Saravanan666/pypi-org huggingface dataset:
Package: tensorflow-fer
Project DescriptionFacial Emotion Recognition using Tensorflow.

It creates a bounding box around the face of the person present in the picture and put a text
at the top of the bounding box representing the recognised emotion.Installpip install tensorflow-ferRequirementstensorflow-gpu>=2.0.0

opencv-python

numpy>=1.18.1Usage:from tensorflow_fer.emotion_recognition import EmotionRecognition

import cv2 as cv

er = EmotionRecognition()

cam = cv.VideoCapture(0)

success, frame = cam.read()

frame = er.recognise(frame, return_type='BGR')

cv.imshow('frame', frame)

cv.waitkey(0)Argumentsframe = er.recognise_emotion(frame, return_type='BGR')

return_type='BGR' or 'RGB'References"Challenges in Representation Learning: A report on three machine learning
contests." I Goodfellow, D Erhan, PL Carrier, A Courville, M Mirza, B
Hamner, W Cukierski, Y Tang, DH Lee, Y Zhou, C Ramaiah, F Feng, R Li,
X Wang, D Athanasakis, J Shawe-Taylor, M Milakov, J Park, R Ionescu,
M Popescu, C Grozea, J Bergstra, J Xie, L Romaszko, B Xu, Z Chuang, and
Y. Bengio. arXiv 2013.
Package: tensorflow-font2char2word2sent2doc
# tensorflow-font2char2word2sent2doc[![PyPI version](https://badge.fury.io/py/tensorflow-font2char2word2sent2doc.svg)](https://badge.fury.io/py/tensorflow-font2char2word2sent2doc)
[![Python versions](https://img.shields.io/pypi/pyversions/tensorflow-font2char2word2sent2doc.svg)](setup.py)
[![wercker status](https://app.wercker.com/status/1d697a9a04d1e9166dfa9705ff10bbe5/s/master“wercker status”)](https://app.wercker.com/project/byKey/1d697a9a04d1e9166dfa9705ff10bbe5)
[![License](https://img.shields.io/badge/license-unlicense-lightgray.svg)](https://unlicense.org)TensorFlow implementation of [Hierarchical Attention Networks for Document Classification](https://www.google.co.jp/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&uact=8&ved=0ahUKEwiNxqa286zRAhUMqY8KHZYlCWEQFggjMAA&url=https%3A%2F%2Fwww.cs.cmu.edu%2F~diyiy%2Fdocs%2Fnaacl16.pdf&usg=AFQjCNFokKFJ1g7WQSDYkYEM82XwhGiDGw&sig2=iHJc5O86dNQrexisfSA7mw) with some extension.## License[The Unlicense](https://unlicense.org)
Package: tensorflow-gnn
TensorFlow GNNSummaryTensorFlow GNN is a library to buildGraph Neural Networkson the TensorFlow platform.
It provides...atfgnn.GraphTensortype to represent
graphs with aheterogeneous schema, that is,
multiple types of nodes and edges;tools fordata preparation,
notably agraph samplerto convert a huge database into a stream of reasonably-sized subgraphs for
training and inference;a collection ofready-to-use modelsand Keras layers to do your ownGNN modeling;a high-level API for trainingorchestration.This library is an OSS port of a Google-internal library used in a broad variety
of contexts, on homogeneous and heterogeneous graphs, and in conjunction with
other scalable graph mining tools.For background and discussion, please see O. Ferludin et al.:TF-GNN: Graph Neural Networks in TensorFlow,
2023 (full citation below).QuickstartGoogle Colab lets you run TF-GNN demos from your browser, no installation
required:Molecular Graph
Classificationwith the MUTAG dataset.Solving OGBN-MAG
end-to-endtrains a model on heterogeneous sampled subgraphs from the popularOGBN-MAGbenchmark.Learning shortest paths with
GraphNetworksdemonstrates an advanced Encoder/Process/Decoder architecture for predicting
the edges of a shortest path.For all colabs and user guides, please see theDocumentation overviewpage, which also links to theAPI docs.Installation InstructionsThe latest stable release of TensorFlow GNN is available frompip install tensorflow_gnnFor installation from source, see ourDeveloper
Guide.Key platform requirements:TensorFlow 2.12, 2.13, 2.14 or 2.15, and any GPU drivers it needs
[instructions].Keras v2, as traditionally included with TensorFlow 2.x.
(TF-GNN does not work with the new multi-backend Keras v3.)Apache Beam for distributed graph sampling.TF-GNN is developed and tested on Linux. Running on other platforms supported
by TensorFlow may be possible.CitationWhen referencing this library in a paper, please cite theTF-GNN paper:@article{tfgnn,
  author  = {Oleksandr Ferludin and Arno Eigenwillig and Martin Blais and
             Dustin Zelle and Jan Pfeifer and Alvaro Sanchez{-}Gonzalez and
             Wai Lok Sibon Li and Sami Abu{-}El{-}Haija and Peter Battaglia and
             Neslihan Bulut and Jonathan Halcrow and
             Filipe Miguel Gon{\c{c}}alves de Almeida and Pedro Gonnet and
             Liangze Jiang and Parth Kothari and Silvio Lattanzi and 
             Andr{\'{e}} Linhares and Brandon Mayer and Vahab Mirrokni and
             John Palowitch and Mihir Paradkar and Jennifer She and
             Anton Tsitsulin and Kevin Villela and Lisa Wang and David Wong and
             Bryan Perozzi},
  title   = {{TF-GNN:} Graph Neural Networks in TensorFlow},
  journal = {CoRR},
  volume  = {abs/2207.03522},
  year    = {2023},
  url     = {http://arxiv.org/abs/2207.03522},
}
Package: tensorflow-gpu
tensorflow-gpu has been removed. Please install tensorflow instead.
The tensorflow package supports GPU accelerated operations via Nvidia CUDA.Removal Informationtensorflow and tensorflow-gpu have been the same package since TensorFlow
2.1, released in September 2019. Although the checksums differ due to metadata,
they were built in the same way and both provide GPU support via Nvidia CUDA.
As of December 2022, tensorflow-gpu has been removed and has been replaced with
this new, empty package that generates an error upon installation.All existing versions of tensorflow-gpu are still available, but the
TensorFlow team has stopped releasing any new tensorflow-gpu packages, and
will not release any patches for existing tensorflow-gpu versions.About this packageThis simple package raises a warning if setup.py is executed as part of a
package installation. This intentionally prevents users from installing
the package.To build and upload this package's source distribution (sdist) to testpypi:$ vim setup.cfg  # update the version number and package name
$ python3 -m pip install --user twine
$ python3 setup.py sdist
$ twine upload --repository testpypi dist/*
$ pip3 install the_name_of_your_test_package -i https://test.pypi.org/simple/ --extra-index-url https://pypi.org/simple
Package: tensorflow-history-plot
TensorflowHistoryPlotA tool for visualize Tensorflow history result.Installpip install tensorflow-history-plotUsenum_classes=5model=Sequential([layers.Conv2D(16,3,padding='same',activation='relu'),layers.MaxPooling2D(),layers.Conv2D(32,3,padding='same',activation='relu'),layers.MaxPooling2D(),layers.Conv2D(64,3,padding='same',activation='relu'),layers.MaxPooling2D(),layers.Flatten(),layers.Dense(128,activation='relu'),layers.Dense(num_classes)])model.compile(optimizer='adam',loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),metrics=['accuracy'])epochs=10history=model.fit(train_ds,validation_data=val_ds,epochs=epochs)fromtensorflow_history_plotimportshow_accshow_acc.plot(history)
Package: tensorflow-image
Tensorflow Image基本的な画像関連AIの学習と推論が簡単にできるライブラリ。このライブラリでは画像の分類、回帰、マルチラベル分類、pix2pixのディープラーニングを 2, 3 行のコードで容易に実行することができます。学習に使用した画像数や学習履歴などの情報も保存されるため、後から確認したりグラフに表示することができます。また、続きから学習を再開することも可能です。そのため、学習の中止と再開を何度も繰り返しても一つの連続した学習履歴として表示できます。テストデータに対する推論結果を、正解のパラメータと比較できる図として表示することができます。ディープラーニングで設定することのできる各パラメータは汎用的な値で最適化されています。インストール方法pipinstalltensorflow-image使用方法"./dataset/" ディレクトリに教師データとなる画像を格納します。分類問題ではクラスごとにディレクトリを分けて画像を格納することで、ディレクトリ名を分類の名前として認識します。回帰問題と多ラベル分類では、各画像の情報を格納した csvファイル のパスを指定する必要があります。それぞれの csvファイル 内構成は以下のようになります。filename は この csvファイル からの相対パスになります。回帰問題|  filename       |  class  || --------------- | ------- ||  ./image/a.png  |  0.3    ||  ./image/b.png  |  0.6    |多ラベル分類|  filename       |  labels                   || --------------- | ------------------------- ||  ./image/a.png  |  label_a,label_c,label_d  ||  ./image/b.png  |  label_b                  |AIをトレーニングする。importtensorflow_imageastfimg# 画像分類icai=tfimg.ImageClassificationAi("model_name_classification")icai.train_model("./dataset/",epochs=6,model_type=tfimg.ModelType.efficient_net_v2_b0,trainable=True)# 画像の回帰問題rai=tfimg.ImageRegressionAi("model_name_regression")rai.train_model("./dataset/data.csv",epochs=6,model_type=tfimg.ModelType.efficient_net_v2_b0,trainable=True)# 多ラベル分類mlai=tfimg.ImageMultiLabelAi("model_name_multilabel")mlai.train_model("./dataset/data.csv",epochs=6,model_type=tfimg.ModelType.efficient_net_v2_b0,trainable=True)学習済みモデルを読み込んで推論する。importtensorflow_imageastfimg# 画像分類icai=tfimg.ImageClassificationAi("model_name_classification")icai.load_model()result=icai.predict("./dataset/sample.png")# 推論するprint(icai.result_to_classname(result))# 画像の回帰問題rai=tfimg.ImageRegressionAi("model_name_regression")rai.load_model()result=rai.predict("./dataset/sample.png")# 推論する# 多ラベル分類mlai=tfimg.ImageMultiLabelAi("model_name_multilabel")mlai.load_model()result=mlai.predict("./dataset/sample.png")# 推論するprint(mlai.result_to_label_dict(result))print(mlai.result_to_labelname(result,0.5))対応モデルMobileNetV2VGG16EfficientNetV2ResNet-RSResNet-RS を使用するには以下のリポジトリを追加で pip install する必要がありますgit+https://github.com/sebastian-sz/resnet-rs-keras@main制作者Nicoyou@NicoyouSoft
Package: tensorflow-image-models
Installpip install tensorflow_image_models==0.0.11Usagefrom tensorflow_image_models import list_modelslist_models()models list:DPN92DPN98DPN131DPN107EfficientNetB0EfficientNetB1..........VGG16VGG19..........ViTGaint_14ViTGaint_16Create Modelfrom tensorflow_image_models import EfficientNetmodel = EfficientNet.EfficientNetB0(classes=10)model.summary()from tensorflow_image_models import Inceptionmodel = Inception.InceptionV4(classes=3, input_shape=(299,299,3)model.summary()Here is a list of input shape expected for each modelEfficientNetB0 = (224, 224, 3)EfficientNetB1 = (240, 240, 3)EfficientNetB2 = (260, 260, 3)EfficientNetB3 = (300, 300, 3)EfficientNetB4 = (380, 380, 3)EfficientNetB5 = (456, 456, 3)EfficientNetB6 = (528, 528, 3)EfficientNetB7 = (600, 600, 3)AlexNet = (224, 224, 3)InceptionResNetV2 = (299, 299, 3)InceptionV3 = (299, 299, 3)LeNet = (32,  32,  1)MobileNetV2 = (224, 224, 3)ResNet18 = (224, 224, 3)ResNet34 = (224, 224, 3)VoVNet27 = (224, 224, 3)VoVNet39 = (224, 224, 3)VoVNet57 = (224, 224, 3)ViTTiny  = (224, 224, 3)ViTSmall_8 = (224, 224, 3)ViTSmall_16 = (224, 224, 3)ViTSmall_32 = (224, 224, 3)ViTBase_8 = (224, 224, 3)ViTBase_16 = (224, 224, 3)ViTBase_32 = (224, 224, 3)ViTLarge_14 = (224, 224, 3)ViTLarge_16 = (224, 224, 3)ViTLarge_32 = (224, 224, 3)ViTHuge_14 = (224, 224, 3)ViTHuge_16 = (224, 224, 3)ViTGaint_14 = (224, 224, 3)ViTGaint_16 = (224, 224, 3)The rest of the models receive any shape, and the default shape is (128,128,3)LicenseThis project is licensed under the MIT LicenseAuthorDEEPOLOGY LAB
Package: tensorflow-intel
TensorFlow is an open source software library for high performance numerical computation. Its flexible architecture allows easy deployment of computation across a variety of platforms, and from desktops to clusters of servers to mobile and edge devices.Originally developed by researchers and engineers from the Google Brain team (https://research.google/teams/brain/) within Google's AI organization, TensorFlow comes with strong support for machine learning and deep learning with a flexible numerical computation core that can be used across many other scientific domains. TensorFlow is licensed underApache 2.0.This optimized version of TensorFlow for Windows OS has been produced by Intel. In order to take full advantage of Intel architecture and to extract maximum performance, the TensorFlow framework has been optimized using oneAPI Deep Neural Network Library (oneDNN) primitives, a popular performance library for deep learning applications. For more information on the optimizations as well as performance data, see this blog postFaster AI Inference with Intel Optimization for TensorFlow. To activate oneDNN, execute the following in the Windows command prompt: set TF_ENABLE_ONEDNN_OPTS=1 before running your AI/ML workload.
Package: tensorflowjs
# tensorflowjs: The Python Package for TensorFlow.jsThetensorflowjspip package contains libraries and tools for
[TensorFlow.js](https://js.tensorflow.org).Use following command to install the library with support of interactive CLI:`bash pip install tensorflowjs[wizard] `Then, run the following to see a list of CLI options`bash tensorflowjs_converter--help`or, use the wizard`bash tensorflowjs_wizard `Alternatively, run the converter via its Bazel target. This must be run from withing the tfjs repo:`bash yarn bazel run//tfjs-converter/python/tensorflowjs/converters:converter----help`## DevelopmentThe python tests are run with Bazel.`bash yarn bazel test//tfjs-converter/python/...`Alternatively, runyarn run-python-teststo run the above command.To debug a specific test case, use the–test_filteroption. For example,`bash yarn bazel test//tfjs-converter/python/tensorflowjs/converters:tf_saved_model_conversion_v2_test--test_filter=ConvertTest.test_convert_saved_model_v1`Interactive debugging with breakpoints is supported bydebugpyin VSCode.
To enable debugging, put this code at the top of the test file you want to
debug.`python import debugpydebugpy.listen(('localhost',5724))print("Waitingfor debugger to connect. Seetfjs-converterpython README") debugpy.wait_for_client() `You may also need to add the following dependency to the test target in the
BazelBUILDfile if it’s not already present.`starlark"//tfjs-converter/python/tensorflowjs:expect_debugpy_installed"`Then, run the test withbazel run –config=debugpyand connect
the VSCode debugger by selecting thePython: Attach (Converter)option.
Package: tensorflow-kernels
Tensorflow-kernelsA package with Tensorflow (both CPU and GPU) implementation of most popular Kernels for kernels methods (SVM, MKL...).Those kernels works with tensor as inputs.The main idea of this project is to exploit the powerfull of GPUs and modern CPUs on matrix and kernels elaborations.
Actually the implemented kernels are:LinearRBFPolynomialCosineSimilarityFourierSplineAttention:Due to the GPUs usage the precision of decimal numbers may be different, and hence, the results may be slightly differs as wellAttention 2:Due to exploit the power of GPUs it's strongly recommended to work with float32 or even in half precision float16.Examples:A simple example withPolynomialKernelimportnumpyasnpimporttensorflowastffromkernels.polynomial_kernelimportPolynomialKernelfromkernelsimportarray_to_tensor,tensor_to_arrayn=2000p=1000a=np.random.random((n,p)).astype(np.float32)b=np.random.random((n,p)).astype(np.float32)x=array_to_tensor(a,dtype=tf.float32)y=array_to_tensor(b,dtype=tf.float32)poly=PolynomialKernel(scale=1,bias=0,degree=4)kernel=poly.compute(x,y)print(tensor_to_array(kernel,dtype=np.float32))A simple example withPSpectrumKernel.Attention:PSpectrum is still experimental and it exploitseager computationin order to work properly.
Furthermore it maybe won't works with Tensorflow 2.0 since some packages have been removed.Attention 2:Due to the usage of the typetf.stringcomputation will be shared between GPUs and CPUs.Attention 3:This kernel return tensor with type tf.int64.importnumpyasnpimporttensorflowastffromkernels.experimental.p_spectrum_kernelimportPSpectrumKernelfromkernelsimportarray_to_tensor,tensor_to_arraya=np.array(['aaaaaaaa','bbbbbbb','ccccc','aaaaaaa','cccccc','bbbbbb'])b=np.array(['aaaaaaaa','bbbbbbb','aaaaaaa','cccccc'])x=array_to_tensor(a,dtype=tf.string)y=array_to_tensor(b,dtype=tf.string)p_spectrum=PSpectrumKernel(p=3)kernel=p_spectrum.compute(x,y)print(tensor_to_array(kernel,dtype=np.float32))Credits:The idea was born by using methods available here:https://github.com/gmum/pykernels
Package: tensorflow-macos
TensorFlow is an open source software library for high performance numerical
computation. Its flexible architecture allows easy deployment of computation
across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters
of servers to mobile and edge devices.Originally developed by researchers and engineers from the Google Brain team
within Google's AI organization, it comes with strong support for machine
learning and deep learning and the flexible numerical computation core is used
across many other scientific domains. TensorFlow is licensed underApache
2.0.
Package: tensorflow-metadata
TensorFlow MetadataTensorFlow Metadata provides standard representations for metadata that are
useful when training machine learning models with TensorFlow.The metadata serialization formats include:A schema describing tabular data (e.g., tf.Examples).A collection of summary statistics over such datasets.A problem statement quantifying the objectives of a model.The metadata may be produced by hand or automatically during input data
analysis, and may be consumed for data validation, exploration, and
transformation.
Package: tensorflow-metal
Accelerate training of machine learning models with TensorFlow right on your Mac.
Install the base tensorflow and the tensorflow-metal PluggableDevice to accelerate training with Metal on Mac GPUs.You can learn more about TensorFlow PluggableDeviceshere.tensorflowtensorflow-metalMacOsfeaturesv2.5v0.1.212.0+Pluggable devicev2.6v0.2.012.0+Variable seq. length RNNv2.7v0.3.012.0+Custom op supportv2.8v0.4.012.0+RNN perf. improvementsv2.9v0.5.012.1+Distributed trainingv2.10v0.6.012.1+v2.11v0.7.012.1+v2.12v0.8.012.1+v2.13v1.0.012.1+FP16 and BF16 supportv2.14v1.1.012.1+
Package: tensorflow-model-remediation
TensorFlow Model RemediationTensorFlow Model Remediation is a library that provides solutions for machine
learning practitioners working to create and train models in a way that reduces
or eliminates user harm resulting from underlying performance biases.InstallationYou can install the package frompip:$pipinstalltensorflow-model-remediationNote: Make sure you are using TensorFlow 2.x.DocumentationThis library contains a collection of machine learning remediation techniques
for addressing potential bias in a model.Currently TensorFlow Model Remediation contains the below techniques:MinDiff technique: Typically used to ensure that a model predicts the
preferred label equally well for all values of a sensitive attribute.
Helpful when trying to achieveequality of
opportunity.Counterfactual Logit Pairing technique: Typically used to ensure that a
model’s prediction does not change between “counterfactual pairs”, where the
sensitive attribute referenced in a feature is different. Helpful when
trying to achievecounterfactual fairness.We recommend starting with theoverview guideto
get an idea of TensorFlow Model Remediation. Next try one of our interactive
guides like theMinDiff tutorial notebook.Counterfactual tutorial notebook.importtensorflow_model_remediationastfmrimporttensorflowastf# Start by defining a Keras model.original_model=...# Next pick the remediation technique you'd like to use. For example, a# MinDiff implementation might look like the below:# Set the MinDiff weight and choose a loss.min_diff_loss=tfmr.min_diff.losses.MMDLoss()min_diff_weight=1.0# Hyperparamater to be tuned.# Create a MinDiff model.min_diff_model=tfmr.min_diff.keras.MinDiffModel(original_model,min_diff_loss,min_diff_weight)# Compile the MinDiff model as you normally would do with the original model.min_diff_model.compile(...)# Create a MinDiff Dataset and train the min_diff_model on it.min_diff_model.fit(min_diff_dataset,...)DisclaimersIf you're interested in learning more about responsible AI practices, includingfairness, please see Google AI'sResponsible AI Practices.tensorflow/model_remediationis Apache 2.0 licensed. See theLICENSEfile.
Package: tensorflow-modules
tensorflow-modulestensorflow layers, modelsRequirementspython >= 3.8.6Setupcreate venv$python-mvenv$(pwd)activate venv$sourcebin/activate
$pipinstall-rrequirements.txtdeactivate venv$deactivatetest$python-munittestdiscovertestspackage releaserelease$rm-rftensorflow_modules.egg-info/*dist/*
$pythonsetup.pysdistbdist_wheel
$twineupload--repositorypypidist/*check$openhttps://pypi.org/project/tensorflow-modules
$pip--no-cache-dirinstall--upgradetensorflow-modules
Package: tensorflow-murmur
tensorflow_murmurSmall TF addons libraryLayersLSTMTransformerLayer, EncoderLayer, DecoderLayerPositionalEmbedding, IdfEmbedding, SpatialEmbeddingRandomIndexing, LanguageMasking, IndexedSlice, Splitter, MultiText, ColumnTransfornerLossesmasked_loss, masked_multi_lossMetricsmasked_accuracy, masked_multi_cosine_similaritysparse_target_accuracy, batch_average
Package: tensorflow-nearest-neighbours
TensorFlow Nearest Neighbours OpGiven an embedding matrixEM, and batch of word embeddingsxfind nearest embedding for each tokenx_ijinEM.importtensorflowastffromtensorflow_nearest_neighboursimportnearest_neighbourstf.debugging.set_log_device_placement(True)x=tf.random.uniform(shape=[8,10,32])EM=tf.random.uniform(shape=[500,32])result=nearest_neighbours(x,EM)print(result.shape)Instalationpipinstalltensorflow_nearest_neighbours
Package: tensorflow_nlp
This is a natural language process toolkit. The toolkit includes implement of segment, pos tagging, named entity recognition, text classification,
text representation, textsum, relation extract, chatbot, QA and so on. so far, the implementation is experimental,
should not be used for the production environment.Implementchatbotnamed entity recognitionpoemspos taggingrelation extractionsegmentsentimenttext classificationtext representationtext summarizationRequirementThe requirement in file [requirements.txt](requirements.txt), and you can use the following command to install required libs:$ pip install -r requirements.txtLicenseThe license is Apache License 2.0
Package: tensorflow-nufft
TensorFlow NUFFTTensorFlow NUFFT is a fast, native non-uniform fast Fourier transform op for
TensorFlow. It provides:Fast CPU/GPU kernels. The TensorFlow framework automatically handles device
placement as usual.A simple, well-documented Python interface.Gradient definitions for automatic differentiation.Shape functions to support static shape inference.The underlying algorithm is based on the NUFFT implementation by the Flatiron
Institute. Please refer toFINUFFTandcuFINUFFTfor more details.InstallationYou can install TensorFlow NUFFT withpip:pip install tensorflow-nufftNote that only Linux wheels are currently being provided.TensorFlow compatibilityEach TensorFlow NUFFT release is compiled against a specific version of
TensorFlow. To ensure compatibility, it is recommended to install matching
versions of TensorFlow and TensorFlow NUFFT according to the table below.TensorFlow NUFFT VersionTensorFlow CompatibilityRelease Datev0.12.0v2.11.xNov 27, 2022v0.11.0v2.10.xOct 12, 2022v0.10.1v2.10.xSep 26, 2022v0.10.0v2.10.xSep 7, 2022v0.9.0v2.9.xSep 5, 2022v0.8.1v2.9.xJun 23, 2022v0.8.0v2.9.xMay 20, 2022v0.7.3v2.8.xMay 4, 2022v0.7.2v2.8.xApr 29, 2022v0.7.1v2.8.xApr 6, 2022v0.7.0v2.8.xFeb 8, 2022v0.6.0v2.7.xJan 27, 2022v0.5.0v2.7.xDec 12, 2021v0.4.0v2.7.xNov 8, 2021v0.3.2v2.6.xAug 18, 2021v0.3.1v2.6.xAug 18, 2021v0.3.0v2.6.xAug 13, 2021UsageOnce installed, you can perform NUFFTs in your TensorFlow code simply as:importtensorflow_nufftastfftoutputs=tfft.nufft(inputs,points)See the documentation for thetfft.nufftfunction to learn more about the different parameters.DocumentationVisit thedocsfor the API
reference and examples of usage.IssuesIf you use this package and something does not work as you expected, pleasefile an issuedescribing your problem. We're here to help!CreditsIf you find this software useful in your research, pleasecite us.ContributorsSee CONTRIBUTORS for a list of people who have contributed to this project.
Thank you!All contributions are welcome. If there is an issue you would like to address
or a feature you would like to add, you might want to begin by commenting on
the corresponding issue (or if it doesn't exist yet, by creating a new issue).