In [1]:

    

from tensorflow.examples.tutorials.mnist import input_data
mnist=input_data.read_data_sets('MNIST_data',one_hot=True)


    

    




Extracting MNIST_data\train-images-idx3-ubyte.gz
Extracting MNIST_data\train-labels-idx1-ubyte.gz
Extracting MNIST_data\t10k-images-idx3-ubyte.gz
Extracting MNIST_data\t10k-labels-idx1-ubyte.gz

In [2]:

    

batch=mnist.train.next_batch(3)


    

In [3]:

    

batch


    

    
Out[3]:





(array([[ 0.,  0.,  0., ...,  0.,  0.,  0.],
        [ 0.,  0.,  0., ...,  0.,  0.,  0.],
        [ 0.,  0.,  0., ...,  0.,  0.,  0.]], dtype=float32),
 array([[ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],
        [ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  1.,  0.],
        [ 0.,  1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.]]))

In [5]:

    

x=batch[0]
y=batch[1]


    

In [7]:

    

y[0]


    

    
Out[7]:





array([ 1.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])

In [9]:

    

x[0].shape


    

    
Out[9]:





(784,)

In [20]:

    

import numpy as np
img=np.reshape(x[0], newshape=(28, -1))


    

In [21]:

    

%matplotlib inline
from matplotlib import pyplot as plt
plt.imshow(img,cmap='gray')


    

    
Out[21]:





<matplotlib.image.AxesImage at 0x250b59bb438>






    

In [ ]: