- to be run sequentially
description | command |
---|---|
run python script | python xx.py |
run the following command : this pauses the process | CTRL + Z ( this works for ubuntu server) |
put the process in background | bg |
This document is about running jupyter notebook on a server and accessing it on a local machine. | |
On the remote machine, start jupyter notebook | |
remote_user@remote_host$ ipython notebook --no-browser --port=8889 | |
when the notebook will start, it will generate a token. | |
On the local machine, start SSH tunnel |
This document shows step by step procedure on how to SSH without using password everytime | |
Type the following commands: | |
1. cd .ssh | |
2. vim config | |
when you open config file, type the following: | |
host snake |
set nu | |
set nocompatible " required | |
filetype off " required | |
" set the runtime path to include Vundle and initialize | |
set rtp+=~/.vim/bundle/Vundle.vim | |
call vundle#begin() | |
" alternatively, pass a path where Vundle should install plugins | |
"call vundle#begin('~/some/path/here') |
import numpy as np | |
def pf(output,target,metric=None): | |
TP = np.count_nonzero(data*target) | |
TN = np.count_nonzero((data - 1) * (target - 1)) | |
FP = np.count_nonzero(data * (target - 1)) | |
FN = np.count_nonzero((data - 1) * target) | |
precision = TP / (TP + FP) | |
recall = TP / (TP + FN) | |
F1 = 2 * precision * recall / (precision + recall) | |
accuracy = (TP+TN)/(TP+TN+FP+FN) |
import torch | |
def pf1(output,target,metric=None): | |
d = output.data | |
t = target.data | |
TP = torch.nonzero(d*t).size(0) | |
TN = torch.nonzero((d - 1) * (t - 1)).size(0) | |
FP = torch.nonzero(d * (t - 1)).size(0) | |
FN = torch.nonzero((d - 1) * t).size(0) | |
precision = TP / (TP + FP) | |
recall = TP / (TP + FN) |
import torch | |
from torch import nn | |
class autoencoder(nn.Module): | |
def __init__(self,downsizing_factor=None,in_channels=1): | |
self.downsize = downsizing_factor | |
self.in_channels = in_channels | |
super(autoencoder,self).__init__() | |
conv_modules=[] | |
self.in_channels = self.in_channels |
import torch | |
from torch import nn | |
class autoencoder(nn.Module): | |
def __init__(self,downsizing_factor=None,in_channels=1): | |
self.downsize = downsizing_factor | |
self.in_channels = in_channels | |
super(autoencoder,self).__init__() | |
conv_modules=[] | |
self.in_channels = self.in_channels |
set tabstop=8 | |
set expandtab | |
set softtabstop=4 | |
set shiftwidth=4 | |
filetype plugin indent on | |
set number | |
set backspace=indent,start | |
set hlsearch |
# Use C-o like we do in screen as action key | |
#unbind C-b | |
set -g prefix C-a | |
#bind-key C-b last-window | |
bind-key a send-prefix | |
bind-key C-n next-window | |
bind-key C-p previous-window | |
# Terminal emulator window title | |
set -g set-titles on |