TensorFlow 1.9 Docs

TensorFlow 1.9 DocumentationTensorFlow is an open source software library for numerical computation using data flow graphs. The graph nodes represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) that flow between them. This flexible architecture lets you deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device without rewriting code. TensorFlow also includes TensorBoard, a data visualization toolkit.TensorFlow was originally developed by researchers and engineers working on the Google Brain team within Googles Machine Intelligence Research organization for the purposes of conducting machine learning and deep neural networks research. The system is general enough to be applicable in a wide variety of other domains, as well.Attribution: the TensorFlow logo and any related marks are trademarks of Google Inc.Table of ContentInstall TensorFlowInstall TensorFlow on UbuntuInstall TensorFlow on macOSInstall TensorFlow on WindowsInstall TensorFlow on RaspbianInstall TensorFlow from SourcesTransitioning to TensorFlow 1.0Install TensorFlow for JavaInstall TensorFlow for GoInstall TensorFlow for CTensorFlow GuideKerasEager ExecutionImporting DataIntroduction to EstimatorsPremade EstimatorsCheckpointsFeature ColumnsDatasets for EstimatorsCreating Custom EstimatorsUsing GPUsUsing TPUsIntroductionTensorsVariablesGraphs and SessionsSave and RestoreEmbeddingsTensorFlow DebuggerVisualizing LearningGraphsHistogramsTensorFlow Version CompatibilityFrequently Asked QuestionsOverviewBasic classificationText classificationRegressionOverfitting and underfittingSave and restore modelsOverviewCustom training: walkthroughLinear model with EstimatorsText classifier with TF-HubBuild a CNN using EstimatorsImage recognitionImage retrainingAdvanced CNNRecurrent neural networkDrawing classificationSimple audio recognitionVector representations of wordsKernel methodsLarge-scale linear modelsMandelbrot setPartial differential equationsNext stepsDeployDistributed TensorFlowHow to run TensorFlow on HadoopHow to run TensorFlow on S3Deploy to JavaScriptIntroductionArchitecture OverviewInstallationServing a TensorFlow ModelRESTful APIBuilding Standard TensorFlow ModelServerServing Inception Model with TensorFlow Serving and KubernetesCreating a new kind of servableCreating a module that discovers new servable pathsSignatureDefs in SavedModel for TensorFlow ServingUsing TensorFlow Serving via DockerPerformancePerformance GuideInput Pipeline Performance GuideBenchmarksFixed Point QuantizationXLA OverviewBroadcasting semanticsDeveloping a new backend for XLAUsing JIT CompilationOperation SemanticsShapes and LayoutUsing AOT compilationExtendTensorFlow ArchitectureAdding a New OpAdding a Custom Filesystem PluginReading custom file and record formatsTensorFlow in other languagesA Tool Developers Guide to TensorFlow Model FilesOverviewIntroduction to TensorFlow LiteDeveloper GuideAndroid Demo AppiOS Demo AppPerformanceIntroduction to TensorFlow MobileBuilding TensorFlow on AndroidBuilding TensorFlow on iOSIntegrating TensorFlow librariesPreparing models for mobile deploymentOptimizing for mobileCommunityRoadmapContributing to TensorFlowMailing ListsUser GroupsWriting TensorFlow DocumentationTensorFlow Style GuideDefining and Running BenchmarksAbout TensorFlowTensorFlow In UseTensorFlow White PapersAttributionOverviewInstallationUsing a ModuleCreating a New ModuleFine-TuningHosting a ModuleImage RetrainingText ClassificationOverviewCommon Signatures for ImagesCommon Signatures for TextOverviewadd_signaturecreate_module_specget_expected_image_sizeget_num_image_channelsimage_embedding_columnLatestModuleExporterload_module_specModuleModuleSpecregister_module_for_exporttext_embedding_columnOverviewImage ModulesText ModulesOther Modules