hasnainv

import logging
import math
import mimetypes
from multiprocessing import Pool
import os

from boto.s3.connection import S3Connection
from filechunkio import FileChunkIO

hasnainv

sudo apt-get install gfortran libopenblas-dev liblapack-dev
sudo pip install numpy scipy

hasnainv

set backup 
175 set backupdir=~/.vim-tmp,~/.tmp,~/tmp,/var/tmp,/tmp
176 set backupskip=/tmp/*,/private/tmp/*
177 set directory=~/.vim-tmp,~/.tmp,~/tmp,/var/tmp,/tmp 
178 set writebackup

hasnainv

http://antirez.com/latest/0

hasnainv

ls | awk '{print "[](results/" $1 ")"}'

hasnainv

wget http://cloud.github.com/downloads/rvoicilas/inotify-tools/inotify-tools-3.14.tar.gz
tar -zxvf inotify-tools-3.14.tar.gz
cd inotify-tools-3.14
./configure
make
make install
updatedb

hasnainv

#!/usr/bin/env python
"""Split large file into multiple pieces for upload to S3.

S3 only supports 5Gb files for uploading directly, so for larger CloudBioLinux
box images we need to use boto's multipart file support.

This parallelizes the task over available cores using multiprocessing.

Usage:
  s3_multipart_upload.py <file_to_transfer> <bucket_name> [<s3_key_name>]

hasnainv

#How to load the model
def build_model(img_width=224, img_height=224):
  from keras.models import Sequential
  from keras.layers import Convolution2D, ZeroPadding2D, MaxPooling2D, Activation
  
  model = Sequential()
  model = Sequential()
  model.add(ZeroPadding2D((1,1),input_shape=(3,img_width,img_height)))
  model.add(Convolution2D(64, 3, 3, activation='relu', name='conv1_1'))
  model.add(Activation('relu'))

hasnainv

import os
from optparse import OptionParser
import cPickle
import skimage
import numpy as np
from scipy import misc
from distutils.dir_util import mkpath

def loadImage(imgpath):
    try: