In [1]:

    

import numpy as np
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Activation, Flatten
from keras.layers import Convolution2D, MaxPooling2D
from keras.utils import np_utils
from PIL import Image
from numpy import array
import math


    

    




Using Theano backend.

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#Loading the training and testing data
(X_train, y_train), (X_test, y_test) = mnist.load_data()


    

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img = Image.fromarray(X_train[0])
img.show()
print(y_train[0])


    

    




5

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no_of_examples = 71


    

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new_im = Image.new('RGB', (280,28))
concat_img = Image.new('RGB', (280,int(math.ceil(no_of_examples/10)*28)))


    

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x_offset = 0
concat_x_offset = 0


    

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for i in range(0,no_of_examples):
    img = Image.fromarray(X_train[i])
    new_im.paste(img, (x_offset,0))
    x_offset += img.size[0]
    if ((i%10==0 and i!=0) or (i == no_of_examples-1)):
        #new_im.show()
        #concat_img = new_im
        concat_img.paste(new_im,(0,concat_x_offset))
        concat_x_offset += new_im.size[1]
        new_im = Image.new('RGB', (280,28))
        x_offset = 0


    

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concat_img.show()


    

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