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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'))

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'''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

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#!/usr/bin/env python
#-*- coding: utf-8 -*-
# Supporting Python 3 

import sys, os, re

try:    bibtexdb = open(sys.argv[1]).read()
except: print("Error: specify the file to be processed!")

if not os.path.isfile('journalList.txt'):

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#!/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