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)

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

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'))

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()

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)