Saravanan666/pypi-org huggingface dataset:
Package: neural-network-from-scratch
neural-network-from-scratchneural_network_from_scratch.core??This file will become your README and also the index of your
documentation.Installpipinstallneural_network_from_scratchHow to useFill me in please! Don’t forget code examples:#minus_one()NameError: name 'minus_one' is not defined#foo()#foo?#add_one(2)NameError: name 'add_one' is not defined#foo(1)NameError: name 'hoo' is not defined1+12#hoo??Object `hoo` not found.#hoo(1)NameError: name 'hoo' is not defined#activation??Object `activation` not found.#activation(10)NameError: name 'activation' is not defined
Package: neural-network-renderer
neural-network-rendererThe code generating the image is writen in Python. This code generates.texand.styfiles that are directly compiled and deleted once the resulting PDF is available.ExampleHere is an example of the code to generate a simple convolutionnal-network representation:fromneural_network_renderer.architectureimportArchitecturefromneural_network_renderer.colorsimportColorsfromneural_network_renderer.layersimportConv,ConvConvRelu,Dense,DottedLines,Input,Pool,Softmax,Spacerdefmain():Colors.Dense("lightgray")Colors.Softmax("lightgray")arch=Architecture(4/32)# first layerarch.add(ConvConvRelu(s_filter=64,n_filter=[32,32],to="(5,0,0)"))arch.add(Pool([32,32,32]))arch.add(Spacer(width=20))# second layerarch.add(ConvConvRelu(s_filter=32,n_filter=[64,64]))arch.add(Pool([16,16,64]))arch.add(Spacer())# third layerarch.add(ConvConvRelu(s_filter=16,n_filter=[64,64]))arch.add(Pool([8,8,64],name="last_pool"))arch.add(Spacer())# GPAarch.add(Conv(s_filter=8,n_filter=1,name="gpa",caption="GPA"))# flattenarch.add(Dense(64,name="flatten",offset="(4,0,0)",caption="Hidden Layer"))arch.add(Softmax(5,5,name="output",caption="Output",offset="(4,0,0)"))arch.add(DottedLines("gpa","flatten"))arch.add(DottedLines("flatten","output"))arch.to_pdf("output_file")if__name__=="__main__":main()
Package: neuralnetworks-shine7
Feed Forward Neural Networks using NumPyThis library is a modification of my previous one.Click Hereto check my previous library.Installation$[sudo]pip3installneuralnetworks-shine7Development Installation$gitclonehttps://github.com/Subhash3/Neural_Net_Using_NumPy.gitUsage>>>fromModelimportNeuralNetworkCreating a Neural Networkinputs=2outputs=1network=NeuralNetwork(inputs,outputs)# Add 2 hidden layers with 16 neurons each and activation function 'tanh'network.addLayer(16,activation_function="tanh")network.addLayer(16,activation_function="tanh")# Finish the neural network by adding the output layer with sigmoid activation function.network.compile(activation_function="sigmoid")Building a datasetThe package contains a Dataset class to create a dataset.>>>fromModelimportDatasetMake sure you have inputs and target values in seperate files in csv format.input_file="inputs.csv"target_file="targets.csv"# Create a dataset object with the same inputs and outputs defined for the network.datasetCreator=Dataset(inputs,outputs)datasetCreator.makeDataset(input_file,target_file)data,size=datasetCreator.getRawData()If you want to manually make a dataset, follow these rules:Dataset must be a list of data samples.A data sample is a list containing inputs and target values.Input and target values are column vector of size (inputs x 1) and (outputs x 1) respectively.For eg, a typical XOR data set looks something like :>>>XOR_data=[[np.array([[0],[0]]),np.array([[0]])],[np.array([[0],[1]]),np.array([[1]])],[np.array([[1],[0]]),np.array([[1]])],[np.array([[1],[1]]),np.array([[0]])]]>>>size=4Training The networkThe library provides aTrainfunction which accepts the dataset, dataset size, and two optional parameters epochs, and logging.defTrain(dataset,size,epochs=5000,logging=True):........For Eg: If you want to train your network for 1000 epochs.>>>network.Train(data,size,epochs=1000)Notice that I didn't change the value of log_outputs as I want the output to printed for each epoch.DebuggingPlot a nice epoch vs error graph>>>network.epoch_vs_error()Know how well the model performed.>>>network.evaluate()To take a look at all the layers' info>>>network.display()Sometimes, learning rate might have to be altered for better convergence.>>>network.setLearningRate(0.1)Exporting ModelYou can export a trained model to a json file which can be loaded and used for predictions in the future.filename="model.json"network.export_model(filename)Load ModelTo load a model from an exported model (json) file.
load_model is a static function, so you must not call this on a NeuralNetwork object!.filename="model.json"network=NeuralNetwork.load_model(filename)
Package: neural-pipeline
Neural PieplineNeural networks training pipeline based on PyTorch. Designed to standardize training process and to increase coding preformance.Core is about 2K lines, covered by tests, that you doesn't need to write againFlexible and customizable training processCheckpoints management and train process resuming (source and target device independent)Metrics processing and visualization by builtin (tensorboard,Matplotlib) or custom monitorsTraining best practices (e.g. learning rate decaying and hard negative mining)Metrics logging and comparison (DVC compatible)Train MNIST example:This code run MNIST image classification with Tensorboard monitoring. Code based on PyTorchexample.See full examplethere.fromneural_pipeline.builtin.monitors.tensorboardimportTensorboardMonitorfromneural_pipelineimportDataProducer,AbstractDataset,TrainConfig,TrainStage,\ValidationStage,Trainer,FileStructManagerimporttorchfromtorchimportnnfromtorchvisionimportdatasets,transformsclassNet(nn.Module):# Network implementationclassMNISTDataset(AbstractDataset):transforms=transforms.Compose([transforms.ToTensor(),transforms.Normalize((0.1307,),(0.3081,))])def__init__(self,data_dir:str,is_train:bool):self.dataset=datasets.MNIST(data_dir,train=is_train,download=True)def__len__(self):returnlen(self.dataset)def__getitem__(self,item):data,target=self.dataset[item]return{'data':self.transforms(data),'target':target}fsm=FileStructManager(base_dir='data',is_continue=False)model=Net()train_dataset=DataProducer([MNISTDataset('data/dataset',True)],batch_size=4,num_workers=2)validation_dataset=DataProducer([MNISTDataset('data/dataset',False)],batch_size=4,num_workers=2)train_config=TrainConfig([TrainStage(train_dataset),ValidationStage(validation_dataset)],torch.nn.NLLLoss(),torch.optim.SGD(model.parameters(),lr=1e-4,momentum=0.5))trainer=Trainer(model,train_config,fsm,torch.device('cuda:0')).set_epoch_num(50)trainer.monitor_hub.add_monitor(TensorboardMonitor(fsm,is_continue=False))trainer.train()Installation:pip install neural-pipelineForbuiltinmodule using install:pip install tensorboardX matplotlibInstall latest version before it's published on PyPipip install -U git+https://github.com/toodef/neural-pipelineGetting started:DocumentationSee the full documentation thereData flow scheme:See the examplesMNIST classification -notebook,fileSegmentation -notebook,fileResume training process -file
Package: neuralplot
Neural PlotNeural Plot is a python library for visualizing Neural Networks.
It helps to plot Keras/Tensorflow model with matplotlib backend.InstallationRun the following to install:$pipinstallneuralplotExample# Importing LibrariesfromneuralplotimportModelPlotimporttensorflowastfimportnumpyasnp# Uncomment while using Colab.# %matplotlib inline# Uncomment while using jupyter notebook. This feature is not working in colab.%matplotlibnotebook#Creating ModelX_input=tf.keras.layers.Input(shape=(32,32,3))X=tf.keras.layers.Conv2D(4,3,activation='relu')(X_input)X=tf.keras.layers.MaxPool2D(2,2)(X)X=tf.keras.layers.Conv2D(16,3,activation='relu')(X)X=tf.keras.layers.MaxPool2D(2,2)(X)X=tf.keras.layers.Conv2D(8,3,activation='relu')(X)X=tf.keras.layers.MaxPool2D(2,2)(X)X=tf.keras.layers.Flatten()(X)X=tf.keras.layers.Dense(10,activation='relu')(X)X=tf.keras.layers.Dense(2,activation='softmax')(X)model=tf.keras.models.Model(inputs=X_input,outputs=X)modelplot=ModelPlot(model=model,grid=True,connection=True,linewidth=0.1)modelplot.show()modelplot=ModelPlot(model=model,grid=False,connection=True,linewidth=0.1)modelplot.show()LicenseMIT
Package: neuralpp
Neural Probabilistic Programs (NeuralPPs)This is an implementation of graphical models inference (Variable Elimination) using PyTorch for implementing
factors (conditional probabilities and priors).
Those can be implemented as tables (the typical discrete graphical model implementation) but also as arbitrary
PyTorch modules, the case of most interest being of course neural networks.The reason we are calling it "probabilistic programs" even though we only have discrete graphical models so far is that factors and variables are implemented in a manner general enough for probabilistic programming symbolic implementations be introduced down the road.PurposeThis project is for researchers interested in exploring the connection between probabilistic and neural network inference.
It is a library that provides the utilities for mixing neural networks and graphical models while offering GPU use and stochastic gradient descent training.ContentThis repository contains:source code, including an experiments directorytest code (to be run with PyTest)setup.pyfor installing with Pipenvironment.ymlfor importing a Conda environmentInstallationPyPIThe library can be installed withpip install neuralppInstallation from sourceFirst, clone the repo locally:git clone https://github.com/rodrigodesalvobraz/neuralpp.git
cd neuralppThen, to install a developer copy, run:python setup.py developAlternatively, for a regular installation:python setup.py installExperimentsInsrc/experimentsone can findsrc/experiments/simple_mnist.py, which shows how to use a "graphical model" with a single factor,
implemented by a convolutional neural network, to learn how to recognize MNIST digits.Insrc/experiments/successive_digits.pythere is code for training a MNIST recognizer from pairs of images of digits,
labeled as being successive digits (positive examples) or not (negative examples).
The correct digit labels are still learned in spite of a total absence of digit labels.
This is possible due to the reasoning performed by the graphical model component of the model
(based on the knowledge of what successive digits are).Insrc/experiments/sum_of_pair.pypairs of images are labeled by the sum of their corresponding digits.
Again the reasoning aspect of graphical models helps by using knowledge about addition.TestsTests inneuralpp.testare split intoquick_testsandslow_tests.
The former include tests of basic data structure implementations while the latter includes  learning sessions with stochastic gradient descent and take several minutes.Run them withpytest .from the root directory if installed from the source code.
Package: neuralpuzzle
NeuralPuzzleA simple Python package that helps anyone on building and running a deep neural network
in second.Free software: GNU General Public License v3Documentation:https://neuralpuzzle.readthedocs.io.QuickstartTo install NeuralPuzzle, run the following command in your terminal:pip install neuralpuzzleQuickly train a simple one hidden layer feed forward nueral network with ReLU activation fucntion, and initialize paramters (weights and bias) using Xavier method:from neuralpuzzle import *

train_network = NeuralPuzzle('mnist.pkl', [784, 50, 10], ['relu'], 'xavier')FeaturesEasy install and useOne-line code to trainCreditsThis package was created withCookiecutterand theaudreyr/cookiecutter-pypackageproject template.History0.1.0 (2017-04-18)First release on PyPI.
Package: neuralpy
Within this package is the most intuitive fully-connected multilayer neural network model. Data science shouldn’t have a high barrier to entry. neuralpy handles the math and overhead while you focus on the data.neuralpy is a neural network model written in python based on Michael Nielsen’s neural networks and deep learning book.Visit theneuralpy websiteGet detailed examples and explanations in theOfficial DocumentationContribute onGithubGetting Started (quick start)The following demonstrates how to download and installneuralpyand how to create and train a simple neural network.
Run the following command to download and install:$ pip install neuralpyCreate a neural network in your project by specifying the number of nodes in each layer. Random weights and biases will automatically be generated:import neuralpy
net = neuralpy.Network([2, 3, 1])The network feeds input vectors as python lists forward and returns the output vector as a list:x = [1, 1]
output = net.forward(x)
print output
# ex: [0.11471727263613461]Train the neural network by first generating training data in the form of a list of tuples. Each tuple has two components and each component is a list representing the input and output respectively. This training set represents the simple OR function
and it can be generated for you to save typing:training_data = neuralpy.load_or()
# [
#   ([1, 1], [1]),
#   ([1, 0], [1]),
#   ...
# ]Then we must specify the remaining hyperparameters. Let’s say we want to limit it to 100 epochs and give it a learning rate of 1:epochs = 100
learning_rate = 1Then run thetrainmethod with the parameters. We’re telling the network to conform to training data:net.train(training_data, epochs, learning_rate)Now feed forward the input from earlier and the output should be closer to 1.0, which is what we trained the network to do:output = net.forward(x)
print output            # ex: [0.9542129706170075]There is more information about advanced options such as monitoring the cost in the official documentation.Since, this is a multilayer feedforward neural network, it is a universal approximator (Hornik, Stinchcombe and White, 1989). Neural Networks can be used for a wide range of applications from image processing to time series prediction.“You abandoned me. You left me to die.”“Well, I wouldn’t have done it if I’d known you were going to hassle me about it.”
Package: neural-python
NeuralPy is a Python library for Artificial Neural Networks.
You can run and test different Neural Network algorithms.Installation$pipinstallneural-pythonLinksDocumentationIssuesTutorialsAvailable algorithmsDependencePython 2.7, 3.3, 3.4NumPy >= 1.9.0SciPy >= 0.14.0Matplotlib >= 1.4.0Next stepsBug fixing and version stabilizationSpeeding up algorithmsAdding more algorithmsLibrary supportRadial Basis Functions Networks (RBFN)Backpropagation and different optimization for itNeural Network EnsemblesAssociative and Autoasociative MemoryCompetitive NetworksStep update algorithms for backpropagationWeight control algorithms for backpropagationBasic Linear NetworksAlgorithmsBackpropagationClassic Gradient DescentMini-batch Gradient DescentConjugate GradientFletcher-ReevesPolak-RibiereHestenes-StiefelConjugate DescentLiu-StoreyDai-Yuanquasi-NewtonBFGSDFPPSBSR1Levenberg-MarquardtHessian diagonalMomentumRPROPiRPROP+QuickPropWeight update rulesWeight DecayWeight EliminationLearning rate update rulesAdaptive Learning RateError difference UpdateLinear search by Golden Search or BrentWolfe line searchSearch than convergeSimple Step MinimizationEnsemblesMixture of ExpertsDynamically Averaged Network (DAN)Radial Basis Functions Networks (RBFN)Generalized Regression Neural Network (GRNN)Probabilistic Neural Network (PNN)Radial basis function K-meansAutoasociative MemoryDiscrete BAM NetworkCMAC NetworkDiscrete Hopfield NetworkCompetitive NetworksAdaptive Resonance Theory (ART1) NetworkSelf-Organizing Feature Map (SOFM or SOM)Linear networksPerceptronLMS NetworkModified Relaxation NetworkAssociativeOJAKohonenInstarHebbTests$pipinstalltox$tox
Package: neural-renderer-paddle
Neural 3D Mesh Renderer in PadddlePaddleA PaddlePaddle version ofNeural Renderer, refer to itsPyTorch versionInstallRun:pipinstallneural-renderer-paddleUsageCheckexamplesfolder for usage.NoteUnittest module is not compatiable with PaddlePaddle. If you want to run test, run:gitclone<THISREPO>neural_renderer_paddle
pipinstallneural_renderer_paddle/cdneural_renderer_paddle/tests
python-munittesttest_*.pyOriginal repo README:Neural 3D Mesh Renderer (CVPR 2018)This repo contains a PyTorch implementation of the paperNeural 3D Mesh Rendererby Hiroharu Kato, Yoshitaka Ushiku, and Tatsuya Harada.
It is a port of theoriginal Chainer implementationreleased by the authors.
Currently the API is the same as in the original implementation with some smalls additions (e.g. render using a general 3x4 camera matrix, lens distortion coefficients etc.). However it is possible that it will change in the future.The library is fully functional and it passes all the test cases supplied by the authors of the original library.
Detailed documentation will be added in the near future.RequirementsPython 2.7+ and PyTorch 0.4.0.The code has been tested only with PyTorch 0.4.0, there are no guarantees that it is compatible with older versions.
Currently the library has both Python 3 and Python 2 support.Note: In some newer PyTorch versions you might see some compilation errors involving AT_ASSERT. In these cases you can use the version of the code that is in the branchat_assert_fix. These changes will be merged into master in the near future.InstallationYou can install the package by runningpip install neural_renderer_pytorchSince running install.py requires PyTorch, make sure to install PyTorch before running the above command.Running examplespython ./examples/example1.py
python ./examples/example2.py
python ./examples/example3.py
python ./examples/example4.pyExample 1: Drawing an object from multiple viewpointsExample 2: Optimizing verticesTransforming the silhouette of a teapot into a rectangle. The loss function is the difference between the rendered image and the reference image.Reference image, optimization, and the result.Example 3: Optimizing texturesMatching the color of a teapot with a reference image.Reference image, result.Example 4: Finding camera parametersThe derivative of images with respect to camera pose can be computed through this renderer. In this example the position of the camera is optimized by gradient descent.From left to right: reference image, initial state, and optimization process.Citation@InProceedings{kato2018renderer
    title={Neural 3D Mesh Renderer},
    author={Kato, Hiroharu and Ushiku, Yoshitaka and Harada, Tatsuya},
    booktitle={The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
    year={2018}
}
Package: neural-response-map
Neural Response MapNeural Response Mapis a tool that allows you to graphically detect the activations of artificial neural networks. To do this, the correlation between neuron activations is calculated and then t-SNE is applied to reduce the dimension. In this way the correlated neurons are plotted close to each other. This process allows observing the activation of neurons and obtaining the response to different stimuli such as the graphs presented by DeepMind in hispaper.InstalationTo install you can use the following commandpipinstallneural-response-mapQuick StartTo make use of the tool, first import the componentsfromneural_response_mapimportNeuralResponseMapThen you need to instantiate the class. For this you must pass the tensorflow model as a parameternrm=NeuralResponseMap(model)Next you need to train the mapper. This function calculates the correlation between the neurons and performs the dimensionality reduction for subsequent graphs. For this you must pass the inputs as a parameter. The inputs are sample input data of the neural network, for example if your network is a VGG16, the inputs are images of your dataset. Also, if you wish, you can pass the layers you want to view as a parameter. If you don't pass the layers as a parameter, the visualization will use all the layers of the model.nrm.TrainMap(inputs,model.layers[:2])Finally you can visualize yourNeural Response Map. To perform the visualization you must call GenerateMap and pass as a parameter the input examples that you want to visualize. If you pass more than one example, the display will be the average of all theNeural Response Mapsof the input data.nrm.GenerateMap(inputs[1:2])GenerateMap allows other optional parameters:method: this parameter controls the display method which can be linear or nearest. The linear method performs a linear interpolation between the values of the neuron's activations. Nearest, on the other hand, does not perform interpolation. The nearest method is the default value.cmap: it is the color map that you want to use, by default it is viridis. If you want to know other visualization maps visit the followinglink.size: it is the size of the image. This value is a tuple with (5,5) by default.file: is the name and type of file in which you want to save the image (for example 'map.png'). If no path is passed the image is not saved.Alternatively after the TrainMap call, you can call CircleProjection. This will make theNeural Response Mapmore circular in shape.nrm.CircleProjection()nrm.GenerateMap(inputs[1:2])AuthorsThis package has been developed by:Joan Alberto Cerretani
Package: neuralsampler
NeuralSampler - PytorchImplementation of Neural Sampler.Install$pipinstallneuralsampleror install the latest version bypipinstall-Ugit+https://JiahaoYao:{password}@github.com/JiahaoYao/neuralsampler.git@mainInstall the jax (follow the official instructionhere)pipinstalljaxjaxlib==0.1.64+[YOUR_CUDA_VERSION]-fhttps://storage.googleapis.com/jax-releases/jax_releases.htmle.g.pipinstall--upgradejaxjaxlib==0.1.64+cuda101-fhttps://storage.googleapis.com/jax-releases/jax_releases.htmlUsageimporttorchfromneuralsamplerimportneuralsamplerRun the codepythonmain.pyRun the scriptspythonscripts/test_job.pyDemonstrations and tutorialsLinkDescriptionTrain the neural sampler for thedouble well potentialin PyTorchTrain the neural sampler for theMüller-Brown Modelin PyTorchTrain the neural sampler for theperiodic potentialin PyTorchTrain the neural sampler for theGinzburg-Landau potentialin PyTorchIgnore me (random things)this repo is to collect all the random results and reproduce the experiments here 

and then jax 

i will use jax and flax, like shown here: https://github.com/yang-song/score_sde

there are the templates of building the jax neural networks (quite interesting to try this functional programming )Todo listthis library is on the pytorchi am also going to prepare the colab notebookthe dataset is through the gdown: you can download the dataset from the google driveplz check the abf-mmd can reproduce the results! I have checked the codes are the same? I guessed the only issue might be just run enough runs! (Sun 09/05/2021 21:06)at least lots of things are connected now!update the colab module, going to download the dataset from the cloud!