Construct a neural network frame by Numpy

Related tags

Deep Learningnumpy_neural_network
Overview

本项目的CSDN博客链接：https://blog.csdn.net/weixin_41578567/article/details/111482022

1. 概览

本项目主要用于神经网络的学习，通过基于numpy的实现，了解神经网络底层前向传播、反向传播以及各类优化器的原理。

该项目目前已实现的功能：

  • 自定义多层的全连接层，并可定义多种激活函数
    • sigmoid
    • tanh
    • relu
    • softmax
  • 定义dropout
  • 支持多分类任务
  • 支持多种优化器
    • SGD
    • BSGD
    • SGD with Momentum
    • AdaGrad
    • AdaDelta
    • RMSProp
    • Adam
    • AdaMax

2. Todo list

  • 加入validation

  • 支持多分类任务

  • 加入卷积层（CNN）

  • 加入池化层

  • 加入循环网络（RNN）

3. 运行

直接执行main_binary_classification.py，即可执行二分类问题的训练和预测

执行main_multi_classification.py，即可执行多分类问题的训练和预测

python main_binary_classification.py

"""
Epoch 1/100 - loss: 0.19804074649934453 - acc: 0.7462311557788944
Epoch 10/100 - loss: 0.05641219571461576 - acc: 0.9447236180904522
Epoch 20/100 - loss: 0.03296407980222495 - acc: 0.9698492462311558
Epoch 30/100 - loss: 0.024833182907967224 - acc: 0.9798994974874372
Epoch 40/100 - loss: 0.02147093826055232 - acc: 0.9824120603015075
Epoch 50/100 - loss: 0.018468433700412783 - acc: 0.9849246231155779
Epoch 60/100 - loss: 0.017207182621404478 - acc: 0.9849246231155779
Epoch 70/100 - loss: 0.016608808691509016 - acc: 0.9824120603015075
Epoch 80/100 - loss: 0.014850895654103682 - acc: 0.9849246231155779
Epoch 90/100 - loss: 0.014590506876720767 - acc: 0.9824120603015075
Epoch 100/100 - loss: 0.013794675383962845 - acc: 0.9874371859296482
"""
Owner
Full stack; AI Engineer
GitHub Repository
Official implementation of "Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation" (RSS 2022)

Intro Official implementation of "Learning Forward Dynamics Model and Informed Trajectory Sampler for Safe Quadruped Navigation" Robotics:Science and

Yunho Kim 21 Dec 07, 2022
fastgradio is a python library to quickly build and share gradio interfaces of your trained fastai models.

fastgradio is a python library to quickly build and share gradio interfaces of your trained fastai models.

Ali Abdalla 34 Jan 05, 2023
Minimal implementation of PAWS (https://arxiv.org/abs/2104.13963) in TensorFlow.

PAWS-TF 🐾 Implementation of Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples (PAWS)

Sayak Paul 43 Jan 08, 2023
A flexible submap-based framework towards spatio-temporally consistent volumetric mapping and scene understanding.

Panoptic Mapping This package contains panoptic_mapping, a general framework for semantic volumetric mapping. We provide, among other, a submap-based

ETHZ ASL 194 Dec 20, 2022
Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting (HMNet)

Hierarchical Motion Encoder-Decoder Network for Trajectory Forecasting (HMNet) Our paper: https://arxiv.org/abs/2111.13324 We will release the complet

15 Oct 17, 2022
natural image generation using ConvNets

The Eyescream Project Generating Natural Images using Neural Networks. For our research summary on this work, please read the Arxiv paper: http://arxi

Meta Archive 601 Nov 23, 2022
Lowest memory consumption and second shortest runtime in NTIRE 2022 challenge on Efficient Super-Resolution

FMEN Lowest memory consumption and second shortest runtime in NTIRE 2022 on Efficient Super-Resolution. Our paper: Fast and Memory-Efficient Network T

33 Dec 01, 2022
Pytorch Implementation of Residual Vision Transformers(ResViT)

ResViT Official Pytorch Implementation of Residual Vision Transformers(ResViT) which is described in the following paper: Onat Dalmaz and Mahmut Yurt

ICON Lab 41 Dec 08, 2022
An onlinel learning to rank python codebase.

OLTR Online learning to rank python codebase. The code related to Pairwise Differentiable Gradient Descent (ranker/PDGDLinearRanker.py) is copied from

ielab 5 Jul 18, 2022
Collision risk estimation using stochastic motion models

collision_risk_estimation Collision risk estimation using stochastic motion models. This is a new approach, based on stochastic models, to predict the

Unmesh 7 Jun 26, 2022
Behind the Curtain: Learning Occluded Shapes for 3D Object Detection

Behind the Curtain: Learning Occluded Shapes for 3D Object Detection Acknowledgement We implement our model, BtcDet, based on [OpenPcdet 0.3.0]. Insta

Qiangeng Xu 163 Dec 19, 2022
Alignment Attention Fusion framework for Few-Shot Object Detection

AAF framework Framework generalities This repository contains the code of the AAF framework proposed in this paper. The main idea behind this work is

Pierre Le Jeune 20 Dec 16, 2022
Official code release for "Learned Spatial Representations for Few-shot Talking-Head Synthesis" ICCV 2021

Official code release for "Learned Spatial Representations for Few-shot Talking-Head Synthesis" ICCV 2021

Moustafa Meshry 16 Oct 05, 2022
Face detection using deep learning.

Face Detection Docker Solution Using Faster R-CNN Dockerface is a deep learning face detector. It deploys a trained Faster R-CNN network on Caffe thro

Nataniel Ruiz 181 Dec 19, 2022
This tool converts a Nondeterministic Finite Automata (NFA) into a Deterministic Finite Automata (DFA)

This tool converts a Nondeterministic Finite Automata (NFA) into a Deterministic Finite Automata (DFA)

Quinn Herden 1 Feb 04, 2022
Python based framework for Automatic AI for Regression and Classification over numerical data.

Python based framework for Automatic AI for Regression and Classification over numerical data. Performs model search, hyper-parameter tuning, and high-quality Jupyter Notebook code generation.

BlobCity, Inc 141 Dec 21, 2022
Official implementation of "Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection" in CVPR 2022.

Jadena Official implementation of "Can You Spot the Chameleon? Adversarially Camouflaging Images from Co-Salient Object Detection" in CVPR 2022. arXiv

Qing Guo 13 Nov 29, 2022
Code implementation for the paper 'Conditional Gaussian PAC-Bayes'.

CondGauss This repository contains PyTorch code for the paper Stochastic Gaussian PAC-Bayes. A novel PAC-Bayesian training method is implemented. Ther

0 Nov 01, 2021
An AI Assistant More Than a Toolkit

tymon An AI Assistant More Than a Toolkit The reason for creating framework tymon is simple. making AI more like an assistant, helping us to complete

TymonXie 46 Oct 24, 2022
The challenge for Quantum Coalition Hackathon 2021

Qchack 2021 Google Challenge This is a challenge for the brave 2021 qchack.io participants. Instructions Hello, intrepid qchacker, welcome to the G|o

quantumlib 18 May 04, 2022