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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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# 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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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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# "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 | |