In [1]:

    

import sys; print('Python \t\t{0[0]}.{0[1]}'.format(sys.version_info))


    

    




Python 		3.6

In [2]:

    

%matplotlib inline 

import numpy as np
import matplotlib.pyplot as plt


    

In [3]:

    

from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("./", one_hot=True)


    

    




Extracting ./train-images-idx3-ubyte.gz
Extracting ./train-labels-idx1-ubyte.gz
Extracting ./t10k-images-idx3-ubyte.gz
Extracting ./t10k-labels-idx1-ubyte.gz

In [4]:

    

mnist.train.images.shape


    

    
Out[4]:





(55000, 784)

In [5]:

    

plt.figure(figsize=(15,5))
for i in list(range(10)):
    plt.subplot(1, 10, i+1)
    pixels = mnist.test.images[i+100]
    pixels = pixels.reshape((28, 28))
    plt.imshow(pixels, cmap='gray_r')
plt.show()


    

In [6]:

    

def test_render(pixels, result, truth):
    #pixels, result and truth are np vectors
    plt.figure(figsize=(10,5))
    plt.subplot(1, 2, 1)
    pixels = pixels.reshape((28, 28))
    plt.imshow(pixels, cmap='gray_r')

    plt.subplot(1, 2, 2)
    
    #index, witdh
    ind = np.arange(len(result))
    width = 0.49

    plt.barh(ind,result, width, color='orange', edgecolor='k', hatch="/")
    plt.barh(ind+width,truth,width, color='g', edgecolor='k')
    plt.yticks(ind+width, range(10))
    plt.margins(y=0)

    plt.show()


    

In [7]:

    

import random
i = 100 #it's a "six" 


pixels = mnist.test.images[i]
truth  = mnist.test.labels[i]
result = [0.3, 0.1, 0., 0., 0., 0., 0.9, 0., 0., 0. ]

test_render(pixels, result, truth)


    

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