Saravanan666/pypi-org huggingface dataset:
Package: neuralhydrology
Python library to train neural networks with a strong focus on hydrological applications.This package has been used extensively in research over the last years and was used in various academic publications.
The core idea of this package is modularity in all places to allow easy integration of new datasets, new model
architectures or any training-related aspects (e.g. loss functions, optimizer, regularization).
One of the core concepts of this code base are configuration files, which let anyone train neural networks without
touching the code itself. The NeuralHydrology package is built on top of the deep learning frameworkPyTorch, since it has proven to be the most flexible and useful for research purposes.We (the AI for Earth Science group at the Institute for Machine Learning, Johannes Kepler University, Linz, Austria) are using
this code in our day-to-day research and will continue to integrate our new research findings into this public repository.Documentation:neuralhydrology.readthedocs.ioResearch Blog:neuralhydrology.github.ioBug reports/Feature requestshttps://github.com/neuralhydrology/neuralhydrology/issuesCite NeuralHydrologyIn case you use NeuralHydrology in your research or work, it would be highly appreciated if you include a reference to ourJOSS paperin any kind of publication.@article{kratzert2022joss,title={NeuralHydrology --- A Python library for Deep Learning research in hydrology},author={Frederik Kratzert and Martin Gauch and Grey Nearing and Daniel Klotz},journal={Journal of Open Source Software},publisher={The Open Journal},year={2022},volume={7},number={71},pages={4050},doi={10.21105/joss.04050},url={https://doi.org/10.21105/joss.04050},}ContactFor questions or comments regarding the usage of this repository, please use thediscussion sectionon Github. For bug reports and feature requests, please open anissueon GitHub.
In special cases, you can also reach out to us by email: neuralhydrology(at)googlegroups.com
Package: neuralink
# Neuralink[![Python 3.7](https://img.shields.io/badge/python-3.7-blue.svg)](https://www.python.org/downloads/release/python-370/)
[![License](https://img.shields.io/hexpm/l/num)](https://github.com/couyang24/DeepLearning/blob/main/LICENSE)
[![Black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)![image](https://t3.ftcdn.net/jpg/02/88/57/34/240_F_288573401_T3cPZFeoRgfUWdPtC5hm99xFoJbWJYcd.jpg)This is a learning notes from deeplearning.AI at Coursera, which also provides basic DeepLearning tools.
Package: neuralintents
neuralintentsStill in a buggy alpha state.Setting Up A Basic Assistantfromneuralintents.assistantsimportBasicAssistantassistant=BasicAssistant('intents.json')assistant.fit_model(epochs=50)assistant.save_model()done=Falsewhilenotdone:message=input("Enter a message: ")ifmessage=="STOP":done=Trueelse:print(assistant.process_input(message))Binding Functions To Requestsfromneuralintents.assistantsimportBasicAssistantstocks=['AAPL','META','TSLA','NVDA']defprint_stocks():print(f'Stocks:{stocks}')assistant=BasicAssistant('intents.json',method_mappings={"stocks":print_stocks,"goodbye":lambda:exit(0)})assistant.fit_model(epochs=50)assistant.save_model()done=Falsewhilenotdone:message=input("Enter a message: ")ifmessage=="STOP":done=Trueelse:print(assistant.process_input(message))Sample intents.json File{"intents":[{"tag":"greeting","patterns":["Hi","How are you","Is anyone there?","Hello","Good day","Whats up","Hey","greetings"],"responses":["Hello!","Good to see you again!","Hi there, how can I help?"],"context_set":""},{"tag":"goodbye","patterns":["cya","See you later","Goodbye","I am Leaving","Have a Good day","bye","cao","see ya"],"responses":["Sad to see you go :(","Talk to you later","Goodbye!"],"context_set":""},{"tag":"programming","patterns":["What is progamming?","What is coding?","Tell me about programming","Tell me about coding","What is software development?"],"responses":["Programming, coding or software development, means writing computer code to automate tasks."],"context_set":""},{"tag":"resource","patterns":["Where can I learn to code?","Best way to learn to code","How can I learn programming","Good programming resources","Can you recommend good coding resources?"],"responses":["Check out the NeuralNine YouTube channel and The Python Bible series (7 in 1)."],"context_set":""},{"tag":"stocks","patterns":["What are my stocks?","Which stocks do I own?","Show my stock portfolio"],"responses":["Here are your stocks!"],"context_set":""}]}
Package: neuralintentsplus
neuralintentsStill in a buggy alpha state.Setting Up A Basic AssistantfromneuralintentsimportGenericAssistantassistant=GenericAssistant('intents.json',model_name="test_model")assistant.train_model()assistant.save_model()done=Falsewhilenotdone:message=input("Enter a message: ")ifmessage=="STOP":done=Trueelse:assistant.request(message)Binding Functions To RequestsfromneuralintentsimportGenericAssistantdeffunction_for_greetings():print("You triggered the greetings intent!")# Some action you want to takedeffunction_for_stocks():print("You triggered the stocks intent!")# Some action you want to takemappings={'greeting':function_for_greetings,'stocks':function_for_stocks}assistant=GenericAssistant('intents.json',intent_methods=mappings,model_name="test_model")assistant.train_model()assistant.save_model()done=Falsewhilenotdone:message=input("Enter a message: ")ifmessage=="STOP":done=Trueelse:assistant.request(message)Sample intents.json File{"intents":[{"tag":"greeting","patterns":["Hi","How are you","Is anyone there?","Hello","Good day","Whats up","Hey","greetings"],"responses":["Hello!","Good to see you again!","Hi there, how can I help?"],"context_set":""},{"tag":"goodbye","patterns":["cya","See you later","Goodbye","I am Leaving","Have a Good day","bye","cao","see ya"],"responses":["Sad to see you go :(","Talk to you later","Goodbye!"],"context_set":""},{"tag":"stocks","patterns":["what stocks do I own?","how are my shares?","what companies am I investing in?","what am I doing in the markets?"],"responses":["You own the following shares: ABBV, AAPL, FB, NVDA and an ETF of the S&P 500 Index!"],"context_set":""}]}
Package: neuralite
NeuraliteA Streamlined Python Framework for Rapidly Building and Deploying Machine Learning-Based Conversational AgentsBuild, deploy, and scale your AI projects with Neuralite! Whether you're a seasoned AI developer or just starting out, Neuralite offers a simple and efficient way to implement AI features into your software.Getting Started 🚀To get started, install Neuralite with pip:pipinstallNeuraliteBasic Chatbot 🤖Creating a basic chatbot is simple:fromneuralite.interpreterimportai# Imports frameworkassistant=ai('dataset.json')# Loads datasetcycle_end=False# Starts the AI cyclewhilenotcycle_end:# Main cyclemessage=input("Enter a message: ")# Prompts user with messageifmessage.lower()=="stop":# Exit with 'stop'cycle_end=True# Ends cycleelse:print(assistant.process_input(message))# Prints the responseCustomizable Training Epochs 🔄Train your AI model with a customizable number of epochs. The higher the epochs, the more refined your model will be.fromneuralite.interpreterimportai# Imports framework# Initialize the AI model with 200 epochsassistant=ai('dataset.json',epochs=200)# Continue with your chatbot as usualCompiling Your Model 📦To make your dataset unreadable, load faster, and to package it into a neat file.fromneuralite.interpreterimportai# Imports frameworkassistant=ai('dataset.json')# Loads datasetassistant.compile('model.nlite')# Packages dataset to 'model.nlite'Loading a Compiled Model 🔓To load a precompiled model.fromneuralite.interpreterimportai# Imports frameworkassistant=ai.compiled('model.nlite')# Loads the compiled modelcycle_end=False# Starts the AI cyclewhilenotcycle_end:# Main cyclemessage=input("Enter a message: ")# Prompts user with messageifmessage.lower()=="stop":# Exit with 'stop'cycle_end=True# Ends cycleelse:print(assistant.process_input(message))# Prints the responseExample Conversational Dataset 💬{"greeting":["Hi","Hello","Hey there"],"greeting_responses":["Hello!","Hi, how can I help?","Hey! What's up?"],"farewell":["Goodbye","See you","Ciao"],"farewell_responses":["Goodbye!","See you soon!","Take care!"],"how_are_you":["How are you?","How's it going?","What's up?"],"how_are_you_responses":["I'm good, thank you!","I'm doing well. How about you?","Not much, what about you?"],"compliment":["You're great!","You're amazing!","You're awesome!"],"compliment_responses":["Thank you! You're pretty awesome too.","Thanks! You're great as well!","Thanks! That means a lot."],"name":["What's your name?","Who are you?","Tell me your name."],"name_responses":["I'm Neuralite, your AI assistant.","Call me Neuralite.","I'm Neuralite! Nice to meet you."],"jokes":["Tell me a joke.","Do you know any jokes?","Make me laugh."],"jokes_responses":["Why did the scarecrow win an award? Because he was outstanding in his field!","I told my computer I needed a break. Now it won't stop sending me Kit-Kats.","Why did the math book look sad? Because it had too many problems."]}
Package: neuralkernel
NeuralkernelThis project uses networks of neuron-like computational units to build a framework of computation. Specifically, it implements characteristics traditionally found in neural networks including synaptic diversity, temporal delays, and voltage spikes. It builds on the ideas proposed in the paperSTICK: Spike Time Interval Computational Kernel, A Framework for General Purpose Computation.Getting StartedTo run a sample network, you can run the module.python-mneuralkernelThe networks currently implemented are:Inverting MemoryLogarithmMaximumNon-Inverting MemoryFull SubtractorFor more information on each of these networks, please check out thedocsfolder.Running the testsTo run the unit tests, you can run the following.pytest
Package: neuralknight
# neuralknight**Authors**: David Snowberger, Shannon Tully, Adam Grandquist**Version**: 1.0.0## OverviewAn occasionally suicidal shell-based chess game.## Getting Started### Requirements- Python 3.5 or greater- pip package manager### InstallationInstallation is as simple as installing from pip:`pip install neuralknight`#### Running the appTo launch neuralknight, type the following into a shell:`neuralknight https://neuralknight.herokuapp.com`Should you wish to run a purely local instance:- Download the source from [here](https://www.github.com/dsnowb/neuralknight)- Create a postgres database named *neuralknight*From inside the source directory:- Initialize the database with `initialize_neuralknight_db`- Launch the local server with`pserve production.ini`- In another shell, run the client with `neuralknight`## ArchitectureThis app is written using Python 3.6, Pyramid, and Postgres, with Heroku as a deployment platform## APISee our API docs [here](https://github.com/dsnowb/neuralknight/blob/master/API.md)## Change Log- 05 April 2018 - Repo Created- 19 April 2018 - 1.0.0 release0.1.0---- Initial version.
Package: neural-logic-machine
Simple NLMA simple Pytorch implementation of Neural Logic Machines (arxiv.org/abs/1904.11694).New in version 1.1Fixed bugrelated to the Pythontypingmodule.Simple-NLM now also works for Python<3.9.Improved efficiencyof both padded and unpadded NLMs, by pruning inner predicates which are not used for computing the NLM output.How to useSimply download fromPyPIby doingpip install neural-logic-machine.
An alternative option is to clone the repository.Assuming it has been installed from PyPI, using it is as simply as importing theneural-logic-machinepackage in Python:from neural_logic_machine import NLM
import torch

# We create an NLM with two layers. The first layer receives different predicates
# and outputs 8 predicates for arities 0-3. The second layer receives these predicates and outputs 2 nullary predicates and one unary predicate.
# We do not need to specify the input size of the NLM (this is automatically inferred from the first forward pass).
nlm = NLM(hidden_features=[[8,8,8,8]], out_features=[2,1,0,0])

# Create a random input for the NLM
# The batch will contain two samples, the first one with 3 objects and the second one with 5 objects
# Note that we are not masking out those tensor positions corresponding to invalid objects (e.g., object 4 for the first batch element,
# despite it only having 3 objects). The reason is that there's no need to, as the NLM does that internally when computing the reduce operations
num_objs = [3,5]
input_NLM = [torch.randn((2,) + (5,)*r + (1,)) for r in range(3)] + [None] # The last position is None, as there are no ternary predicates
# Shapes of tensors in input_NLM: [(2,1), (2,5,1), (2,5,5,1), None]

# Forward pass
output = nlm(input_NLM, num_objs)
print(output) # Note in the result how invalid positions (those correspondings to objects 3 and 4 for the first batch element) are masked to -inf

>>> [tensor([[-0.0104,  0.4631],
        [-0.4774,  0.4594]], grad_fn=<AddmmBackward0>), tensor([[[-0.0508],
         [-0.0756],
         [-0.0551],
         [   -inf],
         [   -inf]],

        [[ 0.0538],
         [ 0.1004],
         [ 0.1422],
         [-0.0173],
         [ 0.0232]]], grad_fn=<MaskedFillBackward0>), None, None]For training, we recommend usingPytorch Lightning.DependenciesPython 3 and Pytorch.AuthorsCarlos Núñez MolinaMasataro Asai
Package: neural-mesh-model
Neural mesh models for 3D reasoning.FeaturesModelsOriginal NeMo (3D pose estimation)3D aware classification NeMo (3D pose estimation and classification)6D pose estimation NeMo(Deformable NeMo ?)BaselinesResNet50StarmapTransformerFaster RCNNDatasetsOOD-CVPascal3D+(Synthetic data ?)(ObjectNet ?)RequirementsPython EnvironmentCreatecondaenvironment:conda create -n nemo python=3.9
conda activate nemoInstallPyTorch(seepytorch.org):conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 cudatoolkit=10.2 -c pytorchInstallPyTorch3D(seegithub.com/facebookresearch/pytorch3d):conda install -c fvcore -c iopath -c conda-forge fvcore iopath
conda install -c bottler nvidiacub
conda install pytorch3d -c pytorch3dInstall other dependencies:conda install numpy matplotlib scipy scikit-image
conda install pillow
conda install -c conda-forge timm tqdm pyyaml transformers
pip install wget BboxTools opencv-pythonInstall NeMo-Extensions:pip install -e .Data PreparationSeedata/README.TODO[ ] Add support for VoGe Renderer[ ] Add support for multiple GPUs support[ ] Add support for some visualizations
Package: neuralnest
NeuralNest: Your Personal AI Assistant (WIP)🚧 This project is currently under heavy development and might not have stable features yet. 🚧NeuralNest aims to be an open-source personal AI assistant designed to seamlessly integrate data from various sources. With the vision to connect to different Language Learning Models (LLMs), both locally hosted and remote, NeuralNest will offer versatility and adaptability. The project will prioritize security and also plans to support human-in-the-loop options for a more personalized AI experience.Planned FeaturesData Integration: Easily fetch and integrate data from multiple sources.Versatile Connections: Connect to various LLMs, both local and remote.Enhanced Security: Multiple security levels to safeguard your data.Human-in-the-Loop: Improve AI responses with human intervention options.Installation (Coming Soon)Details on installation and setup will be provided as the project progresses.Usage (Coming Soon)A comprehensive guide on how to use NeuralNest will be available once core features are implemented.ContributingWhile the project is in its early stages, we appreciate any feedback or ideas. If you're interested in contributing or have suggestions, please open an issue to discuss.LicenseThis project will be licensed under the MIT License. Details will be provided as the project matures.
Package: neuralnet
NeuralnetA high level framework for general purpose neural networks written in Theano.RequirementsTheanoScipyNumpy+mklMatplotlibtqdmvisdomInstallationTo install a stable version, use the following commandpip install neuralnet==0.1.0The version in this repo tends to be newer since I am lazy to make a new version available on Pypi when the change is tiny.
To install the version in this repo executepip install git+git://github.com/justanhduc/neuralnet.git@master (--ignore-installed) (--no-deps)Pretrained ModelsVGG16VGG19ResNet18ResNet34ResNet50ResNet101ResNet152All the tests for these models are available attest.py.
Also, checkout the file for example usages.
Package: neural-net-drawer
# Install the neural-net-drawer package via pippipinstallneural-net-drawer# Import necessary librariesimportmatplotlib.pyplotaspltfromneural_net_drawer.drawerimportNeuralNetworkDiagram# Example usagefig=plt.figure(figsize=(9,9))ax=fig.gca()ax.axis('off')# Create an instance of the NeuralNetworkDiagram classnn_diagram=NeuralNetworkDiagram()# Call the method to draw the neural network diagramnn_diagram.draw_neural_net(ax,[7,5,4,3,4,2,1])plt.show()# Example usage with customizationsfig=plt.figure(figsize=(9,9))ax=fig.gca()ax.axis('off')# Create an instance of the NeuralNetworkDiagram classnn_diagram=NeuralNetworkDiagram()# Set maximum number of layers to displaynn_diagram.max_n_layers_size=3# Set maximum number of neurons per layer to displaynn_diagram.max_layer_size=10# Call the method to draw the neural network diagramnn_diagram.draw_neural_net(ax,[7,5,4,3,4,2,1])plt.show()# Example usage with further customizationsfig=plt.figure(figsize=(9,9))ax=fig.gca()ax.axis('off')# Create an instance of the NeuralNetworkDiagram classnn_diagram=NeuralNetworkDiagram()# Set maximum number of layers to displaynn_diagram.max_n_layers_size=3# Hide neuron numbersnn_diagram.show_neuron_numbers=False# Call the method to draw the neural network diagramnn_diagram.draw_neural_net(ax,[7,5,4,3,4,2,1])plt.show()
Package: neuralnetlib
Handmade NeuralNetwork lib📝 DescriptionThis is a handmade neural network library, made in python,using numpy as the only dependency.I made it to challenge myself and to learn more about neural networks, how they work in depth.The big part of this project was made in 4 hours and a half. The save and load features, and the binary classification support were added later.I intend to improve the neural networks and add more features in the future.📦 FeaturesMany activation functions (sigmoid, tanh, relu, leaky relu, softmax, linear, elu, selu) 📈Many loss functions (mean squared error, mean absolute error, categorical crossentropy, binary crossentropy, huber loss) 📉Many optimizers (sgd, momentum, rmsprop, adam) 📊Supports binary classification, multiclass classification and regression 📖Save and load models 📁Simple to use 📚⚙️ InstallationYou can install the library using pip:pipinstallneuralnetlib💡 How to useSeethis filefor a simple example of how to use the library.
More examples inthis folder.You are free to tweak the hyperparameters and the network architecture to see how it affects the results.I used theMNIST datasetto test the library, but you can use any dataset you want.📜 Output of the example fileHere is an example with Tkinter:Here, I decided to print the first 10 predictions and their respective labels to see how the network is performing.✍️ AuthorsMarc Pinet -Initial work-marcpinet
Package: neuralnet-pytorch
IntroductionA high level framework for general purpose neural networks in Pytorch.Personally, going from Theano to Pytorch is pretty much like time traveling from 90s to the modern day.
However, despite a lot of bells and whistles, I still feel there are some missing elements from Pytorch
which are confirmed to be never added to the library.
Therefore, this library is written to add more features to the current magical Pytorch. All the modules here
directly subclass the corresponding modules from Pytorch, so everything should still be familiar. For example, the
following snippet in Pytorchfrom torch import nn
model = nn.Sequential(
    nn.Conv2d(1, 20, 5, padding=2),
    nn.ReLU(),
    nn.Conv2d(20, 64, 5, padding=2),
    nn.ReLU()
)can be rewritten in Neuralnet-pytorch asimport neuralnet_pytorch as nnt
model = nnt.Sequential(
    nnt.Conv2d(1, 20, 5, padding='half', activation='relu'),
    nnt.Conv2d(20, 64, 5, padding='half', activation='relu')
)which is the same as the native Pytorch, orimport neuralnet_pytorch as nnt
model = nnt.Sequential(input_shape=1)
model.add_module('conv1', nnt.Conv2d(model.output_shape, 20, 5, padding='half', activation='relu'))
model.add_module('conv2', nnt.Conv2d(model.output_shape, 64, 5, padding='half', activation='relu'))which frees you from a lot of memorizations and manual calculations when adding one layer on top of another.
Theano folks will also find some reminiscence as many functions are highly inspired by Theano.RequirementsPytorch>= 1.0.0MatplotlibVisdomTensorboardGin-config(optional)InstallationStable versionpip install --upgrade neuralnet-pytorchBleeding-edge versionpip install git+git://github.com/justanhduc/neuralnet-pytorch.git@masterTo install the version with some collected Cuda/C++ ops, usepip install git+git://github.com/justanhduc/neuralnet-pytorch.git@fancyUsagesThe manual reference is still under development and is available athttps://neuralnet-pytorch.readthedocs.io.TODOAdding introduction and installationWriting documentationsAdding examplesDisclaimerThis package is a product from my little free time during my PhD,
so most but not all the written modules are properly checked.
No replacements or refunds for buggy performance.
All PRs are welcome.AcknowledgementsThe CUDA Chamfer distance is taken from theAtlasNetrepo.The AdaBound optimizer is taken from itsofficial repo.The adapted Gin for Pytorch code is taken fromGin-config.The monitor scheme is inspired fromWGAN.
Package: neuralnet-visualize
Neural Network VisualizerGeneral DescriptionA module which creates a neural network image with the given architecture. It's a handy tool to see how your network is built as compared to a model summary.InstallationBefore installationBefore installing the module, run the below command at your prompt to install the graphviz$sudoaptinstallgraphvizNormal installation$sudopip3installneuralnet-visualizeDevelopment installation$gitclonehttps://github.com/AnuragAnalog/nn_visualize.git
$cdnn_visualizeAfter installationAfter installing the module, if you want to upgrade the module, run the below command.sudopip3installneuralnet-visualize--upgradeFuture WorksAdd Convolutional layers, Maxpooling, Flatten layersAdd Sequence model layersDirectly from the pickle filesSpecific colors for activation functionsDirectly convert from pytorch modelsNeural Network Visualizer Version History0.2.3Added from_pytorch methodFixed a bug of multiple titles in an imageChanged the logic of from_tensorflow methodFixed a bug, unique layer names for non trainable layers0.2.2Added more docstringCan now be downloaded in py20.2.1Added title to the imageOptimized some code0.2.0Made it independent of tensorflowFixed some bugs0.1.4Fixed some bugsAdded more layers maxpooling, avgpooling, flattenchanged the shape of conv2d layer0.1.3Added class docstrings with examplesAdded some more parameters for conv layerDisabled custom addition of layers after from_tensorflow call0.1.2Added Conv layer, with kernel size parameterRefactored add_layer functionAdded Conv layer to from_tensorflow0.1.1Added some possible exceptionsRestructred basic functions in while initial import0.1.0Added documentation0.0.4Refactored summarize functionAdded a code part which colors output layer to redAdded from_tensorflow method0.0.3Added more display orientationsRefactored the build network and add layer codeAdded some more file extensions to which image can be savedAdded summarize function0.0.2Added colors to type of layersAdded Dense layers0.0.1Intial VersionStarting code