In [2]:

    

import tensorflow as tf
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 [23]:

    

batch=mnist.train.next_batch(3)
xs=batch[0]
ys=batch[1]


    

In [24]:

    

xs


    

    
Out[24]:





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

In [25]:

    

ys


    

    
Out[25]:





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

In [26]:

    

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


    

In [27]:

    

%matplotlib inline
from matplotlib import pyplot as plt
plt.imshow(x,'gray')


    

    
Out[27]:





<matplotlib.image.AxesImage at 0x13666b9e8>






    

In [ ]: