Saravanan666/pypi-org huggingface dataset:
Package: tensor-canvas
Tensor Canvas ðŸŽ¨A 2D graphics library for drawing directly onto tensors.Useseagerpyto support a uniform API for pytorch, tensorflow, jax, and numpy backends.
Tensor Canvas uses SDF representations for easy implementation in gpu-accelerated frameworks.Highly inefficient compared to standard gpu rendering, but much better than matplotlib. Integration with ML frameworks also means it is fully-differentiable.Installationpipinstalltensor-canvasExampleimporttensorcanvasastcimporttorchimporttensorflowastfimportjax.numpyasjnpimportnumpyasnp# define 3 cirlces with different positions, radii, and colorsx1,y1,r1,c1=34.8,5.3,2.0,[0.3,0.2,1.0]x2,y2,r2,c2=14.8,15.3,5.0,[0.1,0.9,0.8]x3,y3,r3,c3=30.8,20.3,3.0,[0.0,0.9,0.0]# canvas dimensionsheight,width,channels=32,64,3# draw 3 colored circles on a pytorch image tensorpt_canvas=torch.zeros(channels,height,width)pt_canvas=tc.draw_circle(x1,y1,r1,torch.tensor(c1),pt_canvas)pt_canvas=tc.draw_circle(x2,y2,r2,torch.tensor(c2),pt_canvas)pt_canvas=tc.draw_circle(x3,y3,r3,torch.tensor(c3),pt_canvas)# draw 3 colored cirlces on a tensorflow image tensortf_canvas=tf.zeros([height,width,channels])tf_canvas=tc.draw_circle(x1,y1,r1,tf.convert_to_tensor(c1),tf_canvas)tf_canvas=tc.draw_circle(x2,y2,r2,tf.convert_to_tensor(c2),tf_canvas)tf_canvas=tc.draw_circle(x3,y3,r3,tf.convert_to_tensor(c3),tf_canvas)# draw 3 colored cirlces on a jax image tensorjx_canvas=jnp.zeros([height,width,channels])jx_canvas=tc.draw_circle(x1,y1,r1,jnp.array(c1),jx_canvas)jx_canvas=tc.draw_circle(x2,y2,r2,jnp.array(c2),jx_canvas)jx_canvas=tc.draw_circle(x3,y3,r3,jnp.array(c3),jx_canvas)# draw 3 colored cirlces on a numpy image tensornp_canvas=np.zeros([height,width,channels])np_canvas=tc.draw_circle(x1,y1,r1,np.array(c1),np_canvas)np_canvas=tc.draw_circle(x2,y2,r2,np.array(c2),np_canvas)np_canvas=tc.draw_circle(x3,y3,r3,np.array(c3),np_canvas)# check results are indenticalassert(np.allclose(np_canvas,pt_canvas.permute(1,2,0),atol=1e-6))assert(np.allclose(np_canvas,tf_canvas,atol=1e-6))assert(np.allclose(np_canvas,jx_canvas,atol=1e-6))Notebook Example
Package: tensorcircuit-ng
English |简体中文TensorCircuit is the next generation of quantum circuit simulators with support for automatic differentiation, just-in-time compiling, hardware acceleration, and vectorized parallelism.TensorCircuit is built on top of modern machine learning frameworks and is machine learning backend agnostic. It is specifically suitable for highly efficient simulations of quantum-classical hybrid paradigm and variational quantum algorithms.Getting StartedPlease begin withQuick Start.For more information and introductions, please refer to helpfulexample scriptsandfull documentation. API docstrings and test cases intestsare also informative.The following are some minimal demos.Circuit manipulation:importtensorcircuitastcc=tc.Circuit(2)c.H(0)c.CNOT(0,1)c.rx(1,theta=0.2)print(c.wavefunction())print(c.expectation_ps(z=[0,1]))print(c.sample())Runtime behavior customization:tc.set_backend("tensorflow")tc.set_dtype("complex128")tc.set_contractor("greedy")Automatic differentiations with jit:defforward(theta):c=tc.Circuit(2)c.R(0,theta=theta,alpha=0.5,phi=0.8)returntc.backend.real(c.expectation((tc.gates.z(),[0])))g=tc.backend.grad(forward)g=tc.backend.jit(g)theta=tc.array_to_tensor(1.0)print(g(theta))InstallThe package is purely written in Python and can be obtained via pip as:pipinstalltensorcircuitWe also haveDocker support.AdvantagesTensor network simulation engine basedJIT, AD, vectorized parallelism compatible, GPU supportEfficiencyTime: 10 to 10^6 times acceleration compared to tfq or qiskitSpace: 600+ qubits 1D VQE workflow (converged energy inaccuracy: < 1%)EleganceFlexibility: customized contraction, multiple ML backend/interface choices, multiple dtype precisionsAPI design: quantum for humans, less code, more powerCiting TensorCircuitThis project is released byTencent Quantum Laband is currently maintained byShi-Xin Zhangwith contributions from the lab and open source community.If this project helps in your research, please cite our software whitepaper:TensorCircuit: a Quantum Software Framework for the NISQ Erawhich is also a good introduction for the software.ContributingFor contribution guidelines and notes, seeCONTRIBUTING.We welcomeissues,PRs, anddiscussionsfrom everyone, and these are all hosted on GitHub.Researches and ApplicationsDQASFor the application of Differentiable Quantum Architecture Search, seeapplications.
Reference paper:https://arxiv.org/pdf/2010.08561.pdf.VQNHEFor the application of Variational Quantum-Neural Hybrid Eigensolver, seeapplications.
Reference paper:https://arxiv.org/pdf/2106.05105.pdfandhttps://arxiv.org/pdf/2112.10380.pdf.VQEX - MBLFor the application of VQEX on MBL phase identification, see thetutorial.
Reference paper:https://arxiv.org/pdf/2111.13719.pdf.
Package: tensorcircuitx
English |简体中文TensorCircuit is the next generation of quantum circuit simulators with support for automatic differentiation, just-in-time compiling, hardware acceleration, and vectorized parallelism.TensorCircuit is built on top of modern machine learning frameworks and is machine learning backend agnostic. It is specifically suitable for highly efficient simulations of quantum-classical hybrid paradigm and variational quantum algorithms.Getting StartedPlease begin withQuick Start.For more information and introductions, please refer to helpfulexample scriptsandfull documentation. API docstrings and test cases intestsare also informative.The following are some minimal demos.Circuit manipulation:importtensorcircuitastcc=tc.Circuit(2)c.H(0)c.CNOT(0,1)c.rx(1,theta=0.2)print(c.wavefunction())print(c.expectation_ps(z=[0,1]))print(c.sample())Runtime behavior customization:tc.set_backend("tensorflow")tc.set_dtype("complex128")tc.set_contractor("greedy")Automatic differentiations with jit:defforward(theta):c=tc.Circuit(2)c.R(0,theta=theta,alpha=0.5,phi=0.8)returntc.backend.real(c.expectation((tc.gates.z(),[0])))g=tc.backend.grad(forward)g=tc.backend.jit(g)theta=tc.array_to_tensor(1.0)print(g(theta))InstallThe package is purely written in Python and can be obtained via pip as:pipinstalltensorcircuitWe also haveDocker support.AdvantagesTensor network simulation engine basedJIT, AD, vectorized parallelism compatible, GPU supportEfficiencyTime: 10 to 10^6 times acceleration compared to tfq or qiskitSpace: 600+ qubits 1D VQE workflow (converged energy inaccuracy: < 1%)EleganceFlexibility: customized contraction, multiple ML backend/interface choices, multiple dtype precisionsAPI design: quantum for humans, less code, more powerCiting TensorCircuitThis project is released byTencent Quantum Laband is currently maintained byShi-Xin Zhangwith contributions from the lab and open source community.If this project helps in your research, please cite our software whitepaper:TensorCircuit: a Quantum Software Framework for the NISQ Erawhich is also a good introduction for the software.ContributingFor contribution guidelines and notes, seeCONTRIBUTING.We welcomeissues,PRs, anddiscussionsfrom everyone, and these are all hosted on GitHub.Researches and ApplicationsDQASFor the application of Differentiable Quantum Architecture Search, seeapplications.
Reference paper:https://arxiv.org/pdf/2010.08561.pdf.VQNHEFor the application of Variational Quantum-Neural Hybrid Eigensolver, seeapplications.
Reference paper:https://arxiv.org/pdf/2106.05105.pdfandhttps://arxiv.org/pdf/2112.10380.pdf.VQEX - MBLFor the application of VQEX on MBL phase identification, see thetutorial.
Reference paper:https://arxiv.org/pdf/2111.13719.pdf.
Package: tensorcore
tensorcoretensorcore is a Python package for multilinear algebra supporting basic
operations on dense and sparse tensors.InstallationUsingPyPI:pip install tensorcoreLatest from thesource:git clone https://gitlab.com/cmick/tensorcore.git
cd tensorcore
python setup.py installDependenciestensorcore requires:Python (>= 3.4)NumPy (>= 1.10.0)SciPy (>= 0.14.0)scikit-learn (>= 0.16.0)UsageOptimizationTo optimize the generated bytecode slightly run the library using python-oor-ooflags. As a result, theassertstatements will be omitted (mainly
parameter value and type checking).
Package: tensorcox
TensorCoxTensorflow implementation of Coxs' partial likelihood [^1] based on a counting process representation[^2].
Combining Cox's model with the counting process representation allows for more complex censoring patterns as well as the inclusion of time-varying covariates.
Implementing this in Tensorflow with batch optimization methods gives us a powerful approach that can scale to big data problems and make full use of distributed computing environments.
Since the algorithm is fully written in Tensorflow it is easy to integrate it into larger workflows and combine it with Neural Networks etc.Installationpip install TensorCoxDocumentationDocumentation for TensorCox is available athttps://tensorcox.readthedocs.io/en/latest/References[^1]: Cox, D. R. (1972). Regression models and life‐tables.Journal of the Royal Statistical Society: Series B (Methodological),34(2), 187-202.
[^2]: Andersen, P. K., & Gill, R. D. (1982). Cox's regression model for counting processes: a large sample study.The annals of statistics, 1100-1120.
Package: tensorcro
TensorCRO: A Tensorflow-based implementation of the Coral Reef Optimization algorithm.Table of contentsAboutWhat's new?InstallUsageAboutImplementation author:     A.Palomo-Alonso (alberto.palomo@uah.es) 
Original Algorithm author: S.Salcedo-Sanz  (sancho.salcedo@uah.es)
Universidad de Alcalá (Madrid - Spain). Escuela Politécnica Superior
Signal Processing and Communications Department (TDSC)This is a Tensorflow-based implementation of the Coral Reef Optimization algorithm. The algorithm is implemented
as a Tensorflow graph, which allows to run it in GPU and TPU. The algorithm is implemented as a set of substrate layers
that can be combined with other algorithms such as Differential Evolution, Harmony Search and Random Search. The
framework also allows to implement crossover operators as blxalpha, gaussian, uniform, masked and multipoint.The framework also includes a Jupyter Notebook with an example of use of the algorithm.What's new?1.0.0First release.CRO-SL: Coral Reef Optimization algorithm with substrate layers.GPU runnable: The algorithm can be run in GPU and TPU as a graph, with +``x2` speed-up over the conventional implementations.Substrate crossovers: The framework allows to implement crossover operators as blxalpha, gaussian, uniform,
masked and multipoint.Algorithms: The framework allows to implement algorithms as substrate layers such as Differential Evolution,
Harmony Search and Random Search.Watch Replay: The algorithm also allows to watch the replay of the solutions found in the training process, with
an interactive GUI.Jupyter Notebook: The framework includes a Jupyter Notebook with example of use for the Max-Ones-From-Zeros problem.1.2.0Progress bar: The framework now also includes a progress bar to monitor the training process.Minor bug fixing.Jupyter Notebook: The framework includes a Jupyter Notebook with example of use for the Max-Ones-From-Zeros problem.1.2.1Major bug fixing.Auto-format of parameter specs.InstallTo install it you must install the dependencies. Then, you can install the package with the following command
using PIP:pipinstalltensorcroOr you can clone the repository and install it with the following commands
using Git:gitclonehttps://github.com.iTzAlver/TensorCRO.gitcdTensorCRO/dist/
pipinstall./tensorcro-1.2.0-py3-none-any.whlRequirementsPython 3.6 or higherTensorflow 2.0 or higherNumpy 1.18.1 or higherMatplotlib 3.1.3 or higherPandas 1.0.1 or higherCUDA for GPU support (optional but strongly recommended)Usage:We have a JuPyter Notebook with an example of use of the algorithm. You can find it in the folder/multimedia/notebooksof the repository.Cite:If you use this code, please cite the following paper:@inproceedings{palomo2022tensorcro,title={TensorCRO: A Tensorflow-based implementation of the Coral Reef Optimization algorithm},author={Palomo-Alonso, A and Salcedo-Sanz, S},journal={arXiv preprint arXiv:X.Y},year={2022}}
Package: tensorcross
Programming Language :: Python :: 3.9
Programming Language :: Python :: 3.10
Programming Language :: Python :: 3.11
Programming Language :: Python :: 3.12
Topic :: Software Development
Operating System :: Microsoft :: Windows
Operating System :: POSIX
Operating System :: Unix
Operating System :: MacOSRequires-Python: >=3.9
License-File: LICENSE
Requires-Dist: keras>=2.8
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: scikit-learn>=1.0Cross Validation, Grid Search and Random Search for TensorFlow Datasets. For more information see here:https://github.com/franneck94/TensorCross
Package: tensor-dash
TensorDashTensorDash is an application that lets you remotely monitor your deep learning model's metrics and notifies you when your model training is completed or crashed.Go to docsWhy Tensordash?Watch your model train in real-time.Supports all major deep learning frameworks.Remotely get details on the training and validation metrics.Get notified when your model has completed trainng or when it has crashed.Get detailed graphs on your model’s metrics.InstallationInstalling the Python PackageThere are two ways to install tensordash:Install tensordash from PyPI (recommended):Note: These installation steps assume that you are on a Linux or Mac environment.
If you are on Windows, you will need to removesudoto run the commands below.sudopipinstalltensor-dashIf you are using a virtualenv, you may want to avoid using sudo:pipinstalltensor-dashAlternatively: install tensordash from the GitHub source:First, clone TensorDash usinggit:gitclonehttps://github.com/CleanPegasus/TensorDash.gitThen,cdto the TensorDash folder and run the install command:cdTensorDash
sudopythonsetup.pyinstallInstalling the Android AppInstall the android app from the play store.How to useTensorflowKerasPyTorchFast.ai
Package: tensordata
CV, NLP, DM datasets Toolkit for Machine Learning.|API Document|中文介绍|Version|InstallationTo installthis verson from PyPI, type:pip install tensordataTo get the newest one from this repo (note that we are in the alpha stage, so there may be frequent updates), type:pip install git+git://github.com/Hourout/tensordata.gitContactPlease contact me if you have any related questions or improvements.WeChat
Package: tensordiff
A Python package for finite difference derivatives in any number of dimensions.Features:Differentiate arrays of any number of dimensions along any axisPartial derivatives of any desired orderAccuracy order can be specifiedAccurate treatment of grid boundaryIncludes standard operators from vector calculus like gradient, divergence and curlCan handle uniform and non-uniform gridsCan handle arbitrary linear combinations of derivatives with constant and variable coefficientsFully vectorized for speedCalculate raw finite difference coefficients for any order and accuracy for uniform and non-uniform grids
Package: tensordiffeq
Efficient and Scalable Physics-Informed Deep LearningCollocation-based PINN PDE solvers for prediction and discovery methods on top ofTensorflow2.X for multi-worker distributed computing.Use TensorDiffEq if you require:A meshless PINN solver that can distribute over multiple workers (GPUs) for
forward problems (inference) and inverse problems (discovery)Scalable domains - Iterated solver construction allows for N-D spatio-temporal supportsupport for N-D spatial domains with no time element is includedSelf-Adaptive Collocation methods for forward and inverse PINNsIntuitive user interface allowing for explicit definitions of variable domains,
boundary conditions, initial conditions, and strong-form PDEsWhat makes TensorDiffEq different?Completely open-sourceSelf-Adaptive Solversfor forward and inverse problems, leading to increased accuracy of the solution and stability in training, resulting in
less overall training timeMulti-GPU distributed training for large or fine-grain spatio-temporal domainsBuilt on top of Tensorflow 2.0 for increased support in new functionality exclusive to recent TF releases, such asXLA support,autographfor efficent graph-building, andgrappler supportfor graph optimization* - with no chance of the source code being sunset in a further Tensorflow version releaseIntuitive interface - defining domains, BCs, ICs, and strong-form PDEs in "plain english"*In developmentIf you use TensorDiffEq in your work, please cite it via:@article{mcclenny2021tensordiffeq,
  title={TensorDiffEq: Scalable Multi-GPU Forward and Inverse Solvers for Physics Informed Neural Networks},
  author={McClenny, Levi D and Haile, Mulugeta A and Braga-Neto, Ulisses M},
  journal={arXiv preprint arXiv:2103.16034},
  year={2021}
}Thanks to our additional contributors:@marcelodallaqua, @ragusa, @emiliocoutinho
Package: tensorfaux
A simple neural network implementation which utilizes a simimilar API to TensorFlow's Sequential models.DescriptionAn in-depth paragraph about your project and overview of use.Getting StartedDependenciesNumPypip3 install -r requirements.txtSample UsageBelow is an example of a neural network with twoDense()layers usingTanh()activation functions learning the XOR function. Although seemingly trivial, the XOR function isn'tlinearly separable, meaning linear models such aslogistic regressionand single-layerperceptronscannot learn XOR.fromnnimportSequential,Input,Dense,Tanhnp.random.seed(42)# XOR input/output dataX=np.reshape([[0,0],[0,1],[1,0],[1,1]],(4,2,1))Y=np.reshape([[0],[1],[1],[0]],(4,1,1))# Model instantiationmodel=Sequential([Input(2),Dense(3),Tanh(),Dense(1),Tanh(),])model.compile()# Model trainingmodel.fit(X,Y,epochs=10000,learning_rate=0.01)# PredictY_pred=nn.predict(X)for(y_true,y_pred)inzip(Y,Y_pred):print(f'Actual:{y_true}, Predicted:{y_pred}')Output:Actual: [[0]], Predicted: [[0.0003956]]
Actual: [[1]], Predicted: [[0.97018558]]
Actual: [[1]], Predicted: [[0.97092169]]
Actual: [[0]], Predicted: [[0.00186825]]APISequentialnn.Sequential(layers=[])FunctionDescription__init__Instantiates a newSequentialmodel. If given a list of layers, it will add these to the model, similar toadd().addAdd a single layer to the model.compileTakes the added layers and the parameters ofcompileto generate a trainable model.fitAfter compilation,fit()trains the model on its inputs and outputs.predictMakes predictions after fitting to the data. Takes in a subset of the input data to make a prediction.LayersLayerLayerAbstract class for the layers API. This shouldn't be used in an instance of a model.InputLayerThis should always be the first layer of theSequentialmodel. Since the other layers take in an explicitoutput_sizeas their input, they infer theirinput_sizefrom the previous layer'soutput_size. This means we must declare the model's firstinput_size.DenseLayerJust your regular densely-connected NN layer. At the moment, the dense layer only performs the dot product and bias addition, but no activation function. The activation function is out-sourced to theActivationlayers.ActivationLayerApplies an activation function to an output.TanhLayerHyperbolic tangent activation function.AcknowledgementsTensorFlow Brand Guidelines@allejo/jekyll-docs-themePackaging Projects Tutorial
Package: tensorfit
TensorFitTensorFit is an open source package for curve fitting. This package was designed with the intention of allowing students and researchers to quickly assess parametric functions for explaining experimental data and trends. The package currently only supports univariate functions,i.e.functions with a single independent variable.InstallpipinstalltensorfitUsageImport packages as needed.>>>importnumpyasnp>>>fromtensorfitimportTensorFunctionGenerate from fake experimental data for the purpose of demonstration.>>>x=np.linspace(-1,1,100)>>>y=9.8*x**2+6.1*x+0.87*np.random.randn(*x.shape)Create and initialize TensorFunction instance using a parametric model for your fitting function and a set of starting parameters.>>>tfunc=TensorFunction()>>>my_func="self.a * self.X ** 2 + self.b * self.X + self.c">>>init_params={"a":0.1,"b":0.1,"c":0.1}>>>tfunc.initialize(func=my_func,params=init_params)After initialization, you can make a call to.fit()to fit yourTensorFunction()to the experimental data.>>>tfunc.fit(x,y,num_rounds=10000,early_stopping_rounds=10,verbose_eval=0)Earlystopping,bestiterationis:[Episode-6046]mse:0.81566346Fitted parameters and a summary of your fit can then be looked at.>>>tfunc.Params{'a':9.560993,'b':6.0437393,'c':0.11265278}>>>tfunc.Summary{'mse':0.81566346,'r2':0.9623992666602135}LicenseMIT LicenseThis library uses:numpy, which is distributed under the BSD 3-Clause license.tensorflow, which is distributed under the Apache 2.0 license.
Package: tensorfloe
axtriaalatoraxtriaalator is a Python package that provides various machine learning algorithms for natural language processing tasks.InstallationYou can install axtriaalator using pip:pipinstallaxtriaalator


Usage
Onceinstalled,youcanusetheprogramsprovidedbyaxtriaalatorinyourPythonscriptsorfromthecommandline.


Documentation
FordetaileddocumentationonMyModuleanditsprograms,pleasevisitthedocumentationwebsite.

License
ThisprojectislicensedundertheMITLicense-seetheLICENSEfilefordetails.
Package: tensorflow
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.