title: How to tell which Keras model is better?
body: <p>I don't understand which accuracy in the output to use to compare my 2 Keras models to see which one is better. </p>
  
  <p>Do I use the "acc" (from the training data?) one or the "val acc" (from the validation data?) one?</p>
  
  <p>There are different accs and val accs for each epoch. How do I know the acc or val acc for my model as a whole? Do I average all of the epochs accs or val accs to find the acc or val acc of the model as a whole?</p>
  
  <p><strong>Model 1 Output</strong></p>
  
  <pre><code>Train on 970 samples, validate on 243 samples
  Epoch 1/20
  0s - loss: 0.1708 - acc: 0.7990 - val_loss: 0.2143 - val_acc: 0.7325
  Epoch 2/20
  0s - loss: 0.1633 - acc: 0.8021 - val_loss: 0.2295 - val_acc: 0.7325
  Epoch 3/20
  0s - loss: 0.1657 - acc: 0.7938 - val_loss: 0.2243 - val_acc: 0.7737
  Epoch 4/20
  0s - loss: 0.1847 - acc: 0.7969 - val_loss: 0.2253 - val_acc: 0.7490
  Epoch 5/20
  0s - loss: 0.1771 - acc: 0.8062 - val_loss: 0.2402 - val_acc: 0.7407
  Epoch 6/20
  0s - loss: 0.1789 - acc: 0.8021 - val_loss: 0.2431 - val_acc: 0.7407
  Epoch 7/20
  0s - loss: 0.1789 - acc: 0.8031 - val_loss: 0.2227 - val_acc: 0.7778
  Epoch 8/20
  0s - loss: 0.1810 - acc: 0.8010 - val_loss: 0.2438 - val_acc: 0.7449
  Epoch 9/20
  0s - loss: 0.1711 - acc: 0.8134 - val_loss: 0.2365 - val_acc: 0.7490
  Epoch 10/20
  0s - loss: 0.1852 - acc: 0.7959 - val_loss: 0.2423 - val_acc: 0.7449
  Epoch 11/20
  0s - loss: 0.1889 - acc: 0.7866 - val_loss: 0.2523 - val_acc: 0.7366
  Epoch 12/20
  0s - loss: 0.1838 - acc: 0.8021 - val_loss: 0.2563 - val_acc: 0.7407
  Epoch 13/20
  0s - loss: 0.1835 - acc: 0.8041 - val_loss: 0.2560 - val_acc: 0.7325
  Epoch 14/20
  0s - loss: 0.1868 - acc: 0.8031 - val_loss: 0.2573 - val_acc: 0.7407
  Epoch 15/20
  0s - loss: 0.1829 - acc: 0.8072 - val_loss: 0.2581 - val_acc: 0.7407
  Epoch 16/20
  0s - loss: 0.1878 - acc: 0.8062 - val_loss: 0.2589 - val_acc: 0.7407
  Epoch 17/20
  0s - loss: 0.1833 - acc: 0.8072 - val_loss: 0.2613 - val_acc: 0.7366
  Epoch 18/20
  0s - loss: 0.1837 - acc: 0.8113 - val_loss: 0.2605 - val_acc: 0.7325
  Epoch 19/20
  0s - loss: 0.1906 - acc: 0.8010 - val_loss: 0.2555 - val_acc: 0.7407
  Epoch 20/20
  0s - loss: 0.1884 - acc: 0.8062 - val_loss: 0.2542 - val_acc: 0.7449
  </code></pre>
  
  <p><strong>Model 2 Output</strong></p>
  
  <pre><code>Train on 970 samples, validate on 243 samples
  Epoch 1/20
  0s - loss: 0.1735 - acc: 0.7876 - val_loss: 0.2386 - val_acc: 0.6667
  Epoch 2/20
  0s - loss: 0.1733 - acc: 0.7825 - val_loss: 0.1894 - val_acc: 0.7449
  Epoch 3/20
  0s - loss: 0.1781 - acc: 0.7856 - val_loss: 0.2028 - val_acc: 0.7407
  Epoch 4/20
  0s - loss: 0.1717 - acc: 0.8021 - val_loss: 0.2545 - val_acc: 0.7119
  Epoch 5/20
  0s - loss: 0.1757 - acc: 0.8052 - val_loss: 0.2252 - val_acc: 0.7202
  Epoch 6/20
  0s - loss: 0.1776 - acc: 0.8093 - val_loss: 0.2449 - val_acc: 0.7490
  Epoch 7/20
  0s - loss: 0.1833 - acc: 0.7897 - val_loss: 0.2272 - val_acc: 0.7572
  Epoch 8/20
  0s - loss: 0.1827 - acc: 0.7928 - val_loss: 0.2376 - val_acc: 0.7531
  Epoch 9/20
  0s - loss: 0.1795 - acc: 0.8062 - val_loss: 0.2445 - val_acc: 0.7490
  Epoch 10/20
  0s - loss: 0.1746 - acc: 0.8103 - val_loss: 0.2491 - val_acc: 0.7449
  Epoch 11/20
  0s - loss: 0.1831 - acc: 0.8082 - val_loss: 0.2477 - val_acc: 0.7449
  Epoch 12/20
  0s - loss: 0.1831 - acc: 0.8113 - val_loss: 0.2496 - val_acc: 0.7490
  Epoch 13/20
  0s - loss: 0.1920 - acc: 0.8000 - val_loss: 0.2459 - val_acc: 0.7449
  Epoch 14/20
  0s - loss: 0.1945 - acc: 0.7928 - val_loss: 0.2446 - val_acc: 0.7490
  Epoch 15/20
  0s - loss: 0.1852 - acc: 0.7990 - val_loss: 0.2459 - val_acc: 0.7449
  Epoch 16/20
  0s - loss: 0.1800 - acc: 0.8062 - val_loss: 0.2495 - val_acc: 0.7449
  Epoch 17/20
  0s - loss: 0.1891 - acc: 0.8000 - val_loss: 0.2469 - val_acc: 0.7449
  Epoch 18/20
  0s - loss: 0.1891 - acc: 0.8041 - val_loss: 0.2467 - val_acc: 0.7531
  Epoch 19/20
  0s - loss: 0.1853 - acc: 0.8072 - val_loss: 0.2511 - val_acc: 0.7449
  Epoch 20/20
  0s - loss: 0.1905 - acc: 0.8062 - val_loss: 0.2460 - val_acc: 0.7531
  </code></pre>