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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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