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22 "cells" : [
33  {
4-    "cell_type" : " code" 
5-    "execution_count" : null ,
4+    "cell_type" : " markdown" 
65   "metadata" : {},
7-    "outputs" : [],
86   "source" : [
97    " \n " 
108    " # MNIST Dataset Introduction\n " 
2725   ]
2826  },
2927  {
30-    "cell_type" : " code" 
31-    "execution_count" : null ,
28+    "cell_type" : " markdown" 
3229   "metadata" : {
3330    "collapsed" : true 
3431   },
35-    "outputs" : [],
3632   "source" : [
3733    " # Import MNIST\n " 
3834    " from tensorflow.examples.tutorials.mnist import input_data\n " 
5349   ]
5450  },
5551  {
56-    "cell_type" : " code" 
57-    "execution_count" : null ,
52+    "cell_type" : " markdown" 
5853   "metadata" : {
5954    "collapsed" : true 
6055   },
61-    "outputs" : [],
6256   "source" : [
6357    " # Get the next 64 images array and labels\n " 
6458    " batch_X, batch_Y = mnist.train.next_batch(64)" 
8882   "name" : " python" 
8983   "nbconvert_exporter" : " python" 
9084   "pygments_lexer" : " ipython2" 
91-    "version" : " 2.7.13 " 
85+    "version" : " 2.7.18 " 
9286  }
9387 },
9488 "nbformat" : 4 ,
95-  "nbformat_minor" : 0 
89+  "nbformat_minor" : 1 
9690}
 
 
   
 
     
   
   
          
    
    
     
    
      
     
     
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