where <code>nb_samples</code> corresponds to the total number of images (or samples), and <code>rows</code>, <code>columns</code>, and <code>channels</code> correspond to the number of rows, columns, and channels for each image, respectively.</p> +<p>The <code>path_to_tensor</code> function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape</p> +$$ +(1, 224, 224, 3). +$$<p>The <code>paths_to_tensor</code> function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape</p> +$$ +(\text{nb_samples}, 224, 224, 3). +$$<p>Here, <code>nb_samples</code> is the number of samples, or number of images, in the supplied array of image paths. It is best to think of <code>nb_samples</code> as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [8]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.preprocessing</span> <span class="k">import</span> <span class="n">image</span> +<span class="kn">from</span> <span class="nn">tqdm</span> <span class="k">import</span> <span class="n">tqdm</span> + +<span class="k">def</span> <span class="nf">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span> + <span class="c1"># loads RGB image as PIL.Image.Image type</span> + <span class="n">img</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">load_img</span><span class="p">(</span><span class="n">img_path</span><span class="p">,</span> <span class="n">target_size</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">))</span> + <span class="c1"># convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)</span> + <span class="n">x</span> <span class="o">=</span> <span class="n">image</span><span class="o">.</span><span class="n">img_to_array</span><span class="p">(</span><span class="n">img</span><span class="p">)</span> + <span class="c1"># convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor</span> + <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">x</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span> + +<span class="k">def</span> <span class="nf">paths_to_tensor</span><span class="p">(</span><span class="n">img_paths</span><span class="p">):</span> + <span class="n">list_of_tensors</span> <span class="o">=</span> <span class="p">[</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span> <span class="k">for</span> <span class="n">img_path</span> <span class="ow">in</span> <span class="n">tqdm</span><span class="p">(</span><span class="n">img_paths</span><span class="p">)]</span> + <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">vstack</span><span class="p">(</span><span class="n">list_of_tensors</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="Making-Predictions-with-ResNet-50">Making Predictions with ResNet-50<a class="anchor-link" href="#Making-Predictions-with-ResNet-50">¶</a></h3><p>Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function <code>preprocess_input</code>. If you're curious, you can check the code for <code>preprocess_input</code> <a href="https://github.com/fchollet/keras/blob/master/keras/applications/imagenet_utils.py">here</a>.</p> +<p>Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the <code>predict</code> method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the <code>ResNet50_predict_labels</code> function below.</p> +<p>By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this <a href="https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a">dictionary</a>.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [9]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.applications.resnet50</span> <span class="k">import</span> <span class="n">preprocess_input</span><span class="p">,</span> <span class="n">decode_predictions</span> + +<span class="k">def</span> <span class="nf">ResNet50_predict_labels</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span> + <span class="c1"># returns prediction vector for image located at img_path</span> + <span class="n">img</span> <span class="o">=</span> <span class="n">preprocess_input</span><span class="p">(</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span> + <span class="k">return</span> <span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">ResNet50_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">img</span><span class="p">))</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="Write-a-Dog-Detector">Write a Dog Detector<a class="anchor-link" href="#Write-a-Dog-Detector">¶</a></h3><p>While looking at the <a href="https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a">dictionary</a>, you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from <code>'Chihuahua'</code> to <code>'Mexican hairless'</code>. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the <code>ResNet50_predict_labels</code> function above returns a value between 151 and 268 (inclusive).</p> +<p>We use these ideas to complete the <code>dog_detector</code> function below, which returns <code>True</code> if a dog is detected in an image (and <code>False</code> if not).</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [10]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### returns "True" if a dog is detected in the image stored at img_path</span> +<span class="k">def</span> <span class="nf">dog_detector</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span> + <span class="n">prediction</span> <span class="o">=</span> <span class="n">ResNet50_predict_labels</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span> + <span class="k">return</span> <span class="p">((</span><span class="n">prediction</span> <span class="o"><=</span> <span class="mi">268</span><span class="p">)</span> <span class="o">&</span> <span class="p">(</span><span class="n">prediction</span> <span class="o">>=</span> <span class="mi">151</span><span class="p">))</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="(IMPLEMENTATION)-Assess-the-Dog-Detector">(IMPLEMENTATION) Assess the Dog Detector<a class="anchor-link" href="#(IMPLEMENTATION)-Assess-the-Dog-Detector">¶</a></h3><p><strong>Question 3:</strong> Use the code cell below to test the performance of your <code>dog_detector</code> function.</p> +<ul> +<li>What percentage of the images in <code>human_files_short</code> have a detected dog? </li> +<li>What percentage of the images in <code>dog_files_short</code> have a detected dog?</li> +</ul> +<p><strong>Answer:</strong> Percent of faces detected: 0 % +Percent of dogs detected: 100 %</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [11]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Test the performance of the dog_detector function</span> +<span class="c1">### on the images in human_files_short and dog_files_short.</span> +<span class="n">detected_det_faces</span> <span class="o">=</span> <span class="p">[</span><span class="n">f</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">human_files_short</span> <span class="k">if</span> <span class="n">dog_detector</span><span class="p">(</span><span class="n">f</span><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span><span class="p">]</span> +<span class="n">detected_det_dogs</span> <span class="o">=</span> <span class="p">[</span><span class="n">d</span> <span class="k">for</span> <span class="n">d</span> <span class="ow">in</span> <span class="n">dog_files_short</span> <span class="k">if</span> <span class="n">dog_detector</span><span class="p">(</span><span class="n">d</span><span class="p">)</span> <span class="o">==</span> <span class="kc">True</span><span class="p">]</span> + +<span class="nb">print</span><span class="p">(</span><span class="s1">'Percent of faces detected:'</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">detected_det_faces</span><span class="p">),</span> <span class="s1">'%'</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'Percent of dogs detected:'</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">detected_det_dogs</span><span class="p">),</span> <span class="s1">'%'</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>Percent of faces detected: 0 % +Percent of dogs detected: 100 % +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<hr> +<p><a id='step3'></a></p> +<h2 id="Step-3:-Create-a-CNN-to-Classify-Dog-Breeds-(from-Scratch)">Step 3: Create a CNN to Classify Dog Breeds (from Scratch)<a class="anchor-link" href="#Step-3:-Create-a-CNN-to-Classify-Dog-Breeds-(from-Scratch)">¶</a></h2><p>Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN <em>from scratch</em> (so, you can't use transfer learning <em>yet</em>!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.</p> +<p>Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train.</p> +<p>We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that <em>even a human</em> would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel.</p> +<table> +<thead><tr> +<th>Brittany</th> +<th>Welsh Springer Spaniel</th> +</tr> +</thead> +<tbody> +<tr> +<td><img src="images/Brittany_02625.jpg" width="100"></td> +<td><img src="images/Welsh_springer_spaniel_08203.jpg" width="200"></td> +</tr> +</tbody> +</table> +<p>It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels).</p> +<table> +<thead><tr> +<th>Curly-Coated Retriever</th> +<th>American Water Spaniel</th> +</tr> +</thead> +<tbody> +<tr> +<td><img src="images/Curly-coated_retriever_03896.jpg" width="200"></td> +<td><img src="images/American_water_spaniel_00648.jpg" width="200"></td> +</tr> +</tbody> +</table> +<p>Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed.</p> +<table> +<thead><tr> +<th>Yellow Labrador</th> +<th>Chocolate Labrador</th> +<th>Black Labrador</th> +</tr> +</thead> +<tbody> +<tr> +<td><img src="images/Labrador_retriever_06457.jpg" width="150"></td> +<td><img src="images/Labrador_retriever_06455.jpg" width="240"></td> +<td><img src="images/Labrador_retriever_06449.jpg" width="220"></td> +</tr> +</tbody> +</table> +<p>We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%.</p> +<p>Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun!</p> +<h3 id="Pre-process-the-Data">Pre-process the Data<a class="anchor-link" href="#Pre-process-the-Data">¶</a></h3><p>We rescale the images by dividing every pixel in every image by 255.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [12]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">PIL</span> <span class="k">import</span> <span class="n">ImageFile</span> +<span class="n">ImageFile</span><span class="o">.</span><span class="n">LOAD_TRUNCATED_IMAGES</span> <span class="o">=</span> <span class="kc">True</span> + +<span class="c1"># pre-process the data for Keras</span> +<span class="n">train_tensors</span> <span class="o">=</span> <span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">train_files</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float32'</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span> +<span class="n">valid_tensors</span> <span class="o">=</span> <span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">valid_files</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float32'</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span> +<span class="n">test_tensors</span> <span class="o">=</span> <span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">test_files</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="s1">'float32'</span><span class="p">)</span><span class="o">/</span><span class="mi">255</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stderr output_text"> +<pre>100%|██████████| 6680/6680 [01:13<00:00, 90.78it/s] +100%|██████████| 835/835 [00:08<00:00, 101.98it/s] +100%|██████████| 836/836 [00:08<00:00, 101.72it/s] +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="(IMPLEMENTATION)-Model-Architecture">(IMPLEMENTATION) Model Architecture<a class="anchor-link" href="#(IMPLEMENTATION)-Model-Architecture">¶</a></h3><p>Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:</p> + +<pre><code> model.summary() + +</code></pre> +<p>We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:</p> +<p><img src="images/sample_cnn.png" alt="Sample CNN"></p> +<p><strong>Question 4:</strong> Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.</p> +<p><strong>Answer:</strong> I've selected the architecture proposed because i believe it is a good model to begin with. The first criteria to consider was the fact that it uses an intercalation of convolutional layers and pooling layers, it decreases the complexity of the network by keeping the filters of the map that are required for classification and reducing the resolution of the overall feature map. The other criteria was the number of convolutional layers that I believe was a good configuration for a first test of classification. The use of the global average pool in the end of the convolution is also a good thing about the model, because it keeps the knowledge aquired by the network but flats the output shape. The use of the Dense output layer is also good for the model, but I believe that the use of more than one full connected layer by the end of the network could lead to better results. That was the only change I made on the proposed model, added one more dense layer for the output.</p> +<p>After testing the model for a while, i've decided to remove the additional Dense layer at the end and add a Conv2D layer with another pooling layer. That architecture had better results than the others I've tried.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [13]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">MaxPooling2D</span><span class="p">,</span> <span class="n">GlobalAveragePooling2D</span> +<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Dense</span> +<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span> + +<span class="c1">### TODO: Define your architecture.</span> +<span class="n">model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span> + +<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'same'</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span> +<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span> +<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'same'</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span> +<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span> +<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'same'</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span> +<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span> +<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'same'</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span> +<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span> +<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">GlobalAveragePooling2D</span><span class="p">())</span> +<span class="n">model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">133</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'softmax'</span><span class="p">))</span> + +<span class="n">model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>_________________________________________________________________ +Layer (type) Output Shape Param # +================================================================= +conv2d_1 (Conv2D) (None, 224, 224, 16) 208 +_________________________________________________________________ +max_pooling2d_2 (MaxPooling2 (None, 112, 112, 16) 0 +_________________________________________________________________ +conv2d_2 (Conv2D) (None, 112, 112, 32) 2080 +_________________________________________________________________ +max_pooling2d_3 (MaxPooling2 (None, 56, 56, 32) 0 +_________________________________________________________________ +conv2d_3 (Conv2D) (None, 56, 56, 64) 8256 +_________________________________________________________________ +max_pooling2d_4 (MaxPooling2 (None, 28, 28, 64) 0 +_________________________________________________________________ +conv2d_4 (Conv2D) (None, 28, 28, 128) 32896 +_________________________________________________________________ +max_pooling2d_5 (MaxPooling2 (None, 14, 14, 128) 0 +_________________________________________________________________ +global_average_pooling2d_1 ( (None, 128) 0 +_________________________________________________________________ +dense_1 (Dense) (None, 133) 17157 +================================================================= +Total params: 60,597 +Trainable params: 60,597 +Non-trainable params: 0 +_________________________________________________________________ +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="Compile-the-Model">Compile the Model<a class="anchor-link" href="#Compile-the-Model">¶</a></h3> +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [14]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">'rmsprop'</span><span class="p">,</span> <span class="n">loss</span><span class="o">=</span><span class="s1">'categorical_crossentropy'</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">])</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="(IMPLEMENTATION)-Train-the-Model">(IMPLEMENTATION) Train the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Train-the-Model">¶</a></h3><p>Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.</p> +<p>You are welcome to <a href="https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html">augment the training data</a>, but this is not a requirement.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [15]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="k">import</span> <span class="n">ModelCheckpoint</span> + +<span class="c1">### TODO: specify the number of epochs that you would like to use to train the model.</span> + +<span class="n">epochs</span> <span class="o">=</span> <span class="mi">20</span> + +<span class="c1">### Do NOT modify the code below this line.</span> + +<span class="n">checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">'saved_models/weights.best.from_scratch.hdf5'</span><span class="p">,</span> + <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + +<span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_tensors</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">,</span> + <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">valid_tensors</span><span class="p">,</span> <span class="n">valid_targets</span><span class="p">),</span> + <span class="n">epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">checkpointer</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>Train on 6680 samples, validate on 835 samples +Epoch 1/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.8838 - acc: 0.0080Epoch 00001: val_loss improved from inf to 4.87180, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 24s 4ms/step - loss: 4.8838 - acc: 0.0079 - val_loss: 4.8718 - val_acc: 0.0108 +Epoch 2/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.8640 - acc: 0.0129Epoch 00002: val_loss improved from 4.87180 to 4.84555, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.8639 - acc: 0.0129 - val_loss: 4.8456 - val_acc: 0.0168 +Epoch 3/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.8092 - acc: 0.0204Epoch 00003: val_loss improved from 4.84555 to 4.75984, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.8089 - acc: 0.0204 - val_loss: 4.7598 - val_acc: 0.0251 +Epoch 4/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.7300 - acc: 0.0236Epoch 00004: val_loss improved from 4.75984 to 4.71569, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.7301 - acc: 0.0238 - val_loss: 4.7157 - val_acc: 0.0335 +Epoch 5/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.6662 - acc: 0.0359Epoch 00005: val_loss improved from 4.71569 to 4.66870, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.6662 - acc: 0.0359 - val_loss: 4.6687 - val_acc: 0.0335 +Epoch 6/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.5949 - acc: 0.0413Epoch 00006: val_loss improved from 4.66870 to 4.60348, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.5949 - acc: 0.0412 - val_loss: 4.6035 - val_acc: 0.0479 +Epoch 7/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.5157 - acc: 0.0468Epoch 00007: val_loss improved from 4.60348 to 4.52517, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.5157 - acc: 0.0469 - val_loss: 4.5252 - val_acc: 0.0551 +Epoch 8/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.4474 - acc: 0.0500Epoch 00008: val_loss improved from 4.52517 to 4.48079, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.4475 - acc: 0.0499 - val_loss: 4.4808 - val_acc: 0.0539 +Epoch 9/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.3966 - acc: 0.0539Epoch 00009: val_loss did not improve +6680/6680 [==============================] - 23s 3ms/step - loss: 4.3968 - acc: 0.0537 - val_loss: 4.5209 - val_acc: 0.0551 +Epoch 10/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.3393 - acc: 0.0554Epoch 00010: val_loss improved from 4.48079 to 4.45155, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.3399 - acc: 0.0554 - val_loss: 4.4515 - val_acc: 0.0539 +Epoch 11/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.3024 - acc: 0.0595Epoch 00011: val_loss improved from 4.45155 to 4.42259, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.3019 - acc: 0.0594 - val_loss: 4.4226 - val_acc: 0.0587 +Epoch 12/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.2625 - acc: 0.0691Epoch 00012: val_loss improved from 4.42259 to 4.39708, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.2623 - acc: 0.0690 - val_loss: 4.3971 - val_acc: 0.0551 +Epoch 13/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.2298 - acc: 0.0682Epoch 00013: val_loss improved from 4.39708 to 4.34578, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.2291 - acc: 0.0683 - val_loss: 4.3458 - val_acc: 0.0611 +Epoch 14/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.1869 - acc: 0.0695Epoch 00014: val_loss improved from 4.34578 to 4.27943, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.1877 - acc: 0.0696 - val_loss: 4.2794 - val_acc: 0.0647 +Epoch 15/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.1472 - acc: 0.0755Epoch 00015: val_loss improved from 4.27943 to 4.24986, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.1464 - acc: 0.0757 - val_loss: 4.2499 - val_acc: 0.0731 +Epoch 16/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.1212 - acc: 0.0866Epoch 00016: val_loss improved from 4.24986 to 4.19757, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.1215 - acc: 0.0868 - val_loss: 4.1976 - val_acc: 0.0707 +Epoch 17/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.0690 - acc: 0.0857Epoch 00017: val_loss did not improve +6680/6680 [==============================] - 23s 3ms/step - loss: 4.0689 - acc: 0.0855 - val_loss: 4.2051 - val_acc: 0.0886 +Epoch 18/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.0485 - acc: 0.0983Epoch 00018: val_loss did not improve +6680/6680 [==============================] - 23s 3ms/step - loss: 4.0475 - acc: 0.0988 - val_loss: 4.4219 - val_acc: 0.0707 +Epoch 19/20 +6660/6680 [============================>.] - ETA: 0s - loss: 4.0080 - acc: 0.0949Epoch 00019: val_loss improved from 4.19757 to 4.14352, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 4.0092 - acc: 0.0949 - val_loss: 4.1435 - val_acc: 0.0922 +Epoch 20/20 +6660/6680 [============================>.] - ETA: 0s - loss: 3.9717 - acc: 0.1063Epoch 00020: val_loss improved from 4.14352 to 4.10331, saving model to saved_models/weights.best.from_scratch.hdf5 +6680/6680 [==============================] - 23s 3ms/step - loss: 3.9722 - acc: 0.1064 - val_loss: 4.1033 - val_acc: 0.0898 +</pre> +</div> +</div> + +<div class="output_area"> + + <div class="prompt output_prompt">Out[15]:</div> + + + + +<div class="output_text output_subarea output_execute_result"> +<pre><keras.callbacks.History at 0x7f53f8083630></pre> +</div> + +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="Load-the-Model-with-the-Best-Validation-Loss">Load the Model with the Best Validation Loss<a class="anchor-link" href="#Load-the-Model-with-the-Best-Validation-Loss">¶</a></h3> +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [16]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">'saved_models/weights.best.from_scratch.hdf5'</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="Test-the-Model">Test the Model<a class="anchor-link" href="#Test-the-Model">¶</a></h3><p>Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [17]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># get index of predicted dog breed for each image in test set</span> +<span class="n">dog_breed_predictions</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)))</span> <span class="k">for</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="n">test_tensors</span><span class="p">]</span> + +<span class="c1"># report test accuracy</span> +<span class="n">test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">dog_breed_predictions</span><span class="p">)</span><span class="o">==</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">test_targets</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">dog_breed_predictions</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">'</span> <span class="o">%</span> <span class="n">test_accuracy</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>Test accuracy: 9.5694% +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<hr> +<p><a id='step4'></a></p> +<h2 id="Step-4:-Use-a-CNN-to-Classify-Dog-Breeds">Step 4: Use a CNN to Classify Dog Breeds<a class="anchor-link" href="#Step-4:-Use-a-CNN-to-Classify-Dog-Breeds">¶</a></h2><p>To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning. In the following step, you will get a chance to use transfer learning to train your own CNN.</p> +<h3 id="Obtain-Bottleneck-Features">Obtain Bottleneck Features<a class="anchor-link" href="#Obtain-Bottleneck-Features">¶</a></h3> +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [18]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="n">bottleneck_features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'/data/bottleneck_features/DogVGG16Data.npz'</span><span class="p">)</span> +<span class="n">train_VGG16</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">'train'</span><span class="p">]</span> +<span class="n">valid_VGG16</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">'valid'</span><span class="p">]</span> +<span class="n">test_VGG16</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">'test'</span><span class="p">]</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="Model-Architecture">Model Architecture<a class="anchor-link" href="#Model-Architecture">¶</a></h3><p>The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [19]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG16_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span> +<span class="n">VGG16_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">GlobalAveragePooling2D</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="n">train_VGG16</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span> +<span class="n">VGG16_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">133</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'softmax'</span><span class="p">))</span> + +<span class="n">VGG16_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>_________________________________________________________________ +Layer (type) Output Shape Param # +================================================================= +global_average_pooling2d_2 ( (None, 512) 0 +_________________________________________________________________ +dense_2 (Dense) (None, 133) 68229 +================================================================= +Total params: 68,229 +Trainable params: 68,229 +Non-trainable params: 0 +_________________________________________________________________ +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="Compile-the-Model">Compile the Model<a class="anchor-link" href="#Compile-the-Model">¶</a></h3> +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [20]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG16_model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">'categorical_crossentropy'</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">'rmsprop'</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">])</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="Train-the-Model">Train the Model<a class="anchor-link" href="#Train-the-Model">¶</a></h3> +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [21]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="n">checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">'saved_models/weights.best.VGG16.hdf5'</span><span class="p">,</span> + <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + +<span class="n">VGG16_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_VGG16</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">,</span> + <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">valid_VGG16</span><span class="p">,</span> <span class="n">valid_targets</span><span class="p">),</span> + <span class="n">epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">checkpointer</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>Train on 6680 samples, validate on 835 samples +Epoch 1/20 +6560/6680 [============================>.] - ETA: 0s - loss: 12.4868 - acc: 0.1194Epoch 00001: val_loss improved from inf to 11.07808, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 319us/step - loss: 12.4618 - acc: 0.1205 - val_loss: 11.0781 - val_acc: 0.1976 +Epoch 2/20 +6580/6680 [============================>.] - ETA: 0s - loss: 10.3814 - acc: 0.2807Epoch 00002: val_loss improved from 11.07808 to 10.35501, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 261us/step - loss: 10.3816 - acc: 0.2808 - val_loss: 10.3550 - val_acc: 0.2683 +Epoch 3/20 +6620/6680 [============================>.] - ETA: 0s - loss: 9.9913 - acc: 0.3373Epoch 00003: val_loss improved from 10.35501 to 10.20705, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 259us/step - loss: 10.0056 - acc: 0.3367 - val_loss: 10.2071 - val_acc: 0.2910 +Epoch 4/20 +6620/6680 [============================>.] - ETA: 0s - loss: 9.7211 - acc: 0.3613Epoch 00004: val_loss improved from 10.20705 to 9.97083, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 258us/step - loss: 9.7194 - acc: 0.3611 - val_loss: 9.9708 - val_acc: 0.3066 +Epoch 5/20 +6600/6680 [============================>.] - ETA: 0s - loss: 9.5166 - acc: 0.3891Epoch 00005: val_loss improved from 9.97083 to 9.91203, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 260us/step - loss: 9.5251 - acc: 0.3886 - val_loss: 9.9120 - val_acc: 0.3281 +Epoch 6/20 +6620/6680 [============================>.] - ETA: 0s - loss: 9.3710 - acc: 0.3959Epoch 00006: val_loss improved from 9.91203 to 9.66854, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 259us/step - loss: 9.3661 - acc: 0.3960 - val_loss: 9.6685 - val_acc: 0.3449 +Epoch 7/20 +6620/6680 [============================>.] - ETA: 0s - loss: 9.2993 - acc: 0.4063Epoch 00007: val_loss improved from 9.66854 to 9.62033, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 258us/step - loss: 9.2790 - acc: 0.4075 - val_loss: 9.6203 - val_acc: 0.3509 +Epoch 8/20 +6640/6680 [============================>.] - ETA: 0s - loss: 9.2505 - acc: 0.4142Epoch 00008: val_loss did not improve +6680/6680 [==============================] - 2s 257us/step - loss: 9.2338 - acc: 0.4153 - val_loss: 9.6588 - val_acc: 0.3389 +Epoch 9/20 +6540/6680 [============================>.] - ETA: 0s - loss: 9.2001 - acc: 0.4185Epoch 00009: val_loss improved from 9.62033 to 9.50891, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 261us/step - loss: 9.1652 - acc: 0.4205 - val_loss: 9.5089 - val_acc: 0.3509 +Epoch 10/20 +6620/6680 [============================>.] - ETA: 0s - loss: 9.0310 - acc: 0.4307Epoch 00010: val_loss improved from 9.50891 to 9.38908, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 259us/step - loss: 9.0444 - acc: 0.4298 - val_loss: 9.3891 - val_acc: 0.3772 +Epoch 11/20 +6600/6680 [============================>.] - ETA: 0s - loss: 8.9488 - acc: 0.4318Epoch 00011: val_loss improved from 9.38908 to 9.28354, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 259us/step - loss: 8.9503 - acc: 0.4317 - val_loss: 9.2835 - val_acc: 0.3629 +Epoch 12/20 +6620/6680 [============================>.] - ETA: 0s - loss: 8.6990 - acc: 0.4432Epoch 00012: val_loss improved from 9.28354 to 9.14873, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 259us/step - loss: 8.6991 - acc: 0.4430 - val_loss: 9.1487 - val_acc: 0.3760 +Epoch 13/20 +6620/6680 [============================>.] - ETA: 0s - loss: 8.6075 - acc: 0.4526Epoch 00013: val_loss improved from 9.14873 to 9.12314, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 258us/step - loss: 8.6232 - acc: 0.4516 - val_loss: 9.1231 - val_acc: 0.3844 +Epoch 14/20 +6600/6680 [============================>.] - ETA: 0s - loss: 8.5989 - acc: 0.4576Epoch 00014: val_loss improved from 9.12314 to 9.03359, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 259us/step - loss: 8.5872 - acc: 0.4582 - val_loss: 9.0336 - val_acc: 0.3856 +Epoch 15/20 +6580/6680 [============================>.] - ETA: 0s - loss: 8.3545 - acc: 0.4657Epoch 00015: val_loss improved from 9.03359 to 8.82397, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 260us/step - loss: 8.3389 - acc: 0.4666 - val_loss: 8.8240 - val_acc: 0.3988 +Epoch 16/20 +6620/6680 [============================>.] - ETA: 0s - loss: 8.2313 - acc: 0.4770Epoch 00016: val_loss improved from 8.82397 to 8.75910, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 259us/step - loss: 8.2346 - acc: 0.4769 - val_loss: 8.7591 - val_acc: 0.4084 +Epoch 17/20 +6600/6680 [============================>.] - ETA: 0s - loss: 8.1233 - acc: 0.4809Epoch 00017: val_loss improved from 8.75910 to 8.71061, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 260us/step - loss: 8.1304 - acc: 0.4804 - val_loss: 8.7106 - val_acc: 0.3964 +Epoch 18/20 +6620/6680 [============================>.] - ETA: 0s - loss: 7.9058 - acc: 0.4867Epoch 00018: val_loss improved from 8.71061 to 8.58538, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 258us/step - loss: 7.9190 - acc: 0.4858 - val_loss: 8.5854 - val_acc: 0.3988 +Epoch 19/20 +6620/6680 [============================>.] - ETA: 0s - loss: 7.7842 - acc: 0.5000Epoch 00019: val_loss improved from 8.58538 to 8.46001, saving model to saved_models/weights.best.VGG16.hdf5 +6680/6680 [==============================] - 2s 258us/step - loss: 7.7971 - acc: 0.4993 - val_loss: 8.4600 - val_acc: 0.4168 +Epoch 20/20 +6620/6680 [============================>.] - ETA: 0s - loss: 7.7658 - acc: 0.5076Epoch 00020: val_loss did not improve +6680/6680 [==============================] - 2s 258us/step - loss: 7.7561 - acc: 0.5079 - val_loss: 8.5125 - val_acc: 0.4204 +</pre> +</div> +</div> + +<div class="output_area"> + + <div class="prompt output_prompt">Out[21]:</div> + + + + +<div class="output_text output_subarea output_execute_result"> +<pre><keras.callbacks.History at 0x7f53c9579780></pre> +</div> + +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="Load-the-Model-with-the-Best-Validation-Loss">Load the Model with the Best Validation Loss<a class="anchor-link" href="#Load-the-Model-with-the-Best-Validation-Loss">¶</a></h3> +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [22]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="n">VGG16_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">'saved_models/weights.best.VGG16.hdf5'</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="Test-the-Model">Test the Model<a class="anchor-link" href="#Test-the-Model">¶</a></h3><p>Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [23]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="c1"># get index of predicted dog breed for each image in test set</span> +<span class="n">VGG16_predictions</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">VGG16_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">feature</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)))</span> <span class="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">test_VGG16</span><span class="p">]</span> + +<span class="c1"># report test accuracy</span> +<span class="n">test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">VGG16_predictions</span><span class="p">)</span><span class="o">==</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">test_targets</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">VGG16_predictions</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">'</span> <span class="o">%</span> <span class="n">test_accuracy</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>Test accuracy: 40.6699% +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="Predict-Dog-Breed-with-the-Model">Predict Dog Breed with the Model<a class="anchor-link" href="#Predict-Dog-Breed-with-the-Model">¶</a></h3> +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [24]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">extract_bottleneck_features</span> <span class="k">import</span> <span class="o">*</span> + +<span class="k">def</span> <span class="nf">VGG16_predict_breed</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span> + <span class="c1"># extract bottleneck features</span> + <span class="n">bottleneck_feature</span> <span class="o">=</span> <span class="n">extract_VGG16</span><span class="p">(</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span> + <span class="c1"># obtain predicted vector</span> + <span class="n">predicted_vector</span> <span class="o">=</span> <span class="n">VGG16_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">bottleneck_feature</span><span class="p">)</span> + <span class="c1"># return dog breed that is predicted by the model</span> + <span class="k">return</span> <span class="n">dog_names</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">predicted_vector</span><span class="p">)]</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<hr> +<p><a id='step5'></a></p> +<h2 id="Step-5:-Create-a-CNN-to-Classify-Dog-Breeds-(using-Transfer-Learning)">Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)<a class="anchor-link" href="#Step-5:-Create-a-CNN-to-Classify-Dog-Breeds-(using-Transfer-Learning)">¶</a></h2><p>You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.</p> +<p>In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras. These are already in the workspace, at /data/bottleneck_features. If you wish to download them on a different machine, they can be found at:</p> +<ul> +<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG19Data.npz">VGG-19</a> bottleneck features</li> +<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogResnet50Data.npz">ResNet-50</a> bottleneck features</li> +<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogInceptionV3Data.npz">Inception</a> bottleneck features</li> +<li><a href="https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogXceptionData.npz">Xception</a> bottleneck features</li> +</ul> +<p>The files are encoded as such:</p> + +<pre><code>Dog{network}Data.npz + +</code></pre> +<p>where <code>{network}</code>, in the above filename, can be one of <code>VGG19</code>, <code>Resnet50</code>, <code>InceptionV3</code>, or <code>Xception</code>.</p> +<p>The above architectures are downloaded and stored for you in the <code>/data/bottleneck_features/</code> folder.</p> +<p>This means the following will be in the <code>/data/bottleneck_features/</code> folder:</p> +<p><code>DogVGG19Data.npz</code> +<code>DogResnet50Data.npz</code> +<code>DogInceptionV3Data.npz</code> +<code>DogXceptionData.npz</code></p> +<h3 id="(IMPLEMENTATION)-Obtain-Bottleneck-Features">(IMPLEMENTATION) Obtain Bottleneck Features<a class="anchor-link" href="#(IMPLEMENTATION)-Obtain-Bottleneck-Features">¶</a></h3><p>In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:</p> + +<pre><code>bottleneck_features = np.load('/data/bottleneck_features/Dog{network}Data.npz') +train_{network} = bottleneck_features['train'] +valid_{network} = bottleneck_features['valid'] +test_{network} = bottleneck_features['test']</code></pre> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [25]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Obtain bottleneck features from another pre-trained CNN.</span> +<span class="n">bottleneck_features</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="s1">'/data/bottleneck_features/DogResnet50Data.npz'</span><span class="p">)</span> +<span class="n">train_ResNet50</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">'train'</span><span class="p">]</span> +<span class="n">valid_ResNet50</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">'valid'</span><span class="p">]</span> +<span class="n">test_ResNet50</span> <span class="o">=</span> <span class="n">bottleneck_features</span><span class="p">[</span><span class="s1">'test'</span><span class="p">]</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="(IMPLEMENTATION)-Model-Architecture">(IMPLEMENTATION) Model Architecture<a class="anchor-link" href="#(IMPLEMENTATION)-Model-Architecture">¶</a></h3><p>Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:</p> + +<pre><code> <your model's name>.summary() + +</code></pre> +<p><strong>Question 5:</strong> Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.</p> +<p><strong>Answer:</strong> I believe that the simple architecture is enougth, since the learning of the ResNet might be sufficient for generating good results by simply changing the last Dense layer and trainig the CNN with my dog breeds' dataset.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [26]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Define your architecture.</span> + +<span class="n">res_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span> + +<span class="n">res_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">GlobalAveragePooling2D</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="n">train_ResNet50</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]))</span> +<span class="n">res_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">133</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'softmax'</span><span class="p">))</span> + +<span class="n">res_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>_________________________________________________________________ +Layer (type) Output Shape Param # +================================================================= +global_average_pooling2d_3 ( (None, 2048) 0 +_________________________________________________________________ +dense_3 (Dense) (None, 133) 272517 +================================================================= +Total params: 272,517 +Trainable params: 272,517 +Non-trainable params: 0 +_________________________________________________________________ +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="(IMPLEMENTATION)-Compile-the-Model">(IMPLEMENTATION) Compile the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Compile-the-Model">¶</a></h3> +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [27]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="n">res_model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">'categorical_crossentropy'</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">'rmsprop'</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">])</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="(IMPLEMENTATION)-Train-the-Model">(IMPLEMENTATION) Train the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Train-the-Model">¶</a></h3><p>Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.</p> +<p>You are welcome to <a href="https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html">augment the training data</a>, but this is not a requirement.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [28]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="n">res_checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">'saved_models/weights.best.RESNET.hdf5'</span><span class="p">,</span> + <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + +<span class="n">res_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_ResNet50</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">,</span> + <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">valid_ResNet50</span><span class="p">,</span> <span class="n">valid_targets</span><span class="p">),</span> + <span class="n">epochs</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">res_checkpointer</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>Train on 6680 samples, validate on 835 samples +Epoch 1/20 +6520/6680 [============================>.] - ETA: 0s - loss: 1.6652 - acc: 0.5937Epoch 00001: val_loss improved from inf to 0.83093, saving model to saved_models/weights.best.RESNET.hdf5 +6680/6680 [==============================] - 2s 285us/step - loss: 1.6447 - acc: 0.5973 - val_loss: 0.8309 - val_acc: 0.7377 +Epoch 2/20 +6620/6680 [============================>.] - ETA: 0s - loss: 0.4451 - acc: 0.8636Epoch 00002: val_loss improved from 0.83093 to 0.68045, saving model to saved_models/weights.best.RESNET.hdf5 +6680/6680 [==============================] - 2s 238us/step - loss: 0.4454 - acc: 0.8630 - val_loss: 0.6805 - val_acc: 0.7928 +Epoch 3/20 +6660/6680 [============================>.] - ETA: 0s - loss: 0.2622 - acc: 0.9153Epoch 00003: val_loss improved from 0.68045 to 0.60472, saving model to saved_models/weights.best.RESNET.hdf5 +6680/6680 [==============================] - 2s 255us/step - loss: 0.2622 - acc: 0.9151 - val_loss: 0.6047 - val_acc: 0.8072 +Epoch 4/20 +6620/6680 [============================>.] - ETA: 0s - loss: 0.1718 - acc: 0.9443Epoch 00004: val_loss did not improve +6680/6680 [==============================] - 2s 254us/step - loss: 0.1720 - acc: 0.9443 - val_loss: 0.6772 - val_acc: 0.8096 +Epoch 5/20 +6660/6680 [============================>.] - ETA: 0s - loss: 0.1213 - acc: 0.9617Epoch 00005: val_loss did not improve +6680/6680 [==============================] - 2s 251us/step - loss: 0.1212 - acc: 0.9617 - val_loss: 0.7300 - val_acc: 0.8036 +Epoch 6/20 +6520/6680 [============================>.] - ETA: 0s - loss: 0.0873 - acc: 0.9736Epoch 00006: val_loss did not improve +6680/6680 [==============================] - 2s 229us/step - loss: 0.0865 - acc: 0.9738 - val_loss: 0.6814 - val_acc: 0.8120 +Epoch 7/20 +6560/6680 [============================>.] - ETA: 0s - loss: 0.0590 - acc: 0.9823Epoch 00007: val_loss did not improve +6680/6680 [==============================] - 2s 228us/step - loss: 0.0597 - acc: 0.9819 - val_loss: 0.6965 - val_acc: 0.8168 +Epoch 8/20 +6540/6680 [============================>.] - ETA: 0s - loss: 0.0450 - acc: 0.9869Epoch 00008: val_loss did not improve +6680/6680 [==============================] - 2s 229us/step - loss: 0.0461 - acc: 0.9862 - val_loss: 0.6680 - val_acc: 0.8299 +Epoch 9/20 +6540/6680 [============================>.] - ETA: 0s - loss: 0.0354 - acc: 0.9907Epoch 00009: val_loss did not improve +6680/6680 [==============================] - 2s 228us/step - loss: 0.0357 - acc: 0.9907 - val_loss: 0.7417 - val_acc: 0.8180 +Epoch 10/20 +6540/6680 [============================>.] - ETA: 0s - loss: 0.0234 - acc: 0.9946Epoch 00010: val_loss did not improve +6680/6680 [==============================] - 2s 229us/step - loss: 0.0252 - acc: 0.9945 - val_loss: 0.7301 - val_acc: 0.8299 +Epoch 11/20 +6520/6680 [============================>.] - ETA: 0s - loss: 0.0207 - acc: 0.9946Epoch 00011: val_loss did not improve +6680/6680 [==============================] - 2s 230us/step - loss: 0.0212 - acc: 0.9945 - val_loss: 0.7545 - val_acc: 0.8359 +Epoch 12/20 +6520/6680 [============================>.] - ETA: 0s - loss: 0.0152 - acc: 0.9957Epoch 00012: val_loss did not improve +6680/6680 [==============================] - 2s 230us/step - loss: 0.0163 - acc: 0.9955 - val_loss: 0.7939 - val_acc: 0.8275 +Epoch 13/20 +6540/6680 [============================>.] - ETA: 0s - loss: 0.0142 - acc: 0.9965Epoch 00013: val_loss did not improve +6680/6680 [==============================] - 2s 229us/step - loss: 0.0143 - acc: 0.9963 - val_loss: 0.8071 - val_acc: 0.8299 +Epoch 14/20 +6540/6680 [============================>.] - ETA: 0s - loss: 0.0109 - acc: 0.9977Epoch 00014: val_loss did not improve +6680/6680 [==============================] - 2s 230us/step - loss: 0.0107 - acc: 0.9978 - val_loss: 0.8540 - val_acc: 0.8156 +Epoch 15/20 +6500/6680 [============================>.] - ETA: 0s - loss: 0.0097 - acc: 0.9980Epoch 00015: val_loss did not improve +6680/6680 [==============================] - 2s 229us/step - loss: 0.0096 - acc: 0.9981 - val_loss: 0.8420 - val_acc: 0.8287 +Epoch 16/20 +6500/6680 [============================>.] - ETA: 0s - loss: 0.0082 - acc: 0.9980Epoch 00016: val_loss did not improve +6680/6680 [==============================] - 2s 230us/step - loss: 0.0088 - acc: 0.9979 - val_loss: 0.8458 - val_acc: 0.8347 +Epoch 17/20 +6440/6680 [===========================>..] - ETA: 0s - loss: 0.0081 - acc: 0.9980Epoch 00017: val_loss did not improve +6680/6680 [==============================] - 2s 232us/step - loss: 0.0079 - acc: 0.9981 - val_loss: 0.8772 - val_acc: 0.8204 +Epoch 18/20 +6500/6680 [============================>.] - ETA: 0s - loss: 0.0072 - acc: 0.9985Epoch 00018: val_loss did not improve +6680/6680 [==============================] - 2s 231us/step - loss: 0.0070 - acc: 0.9985 - val_loss: 0.8823 - val_acc: 0.8275 +Epoch 19/20 +6460/6680 [============================>.] - ETA: 0s - loss: 0.0058 - acc: 0.9985Epoch 00019: val_loss did not improve +6680/6680 [==============================] - 2s 232us/step - loss: 0.0061 - acc: 0.9984 - val_loss: 0.9441 - val_acc: 0.8323 +Epoch 20/20 +6500/6680 [============================>.] - ETA: 0s - loss: 0.0055 - acc: 0.9982Epoch 00020: val_loss did not improve +6680/6680 [==============================] - 2s 231us/step - loss: 0.0054 - acc: 0.9982 - val_loss: 0.9053 - val_acc: 0.8311 +</pre> +</div> +</div> + +<div class="output_area"> + + <div class="prompt output_prompt">Out[28]:</div> + + + + +<div class="output_text output_subarea output_execute_result"> +<pre><keras.callbacks.History at 0x7f53c9151b38></pre> +</div> + +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="(IMPLEMENTATION)-Load-the-Model-with-the-Best-Validation-Loss">(IMPLEMENTATION) Load the Model with the Best Validation Loss<a class="anchor-link" href="#(IMPLEMENTATION)-Load-the-Model-with-the-Best-Validation-Loss">¶</a></h3> +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [29]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Load the model weights with the best validation loss.</span> +<span class="n">res_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">'saved_models/weights.best.RESNET.hdf5'</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="(IMPLEMENTATION)-Test-the-Model">(IMPLEMENTATION) Test the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Test-the-Model">¶</a></h3><p>Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [30]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Calculate classification accuracy on the test dataset.</span> +<span class="c1"># get index of predicted dog breed for each image in test set</span> +<span class="n">res_predictions</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">res_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">feature</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)))</span> <span class="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">test_ResNet50</span><span class="p">]</span> + +<span class="c1"># report test accuracy</span> +<span class="n">res_test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">res_predictions</span><span class="p">)</span><span class="o">==</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">test_targets</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">res_predictions</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">'</span> <span class="o">%</span> <span class="n">res_test_accuracy</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>Test accuracy: 81.3397% +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h3 id="(IMPLEMENTATION)-Predict-Dog-Breed-with-the-Model">(IMPLEMENTATION) Predict Dog Breed with the Model<a class="anchor-link" href="#(IMPLEMENTATION)-Predict-Dog-Breed-with-the-Model">¶</a></h3><p>Write a function that takes an image path as input and returns the dog breed (<code>Affenpinscher</code>, <code>Afghan_hound</code>, etc) that is predicted by your model.</p> +<p>Similar to the analogous function in Step 5, your function should have three steps:</p> +<ol> +<li>Extract the bottleneck features corresponding to the chosen CNN model.</li> +<li>Supply the bottleneck features as input to the model to return the predicted vector. Note that the argmax of this prediction vector gives the index of the predicted dog breed.</li> +<li>Use the <code>dog_names</code> array defined in Step 0 of this notebook to return the corresponding breed.</li> +</ol> +<p>The functions to extract the bottleneck features can be found in <code>extract_bottleneck_features.py</code>, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function</p> + +<pre><code>extract_{network} + +</code></pre> +<p>where <code>{network}</code>, in the above filename, should be one of <code>VGG19</code>, <code>Resnet50</code>, <code>InceptionV3</code>, or <code>Xception</code>.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [31]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Write a function that takes a path to an image as input</span> +<span class="c1">### and returns the dog breed that is predicted by the model.</span> +<span class="kn">from</span> <span class="nn">extract_bottleneck_features</span> <span class="k">import</span> <span class="o">*</span> + +<span class="k">def</span> <span class="nf">res_predict_breed</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span> + <span class="c1"># extract bottleneck features</span> + <span class="n">bottleneck_feature</span> <span class="o">=</span> <span class="n">extract_Resnet50</span><span class="p">(</span><span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">))</span> + <span class="c1"># obtain predicted vector</span> + <span class="n">predicted_vector</span> <span class="o">=</span> <span class="n">res_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">bottleneck_feature</span><span class="p">)</span> + <span class="c1"># return dog breed that is predicted by the model</span> + <span class="k">return</span> <span class="n">dog_names</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">predicted_vector</span><span class="p">)]</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<hr> +<p><a id='step6'></a></p> +<h2 id="Step-6:-Write-your-Algorithm">Step 6: Write your Algorithm<a class="anchor-link" href="#Step-6:-Write-your-Algorithm">¶</a></h2><p>Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,</p> +<ul> +<li>if a <strong>dog</strong> is detected in the image, return the predicted breed.</li> +<li>if a <strong>human</strong> is detected in the image, return the resembling dog breed.</li> +<li>if <strong>neither</strong> is detected in the image, provide output that indicates an error.</li> +</ul> +<p>You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the <code>face_detector</code> and <code>dog_detector</code> functions developed above. You are <strong>required</strong> to use your CNN from Step 5 to predict dog breed.</p> +<p>Some sample output for our algorithm is provided below, but feel free to design your own user experience!</p> +<p><img src="images/sample_human_output.png" alt="Sample Human Output"></p> +<h3 id="(IMPLEMENTATION)-Write-your-Algorithm">(IMPLEMENTATION) Write your Algorithm<a class="anchor-link" href="#(IMPLEMENTATION)-Write-your-Algorithm">¶</a></h3> +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [45]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">### TODO: Write your algorithm.</span> +<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Conv2D</span><span class="p">,</span> <span class="n">MaxPooling2D</span><span class="p">,</span> <span class="n">GlobalAveragePooling2D</span> +<span class="kn">from</span> <span class="nn">keras.layers</span> <span class="k">import</span> <span class="n">Dropout</span><span class="p">,</span> <span class="n">Flatten</span><span class="p">,</span> <span class="n">Dense</span> +<span class="kn">from</span> <span class="nn">keras.models</span> <span class="k">import</span> <span class="n">Sequential</span> + +<span class="n">my_model</span> <span class="o">=</span> <span class="n">Sequential</span><span class="p">()</span> + +<span class="n">my_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">16</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">11</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">4</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'same'</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">,</span> <span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">224</span><span class="p">,</span> <span class="mi">224</span><span class="p">,</span> <span class="mi">3</span><span class="p">)))</span> +<span class="n">my_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">32</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">5</span><span class="p">,</span> <span class="n">strides</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'same'</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span> +<span class="n">my_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span> +<span class="n">my_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">))</span> +<span class="n">my_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">64</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'same'</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span> +<span class="n">my_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">MaxPooling2D</span><span class="p">(</span><span class="n">pool_size</span><span class="o">=</span><span class="mi">2</span><span class="p">))</span> +<span class="n">my_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">))</span> +<span class="n">my_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Conv2D</span><span class="p">(</span><span class="n">filters</span><span class="o">=</span><span class="mi">128</span><span class="p">,</span> <span class="n">kernel_size</span><span class="o">=</span><span class="mi">2</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="s1">'same'</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">))</span> +<span class="n">my_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">GlobalAveragePooling2D</span><span class="p">())</span> +<span class="n">my_model</span><span class="o">.</span><span class="n">add</span><span class="p">(</span><span class="n">Dense</span><span class="p">(</span><span class="mi">133</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'softmax'</span><span class="p">))</span> + +<span class="n">my_model</span><span class="o">.</span><span class="n">summary</span><span class="p">()</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>_________________________________________________________________ +Layer (type) Output Shape Param # +================================================================= +conv2d_13 (Conv2D) (None, 56, 56, 16) 5824 +_________________________________________________________________ +max_pooling2d_12 (MaxPooling (None, 28, 28, 16) 0 +_________________________________________________________________ +dropout_7 (Dropout) (None, 28, 28, 16) 0 +_________________________________________________________________ +conv2d_14 (Conv2D) (None, 14, 14, 32) 12832 +_________________________________________________________________ +max_pooling2d_13 (MaxPooling (None, 7, 7, 32) 0 +_________________________________________________________________ +dropout_8 (Dropout) (None, 7, 7, 32) 0 +_________________________________________________________________ +conv2d_15 (Conv2D) (None, 7, 7, 64) 8256 +_________________________________________________________________ +max_pooling2d_14 (MaxPooling (None, 3, 3, 64) 0 +_________________________________________________________________ +dropout_9 (Dropout) (None, 3, 3, 64) 0 +_________________________________________________________________ +conv2d_16 (Conv2D) (None, 3, 3, 128) 32896 +_________________________________________________________________ +global_average_pooling2d_6 ( (None, 128) 0 +_________________________________________________________________ +dense_6 (Dense) (None, 133) 17157 +================================================================= +Total params: 76,965 +Trainable params: 76,965 +Non-trainable params: 0 +_________________________________________________________________ +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [46]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="kn">from</span> <span class="nn">keras.callbacks</span> <span class="k">import</span> <span class="n">ModelCheckpoint</span> + +<span class="n">my_model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">loss</span><span class="o">=</span><span class="s1">'categorical_crossentropy'</span><span class="p">,</span> <span class="n">optimizer</span><span class="o">=</span><span class="s1">'rmsprop'</span><span class="p">,</span> <span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">])</span> + +<span class="n">checkpointer</span> <span class="o">=</span> <span class="n">ModelCheckpoint</span><span class="p">(</span><span class="n">filepath</span><span class="o">=</span><span class="s1">'saved_models/weights.best.my_model.hdf5'</span><span class="p">,</span> + <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span> <span class="n">save_best_only</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span> + +<span class="n">my_model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">train_tensors</span><span class="p">,</span> <span class="n">train_targets</span><span class="p">,</span> + <span class="n">validation_data</span><span class="o">=</span><span class="p">(</span><span class="n">valid_tensors</span><span class="p">,</span> <span class="n">valid_targets</span><span class="p">),</span> + <span class="n">epochs</span><span class="o">=</span><span class="mi">70</span><span class="p">,</span> <span class="n">batch_size</span><span class="o">=</span><span class="mi">20</span><span class="p">,</span> <span class="n">callbacks</span><span class="o">=</span><span class="p">[</span><span class="n">checkpointer</span><span class="p">],</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>Train on 6680 samples, validate on 835 samples +Epoch 1/70 +6620/6680 [============================>.] - ETA: 0s - loss: 4.8869 - acc: 0.0101Epoch 00001: val_loss improved from inf to 4.87386, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 8s 1ms/step - loss: 4.8867 - acc: 0.0102 - val_loss: 4.8739 - val_acc: 0.0108 +Epoch 2/70 +6620/6680 [============================>.] - ETA: 0s - loss: 4.8686 - acc: 0.0080Epoch 00002: val_loss improved from 4.87386 to 4.85085, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 4.8690 - acc: 0.0081 - val_loss: 4.8508 - val_acc: 0.0096 +Epoch 3/70 +6620/6680 [============================>.] - ETA: 0s - loss: 4.8187 - acc: 0.0174Epoch 00003: val_loss improved from 4.85085 to 4.76682, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 4.8180 - acc: 0.0175 - val_loss: 4.7668 - val_acc: 0.0168 +Epoch 4/70 +6620/6680 [============================>.] - ETA: 0s - loss: 4.7446 - acc: 0.0215Epoch 00004: val_loss improved from 4.76682 to 4.68400, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 4.7431 - acc: 0.0214 - val_loss: 4.6840 - val_acc: 0.0240 +Epoch 5/70 +6620/6680 [============================>.] - ETA: 0s - loss: 4.6704 - acc: 0.0316Epoch 00005: val_loss improved from 4.68400 to 4.64125, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 4.6734 - acc: 0.0314 - val_loss: 4.6412 - val_acc: 0.0251 +Epoch 6/70 +6620/6680 [============================>.] - ETA: 0s - loss: 4.5674 - acc: 0.0352Epoch 00006: val_loss improved from 4.64125 to 4.53998, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 4.5675 - acc: 0.0353 - val_loss: 4.5400 - val_acc: 0.0431 +Epoch 7/70 +6620/6680 [============================>.] - ETA: 0s - loss: 4.4595 - acc: 0.0399Epoch 00007: val_loss improved from 4.53998 to 4.40958, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 4.4584 - acc: 0.0401 - val_loss: 4.4096 - val_acc: 0.0491 +Epoch 8/70 +6620/6680 [============================>.] - ETA: 0s - loss: 4.3842 - acc: 0.0485Epoch 00008: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 4.3828 - acc: 0.0488 - val_loss: 4.4543 - val_acc: 0.0431 +Epoch 9/70 +6620/6680 [============================>.] - ETA: 0s - loss: 4.2847 - acc: 0.0576Epoch 00009: val_loss improved from 4.40958 to 4.28609, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 4.2859 - acc: 0.0575 - val_loss: 4.2861 - val_acc: 0.0527 +Epoch 10/70 +6620/6680 [============================>.] - ETA: 0s - loss: 4.2211 - acc: 0.0625Epoch 00010: val_loss improved from 4.28609 to 4.22521, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 4.2187 - acc: 0.0627 - val_loss: 4.2252 - val_acc: 0.0635 +Epoch 11/70 +6620/6680 [============================>.] - ETA: 0s - loss: 4.1638 - acc: 0.0648Epoch 00011: val_loss improved from 4.22521 to 4.18640, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 4.1661 - acc: 0.0644 - val_loss: 4.1864 - val_acc: 0.0575 +Epoch 12/70 +6620/6680 [============================>.] - ETA: 0s - loss: 4.1176 - acc: 0.0764Epoch 00012: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 4.1176 - acc: 0.0763 - val_loss: 4.1892 - val_acc: 0.0611 +Epoch 13/70 +6620/6680 [============================>.] - ETA: 0s - loss: 4.0547 - acc: 0.0841Epoch 00013: val_loss improved from 4.18640 to 4.09288, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 4.0541 - acc: 0.0840 - val_loss: 4.0929 - val_acc: 0.0743 +Epoch 14/70 +6620/6680 [============================>.] - ETA: 0s - loss: 4.0094 - acc: 0.0849Epoch 00014: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 4.0074 - acc: 0.0862 - val_loss: 4.1020 - val_acc: 0.0611 +Epoch 15/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.9582 - acc: 0.0971Epoch 00015: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.9587 - acc: 0.0970 - val_loss: 4.1063 - val_acc: 0.0623 +Epoch 16/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.9007 - acc: 0.1015Epoch 00016: val_loss improved from 4.09288 to 4.01187, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 3.8985 - acc: 0.1019 - val_loss: 4.0119 - val_acc: 0.0814 +Epoch 17/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.8653 - acc: 0.1063Epoch 00017: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.8652 - acc: 0.1063 - val_loss: 4.0395 - val_acc: 0.0802 +Epoch 18/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.8294 - acc: 0.1168Epoch 00018: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.8278 - acc: 0.1163 - val_loss: 4.0197 - val_acc: 0.0778 +Epoch 19/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.7896 - acc: 0.1169Epoch 00019: val_loss improved from 4.01187 to 3.94036, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 3.7895 - acc: 0.1166 - val_loss: 3.9404 - val_acc: 0.0958 +Epoch 20/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.7515 - acc: 0.1248Epoch 00020: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.7506 - acc: 0.1246 - val_loss: 4.0709 - val_acc: 0.0743 +Epoch 21/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.7273 - acc: 0.1255Epoch 00021: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.7274 - acc: 0.1260 - val_loss: 3.9446 - val_acc: 0.0886 +Epoch 22/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.6885 - acc: 0.1319Epoch 00022: val_loss improved from 3.94036 to 3.87154, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 3.6891 - acc: 0.1319 - val_loss: 3.8715 - val_acc: 0.0886 +Epoch 23/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.6627 - acc: 0.1403Epoch 00023: val_loss improved from 3.87154 to 3.86570, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 3.6637 - acc: 0.1397 - val_loss: 3.8657 - val_acc: 0.0994 +Epoch 24/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.6331 - acc: 0.1388Epoch 00024: val_loss improved from 3.86570 to 3.82946, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 3.6310 - acc: 0.1395 - val_loss: 3.8295 - val_acc: 0.1018 +Epoch 25/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.6027 - acc: 0.1441Epoch 00025: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.6046 - acc: 0.1437 - val_loss: 3.8820 - val_acc: 0.0958 +Epoch 26/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.5685 - acc: 0.1486Epoch 00026: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.5682 - acc: 0.1488 - val_loss: 3.8985 - val_acc: 0.0970 +Epoch 27/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.5516 - acc: 0.1598Epoch 00027: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.5522 - acc: 0.1594 - val_loss: 3.8917 - val_acc: 0.1114 +Epoch 28/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.5003 - acc: 0.1601Epoch 00028: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.5028 - acc: 0.1594 - val_loss: 3.8673 - val_acc: 0.1006 +Epoch 29/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.4707 - acc: 0.1625Epoch 00029: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.4726 - acc: 0.1627 - val_loss: 4.0873 - val_acc: 0.0826 +Epoch 30/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.4687 - acc: 0.1707Epoch 00030: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.4721 - acc: 0.1707 - val_loss: 3.8861 - val_acc: 0.0970 +Epoch 31/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.4408 - acc: 0.1702Epoch 00031: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.4425 - acc: 0.1698 - val_loss: 3.9220 - val_acc: 0.1090 +Epoch 32/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.3969 - acc: 0.1781Epoch 00032: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.3976 - acc: 0.1774 - val_loss: 3.8863 - val_acc: 0.1030 +Epoch 33/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.3919 - acc: 0.1749Epoch 00033: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.3957 - acc: 0.1746 - val_loss: 3.8761 - val_acc: 0.1030 +Epoch 34/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.3673 - acc: 0.1870Epoch 00034: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.3661 - acc: 0.1876 - val_loss: 3.8526 - val_acc: 0.1126 +Epoch 35/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.3567 - acc: 0.1834Epoch 00035: val_loss improved from 3.82946 to 3.77920, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 3.3551 - acc: 0.1843 - val_loss: 3.7792 - val_acc: 0.1198 +Epoch 36/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.3355 - acc: 0.1890Epoch 00036: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.3358 - acc: 0.1888 - val_loss: 3.8441 - val_acc: 0.1198 +Epoch 37/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.3089 - acc: 0.1869Epoch 00037: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.3105 - acc: 0.1871 - val_loss: 4.0146 - val_acc: 0.0874 +Epoch 38/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.2829 - acc: 0.1971Epoch 00038: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.2835 - acc: 0.1975 - val_loss: 3.8140 - val_acc: 0.1186 +Epoch 39/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.2996 - acc: 0.1979Epoch 00039: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.3005 - acc: 0.1975 - val_loss: 3.8752 - val_acc: 0.1066 +Epoch 40/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.2607 - acc: 0.2071Epoch 00040: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.2605 - acc: 0.2064 - val_loss: 3.8568 - val_acc: 0.1162 +Epoch 41/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.2237 - acc: 0.2039Epoch 00041: val_loss improved from 3.77920 to 3.77177, saving model to saved_models/weights.best.my_model.hdf5 +6680/6680 [==============================] - 7s 1ms/step - loss: 3.2248 - acc: 0.2033 - val_loss: 3.7718 - val_acc: 0.1305 +Epoch 42/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.2078 - acc: 0.2162Epoch 00042: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.2070 - acc: 0.2162 - val_loss: 3.9256 - val_acc: 0.1042 +Epoch 43/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.1947 - acc: 0.2154Epoch 00043: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.1931 - acc: 0.2156 - val_loss: 3.8721 - val_acc: 0.1246 +Epoch 44/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.1847 - acc: 0.2198Epoch 00044: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.1862 - acc: 0.2198 - val_loss: 3.7960 - val_acc: 0.1317 +Epoch 45/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.1671 - acc: 0.2216Epoch 00045: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.1660 - acc: 0.2217 - val_loss: 3.8285 - val_acc: 0.1222 +Epoch 46/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.1758 - acc: 0.2193Epoch 00046: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.1739 - acc: 0.2195 - val_loss: 3.8622 - val_acc: 0.1257 +Epoch 47/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.1576 - acc: 0.2213Epoch 00047: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.1560 - acc: 0.2213 - val_loss: 3.8289 - val_acc: 0.1162 +Epoch 48/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.1389 - acc: 0.2257Epoch 00048: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.1422 - acc: 0.2250 - val_loss: 4.0203 - val_acc: 0.0922 +Epoch 49/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.1103 - acc: 0.2350Epoch 00049: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.1107 - acc: 0.2347 - val_loss: 3.8417 - val_acc: 0.1174 +Epoch 50/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.0985 - acc: 0.2358Epoch 00050: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.0973 - acc: 0.2362 - val_loss: 3.8736 - val_acc: 0.1090 +Epoch 51/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.0987 - acc: 0.2266Epoch 00051: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.0993 - acc: 0.2260 - val_loss: 3.8226 - val_acc: 0.1174 +Epoch 52/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.0941 - acc: 0.2340Epoch 00052: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.0954 - acc: 0.2331 - val_loss: 3.8596 - val_acc: 0.1198 +Epoch 53/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.0688 - acc: 0.2338Epoch 00053: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.0677 - acc: 0.2347 - val_loss: 3.8691 - val_acc: 0.1198 +Epoch 54/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.0602 - acc: 0.2452Epoch 00054: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.0584 - acc: 0.2452 - val_loss: 3.8169 - val_acc: 0.1281 +Epoch 55/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.0388 - acc: 0.2468Epoch 00055: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.0390 - acc: 0.2466 - val_loss: 3.8522 - val_acc: 0.1126 +Epoch 56/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.0493 - acc: 0.2376Epoch 00056: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.0468 - acc: 0.2376 - val_loss: 3.8391 - val_acc: 0.1198 +Epoch 57/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.0074 - acc: 0.2557Epoch 00057: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.0073 - acc: 0.2560 - val_loss: 3.8511 - val_acc: 0.1246 +Epoch 58/70 +6620/6680 [============================>.] - ETA: 0s - loss: 3.0000 - acc: 0.2488Epoch 00058: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 3.0019 - acc: 0.2488 - val_loss: 3.8591 - val_acc: 0.1114 +Epoch 59/70 +6620/6680 [============================>.] - ETA: 0s - loss: 2.9860 - acc: 0.2545Epoch 00059: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 2.9809 - acc: 0.2551 - val_loss: 3.8320 - val_acc: 0.1174 +Epoch 60/70 +6620/6680 [============================>.] - ETA: 0s - loss: 2.9831 - acc: 0.2468Epoch 00060: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 2.9837 - acc: 0.2472 - val_loss: 3.8783 - val_acc: 0.1186 +Epoch 61/70 +6620/6680 [============================>.] - ETA: 0s - loss: 2.9873 - acc: 0.2541Epoch 00061: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 2.9889 - acc: 0.2540 - val_loss: 3.9328 - val_acc: 0.1066 +Epoch 62/70 +6620/6680 [============================>.] - ETA: 0s - loss: 2.9851 - acc: 0.2559Epoch 00062: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 2.9825 - acc: 0.2564 - val_loss: 3.9469 - val_acc: 0.1162 +Epoch 63/70 +6620/6680 [============================>.] - ETA: 0s - loss: 2.9550 - acc: 0.2583Epoch 00063: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 2.9540 - acc: 0.2585 - val_loss: 3.8602 - val_acc: 0.1317 +Epoch 64/70 +6620/6680 [============================>.] - ETA: 0s - loss: 2.9563 - acc: 0.2598Epoch 00064: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 2.9564 - acc: 0.2597 - val_loss: 3.8396 - val_acc: 0.1210 +Epoch 65/70 +6620/6680 [============================>.] - ETA: 0s - loss: 2.9298 - acc: 0.2672Epoch 00065: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 2.9309 - acc: 0.2674 - val_loss: 3.9362 - val_acc: 0.1066 +Epoch 66/70 +6620/6680 [============================>.] - ETA: 0s - loss: 2.9376 - acc: 0.2630Epoch 00066: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 2.9405 - acc: 0.2623 - val_loss: 3.8559 - val_acc: 0.1150 +Epoch 67/70 +6620/6680 [============================>.] - ETA: 0s - loss: 2.9171 - acc: 0.2640Epoch 00067: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 2.9197 - acc: 0.2636 - val_loss: 3.9044 - val_acc: 0.1114 +Epoch 68/70 +6620/6680 [============================>.] - ETA: 0s - loss: 2.8975 - acc: 0.2692Epoch 00068: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 2.8956 - acc: 0.2692 - val_loss: 3.8261 - val_acc: 0.1377 +Epoch 69/70 +6620/6680 [============================>.] - ETA: 0s - loss: 2.9108 - acc: 0.2671Epoch 00069: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 2.9107 - acc: 0.2668 - val_loss: 3.9664 - val_acc: 0.1162 +Epoch 70/70 +6620/6680 [============================>.] - ETA: 0s - loss: 2.8951 - acc: 0.2728Epoch 00070: val_loss did not improve +6680/6680 [==============================] - 7s 1ms/step - loss: 2.8922 - acc: 0.2735 - val_loss: 3.9087 - val_acc: 0.1246 +</pre> +</div> +</div> + +<div class="output_area"> + + <div class="prompt output_prompt">Out[46]:</div> + + + + +<div class="output_text output_subarea output_execute_result"> +<pre><keras.callbacks.History at 0x7f53d8073198></pre> +</div> + +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [48]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="n">my_model</span><span class="o">.</span><span class="n">load_weights</span><span class="p">(</span><span class="s1">'saved_models/weights.best.my_model.hdf5'</span><span class="p">)</span> + +<span class="n">my_predictions</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">my_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">feature</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)))</span> <span class="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">test_tensors</span><span class="p">]</span> + +<span class="c1"># report test accuracy</span> +<span class="n">my_test_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">my_predictions</span><span class="p">)</span><span class="o">==</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">test_targets</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">my_predictions</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">'</span> <span class="o">%</span> <span class="n">my_test_accuracy</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>Test accuracy: 13.6364% +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<hr> +<p><a id='step7'></a></p> +<h2 id="Step-7:-Test-Your-Algorithm">Step 7: Test Your Algorithm<a class="anchor-link" href="#Step-7:-Test-Your-Algorithm">¶</a></h2><p>In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that <strong>you</strong> look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?</p> +<h3 id="(IMPLEMENTATION)-Test-Your-Algorithm-on-Sample-Images!">(IMPLEMENTATION) Test Your Algorithm on Sample Images!<a class="anchor-link" href="#(IMPLEMENTATION)-Test-Your-Algorithm-on-Sample-Images!">¶</a></h3><p>Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images.</p> +<p><strong>Question 6:</strong> Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.</p> +<p><strong>Answer:</strong> The numbers were similar to the accuracy that the algorithm provided, but after all the testing I've made, I'm a bit frustrated about the results. The main improvements that I could apply are data augmentation and work better with hyperparams calibration, since small changes makes a lot of difference in the overall result, I must spend more time working with them to better undertand the impact of each in the process of training the CNN.</p> + +</div> +</div> +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [49]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="c1">## TODO: Execute your algorithm from Step 6 on</span> +<span class="c1">## at least 6 images on your computer.</span> +<span class="c1">## Feel free to use as many code cells as needed.</span> + +<span class="k">def</span> <span class="nf">my_predict_breed</span><span class="p">(</span><span class="n">img_path</span><span class="p">):</span> + <span class="n">tensor</span> <span class="o">=</span> <span class="n">path_to_tensor</span><span class="p">(</span><span class="n">img_path</span><span class="p">)</span> + <span class="c1"># obtain predicted vector</span> + <span class="n">predicted_vector</span> <span class="o">=</span> <span class="n">my_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span> + <span class="c1"># return dog breed that is predicted by the model</span> + <span class="k">return</span> <span class="n">dog_names</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">predicted_vector</span><span class="p">)]</span> +</pre></div> + + </div> +</div> +</div> + +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [61]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="n">sample_dogs</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">glob</span><span class="p">(</span><span class="s2">"./images/dogs/*"</span><span class="p">))</span> + +<span class="n">my_tensors</span> <span class="o">=</span> <span class="n">paths_to_tensor</span><span class="p">(</span><span class="n">sample_dogs</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stderr output_text"> +<pre> + 0%| | 0/6 [00:00<?, ?it/s] +100%|██████████| 6/6 [00:00<00:00, 59.90it/s] +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing code_cell rendered"> +<div class="input"> +<div class="prompt input_prompt">In [75]:</div> +<div class="inner_cell"> + <div class="input_area"> +<div class=" highlight hl-ipython3"><pre><span></span><span class="n">my_predictions</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">argmax</span><span class="p">(</span><span class="n">my_model</span><span class="o">.</span><span class="n">predict</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">feature</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)))</span> <span class="k">for</span> <span class="n">feature</span> <span class="ow">in</span> <span class="n">my_tensors</span><span class="p">]</span> + +<span class="n">names</span> <span class="o">=</span> <span class="p">[</span><span class="n">dog_names</span><span class="p">[</span><span class="n">a</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">my_predictions</span><span class="p">]</span> + +<span class="n">right_numbers</span> <span class="o">=</span> <span class="p">[</span><span class="mi">114</span><span class="p">,</span> <span class="mi">23</span><span class="p">,</span> <span class="mi">115</span><span class="p">,</span> <span class="mi">31</span><span class="p">,</span> <span class="mi">25</span><span class="p">,</span> <span class="mi">64</span><span class="p">]</span> + +<span class="n">my_accuracy</span> <span class="o">=</span> <span class="mi">100</span><span class="o">*</span><span class="n">np</span><span class="o">.</span><span class="n">sum</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">my_predictions</span><span class="p">)</span><span class="o">==</span><span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">right_numbers</span><span class="p">))</span><span class="o">/</span><span class="nb">len</span><span class="p">(</span><span class="n">my_predictions</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s1">'Test accuracy: </span><span class="si">%.4f%%</span><span class="s1">'</span> <span class="o">%</span> <span class="n">my_accuracy</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"predicted numbers: "</span><span class="p">,</span> <span class="n">my_predictions</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"right numbers: "</span><span class="p">,</span> <span class="n">right_numbers</span><span class="p">)</span> +<span class="nb">print</span><span class="p">(</span><span class="s2">"predicted breeds: "</span><span class="p">,</span> <span class="n">names</span><span class="p">)</span> +</pre></div> + + </div> +</div> +</div> + +<div class="output_wrapper"> +<div class="output"> + + +<div class="output_area"> + + <div class="prompt"></div> + + +<div class="output_subarea output_stream output_stdout output_text"> +<pre>Test accuracy: 16.6667% +predicted numbers: [114, 14, 14, 23, 14, 14] +right numbers: [114, 23, 115, 31, 25, 64] +predicted breeds: ['in/114.Otterhound', 'in/014.Basenji', 'in/014.Basenji', 'in/023.Bernese_mountain_dog', 'in/014.Basenji', 'in/014.Basenji'] +</pre> +</div> +</div> + +</div> +</div> + +</div> +<div class="cell border-box-sizing text_cell rendered"><div class="prompt input_prompt"> +</div><div class="inner_cell"> +<div class="text_cell_render border-box-sizing rendered_html"> +<h1 id="Please-download-your-notebook-to-submit">Please download your notebook to submit<a class="anchor-link" href="#Please-download-your-notebook-to-submit">¶</a></h1><p>In order to submit, please do the following:</p> +<ol> +<li>Download an HTML version of the notebook to your computer using 'File: Download as...'</li> +<li>Click on the orange Jupyter circle on the top left of the workspace.</li> +<li>Navigate into the dog-project folder to ensure that you are using the provided dog_images, lfw, and bottleneck_features folders; this means that those folders will <em>not</em> appear in the dog-project folder. If they do appear because you downloaded them, delete them.</li> +<li>While in the dog-project folder, upload the HTML version of this notebook you just downloaded. The upload button is on the top right.</li> +<li>Navigate back to the home folder by clicking on the two dots next to the folder icon, and then open up a terminal under the 'new' tab on the top right</li> +<li>Zip the dog-project folder with the following command in the terminal: +<code>zip -r dog-project.zip dog-project</code></li> +<li>Download the zip file by clicking on the square next to it and selecting 'download'. This will be the zip file you turn in on the next node after this workspace!</li> +</ol> + +</div> +</div> +</div> + </div> + </div> +</body> + + + + +</html> diff --git a/dog_app.ipynb b/dog_app.ipynb new file mode 100644 index 0000000..59ba005 --- /dev/null +++ b/dog_app.ipynb @@ -0,0 +1,1041 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Artificial Intelligence Nanodegree\n", + "\n", + "## Convolutional Neural Networks\n", + "\n", + "## Project: Write an Algorithm for a Dog Identification App \n", + "\n", + "---\n", + "\n", + "In this notebook, some template code has already been provided for you, and you will need to implement additional functionality to successfully complete this project. You will not need to modify the included code beyond what is requested. Sections that begin with **'(IMPLEMENTATION)'** in the header indicate that the following block of code will require additional functionality which you must provide. Instructions will be provided for each section, and the specifics of the implementation are marked in the code block with a 'TODO' statement. Please be sure to read the instructions carefully! \n", + "\n", + "> **Note**: Once you have completed all of the code implementations, you need to finalize your work by exporting the iPython Notebook as an HTML document. Before exporting the notebook to html, all of the code cells need to have been run so that reviewers can see the final implementation and output. You can then export the notebook by using the menu above and navigating to \\n\",\n", + " \"**File -> Download as -> HTML (.html)**. Include the finished document along with this notebook as your submission.\n", + "\n", + "In addition to implementing code, there will be questions that you must answer which relate to the project and your implementation. Each section where you will answer a question is preceded by a **'Question X'** header. Carefully read each question and provide thorough answers in the following text boxes that begin with **'Answer:'**. Your project submission will be evaluated based on your answers to each of the questions and the implementation you provide.\n", + "\n", + ">**Note:** Code and Markdown cells can be executed using the **Shift + Enter** keyboard shortcut. Markdown cells can be edited by double-clicking the cell to enter edit mode.\n", + "\n", + "The rubric contains _optional_ \"Stand Out Suggestions\" for enhancing the project beyond the minimum requirements. If you decide to pursue the \"Stand Out Suggestions\", you should include the code in this IPython notebook.\n", + "\n", + "\n", + "\n", + "---\n", + "### Why We're Here \n", + "\n", + "In this notebook, you will make the first steps towards developing an algorithm that could be used as part of a mobile or web app. At the end of this project, your code will accept any user-supplied image as input. If a dog is detected in the image, it will provide an estimate of the dog's breed. If a human is detected, it will provide an estimate of the dog breed that is most resembling. The image below displays potential sample output of your finished project (... but we expect that each student's algorithm will behave differently!). \n", + "\n", + "![Sample Dog Output](images/sample_dog_output.png)\n", + "\n", + "In this real-world setting, you will need to piece together a series of models to perform different tasks; for instance, the algorithm that detects humans in an image will be different from the CNN that infers dog breed. There are many points of possible failure, and no perfect algorithm exists. Your imperfect solution will nonetheless create a fun user experience!\n", + "\n", + "### The Road Ahead\n", + "\n", + "We break the notebook into separate steps. Feel free to use the links below to navigate the notebook.\n", + "\n", + "* [Step 0](#step0): Import Datasets\n", + "* [Step 1](#step1): Detect Humans\n", + "* [Step 2](#step2): Detect Dogs\n", + "* [Step 3](#step3): Create a CNN to Classify Dog Breeds (from Scratch)\n", + "* [Step 4](#step4): Use a CNN to Classify Dog Breeds (using Transfer Learning)\n", + "* [Step 5](#step5): Create a CNN to Classify Dog Breeds (using Transfer Learning)\n", + "* [Step 6](#step6): Write your Algorithm\n", + "* [Step 7](#step7): Test Your Algorithm\n", + "\n", + "---\n", + "<a id='step0'></a>\n", + "## Step 0: Import Datasets\n", + "\n", + "### Import Dog Dataset\n", + "\n", + "In the code cell below, we import a dataset of dog images. We populate a few variables through the use of the `load_files` function from the scikit-learn library:\n", + "- `train_files`, `valid_files`, `test_files` - numpy arrays containing file paths to images\n", + "- `train_targets`, `valid_targets`, `test_targets` - numpy arrays containing onehot-encoded classification labels \n", + "- `dog_names` - list of string-valued dog breed names for translating labels" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from sklearn.datasets import load_files \n", + "from keras.utils import np_utils\n", + "import numpy as np\n", + "from glob import glob\n", + "\n", + "# define function to load train, test, and validation datasets\n", + "def load_dataset(path):\n", + " data = load_files(path)\n", + " dog_files = np.array(data['filenames'])\n", + " dog_targets = np_utils.to_categorical(np.array(data['target']), 133)\n", + " return dog_files, dog_targets\n", + "\n", + "# load train, test, and validation datasets\n", + "train_files, train_targets = load_dataset('dogImages/train')\n", + "valid_files, valid_targets = load_dataset('dogImages/valid')\n", + "test_files, test_targets = load_dataset('dogImages/test')\n", + "\n", + "# load list of dog names\n", + "dog_names = [item[20:-1] for item in sorted(glob(\"dogImages/train/*/\"))]\n", + "\n", + "# print statistics about the dataset\n", + "print('There are %d total dog categories.' % len(dog_names))\n", + "print('There are %s total dog images.\\n' % len(np.hstack([train_files, valid_files, test_files])))\n", + "print('There are %d training dog images.' % len(train_files))\n", + "print('There are %d validation dog images.' % len(valid_files))\n", + "print('There are %d test dog images.'% len(test_files))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Import Human Dataset\n", + "\n", + "In the code cell below, we import a dataset of human images, where the file paths are stored in the numpy array `human_files`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import random\n", + "random.seed(8675309)\n", + "\n", + "# load filenames in shuffled human dataset\n", + "human_files = np.array(glob(\"lfw/*/*\"))\n", + "random.shuffle(human_files)\n", + "\n", + "# print statistics about the dataset\n", + "print('There are %d total human images.' % len(human_files))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "<a id='step1'></a>\n", + "## Step 1: Detect Humans\n", + "\n", + "We use OpenCV's implementation of [Haar feature-based cascade classifiers](http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html) to detect human faces in images. OpenCV provides many pre-trained face detectors, stored as XML files on [github](https://github.com/opencv/opencv/tree/master/data/haarcascades). We have downloaded one of these detectors and stored it in the `haarcascades` directory.\n", + "\n", + "In the next code cell, we demonstrate how to use this detector to find human faces in a sample image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import cv2 \n", + "import matplotlib.pyplot as plt \n", + "%matplotlib inline \n", + "\n", + "# extract pre-trained face detector\n", + "face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')\n", + "\n", + "# load color (BGR) image\n", + "img = cv2.imread(human_files[3])\n", + "# convert BGR image to grayscale\n", + "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", + "\n", + "# find faces in image\n", + "faces = face_cascade.detectMultiScale(gray)\n", + "\n", + "# print number of faces detected in the image\n", + "print('Number of faces detected:', len(faces))\n", + "\n", + "# get bounding box for each detected face\n", + "for (x,y,w,h) in faces:\n", + " # add bounding box to color image\n", + " cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n", + " \n", + "# convert BGR image to RGB for plotting\n", + "cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", + "\n", + "# display the image, along with bounding box\n", + "plt.imshow(cv_rgb)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Before using any of the face detectors, it is standard procedure to convert the images to grayscale. The `detectMultiScale` function executes the classifier stored in `face_cascade` and takes the grayscale image as a parameter. \n", + "\n", + "In the above code, `faces` is a numpy array of detected faces, where each row corresponds to a detected face. Each detected face is a 1D array with four entries that specifies the bounding box of the detected face. The first two entries in the array (extracted in the above code as `x` and `y`) specify the horizontal and vertical positions of the top left corner of the bounding box. The last two entries in the array (extracted here as `w` and `h`) specify the width and height of the box.\n", + "\n", + "### Write a Human Face Detector\n", + "\n", + "We can use this procedure to write a function that returns `True` if a human face is detected in an image and `False` otherwise. This function, aptly named `face_detector`, takes a string-valued file path to an image as input and appears in the code block below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# returns \"True\" if face is detected in image stored at img_path\n", + "def face_detector(img_path):\n", + " img = cv2.imread(img_path)\n", + " gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", + " faces = face_cascade.detectMultiScale(gray)\n", + " return len(faces) > 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (IMPLEMENTATION) Assess the Human Face Detector\n", + "\n", + "__Question 1:__ Use the code cell below to test the performance of the `face_detector` function. \n", + "- What percentage of the first 100 images in `human_files` have a detected human face? \n", + "- What percentage of the first 100 images in `dog_files` have a detected human face? \n", + "\n", + "Ideally, we would like 100% of human images with a detected face and 0% of dog images with a detected face. You will see that our algorithm falls short of this goal, but still gives acceptable performance. We extract the file paths for the first 100 images from each of the datasets and store them in the numpy arrays `human_files_short` and `dog_files_short`.\n", + "\n", + "__Answer:__ " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "human_files_short = human_files[:100]\n", + "dog_files_short = train_files[:100]\n", + "# Do NOT modify the code above this line.\n", + "\n", + "## TODO: Test the performance of the face_detector algorithm \n", + "## on the images in human_files_short and dog_files_short." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "__Question 2:__ This algorithmic choice necessitates that we communicate to the user that we accept human images only when they provide a clear view of a face (otherwise, we risk having unneccessarily frustrated users!). In your opinion, is this a reasonable expectation to pose on the user? If not, can you think of a way to detect humans in images that does not necessitate an image with a clearly presented face?\n", + "\n", + "__Answer:__\n", + "\n", + "We suggest the face detector from OpenCV as a potential way to detect human images in your algorithm, but you are free to explore other approaches, especially approaches that make use of deep learning :). Please use the code cell below to design and test your own face detection algorithm. If you decide to pursue this _optional_ task, report performance on each of the datasets." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "## (Optional) TODO: Report the performance of another \n", + "## face detection algorithm on the LFW dataset\n", + "### Feel free to use as many code cells as needed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "<a id='step2'></a>\n", + "## Step 2: Detect Dogs\n", + "\n", + "In this section, we use a pre-trained [ResNet-50](http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006) model to detect dogs in images. Our first line of code downloads the ResNet-50 model, along with weights that have been trained on [ImageNet](http://www.image-net.org/), a very large, very popular dataset used for image classification and other vision tasks. ImageNet contains over 10 million URLs, each linking to an image containing an object from one of [1000 categories](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a). Given an image, this pre-trained ResNet-50 model returns a prediction (derived from the available categories in ImageNet) for the object that is contained in the image." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.applications.resnet50 import ResNet50\n", + "\n", + "# define ResNet50 model\n", + "ResNet50_model = ResNet50(weights='imagenet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Pre-process the Data\n", + "\n", + "When using TensorFlow as backend, Keras CNNs require a 4D array (which we'll also refer to as a 4D tensor) as input, with shape\n", + "\n", + "$$\n", + "(\\text{nb_samples}, \\text{rows}, \\text{columns}, \\text{channels}),\n", + "$$\n", + "\n", + "where `nb_samples` corresponds to the total number of images (or samples), and `rows`, `columns`, and `channels` correspond to the number of rows, columns, and channels for each image, respectively. \n", + "\n", + "The `path_to_tensor` function below takes a string-valued file path to a color image as input and returns a 4D tensor suitable for supplying to a Keras CNN. The function first loads the image and resizes it to a square image that is $224 \\times 224$ pixels. Next, the image is converted to an array, which is then resized to a 4D tensor. In this case, since we are working with color images, each image has three channels. Likewise, since we are processing a single image (or sample), the returned tensor will always have shape\n", + "\n", + "$$\n", + "(1, 224, 224, 3).\n", + "$$\n", + "\n", + "The `paths_to_tensor` function takes a numpy array of string-valued image paths as input and returns a 4D tensor with shape \n", + "\n", + "$$\n", + "(\\text{nb_samples}, 224, 224, 3).\n", + "$$\n", + "\n", + "Here, `nb_samples` is the number of samples, or number of images, in the supplied array of image paths. It is best to think of `nb_samples` as the number of 3D tensors (where each 3D tensor corresponds to a different image) in your dataset!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.preprocessing import image \n", + "from tqdm import tqdm\n", + "\n", + "def path_to_tensor(img_path):\n", + " # loads RGB image as PIL.Image.Image type\n", + " img = image.load_img(img_path, target_size=(224, 224))\n", + " # convert PIL.Image.Image type to 3D tensor with shape (224, 224, 3)\n", + " x = image.img_to_array(img)\n", + " # convert 3D tensor to 4D tensor with shape (1, 224, 224, 3) and return 4D tensor\n", + " return np.expand_dims(x, axis=0)\n", + "\n", + "def paths_to_tensor(img_paths):\n", + " list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]\n", + " return np.vstack(list_of_tensors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Making Predictions with ResNet-50\n", + "\n", + "Getting the 4D tensor ready for ResNet-50, and for any other pre-trained model in Keras, requires some additional processing. First, the RGB image is converted to BGR by reordering the channels. All pre-trained models have the additional normalization step that the mean pixel (expressed in RGB as $[103.939, 116.779, 123.68]$ and calculated from all pixels in all images in ImageNet) must be subtracted from every pixel in each image. This is implemented in the imported function `preprocess_input`. If you're curious, you can check the code for `preprocess_input` [here](https://github.com/fchollet/keras/blob/master/keras/applications/imagenet_utils.py).\n", + "\n", + "Now that we have a way to format our image for supplying to ResNet-50, we are now ready to use the model to extract the predictions. This is accomplished with the `predict` method, which returns an array whose $i$-th entry is the model's predicted probability that the image belongs to the $i$-th ImageNet category. This is implemented in the `ResNet50_predict_labels` function below.\n", + "\n", + "By taking the argmax of the predicted probability vector, we obtain an integer corresponding to the model's predicted object class, which we can identify with an object category through the use of this [dictionary](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a). " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.applications.resnet50 import preprocess_input, decode_predictions\n", + "\n", + "def ResNet50_predict_labels(img_path):\n", + " # returns prediction vector for image located at img_path\n", + " img = preprocess_input(path_to_tensor(img_path))\n", + " return np.argmax(ResNet50_model.predict(img))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Write a Dog Detector\n", + "\n", + "While looking at the [dictionary](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a), you will notice that the categories corresponding to dogs appear in an uninterrupted sequence and correspond to dictionary keys 151-268, inclusive, to include all categories from `'Chihuahua'` to `'Mexican hairless'`. Thus, in order to check to see if an image is predicted to contain a dog by the pre-trained ResNet-50 model, we need only check if the `ResNet50_predict_labels` function above returns a value between 151 and 268 (inclusive).\n", + "\n", + "We use these ideas to complete the `dog_detector` function below, which returns `True` if a dog is detected in an image (and `False` if not)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### returns \"True\" if a dog is detected in the image stored at img_path\n", + "def dog_detector(img_path):\n", + " prediction = ResNet50_predict_labels(img_path)\n", + " return ((prediction <= 268) & (prediction >= 151)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (IMPLEMENTATION) Assess the Dog Detector\n", + "\n", + "__Question 3:__ Use the code cell below to test the performance of your `dog_detector` function. \n", + "- What percentage of the images in `human_files_short` have a detected dog? \n", + "- What percentage of the images in `dog_files_short` have a detected dog?\n", + "\n", + "__Answer:__ " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: Test the performance of the dog_detector function\n", + "### on the images in human_files_short and dog_files_short." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "<a id='step3'></a>\n", + "## Step 3: Create a CNN to Classify Dog Breeds (from Scratch)\n", + "\n", + "Now that we have functions for detecting humans and dogs in images, we need a way to predict breed from images. In this step, you will create a CNN that classifies dog breeds. You must create your CNN _from scratch_ (so, you can't use transfer learning _yet_!), and you must attain a test accuracy of at least 1%. In Step 5 of this notebook, you will have the opportunity to use transfer learning to create a CNN that attains greatly improved accuracy.\n", + "\n", + "Be careful with adding too many trainable layers! More parameters means longer training, which means you are more likely to need a GPU to accelerate the training process. Thankfully, Keras provides a handy estimate of the time that each epoch is likely to take; you can extrapolate this estimate to figure out how long it will take for your algorithm to train. \n", + "\n", + "We mention that the task of assigning breed to dogs from images is considered exceptionally challenging. To see why, consider that *even a human* would have great difficulty in distinguishing between a Brittany and a Welsh Springer Spaniel. \n", + "\n", + "Brittany | Welsh Springer Spaniel\n", + "- | - \n", + "<img src=\"images/Brittany_02625.jpg\" width=\"100\"> | <img src=\"images/Welsh_springer_spaniel_08203.jpg\" width=\"200\">\n", + "\n", + "It is not difficult to find other dog breed pairs with minimal inter-class variation (for instance, Curly-Coated Retrievers and American Water Spaniels). \n", + "\n", + "Curly-Coated Retriever | American Water Spaniel\n", + "- | -\n", + "<img src=\"images/Curly-coated_retriever_03896.jpg\" width=\"200\"> | <img src=\"images/American_water_spaniel_00648.jpg\" width=\"200\">\n", + "\n", + "\n", + "Likewise, recall that labradors come in yellow, chocolate, and black. Your vision-based algorithm will have to conquer this high intra-class variation to determine how to classify all of these different shades as the same breed. \n", + "\n", + "Yellow Labrador | Chocolate Labrador | Black Labrador\n", + "- | -\n", + "<img src=\"images/Labrador_retriever_06457.jpg\" width=\"150\"> | <img src=\"images/Labrador_retriever_06455.jpg\" width=\"240\"> | <img src=\"images/Labrador_retriever_06449.jpg\" width=\"220\">\n", + "\n", + "We also mention that random chance presents an exceptionally low bar: setting aside the fact that the classes are slightly imabalanced, a random guess will provide a correct answer roughly 1 in 133 times, which corresponds to an accuracy of less than 1%. \n", + "\n", + "Remember that the practice is far ahead of the theory in deep learning. Experiment with many different architectures, and trust your intuition. And, of course, have fun! \n", + "\n", + "### Pre-process the Data\n", + "\n", + "We rescale the images by dividing every pixel in every image by 255." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from PIL import ImageFile \n", + "ImageFile.LOAD_TRUNCATED_IMAGES = True \n", + "\n", + "# pre-process the data for Keras\n", + "train_tensors = paths_to_tensor(train_files).astype('float32')/255\n", + "valid_tensors = paths_to_tensor(valid_files).astype('float32')/255\n", + "test_tensors = paths_to_tensor(test_files).astype('float32')/255" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (IMPLEMENTATION) Model Architecture\n", + "\n", + "Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:\n", + " \n", + " model.summary()\n", + "\n", + "We have imported some Python modules to get you started, but feel free to import as many modules as you need. If you end up getting stuck, here's a hint that specifies a model that trains relatively fast on CPU and attains >1% test accuracy in 5 epochs:\n", + "\n", + "![Sample CNN](images/sample_cnn.png)\n", + " \n", + "__Question 4:__ Outline the steps you took to get to your final CNN architecture and your reasoning at each step. If you chose to use the hinted architecture above, describe why you think that CNN architecture should work well for the image classification task.\n", + "\n", + "__Answer:__ " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n", + "from keras.layers import Dropout, Flatten, Dense\n", + "from keras.models import Sequential\n", + "\n", + "model = Sequential()\n", + "\n", + "### TODO: Define your architecture.\n", + "\n", + "model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (IMPLEMENTATION) Train the Model\n", + "\n", + "Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss.\n", + "\n", + "You are welcome to [augment the training data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html), but this is not a requirement. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.callbacks import ModelCheckpoint \n", + "\n", + "### TODO: specify the number of epochs that you would like to use to train the model.\n", + "\n", + "epochs = ...\n", + "\n", + "### Do NOT modify the code below this line.\n", + "\n", + "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', \n", + " verbose=1, save_best_only=True)\n", + "\n", + "model.fit(train_tensors, train_targets, \n", + " validation_data=(valid_tensors, valid_targets),\n", + " epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the Model with the Best Validation Loss" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "model.load_weights('saved_models/weights.best.from_scratch.hdf5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test the Model\n", + "\n", + "Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 1%." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# get index of predicted dog breed for each image in test set\n", + "dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]\n", + "\n", + "# report test accuracy\n", + "test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)\n", + "print('Test accuracy: %.4f%%' % test_accuracy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "<a id='step4'></a>\n", + "## Step 4: Use a CNN to Classify Dog Breeds\n", + "\n", + "To reduce training time without sacrificing accuracy, we show you how to train a CNN using transfer learning. In the following step, you will get a chance to use transfer learning to train your own CNN.\n", + "\n", + "### Obtain Bottleneck Features" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bottleneck_features = np.load('bottleneck_features/DogVGG16Data.npz')\n", + "train_VGG16 = bottleneck_features['train']\n", + "valid_VGG16 = bottleneck_features['valid']\n", + "test_VGG16 = bottleneck_features['test']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Model Architecture\n", + "\n", + "The model uses the the pre-trained VGG-16 model as a fixed feature extractor, where the last convolutional output of VGG-16 is fed as input to our model. We only add a global average pooling layer and a fully connected layer, where the latter contains one node for each dog category and is equipped with a softmax." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "VGG16_model = Sequential()\n", + "VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))\n", + "VGG16_model.add(Dense(133, activation='softmax'))\n", + "\n", + "VGG16_model.summary()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compile the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Train the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', \n", + " verbose=1, save_best_only=True)\n", + "\n", + "VGG16_model.fit(train_VGG16, train_targets, \n", + " validation_data=(valid_VGG16, valid_targets),\n", + " epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Load the Model with the Best Validation Loss" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Test the Model\n", + "\n", + "Now, we can use the CNN to test how well it identifies breed within our test dataset of dog images. We print the test accuracy below." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# get index of predicted dog breed for each image in test set\n", + "VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]\n", + "\n", + "# report test accuracy\n", + "test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)\n", + "print('Test accuracy: %.4f%%' % test_accuracy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Predict Dog Breed with the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from extract_bottleneck_features import *\n", + "\n", + "def VGG16_predict_breed(img_path):\n", + " # extract bottleneck features\n", + " bottleneck_feature = extract_VGG16(path_to_tensor(img_path))\n", + " # obtain predicted vector\n", + " predicted_vector = VGG16_model.predict(bottleneck_feature)\n", + " # return dog breed that is predicted by the model\n", + " return dog_names[np.argmax(predicted_vector)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "<a id='step5'></a>\n", + "## Step 5: Create a CNN to Classify Dog Breeds (using Transfer Learning)\n", + "\n", + "You will now use transfer learning to create a CNN that can identify dog breed from images. Your CNN must attain at least 60% accuracy on the test set.\n", + "\n", + "In Step 4, we used transfer learning to create a CNN using VGG-16 bottleneck features. In this section, you must use the bottleneck features from a different pre-trained model. To make things easier for you, we have pre-computed the features for all of the networks that are currently available in Keras:\n", + "- [VGG-19](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG19Data.npz) bottleneck features\n", + "- [ResNet-50](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogResnet50Data.npz) bottleneck features\n", + "- [Inception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogInceptionV3Data.npz) bottleneck features\n", + "- [Xception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogXceptionData.npz) bottleneck features\n", + "\n", + "The files are encoded as such:\n", + "\n", + " Dog{network}Data.npz\n", + " \n", + "where `{network}`, in the above filename, can be one of `VGG19`, `Resnet50`, `InceptionV3`, or `Xception`. Pick one of the above architectures, download the corresponding bottleneck features, and store the downloaded file in the `bottleneck_features/` folder in the repository.\n", + "\n", + "### (IMPLEMENTATION) Obtain Bottleneck Features\n", + "\n", + "In the code block below, extract the bottleneck features corresponding to the train, test, and validation sets by running the following:\n", + "\n", + " bottleneck_features = np.load('bottleneck_features/Dog{network}Data.npz')\n", + " train_{network} = bottleneck_features['train']\n", + " valid_{network} = bottleneck_features['valid']\n", + " test_{network} = bottleneck_features['test']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: Obtain bottleneck features from another pre-trained CNN." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (IMPLEMENTATION) Model Architecture\n", + "\n", + "Create a CNN to classify dog breed. At the end of your code cell block, summarize the layers of your model by executing the line:\n", + " \n", + " <your model's name>.summary()\n", + " \n", + "__Question 5:__ Outline the steps you took to get to your final CNN architecture and your reasoning at each step. Describe why you think the architecture is suitable for the current problem.\n", + "\n", + "__Answer:__ \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: Define your architecture." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (IMPLEMENTATION) Compile the Model" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: Compile the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (IMPLEMENTATION) Train the Model\n", + "\n", + "Train your model in the code cell below. Use model checkpointing to save the model that attains the best validation loss. \n", + "\n", + "You are welcome to [augment the training data](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html), but this is not a requirement. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: Train the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (IMPLEMENTATION) Load the Model with the Best Validation Loss" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: Load the model weights with the best validation loss." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (IMPLEMENTATION) Test the Model\n", + "\n", + "Try out your model on the test dataset of dog images. Ensure that your test accuracy is greater than 60%." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: Calculate classification accuracy on the test dataset." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### (IMPLEMENTATION) Predict Dog Breed with the Model\n", + "\n", + "Write a function that takes an image path as input and returns the dog breed (`Affenpinscher`, `Afghan_hound`, etc) that is predicted by your model. \n", + "\n", + "Similar to the analogous function in Step 5, your function should have three steps:\n", + "1. Extract the bottleneck features corresponding to the chosen CNN model.\n", + "2. Supply the bottleneck features as input to the model to return the predicted vector. Note that the argmax of this prediction vector gives the index of the predicted dog breed.\n", + "3. Use the `dog_names` array defined in Step 0 of this notebook to return the corresponding breed.\n", + "\n", + "The functions to extract the bottleneck features can be found in `extract_bottleneck_features.py`, and they have been imported in an earlier code cell. To obtain the bottleneck features corresponding to your chosen CNN architecture, you need to use the function\n", + "\n", + " extract_{network}\n", + " \n", + "where `{network}`, in the above filename, should be one of `VGG19`, `Resnet50`, `InceptionV3`, or `Xception`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: Write a function that takes a path to an image as input\n", + "### and returns the dog breed that is predicted by the model." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "<a id='step6'></a>\n", + "## Step 6: Write your Algorithm\n", + "\n", + "Write an algorithm that accepts a file path to an image and first determines whether the image contains a human, dog, or neither. Then,\n", + "- if a __dog__ is detected in the image, return the predicted breed.\n", + "- if a __human__ is detected in the image, return the resembling dog breed.\n", + "- if __neither__ is detected in the image, provide output that indicates an error.\n", + "\n", + "You are welcome to write your own functions for detecting humans and dogs in images, but feel free to use the `face_detector` and `dog_detector` functions developed above. You are __required__ to use your CNN from Step 5 to predict dog breed. \n", + "\n", + "Some sample output for our algorithm is provided below, but feel free to design your own user experience!\n", + "\n", + "![Sample Human Output](images/sample_human_output.png)\n", + "\n", + "\n", + "### (IMPLEMENTATION) Write your Algorithm" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: Write your algorithm.\n", + "### Feel free to use as many code cells as needed." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "<a id='step7'></a>\n", + "## Step 7: Test Your Algorithm\n", + "\n", + "In this section, you will take your new algorithm for a spin! What kind of dog does the algorithm think that __you__ look like? If you have a dog, does it predict your dog's breed accurately? If you have a cat, does it mistakenly think that your cat is a dog?\n", + "\n", + "### (IMPLEMENTATION) Test Your Algorithm on Sample Images!\n", + "\n", + "Test your algorithm at least six images on your computer. Feel free to use any images you like. Use at least two human and two dog images. \n", + "\n", + "__Question 6:__ Is the output better than you expected :) ? Or worse :( ? Provide at least three possible points of improvement for your algorithm.\n", + "\n", + "__Answer:__ " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "## TODO: Execute your algorithm from Step 6 on\n", + "## at least 6 images on your computer.\n", + "## Feel free to use as many code cells as needed." + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "dog-project", + "language": "python", + "name": "dog-project" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.2" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/dog_app_zh.ipynb b/dog_app_zh.ipynb new file mode 100644 index 0000000..4ec1d72 --- /dev/null +++ b/dog_app_zh.ipynb @@ -0,0 +1,1122 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 卷积神经网络(Convolutional Neural Network, CNN)\n", + "\n", + "## 项目:实现一个狗品种识别算法App\n", + "\n", + "在这个notebook文件中,有些模板代码已经提供给你,但你还需要实现更多的功能来完成这个项目。除非有明确要求,你无须修改任何已给出的代码。以**'(练习)'**开始的标题表示接下来的代码部分中有你需要实现的功能。这些部分都配有详细的指导,需要实现的部分也会在注释中以'TODO'标出。请仔细阅读所有的提示。\n", + "\n", + "除了实现代码外,你还**需要**回答一些与项目及代码相关的问题。每个需要回答的问题都会以 **'问题 X'** 标记。请仔细阅读每个问题,并且在问题后的 **'回答'** 部分写出完整的答案。我们将根据 你对问题的回答 和 撰写代码实现的功能 来对你提交的项目进行评分。\n", + "\n", + ">**提示:**Code 和 Markdown 区域可通过 **Shift + Enter** 快捷键运行。此外,Markdown可以通过双击进入编辑模式。\n", + "\n", + "项目中显示为_选做_的部分可以帮助你的项目脱颖而出,而不是仅仅达到通过的最低要求。如果你决定追求更高的挑战,请在此 notebook 中完成_选做_部分的代码。\n", + "\n", + "---\n", + "\n", + "### 让我们开始吧\n", + "在这个notebook中,你将迈出第一步,来开发可以作为移动端或 Web应用程序一部分的算法。在这个项目的最后,你的程序将能够把用户提供的任何一个图像作为输入。如果可以从图像中检测到一只狗,它会输出对狗品种的预测。如果图像中是一个人脸,它会预测一个与其最相似的狗的种类。下面这张图展示了完成项目后可能的输出结果。(……实际上我们希望每个学生的输出结果不相同!)\n", + "\n", + "![Sample Dog Output](images/sample_dog_output.png)\n", + "\n", + "在现实世界中,你需要拼凑一系列的模型来完成不同的任务;举个例子,用来预测狗种类的算法会与预测人类的算法不同。在做项目的过程中,你可能会遇到不少失败的预测,因为并不存在完美的算法和模型。你最终提交的不完美的解决方案也一定会给你带来一个有趣的学习经验!\n", + "\n", + "### 项目内容\n", + "\n", + "我们将这个notebook分为不同的步骤,你可以使用下面的链接来浏览此notebook。\n", + "\n", + "* [Step 0](#step0): 导入数据集\n", + "* [Step 1](#step1): 检测人脸\n", + "* [Step 2](#step2): 检测狗狗\n", + "* [Step 3](#step3): 从头创建一个CNN来分类狗品种\n", + "* [Step 4](#step4): 使用一个CNN来区分狗的品种(使用迁移学习)\n", + "* [Step 5](#step5): 建立一个CNN来分类狗的品种(使用迁移学习)\n", + "* [Step 6](#step6): 完成你的算法\n", + "* [Step 7](#step7): 测试你的算法\n", + "\n", + "在该项目中包含了如下的问题:\n", + "\n", + "* [问题 1](#question1)\n", + "* [问题 2](#question2)\n", + "* [问题 3](#question3)\n", + "* [问题 4](#question4)\n", + "* [问题 5](#question5)\n", + "* [问题 6](#question6)\n", + "* [问题 7](#question7)\n", + "* [问题 8](#question8)\n", + "* [问题 9](#question9)\n", + "* [问题 10](#question10)\n", + "* [问题 11](#question11)\n", + "\n", + "\n", + "---\n", + "<a id='step0'></a>\n", + "## 步骤 0: 导入数据集\n", + "\n", + "### 导入狗数据集\n", + "在下方的代码单元(cell)中,我们导入了一个狗图像的数据集。我们使用 scikit-learn 库中的 `load_files` 函数来获取一些变量:\n", + "- `train_files`, `valid_files`, `test_files` - 包含图像的文件路径的numpy数组\n", + "- `train_targets`, `valid_targets`, `test_targets` - 包含独热编码分类标签的numpy数组\n", + "- `dog_names` - 由字符串构成的与标签相对应的狗的种类" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "from sklearn.datasets import load_files \n", + "from keras.utils import np_utils\n", + "import numpy as np\n", + "from glob import glob\n", + "\n", + "# define function to load train, test, and validation datasets\n", + "def load_dataset(path):\n", + " data = load_files(path)\n", + " dog_files = np.array(data['filenames'])\n", + " dog_targets = np_utils.to_categorical(np.array(data['target']), 133)\n", + " return dog_files, dog_targets\n", + "\n", + "# load train, test, and validation datasets\n", + "train_files, train_targets = load_dataset('/data/dog_images/train')\n", + "valid_files, valid_targets = load_dataset('/data/dog_images/valid')\n", + "test_files, test_targets = load_dataset('/data/dog_images/test')\n", + "\n", + "# load list of dog names\n", + "dog_names = [item[20:-1] for item in sorted(glob(\"/data/dog_images/train/*/\"))]\n", + "\n", + "# print statistics about the dataset\n", + "print('There are %d total dog categories.' % len(dog_names))\n", + "print('There are %s total dog images.\\n' % len(np.hstack([train_files, valid_files, test_files])))\n", + "print('There are %d training dog images.' % len(train_files))\n", + "print('There are %d validation dog images.' % len(valid_files))\n", + "print('There are %d test dog images.'% len(test_files))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 导入人脸数据集\n", + "\n", + "在下方的代码单元中,我们导入人脸图像数据集,文件所在路径存储在名为 `human_files` 的 numpy 数组。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import random\n", + "random.seed(8675309)\n", + "\n", + "# 加载打乱后的人脸数据集的文件名\n", + "human_files = np.array(glob(\"/data/lfw/*/*\"))\n", + "random.shuffle(human_files)\n", + "\n", + "# 打印数据集的数据量\n", + "print('There are %d total human images.' % len(human_files))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "<a id='step1'></a>\n", + "## 步骤1:检测人脸\n", + " \n", + "我们将使用 OpenCV 中的 [Haar feature-based cascade classifiers](http://docs.opencv.org/trunk/d7/d8b/tutorial_py_face_detection.html) 来检测图像中的人脸。OpenCV 提供了很多预训练的人脸检测模型,它们以XML文件保存在 [github](https://github.com/opencv/opencv/tree/master/data/haarcascades)。我们已经下载了其中一个检测模型,并且把它存储在 `haarcascades` 的目录中。\n", + "\n", + "在如下代码单元中,我们将演示如何使用这个检测模型在样本图像中找到人脸。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import cv2 \n", + "import matplotlib.pyplot as plt \n", + "%matplotlib inline \n", + "\n", + "# 提取预训练的人脸检测模型\n", + "face_cascade = cv2.CascadeClassifier('haarcascades/haarcascade_frontalface_alt.xml')\n", + "\n", + "# 加载彩色(通道顺序为BGR)图像\n", + "img = cv2.imread(human_files[3])\n", + "\n", + "# 将BGR图像进行灰度处理\n", + "gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", + "\n", + "# 在图像中找出脸\n", + "faces = face_cascade.detectMultiScale(gray)\n", + "\n", + "# 打印图像中检测到的脸的个数\n", + "print('Number of faces detected:', len(faces))\n", + "\n", + "# 获取每一个所检测到的脸的识别框\n", + "for (x,y,w,h) in faces:\n", + " # 在人脸图像中绘制出识别框\n", + " cv2.rectangle(img,(x,y),(x+w,y+h),(255,0,0),2)\n", + " \n", + "# 将BGR图像转变为RGB图像以打印\n", + "cv_rgb = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n", + "\n", + "# 展示含有识别框的图像\n", + "plt.imshow(cv_rgb)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "在使用任何一个检测模型之前,将图像转换为灰度图是常用过程。`detectMultiScale` 函数使用储存在 `face_cascade` 中的的数据,对输入的灰度图像进行分类。\n", + "\n", + "在上方的代码中,`faces` 以 numpy 数组的形式,保存了识别到的面部信息。它其中每一行表示一个被检测到的脸,该数据包括如下四个信息:前两个元素 `x`、`y` 代表识别框左上角的 x 和 y 坐标(参照上图,注意 y 坐标的方向和我们默认的方向不同);后两个元素代表识别框在 x 和 y 轴两个方向延伸的长度 `w` 和 `d`。 \n", + "\n", + "### 写一个人脸识别器\n", + "\n", + "我们可以将这个程序封装为一个函数。该函数的输入为人脸图像的**路径**,当图像中包含人脸时,该函数返回 `True`,反之返回 `False`。该函数定义如下所示。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 如果img_path路径表示的图像检测到了脸,返回\"True\" \n", + "def face_detector(img_path):\n", + " img = cv2.imread(img_path)\n", + " gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)\n", + " faces = face_cascade.detectMultiScale(gray)\n", + " return len(faces) > 0" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### **【练习】** 评估人脸检测模型" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "---\n", + "\n", + "<a id='question1'></a>\n", + "### __问题 1:__ \n", + "\n", + "在下方的代码块中,使用 `face_detector` 函数,计算:\n", + "\n", + "- `human_files` 的前100张图像中,能够检测到**人脸**的图像占比多少?\n", + "- `dog_files` 的前100张图像中,能够检测到**人脸**的图像占比多少?\n", + "\n", + "理想情况下,人图像中检测到人脸的概率应当为100%,而狗图像中检测到人脸的概率应该为0%。你会发现我们的算法并非完美,但结果仍然是可以接受的。我们从每个数据集中提取前100个图像的文件路径,并将它们存储在`human_files_short`和`dog_files_short`中。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "human_files_short = human_files[:100]\n", + "dog_files_short = train_files[:100]\n", + "## 请不要修改上方代码\n", + "\n", + "\n", + "## TODO: 基于human_files_short和dog_files_short\n", + "## 中的图像测试face_detector的表现\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "<a id='question2'></a>\n", + "\n", + "### __问题 2:__ \n", + "\n", + "就算法而言,该算法成功与否的关键在于,用户能否提供含有清晰面部特征的人脸图像。\n", + "那么你认为,这样的要求在实际使用中对用户合理吗?如果你觉得不合理,你能否想到一个方法,即使图像中并没有清晰的面部特征,也能够检测到人脸?\n", + "\n", + "__回答:__\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "<a id='Selection1'></a>\n", + "### 选做:\n", + "\n", + "我们建议在你的算法中使用opencv的人脸检测模型去检测人类图像,不过你可以自由地探索其他的方法,尤其是尝试使用深度学习来解决它:)。请用下方的代码单元来设计和测试你的面部监测算法。如果你决定完成这个_选做_任务,你需要报告算法在每一个数据集上的表现。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "## (选做) TODO: 报告另一个面部检测算法在LFW数据集上的表现\n", + "### 你可以随意使用所需的代码单元数" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "<a id='step2'></a>\n", + "\n", + "## 步骤 2: 检测狗狗\n", + "\n", + "在这个部分中,我们使用预训练的 [ResNet-50](http://ethereon.github.io/netscope/#/gist/db945b393d40bfa26006) 模型去检测图像中的狗。下方的第一行代码就是下载了 ResNet-50 模型的网络结构参数,以及基于 [ImageNet](http://www.image-net.org/) 数据集的预训练权重。\n", + "\n", + "ImageNet 这目前一个非常流行的数据集,常被用来测试图像分类等计算机视觉任务相关的算法。它包含超过一千万个 URL,每一个都链接到 [1000 categories](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) 中所对应的一个物体的图像。任给输入一个图像,该 ResNet-50 模型会返回一个对图像中物体的预测结果。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.applications.resnet50 import ResNet50\n", + "\n", + "# 定义ResNet50模型\n", + "ResNet50_model = ResNet50(weights='imagenet')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 数据预处理\n", + "\n", + "- 在使用 TensorFlow 作为后端的时候,在 Keras 中,CNN 的输入是一个4维数组(也被称作4维张量),它的各维度尺寸为 `(nb_samples, rows, columns, channels)`。其中 `nb_samples` 表示图像(或者样本)的总数,`rows`, `columns`, 和 `channels` 分别表示图像的行数、列数和通道数。\n", + "\n", + "\n", + "- 下方的 `path_to_tensor` 函数实现如下将彩色图像的字符串型的文件路径作为输入,返回一个4维张量,作为 Keras CNN 输入。因为我们的输入图像是彩色图像,因此它们具有三个通道( `channels` 为 `3`)。\n", + " 1. 该函数首先读取一张图像,然后将其缩放为 224×224 的图像。\n", + " 2. 随后,该图像被调整为具有4个维度的张量。\n", + " 3. 对于任一输入图像,最后返回的张量的维度是:`(1, 224, 224, 3)`。\n", + "\n", + "\n", + "- `paths_to_tensor` 函数将图像路径的字符串组成的 numpy 数组作为输入,并返回一个4维张量,各维度尺寸为 `(nb_samples, 224, 224, 3)`。 在这里,`nb_samples`是提供的图像路径的数据中的样本数量或图像数量。你也可以将 `nb_samples` 理解为数据集中3维张量的个数(每个3维张量表示一个不同的图像。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.preprocessing import image \n", + "from tqdm import tqdm\n", + "\n", + "def path_to_tensor(img_path):\n", + " # 用PIL加载RGB图像为PIL.Image.Image类型\n", + " img = image.load_img(img_path, target_size=(224, 224))\n", + " # 将PIL.Image.Image类型转化为格式为(224, 224, 3)的3维张量\n", + " x = image.img_to_array(img)\n", + " # 将3维张量转化为格式为(1, 224, 224, 3)的4维张量并返回\n", + " return np.expand_dims(x, axis=0)\n", + "\n", + "def paths_to_tensor(img_paths):\n", + " list_of_tensors = [path_to_tensor(img_path) for img_path in tqdm(img_paths)]\n", + " return np.vstack(list_of_tensors)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 基于 ResNet-50 架构进行预测\n", + "\n", + "对于通过上述步骤得到的四维张量,在把它们输入到 ResNet-50 网络、或 Keras 中其他类似的预训练模型之前,还需要进行一些额外的处理:\n", + "1. 首先,这些图像的通道顺序为 RGB,我们需要重排他们的通道顺序为 BGR。\n", + "2. 其次,预训练模型的输入都进行了额外的归一化过程。因此我们在这里也要对这些张量进行归一化,即对所有图像所有像素都减去像素均值 `[103.939, 116.779, 123.68]`(以 RGB 模式表示,根据所有的 ImageNet 图像算出)。\n", + "\n", + "导入的 `preprocess_input` 函数实现了这些功能。如果你对此很感兴趣,可以在 [这里](https://github.com/fchollet/keras/blob/master/keras/applications/imagenet_utils.py) 查看 `preprocess_input`的代码。\n", + "\n", + "\n", + "在实现了图像处理的部分之后,我们就可以使用模型来进行预测。这一步通过 `predict` 方法来实现,它返回一个向量,向量的第 i 个元素表示该图像属于第 i 个 ImageNet 类别的概率。这通过如下的 `ResNet50_predict_labels` 函数实现。\n", + "\n", + "通过对预测出的向量取用 argmax 函数(找到有最大概率值的下标序号),我们可以得到一个整数,即模型预测到的物体的类别。进而根据这个 [清单](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a),我们能够知道这具体是哪个品种的狗狗。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.applications.resnet50 import preprocess_input, decode_predictions\n", + "def ResNet50_predict_labels(img_path):\n", + " # 返回img_path路径的图像的预测向量\n", + " img = preprocess_input(path_to_tensor(img_path))\n", + " return np.argmax(ResNet50_model.predict(img))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 完成狗检测模型\n", + "\n", + "\n", + "在研究该 [清单](https://gist.github.com/yrevar/942d3a0ac09ec9e5eb3a) 的时候,你会注意到,狗类别对应的序号为151-268。因此,在检查预训练模型判断图像是否包含狗的时候,我们只需要检查如上的 `ResNet50_predict_labels` 函数是否返回一个介于151和268之间(包含区间端点)的值。\n", + "\n", + "我们通过这些想法来完成下方的 `dog_detector` 函数,如果从图像中检测到狗就返回 `True`,否则返回 `False`。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def dog_detector(img_path):\n", + " prediction = ResNet50_predict_labels(img_path)\n", + " return ((prediction <= 268) & (prediction >= 151)) " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 【作业】评估狗狗检测模型\n", + "\n", + "---\n", + "\n", + "<a id='question3'></a>\n", + "### __问题 3:__ \n", + "\n", + "在下方的代码块中,使用 `dog_detector` 函数,计算:\n", + "\n", + "- `human_files_short`中图像检测到狗狗的百分比?\n", + "- `dog_files_short`中图像检测到狗狗的百分比?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: 测试dog_detector函数在human_files_short和dog_files_short的表现" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "<a id='step3'></a>\n", + "\n", + "## 步骤 3: 从头开始创建一个CNN来分类狗品种\n", + "\n", + "\n", + "现在我们已经实现了一个函数,能够在图像中识别人类及狗狗。但我们需要更进一步的方法,来对狗的类别进行识别。在这一步中,你需要实现一个卷积神经网络来对狗的品种进行分类。你需要__从头实现__你的卷积神经网络(在这一阶段,你还不能使用迁移学习),并且你需要达到超过1%的测试集准确率。在本项目的步骤五种,你还有机会使用迁移学习来实现一个准确率大大提高的模型。\n", + "\n", + "在添加卷积层的时候,注意不要加上太多的(可训练的)层。更多的参数意味着更长的训练时间,也就是说你更可能需要一个 GPU 来加速训练过程。万幸的是,Keras 提供了能够轻松预测每次迭代(epoch)花费时间所需的函数。你可以据此推断你算法所需的训练时间。\n", + "\n", + "值得注意的是,对狗的图像进行分类是一项极具挑战性的任务。因为即便是一个正常人,也很难区分布列塔尼犬和威尔士史宾格犬。\n", + "\n", + "\n", + "布列塔尼犬(Brittany) | 威尔士史宾格犬(Welsh Springer Spaniel)\n", + "- | - \n", + "<img src=\"images/Brittany_02625.jpg\" width=\"100\"> | <img src=\"images/Welsh_springer_spaniel_08203.jpg\" width=\"200\">\n", + "\n", + "不难发现其他的狗品种会有很小的类间差别(比如金毛寻回犬和美国水猎犬)。\n", + "\n", + "\n", + "金毛寻回犬(Curly-Coated Retriever) | 美国水猎犬(American Water Spaniel)\n", + "- | -\n", + "<img src=\"images/Curly-coated_retriever_03896.jpg\" width=\"200\"> | <img src=\"images/American_water_spaniel_00648.jpg\" width=\"200\">\n", + "\n", + "同样,拉布拉多犬(labradors)有黄色、棕色和黑色这三种。那么你设计的基于视觉的算法将不得不克服这种较高的类间差别,以达到能够将这些不同颜色的同类狗分到同一个品种中。\n", + "\n", + "黄色拉布拉多犬(Yellow Labrador) | 棕色拉布拉多犬(Chocolate Labrador) | 黑色拉布拉多犬(Black Labrador)\n", + "- | -\n", + "<img src=\"images/Labrador_retriever_06457.jpg\" width=\"150\"> | <img src=\"images/Labrador_retriever_06455.jpg\" width=\"240\"> | <img src=\"images/Labrador_retriever_06449.jpg\" width=\"220\">\n", + "\n", + "我们也提到了随机分类将得到一个非常低的结果:不考虑品种略有失衡的影响,随机猜测到正确品种的概率是1/133,相对应的准确率是低于1%的。\n", + "\n", + "请记住,在深度学习领域,实践远远高于理论。大量尝试不同的框架吧,相信你的直觉!当然,玩得开心!\n", + "\n", + "\n", + "### 数据预处理\n", + "\n", + "\n", + "通过对每张图像的像素值除以255,我们对图像实现了归一化处理。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from PIL import ImageFile \n", + "ImageFile.LOAD_TRUNCATED_IMAGES = True \n", + "\n", + "# Keras中的数据预处理过程\n", + "train_tensors = paths_to_tensor(train_files).astype('float32')/255\n", + "valid_tensors = paths_to_tensor(valid_files).astype('float32')/255\n", + "test_tensors = paths_to_tensor(test_files).astype('float32')/255" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 【练习】模型架构\n", + "\n", + "\n", + "创建一个卷积神经网络来对狗品种进行分类。在你代码块的最后,执行 `model.summary()` 来输出你模型的总结信息。\n", + " \n", + "我们已经帮你导入了一些所需的 Python 库,如有需要你可以自行导入。如果你在过程中遇到了困难,如下是给你的一点小提示——该模型能够在5个 epoch 内取得超过1%的测试准确率,并且能在CPU上很快地训练。\n", + "\n", + "![Sample CNN](images/sample_cnn.png)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "<a id='question4'></a> \n", + "\n", + "### __问题 4:__ \n", + "\n", + "在下方的代码块中尝试使用 Keras 搭建卷积网络的架构,并回答相关的问题。\n", + "\n", + "1. 你可以尝试自己搭建一个卷积网络的模型,那么你需要回答你搭建卷积网络的具体步骤(用了哪些层)以及为什么这样搭建。\n", + "2. 你也可以根据上图提示的步骤搭建卷积网络,那么请说明为何如上的架构能够在该问题上取得很好的表现。\n", + "\n", + "__回答:__ " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D\n", + "from keras.layers import Dropout, Flatten, Dense\n", + "from keras.models import Sequential\n", + "\n", + "model = Sequential()\n", + "\n", + "### TODO: 定义你的网络架构\n", + " \n", + "model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "## 编译模型\n", + "model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy'])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 【练习】训练模型\n", + "\n", + "\n", + "---\n", + "\n", + "<a id='question5'></a> \n", + "\n", + "### __问题 5:__ \n", + "\n", + "在下方代码单元训练模型。使用模型检查点(model checkpointing)来储存具有最低验证集 loss 的模型。\n", + "\n", + "可选题:你也可以对训练集进行 [数据增强](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html),来优化模型的表现。\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from keras.callbacks import ModelCheckpoint \n", + "\n", + "### TODO: 设置训练模型的epochs的数量\n", + "\n", + "epochs = None\n", + "\n", + "### 不要修改下方代码\n", + "\n", + "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.from_scratch.hdf5', \n", + " verbose=1, save_best_only=True)\n", + "\n", + "model.fit(train_tensors, train_targets, \n", + " validation_data=(valid_tensors, valid_targets),\n", + " epochs=epochs, batch_size=20, callbacks=[checkpointer], verbose=1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "## 加载具有最好验证loss的模型\n", + "\n", + "model.load_weights('saved_models/weights.best.from_scratch.hdf5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 测试模型\n", + "\n", + "在狗图像的测试数据集上试用你的模型。确保测试准确率大于1%。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 获取测试数据集中每一个图像所预测的狗品种的index\n", + "dog_breed_predictions = [np.argmax(model.predict(np.expand_dims(tensor, axis=0))) for tensor in test_tensors]\n", + "\n", + "# 报告测试准确率\n", + "test_accuracy = 100*np.sum(np.array(dog_breed_predictions)==np.argmax(test_targets, axis=1))/len(dog_breed_predictions)\n", + "print('Test accuracy: %.4f%%' % test_accuracy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "<a id='step4'></a>\n", + "## 步骤 4: 使用一个CNN来区分狗的品种\n", + "\n", + "\n", + "使用 迁移学习(Transfer Learning)的方法,能帮助我们在不损失准确率的情况下大大减少训练时间。在以下步骤中,你可以尝试使用迁移学习来训练你自己的CNN。\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 得到从图像中提取的特征向量(Bottleneck Features)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "bottleneck_features = np.load('/data/bottleneck_features/DogVGG16Data.npz')\n", + "train_VGG16 = bottleneck_features['train']\n", + "valid_VGG16 = bottleneck_features['valid']\n", + "test_VGG16 = bottleneck_features['test']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 模型架构\n", + "\n", + "该模型使用预训练的 VGG-16 模型作为固定的图像特征提取器,其中 VGG-16 最后一层卷积层的输出被直接输入到我们的模型。我们只需要添加一个全局平均池化层以及一个全连接层,其中全连接层使用 softmax 激活函数,对每一个狗的种类都包含一个节点。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "VGG16_model = Sequential()\n", + "VGG16_model.add(GlobalAveragePooling2D(input_shape=train_VGG16.shape[1:]))\n", + "VGG16_model.add(Dense(133, activation='softmax'))\n", + "\n", + "VGG16_model.summary()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "## 编译模型\n", + "\n", + "VGG16_model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "## 训练模型\n", + "\n", + "checkpointer = ModelCheckpoint(filepath='saved_models/weights.best.VGG16.hdf5', \n", + " verbose=1, save_best_only=True)\n", + "\n", + "VGG16_model.fit(train_VGG16, train_targets, \n", + " validation_data=(valid_VGG16, valid_targets),\n", + " epochs=20, batch_size=20, callbacks=[checkpointer], verbose=1)\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "## 加载具有最好验证loss的模型\n", + "\n", + "VGG16_model.load_weights('saved_models/weights.best.VGG16.hdf5')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 测试模型\n", + "现在,我们可以测试此CNN在狗图像测试数据集中识别品种的效果如何。我们在下方打印出测试准确率。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# 获取测试数据集中每一个图像所预测的狗品种的index\n", + "VGG16_predictions = [np.argmax(VGG16_model.predict(np.expand_dims(feature, axis=0))) for feature in test_VGG16]\n", + "\n", + "# 报告测试准确率\n", + "test_accuracy = 100*np.sum(np.array(VGG16_predictions)==np.argmax(test_targets, axis=1))/len(VGG16_predictions)\n", + "print('Test accuracy: %.4f%%' % test_accuracy)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 使用模型预测狗的品种" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from extract_bottleneck_features import *\n", + "\n", + "def VGG16_predict_breed(img_path):\n", + " # 提取bottleneck特征\n", + " bottleneck_feature = extract_VGG16(path_to_tensor(img_path))\n", + " # 获取预测向量\n", + " predicted_vector = VGG16_model.predict(bottleneck_feature)\n", + " # 返回此模型预测的狗的品种\n", + " return dog_names[np.argmax(predicted_vector)]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "<a id='step5'></a>\n", + "## 步骤 5: 建立一个CNN来分类狗的品种(使用迁移学习)\n", + "\n", + "现在你将使用迁移学习来建立一个CNN,从而可以从图像中识别狗的品种。你的 CNN 在测试集上的准确率必须至少达到60%。\n", + "\n", + "在步骤4中,我们使用了迁移学习来创建一个使用基于 VGG-16 提取的特征向量来搭建一个 CNN。在本部分内容中,你必须使用另一个预训练模型来搭建一个 CNN。为了让这个任务更易实现,我们已经预先对目前 keras 中可用的几种网络进行了预训练:\n", + "\n", + "- [VGG-19](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogVGG19Data.npz) bottleneck features\n", + "- [ResNet-50](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogResnet50Data.npz) bottleneck features\n", + "- [Inception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogInceptionV3Data.npz) bottleneck features\n", + "- [Xception](https://s3-us-west-1.amazonaws.com/udacity-aind/dog-project/DogXceptionData.npz) bottleneck features\n", + "\n", + "这些文件被命名为为:\n", + "\n", + " Dog{network}Data.npz\n", + "\n", + "其中 `{network}` 可以是 `VGG19`、`Resnet50`、`InceptionV3` 或 `Xception` 中的一个。选择上方网络架构中的一个,他们已经保存在目录 `/data/bottleneck_features/` 中。\n", + "\n", + "\n", + "### 【练习】获取模型的特征向量\n", + "\n", + "在下方代码块中,通过运行下方代码提取训练、测试与验证集相对应的bottleneck特征。\n", + "\n", + " bottleneck_features = np.load('/data/bottleneck_features/Dog{network}Data.npz')\n", + " train_{network} = bottleneck_features['train']\n", + " valid_{network} = bottleneck_features['valid']\n", + " test_{network} = bottleneck_features['test']" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: 从另一个预训练的CNN获取bottleneck特征" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### 【练习】模型架构\n", + "\n", + "建立一个CNN来分类狗品种。在你的代码单元块的最后,通过运行如下代码输出网络的结构:\n", + " \n", + " <your model's name>.summary()\n", + " \n", + "---\n", + "\n", + "<a id='question6'></a> \n", + "\n", + "### __问题 6:__ \n", + "\n", + "\n", + "在下方的代码块中尝试使用 Keras 搭建最终的网络架构,并回答你实现最终 CNN 架构的步骤与每一步的作用,并描述你在迁移学习过程中,使用该网络架构的原因。\n", + "\n", + "\n", + "__回答:__ \n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: 定义你的框架" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: 编译模型" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### 【练习】训练模型\n", + "\n", + "<a id='question7'></a> \n", + "\n", + "### __问题 7:__ \n", + "\n", + "在下方代码单元中训练你的模型。使用模型检查点(model checkpointing)来储存具有最低验证集 loss 的模型。\n", + "\n", + "当然,你也可以对训练集进行 [数据增强](https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html) 以优化模型的表现,不过这不是必须的步骤。\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: 训练模型" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: 加载具有最佳验证loss的模型权重" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### 【练习】测试模型\n", + "\n", + "<a id='question8'></a> \n", + "\n", + "### __问题 8:__ \n", + "\n", + "在狗图像的测试数据集上试用你的模型。确保测试准确率大于60%。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: 在测试集上计算分类准确率" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "### 【练习】使用模型测试狗的品种\n", + "\n", + "\n", + "实现一个函数,它的输入为图像路径,功能为预测对应图像的类别,输出为你模型预测出的狗类别(`Affenpinscher`, `Afghan_hound` 等)。\n", + "\n", + "与步骤5中的模拟函数类似,你的函数应当包含如下三个步骤:\n", + "\n", + "1. 根据选定的模型载入图像特征(bottleneck features)\n", + "2. 将图像特征输输入到你的模型中,并返回预测向量。注意,在该向量上使用 argmax 函数可以返回狗种类的序号。\n", + "3. 使用在步骤0中定义的 `dog_names` 数组来返回对应的狗种类名称。\n", + "\n", + "提取图像特征过程中使用到的函数可以在 `extract_bottleneck_features.py` 中找到。同时,他们应已在之前的代码块中被导入。根据你选定的 CNN 网络,你可以使用 `extract_{network}` 函数来获得对应的图像特征,其中 `{network}` 代表 `VGG19`, `Resnet50`, `InceptionV3`, 或 `Xception` 中的一个。\n", + " \n", + "---\n", + "\n", + "<a id='question9'></a> \n", + "\n", + "### __问题 9:__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: 写一个函数,该函数将图像的路径作为输入\n", + "### 然后返回此模型所预测的狗的品种" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "\n", + "<a id='step6'></a>\n", + "## 步骤 6: 完成你的算法\n", + "\n", + "\n", + "\n", + "实现一个算法,它的输入为图像的路径,它能够区分图像是否包含一个人、狗或两者都不包含,然后:\n", + "\n", + "- 如果从图像中检测到一只__狗__,返回被预测的品种。\n", + "- 如果从图像中检测到__人__,返回最相像的狗品种。\n", + "- 如果两者都不能在图像中检测到,输出错误提示。\n", + "\n", + "我们非常欢迎你来自己编写检测图像中人类与狗的函数,你可以随意地使用上方完成的 `face_detector` 和 `dog_detector` 函数。你__需要__在步骤5使用你的CNN来预测狗品种。\n", + "\n", + "下面提供了算法的示例输出,但你可以自由地设计自己的模型!\n", + "\n", + "![Sample Human Output](images/sample_human_output.png)\n", + "\n", + "\n", + "\n", + "\n", + "<a id='question10'></a> \n", + "\n", + "### __问题 10:__\n", + "\n", + "在下方代码块中完成你的代码。\n", + "\n", + "---\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "### TODO: 设计你的算法\n", + "### 自由地使用所需的代码单元数吧" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "---\n", + "<a id='step7'></a>\n", + "## 步骤 7: 测试你的算法\n", + "\n", + "在这个部分中,你将尝试一下你的新算法!算法认为__你__看起来像什么类型的狗?如果你有一只狗,它可以准确地预测你的狗的品种吗?如果你有一只猫,它会将你的猫误判为一只狗吗?\n", + "\n", + "**上传方式:点击左上角的Jupyter回到上级菜单,你可以看到Jupyter Notebook的右上方会有Upload按钮。**\n", + "\n", + "<a id='question11'></a> \n", + "\n", + "### __问题 11:__\n", + "\n", + "在下方编写代码,用至少6张现实中的图片来测试你的算法。你可以使用任意照片,不过请至少使用两张人类图片(要征得当事人同意哦)和两张狗的图片。\n", + "同时请回答如下问题:\n", + "\n", + "1. 输出结果比你预想的要好吗 :) ?或者更糟 :( ?\n", + "2. 提出至少三点改进你的模型的想法。" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "## TODO: 在你的电脑上,在步骤6中,至少在6张图片上运行你的算法。\n", + "## 自由地使用所需的代码单元数吧" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "**注意: 当你写完了所有的代码,并且回答了所有的问题。你就可以把你的 iPython Notebook 导出成 HTML 文件。你可以在菜单栏,这样导出File -> Download as -> HTML (.html)把这个 HTML 和这个 iPython notebook 一起做为你的作业提交。**" + ] + } + ], + "metadata": { + "anaconda-cloud": {}, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.3" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/extract_bottleneck_features.py b/extract_bottleneck_features.py new file mode 100644 index 0000000..000f7db --- /dev/null +++ b/extract_bottleneck_features.py @@ -0,0 +1,19 @@ +def extract_VGG16(tensor): + from keras.applications.vgg16 import VGG16, preprocess_input + return VGG16(weights='imagenet', include_top=False).predict(preprocess_input(tensor)) + +def extract_VGG19(tensor): + from keras.applications.vgg19 import VGG19, preprocess_input + return VGG19(weights='imagenet', include_top=False).predict(preprocess_input(tensor)) + +def extract_Resnet50(tensor): + from keras.applications.resnet50 import ResNet50, preprocess_input + return ResNet50(weights='imagenet', include_top=False).predict(preprocess_input(tensor)) + +def extract_Xception(tensor): + from keras.applications.xception import Xception, preprocess_input + return Xception(weights='imagenet', include_top=False).predict(preprocess_input(tensor)) + +def extract_InceptionV3(tensor): + from keras.applications.inception_v3 import InceptionV3, preprocess_input + return InceptionV3(weights='imagenet', include_top=False).predict(preprocess_input(tensor)) \ No newline at end of file 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b/haarcascades/haarcascade_frontalface_alt2.xml new file mode 100644 index 0000000..b49cf5d --- /dev/null +++ b/haarcascades/haarcascade_frontalface_alt2.xml @@ -0,0 +1,20719 @@ +<?xml version="1.0"?> +<!-- + Tree-based 20x20 gentle adaboost frontal face detector. + Created by Rainer Lienhart. + +//////////////////////////////////////////////////////////////////////////////////////// + + IMPORTANT: READ BEFORE DOWNLOADING, COPYING, INSTALLING OR USING. + + By downloading, copying, installing or using the software you agree to this license. + If you do not agree to this license, do not download, install, + copy or use the software. + + + Intel License Agreement + For Open Source Computer Vision Library + + Copyright (C) 2000, Intel Corporation, all rights reserved. + Third party copyrights are property of their respective owners. + + Redistribution and use in source and binary forms, with or without modification, + are permitted provided that the following conditions are met: + + * Redistribution's of source code must retain the above copyright notice, + this list of conditions and the following disclaimer. + + * Redistribution's in binary form must reproduce the above copyright notice, + this list of conditions and the following disclaimer in the documentation + and/or other materials provided with the distribution. + + * The name of Intel Corporation may not be used to endorse or promote products + derived from this software without specific prior written permission. + + This software is provided by the copyright holders and contributors "as is" and + any express or implied warranties, including, but not limited to, the implied + warranties of merchantability and fitness for a particular purpose are disclaimed. + In no event shall the Intel Corporation or contributors be liable for any direct, + indirect, incidental, special, exemplary, or consequential damages + (including, but not limited to, procurement of substitute goods or services; + loss of use, data, or profits; or business interruption) however caused + and on any theory of liability, whether in contract, strict liability, + or tort (including negligence or otherwise) arising in any way out of + the use of this software, even if advised of the possibility of such damage. +--> +<opencv_storage> +<cascade type_id="opencv-cascade-classifier"><stageType>BOOST</stageType> + <featureType>HAAR</featureType> + <height>20</height> + <width>20</width> + <stageParams> + <maxWeakCount>109</maxWeakCount></stageParams> + <featureParams> + <maxCatCount>0</maxCatCount></featureParams> + <stageNum>20</stageNum> + <stages> + <_> + <maxWeakCount>3</maxWeakCount> + <stageThreshold>3.5069230198860168e-01</stageThreshold> + <weakClassifiers> + <_> + <internalNodes> + 0 1 0 4.3272329494357109e-03 -1 -2 1 1.3076160103082657e-02</internalNodes> + <leafValues> + 3.8381900638341904e-02 8.9652568101882935e-01 + 2.6293140649795532e-01</leafValues></_> + <_> + <internalNodes> + 0 1 2 5.2434601821005344e-04 -1 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tk=8.5.18=0 +- wheel=0.29.0=py36_0 +- xz=5.2.2=1 +- zlib=1.2.8=3 +- pip: + - bleach==2.0.0 + - cycler==0.10.0 + - decorator==4.0.11 + - entrypoints==0.2.3 + - h5py==2.6.0 + - html5lib==0.999999999 + - ipykernel==4.6.1 + - ipython==6.1.0 + - ipython-genutils==0.2.0 + - ipywidgets==6.0.0 + - jedi==0.10.2 + - jinja2==2.9.6 + - jsonschema==2.6.0 + - jupyter==1.0.0 + - jupyter-client==5.0.1 + - jupyter-console==5.1.0 + - jupyter-core==4.3.0 + - keras==2.0.2 + - markupsafe==1.0 + - matplotlib==2.0.0 + - mistune==0.7.4 + - nbconvert==5.2.1 + - nbformat==4.3.0 + - notebook==5.0.0 + - numpy==1.12.0 + - olefile==0.44 + - opencv-python==3.2.0.6 + - pandocfilters==1.4.1 + - pexpect==4.2.1 + - pickleshare==0.7.4 + - pillow==4.0.0 + - prompt-toolkit==1.0.14 + - protobuf==3.3.0 + - ptyprocess==0.5.1 + - pygments==2.2.0 + - pyparsing==2.2.0 + - python-dateutil==2.6.0 + - pytz==2017.2 + - pyyaml==3.12 + - pyzmq==16.0.2 + - qtconsole==4.3.0 + - scikit-learn==0.18.1 + - scipy==0.18.1 + - simplegeneric==0.8.1 + - six==1.10.0 + - tensorflow-gpu==1.0.0 + - terminado==0.6 + - testpath==0.3.1 + - theano==0.9.0 + - tornado==4.5.1 + - tqdm==4.11.2 + - traitlets==4.3.2 + - wcwidth==0.1.7 + - webencodings==0.5.1 + - widgetsnbextension==2.0.0 diff --git a/requirements/dog-linux.yml b/requirements/dog-linux.yml new file mode 100644 index 0000000..65f714a --- /dev/null +++ b/requirements/dog-linux.yml @@ -0,0 +1,70 @@ +name: dog-project +channels: +- defaults +dependencies: +- openssl=1.0.2l=0 +- pip=9.0.1=py36_1 +- python=3.6.1=2 +- readline=6.2=2 +- setuptools=27.2.0=py36_0 +- sqlite=3.13.0=0 +- tk=8.5.18=0 +- wheel=0.29.0=py36_0 +- xz=5.2.2=1 +- zlib=1.2.8=3 +- pip: + - bleach==2.0.0 + - cycler==0.10.0 + - decorator==4.0.11 + - entrypoints==0.2.3 + - h5py==2.6.0 + - html5lib==0.999999999 + - ipykernel==4.6.1 + - ipython==6.1.0 + - ipython-genutils==0.2.0 + - ipywidgets==6.0.0 + - jedi==0.10.2 + - jinja2==2.9.6 + - jsonschema==2.6.0 + - jupyter==1.0.0 + - jupyter-client==5.0.1 + - jupyter-console==5.1.0 + - jupyter-core==4.3.0 + - keras==2.0.2 + - markupsafe==1.0 + - matplotlib==2.0.0 + - mistune==0.7.4 + - nbconvert==5.2.1 + - nbformat==4.3.0 + - notebook==5.0.0 + - numpy==1.12.0 + - olefile==0.44 + - opencv-python==3.2.0.6 + - pandocfilters==1.4.1 + - pexpect==4.2.1 + - pickleshare==0.7.4 + - pillow==4.0.0 + - prompt-toolkit==1.0.14 + - protobuf==3.3.0 + - ptyprocess==0.5.1 + - pygments==2.2.0 + - pyparsing==2.2.0 + - python-dateutil==2.6.0 + - pytz==2017.2 + - pyyaml==3.12 + - pyzmq==16.0.2 + - qtconsole==4.3.0 + - scikit-learn==0.18.1 + - scipy==0.18.1 + - simplegeneric==0.8.1 + - six==1.10.0 + - tensorflow==1.0.0 + - terminado==0.6 + - testpath==0.3.1 + - theano==0.9.0 + - tornado==4.5.1 + - tqdm==4.11.2 + - traitlets==4.3.2 + - wcwidth==0.1.7 + - webencodings==0.5.1 + - widgetsnbextension==2.0.0 diff --git a/requirements/dog-mac-gpu.yml b/requirements/dog-mac-gpu.yml new file mode 100644 index 0000000..5c3aa6c --- /dev/null +++ b/requirements/dog-mac-gpu.yml @@ -0,0 +1,204 @@ +name: dog-project +channels: +- damianavila82 +- defaults +dependencies: +- rise=4.0.0b1=py35_0 +- _license=1.1=py35_1 +- alabaster=0.7.10=py35_0 +- anaconda-client=1.6.2=py35_0 +- anaconda=custom=py35_0 +- anaconda-navigator=1.5.0=py35_0 +- anaconda-project=0.4.1=py35_0 +- appnope=0.1.0=py35_0 +- appscript=1.0.1=py35_0 +- astroid=1.4.9=py35_0 +- astropy=1.3=np112py35_0 +- babel=2.3.4=py35_0 +- backports=1.0=py35_0 +- beautifulsoup4=4.5.3=py35_0 +- bitarray=0.8.1=py35_0 +- blaze=0.10.1=py35_0 +- bleach=1.5.0=py35_0 +- bokeh=0.12.4=py35_0 +- boto=2.46.1=py35_0 +- bottleneck=1.2.0=np112py35_0 +- cffi=1.9.1=py35_0 +- chardet=2.3.0=py35_0 +- chest=0.2.3=py35_0 +- click=6.7=py35_0 +- cloudpickle=0.2.2=py35_0 +- clyent=1.2.2=py35_0 +- colorama=0.3.7=py35_0 +- configobj=5.0.6=py35_0 +- contextlib2=0.5.4=py35_0 +- cryptography=1.7.1=py35_0 +- curl=7.52.1=0 +- cycler=0.10.0=py35_0 +- cython=0.25.2=py35_0 +- cytoolz=0.8.2=py35_0 +- dask=0.14.0=py35_0 +- datashape=0.5.4=py35_0 +- decorator=4.0.11=py35_0 +- dill=0.2.5=py35_0 +- docutils=0.13.1=py35_0 +- entrypoints=0.2.2=py35_1 +- et_xmlfile=1.0.1=py35_0 +- fastcache=1.0.2=py35_1 +- flask=0.12=py35_0 +- flask-cors=3.0.2=py35_0 +- freetype=2.5.5=2 +- get_terminal_size=1.0.0=py35_0 +- gevent=1.2.1=py35_0 +- greenlet=0.4.12=py35_0 +- h5py=2.6.0=np112py35_2 +- hdf5=1.8.17=1 +- heapdict=1.0.0=py35_1 +- html5lib=0.999=py35_0 +- icu=54.1=0 +- idna=2.2=py35_0 +- imagesize=0.7.1=py35_0 +- ipykernel=4.5.2=py35_0 +- ipython=5.3.0=py35_0 +- ipython_genutils=0.1.0=py35_0 +- ipywidgets=6.0.0=py35_0 +- isort=4.2.5=py35_0 +- itsdangerous=0.24=py35_0 +- jbig=2.1=0 +- jdcal=1.3=py35_0 +- jedi=0.9.0=py35_1 +- jinja2=2.9.5=py35_0 +- jpeg=9b=0 +- jsonschema=2.5.1=py35_0 +- jupyter=1.0.0=py35_3 +- jupyter_client=5.0.0=py35_0 +- jupyter_console=5.1.0=py35_0 +- jupyter_core=4.3.0=py35_0 +- lazy-object-proxy=1.2.2=py35_0 +- libiconv=1.14=0 +- libpng=1.6.27=0 +- libtiff=4.0.6=3 +- libxml2=2.9.4=0 +- libxslt=1.1.29=0 +- llvmlite=0.16.0=py35_0 +- locket=0.2.0=py35_1 +- lxml=3.7.3=py35_0 +- markupsafe=0.23=py35_2 +- matplotlib=2.0.0=np112py35_0 +- mistune=0.7.4=py35_0 +- mkl=2017.0.1=0 +- mkl-service=1.1.2=py35_3 +- mpmath=0.19=py35_1 +- multipledispatch=0.4.9=py35_0 +- nbconvert=5.1.1=py35_0 +- nbformat=4.3.0=py35_0 +- networkx=1.11=py35_0 +- nltk=3.2.2=py35_0 +- nose=1.3.7=py35_1 +- notebook=4.4.1=py35_0 +- numba=0.31.0=np112py35_0 +- numexpr=2.6.2=np112py35_0 +- numpy=1.12.0=py35_0 +- numpydoc=0.6.0=py35_0 +- odo=0.5.0=py35_1 +- olefile=0.44=py35_0 +- openpyxl=2.4.1=py35_0 +- openssl=1.0.2k=0 +- pandas=0.19.2=np112py35_1 +- pandocfilters=1.4.1=py35_0 +- partd=0.3.7=py35_0 +- path.py=10.1=py35_0 +- pathlib2=2.2.0=py35_0 +- patsy=0.4.1=py35_0 +- pep8=1.7.0=py35_0 +- pexpect=4.2.1=py35_0 +- pickleshare=0.7.4=py35_0 +- pillow=4.0.0=py35_1 +- pip=9.0.1=py35_1 +- ply=3.10=py35_0 +- prompt_toolkit=1.0.13=py35_0 +- psutil=5.2.0=py35_0 +- ptyprocess=0.5.1=py35_0 +- py=1.4.32=py35_0 +- pyasn1=0.2.3=py35_0 +- pycosat=0.6.1=py35_1 +- pycparser=2.17=py35_0 +- pycrypto=2.6.1=py35_4 +- pycurl=7.43.0=py35_2 +- pyflakes=1.5.0=py35_0 +- pygments=2.2.0=py35_0 +- pylint=1.6.4=py35_1 +- pyopenssl=16.2.0=py35_0 +- pyparsing=2.1.4=py35_0 +- pyqt=5.6.0=py35_2 +- pytables=3.3.0=np112py35_0 +- pytest=3.0.6=py35_0 +- python=3.5.3=1 +- python-dateutil=2.6.0=py35_0 +- python.app=1.2=py35_4 +- pytz=2016.10=py35_0 +- pyyaml=3.12=py35_0 +- pyzmq=16.0.2=py35_0 +- qt=5.6.2=0 +- qtawesome=0.4.4=py35_0 +- qtconsole=4.2.1=py35_1 +- qtpy=1.2.1=py35_0 +- readline=6.2=2 +- redis=3.2.0=0 +- redis-py=2.10.5=py35_0 +- requests=2.13.0=py35_0 +- rope=0.9.4=py35_1 +- ruamel_yaml=0.11.14=py35_1 +- scikit-image=0.12.3=np112py35_1 +- scikit-learn=0.18.1=np112py35_1 +- scipy=0.19.0=np112py35_0 +- seaborn=0.7.1=py35_0 +- setuptools=27.2.0=py35_0 +- simplegeneric=0.8.1=py35_1 +- singledispatch=3.4.0.3=py35_0 +- sip=4.18=py35_0 +- six=1.10.0=py35_0 +- snowballstemmer=1.2.1=py35_0 +- sockjs-tornado=1.0.3=py35_0 +- sphinx=1.5.1=py35_0 +- spyder=3.1.3=py35_0 +- sqlalchemy=1.1.6=py35_0 +- sqlite=3.13.0=0 +- statsmodels=0.8.0=np112py35_0 +- sympy=1.0=py35_0 +- terminado=0.6=py35_0 +- testpath=0.3=py35_0 +- tk=8.5.18=0 +- toolz=0.8.2=py35_0 +- tornado=4.4.2=py35_0 +- traitlets=4.3.2=py35_0 +- unicodecsv=0.14.1=py35_0 +- wcwidth=0.1.7=py35_0 +- werkzeug=0.12=py35_0 +- wheel=0.29.0=py35_0 +- widgetsnbextension=2.0.0=py35_0 +- wrapt=1.10.8=py35_0 +- xlrd=1.0.0=py35_0 +- xlsxwriter=0.9.6=py35_0 +- xlwings=0.10.2=py35_0 +- xlwt=1.2.0=py35_0 +- xz=5.2.2=1 +- yaml=0.1.6=0 +- zlib=1.2.8=3 +- pip: + - backports.shutil-get-terminal-size==1.0.0 + - cvxopt==1.1.9 + - et-xmlfile==1.0.1 + - ipython-genutils==0.1.0 + - jupyter-client==5.0.0 + - jupyter-console==5.1.0 + - jupyter-core==4.3.0 + - keras==2.0.0 + - opencv-python==3.2.0.6 + - prompt-toolkit==1.0.13 + - protobuf==3.2.0 + - rope-py3k==0.9.4.post1 + - tables==3.3.0 + - tensorflow-gpu==1.0.0 + - theano==0.8.2 + - tqdm==4.11.2 \ No newline at end of file diff --git a/requirements/dog-mac.yml b/requirements/dog-mac.yml new file mode 100644 index 0000000..b89af32 --- /dev/null +++ b/requirements/dog-mac.yml @@ -0,0 +1,204 @@ +name: dog-project +channels: +- damianavila82 +- defaults +dependencies: +- rise=4.0.0b1=py35_0 +- _license=1.1=py35_1 +- alabaster=0.7.10=py35_0 +- anaconda-client=1.6.2=py35_0 +- anaconda=custom=py35_0 +- anaconda-navigator=1.5.0=py35_0 +- anaconda-project=0.4.1=py35_0 +- appnope=0.1.0=py35_0 +- appscript=1.0.1=py35_0 +- astroid=1.4.9=py35_0 +- astropy=1.3=np112py35_0 +- babel=2.3.4=py35_0 +- backports=1.0=py35_0 +- beautifulsoup4=4.5.3=py35_0 +- bitarray=0.8.1=py35_0 +- blaze=0.10.1=py35_0 +- bleach=1.5.0=py35_0 +- bokeh=0.12.4=py35_0 +- boto=2.46.1=py35_0 +- bottleneck=1.2.0=np112py35_0 +- cffi=1.9.1=py35_0 +- chardet=2.3.0=py35_0 +- chest=0.2.3=py35_0 +- click=6.7=py35_0 +- cloudpickle=0.2.2=py35_0 +- clyent=1.2.2=py35_0 +- colorama=0.3.7=py35_0 +- configobj=5.0.6=py35_0 +- contextlib2=0.5.4=py35_0 +- cryptography=1.7.1=py35_0 +- curl=7.52.1=0 +- cycler=0.10.0=py35_0 +- cython=0.25.2=py35_0 +- cytoolz=0.8.2=py35_0 +- dask=0.14.0=py35_0 +- datashape=0.5.4=py35_0 +- decorator=4.0.11=py35_0 +- dill=0.2.5=py35_0 +- docutils=0.13.1=py35_0 +- entrypoints=0.2.2=py35_1 +- et_xmlfile=1.0.1=py35_0 +- fastcache=1.0.2=py35_1 +- flask=0.12=py35_0 +- flask-cors=3.0.2=py35_0 +- freetype=2.5.5=2 +- get_terminal_size=1.0.0=py35_0 +- gevent=1.2.1=py35_0 +- greenlet=0.4.12=py35_0 +- h5py=2.6.0=np112py35_2 +- hdf5=1.8.17=1 +- heapdict=1.0.0=py35_1 +- html5lib=0.999=py35_0 +- icu=54.1=0 +- idna=2.2=py35_0 +- imagesize=0.7.1=py35_0 +- ipykernel=4.5.2=py35_0 +- ipython=5.3.0=py35_0 +- ipython_genutils=0.1.0=py35_0 +- ipywidgets=6.0.0=py35_0 +- isort=4.2.5=py35_0 +- itsdangerous=0.24=py35_0 +- jbig=2.1=0 +- jdcal=1.3=py35_0 +- jedi=0.9.0=py35_1 +- jinja2=2.9.5=py35_0 +- jpeg=9b=0 +- jsonschema=2.5.1=py35_0 +- jupyter=1.0.0=py35_3 +- jupyter_client=5.0.0=py35_0 +- jupyter_console=5.1.0=py35_0 +- jupyter_core=4.3.0=py35_0 +- lazy-object-proxy=1.2.2=py35_0 +- libiconv=1.14=0 +- libpng=1.6.27=0 +- libtiff=4.0.6=3 +- libxml2=2.9.4=0 +- libxslt=1.1.29=0 +- llvmlite=0.16.0=py35_0 +- locket=0.2.0=py35_1 +- lxml=3.7.3=py35_0 +- markupsafe=0.23=py35_2 +- matplotlib=2.0.0=np112py35_0 +- mistune=0.7.4=py35_0 +- mkl=2017.0.1=0 +- mkl-service=1.1.2=py35_3 +- mpmath=0.19=py35_1 +- multipledispatch=0.4.9=py35_0 +- nbconvert=5.1.1=py35_0 +- nbformat=4.3.0=py35_0 +- networkx=1.11=py35_0 +- nltk=3.2.2=py35_0 +- nose=1.3.7=py35_1 +- notebook=4.4.1=py35_0 +- numba=0.31.0=np112py35_0 +- numexpr=2.6.2=np112py35_0 +- numpy=1.12.0=py35_0 +- numpydoc=0.6.0=py35_0 +- odo=0.5.0=py35_1 +- olefile=0.44=py35_0 +- openpyxl=2.4.1=py35_0 +- openssl=1.0.2k=0 +- pandas=0.19.2=np112py35_1 +- pandocfilters=1.4.1=py35_0 +- partd=0.3.7=py35_0 +- path.py=10.1=py35_0 +- pathlib2=2.2.0=py35_0 +- patsy=0.4.1=py35_0 +- pep8=1.7.0=py35_0 +- pexpect=4.2.1=py35_0 +- pickleshare=0.7.4=py35_0 +- pillow=4.0.0=py35_1 +- pip=9.0.1=py35_1 +- ply=3.10=py35_0 +- prompt_toolkit=1.0.13=py35_0 +- psutil=5.2.0=py35_0 +- ptyprocess=0.5.1=py35_0 +- py=1.4.32=py35_0 +- pyasn1=0.2.3=py35_0 +- pycosat=0.6.1=py35_1 +- pycparser=2.17=py35_0 +- pycrypto=2.6.1=py35_4 +- pycurl=7.43.0=py35_2 +- pyflakes=1.5.0=py35_0 +- pygments=2.2.0=py35_0 +- pylint=1.6.4=py35_1 +- pyopenssl=16.2.0=py35_0 +- pyparsing=2.1.4=py35_0 +- pyqt=5.6.0=py35_2 +- pytables=3.3.0=np112py35_0 +- pytest=3.0.6=py35_0 +- python=3.5.3=1 +- python-dateutil=2.6.0=py35_0 +- python.app=1.2=py35_4 +- pytz=2016.10=py35_0 +- pyyaml=3.12=py35_0 +- pyzmq=16.0.2=py35_0 +- qt=5.6.2=0 +- qtawesome=0.4.4=py35_0 +- qtconsole=4.2.1=py35_1 +- qtpy=1.2.1=py35_0 +- readline=6.2=2 +- redis=3.2.0=0 +- redis-py=2.10.5=py35_0 +- requests=2.13.0=py35_0 +- rope=0.9.4=py35_1 +- ruamel_yaml=0.11.14=py35_1 +- scikit-image=0.12.3=np112py35_1 +- scikit-learn=0.18.1=np112py35_1 +- scipy=0.19.0=np112py35_0 +- seaborn=0.7.1=py35_0 +- setuptools=27.2.0=py35_0 +- simplegeneric=0.8.1=py35_1 +- singledispatch=3.4.0.3=py35_0 +- sip=4.18=py35_0 +- six=1.10.0=py35_0 +- snowballstemmer=1.2.1=py35_0 +- sockjs-tornado=1.0.3=py35_0 +- sphinx=1.5.1=py35_0 +- spyder=3.1.3=py35_0 +- sqlalchemy=1.1.6=py35_0 +- sqlite=3.13.0=0 +- statsmodels=0.8.0=np112py35_0 +- sympy=1.0=py35_0 +- terminado=0.6=py35_0 +- testpath=0.3=py35_0 +- tk=8.5.18=0 +- toolz=0.8.2=py35_0 +- tornado=4.4.2=py35_0 +- traitlets=4.3.2=py35_0 +- unicodecsv=0.14.1=py35_0 +- wcwidth=0.1.7=py35_0 +- werkzeug=0.12=py35_0 +- wheel=0.29.0=py35_0 +- widgetsnbextension=2.0.0=py35_0 +- wrapt=1.10.8=py35_0 +- xlrd=1.0.0=py35_0 +- xlsxwriter=0.9.6=py35_0 +- xlwings=0.10.2=py35_0 +- xlwt=1.2.0=py35_0 +- xz=5.2.2=1 +- yaml=0.1.6=0 +- zlib=1.2.8=3 +- pip: + - backports.shutil-get-terminal-size==1.0.0 + - cvxopt==1.1.9 + - et-xmlfile==1.0.1 + - ipython-genutils==0.1.0 + - jupyter-client==5.0.0 + - jupyter-console==5.1.0 + - jupyter-core==4.3.0 + - keras==2.0.0 + - opencv-python==3.2.0.6 + - prompt-toolkit==1.0.13 + - protobuf==3.2.0 + - rope-py3k==0.9.4.post1 + - tables==3.3.0 + - tensorflow==1.0.0 + - theano==0.8.2 + - tqdm==4.11.2 \ No newline at end of file diff --git a/requirements/dog-windows-gpu.yml b/requirements/dog-windows-gpu.yml new file mode 100644 index 0000000..da89f99 --- /dev/null +++ b/requirements/dog-windows-gpu.yml @@ -0,0 +1,96 @@ +name: dog-project +channels: +- defaults +dependencies: +- _nb_ext_conf=0.3.0=py35_0 +- anaconda-client=1.6.2=py35_0 +- bleach=1.5.0=py35_0 +- bzip2=1.0.6=vc14_3 +- clyent=1.2.2=py35_0 +- colorama=0.3.7=py35_0 +- cycler=0.10.0=py35_0 +- decorator=4.0.11=py35_0 +- entrypoints=0.2.2=py35_1 +- freetype=2.5.5=vc14_2 +- h5py=2.7.0=np112py35_0 +- hdf5=1.8.15.1=vc14_4 +- html5lib=0.999=py35_0 +- icu=57.1=vc14_0 +- ipykernel=4.5.2=py35_0 +- ipython=5.3.0=py35_0 +- ipython_genutils=0.1.0=py35_0 +- ipywidgets=6.0.0=py35_0 +- jinja2=2.9.5=py35_0 +- jpeg=9b=vc14_0 +- jsonschema=2.5.1=py35_0 +- jupyter=1.0.0=py35_3 +- jupyter_client=5.0.0=py35_0 +- jupyter_console=5.1.0=py35_0 +- jupyter_core=4.3.0=py35_0 +- libpng=1.6.27=vc14_0 +- libtiff=4.0.6=vc14_3 +- markupsafe=0.23=py35_2 +- matplotlib=2.0.0=np112py35_0 +- mistune=0.7.4=py35_0 +- mkl=2017.0.1=0 +- nb_anacondacloud=1.2.0=py35_0 +- nb_conda=2.0.0=py35_0 +- nb_conda_kernels=2.0.0=py35_0 +- nbconvert=5.1.1=py35_0 +- nbformat=4.3.0=py35_0 +- nbpresent=3.0.2=py35_0 +- notebook=4.4.1=py35_0 +- numpy=1.12.1=py35_0 +- olefile=0.44=py35_0 +- openssl=1.0.2k=vc14_0 +- pandocfilters=1.4.1=py35_0 +- path.py=10.1=py35_0 +- pickleshare=0.7.4=py35_0 +- pillow=4.0.0=py35_1 +- pip=9.0.1=py35_1 +- prompt_toolkit=1.0.13=py35_0 +- pygments=2.2.0=py35_0 +- pyparsing=2.1.4=py35_0 +- pyqt=5.6.0=py35_2 +- python=3.5.3=0 +- python-dateutil=2.6.0=py35_0 +- pytz=2016.10=py35_0 +- pyyaml=3.12=py35_0 +- pyzmq=16.0.2=py35_0 +- qt=5.6.2=vc14_3 +- qtconsole=4.2.1=py35_2 +- requests=2.13.0=py35_0 +- scikit-learn=0.18.1=np112py35_1 +- scipy=0.19.0=np112py35_0 +- setuptools=27.2.0=py35_1 +- simplegeneric=0.8.1=py35_1 +- sip=4.18=py35_0 +- six=1.10.0=py35_0 +- testpath=0.3=py35_0 +- tk=8.5.18=vc14_0 +- tornado=4.4.2=py35_0 +- traitlets=4.3.2=py35_0 +- vs2015_runtime=14.0.25123=0 +- wcwidth=0.1.7=py35_0 +- wheel=0.29.0=py35_0 +- widgetsnbextension=2.0.0=py35_0 +- win_unicode_console=0.5=py35_0 +- zlib=1.2.8=vc14_3 +- pip: + - ipython-genutils==0.1.0 + - jupyter-client==5.0.0 + - jupyter-console==5.1.0 + - jupyter-core==4.3.0 + - keras==2.0.2 + - nb-anacondacloud==1.2.0 + - nb-conda==2.0.0 + - nb-conda-kernels==2.0.0 + - opencv-python==3.1.0.0 + - prompt-toolkit==1.0.13 + - protobuf==3.2.0 + - tensorflow-gpu==1.0.1 + - theano==0.9.0 + - tqdm==4.11.2 + - win-unicode-console==0.5 + + diff --git a/requirements/dog-windows.yml b/requirements/dog-windows.yml new file mode 100644 index 0000000..6362793 --- /dev/null +++ b/requirements/dog-windows.yml @@ -0,0 +1,96 @@ +name: dog-project +channels: +- defaults +dependencies: +- _nb_ext_conf=0.3.0=py35_0 +- anaconda-client=1.6.2=py35_0 +- bleach=1.5.0=py35_0 +- bzip2=1.0.6=vc14_3 +- clyent=1.2.2=py35_0 +- colorama=0.3.7=py35_0 +- cycler=0.10.0=py35_0 +- decorator=4.0.11=py35_0 +- entrypoints=0.2.2=py35_1 +- freetype=2.5.5=vc14_2 +- h5py=2.7.0=np112py35_0 +- hdf5=1.8.15.1=vc14_4 +- html5lib=0.999=py35_0 +- icu=57.1=vc14_0 +- ipykernel=4.5.2=py35_0 +- ipython=5.3.0=py35_0 +- ipython_genutils=0.1.0=py35_0 +- ipywidgets=6.0.0=py35_0 +- jinja2=2.9.5=py35_0 +- jpeg=9b=vc14_0 +- jsonschema=2.5.1=py35_0 +- jupyter=1.0.0=py35_3 +- jupyter_client=5.0.0=py35_0 +- jupyter_console=5.1.0=py35_0 +- jupyter_core=4.3.0=py35_0 +- libpng=1.6.27=vc14_0 +- libtiff=4.0.6=vc14_3 +- markupsafe=0.23=py35_2 +- matplotlib=2.0.0=np112py35_0 +- mistune=0.7.4=py35_0 +- mkl=2017.0.1=0 +- nb_anacondacloud=1.2.0=py35_0 +- nb_conda=2.0.0=py35_0 +- nb_conda_kernels=2.0.0=py35_0 +- nbconvert=5.1.1=py35_0 +- nbformat=4.3.0=py35_0 +- nbpresent=3.0.2=py35_0 +- notebook=4.4.1=py35_0 +- numpy=1.12.1=py35_0 +- olefile=0.44=py35_0 +- openssl=1.0.2k=vc14_0 +- pandocfilters=1.4.1=py35_0 +- path.py=10.1=py35_0 +- pickleshare=0.7.4=py35_0 +- pillow=4.0.0=py35_1 +- pip=9.0.1=py35_1 +- prompt_toolkit=1.0.13=py35_0 +- pygments=2.2.0=py35_0 +- pyparsing=2.1.4=py35_0 +- pyqt=5.6.0=py35_2 +- python=3.5.3=0 +- python-dateutil=2.6.0=py35_0 +- pytz=2016.10=py35_0 +- pyyaml=3.12=py35_0 +- pyzmq=16.0.2=py35_0 +- qt=5.6.2=vc14_3 +- qtconsole=4.2.1=py35_2 +- requests=2.13.0=py35_0 +- scikit-learn=0.18.1=np112py35_1 +- scipy=0.19.0=np112py35_0 +- setuptools=27.2.0=py35_1 +- simplegeneric=0.8.1=py35_1 +- sip=4.18=py35_0 +- six=1.10.0=py35_0 +- testpath=0.3=py35_0 +- tk=8.5.18=vc14_0 +- tornado=4.4.2=py35_0 +- traitlets=4.3.2=py35_0 +- vs2015_runtime=14.0.25123=0 +- wcwidth=0.1.7=py35_0 +- wheel=0.29.0=py35_0 +- widgetsnbextension=2.0.0=py35_0 +- win_unicode_console=0.5=py35_0 +- zlib=1.2.8=vc14_3 +- pip: + - ipython-genutils==0.1.0 + - jupyter-client==5.0.0 + - jupyter-console==5.1.0 + - jupyter-core==4.3.0 + - keras==2.0.2 + - nb-anacondacloud==1.2.0 + - nb-conda==2.0.0 + - nb-conda-kernels==2.0.0 + - opencv-python==3.1.0.0 + - prompt-toolkit==1.0.13 + - protobuf==3.2.0 + - tensorflow==1.0.1 + - theano==0.9.0 + - tqdm==4.11.2 + - win-unicode-console==0.5 + + diff --git a/requirements/requirements-gpu.txt b/requirements/requirements-gpu.txt new file mode 100644 index 0000000..7771375 --- /dev/null +++ b/requirements/requirements-gpu.txt @@ -0,0 +1,11 @@ +opencv-python==3.2.0.6 +h5py==2.6.0 +matplotlib==2.0.0 +numpy==1.12.0 +scipy==0.18.1 +tqdm==4.11.2 +keras==2.0.2 +scikit-learn==0.18.1 +pillow==4.0.0 +ipykernel==4.6.1 +tensorflow-gpu==1.0.0 \ No newline at end of file diff --git a/requirements/requirements.txt b/requirements/requirements.txt new file mode 100644 index 0000000..628fb75 --- /dev/null +++ b/requirements/requirements.txt @@ -0,0 +1,11 @@ +opencv-python==3.2.0.6 +h5py==2.6.0 +matplotlib==2.0.0 +numpy==1.12.0 +scipy==0.18.1 +tqdm==4.11.2 +keras==2.0.2 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