Over9000 optimizer

Related tags

Pytorch Utilitiesover9000
Overview

Optimizers and tests

Every result is avg of 20 runs.

Dataset LR Schedule Imagenette size 128, 5 epoch Imagewoof size 128, 5 epoch
Adam - baseline OneCycle 0.8493 0.6125
RangerLars (RAdam + LARS + Lookahead) Flat and anneal 0.8732 0.6523
Ralamb (RAdam + LARS) Flat and anneal 0.8675 0.6367
Ranger (RAdam + Lookahead) Flat and anneal 0.8594 0.5946
Novograd Flat and anneal 0.8711 0.6126
Radam Flat and anneal 0.8444 0.537
Lookahead OneCycle 0.8578 0.6106
Lamb OneCycle 0.8400 0.5597
DiffGrad OneCycle 0.8527 0.5912
AdaMod OneCycle 0.8473 0.6132
Owner
Mikhail Grankin
Mikhail Grankin
GitHub Repository
PyTorch Implementation of [1611.06440] Pruning Convolutional Neural Networks for Resource Efficient Inference

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GPU-accelerated PyTorch implementation of Zero-shot User Intent Detection via Capsule Neural Networks

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A Pytorch Implementation for Compact Bilinear Pooling.

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Code for paper "Energy-Constrained Compression for Deep Neural Networks via Weighted Sparse Projection and Layer Input Masking"

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Pytorch implementation of Distributed Proximal Policy Optimization

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Over9000 optimizer

Optimizers and tests Every result is avg of 20 runs. Dataset LR Schedule Imagenette size 128, 5 epoch Imagewoof size 128, 5 epoch Adam - baseline OneC

Mikhail Grankin 405 Nov 27, 2022
Differentiable SDE solvers with GPU support and efficient sensitivity analysis.

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PyTorch to TensorFlow Lite converter

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PyTorch implementation of TabNet paper : https://arxiv.org/pdf/1908.07442.pdf

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pip install antialiased-cnns to improve stability and accuracy

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On the Variance of the Adaptive Learning Rate and Beyond

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Model summary in PyTorch similar to `model.summary()` in Keras

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Bunch of optimizer implementations in PyTorch

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PyTorch Extension Library of Optimized Scatter Operations

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A tiny scalar-valued autograd engine and a neural net library on top of it with PyTorch-like API

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S3-plugin is a high performance PyTorch dataset library to efficiently access datasets stored in S3 buckets.

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A lightweight wrapper for PyTorch that provides a simple declarative API for context switching between devices, distributed modes, mixed-precision, and PyTorch extensions.

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