jskDr

tolerence = 1e-10

M = rand(2, 2)

#w0 = [1., 1]
#w = copy( w0)
w_org = [1, 1] / norm( [1, 1])
w_full = rand(2, 1)
w = w_full / norm( w_full)

napsternxg

"""
Author: Shubhanshu Mishra
Posted this on the keras issue tracker at: https://github.com/fchollet/keras/issues/108
Implementing a linear regression using Keras. 
"""

from keras.models import Sequential
from keras.layers.core import Dense, Activation

model = Sequential()

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'))

falsetru

#!/usr/bin/kivy
import kivy
#kivy.require('1.0.6')

from kivy.app import App
from kivy.uix.floatlayout import FloatLayout
from kivy.uix.label import Label
from kivy.graphics import Color, Rectangle, Point, GraphicException
from kivy.clock import Clock
from kivy.logger import Logger

mywebserver.py

#############
# mywebserver.py
import BaseHTTPServer, SimpleHTTPServer, CGIHTTPServer

class myRequestHandler(CGIHTTPServer.CGIHTTPRequestHandler):
     def is_executable(self, path):
         return self.is_python(path)

if __name__ == '__main__':
     SimpleHTTPServer.test(myRequestHandler, BaseHTTPServer.HTTPServer)