kwd

# Extract Files ( https://wiki.archlinux.org/index.php/Bashrc_helpers )
extract() {
  if [ -f $1 ] ; then
      case $1 in
          *.tar.bz2)   tar xvjf $1    ;;
          *.tar.gz)    tar xvzf $1    ;;
          *.tar.xz)    tar xvJf $1    ;;
          *.bz2)       bunzip2 $1     ;;
          *.rar)       unrar x $1     ;;
          *.gz)        gunzip $1      ;;

soarez

How to setup your own CA with OpenSSL

For educational reasons I've decided to create my own CA. Here is what I learned.

First things first

Lets get some context first.

fayeip

titipata

Caffe Installation

Note on how to install caffe on Ubuntu. Sucessfully install using CPU, more information for GPU see this link

###Installation

• verify all the preinstallation according to CUDA guide e.g.

lspci | grep -i nvidia

GilLevi

Age and Gender Classification using Convolutional Neural Networks

name: Age Classification CNN

caffemodel: age_net.caffemodel

caffemodel_url: https://github.com/GilLevi/AgeGenderDeepLearning/raw/master/models/age_net.caffemodel

JBed

from keras.models import Sequential
from keras.layers.core import Dense, Dropout, Activation, Flatten
from keras.layers.convolutional import Convolution2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization

#AlexNet with batch normalization in Keras 
#input image is 224x224

model = Sequential()
model.add(Convolution2D(64, 3, 11, 11, border_mode='full'))

GilLevi

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns

Convolutional neural networks for emotion classification from facial images as described in the following work:

Gil Levi and Tal Hassner, Emotion Recognition in the Wild via Convolutional Neural Networks and Mapped Binary Patterns, Proc. ACM International Conference on Multimodal Interaction (ICMI), Seattle, Nov. 2015

Project page: http://www.openu.ac.il/home/hassner/projects/cnn_emotions/

If you find our models useful, please add suitable reference to our paper in your work.

yrevar

{0: 'tench, Tinca tinca',
 1: 'goldfish, Carassius auratus',
 2: 'great white shark, white shark, man-eater, man-eating shark, Carcharodon carcharias',
 3: 'tiger shark, Galeocerdo cuvieri',
 4: 'hammerhead, hammerhead shark',
 5: 'electric ray, crampfish, numbfish, torpedo',
 6: 'stingray',
 7: 'cock',
 8: 'hen',
 9: 'ostrich, Struthio camelus',

udibr

import numpy as np
import h5py
import keras.backend as K

def str_shape(x):
    return 'x'.join(map(str, x.shape))

def load_weights(model, filepath, lookup={}, ignore=[], transform=None, verbose=True):
    """Modified version of keras load_weights that loads as much as it can.
    Useful for transfer learning.

fchollet

'''This script goes along the blog post
"Building powerful image classification models using very little data"
from blog.keras.io.
It uses data that can be downloaded at:
https://www.kaggle.com/c/dogs-vs-cats/data
In our setup, we:
- created a data/ folder
- created train/ and validation/ subfolders inside data/
- created cats/ and dogs/ subfolders inside train/ and validation/
- put the cat pictures index 0-999 in data/train/cats