--
- install python-3:
$ pyenv install 3.5.2
class Ralamb(Optimizer): | |
def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8, weight_decay=0): | |
defaults = dict(lr=lr, betas=betas, eps=eps, weight_decay=weight_decay) | |
self.buffer = [[None, None, None] for ind in range(10)] | |
super(Ralamb, self).__init__(params, defaults) | |
def __setstate__(self, state): | |
super(Ralamb, self).__setstate__(state) |
"""Implementation of NEAT. | |
python neat.py --task {xor, lunar, cartpole} | |
See the post at https://wellecks.wordpress.com/ for details. | |
Parts of this implementation are based on Neat-Python. | |
""" | |
from itertools import count | |
import numpy as np | |
import math |
git log --author="Linus Torvalds" --date=iso | perl -nalE 'if (/^Date:\s+[\d-]{10}\s(\d{2})/) { say $1+0 }' | sort | uniq -c|perl -MList::Util=max -nalE '$h{$F[1]} = $F[0]; }{ $m = max values %h; foreach (0..23) { $h{$_} = 0 if not exists $h{$_} } foreach (sort {$a <=> $b } keys %h) { say sprintf "%02d - %4d %s", $_, $h{$_}, "*"x ($h{$_} / $m * 50); }' | |
# Copyright (c) 2019-present, Thomas Wolf. | |
# All rights reserved. This source code is licensed under the MIT-style license. | |
""" A very small and self-contained gist to train a GPT-2 transformer model on wikitext-103 """ | |
import os | |
from collections import namedtuple | |
from tqdm import tqdm | |
import torch | |
import torch.nn as nn | |
from torch.utils.data import DataLoader | |
from ignite.engine import Engine, Events |
### JHW 2018 | |
import numpy as np | |
import umap | |
# This code from the excellent module at: | |
# https://stackoverflow.com/questions/4643647/fast-prime-factorization-module | |
import random |
import torch | |
import torch.nn as nn | |
def log_sum_exp(x): | |
# See implementation detail in | |
# http://timvieira.github.io/blog/post/2014/02/11/exp-normalize-trick/ | |
# b is a shift factor. see link. | |
# x.size() = [N, C]: | |
b, _ = torch.max(x, 1) |