950
950
" test_accurate_predictions += int(np.argmax(layer_2) == np.argmax(test_labels[i:i+1]))\n " ,
951
951
" \n " ,
952
952
" # 3. Display the error and accuracy metrics in the output.\n " ,
953
- " sys.stdout.write (\"\\ n\" +\\\n " ,
954
- " \" Epoch:\" + str(j+10) +\\\n " ,
955
- " \" Training set error:\" + str(training_loss/ float(len(training_images)))[0:5] +\\\n " ,
956
- " \" Training set accuracy:\" + str(training_accurate_predictions/ float(len(training_images))) +\\\n " ,
957
- " \" Test set error:\" + str(test_loss/ float(len(test_images)))[0:5] +\\\n " ,
958
- " \" Test set accuracy:\" + str(test_accurate_predictions/ float(len(test_images))))"
953
+ " print (\"\\ n\" +\\\n " ,
954
+ " \" Epoch:\" + str(j+10) +\\\n " ,
955
+ " \" Training set error:\" + str(training_loss/ float(len(training_images)))[0:5] +\\\n " ,
956
+ " \" Training set accuracy:\" + str(training_accurate_predictions/ float(len(training_images))) +\\\n " ,
957
+ " \" Test set error:\" + str(test_loss/ float(len(test_images)))[0:5] +\\\n " ,
958
+ " \" Test set accuracy:\" + str(test_accurate_predictions/ float(len(test_images))))\n "
959
959
]
960
960
},
961
961
{
1014
1014
},
1015
1015
"nbformat" :4 ,
1016
1016
"nbformat_minor" :0
1017
- }
1017
+ }