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| #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')) |
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| import logging | |
| import math | |
| import mimetypes | |
| from multiprocessing import Pool | |
| import os | |
| from boto.s3.connection import S3Connection | |
| from filechunkio import FileChunkIO | |
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| #!/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>] |
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| 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 |
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| ls | awk '{print "[](results/" $1 ")"}' |
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| http://antirez.com/latest/0 |
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| 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 |
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| 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: |
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| sudo apt-get install gfortran libopenblas-dev liblapack-dev | |
| sudo pip install numpy scipy |