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data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="feedback-dialog" aria-modal="true" aria-labelledby="feedback-dialog-title" aria-describedby="feedback-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow Overlay--size-medium Overlay--motion-scaleFade Overlay--disableScroll"> <div data-view-component="true" class="Overlay-header"> <div class="Overlay-headerContentWrap"> <div class="Overlay-titleWrap"> <h1 class="Overlay-title " id="feedback-dialog-title"> Provide feedback </h1> </div> <div class="Overlay-actionWrap"> <button data-close-dialog-id="feedback-dialog" aria-label="Close" type="button" data-view-component="true" class="close-button Overlay-closeButton"><svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 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<label for="include_email" style="font-weight: normal">Include my email address so I can be contacted</label> </form></div> </scrollable-region> <div data-view-component="true" class="Overlay-footer Overlay-footer--alignEnd"> <button data-close-dialog-id="feedback-dialog" type="button" data-view-component="true" class="btn"> Cancel </button> <button form="code-search-feedback-form" data-action="click:qbsearch-input#submitFeedback" type="submit" data-view-component="true" class="btn-primary btn"> Submit feedback </button> </div> </dialog></dialog-helper> <custom-scopes data-target="qbsearch-input.customScopesManager"> <dialog-helper> <dialog data-target="custom-scopes.customScopesModalDialog" data-action="close:qbsearch-input#handleDialogClose cancel:qbsearch-input#handleDialogClose" id="custom-scopes-dialog" aria-modal="true" aria-labelledby="custom-scopes-dialog-title" aria-describedby="custom-scopes-dialog-description" data-view-component="true" class="Overlay Overlay-whenNarrow 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src=\"https://camo.githubusercontent.com/28bfa76ac1791903dc5b7b56e704e616d896da0b8349625bb533aec64eeef359/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4170616368652d6c69676874677265792e7376673f7374796c653d666c6174\" alt=\"License Apache\" data-canonical-src=\"https://img.shields.io/badge/License-Apache-lightgrey.svg?style=flat\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"http://twitter.com/PerfectlySoft\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/b413f124f3860f984253302c319a722643c85cbba52debc83437fb89410cd47d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f547769747465722d40506572666563746c79536f66742d626c75652e7376673f7374796c653d666c6174\" alt=\"PerfectlySoft Twitter\" data-canonical-src=\"https://img.shields.io/badge/Twitter-@PerfectlySoft-blue.svg?style=flat\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n \u003ca href=\"http://perfect.ly\" rel=\"nofollow\"\u003e\n \u003cimg src=\"https://camo.githubusercontent.com/8f5052c851792fd8745d412731979c077b264f4aeca01860adb8d3d17bd8c358/687474703a2f2f706572666563742e6c792f62616467652e737667\" alt=\"Slack Status\" data-canonical-src=\"http://perfect.ly/badge.svg\" style=\"max-width: 100%;\"\u003e\n \u003c/a\u003e\n\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis project is an experimental wrapper of TensorFlow C API which enables Machine Learning in Server Side Swift.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eThis package builds with Swift Package Manager and is part of the \u003ca href=\"https://github.com/PerfectlySoft/Perfect\"\u003ePerfect\u003c/a\u003e project but can also be used as an independent module.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eEnsure you have installed and activated the latest Swift 4.1.1 / Xcode 9.3\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eProject Status\u003c/h2\u003e\u003ca id=\"user-content-project-status\" class=\"anchor\" aria-label=\"Permalink: Project Status\" href=\"#project-status\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThe framework conforms to TensorFlow v1.8.0 C API functionality.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eDevelopment Notes\u003c/h2\u003e\u003ca id=\"user-content-development-notes\" class=\"anchor\" aria-label=\"Permalink: Development Notes\" href=\"#development-notes\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThese files are the key part of Perfect-TensorFlow:\u003c/p\u003e\n\u003cdiv class=\"snippet-clipboard-content notranslate position-relative overflow-auto\" data-snippet-clipboard-copy-content=\"Sources\n├── PerfectTensorFlow\n│ ├── APILoader.swift (1000+ lines, translated from tensorflow/c/c_api.h)\n│ ├── PerfectTensorFlow.swift (2700+ lines)\n└── TensorFlowAPI\n ├── TensorFlowAPI.c (72 lines)\n └── include\n └── TensorFlowAPI.h (138 lines)\"\u003e\u003cpre class=\"notranslate\"\u003e\u003ccode\u003eSources\n├── PerfectTensorFlow\n│ ├── APILoader.swift (1000+ lines, translated from tensorflow/c/c_api.h)\n│ ├── PerfectTensorFlow.swift (2700+ lines)\n└── TensorFlowAPI\n ├── TensorFlowAPI.c (72 lines)\n └── include\n └── TensorFlowAPI.h (138 lines)\n\u003c/code\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAll other Swift sources named as 'pb.*.swift', which is totally up to 45,000+ lines of code, are automatically generated by \u003ccode\u003eupdateprotos.sh\u003c/code\u003e in the root directory. Unfortunately, if using such a script, you still need to manually edit the \u003ccode\u003epublic typealias\u003c/code\u003e part listed in the \u003cstrong\u003ePerfectTensorFlow.swift\u003c/strong\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eUp to now there is no such a plan to generate these protocol buffer files dynamically in the Swift Source since Perfect-TensorFlow is a part of Perfect, although it can run independently, all features of Perfect framework are built by Swift Package Manager for consistency consideration. However, since the project is also fast growing, all pull request, ideas, suggestions and comments are welcome!\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eAPI Guide\u003c/h2\u003e\u003ca id=\"user-content-api-guide\" class=\"anchor\" aria-label=\"Permalink: API Guide\" href=\"#api-guide\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAPI programming topics can be found in \u003ca href=\"/PerfectlySoft/Perfect-TensorFlow/blob/master/GUIDE.md\"\u003ePerfect TensorFlow Guide\u003c/a\u003e.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eAlso, there are many features that has already embedded in the testing script, such as \u003cstrong\u003eTensorFlow event\u003c/strong\u003e and \u003cstrong\u003esummary\u003c/strong\u003e for \u003cstrong\u003eTensorBoard\u003c/strong\u003e report and benchmarking. Please check the \u003ca href=\"/PerfectlySoft/Perfect-TensorFlow/blob/master/Tests/PerfectTensorFlowTests/PerfectTensorFlowTests.swift\"\u003ePerfect TensorFlow Testing Script\u003c/a\u003e for detail.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eQuick Start\u003c/h2\u003e\u003ca id=\"user-content-quick-start\" class=\"anchor\" aria-label=\"Permalink: Quick Start\" href=\"#quick-start\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eTensorFlow C API Library Installation\u003c/h3\u003e\u003ca id=\"user-content-tensorflow-c-api-library-installation\" class=\"anchor\" aria-label=\"Permalink: TensorFlow C API Library Installation\" href=\"#tensorflow-c-api-library-installation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePerfect-TensorFlow is based on TensorFlow C API, i.e., \u003ccode\u003elibtensorflow.so\u003c/code\u003e and \u003ccode\u003elibtensorflow_framework.so\u003c/code\u003e on runtime.\nThis project contains an express CPU version installation script for this module on both macOS / Ubuntu Linux, and will install both dynamic libraries into path \u003ccode\u003e/usr/local/lib\u003c/code\u003e. You can download \u0026amp; run \u003ca href=\"https://github.com/PerfectlySoft/Perfect-TensorFlow/blob/master/install.sh\"\u003e\u003ccode\u003einstall.sh\u003c/code\u003e\u003c/a\u003e. Before running this script, please make sure that \u003ccode\u003ecurl\u003c/code\u003e has been installed onto your computer.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eFor more installation options, such as GPU/CPU and multiple versions on the same machine, please check TensorFlow website: \u003ca href=\"https://www.tensorflow.org/install/install_c\" rel=\"nofollow\"\u003eInstalling TensorFlow for C\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003ePerfect TensorFlow Application\u003c/h3\u003e\u003ca id=\"user-content-perfect-tensorflow-application\" class=\"anchor\" aria-label=\"Permalink: Perfect TensorFlow Application\" href=\"#perfect-tensorflow-application\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eTo use this library, add dependencies to your project's Package.swift with the \u003cstrong\u003eLATEST TAG\u003c/strong\u003e:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-swift notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\".package(url: \u0026quot;https://github.com/PerfectlySoft/Perfect-TensorFlow.git\u0026quot;, from: \u0026quot;1.4.0\u0026quot;)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003epackage\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003eurl\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003ehttps://github.com/PerfectlySoft/Perfect-TensorFlow.git\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e from\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e1.4.0\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eand it also requires a dependency declaration in the same file, \u003ccode\u003etarget\u003c/code\u003e section:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-swift notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"dependencies: [\u0026quot;PerfectTensorFlow\u0026quot;]\"\u003e\u003cpre\u003edependencies: \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003ePerfectTensorFlow\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eThen declare the library:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-swift notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"// TensorFlowAPI contains most API functions defined in libtensorflow.so\nimport TensorFlowAPI\n\n// This is the Swift version of TensorFlow classes and objects\nimport PerfectTensorFlow\n\n// To keep the naming consistency with TensorFlow in other languages such as\n// Python or Java, making an alias of `TensorFlow` Class is a good idea:\npublic typealias TF = TensorFlow\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e// TensorFlowAPI contains most API functions defined in libtensorflow.so\u003c/span\u003e\nimport TensorFlowAPI\n\n\u003cspan class=\"pl-c\"\u003e// This is the Swift version of TensorFlow classes and objects\u003c/span\u003e\nimport PerfectTensorFlow\n\n\u003cspan class=\"pl-c\"\u003e// To keep the naming consistency with TensorFlow in other languages such as\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// Python or Java, making an alias of `TensorFlow` Class is a good idea:\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003epublic\u003c/span\u003e \u003cspan class=\"pl-k\"\u003etypealias\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eTF\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eTensorFlow\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLibrary Activation\u003c/h3\u003e\u003ca id=\"user-content-library-activation\" class=\"anchor\" aria-label=\"Permalink: Library Activation\" href=\"#library-activation\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003e\u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e⚠️\u003c/g-emoji\u003eNOTE\u003cg-emoji class=\"g-emoji\" alias=\"warning\"\u003e⚠️\u003c/g-emoji\u003e Prior to use \u003cstrong\u003eANY ACTUAL FUNCTIONS\u003c/strong\u003e of Perfect TensorFlow framework, \u003ccode\u003eTF.Open()\u003c/code\u003e must be called first:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-swift notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"// this action will load all api functions defined\n// in /usr/local/lib/libtensorflow.so\ntry TF.Open()\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e// this action will load all api functions defined\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// in /usr/local/lib/libtensorflow.so\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eTF\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eOpen\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003ePlease also note that you can active the library with a specific path, alternatively, especially in case of different versions or CPU/GPU library adjustment required:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-swift notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"// this action will load the library with the path\ntry TF.Open(\u0026quot;/path/to/DLL/of/libtensorflow.so\u0026quot;)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e// this action will load the library with the path\u003c/span\u003e\n\u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eTF\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eOpen\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e/path/to/DLL/of/libtensorflow.so\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003e\"Hello, Perfect TensorFlow!\"\u003c/h3\u003e\u003ca id=\"user-content-hello-perfect-tensorflow\" class=\"anchor\" aria-label=\"Permalink: \u0026quot;Hello, Perfect TensorFlow!\u0026quot;\" href=\"#hello-perfect-tensorflow\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eHere is the Swift version of \"Hello, TensorFlow!\":\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-swift notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"// define a string tensor\nlet tensor = try TF.Tensor.Scalar(\u0026quot;Hello, Perfect TensorFlow! 🇨🇳🇨🇦\u0026quot;)\n\n// declare a new graph\nlet g = try TF.Graph()\n\n// turn the tensor into an operation\nlet op = try g.const(tensor: tensor, name: \u0026quot;hello\u0026quot;)\n\n// run a session\nlet o = try g.runner().fetch(op).addTarget(op).run()\n\n// decode the result \nlet decoded = try TF.Decode(strings: o[0].data, count: 1)\n\n// check the result\nlet s2 = decoded[0].string\nprint(s2)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e// define a string tensor\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etensor\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eTF\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003eTensor\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eScalar\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003eHello, Perfect TensorFlow! 🇨🇳🇨🇦\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// declare a new graph\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eg\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eTF\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eGraph\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// turn the tensor into an operation\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eop\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e g\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003econst\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003etensor\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e tensor\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e name\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003ehello\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// run a session\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eo\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e g\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003erunner\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003efetch\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003eop\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eaddTarget\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003eop\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003erun\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// decode the result \u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003edecoded\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eTF\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eDecode\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003estrings\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eo\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003edata\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e count\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// check the result\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003es2\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-en\"\u003edecoded\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003estring\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003es2\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eMatrix Operations\u003c/h3\u003e\u003ca id=\"user-content-matrix-operations\" class=\"anchor\" aria-label=\"Permalink: Matrix Operations\" href=\"#matrix-operations\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eAs you can see, Swift version of TensorFlow keeps the same principals of the original one, i.e., create tensors, save tensors into graph, define the operations and then run the session \u0026amp; check the result.\u003c/p\u003e\n\u003cp dir=\"auto\"\u003eHere is an other simple example of matrix operations in Perfect TensorFlow:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-swift notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"/* Matrix Multiply:\n| 1 2 | |0 1| |0 1|\n| 3 4 | * |0 0| = |0 3|\n*/\n// input the matrix.\nlet tA = try TF.Tensor.Matrix([[1, 2], [3, 4]])\nlet tB = try TF.Tensor.Matrix([[0, 0], [1, 0]])\n\n// adding tensors to graph\nlet g = try TF.Graph()\nlet A = try g.const(tensor: tA, name: \u0026quot;Const_0\u0026quot;)\nlet B = try g.const(tensor: tB, name: \u0026quot;Const_1\u0026quot;)\n\n// define matrix multiply operation\nlet v = try g.matMul(l: A, r: B, name: \u0026quot;v\u0026quot;, transposeB: true)\n\n// run the session\nlet o = try g.runner().fetch(v).addTarget(v).run()\nlet m:[Float] = try o[0].asArray()\nprint(m)\n// m shall be [0, 1, 0, 3]\"\u003e\u003cpre\u003e\u003cspan class=\"pl-c\"\u003e/* Matrix Multiply:\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e| 1 2 | |0 1| |0 1|\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e| 3 4 | * |0 0| = |0 3|\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e*/\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// input the matrix.\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etA\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eTF\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003eTensor\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eMatrix\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e2\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e3\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e4\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003etB\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eTF\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003eTensor\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eMatrix\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e1\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// adding tensors to graph\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eg\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eTF\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eGraph\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eA\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e g\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003econst\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003etensor\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e tA\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e name\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003eConst_0\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eB\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e g\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003econst\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003etensor\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e tB\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e name\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003eConst_1\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// define matrix multiply operation\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003ev\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e g\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003ematMul\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003el\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e A\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e r\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e B\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e name\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003ev\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e transposeB\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e true\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// run the session\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eo\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e g\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003erunner\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003efetch\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003ev\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eaddTarget\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003ev\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003erun\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003em\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-smi\"\u003eFloat\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-en\"\u003eo\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-c1\"\u003e0\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003easArray\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\u003cspan class=\"pl-en\"\u003eprint\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003em\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\u003cspan class=\"pl-c\"\u003e// m shall be [0, 1, 0, 3]\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eLoad a Saved Artificial Neural Network Model\u003c/h3\u003e\u003ca id=\"user-content-load-a-saved-artificial-neural-network-model\" class=\"anchor\" aria-label=\"Permalink: Load a Saved Artificial Neural Network Model\" href=\"#load-a-saved-artificial-neural-network-model\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eBesides building graph \u0026amp; sessions in code, Perfect TensorFlow also provides a handy method to load models into runtime, i.e, generate a new session by loading a model file:\u003c/p\u003e\n\u003cdiv class=\"highlight highlight-source-swift notranslate position-relative overflow-auto\" dir=\"auto\" data-snippet-clipboard-copy-content=\"let g = try TF.Graph()\n\n// the meta signature info defined in a saved model\nlet metaBuf = try TF.Buffer()\n\n// load the session\nlet session = try g.load(\n\texportDir: \u0026quot;/path/to/saved/model\u0026quot;,\n\ttags: [\u0026quot;tag1\u0026quot;, \u0026quot;tag2\u0026quot;, ...],\n\tmetaGraphDef: metaBuf)\"\u003e\u003cpre\u003e\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003eg\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eTF\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eGraph\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// the meta signature info defined in a saved model\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003emetaBuf\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e \u003cspan class=\"pl-smi\"\u003eTF\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eBuffer\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\n\n\u003cspan class=\"pl-c\"\u003e// load the session\u003c/span\u003e\n\u003cspan class=\"pl-k\"\u003elet\u003c/span\u003e \u003cspan class=\"pl-s1\"\u003esession\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e=\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e\u003cspan class=\"pl-k\"\u003etry\u003c/span\u003e\u003c/span\u003e g\u003cspan class=\"pl-kos\"\u003e.\u003c/span\u003e\u003cspan class=\"pl-en\"\u003eload\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e(\u003c/span\u003e\n\texportDir\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e/path/to/saved/model\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n\ttags\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e \u003cspan class=\"pl-kos\"\u003e[\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003etag1\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-s\"\u003etag2\u003c/span\u003e\u003cspan class=\"pl-s\"\u003e\"\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e \u003cspan class=\"pl-c1\"\u003e...\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e]\u003c/span\u003e\u003cspan class=\"pl-kos\"\u003e,\u003c/span\u003e\n\tmetaGraphDef\u003cspan class=\"pl-kos\"\u003e:\u003c/span\u003e metaBuf\u003cspan class=\"pl-kos\"\u003e)\u003c/span\u003e\u003c/pre\u003e\u003c/div\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch3 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eComputer Vision Demo\u003c/h3\u003e\u003ca id=\"user-content-computer-vision-demo\" class=\"anchor\" aria-label=\"Permalink: Computer Vision Demo\" href=\"#computer-vision-demo\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eA detailed example of Perfect TensorFlow for Computer Vision can be found in this repo: \u003ca href=\"https://github.com/PerfectExamples/Perfect-TensorFlow-Demo-Vision\"\u003ePerfect TensorFlow Demo\u003c/a\u003e, where you can upload any local images or draw a scribble online to test if the server can recognize the picture content:\u003c/p\u003e\n\u003cp dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer\" href=\"https://github.com/PerfectExamples/Perfect-TensorFlow-Demo-Vision/blob/master/scrshot2.png?raw=true\"\u003e\u003cimg src=\"https://github.com/PerfectExamples/Perfect-TensorFlow-Demo-Vision/raw/master/scrshot2.png?raw=true\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eFurther Information\u003c/h2\u003e\u003ca id=\"user-content-further-information\" class=\"anchor\" aria-label=\"Permalink: Further Information\" href=\"#further-information\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp dir=\"auto\"\u003eFor more information on the Perfect project, please visit \u003ca href=\"http://perfect.org\" rel=\"nofollow\"\u003eperfect.org\u003c/a\u003e.\u003c/p\u003e\n\u003cdiv class=\"markdown-heading\" dir=\"auto\"\u003e\u003ch2 tabindex=\"-1\" class=\"heading-element\" dir=\"auto\"\u003eNow WeChat Subscription is Available (Chinese)\u003c/h2\u003e\u003ca id=\"user-content-now-wechat-subscription-is-available-chinese\" class=\"anchor\" aria-label=\"Permalink: Now WeChat Subscription is Available (Chinese)\" href=\"#now-wechat-subscription-is-available-chinese\"\u003e\u003csvg class=\"octicon octicon-link\" viewBox=\"0 0 16 16\" version=\"1.1\" width=\"16\" height=\"16\" aria-hidden=\"true\"\u003e\u003cpath d=\"m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z\"\u003e\u003c/path\u003e\u003c/svg\u003e\u003c/a\u003e\u003c/div\u003e\n\u003cp align=\"center\" dir=\"auto\"\u003e\u003ca target=\"_blank\" rel=\"noopener noreferrer nofollow\" href=\"https://raw.githubusercontent.com/PerfectExamples/Perfect-Cloudinary-ImageUploader-Demo/master/qr.png\"\u003e\u003cimg src=\"https://raw.githubusercontent.com/PerfectExamples/Perfect-Cloudinary-ImageUploader-Demo/master/qr.png\" style=\"max-width: 100%;\"\u003e\u003c/a\u003e\u003c/p\u003e\n\u003c/article\u003e","loaded":true,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":[{"level":1,"text":"Perfect TensorFlow 简体中文","anchor":"perfect-tensorflow-简体中文","htmlText":"Perfect TensorFlow 简体中文"},{"level":2,"text":"Project Status","anchor":"project-status","htmlText":"Project Status"},{"level":2,"text":"Development Notes","anchor":"development-notes","htmlText":"Development Notes"},{"level":2,"text":"API Guide","anchor":"api-guide","htmlText":"API Guide"},{"level":2,"text":"Quick Start","anchor":"quick-start","htmlText":"Quick Start"},{"level":3,"text":"TensorFlow C API Library Installation","anchor":"tensorflow-c-api-library-installation","htmlText":"TensorFlow C API Library Installation"},{"level":3,"text":"Perfect TensorFlow Application","anchor":"perfect-tensorflow-application","htmlText":"Perfect TensorFlow Application"},{"level":3,"text":"Library Activation","anchor":"library-activation","htmlText":"Library Activation"},{"level":3,"text":"\"Hello, Perfect TensorFlow!\"","anchor":"hello-perfect-tensorflow","htmlText":"\"Hello, Perfect TensorFlow!\""},{"level":3,"text":"Matrix Operations","anchor":"matrix-operations","htmlText":"Matrix Operations"},{"level":3,"text":"Load a Saved Artificial Neural Network Model","anchor":"load-a-saved-artificial-neural-network-model","htmlText":"Load a Saved Artificial Neural Network Model"},{"level":3,"text":"Computer Vision Demo","anchor":"computer-vision-demo","htmlText":"Computer Vision Demo"},{"level":2,"text":"Further Information","anchor":"further-information","htmlText":"Further Information"},{"level":2,"text":"Now WeChat Subscription is Available (Chinese)","anchor":"now-wechat-subscription-is-available-chinese","htmlText":"Now WeChat Subscription is Available (Chinese)"}],"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FPerfectlySoft%2FPerfect-TensorFlow"}},{"displayName":"CODE_OF_CONDUCT.md","repoName":"Perfect-TensorFlow","refName":"master","path":"CODE_OF_CONDUCT.md","preferredFileType":"code_of_conduct","tabName":"Code of conduct","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FPerfectlySoft%2FPerfect-TensorFlow"}},{"displayName":"LICENSE","repoName":"Perfect-TensorFlow","refName":"master","path":"LICENSE","preferredFileType":"license","tabName":"Apache-2.0","richText":null,"loaded":false,"timedOut":false,"errorMessage":null,"headerInfo":{"toc":null,"siteNavLoginPath":"/login?return_to=https%3A%2F%2Fgithub.com%2FPerfectlySoft%2FPerfect-TensorFlow"}}],"overviewFilesProcessingTime":0}},"appPayload":{"helpUrl":"https://docs.github.com","findFileWorkerPath":"/assets-cdn/worker/find-file-worker-1583894afd38.js","findInFileWorkerPath":"/assets-cdn/worker/find-in-file-worker-67668e8c2caa.js","githubDevUrl":null,"enabled_features":{"code_nav_ui_events":false,"overview_shared_code_dropdown_button":false,"react_blob_overlay":false,"copilot_conversational_ux_embedding_update":false,"copilot_smell_icebreaker_ux":true,"copilot_workspace":false,"accessible_code_button":true}}}}</script> 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src="https://camo.githubusercontent.com/28bfa76ac1791903dc5b7b56e704e616d896da0b8349625bb533aec64eeef359/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f4c6963656e73652d4170616368652d6c69676874677265792e7376673f7374796c653d666c6174" alt="License Apache" data-canonical-src="https://img.shields.io/badge/License-Apache-lightgrey.svg?style=flat" style="max-width: 100%;"> </a> <a href="http://twitter.com/PerfectlySoft" rel="nofollow"> <img src="https://camo.githubusercontent.com/b413f124f3860f984253302c319a722643c85cbba52debc83437fb89410cd47d/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f547769747465722d40506572666563746c79536f66742d626c75652e7376673f7374796c653d666c6174" alt="PerfectlySoft Twitter" data-canonical-src="https://img.shields.io/badge/Twitter-@PerfectlySoft-blue.svg?style=flat" style="max-width: 100%;"> </a> <a href="http://perfect.ly" rel="nofollow"> <img src="https://camo.githubusercontent.com/8f5052c851792fd8745d412731979c077b264f4aeca01860adb8d3d17bd8c358/687474703a2f2f706572666563742e6c792f62616467652e737667" alt="Slack Status" data-canonical-src="http://perfect.ly/badge.svg" style="max-width: 100%;"> </a> </p> <p dir="auto">This project is an experimental wrapper of TensorFlow C API which enables Machine Learning in Server Side Swift.</p> <p dir="auto">This package builds with Swift Package Manager and is part of the <a href="https://github.com/PerfectlySoft/Perfect">Perfect</a> project but can also be used as an independent module.</p> <p dir="auto">Ensure you have installed and activated the latest Swift 4.1.1 / Xcode 9.3</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Project Status</h2><a id="user-content-project-status" class="anchor" aria-label="Permalink: Project Status" href="#project-status"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">The framework conforms to TensorFlow v1.8.0 C API functionality.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Development Notes</h2><a id="user-content-development-notes" class="anchor" aria-label="Permalink: Development Notes" href="#development-notes"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">These files are the key part of Perfect-TensorFlow:</p> <div class="snippet-clipboard-content notranslate position-relative overflow-auto" data-snippet-clipboard-copy-content="Sources ├── PerfectTensorFlow │ ├── APILoader.swift (1000+ lines, translated from tensorflow/c/c_api.h) │ ├── PerfectTensorFlow.swift (2700+ lines) └── TensorFlowAPI ├── TensorFlowAPI.c (72 lines) └── include └── TensorFlowAPI.h (138 lines)"><pre class="notranslate"><code>Sources ├── PerfectTensorFlow │ ├── APILoader.swift (1000+ lines, translated from tensorflow/c/c_api.h) │ ├── PerfectTensorFlow.swift (2700+ lines) └── TensorFlowAPI ├── TensorFlowAPI.c (72 lines) └── include └── TensorFlowAPI.h (138 lines) </code></pre></div> <p dir="auto">All other Swift sources named as 'pb.*.swift', which is totally up to 45,000+ lines of code, are automatically generated by <code>updateprotos.sh</code> in the root directory. Unfortunately, if using such a script, you still need to manually edit the <code>public typealias</code> part listed in the <strong>PerfectTensorFlow.swift</strong>.</p> <p dir="auto">Up to now there is no such a plan to generate these protocol buffer files dynamically in the Swift Source since Perfect-TensorFlow is a part of Perfect, although it can run independently, all features of Perfect framework are built by Swift Package Manager for consistency consideration. However, since the project is also fast growing, all pull request, ideas, suggestions and comments are welcome!</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">API Guide</h2><a id="user-content-api-guide" class="anchor" aria-label="Permalink: API Guide" href="#api-guide"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">API programming topics can be found in <a href="/PerfectlySoft/Perfect-TensorFlow/blob/master/GUIDE.md">Perfect TensorFlow Guide</a>.</p> <p dir="auto">Also, there are many features that has already embedded in the testing script, such as <strong>TensorFlow event</strong> and <strong>summary</strong> for <strong>TensorBoard</strong> report and benchmarking. Please check the <a href="/PerfectlySoft/Perfect-TensorFlow/blob/master/Tests/PerfectTensorFlowTests/PerfectTensorFlowTests.swift">Perfect TensorFlow Testing Script</a> for detail.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Quick Start</h2><a id="user-content-quick-start" class="anchor" aria-label="Permalink: Quick Start" href="#quick-start"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">TensorFlow C API Library Installation</h3><a id="user-content-tensorflow-c-api-library-installation" class="anchor" aria-label="Permalink: TensorFlow C API Library Installation" href="#tensorflow-c-api-library-installation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Perfect-TensorFlow is based on TensorFlow C API, i.e., <code>libtensorflow.so</code> and <code>libtensorflow_framework.so</code> on runtime. This project contains an express CPU version installation script for this module on both macOS / Ubuntu Linux, and will install both dynamic libraries into path <code>/usr/local/lib</code>. You can download & run <a href="https://github.com/PerfectlySoft/Perfect-TensorFlow/blob/master/install.sh"><code>install.sh</code></a>. Before running this script, please make sure that <code>curl</code> has been installed onto your computer.</p> <p dir="auto">For more installation options, such as GPU/CPU and multiple versions on the same machine, please check TensorFlow website: <a href="https://www.tensorflow.org/install/install_c" rel="nofollow">Installing TensorFlow for C</a></p> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Perfect TensorFlow Application</h3><a id="user-content-perfect-tensorflow-application" class="anchor" aria-label="Permalink: Perfect TensorFlow Application" href="#perfect-tensorflow-application"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">To use this library, add dependencies to your project's Package.swift with the <strong>LATEST TAG</strong>:</p> <div class="highlight highlight-source-swift notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content=".package(url: "https://github.com/PerfectlySoft/Perfect-TensorFlow.git", from: "1.4.0")"><pre><span class="pl-kos">.</span>package<span class="pl-kos">(</span>url<span class="pl-kos">:</span> <span class="pl-s">"</span><span class="pl-s">https://github.com/PerfectlySoft/Perfect-TensorFlow.git</span><span class="pl-s">"</span><span class="pl-kos">,</span> from<span class="pl-kos">:</span> <span class="pl-s">"</span><span class="pl-s">1.4.0</span><span class="pl-s">"</span><span class="pl-kos">)</span></pre></div> <p dir="auto">and it also requires a dependency declaration in the same file, <code>target</code> section:</p> <div class="highlight highlight-source-swift notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="dependencies: ["PerfectTensorFlow"]"><pre>dependencies: <span class="pl-kos">[</span><span class="pl-s">"</span><span class="pl-s">PerfectTensorFlow</span><span class="pl-s">"</span><span class="pl-kos">]</span></pre></div> <p dir="auto">Then declare the library:</p> <div class="highlight highlight-source-swift notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="// TensorFlowAPI contains most API functions defined in libtensorflow.so import TensorFlowAPI // This is the Swift version of TensorFlow classes and objects import PerfectTensorFlow // To keep the naming consistency with TensorFlow in other languages such as // Python or Java, making an alias of `TensorFlow` Class is a good idea: public typealias TF = TensorFlow"><pre><span class="pl-c">// TensorFlowAPI contains most API functions defined in libtensorflow.so</span> import TensorFlowAPI <span class="pl-c">// This is the Swift version of TensorFlow classes and objects</span> import PerfectTensorFlow <span class="pl-c">// To keep the naming consistency with TensorFlow in other languages such as</span> <span class="pl-c">// Python or Java, making an alias of `TensorFlow` Class is a good idea:</span> <span class="pl-k">public</span> <span class="pl-k">typealias</span> <span class="pl-smi">TF</span> <span class="pl-c1">=</span> <span class="pl-smi">TensorFlow</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Library Activation</h3><a id="user-content-library-activation" class="anchor" aria-label="Permalink: Library Activation" href="#library-activation"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto"><g-emoji class="g-emoji" alias="warning">⚠️</g-emoji>NOTE<g-emoji class="g-emoji" alias="warning">⚠️</g-emoji> Prior to use <strong>ANY ACTUAL FUNCTIONS</strong> of Perfect TensorFlow framework, <code>TF.Open()</code> must be called first:</p> <div class="highlight highlight-source-swift notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="// this action will load all api functions defined // in /usr/local/lib/libtensorflow.so try TF.Open()"><pre><span class="pl-c">// this action will load all api functions defined</span> <span class="pl-c">// in /usr/local/lib/libtensorflow.so</span> <span class="pl-c1"><span class="pl-k">try</span></span> <span class="pl-smi">TF</span><span class="pl-kos">.</span><span class="pl-en">Open</span><span class="pl-kos">(</span><span class="pl-kos">)</span></pre></div> <p dir="auto">Please also note that you can active the library with a specific path, alternatively, especially in case of different versions or CPU/GPU library adjustment required:</p> <div class="highlight highlight-source-swift notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="// this action will load the library with the path try TF.Open("/path/to/DLL/of/libtensorflow.so")"><pre><span class="pl-c">// this action will load the library with the path</span> <span class="pl-c1"><span class="pl-k">try</span></span> <span class="pl-smi">TF</span><span class="pl-kos">.</span><span class="pl-en">Open</span><span class="pl-kos">(</span><span class="pl-s">"</span><span class="pl-s">/path/to/DLL/of/libtensorflow.so</span><span class="pl-s">"</span><span class="pl-kos">)</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">"Hello, Perfect TensorFlow!"</h3><a id="user-content-hello-perfect-tensorflow" class="anchor" aria-label="Permalink: "Hello, Perfect TensorFlow!"" href="#hello-perfect-tensorflow"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Here is the Swift version of "Hello, TensorFlow!":</p> <div class="highlight highlight-source-swift notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="// define a string tensor let tensor = try TF.Tensor.Scalar("Hello, Perfect TensorFlow! 🇨🇳🇨🇦") // declare a new graph let g = try TF.Graph() // turn the tensor into an operation let op = try g.const(tensor: tensor, name: "hello") // run a session let o = try g.runner().fetch(op).addTarget(op).run() // decode the result let decoded = try TF.Decode(strings: o[0].data, count: 1) // check the result let s2 = decoded[0].string print(s2)"><pre><span class="pl-c">// define a string tensor</span> <span class="pl-k">let</span> <span class="pl-s1">tensor</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> <span class="pl-smi">TF</span><span class="pl-kos">.</span>Tensor<span class="pl-kos">.</span><span class="pl-en">Scalar</span><span class="pl-kos">(</span><span class="pl-s">"</span><span class="pl-s">Hello, Perfect TensorFlow! 🇨🇳🇨🇦</span><span class="pl-s">"</span><span class="pl-kos">)</span> <span class="pl-c">// declare a new graph</span> <span class="pl-k">let</span> <span class="pl-s1">g</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> <span class="pl-smi">TF</span><span class="pl-kos">.</span><span class="pl-en">Graph</span><span class="pl-kos">(</span><span class="pl-kos">)</span> <span class="pl-c">// turn the tensor into an operation</span> <span class="pl-k">let</span> <span class="pl-s1">op</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> g<span class="pl-kos">.</span><span class="pl-en">const</span><span class="pl-kos">(</span>tensor<span class="pl-kos">:</span> tensor<span class="pl-kos">,</span> name<span class="pl-kos">:</span> <span class="pl-s">"</span><span class="pl-s">hello</span><span class="pl-s">"</span><span class="pl-kos">)</span> <span class="pl-c">// run a session</span> <span class="pl-k">let</span> <span class="pl-s1">o</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> g<span class="pl-kos">.</span><span class="pl-en">runner</span><span class="pl-kos">(</span><span class="pl-kos">)</span><span class="pl-kos">.</span><span class="pl-en">fetch</span><span class="pl-kos">(</span>op<span class="pl-kos">)</span><span class="pl-kos">.</span><span class="pl-en">addTarget</span><span class="pl-kos">(</span>op<span class="pl-kos">)</span><span class="pl-kos">.</span><span class="pl-en">run</span><span class="pl-kos">(</span><span class="pl-kos">)</span> <span class="pl-c">// decode the result </span> <span class="pl-k">let</span> <span class="pl-s1">decoded</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> <span class="pl-smi">TF</span><span class="pl-kos">.</span><span class="pl-en">Decode</span><span class="pl-kos">(</span>strings<span class="pl-kos">:</span> <span class="pl-en">o</span><span class="pl-kos">[</span><span class="pl-c1">0</span><span class="pl-kos">]</span><span class="pl-kos">.</span>data<span class="pl-kos">,</span> count<span class="pl-kos">:</span> <span class="pl-c1">1</span><span class="pl-kos">)</span> <span class="pl-c">// check the result</span> <span class="pl-k">let</span> <span class="pl-s1">s2</span> <span class="pl-c1">=</span> <span class="pl-en">decoded</span><span class="pl-kos">[</span><span class="pl-c1">0</span><span class="pl-kos">]</span><span class="pl-kos">.</span>string <span class="pl-en">print</span><span class="pl-kos">(</span>s2<span class="pl-kos">)</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Matrix Operations</h3><a id="user-content-matrix-operations" class="anchor" aria-label="Permalink: Matrix Operations" href="#matrix-operations"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">As you can see, Swift version of TensorFlow keeps the same principals of the original one, i.e., create tensors, save tensors into graph, define the operations and then run the session & check the result.</p> <p dir="auto">Here is an other simple example of matrix operations in Perfect TensorFlow:</p> <div class="highlight highlight-source-swift notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="/* Matrix Multiply: | 1 2 | |0 1| |0 1| | 3 4 | * |0 0| = |0 3| */ // input the matrix. let tA = try TF.Tensor.Matrix([[1, 2], [3, 4]]) let tB = try TF.Tensor.Matrix([[0, 0], [1, 0]]) // adding tensors to graph let g = try TF.Graph() let A = try g.const(tensor: tA, name: "Const_0") let B = try g.const(tensor: tB, name: "Const_1") // define matrix multiply operation let v = try g.matMul(l: A, r: B, name: "v", transposeB: true) // run the session let o = try g.runner().fetch(v).addTarget(v).run() let m:[Float] = try o[0].asArray() print(m) // m shall be [0, 1, 0, 3]"><pre><span class="pl-c">/* Matrix Multiply:</span> <span class="pl-c">| 1 2 | |0 1| |0 1|</span> <span class="pl-c">| 3 4 | * |0 0| = |0 3|</span> <span class="pl-c">*/</span> <span class="pl-c">// input the matrix.</span> <span class="pl-k">let</span> <span class="pl-s1">tA</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> <span class="pl-smi">TF</span><span class="pl-kos">.</span>Tensor<span class="pl-kos">.</span><span class="pl-en">Matrix</span><span class="pl-kos">(</span><span class="pl-kos">[</span><span class="pl-kos">[</span><span class="pl-c1">1</span><span class="pl-kos">,</span> <span class="pl-c1">2</span><span class="pl-kos">]</span><span class="pl-kos">,</span> <span class="pl-kos">[</span><span class="pl-c1">3</span><span class="pl-kos">,</span> <span class="pl-c1">4</span><span class="pl-kos">]</span><span class="pl-kos">]</span><span class="pl-kos">)</span> <span class="pl-k">let</span> <span class="pl-s1">tB</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> <span class="pl-smi">TF</span><span class="pl-kos">.</span>Tensor<span class="pl-kos">.</span><span class="pl-en">Matrix</span><span class="pl-kos">(</span><span class="pl-kos">[</span><span class="pl-kos">[</span><span class="pl-c1">0</span><span class="pl-kos">,</span> <span class="pl-c1">0</span><span class="pl-kos">]</span><span class="pl-kos">,</span> <span class="pl-kos">[</span><span class="pl-c1">1</span><span class="pl-kos">,</span> <span class="pl-c1">0</span><span class="pl-kos">]</span><span class="pl-kos">]</span><span class="pl-kos">)</span> <span class="pl-c">// adding tensors to graph</span> <span class="pl-k">let</span> <span class="pl-s1">g</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> <span class="pl-smi">TF</span><span class="pl-kos">.</span><span class="pl-en">Graph</span><span class="pl-kos">(</span><span class="pl-kos">)</span> <span class="pl-k">let</span> <span class="pl-s1">A</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> g<span class="pl-kos">.</span><span class="pl-en">const</span><span class="pl-kos">(</span>tensor<span class="pl-kos">:</span> tA<span class="pl-kos">,</span> name<span class="pl-kos">:</span> <span class="pl-s">"</span><span class="pl-s">Const_0</span><span class="pl-s">"</span><span class="pl-kos">)</span> <span class="pl-k">let</span> <span class="pl-s1">B</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> g<span class="pl-kos">.</span><span class="pl-en">const</span><span class="pl-kos">(</span>tensor<span class="pl-kos">:</span> tB<span class="pl-kos">,</span> name<span class="pl-kos">:</span> <span class="pl-s">"</span><span class="pl-s">Const_1</span><span class="pl-s">"</span><span class="pl-kos">)</span> <span class="pl-c">// define matrix multiply operation</span> <span class="pl-k">let</span> <span class="pl-s1">v</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> g<span class="pl-kos">.</span><span class="pl-en">matMul</span><span class="pl-kos">(</span>l<span class="pl-kos">:</span> A<span class="pl-kos">,</span> r<span class="pl-kos">:</span> B<span class="pl-kos">,</span> name<span class="pl-kos">:</span> <span class="pl-s">"</span><span class="pl-s">v</span><span class="pl-s">"</span><span class="pl-kos">,</span> transposeB<span class="pl-kos">:</span> true<span class="pl-kos">)</span> <span class="pl-c">// run the session</span> <span class="pl-k">let</span> <span class="pl-s1">o</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> g<span class="pl-kos">.</span><span class="pl-en">runner</span><span class="pl-kos">(</span><span class="pl-kos">)</span><span class="pl-kos">.</span><span class="pl-en">fetch</span><span class="pl-kos">(</span>v<span class="pl-kos">)</span><span class="pl-kos">.</span><span class="pl-en">addTarget</span><span class="pl-kos">(</span>v<span class="pl-kos">)</span><span class="pl-kos">.</span><span class="pl-en">run</span><span class="pl-kos">(</span><span class="pl-kos">)</span> <span class="pl-k">let</span> <span class="pl-s1">m</span><span class="pl-kos">:</span><span class="pl-kos">[</span><span class="pl-smi">Float</span><span class="pl-kos">]</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> <span class="pl-en">o</span><span class="pl-kos">[</span><span class="pl-c1">0</span><span class="pl-kos">]</span><span class="pl-kos">.</span><span class="pl-en">asArray</span><span class="pl-kos">(</span><span class="pl-kos">)</span> <span class="pl-en">print</span><span class="pl-kos">(</span>m<span class="pl-kos">)</span> <span class="pl-c">// m shall be [0, 1, 0, 3]</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Load a Saved Artificial Neural Network Model</h3><a id="user-content-load-a-saved-artificial-neural-network-model" class="anchor" aria-label="Permalink: Load a Saved Artificial Neural Network Model" href="#load-a-saved-artificial-neural-network-model"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">Besides building graph & sessions in code, Perfect TensorFlow also provides a handy method to load models into runtime, i.e, generate a new session by loading a model file:</p> <div class="highlight highlight-source-swift notranslate position-relative overflow-auto" dir="auto" data-snippet-clipboard-copy-content="let g = try TF.Graph() // the meta signature info defined in a saved model let metaBuf = try TF.Buffer() // load the session let session = try g.load( exportDir: "/path/to/saved/model", tags: ["tag1", "tag2", ...], metaGraphDef: metaBuf)"><pre><span class="pl-k">let</span> <span class="pl-s1">g</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> <span class="pl-smi">TF</span><span class="pl-kos">.</span><span class="pl-en">Graph</span><span class="pl-kos">(</span><span class="pl-kos">)</span> <span class="pl-c">// the meta signature info defined in a saved model</span> <span class="pl-k">let</span> <span class="pl-s1">metaBuf</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> <span class="pl-smi">TF</span><span class="pl-kos">.</span><span class="pl-en">Buffer</span><span class="pl-kos">(</span><span class="pl-kos">)</span> <span class="pl-c">// load the session</span> <span class="pl-k">let</span> <span class="pl-s1">session</span> <span class="pl-c1">=</span> <span class="pl-c1"><span class="pl-k">try</span></span> g<span class="pl-kos">.</span><span class="pl-en">load</span><span class="pl-kos">(</span> exportDir<span class="pl-kos">:</span> <span class="pl-s">"</span><span class="pl-s">/path/to/saved/model</span><span class="pl-s">"</span><span class="pl-kos">,</span> tags<span class="pl-kos">:</span> <span class="pl-kos">[</span><span class="pl-s">"</span><span class="pl-s">tag1</span><span class="pl-s">"</span><span class="pl-kos">,</span> <span class="pl-s">"</span><span class="pl-s">tag2</span><span class="pl-s">"</span><span class="pl-kos">,</span> <span class="pl-c1">...</span><span class="pl-kos">]</span><span class="pl-kos">,</span> metaGraphDef<span class="pl-kos">:</span> metaBuf<span class="pl-kos">)</span></pre></div> <div class="markdown-heading" dir="auto"><h3 tabindex="-1" class="heading-element" dir="auto">Computer Vision Demo</h3><a id="user-content-computer-vision-demo" class="anchor" aria-label="Permalink: Computer Vision Demo" href="#computer-vision-demo"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">A detailed example of Perfect TensorFlow for Computer Vision can be found in this repo: <a href="https://github.com/PerfectExamples/Perfect-TensorFlow-Demo-Vision">Perfect TensorFlow Demo</a>, where you can upload any local images or draw a scribble online to test if the server can recognize the picture content:</p> <p dir="auto"><a target="_blank" rel="noopener noreferrer" href="https://github.com/PerfectExamples/Perfect-TensorFlow-Demo-Vision/blob/master/scrshot2.png?raw=true"><img src="https://github.com/PerfectExamples/Perfect-TensorFlow-Demo-Vision/raw/master/scrshot2.png?raw=true" style="max-width: 100%;"></a></p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Further Information</h2><a id="user-content-further-information" class="anchor" aria-label="Permalink: Further Information" href="#further-information"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p dir="auto">For more information on the Perfect project, please visit <a href="http://perfect.org" rel="nofollow">perfect.org</a>.</p> <div class="markdown-heading" dir="auto"><h2 tabindex="-1" class="heading-element" dir="auto">Now WeChat Subscription is Available (Chinese)</h2><a id="user-content-now-wechat-subscription-is-available-chinese" class="anchor" aria-label="Permalink: Now WeChat Subscription is Available (Chinese)" href="#now-wechat-subscription-is-available-chinese"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path d="m7.775 3.275 1.25-1.25a3.5 3.5 0 1 1 4.95 4.95l-2.5 2.5a3.5 3.5 0 0 1-4.95 0 .751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018 1.998 1.998 0 0 0 2.83 0l2.5-2.5a2.002 2.002 0 0 0-2.83-2.83l-1.25 1.25a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042Zm-4.69 9.64a1.998 1.998 0 0 0 2.83 0l1.25-1.25a.751.751 0 0 1 1.042.018.751.751 0 0 1 .018 1.042l-1.25 1.25a3.5 3.5 0 1 1-4.95-4.95l2.5-2.5a3.5 3.5 0 0 1 4.95 0 .751.751 0 0 1-.018 1.042.751.751 0 0 1-1.042.018 1.998 1.998 0 0 0-2.83 0l-2.5 2.5a1.998 1.998 0 0 0 0 2.83Z"></path></svg></a></div> <p align="center" dir="auto"><a target="_blank" rel="noopener noreferrer nofollow" href="https://raw.githubusercontent.com/PerfectExamples/Perfect-Cloudinary-ImageUploader-Demo/master/qr.png"><img 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id="site-details-dialog"> <details class="details-reset details-overlay details-overlay-dark lh-default color-fg-default hx_rsm" open> <summary role="button" aria-label="Close dialog"></summary> <details-dialog class="Box Box--overlay d-flex flex-column anim-fade-in fast hx_rsm-dialog hx_rsm-modal"> <button class="Box-btn-octicon m-0 btn-octicon position-absolute right-0 top-0" type="button" aria-label="Close dialog" data-close-dialog> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-x"> <path d="M3.72 3.72a.75.75 0 0 1 1.06 0L8 6.94l3.22-3.22a.749.749 0 0 1 1.275.326.749.749 0 0 1-.215.734L9.06 8l3.22 3.22a.749.749 0 0 1-.326 1.275.749.749 0 0 1-.734-.215L8 9.06l-3.22 3.22a.751.751 0 0 1-1.042-.018.751.751 0 0 1-.018-1.042L6.94 8 3.72 4.78a.75.75 0 0 1 0-1.06Z"></path> </svg> </button> <div class="octocat-spinner my-6 js-details-dialog-spinner"></div> </details-dialog> </details> </template> <div class="Popover js-hovercard-content position-absolute" style="display: none; outline: none;"> <div class="Popover-message Popover-message--bottom-left Popover-message--large Box color-shadow-large" style="width:360px;"> </div> </div> <template id="snippet-clipboard-copy-button"> <div class="zeroclipboard-container position-absolute right-0 top-0"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn js-clipboard-copy m-2 p-0" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon m-2"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none m-2"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> <template id="snippet-clipboard-copy-button-unpositioned"> <div class="zeroclipboard-container"> <clipboard-copy aria-label="Copy" class="ClipboardButton btn btn-invisible js-clipboard-copy m-2 p-0 d-flex flex-justify-center flex-items-center" data-copy-feedback="Copied!" data-tooltip-direction="w"> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-copy js-clipboard-copy-icon"> <path d="M0 6.75C0 5.784.784 5 1.75 5h1.5a.75.75 0 0 1 0 1.5h-1.5a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-1.5a.75.75 0 0 1 1.5 0v1.5A1.75 1.75 0 0 1 9.25 16h-7.5A1.75 1.75 0 0 1 0 14.25Z"></path><path d="M5 1.75C5 .784 5.784 0 6.75 0h7.5C15.216 0 16 .784 16 1.75v7.5A1.75 1.75 0 0 1 14.25 11h-7.5A1.75 1.75 0 0 1 5 9.25Zm1.75-.25a.25.25 0 0 0-.25.25v7.5c0 .138.112.25.25.25h7.5a.25.25 0 0 0 .25-.25v-7.5a.25.25 0 0 0-.25-.25Z"></path> </svg> <svg aria-hidden="true" height="16" viewBox="0 0 16 16" version="1.1" width="16" data-view-component="true" class="octicon octicon-check js-clipboard-check-icon color-fg-success d-none"> <path d="M13.78 4.22a.75.75 0 0 1 0 1.06l-7.25 7.25a.75.75 0 0 1-1.06 0L2.22 9.28a.751.751 0 0 1 .018-1.042.751.751 0 0 1 1.042-.018L6 10.94l6.72-6.72a.75.75 0 0 1 1.06 0Z"></path> </svg> </clipboard-copy> </div> </template> </div> <div id="js-global-screen-reader-notice" class="sr-only mt-n1" aria-live="polite" aria-atomic="true" ></div> <div id="js-global-screen-reader-notice-assertive" class="sr-only mt-n1" aria-live="assertive" aria-atomic="true"></div> </body> </html>