注意:本文内容适用于 Tmux 2.3 及以上的版本,但是绝大部分的特性低版本也都适用,鼠标支持、VI 模式、插件管理在低版本可能会与本文不兼容。
启动新会话:
tmux [new -s 会话名 -n 窗口名]
恢复会话:
| curl -O https://gist.github.com/akimach/27e87cb7f97dc253921b9ea8b7b332b5/raw/fd112a66a3ca953dd7ad26098cccfe1d532ba0d7/binominal_confidience_interval.ipynb | |
| jupyter-nbconvert binominal_confidience_interval.ipynb --to markdown | |
| pandoc binominal_confidience_interval.ipynb -t docx -o binominal_confidience_interval.docx |
| from typing import List, Tuple, Optional, Union, Any, ContextManager, Callable, overload | |
| import builtins | |
| import math | |
| import pickle | |
| class dtype: ... | |
| _dtype = dtype |
| """ | |
| Minimal character-level Vanilla RNN model. Written by Andrej Karpathy (@karpathy) | |
| BSD License | |
| """ | |
| import numpy as np | |
| # data I/O | |
| data = open('input.txt', 'r').read() # should be simple plain text file | |
| chars = list(set(data)) | |
| data_size, vocab_size = len(data), len(chars) |
| """ | |
| Create train, valid, test iterators for CIFAR-10 [1]. | |
| Easily extended to MNIST, CIFAR-100 and Imagenet. | |
| [1]: https://discuss.pytorch.org/t/feedback-on-pytorch-for-kaggle-competitions/2252/4 | |
| """ | |
| import torch | |
| import numpy as np |
| /* | |
| * 二分幂法 求x^n | |
| */ | |
| // 求整数幂 | |
| package main | |
| import ( | |
| "fmt" |