learning-rate-scheduling
There are 37 repositories under learning-rate-scheduling topic.
Tony-Y/pytorch_warmup
Learning Rate Warmup in PyTorch
awslabs/adatune
Gradient based Hyperparameter Tuning library in PyTorch
kozistr/pytorch_optimizer
optimizer & lr scheduler & loss function collections in PyTorch
cmpark0126/pytorch-polynomial-lr-decay
Polynomial Learning Rate Decay Scheduler for PyTorch
Lance0218/Pytorch-DistributedDataParallel-Training-Tricks
A guide that integrates Pytorch DistributedDataParallel, Apex, warmup, learning rate scheduler, also mentions the set-up of early-stopping and random seed.
abhuse/cyclic-cosine-decay
Pytorch cyclic cosine decay learning rate scheduler
YuchenJin/autolrs
Automatic learning-rate scheduler
lehduong/torch-warmup-lr
Warmup learning rate wrapper for Pytorch Scheduler
git-disl/LRBench
A learning rate recommending and benchmarking tool.
TheQuantScientist/LiteFormer
[PENDING] The official repo for the paper: "A Lightweight Multi-Head Attention Transformer for Stock Price Forecasting".
ahundt/sharpDARTS
sharpDARTS: Faster and More Accurate Differentiable Architecture Search
JonnoFTW/keras_find_lr_on_plateau
Keras Callback to Automatically Adjust the learning rate when it stops improving
timesler/lr-momentum-scheduler
Pytorch implementation of arbitrary learning rate and momentum schedules, including the One Cycle Policy
cybertronai/pytorch-fd
Implementation of fluctuation dissipation relations for automatic learning rate annealing.
ZIB-IOL/BIMP
Code to reproduce the experiments of ICLR2023-paper: How I Learned to Stop Worrying and Love Retraining
kardasbart/MultiLR
A method for assigning separate learning rate schedulers to different parameters group in a model.
nomuramasahir0/cma-learning-rate-adaptation
(GECCO2023 Best Paper Nomination & ACM TELO) CMA-ES with Learning Rate Adaptation
sdamadi/image-classification
Comprehensive image classification for training multilayer perceptron (MLP), LeNet, LeNet5, conv2, conv4, conv6, VGG11, VGG13, VGG16, VGG19 with batch normalization, ResNet18, ResNet34, ResNet50, MobilNetV2 on MNIST, CIFAR10, CIFAR100, and ImageNet1K.
vballoli/abel-pytorch
ABEL implemented in PyTorch
iSiddharth20/DeepLearning-ImageClassification-Toolkit
End-to-end Image Classification using Deep Learning toolkit for custom image datasets. Features include Pre-Processing, Training with Multiple CNN Architectures and Statistical Inference Tools. Special utilities for RAM optimization, Learning Rate Scheduling, and Detailed Code Comments are included.
agiokap/Dogs-vs-Cats-An-Image-Classification-Task-using-TensorFlow
In this repository, I put into test my newly acquired Deep Learning skills in order to solve the Kaggle's famous Image Classification Problem, called "Dogs vs. Cats".
metalmerge/SPECTRA
SPECTRA: Solar Panel Evaluation through Computer Vision and Advanced Techniques for Reliable Analysis
ndtands/CNN_Color_Car_Pytorch
Build from scratch
rharish101/multistage-step-size-scheduling-minimax
Master's thesis: Experiments on multistage step size schedulers for first-order optimization in minimax problems
synml/lr-scheduler-visualization
Visualize the progress of the learning rate scheduler graphically.
draktr/optschedule
Flexible parameter scheduler that can be implemented with proprietary and open source optimizers.
elaaj/cat-vs-dog-classifier
The goal of this project is to devise an accurate CNN-based classifier able to distinguish between Cat and Dog in images where the animal is predominant.
izdihartinoni/Image-Classification-Rock-Paper-Scissors-with-Model-Deployment
Submission Akhir - Image Classification Model Deployment - Belajar Pengembangan Machine Learning - Dicoding
kbmclaren/assn5-CMSC478-ML
The machine learning task in this assignment is image classification using Convolutional Neural Networks in Tensorflow and Keras
KhoiDOO/tvlars
TVLARS - A Fast Convergence Optimizer for Large Batch Training
omkar-nitsure/Mumbai_RainFall_Forecasting
Used different Transformer based and LSTM based models for forecasting rainfall in different areas of Mumbai. Employed different smart training techniques to improve correlation with the true time-series.
razamehar/Weather-Time-Series-Analysis-using-Statistical-Methods-and-Deep-Learning-Models
This project conducts a thorough analysis of weather time series data using diverse statistical and deep learning models. Each model was rigorously applied to the same weather time series data to assess and compare their forecasting accuracy. Detailed results and analyses are provided to delineate the strengths and weaknesses of each approach.
rharish101/label-noise-and-generalization
Semester project on the impact of label noise on deep learning optimization