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# "Defaults of loans" is predicted with "customer data + images in SNS" 


#  1.import library
import keras
from keras.layers import Input, Embedding, LSTM, Dense,Conv2D,MaxPooling2D,Flatten
from keras.models import Model, Sequential

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import os
import h5py
import keras
import numpy as np
from keras.preprocessing.image import ImageDataGenerator, array_to_img, img_to_array, load_img
from keras.models import Sequential
from keras.layers import Convolution2D, MaxPooling2D, ZeroPadding2D,Cropping2D 
from keras.layers import Activation, Dropout, Flatten, Dense, Lambda
from keras.utils.np_utils import to_categorical
from keras.regularizers import l2, activity_l2

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# 1. Import library
import os
import h5py
import numpy as np
import tensorflow as tf
import time
import glob
import json
import csv
from PIL import Image

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#  1.Import library
import time
import numpy as np
from numpy.random import *
seed(100)

#  2.Setting  up the data
conv = np.array([(6,-5,4), (-5,17,-11),(4,-11,24)])
mu=np.array([8,12,15])
print(conv)

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