Follow the simple steps in the order mentioned below to have your USB drive mounted on your Raspberry Pi every time you boot it.
These steps are required especially if your are setting up a Samba share, or a 24x7 torrent downloader, or alike where your Raspberry Pi must have your external storage already mounted and ready for access by the services / daemons.
Step 0. Plug in your USB HDD / Drive to Raspberry Pi If you are using a NTFS formatted drive, install the following
| """Another way, note this one will load the whole array into memory .""" | |
| from keras.preprocessing.image import ImageDataGenerator | |
| import h5py | |
| from keras.utils.io_utils import HDF5Matrix | |
| seed=0 | |
| batch_size=32 | |
| # we create two instances with the same arguments | |
| data_gen_args = dict( | |
| rotation_range=90., |
| # /etc/default/keyboard | |
| # KEYBOARD CONFIGURATION FILE | |
| # Consult the keyboard(5) manual page. | |
| XKBMODEL="abnt2" | |
| XKBLAYOUT="br" | |
| #XKBVARIANT="nodeadkeys" | |
| #XKBOPTIONS="" |
| # Standard scientific Python imports | |
| import pylab as pl | |
| import numpy as np | |
| from time import time | |
| # Import datasets, classifiers and performance metrics | |
| from sklearn import datasets, svm, pipeline | |
| from sklearn.kernel_approximation import (RBFSampler, | |
| Nystroem) | |
| from sklearn.utils import shuffle |
| """Experiment with soft-thresholded k-means feature for MNIST classification | |
| This is experiment is a tentative alternative to approximate kernel expansions | |
| explored on the same dataset by @amueller on this blog post: | |
| http://peekaboo-vision.blogspot.fr/2012/12/kernel-approximations-for-efficient.html | |
| Meant to be run with ``%run script.py`` in IPython. | |
| The 1000-dim k-means based feature expansion should yield ~96% test accuracy |
| # required: RGtk2 | |
| calculateGUI <- function() { | |
| ## Run on "OK" | |
| performStatistics <- function(button, user.data) { | |
| res <- NULL | |
| d <- NULL | |
| error <- NULL | |
| warning <- NULL | |
| # Get the information about data and the file | |
| the.file <- filename$getText() |