TensorFlow, PyTorch and Numpy layers for generating Orthogonal Polynomials

Related tags

Deep LearningOrthNet
Overview

OrthNet

TensorFlow, PyTorch and Numpy layers for generating multi-dimensional Orthogonal Polynomials

1. Installation
2. Usage
3. Polynomials
4. Base Class(Poly)

Installation:

  1. the stable version:
    pip3 install orthnet

  2. the dev version:

git clone https://github.com/orcuslc/orthnet.git && cd orthnet
python3 setup.py build_ext --inplace && python3 setup.py install

Usage:

with TensorFlow

import tensorflow as tf
import numpy as np
from orthnet import Legendre

x_data = np.random.random((10, 2))
x = tf.placeholder(dtype = tf.float32, shape = [None, 2])
L = Legendre(x, 5)

with tf.Session() as sess:
    print(L.tensor, feed_dict = {x: x_data})

with PyTorch

import torch
import numpy as np
from orthnet import Legendre

x = torch.DoubleTensor(np.random.random((10, 2)))
L = Legendre(x, 5)
print(L.tensor)

with Numpy

import numpy as np
from orthnet import Legendre

x = np.random.random((10, 2))
L = Legendre(x, 5)
print(L.tensor)

Specify Backend

In some scenarios, users can specify the exact backend compatible with the input x. The backends provided are:

An example to specify the backend is as follows.

import numpy as np
from orthnet import Legendre, NumpyBackend

x = np.random.random((10, 2))
L = Legendre(x, 5, backend = NumpyBackend())
print(L.tensor)

Specify tensor product combinations

In some scenarios, users may provide pre-computed tensor product combinations to save computing time. An example of providing combinations is as follows.

import numpy as np
from orthnet import Legendre, enum_dim

dim = 2
degree = 5
x = np.random.random((10, dim))
L = Legendre(x, degree, combinations = enum_dim(degree, dim))
print(L.tensor)

Polynomials:

Class Polynomial
orthnet.Legendre(Poly) Legendre polynomial
orthnet.Legendre_Normalized(Poly) Normalized Legendre polynomial
orthnet.Laguerre(Poly) Laguerre polynomial
orthnet.Hermite(Poly) Hermite polynomial of the first kind (in probability theory)
orthnet.Hermite2(Poly) Hermite polynomial of the second kind (in physics)
orthnet.Chebyshev(Poly) Chebyshev polynomial of the first kind
orthnet.Chebyshev2(Poly) Chebyshev polynomial of the second kind
orthnet.Jacobi(Poly, alpha, beta) Jacobi polynomial

Base class:

Class Poly(x, degree, combination = None):

  • Inputs:
    • x a tensor
    • degree highest degree for target polynomials
    • combination optional, tensor product combinations
  • Attributes:
    • Poly.tensor the tensor of function values (with degree from 0 to Poly.degree(included))
    • Poly.length the number of function basis (columns) in Poly.tensor
    • Poly.index the index of the first combination of each degree in Poly.combinations
    • Poly.combinations all combinations of tensor product
    • Poly.tensor_of_degree(degree) return all polynomials of given degrees
    • Poly.eval(coefficients) return the function values with given coefficients
    • Poly.quadrature(function, weight) return Gauss quadrature with given function and weight
You might also like...
A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. ONNX. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin, .onnx]
A repository that shares tuning results of trained models generated by TensorFlow / Keras. Post-training quantization (Weight Quantization, Integer Quantization, Full Integer Quantization, Float16 Quantization), Quantization-aware training. TensorFlow Lite. OpenVINO. CoreML. TensorFlow.js. TF-TRT. MediaPipe. ONNX. [.tflite,.h5,.pb,saved_model,tfjs,tftrt,mlmodel,.xml/.bin, .onnx]

PINTO_model_zoo Please read the contents of the LICENSE file located directly under each folder before using the model. My model conversion scripts ar

Katsuya Hyodo 2.4k Jan 5, 2023
An Ensemble of CNN (Python 3.5.1 Tensorflow 1.3 numpy 1.13)
An Ensemble of CNN (Python 3.5.1 Tensorflow 1.3 numpy 1.13)

An Ensemble of CNN (Python 3.5.1 Tensorflow 1.3 numpy 1.13)

null 0 May 6, 2022
Unofficial PyTorch implementation of Attention Free Transformer (AFT) layers by Apple Inc.
Unofficial PyTorch implementation of Attention Free Transformer (AFT) layers by Apple Inc.

aft-pytorch Unofficial PyTorch implementation of Attention Free Transformer's layers by Zhai, et al. [abs, pdf] from Apple Inc. Installation You can i

Rishabh Anand 184 Dec 12, 2022
A library to inspect itermediate layers of PyTorch models.
A library to inspect itermediate layers of PyTorch models.

A library to inspect itermediate layers of PyTorch models. Why? It's often the case that we want to inspect intermediate layers of a model without mod

archinet.ai 380 Dec 28, 2022
a reccurrent neural netowrk that when trained on a peice of text and fed a starting prompt will write its on 250 character text using LSTM layers

RNN-Playwrite a reccurrent neural netowrk that when trained on a peice of text and fed a starting prompt will write its on 250 character text using LS

Arno Barton 1 Oct 29, 2021
Meta Language-Specific Layers in Multilingual Language Models

Meta Language-Specific Layers in Multilingual Language Models This repo contains the source codes for our paper On Negative Interference in Multilingu

Zirui Wang 20 Feb 13, 2022
Improving Deep Network Debuggability via Sparse Decision Layers
Improving Deep Network Debuggability via Sparse Decision Layers

Improving Deep Network Debuggability via Sparse Decision Layers This repository contains the code for our paper: Leveraging Sparse Linear Layers for D

Madry Lab 35 Nov 14, 2022
Spectral Tensor Train Parameterization of Deep Learning Layers
Spectral Tensor Train Parameterization of Deep Learning Layers

Spectral Tensor Train Parameterization of Deep Learning Layers This repository is the official implementation of our AISTATS 2021 paper titled "Spectr

Anton Obukhov 12 Oct 23, 2022
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition

RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition (PyTorch) Paper: https://arxiv.org/abs/2105.01883 Citation: @

null 260 Jan 3, 2023
Comments
  • Cuda support

    Cuda support

    Hi,

    First of all thank your for developing this project. Is it possible to create the Jacobi.tensor in the gpu? Currently I am creating the tensor in the cpu and then moving them to gpu, which is time consuming.

    Cheers

    opened by mariolinovIC 1
  • Jacobi polynomial incorrect evaluation

    Jacobi polynomial incorrect evaluation

    Hi, I have noticed than when I evaluate Jacobi polynomial with alpha=1 and beta=1 the results are not ok. Particularly I tried in range (-1,1) and I noticed the problem for n greater than 1 (i.e., 2,3,4). Thank you for your support.

    opened by mariolinovIC 0
Owner
Chuan
+1s.
Chuan
GitHub Repository
Losslandscapetaxonomy - Taxonomizing local versus global structure in neural network loss landscapes

Taxonomizing local versus global structure in neural network loss landscapes Int

Yaoqing Yang 8 Dec 30, 2022
CCP dataset from Clothing Co-Parsing by Joint Image Segmentation and Labeling

Clothing Co-Parsing (CCP) Dataset Clothing Co-Parsing (CCP) dataset is a new clothing database including elaborately annotated clothing items. 2, 098

Wei Yang 434 Dec 24, 2022
Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation （ICCV2021）

Dynamic Divide-and-Conquer Adversarial Training for Robust Semantic Segmentation This is a pytorch project for the paper Dynamic Divide-and-Conquer Ad

DV Lab 29 Nov 21, 2022
Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab.

CLIP-Guided-Diffusion Just playing with getting CLIP Guided Diffusion running locally, rather than having to use colab. Original colab notebooks by Ka

Nerdy Rodent 336 Dec 09, 2022
A modular, open and non-proprietary toolkit for core robotic functionalities by harnessing deep learning

A modular, open and non-proprietary toolkit for core robotic functionalities by harnessing deep learning Website • About • Installation • Using OpenDR

OpenDR 304 Dec 28, 2022
PyTorch IPFS Dataset

PyTorch IPFS Dataset IPFSDataset(Dataset) See the jupyter notepad to see how it works and how it interacts with a standard pytorch DataLoader You need

Jake Kalstad 2 Apr 13, 2022
Only valid pull requests will be allowed. Use python only and readme changes will not be accepted.

❌ This repo is excluded from hacktoberfest This repo is for python beginners and contains lot of beginner python projects for practice. You can also s

Prajjwal Pathak 50 Dec 28, 2022
CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation

CyTran: Cycle-Consistent Transformers for Non-Contrast to Contrast CT Translation We propose a novel approach to translate unpaired contrast computed

Nicolae Catalin Ristea 13 Jan 02, 2023
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees

ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees This repository is the official implementation of the empirica

Kuan-Lin (Jason) Chen 2 Oct 02, 2022
Source code for Acorn, the precision farming rover by Twisted Fields

Acorn precision farming rover This is the software repository for Acorn, the precision farming rover by Twisted Fields. For more information see twist

Twisted Fields 198 Jan 02, 2023
Author Disambiguation using Knowledge Graph Embeddings with Literals

Author Name Disambiguation with Knowledge Graph Embeddings using Literals This is the repository for the master thesis project on Knowledge Graph Embe

12 Oct 19, 2022
PyTorch Implementation of AnimeGANv2

PyTorch implementation of AnimeGANv2

4k Jan 07, 2023
CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching（CVPR2021）

CFNet(CVPR 2021) This is the implementation of the paper CFNet: Cascade and Fused Cost Volume for Robust Stereo Matching, CVPR 2021, Zhelun Shen, Yuch

106 Dec 28, 2022
[CoRL 2021] A robotics benchmark for cross-embodiment imitation.

x-magical x-magical is a benchmark extension of MAGICAL specifically geared towards cross-embodiment imitation. The tasks still provide the Demo/Test

Kevin Zakka 36 Nov 26, 2022
A spatial genome aligner for analyzing multiplexed DNA-FISH imaging data.

jie jie is a spatial genome aligner. This package parses true chromatin imaging signal from noise by aligning signals to a reference DNA polymer model

Bojing Jia 9 Sep 29, 2022
CountDown to New Year and shoot fireworks

CountDown and Shoot Fireworks About App This is an small application make you re

5 Dec 31, 2022
Official repository for the paper "Self-Supervised Models are Continual Learners" (CVPR 2022)

Self-Supervised Models are Continual Learners This is the official repository for the paper: Self-Supervised Models are Continual Learners Enrico Fini

Enrico Fini 73 Dec 18, 2022
ONNX-PackNet-SfM: Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Python scripts for performing monocular depth estimation using the PackNet-SfM model in ONNX

Ibai Gorordo 14 Dec 09, 2022
Tool for live presentations using manim

manim-presentation Tool for live presentations using manim Install pip install manim-presentation opencv-python Usage Use the class Slide as your sce

Federico Galatolo 146 Jan 06, 2023
This repository contains a toolkit for collecting, labeling and tracking object keypoints

This repository contains a toolkit for collecting, labeling and tracking object keypoints. Object keypoints are semantic points in an object's coordinate frame.

ETHZ ASL 13 Dec 12, 2022