Saravanan666/pypi-org huggingface dataset:
Package: tflite2onnx
tflite2onnx - Convert TensorFlow Lite models to ONNXtflite2onnxconverts TensorFlow Lite (TFLite) models (*.tflite) to ONNX models (*.onnx),
with data layout and quantization semantic properly handled (check theintroduction blogfor detail).HighlightsIf you'd like to convert a TensorFlow model (frozen graph*.pb,SavedModelor whatever) to ONNX, trytf2onnx.
Or, you can firstlyconvertit to a TFLite (*.tflite) model,
and then convert the TFLite model to ONNX.Microsoft has implemented anotherTensorFlow Lite to ONNX model converterintf2onnxat Feb 2021(we open sourcedtflite2onnxin May 2020).tf2onnxseems to able to convert Quantization
just like us, and it seems able to convert RNN networks which we are not supported yet.
Please trytf2onnx --tfliteiftflite2onnxmissing any functionality.InstallationInstall viapippip install tflite2onnx.Or install from source to get latest features (please try out withvirtualenv):Download the repo:git clone https://github.com/zhenhuaw-me/tflite2onnx.git && cd tflite2onnxBuild the package:./scripts/build-wheel.shInstall the built package:pip install assets/dist/tflite2onnx-*.whlOr you can just add the code tree to your$PYTHONPATH.
(Command line tool is not avaiable in this mode.)exportPYTHONPATH=$(pwd):${PYTHONPATH}UsagePython Interfaceimporttflite2onnxtflite_path='/path/to/original/tflite/model'onnx_path='/path/to/save/converted/onnx/model'tflite2onnx.convert(tflite_path,onnx_path)tflite2onnxnow supportsexplicit layout, check thetest example.Command Linetflite2onnx/path/to/original/tflite/model/path/to/save/converted/onnx/modelDocumentationFAQRelease noteContribution guideIntroduction blog - the background, design and implementationHow to enable a new operatorData layout semanticContributingIf something seems wrong to you,report bugs.If some operators are not supported yet, you mayrequest a new operator.It would be great if you can help to enable new operators, please join us withHow to enable a new operator.Feel free to open any other related discussions.Checkcontribution guidefor more.LicenseApache License Version 2.0.
Package: tfliteiorewriter
tflite-input-output-rewriterThis tool displays tflite signatures and rewrites the input/output OP name to the name of the signature. There is no need to install TensorFlow or TFLite.EnvironmentUbuntu 20.04+flatbuffers-compiler (Note: Official binaries are degraded by lack of precision when processing INT8 quantized tflite files.)requestsMotivationThe purpose is to solve the following problems by forcibly rewriting tflite's input/output OP names.When TFLite models are generated, TensorFlow automatically prefixes the input OP name withserving_default_, resulting in very difficult-to-read models. Also, an unnecessary index:nis added to the end of the name.Also, the output OP name is arbitrarily rewritten to the unintelligibleStatefulPartitionedCall:n.ExecutionDocker$ docker login ghcr.io

Username (xxxx): {Enter}
Password: {Personal Access Token}
Login Succeeded

$ docker run --rm -it \
-v `pwd`:/home/user \
ghcr.io/pinto0309/tflite-input-output-rewriter:latest

$ tfliteiorewriter -i xxxx.tfliteLocal$sudoapt-getupdate&&sudoapt-getinstall-yflatbuffers-compiler# Other than debian/ubuntu: https://github.com/google/flatbuffers/releases$pipinstall-Utfliteiorewriter

$tfliteiorewriter-ixxxx.tfliteusage: tfliteiorewriter
  [-h]
  -i INPUT_TFLITE_FILE_PATH
  [-v]
  [-o OUTPUT_FOLDER_PATH]
  [-r RENAME RENAME]

optional arguments:
  -h, --help
      show this help message and exit

  -i INPUT_TFLITE_FILE_PATH, --input_tflite_file_path INPUT_TFLITE_FILE_PATH
      Input tflite file path.
      If `--rename` is not used, the input/output OP name is overwritten with the definition
      information in signature_defs.

  -v, --view
      Runs in a mode that only displays the signature_defs recorded in the model.
      This mode does not rewrite the model.

  -o OUTPUT_FOLDER_PATH, --output_folder_path OUTPUT_FOLDER_PATH
      Output tflite file folder path.

  -r RENAME RENAME, --rename RENAME RENAME
      Replace with any specified name.
      --rename {from_name1} {to_name1} --rename {from_name2} {to_name2} ...
      --rename serving_default_input_1:0 aaa --rename StatefulPartitionedCall:0 bbbExecution ResultIf this tool is run without additional options, it will overwrite the input/output OP names for Netron display with the input/output name definition information insignature_defs.InputsOutputsView Mode Resulttfliteiorewriter-ixxxx.tflite-vRename Mode ResultReplace with any name by specifying{From}and{To}in the--renmae (-r)option.Beforetfliteiorewriter\-ixxxx.tflite\-rserving_default_input_1:0aaa\-rStatefulPartitionedCall:0bbbAfter
Package: tflite-model-maker
TFLite Model MakerOverviewThe TFLite Model Maker library simplifies the process of adapting and converting
a TensorFlow neural-network model to particular input data when deploying this
model for on-device ML applications.RequirementsRefer torequirements.txtfor dependent libraries that're needed to use the library and run the demo
code.Note that you might also need to installsndfilefor Audio tasks.
On Debian/Ubuntu, you can do so bysudo apt-get install libsndfile1InstallationThere are two ways to install Model Maker.Install a prebuilt pip package:tflite-model-maker.pipinstalltflite-model-makerIf you want to install nightly versiontflite-model-maker-nightly,
please follow the command:pipinstalltflite-model-maker-nightlyClone the source code from GitHub and install.gitclonehttps://github.com/tensorflow/examplescdexamples/tensorflow_examples/lite/model_maker/pip_package
pipinstall-e.TensorFlow Lite Model Maker depends on TensorFlowpip package. For GPU support, please
refer to TensorFlow'sGPU guideorinstallation guide.End-to-End ExampleFor instance, it could have an end-to-end image classification example that
utilizes this library with just 4 lines of code, each of which representing one
step of the overall process. For more detail, you could refer toColab for image classification.Step 1. Import the required modules.fromtflite_model_makerimportimage_classifierfromtflite_model_maker.image_classifierimportDataLoaderStep 2. Load input data specific to an on-device ML app.data=DataLoader.from_folder('flower_photos/')Step 3. Customize the TensorFlow model.model=image_classifier.create(data)Step 4. Evaluate the model.loss,accuracy=model.evaluate()Step 5. Export to Tensorflow Lite model and label file inexport_dir.model.export(export_dir='/tmp/')NotebookCurrently, we support image classification, text classification and question
answer tasks. Meanwhile, we provide demo code for each of them in demo folder.Overview for TensorFlow Lite Model MakerPython API ReferenceColab for image classificationColab for text classificationColab for BERT question answerColab for object detection
Package: tflite-model-maker-nightly
TFLite Model MakerOverviewThe TFLite Model Maker library simplifies the process of adapting and converting
a TensorFlow neural-network model to particular input data when deploying this
model for on-device ML applications.RequirementsRefer torequirements.txtfor dependent libraries that're needed to use the library and run the demo
code.Note that you might also need to installsndfilefor Audio tasks.
On Debian/Ubuntu, you can do so bysudo apt-get install libsndfile1InstallationThere are two ways to install Model Maker.Install a prebuilt pip package:tflite-model-maker.pipinstalltflite-model-makerIf you want to install nightly versiontflite-model-maker-nightly,
please follow the command:pipinstalltflite-model-maker-nightlyClone the source code from GitHub and install.gitclonehttps://github.com/tensorflow/examplescdexamples/tensorflow_examples/lite/model_maker/pip_package
pipinstall-e.TensorFlow Lite Model Maker depends on TensorFlowpip package. For GPU support, please
refer to TensorFlow'sGPU guideorinstallation guide.End-to-End ExampleFor instance, it could have an end-to-end image classification example that
utilizes this library with just 4 lines of code, each of which representing one
step of the overall process. For more detail, you could refer toColab for image classification.Step 1. Import the required modules.fromtflite_model_makerimportimage_classifierfromtflite_model_maker.image_classifierimportDataLoaderStep 2. Load input data specific to an on-device ML app.data=DataLoader.from_folder('flower_photos/')Step 3. Customize the TensorFlow model.model=image_classifier.create(data)Step 4. Evaluate the model.loss,accuracy=model.evaluate()Step 5. Export to Tensorflow Lite model and label file inexport_dir.model.export(export_dir='/tmp/')NotebookCurrently, we support image classification, text classification and question
answer tasks. Meanwhile, we provide demo code for each of them in demo folder.Overview for TensorFlow Lite Model MakerPython API ReferenceColab for image classificationColab for text classificationColab for BERT question answerColab for object detection
Package: tf-literature-based-discovery
# TextFlows Literature Based Discovery Module #A [TextFlows](https://github.com/xflows/textflows/) package which text mining widgets (UI components) for literature based cross-domain discovery. The package can also be used with [ClowdFlows](https://github.com/xflows/clowdflows/) 2.0.[![Documentation Status](https://readthedocs.org/projects/rdm/badge/?version=latest)](http://docs.textflows.org/)## Installation, documentation ##Please find installation instructions, examples and API reference on [Read the Docs](http://docs.textflows.org/).## Note ##Please note that this is a research project and that drastic changes can be (and are) made pretty regularly. Changes are documented in the [CHANGELOG](CHANGELOG.md).Pull requests and issues are welcome.## Contributors to the tf_literature_based_discovery package code ##Matic Perovšek (@mperice), Matjaž Juršič (@mjursic)[Knowldge Technologies Department](http://kt.ijs.si), Jožef Stefan Institute, Ljubljana
Package: tflite-support
TFLite Support is a toolkit that helps users to develop ML and deploy TFLite models onto mobile devices.This PyPI package includes the Python bindings for following features:Task Library: a set of powerful and easy-to-use task-specific libraries to
integrate TFLite models onto various platforms. See theTask Library
documentationfor more information.Metadata schemas: wraps TFLite model schema and metadata schema in Python.Metadata writer and displayer: can be used to populate the metadata and
associated files into the model, as well as converting the populated metadata
into the json format. See theMetadata
documentationfor more
information.Android Codegen tool: generates the Java model interface used in Android for
a particular model. See theCodegen tool
documentationfor more information.
Package: tflite-support-nightly
TFLite Support is a toolkit that helps users to develop ML and deploy TFLite models onto mobile devices.This PyPI package includes the Python bindings for following features:Task Library: a set of powerful and easy-to-use task-specific libraries to
integrate TFLite models onto various platforms. See theTask Library
documentationfor more information.Metadata schemas: wraps TFLite model schema and metadata schema in Python.Metadata writer and displayer: can be used to populate the metadata and
associated files into the model, as well as converting the populated metadata
into the json format. See theMetadata
documentationfor more
information.Android Codegen tool: generates the Java model interface used in Android for
a particular model. See theCodegen tool
documentationfor more information.
Package: tf-livepatch-lr
Tensorflow learning rate patcherDescriptionThis is a simple project which implements one feature: it allows you to
change the learning rate schedule without stopping the training process by
editing the specified file. The file is just a json containing the output
of thetensorflow.keras.optimizers.schedules.deserializemethod, for example:{"class_name":"PolynomialDecay","config":{"cycle":false,"decay_steps":1000,"end_learning_rate":0.01,"initial_learning_rate":0.1,"power":0.5}}Custom learning rate schedules are also supported. In order to use them passcustom_objectstoLiveLrScheduleconstructor as you'd do fortensorflow.keras.optimizers.schedules.deserialize.
Package: tflogger
TFLoggerThis is a simple library that has been carved out ofTensorFlow's codebase for being able to generate the required output format forTensorBoard.BuildingIn order to build from source the following packages are required:cmakeprotobufTo build the library simply run the following commands:git clone https://github.com/shogun-toolbox/tflogger.git
cd tflogger
mkdir build
cd build
cmake ..
makeTestingIn order to enable the unit tests add the-DBUILD-TESTS=ONcmake option to the cmake command. This will automatically create a target for building the unit tests and adds it to the ctest targets.In other words to run the unit tests:cmake -DBUILD-TESTS=ON [other cmake flags] ..
make
ctestPython interfaceCurrently only the python wrapper is available, but further targets could be specified using SWIG.For building the python wrapper you need to add the-DINTERFACE_PYTHON=ONcmake flag to the cmake command:cmake -DINTERFACE_PYTHON=ON ..UsingExamples of how to use the library are available underexampledirectory.
Package: tflstatuscli
tflstatusclitflstatuscli is a Python CLI written using the Typer CLI framework for querying the TFL Tube line statusesInstallationpipinstalltflstatuscliUsageShow status of all tube linestflclistatusShow status of particular tube linetflclistatus--line<line_name>NOTE: When searching a particular tube line, validity is checked against the TFL APIs list of tube lines which at the time of writing this includes;BakerlooCentralCircleDistrictHammersmith & CityJubileeMetropolitanNorthernPiccadillyVictoriaWaterloo & CityShow future disruptions of particular tube linetflclistatus--line<line_name>--startdateYYYY-MM-DD--enddateYYYY-MM-DDNOTE: When searching a particular tube line, validity is checked against the TFL APIs list of tube lines which at the time of writing this includes;BakerlooCentralCircleDistrictHammersmith & CityJubileeMetropolitanNorthernPiccadillyVictoriaWaterloo & CityShow all disruptions on TFL tube linestflclidisruptionsPoetry UsageRun these commands within project root dir with python poetry installed.Install package and dependancies locallypoetryinstallRun testspoetryrunpytest-vBuild wheelpoetry buildNOTE: Wheel located under /dist in project root dir
Package: tfluna-driver
TF-Luna LiDAR Python DriverTheTF-Luna sensoris a LiDAR sensor, which can be read either with I2C or Serial.This repository contains the code permitting to use the TF-Luna sensor with the serial interface.This repository is a fork oftfluna-python, and is a rewrite of the driver to a class-based structure.
The new code is also more careful when it comes to handling the serial interface: this driver is indeed intended to be used (only) as a Python resource, i.e. as follows (more details in theexamplefolder):import tfluna
import timeout_decorator

with tfluna.TfLuna(baud_speed=115200, serial_name="/dev/serial0") as tfluna:
    tfluna.get_version()
    tfluna.set_samp_rate(10)
    #tfluna.set_baudrate(57600) # can be used to change the baud_speed
    distance,strength,temperature = tfluna.read_tfluna_data()- TF-Luna + Raspberry Pi Wiring -The TF-Luna can be wired to the Raspberry Pi via the mini UART port:
Package: tflunifiedapi
TfL-python-apiTransport for London (TfL) Unified API is releasedhereis for open data users to use in their own software and services.Get StartedTo use the TfL Unified API, developers shouldregisterfor an Application ID (app_id) and Application Key (app_key).fromtfl.clientimportClientfromtfl.api_tokenimportApiTokenapp_id='APPLICATION ID'app_key='APPLICATION KEY'token=ApiToken(app_id,app_key)client=Client(token)print(client.get_line_meta_modes())print(client.get_lines(mode="bus")[0])print(client.get_lines(line_id="victoria")[0])API DocumentationThis Python library provide a thin wrapper around the TfL Unified APIs. See theTransport for London Unified APIfor more details.
Package: tflwrapper
TfL API WrapperAn API wrapper for the TfL Unified API, made with Python.Read theDocumentationInstallationpip install tflwrapperContributeThere are many ways to contribute to this repo.Submit bugsand help us verify fixes as they are checked in.Review thesource code changes.Example UsageStopPointfromtflwrapperimportstopPointapp_key="YOUR_API_KEY_HERE"stoppoint=stopPoint(app_key)arrivals=stoppoint.getStationArrivals('940GZZLUAS')print(arrivals)DisclaimerThis repository is not affiliated, associated, authorized, endorsed by, or in any way officially connected with Transport for London (TfL) or it's parent organisation Greater London Authority (GLA)
Package: tflyrics
tflyricsGenerate intriguing lyrics with TensorFlow and an internet connection.tflyricsis a Python package that allows you to easily select lyrics of
specific artists fromgenius.com, and train a deep
neural network to generate text that sounds similar to those lyrics. This
work was inspired fromThe Unreasonable Effectiveness of Recurrent Neural
NetworksandText generation with an
RNN.Example:fromtflyricsimportPoet,LyricsGeneratorartists=['Bob Dylan','Tim Buckley','The Beatles']gen=LyricsGenerator(artists,per_artist=5)ds=gen.as_dataset(batch_size=16)p=Poet()p.train_on(ds,n_epochs=10)poem=p.generate(start_string='Hey ',n_gen_chars=1000)print(poem)Quick paraphrase: aLyricsGeneratorobject makes it easy for you to create a
data pipeline that feeds from the Genius API directly into a recurrent neural
network; aPoetobject is a wrapper around a recurrent neural network.Note that the Genius API requires you to have anaccess token. Without
that,tflyricswon't be able to get lyrics for you. You can get an access
token for free atdocs.genius.com. Once you have
it you can either pass it under thetokenargument of aLyricsGeneratorconstructor, or store it as en environment variable (withexport GENIUS_ACCESS_TOKEN='<your token here>').tflyricswill detect this
environment variable automatically, if it exists.
Package: tfm
tfmSimple file manager implemented in Qt for Python (Qt6). This project aims to create a simple, desktop agnostic linux file-manager.
The project is in an alpha stage right now and thus it is not advised to use it at the moment.
If you still want to try it, see below.Featuresbasic directory traversalwindow layout with toolbar, statusbar, main table view, fs tree, bookmarks and mountsopen files using xdg-opencut, copy, paste (with progress information), renamenamed bookmarks to directoriesshow/hide dotfilesthrow files and directories in the trashdrag and drop (always moves)cli to open with a path or file url suppliedshow, mount and unmount devices and iso files using udisks2auto extract zip, rar and tar filesHow to installThere are python packages for the alpha version to download on pypi. Install at your own risk using:pip install tfmThen run using:python3 -m tfmAlso install the external requirementsudisks2,unrar(orunarorbsdtar) andutil-linux.ScreenshotsComing soon.Roadmaphigh priorityfunctionalitygive choices on handling existing files on pasteimplement network mounts, especially SMB shares (probably using smbnetfs)normal priorityfunctionalitydifferentiate default dropAction by destinationdifferentiate delete action by sourcefuzzy search in folderrestore from trash (should be implemented via a custom model/view)empty trashload file / dir / partition info in a background threaddelete foreveruiopen with dialogmake links visually distinguishablefile property dialogbookmark order configurable in the UIdrag and drop folders as bookmarks in the bookmark viewbookmark current dirbookmark directory name as tool tipabout dialoginfrastructureaddress all in code #TODOslow priorityfunctionalitymount MTP devicesopen terminal in current directoryuioptional preview panecontext sensitive views and columnsuser configurable views and columnsaccessabilitylocalizationNon-FeaturesThese features are never going to be part of tfm:dependencies on specific desktop environmentswindow management like tabs, split views or similarplugins or script supporta lot of configuration options
Package: tf-madgrad
MADGRAD Optimization Algorithm For TensorflowThis package implements the MadGrad Algorithm proposed inAdaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization(Aaron Defazio and Samy Jelassi, 2021).Table of ContentsAbout The ProjectGetting StartedPrerequisitesInstallationUsageContributingLicenseContactCitationsAbout The ProjectThe MadGrad algorithm of optimization uses Dual averaging of gradients along with momentum based adaptivity to attain results that match or outperform Adam or SGD + momentum based algorithms. This project offers a Tensorflow implementation of the algorithm along with a few usage examples and tests.PrerequisitesPrerequisites can be installed separately through therequirements.txtfile as belowpipinstall-rrequirements.txtInstallationThis project is built with Python 3 and can bepipinstalled directlypipinstalltf-madgradUsageTo use the optimizer in any tf.keras model, you just need to import and instantiate theMadGradoptimizer from thetf_madgradpackage.frommadgradimportMadGrad# Create the architectureinp=tf.keras.layers.Input(shape=shape)...op=tf.keras.layers.Dense(classes,activation=activation)# Instantiate the modelmodel=tf.keras.models.Model(inp,op)# Pass the MadGrad optimizer to the compile functionmodel.compile(optimizer=MadGrad(lr=0.01),loss=loss)# Fit the keras model as normalmodel.fit(...)This implementation is also supported for distributed training usingtf.strategySee a MNIST examplehereContributingAny and all contributions are welcome. Please raise an issue if the optimizer gives incorrect results or crashes unexpectedly during training.LicenseDistributed under the MIT License. SeeLICENSEfor more information.ContactFeel free to reach out for any issues or requests related to this implementationDarshan Deshpande -Email|LinkedInCitations@misc{defazio2021adaptivity,
      title={Adaptivity without Compromise: A Momentumized, Adaptive, Dual Averaged Gradient Method for Stochastic Optimization}, 
      author={Aaron Defazio and Samy Jelassi},
      year={2021},
      eprint={2101.11075},
      archivePrefix={arXiv},
      primaryClass={cs.LG}
}
Package: tfmcutil
tfmcutil介绍文本文件合并、分割模块：练习用的小模块。模块结构tfmcutil 模块包含一个 TextFiles 类，文件的分割、合并通过该类实例的 sizedfiles、onefile 方法完成。安装教程python3 -m pip install tfmcutil使用说明# 假设以下文件都存在且路径正确fromtfmcutilimportTextFilesf1=TextFiles(("test.txt","utf-8"))f2=TextFiles("test1.txt",("test2.txt","ascii"))f3=f1+f2print(len(f3))# 实例中包含的文件数print(f3.items())# 实例中所有的 (文件路径, 编码) 元组f3.onefile("result.txt")# 合并成一个文件f3.sizedfiles(1000,"sized.txt")# 按字符数分割成多个文件， sized_1.txt、sized_2.txt ...f4=TextFiles.fromstring("测试内容\n第二行测试内容","new.txt")# 将字符串写入 "new.txt" 并保存，返回 TextFiles 实例f5=f4+f1print(f5.totalsize())# 返回实例中所有文件大小总和，单位字节# 更多请查看源代码或 help(TextFiles)参与贡献Fork 本仓库新建 feat_xxx 分支提交代码新建 Pull Request特技使用 Readme_XXX.md 来支持不同的语言，例如 Readme_en.md, Readme_zh.mdGitee 官方博客blog.gitee.com你可以https://gitee.com/explore这个地址来了解 Gitee 上的优秀开源项目GVP全称是 Gitee 最有价值开源项目，是综合评定出的优秀开源项目Gitee 官方提供的使用手册https://gitee.com/helpGitee 封面人物是一档用来展示 Gitee 会员风采的栏目https://gitee.com/gitee-stars/
Package: tfmesos2
Tensorflow V2 for MesosWith these python module it's possible to run Tensorflow scripts against a Mesos cluster. For
detailed examples, please have a look into the examples directory.IssuesTo open an issue, please use this place:https://github.com/AVENTER-UG/tensorflow-mesos/issuesRequirementsApache Mesos minimum 1.6.xHow to installTo install tensorflow-mesos and all required packages, execute the following command.pipinstalltfmesos2How to use?Tensorflow for Mesos need some environment variables to know how and which Mesos it should use.exportMESOS_SSL=trueexportMESOS_MASTER=localhost:5050exportMESOS_USERNAME=<MESOS_PRINCIPAL>exportMESOS_PASSWORD=<MESOS_SECRET>

pythonexamples/plus.py