In [2]:

    

import tensorflow as tf
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import matplotlib
%matplotlib inline
import input_data
import numpy


    

In [3]:

    

import sys
import input_data
mnist = input_data.read_data_sets("/tmp/data/", one_hot=True)


    

    




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






    




/Library/Frameworks/Python.framework/Versions/2.7/lib/python2.7/gzip.py:275: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future
  chunk = self.extrabuf[offset: offset + size]
input_data.py:35: VisibleDeprecationWarning: converting an array with ndim > 0 to an index will result in an error in the future
  data = data.reshape(num_images, rows, cols, 1)

In [4]:

    

session = tf.Session()


    

In [5]:

    

x = tf.placeholder(tf.float32,shape=[28*28])


    

In [6]:

    

allx = tf.placeholder(tf.float32,shape=[None,28**2])


    

In [7]:

    

testimage = mnist.test.images[0]


    

In [8]:

    

dist = tf.reduce_sum(tf.abs(tf.sub(x,allx)), reduction_indices=1)


    

In [9]:

    

l2dist = session.run(tf.reduce_sum(tf.sub(x,allx),1),feed_dict={x:testimage,allx:mnist.train.images})


    

In [10]:

    

predict = tf.arg_min(dist,0)
predictl2 = tf.arg_min(l2dist,0)


    

In [11]:

    

def dopredict(testimage):
    return (session.run(predict,feed_dict={x:testimage,allx:mnist.train.images}),
            session.run(predict,feed_dict={x:testimage,allx:mnist.train.images}))


    

In [12]:

    

def draw(img):
    tmp = img
    tmp2 = tmp.reshape((28,28))

    plt.imshow(tmp2, cmap = cm.Greys)
    plt.show()


    

In [16]:

    

import random
for img in  list(map(lambda _: random.choice(mnist.test.images), range(1))): #mnist.train.images[50:55]:
    draw(img)
    
    p,p2 = dopredict(img)
#     print p,p2
    print(numpy.argmax(mnist.train.labels[p]))
    print(numpy.argmax(mnist.train.labels[p2]))


    

    













    




5
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