neural_nets_intro.ipynb

      "Epoch 143/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.2030 - acc: 0.9600 - val_loss: 0.2516 - val_acc: 0.9400\n",
      "Epoch 144/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.2017 - acc: 0.9629 - val_loss: 0.2507 - val_acc: 0.9400\n",
      "Epoch 145/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.2007 - acc: 0.9600 - val_loss: 0.2494 - val_acc: 0.9400\n",
      "Epoch 146/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1994 - acc: 0.9600 - val_loss: 0.2480 - val_acc: 0.9400\n",
      "Epoch 147/300\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.1982 - acc: 0.9600 - val_loss: 0.2468 - val_acc: 0.9400\n",
      "Epoch 148/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1973 - acc: 0.9600 - val_loss: 0.2456 - val_acc: 0.9400\n",
      "Epoch 149/300\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.1960 - acc: 0.9629 - val_loss: 0.2439 - val_acc: 0.9400\n",
      "Epoch 150/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1949 - acc: 0.9600 - val_loss: 0.2429 - val_acc: 0.9400\n",
      "Epoch 151/300\n",
      "350/350 [==============================] - 0s 116us/step - loss: 0.1937 - acc: 0.9629 - val_loss: 0.2418 - val_acc: 0.9400\n",
      "Epoch 152/300\n",
      "350/350 [==============================] - 0s 97us/step - loss: 0.1925 - acc: 0.9600 - val_loss: 0.2403 - val_acc: 0.9333\n",
      "Epoch 153/300\n",
      "350/350 [==============================] - 0s 99us/step - loss: 0.1916 - acc: 0.9629 - val_loss: 0.2391 - val_acc: 0.9333\n",
      "Epoch 154/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1904 - acc: 0.9629 - val_loss: 0.2376 - val_acc: 0.9333\n",
      "Epoch 155/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1895 - acc: 0.9629 - val_loss: 0.2364 - val_acc: 0.9333\n",
      "Epoch 156/300\n",
      "350/350 [==============================] - 0s 82us/step - loss: 0.1886 - acc: 0.9629 - val_loss: 0.2349 - val_acc: 0.9333\n",
      "Epoch 157/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.1873 - acc: 0.9629 - val_loss: 0.2343 - val_acc: 0.9333\n",
      "Epoch 158/300\n",
      "350/350 [==============================] - 0s 82us/step - loss: 0.1864 - acc: 0.9629 - val_loss: 0.2329 - val_acc: 0.9333\n",
      "Epoch 159/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1854 - acc: 0.9629 - val_loss: 0.2319 - val_acc: 0.9333\n",
      "Epoch 160/300\n",
      "350/350 [==============================] - 0s 78us/step - loss: 0.1845 - acc: 0.9629 - val_loss: 0.2307 - val_acc: 0.9333\n",
      "Epoch 161/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1833 - acc: 0.9629 - val_loss: 0.2297 - val_acc: 0.9333\n",
      "Epoch 162/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.1824 - acc: 0.9629 - val_loss: 0.2285 - val_acc: 0.9333\n",
      "Epoch 163/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1815 - acc: 0.9629 - val_loss: 0.2272 - val_acc: 0.9333\n",
      "Epoch 164/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.1805 - acc: 0.9629 - val_loss: 0.2265 - val_acc: 0.9333\n",
      "Epoch 165/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1796 - acc: 0.9629 - val_loss: 0.2256 - val_acc: 0.9333\n",
      "Epoch 166/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1791 - acc: 0.9629 - val_loss: 0.2245 - val_acc: 0.9333\n",
      "Epoch 167/300\n",
      "350/350 [==============================] - 0s 110us/step - loss: 0.1781 - acc: 0.9629 - val_loss: 0.2234 - val_acc: 0.9333\n",
      "Epoch 168/300\n",
      "350/350 [==============================] - 0s 100us/step - loss: 0.1772 - acc: 0.9629 - val_loss: 0.2228 - val_acc: 0.9333\n",
      "Epoch 169/300\n",
      "350/350 [==============================] - 0s 101us/step - loss: 0.1761 - acc: 0.9629 - val_loss: 0.2214 - val_acc: 0.9333\n",
      "Epoch 170/300\n",
      "350/350 [==============================] - 0s 98us/step - loss: 0.1754 - acc: 0.9657 - val_loss: 0.2207 - val_acc: 0.9333\n",
      "Epoch 171/300\n",
      "350/350 [==============================] - 0s 97us/step - loss: 0.1745 - acc: 0.9629 - val_loss: 0.2198 - val_acc: 0.9333\n",
      "Epoch 172/300\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.1740 - acc: 0.9629 - val_loss: 0.2183 - val_acc: 0.9333\n",
      "Epoch 173/300\n",
      "350/350 [==============================] - 0s 78us/step - loss: 0.1727 - acc: 0.9629 - val_loss: 0.2173 - val_acc: 0.9400\n",
      "Epoch 174/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.1720 - acc: 0.9629 - val_loss: 0.2163 - val_acc: 0.9400\n",
      "Epoch 175/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1711 - acc: 0.9629 - val_loss: 0.2155 - val_acc: 0.9400\n",
      "Epoch 176/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.1702 - acc: 0.9629 - val_loss: 0.2143 - val_acc: 0.9400\n",
      "Epoch 177/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.1693 - acc: 0.9629 - val_loss: 0.2130 - val_acc: 0.9467\n",
      "Epoch 178/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1687 - acc: 0.9657 - val_loss: 0.2124 - val_acc: 0.9400\n",
      "Epoch 179/300\n",
      "350/350 [==============================] - 0s 77us/step - loss: 0.1677 - acc: 0.9629 - val_loss: 0.2111 - val_acc: 0.9533\n",
      "Epoch 180/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.1671 - acc: 0.9629 - val_loss: 0.2098 - val_acc: 0.9533\n",
      "Epoch 181/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350/350 [==============================] - 0s 81us/step - loss: 0.1664 - acc: 0.9629 - val_loss: 0.2093 - val_acc: 0.9533\n",
      "Epoch 182/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1658 - acc: 0.9629 - val_loss: 0.2082 - val_acc: 0.9533\n",
      "Epoch 183/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1650 - acc: 0.9629 - val_loss: 0.2071 - val_acc: 0.9533\n",
      "Epoch 184/300\n",
      "350/350 [==============================] - 0s 82us/step - loss: 0.1643 - acc: 0.9629 - val_loss: 0.2063 - val_acc: 0.9533\n",
      "Epoch 185/300\n",
      "350/350 [==============================] - 0s 78us/step - loss: 0.1635 - acc: 0.9629 - val_loss: 0.2052 - val_acc: 0.9533\n",
      "Epoch 186/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.1629 - acc: 0.9629 - val_loss: 0.2044 - val_acc: 0.9533\n",
      "Epoch 187/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.1621 - acc: 0.9629 - val_loss: 0.2037 - val_acc: 0.9533\n",
      "Epoch 188/300\n",
      "350/350 [==============================] - 0s 80us/step - loss: 0.1615 - acc: 0.9629 - val_loss: 0.2028 - val_acc: 0.9533\n",
      "Epoch 189/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1605 - acc: 0.9629 - val_loss: 0.2024 - val_acc: 0.9533\n",
      "Epoch 190/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.1602 - acc: 0.9657 - val_loss: 0.2011 - val_acc: 0.9533\n",
      "Epoch 191/300\n",
      "350/350 [==============================] - 0s 78us/step - loss: 0.1593 - acc: 0.9629 - val_loss: 0.2007 - val_acc: 0.9533\n",
      "Epoch 192/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.1587 - acc: 0.9629 - val_loss: 0.2001 - val_acc: 0.9533\n",
      "Epoch 193/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1579 - acc: 0.9657 - val_loss: 0.1995 - val_acc: 0.9533\n",
      "Epoch 194/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.1574 - acc: 0.9629 - val_loss: 0.1989 - val_acc: 0.9533\n",
      "Epoch 195/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1569 - acc: 0.9629 - val_loss: 0.1986 - val_acc: 0.9533\n",
      "Epoch 196/300\n",
      "350/350 [==============================] - 0s 80us/step - loss: 0.1561 - acc: 0.9657 - val_loss: 0.1979 - val_acc: 0.9533\n",
      "Epoch 197/300\n",
      "350/350 [==============================] - 0s 82us/step - loss: 0.1555 - acc: 0.9629 - val_loss: 0.1967 - val_acc: 0.9533\n",
      "Epoch 198/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.1548 - acc: 0.9629 - val_loss: 0.1957 - val_acc: 0.9533\n",
      "Epoch 199/300\n",
      "350/350 [==============================] - 0s 76us/step - loss: 0.1543 - acc: 0.9629 - val_loss: 0.1950 - val_acc: 0.9533\n",
      "Epoch 200/300\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.1538 - acc: 0.9629 - val_loss: 0.1945 - val_acc: 0.9533\n",
      "Epoch 201/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.1528 - acc: 0.9629 - val_loss: 0.1937 - val_acc: 0.9533\n",
      "Epoch 202/300\n",
      "350/350 [==============================] - 0s 75us/step - loss: 0.1522 - acc: 0.9629 - val_loss: 0.1926 - val_acc: 0.9533\n",
      "Epoch 203/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.1519 - acc: 0.9629 - val_loss: 0.1927 - val_acc: 0.9533\n",
      "Epoch 204/300\n",
      "350/350 [==============================] - 0s 76us/step - loss: 0.1511 - acc: 0.9629 - val_loss: 0.1919 - val_acc: 0.9533\n",
      "Epoch 205/300\n",
      "350/350 [==============================] - 0s 76us/step - loss: 0.1504 - acc: 0.9657 - val_loss: 0.1907 - val_acc: 0.9533\n",
      "Epoch 206/300\n",
      "350/350 [==============================] - 0s 82us/step - loss: 0.1499 - acc: 0.9629 - val_loss: 0.1893 - val_acc: 0.9533\n",
      "Epoch 207/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1496 - acc: 0.9657 - val_loss: 0.1886 - val_acc: 0.9533\n",
      "Epoch 208/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.1486 - acc: 0.9657 - val_loss: 0.1875 - val_acc: 0.9533\n",
      "Epoch 209/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1481 - acc: 0.9657 - val_loss: 0.1866 - val_acc: 0.9533\n",
      "Epoch 210/300\n",
      "350/350 [==============================] - 0s 79us/step - loss: 0.1475 - acc: 0.9657 - val_loss: 0.1858 - val_acc: 0.9533\n",
      "Epoch 211/300\n",
      "350/350 [==============================] - 0s 74us/step - loss: 0.1468 - acc: 0.9657 - val_loss: 0.1856 - val_acc: 0.9533\n",
      "Epoch 212/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.1465 - acc: 0.9657 - val_loss: 0.1844 - val_acc: 0.9533\n",
      "Epoch 213/300\n",
      "350/350 [==============================] - 0s 79us/step - loss: 0.1458 - acc: 0.9657 - val_loss: 0.1840 - val_acc: 0.9533\n",
      "Epoch 214/300\n",
      "350/350 [==============================] - 0s 75us/step - loss: 0.1452 - acc: 0.9657 - val_loss: 0.1838 - val_acc: 0.9533\n",
      "Epoch 215/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.1451 - acc: 0.9629 - val_loss: 0.1833 - val_acc: 0.9533\n",
      "Epoch 216/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1442 - acc: 0.9657 - val_loss: 0.1820 - val_acc: 0.9533\n",
      "Epoch 217/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1437 - acc: 0.9657 - val_loss: 0.1812 - val_acc: 0.9533\n",
      "Epoch 218/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1432 - acc: 0.9657 - val_loss: 0.1812 - val_acc: 0.9533\n",
      "Epoch 219/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.1426 - acc: 0.9657 - val_loss: 0.1808 - val_acc: 0.9533\n",
      "Epoch 220/300\n",
      "350/350 [==============================] - 0s 96us/step - loss: 0.1419 - acc: 0.9629 - val_loss: 0.1800 - val_acc: 0.9533\n",
      "Epoch 221/300\n",
      "350/350 [==============================] - 0s 91us/step - loss: 0.1417 - acc: 0.9657 - val_loss: 0.1796 - val_acc: 0.9533\n",
      "Epoch 222/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1412 - acc: 0.9657 - val_loss: 0.1784 - val_acc: 0.9533\n",
      "Epoch 223/300\n",
      "350/350 [==============================] - 0s 105us/step - loss: 0.1404 - acc: 0.9657 - val_loss: 0.1780 - val_acc: 0.9533\n",
      "Epoch 224/300\n",
      "350/350 [==============================] - 0s 77us/step - loss: 0.1397 - acc: 0.9657 - val_loss: 0.1771 - val_acc: 0.9533\n",
      "Epoch 225/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.1395 - acc: 0.9657 - val_loss: 0.1764 - val_acc: 0.9533\n",
      "Epoch 226/300\n",
      "350/350 [==============================] - 0s 78us/step - loss: 0.1389 - acc: 0.9657 - val_loss: 0.1765 - val_acc: 0.9533\n",
      "Epoch 227/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.1382 - acc: 0.9657 - val_loss: 0.1760 - val_acc: 0.9533\n",
      "Epoch 228/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.1381 - acc: 0.9657 - val_loss: 0.1747 - val_acc: 0.9533\n",
      "Epoch 229/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.1372 - acc: 0.9657 - val_loss: 0.1740 - val_acc: 0.9533\n",
      "Epoch 230/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.1367 - acc: 0.9657 - val_loss: 0.1737 - val_acc: 0.9533\n",
      "Epoch 231/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1361 - acc: 0.9657 - val_loss: 0.1730 - val_acc: 0.9533\n",
      "Epoch 232/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1358 - acc: 0.9657 - val_loss: 0.1723 - val_acc: 0.9533\n",
      "Epoch 233/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1352 - acc: 0.9657 - val_loss: 0.1713 - val_acc: 0.9533\n",
      "Epoch 234/300\n",
      "350/350 [==============================] - 0s 92us/step - loss: 0.1347 - acc: 0.9657 - val_loss: 0.1705 - val_acc: 0.9600\n",
      "Epoch 235/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.1342 - acc: 0.9657 - val_loss: 0.1703 - val_acc: 0.9600\n",
      "Epoch 236/300\n",
      "350/350 [==============================] - 0s 93us/step - loss: 0.1336 - acc: 0.9657 - val_loss: 0.1692 - val_acc: 0.9600\n",
      "Epoch 237/300\n",
      "350/350 [==============================] - 0s 89us/step - loss: 0.1331 - acc: 0.9657 - val_loss: 0.1689 - val_acc: 0.9600\n",
      "Epoch 238/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.1327 - acc: 0.9657 - val_loss: 0.1687 - val_acc: 0.9600\n",
      "Epoch 239/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.1321 - acc: 0.9657 - val_loss: 0.1679 - val_acc: 0.9600\n",
      "Epoch 240/300\n",
      "350/350 [==============================] - 0s 78us/step - loss: 0.1316 - acc: 0.9657 - val_loss: 0.1670 - val_acc: 0.9600\n",
      "Epoch 241/300\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "350/350 [==============================] - 0s 86us/step - loss: 0.1311 - acc: 0.9657 - val_loss: 0.1665 - val_acc: 0.9600\n",
      "Epoch 242/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.1305 - acc: 0.9657 - val_loss: 0.1661 - val_acc: 0.9600\n",
      "Epoch 243/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.1303 - acc: 0.9657 - val_loss: 0.1655 - val_acc: 0.9600\n",
      "Epoch 244/300\n",
      "350/350 [==============================] - 0s 78us/step - loss: 0.1296 - acc: 0.9657 - val_loss: 0.1655 - val_acc: 0.9600\n",
      "Epoch 245/300\n",
      "350/350 [==============================] - 0s 76us/step - loss: 0.1294 - acc: 0.9657 - val_loss: 0.1653 - val_acc: 0.9600\n",
      "Epoch 246/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1289 - acc: 0.9686 - val_loss: 0.1642 - val_acc: 0.9600\n",
      "Epoch 247/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.1286 - acc: 0.9657 - val_loss: 0.1639 - val_acc: 0.9600\n",
      "Epoch 248/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1281 - acc: 0.9657 - val_loss: 0.1634 - val_acc: 0.9600\n",
      "Epoch 249/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1279 - acc: 0.9657 - val_loss: 0.1629 - val_acc: 0.9600\n",
      "Epoch 250/300\n",
      "350/350 [==============================] - 0s 76us/step - loss: 0.1274 - acc: 0.9657 - val_loss: 0.1621 - val_acc: 0.9600\n",
      "Epoch 251/300\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.1270 - acc: 0.9657 - val_loss: 0.1615 - val_acc: 0.9600\n",
      "Epoch 252/300\n",
      "350/350 [==============================] - 0s 79us/step - loss: 0.1264 - acc: 0.9657 - val_loss: 0.1615 - val_acc: 0.9600\n",
      "Epoch 253/300\n",
      "350/350 [==============================] - 0s 75us/step - loss: 0.1263 - acc: 0.9657 - val_loss: 0.1610 - val_acc: 0.9600\n",
      "Epoch 254/300\n",
      "350/350 [==============================] - 0s 79us/step - loss: 0.1257 - acc: 0.9657 - val_loss: 0.1600 - val_acc: 0.9600\n",
      "Epoch 255/300\n",
      "350/350 [==============================] - 0s 82us/step - loss: 0.1253 - acc: 0.9657 - val_loss: 0.1598 - val_acc: 0.9600\n",
      "Epoch 256/300\n",
      "350/350 [==============================] - 0s 79us/step - loss: 0.1251 - acc: 0.9657 - val_loss: 0.1590 - val_acc: 0.9600\n",
      "Epoch 257/300\n",
      "350/350 [==============================] - 0s 90us/step - loss: 0.1244 - acc: 0.9657 - val_loss: 0.1587 - val_acc: 0.9600\n",
      "Epoch 258/300\n",
      "350/350 [==============================] - 0s 76us/step - loss: 0.1243 - acc: 0.9657 - val_loss: 0.1586 - val_acc: 0.9600\n",
      "Epoch 259/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.1238 - acc: 0.9657 - val_loss: 0.1581 - val_acc: 0.9600\n",
      "Epoch 260/300\n",
      "350/350 [==============================] - 0s 79us/step - loss: 0.1233 - acc: 0.9657 - val_loss: 0.1575 - val_acc: 0.9600\n",
      "Epoch 261/300\n",
      "350/350 [==============================] - 0s 75us/step - loss: 0.1230 - acc: 0.9657 - val_loss: 0.1568 - val_acc: 0.9600\n",
      "Epoch 262/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.1226 - acc: 0.9657 - val_loss: 0.1569 - val_acc: 0.9600\n",
      "Epoch 263/300\n",
      "350/350 [==============================] - 0s 77us/step - loss: 0.1223 - acc: 0.9657 - val_loss: 0.1555 - val_acc: 0.9600\n",
      "Epoch 264/300\n",
      "350/350 [==============================] - 0s 80us/step - loss: 0.1217 - acc: 0.9657 - val_loss: 0.1547 - val_acc: 0.9600\n",
      "Epoch 265/300\n",
      "350/350 [==============================] - 0s 82us/step - loss: 0.1215 - acc: 0.9657 - val_loss: 0.1551 - val_acc: 0.9600\n",
      "Epoch 266/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.1211 - acc: 0.9657 - val_loss: 0.1548 - val_acc: 0.9600\n",
      "Epoch 267/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.1206 - acc: 0.9657 - val_loss: 0.1540 - val_acc: 0.9600\n",
      "Epoch 268/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1206 - acc: 0.9657 - val_loss: 0.1533 - val_acc: 0.9600\n",
      "Epoch 269/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1202 - acc: 0.9686 - val_loss: 0.1534 - val_acc: 0.9600\n",
      "Epoch 270/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.1195 - acc: 0.9657 - val_loss: 0.1528 - val_acc: 0.9600\n",
      "Epoch 271/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.1197 - acc: 0.9657 - val_loss: 0.1524 - val_acc: 0.9600\n",
      "Epoch 272/300\n",
      "350/350 [==============================] - 0s 75us/step - loss: 0.1190 - acc: 0.9657 - val_loss: 0.1518 - val_acc: 0.9600\n",
      "Epoch 273/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.1188 - acc: 0.9657 - val_loss: 0.1520 - val_acc: 0.9600\n",
      "Epoch 274/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1184 - acc: 0.9657 - val_loss: 0.1519 - val_acc: 0.9600\n",
      "Epoch 275/300\n",
      "350/350 [==============================] - 0s 79us/step - loss: 0.1181 - acc: 0.9657 - val_loss: 0.1510 - val_acc: 0.9600\n",
      "Epoch 276/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1178 - acc: 0.9657 - val_loss: 0.1501 - val_acc: 0.9600\n",
      "Epoch 277/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.1173 - acc: 0.9657 - val_loss: 0.1496 - val_acc: 0.9600\n",
      "Epoch 278/300\n",
      "350/350 [==============================] - 0s 74us/step - loss: 0.1169 - acc: 0.9657 - val_loss: 0.1495 - val_acc: 0.9600\n",
      "Epoch 279/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.1168 - acc: 0.9657 - val_loss: 0.1489 - val_acc: 0.9600\n",
      "Epoch 280/300\n",
      "350/350 [==============================] - 0s 75us/step - loss: 0.1165 - acc: 0.9657 - val_loss: 0.1488 - val_acc: 0.9600\n",
      "Epoch 281/300\n",
      "350/350 [==============================] - 0s 78us/step - loss: 0.1162 - acc: 0.9657 - val_loss: 0.1476 - val_acc: 0.9600\n",
      "Epoch 282/300\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.1158 - acc: 0.9657 - val_loss: 0.1474 - val_acc: 0.9600\n",
      "Epoch 283/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.1155 - acc: 0.9657 - val_loss: 0.1473 - val_acc: 0.9600\n",
      "Epoch 284/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1153 - acc: 0.9657 - val_loss: 0.1474 - val_acc: 0.9600\n",
      "Epoch 285/300\n",
      "350/350 [==============================] - 0s 74us/step - loss: 0.1152 - acc: 0.9686 - val_loss: 0.1472 - val_acc: 0.9600\n",
      "Epoch 286/300\n",
      "350/350 [==============================] - 0s 84us/step - loss: 0.1146 - acc: 0.9657 - val_loss: 0.1465 - val_acc: 0.9600\n",
      "Epoch 287/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.1145 - acc: 0.9657 - val_loss: 0.1459 - val_acc: 0.9600\n",
      "Epoch 288/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1142 - acc: 0.9657 - val_loss: 0.1457 - val_acc: 0.9600\n",
      "Epoch 289/300\n",
      "350/350 [==============================] - 0s 83us/step - loss: 0.1139 - acc: 0.9657 - val_loss: 0.1454 - val_acc: 0.9600\n",
      "Epoch 290/300\n",
      "350/350 [==============================] - 0s 79us/step - loss: 0.1136 - acc: 0.9657 - val_loss: 0.1454 - val_acc: 0.9600\n",
      "Epoch 291/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1133 - acc: 0.9657 - val_loss: 0.1448 - val_acc: 0.9600\n",
      "Epoch 292/300\n",
      "350/350 [==============================] - 0s 87us/step - loss: 0.1129 - acc: 0.9657 - val_loss: 0.1443 - val_acc: 0.9600\n",
      "Epoch 293/300\n",
      "350/350 [==============================] - 0s 88us/step - loss: 0.1127 - acc: 0.9657 - val_loss: 0.1440 - val_acc: 0.9600\n",
      "Epoch 294/300\n",
      "350/350 [==============================] - 0s 79us/step - loss: 0.1124 - acc: 0.9657 - val_loss: 0.1439 - val_acc: 0.9600\n",
      "Epoch 295/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.1121 - acc: 0.9657 - val_loss: 0.1436 - val_acc: 0.9600\n",
      "Epoch 296/300\n",
      "350/350 [==============================] - 0s 78us/step - loss: 0.1116 - acc: 0.9657 - val_loss: 0.1429 - val_acc: 0.9600\n",
      "Epoch 297/300\n",
      "350/350 [==============================] - 0s 81us/step - loss: 0.1118 - acc: 0.9657 - val_loss: 0.1420 - val_acc: 0.9600\n",
      "Epoch 298/300\n",
      "350/350 [==============================] - 0s 85us/step - loss: 0.1113 - acc: 0.9657 - val_loss: 0.1418 - val_acc: 0.9600\n",
      "Epoch 299/300\n",
      "350/350 [==============================] - 0s 86us/step - loss: 0.1110 - acc: 0.9657 - val_loss: 0.1414 - val_acc: 0.9600\n",
      "Epoch 300/300\n",
      "350/350 [==============================] - 0s 94us/step - loss: 0.1106 - acc: 0.9657 - val_loss: 0.1417 - val_acc: 0.9600\n"
     ]
    }
   ],
   "source": [
    "# Instantiating the model\n",
    "model = a_simple_NN()\n",
    "\n",
    "# Splitting the dataset into training (70%) and validation sets (30%)\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    features, labels, test_size=0.3)\n",
    "\n",
    "# Setting the number of passes through the entire training set\n",
    "num_epochs = 300\n",
    "\n",
    "# We can pass validation data while training\n",
    "model_run = model.fit(X_train, y_train, epochs=num_epochs,\n",
    "                      validation_data=(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The history has the following data:  dict_keys(['val_loss', 'val_acc', 'loss', 'acc'])\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looking at the loss and accuracy on the training and validation sets during the training\n",
    "# This can be done by using Keras callback \"history\" which is applied by default\n",
    "history_model = model_run.history\n",
    "\n",
    "print(\"The history has the following data: \", history_model.keys())\n",
    "\n",
    "# Plotting the training and validation accuracy during the training\n",
    "plt.plot(np.arange(1, num_epochs+1), history_model[\"acc\"], \"blue\") ;\n",
    "\n",
    "plt.plot(np.arange(1, num_epochs+1), history_model[\"val_acc\"], \"red\") ;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Here we dont't really see a big difference between the training and validation data because the function we are trying to fit is quiet simple and there is not too much noise. We will come back to these curves in a later example**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the example above we splitted our dataset into a 70-30 train-validation set. We know from previous chapters that to more robustly calculate accuracy we can use **K-fold crossvalidation**.\n",
    "This is even more important when we have small datasets and cannot afford to reserve a validation set!\n",
    "\n",
    "One way to do the cross validation here would be to write our own function to do this. However, we also know that **SciKit learn** provides several handy functions to evaluate and tune the models. So the question is:\n",
    "\n",
    "Can we somehow use these **Scikit learn** functions or ones we wrote ourselves for **Scikit learn** models to evaluate and tune our Keras models?\n",
    "\n",
    "The Answer is **YES !**\n",
    "\n",
    "We show how to do this in the following section."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using SciKit learn functions on Keras models\n",
    "\n",
    "Keras offers 2 wrappers which allow its Sequential models to be used with SciKit learn. \n",
    "\n",
    "There are: **KerasClassifier** and **KerasRegressor**.\n",
    "\n",
    "For more information:\n",
    "https://keras.io/scikit-learn-api/\n",
    "\n",
    "**Now lets see how this works!**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We wrap the Keras model we created above with KerasClassifier\n",
    "from keras.wrappers.scikit_learn import KerasClassifier\n",
    "from sklearn.model_selection import cross_val_score\n",
    "# Wrapping Keras model\n",
    "# NOTE: We pass verbose=0 to suppress the model output\n",
    "num_epochs = 400\n",
    "model_scikit = KerasClassifier(\n",
    "    build_fn=a_simple_NN, **{\"epochs\": num_epochs, \"verbose\": 0})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Let's reuse the function to visualize the decision boundary which we saw in chapter 2 with minimal change\n",
    "\n",
    "def list_flatten(list_of_list):\n",
    "    flattened_list = [i for j in list_of_list for i in j]\n",
    "    return flattened_list\n",
    "\n",
    "def plot_points(plt=plt, marker='o'):\n",
    "    colors = [[\"steelblue\", \"chocolate\"][i] for i in labels]\n",
    "    plt.scatter(features.iloc[:, 0], features.iloc[:, 1], color=colors, marker=marker);\n",
    "\n",
    "def train_and_plot_decision_surface(\n",
    "    name, classifier, features_2d, labels, preproc=None, plt=plt, marker='o', N=400\n",
    "):\n",
    "\n",
    "    features_2d = np.array(features_2d)\n",
    "    xmin, ymin = features_2d.min(axis=0)\n",
    "    xmax, ymax = features_2d.max(axis=0)\n",
    "\n",
    "    x = np.linspace(xmin, xmax, N)\n",
    "    y = np.linspace(ymin, ymax, N)\n",
    "    points = np.array(np.meshgrid(x, y)).T.reshape(-1, 2)\n",
    "\n",
    "    if preproc is not None:\n",
    "        points_for_classifier = preproc.fit_transform(points)\n",
    "        features_2d = preproc.fit_transform(features_2d)\n",
    "    else:\n",
    "        points_for_classifier = points\n",
    "\n",
    "    classifier.fit(features_2d, labels, verbose=0)\n",
    "    predicted = classifier.predict(features_2d)\n",
    "    \n",
    "    if name == \"Neural Net\":\n",
    "        predicted = list_flatten(predicted)\n",
    "    \n",
    "    \n",
    "    if preproc is not None:\n",
    "        name += \" (w/ preprocessing)\"\n",
    "    print(name + \":\\t\", sum(predicted == labels), \"/\", len(labels), \"correct\")\n",
    "    \n",
    "    if name == \"Neural Net\":\n",
    "        classes = np.array(list_flatten(classifier.predict(points_for_classifier)), dtype=bool)\n",
    "    else:\n",
    "        classes = np.array(classifier.predict(points_for_classifier), dtype=bool)\n",
    "    plt.plot(\n",
    "        points[~classes][:, 0],\n",
    "        points[~classes][:, 1],\n",
    "        \"o\",\n",
    "        color=\"steelblue\",\n",
    "        markersize=1,\n",
    "        alpha=0.01,\n",
    "    )\n",
    "    plt.plot(\n",
    "        points[classes][:, 0],\n",
    "        points[classes][:, 1],\n",
    "        \"o\",\n",
    "        color=\"chocolate\",\n",
    "        markersize=1,\n",
    "        alpha=0.04,\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Neural Net:\t 484 / 500 correct\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "_, ax = plt.subplots(figsize=(6, 6))\n",
    "\n",
    "train_and_plot_decision_surface(\"Neural Net\", model_scikit, features, labels, plt=ax)\n",
    "plot_points(plt=ax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The acuracy on the  5  validation folds: [0.72 0.94 0.95 0.75 0.94]\n",
      "The Average acuracy on the  5  validation folds: 0.86\n"
     ]
    }
   ],
   "source": [
    "# Applying K-fold cross-validation\n",
    "# Here we pass the whole dataset, i.e. features and labels, instead of splitting it.\n",
    "num_folds = 5\n",
    "cross_validation = cross_val_score(\n",
    "    model_scikit, features, labels, cv=num_folds, verbose=0)\n",
    "\n",
    "print(\"The acuracy on the \", num_folds, \" validation folds:\", cross_validation)\n",
    "print(\"The Average acuracy on the \", num_folds, \" validation folds:\", np.mean(cross_validation))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### NOTE: The above code took quiet long even though we used only 5  CV folds and the neural network and data size are very small!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Hyperparameter optimization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We know from chapter 6 that there are 2 types of parameters which need to be tuned for a machine learning model.\n",
    "* Normal model parameters which can be learned for e.g. by gradient-descent\n",
    "* Hyperparameters\n",
    "\n",
    "In the model which we created above we made some arbitrary choices like which optimizer we use, what is its learning rate, number of hidden units and so on ...\n",
    "\n",
    "Now that we have the keras model wrapped as a scikit model we can use the grid search functions we have seen in chapter 6."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.9580000002384186 {'epochs': 500}\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "HP_grid = dict(epochs=[300, 500, 1000])\n",
    "search = GridSearchCV(estimator=model_scikit, param_grid=HP_grid)\n",
    "search.fit(features, labels)\n",
    "print(search.best_score_, search.best_params_)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Exercise: Create a neural network to classify the 2d points example from chapter 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          x         y  label\n",
      "0 -0.501840  1.802857  False\n",
      "1  0.927976  0.394634   True\n",
      "2 -1.375925 -1.376022  False\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4e0457ca20>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "circle = pd.read_csv(\"2d_points.csv\")\n",
    "# Using x and y coordinates as featues\n",
    "features = circle.iloc[:, :-1]\n",
    "# Convert boolean to integer values (True->1 and False->0)\n",
    "labels = circle.iloc[:, -1].astype(int)\n",
    "\n",
    "colors = [[\"steelblue\", \"chocolate\"][i] for i in circle[\"label\"]]\n",
    "plt.figure(figsize=(5, 5))\n",
    "plt.xlim([-2, 2])\n",
    "plt.ylim([-2, 2])\n",
    "\n",
    "plt.scatter(features[\"x\"], features[\"y\"], color=colors, marker=\"o\");\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Insert Code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### MNIST Dataset\n",
    "\n",
    "MNIST datasets is a very common dataset used in machine learning. It is widely used to train and validate models.\n",
    "\n",
    "\n",
    ">The MNIST database of handwritten digits, available from this page, has a training set of 60,000 examples, and a >test set of 10,000 examples. It is a subset of a larger set available from NIST. The digits have been size->normalized and centered in a fixed-size image.\n",
    ">It is a good database for people who want to try learning techniques and pattern recognition methods on real-world >data while spending minimal efforts on preprocessing and formatting.\n",
    ">source: http://yann.lecun.com/exdb/mnist/\n",
    "\n",
    "The problem we want to solve using this dataset is: multi-class classification\n",
    "This dataset consists of images of handwritten digits between 0-9 and their corresponsing labels. We want to train a neural network which is able to predict the correct digit on the image. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Loading the dataset in keras\n",
    "# Later you can explore and play with other datasets with come with Keras\n",
    "from keras.datasets import mnist\n",
    "\n",
    "# Loading the train and test data\n",
    "\n",
    "(X_train, y_train), (X_test, y_test) = mnist.load_data()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 28, 28)\n"
     ]
    }
   ],
   "source": [
    "# Looking at the dataset\n",
    "print(X_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "This digit is:  8\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe8e68579e8>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 253
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# We can see that the training set consists of 60,000 images of size 28x28 pixels\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "i=np.random.randint(0,X_train.shape[0])\n",
    "plt.imshow(X_train[i], cmap=\"gray_r\") ;\n",
    "print(\"This digit is: \" , y_train[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 255\n"
     ]
    }
   ],
   "source": [
    "# Look at the data values for a couple of images\n",
    "print(X_train[0].min(), X_train[1].max())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The data consists of values between 0-255 representing the **grayscale level**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000,)\n"
     ]
    }
   ],
   "source": [
    "# The labels are the digit on the image\n",
    "print(y_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Scaling the data\n",
    "# It is important to normalize the input data to (0-1) before providing it to a neural net\n",
    "# We could use the previously introduced function from SciKit learn. However, here it is sufficient to\n",
    "# just divide the input data by 255\n",
    "X_train_norm = X_train/255.\n",
    "X_test_norm = X_test/255.\n",
    "\n",
    "# Also we need to reshape the input data such that each sample is a vector and not a 2D matrix\n",
    "X_train_prep = X_train_norm.reshape(X_train_norm.shape[0],28*28)\n",
    "X_test_prep = X_test_norm.reshape(X_test_norm.shape[0],28*28)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**IMPORTANT: One-Hot encoding**\n",
    "\n",
    "**TODO: Better frame the explaination**\n",
    "\n",
    "In such problems the labels are provided as something called **One-hot encodings**. What this does is to convert a categorical label to a vector.\n",
    "\n",
    "For the MNIST problem where we have **10 categories** one-hot encoding will create a vector of length 10 for each of the labels. All the entries of this vector will be zero **except** for the index which is equal to the integer value of the label.\n",
    "\n",
    "For example:\n",
    "if label is 4. The one-hot vector will look like **[0 0 0 0 1 0 0 0 0 0]**\n",
    "\n",
    "Fortunately, we don't have to code this ourselves because Keras has a built-in function for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(60000, 10)\n"
     ]
    }
   ],
   "source": [
    "from keras.utils.np_utils import to_categorical\n",
    "\n",
    "y_train_onehot = to_categorical(y_train, num_classes=10)\n",
    "y_test_onehot = to_categorical(y_test, num_classes=10)\n",
    "\n",
    "print(y_train_onehot.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/20\n",
      "60000/60000 [==============================] - 2s 34us/step - loss: 0.5888 - acc: 0.8434\n",
      "Epoch 2/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.2569 - acc: 0.9267\n",
      "Epoch 3/20\n",
      "60000/60000 [==============================] - 1s 16us/step - loss: 0.2024 - acc: 0.9416\n",
      "Epoch 4/20\n",
      "60000/60000 [==============================] - 1s 17us/step - loss: 0.1706 - acc: 0.9497\n",
      "Epoch 5/20\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1475 - acc: 0.9563\n",
      "Epoch 6/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.1290 - acc: 0.9627\n",
      "Epoch 7/20\n",
      "60000/60000 [==============================] - 1s 23us/step - loss: 0.1162 - acc: 0.9651\n",
      "Epoch 8/20\n",
      "60000/60000 [==============================] - 1s 19us/step - loss: 0.1035 - acc: 0.9691\n",
      "Epoch 9/20\n",
      "60000/60000 [==============================] - 2s 28us/step - loss: 0.0939 - acc: 0.9716\n",
      "Epoch 10/20\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0848 - acc: 0.9743\n",
      "Epoch 11/20\n",
      "60000/60000 [==============================] - 1s 25us/step - loss: 0.0777 - acc: 0.9763\n",
      "Epoch 12/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0720 - acc: 0.9780\n",
      "Epoch 13/20\n",
      "60000/60000 [==============================] - 1s 22us/step - loss: 0.0655 - acc: 0.9808\n",
      "Epoch 14/20\n",
      "60000/60000 [==============================] - 2s 30us/step - loss: 0.0610 - acc: 0.9817\n",
      "Epoch 15/20\n",
      "60000/60000 [==============================] - 1s 16us/step - loss: 0.0563 - acc: 0.9832\n",
      "Epoch 16/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0527 - acc: 0.9842\n",
      "Epoch 17/20\n",
      "60000/60000 [==============================] - 1s 21us/step - loss: 0.0478 - acc: 0.9854\n",
      "Epoch 18/20\n",
      "60000/60000 [==============================] - 1s 15us/step - loss: 0.0453 - acc: 0.9864\n",
      "Epoch 19/20\n",
      "60000/60000 [==============================] - 1s 18us/step - loss: 0.0419 - acc: 0.9874\n",
      "Epoch 20/20\n",
      "60000/60000 [==============================] - 1s 20us/step - loss: 0.0387 - acc: 0.9885\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x7fe8e7465438>"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Building the keras model\n",
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(64,input_shape=(28*28,), activation=\"relu\"))\n",
    "\n",
    "model.add(Dense(64, activation = \"relu\"))\n",
    "\n",
    "model.add(Dense(10, activation = \"softmax\"))\n",
    "\n",
    "model.compile(loss=\"categorical_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "\n",
    "model_history = model.fit(X_train_prep, y_train_cat, epochs=20, batch_size=512);"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10000/10000 [==============================] - 1s 85us/step\n",
      "The [loss, accuracy] are:  [0.08737125840586377, 0.974]\n"
     ]
    }
   ],
   "source": [
    "# Evaluating the model on test dataset\n",
    "print(\"The [loss, accuracy] on test dataset are: \" , model.evaluate(X_test_prep, y_test_onehot))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Work in Progress\n",
    "\n",
    "## Network results on dataset used in previous notebooks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Might Remove the following: This beer example is not good for neural networks. Basically the dataset is far too small**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(225, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Revisiting the beer example\n",
    "\n",
    "import pandas as pd\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",