An implementation of Efficientnet in PyTorch
Pytorch
B0:
call plug#begin("~/.vim/plugged") | |
" Theme | |
Plug 'arcticicestudio/nord-vim' | |
" Language Client | |
Plug 'neoclide/coc.nvim', {'branch': 'release'} | |
let g:coc_global_extensions = ['coc-emmet', 'coc-css', 'coc-html', 'coc-json', 'coc-prettier', 'coc-tsserver', 'coc-python'] | |
" TypeScript Highlighting | |
Plug 'vim-airline/vim-airline' | |
Plug 'vim-airline/vim-airline-themes' |
// | |
// MetalAdder.h | |
// MetalLearn1 | |
// | |
// Created by Sharan Sajiv Menon on 4/3/22. | |
// | |
#import <Foundation/Foundation.h> | |
#import <Metal/Metal.h> | |
#ifndef MetalAdder_h |
extern crate vulkano; | |
extern crate vulkano_shaders; | |
use vulkano::{ | |
sync, | |
buffer::{BufferUsage, CpuAccessibleBuffer}, | |
command_buffer::{AutoCommandBufferBuilder, CommandBufferUsage}, | |
device::{ | |
physical::{PhysicalDevice}, | |
QueueCreateInfo, |
#include<stdio.h> | |
#include<iostream> | |
#include<Accelerate/Accelerate.h> | |
#include <random> | |
#include <stdlib.h> | |
#include <vector> | |
#include <chrono> | |
void fill(float *a, int n) { | |
for (int i = 0; i < n; i++) { |
Based on this paper: https://arxiv.org/pdf/1703.03864.pdf
More info can be found in this blog post: https://openai.com/blog/evolution-strategies/
Based on DCGAN from this paper: https://arxiv.org/pdf/1511.06434.pdf
Implementation of SRGAN in PyTorch. Superresolves to 4x
Based on this paper: https://arxiv.org/pdf/1609.04802.pdf
Model is to be trained for 150 epochs.