neural_nets_intro.ipynb

      "Epoch 858/1000\n",
      "157/157 [==============================] - 0s 106us/step - loss: 0.0512 - acc: 0.9936 - val_loss: 0.0918 - val_acc: 0.9559\n",
      "Epoch 859/1000\n",
      "157/157 [==============================] - 0s 169us/step - loss: 0.0507 - acc: 0.9936 - val_loss: 0.0846 - val_acc: 0.9706\n",
      "Epoch 860/1000\n",
      "157/157 [==============================] - 0s 180us/step - loss: 0.0494 - acc: 0.9873 - val_loss: 0.0875 - val_acc: 0.9559\n",
      "Epoch 861/1000\n",
      "157/157 [==============================] - 0s 154us/step - loss: 0.0507 - acc: 0.9873 - val_loss: 0.0861 - val_acc: 0.9706\n",
      "Epoch 862/1000\n",
      "157/157 [==============================] - 0s 178us/step - loss: 0.0524 - acc: 0.9873 - val_loss: 0.0799 - val_acc: 0.9706\n",
      "Epoch 863/1000\n",
      "157/157 [==============================] - 0s 252us/step - loss: 0.0493 - acc: 0.9936 - val_loss: 0.0896 - val_acc: 0.9559\n",
      "Epoch 864/1000\n",
      "157/157 [==============================] - 0s 216us/step - loss: 0.0494 - acc: 0.9936 - val_loss: 0.0929 - val_acc: 0.9559\n",
      "Epoch 865/1000\n",
      "157/157 [==============================] - 0s 167us/step - loss: 0.0505 - acc: 0.9936 - val_loss: 0.0885 - val_acc: 0.9559\n",
      "Epoch 866/1000\n",
      "157/157 [==============================] - 0s 177us/step - loss: 0.0513 - acc: 0.9936 - val_loss: 0.0919 - val_acc: 0.9559\n",
      "Epoch 867/1000\n",
      "157/157 [==============================] - 0s 121us/step - loss: 0.0500 - acc: 0.9873 - val_loss: 0.0859 - val_acc: 0.9706\n",
      "Epoch 868/1000\n",
      "157/157 [==============================] - 0s 170us/step - loss: 0.0502 - acc: 0.9936 - val_loss: 0.0889 - val_acc: 0.9559\n",
      "Epoch 869/1000\n",
      "157/157 [==============================] - 0s 186us/step - loss: 0.0502 - acc: 0.9936 - val_loss: 0.0886 - val_acc: 0.9559\n",
      "Epoch 870/1000\n",
      "157/157 [==============================] - 0s 241us/step - loss: 0.0486 - acc: 0.9936 - val_loss: 0.0878 - val_acc: 0.9559\n",
      "Epoch 871/1000\n",
      "157/157 [==============================] - 0s 252us/step - loss: 0.0483 - acc: 0.9873 - val_loss: 0.0808 - val_acc: 0.9706\n",
      "Epoch 872/1000\n",
      "157/157 [==============================] - 0s 239us/step - loss: 0.0482 - acc: 0.9936 - val_loss: 0.0869 - val_acc: 0.9559\n",
      "Epoch 873/1000\n",
      "157/157 [==============================] - 0s 199us/step - loss: 0.0500 - acc: 0.9936 - val_loss: 0.0901 - val_acc: 0.9559\n",
      "Epoch 874/1000\n",
      "157/157 [==============================] - 0s 175us/step - loss: 0.0495 - acc: 0.9873 - val_loss: 0.0881 - val_acc: 0.9559\n",
      "Epoch 875/1000\n",
      "157/157 [==============================] - 0s 155us/step - loss: 0.0485 - acc: 0.9936 - val_loss: 0.0923 - val_acc: 0.9559\n",
      "Epoch 876/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.0482 - acc: 0.9873 - val_loss: 0.0821 - val_acc: 0.9706\n",
      "Epoch 877/1000\n",
      "157/157 [==============================] - 0s 177us/step - loss: 0.0493 - acc: 0.9936 - val_loss: 0.0955 - val_acc: 0.9559\n",
      "Epoch 878/1000\n",
      "157/157 [==============================] - 0s 218us/step - loss: 0.0499 - acc: 0.9809 - val_loss: 0.0851 - val_acc: 0.9706\n",
      "Epoch 879/1000\n",
      "157/157 [==============================] - 0s 204us/step - loss: 0.0475 - acc: 0.9936 - val_loss: 0.0855 - val_acc: 0.9706\n",
      "Epoch 880/1000\n",
      "157/157 [==============================] - 0s 209us/step - loss: 0.0490 - acc: 0.9936 - val_loss: 0.0826 - val_acc: 0.9706\n",
      "Epoch 881/1000\n",
      "157/157 [==============================] - 0s 349us/step - loss: 0.0474 - acc: 0.9936 - val_loss: 0.0813 - val_acc: 0.9706\n",
      "Epoch 882/1000\n",
      "157/157 [==============================] - 0s 153us/step - loss: 0.0479 - acc: 0.9936 - val_loss: 0.0924 - val_acc: 0.9559\n",
      "Epoch 883/1000\n",
      "157/157 [==============================] - 0s 188us/step - loss: 0.0527 - acc: 0.9873 - val_loss: 0.0853 - val_acc: 0.9706\n",
      "Epoch 884/1000\n",
      "157/157 [==============================] - 0s 122us/step - loss: 0.0473 - acc: 0.9936 - val_loss: 0.0863 - val_acc: 0.9706\n",
      "Epoch 885/1000\n",
      "157/157 [==============================] - 0s 152us/step - loss: 0.0469 - acc: 0.9873 - val_loss: 0.0787 - val_acc: 0.9706\n",
      "Epoch 886/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "157/157 [==============================] - 0s 161us/step - loss: 0.0477 - acc: 0.9936 - val_loss: 0.0853 - val_acc: 0.9706\n",
      "Epoch 887/1000\n",
      "157/157 [==============================] - 0s 176us/step - loss: 0.0475 - acc: 0.9936 - val_loss: 0.0957 - val_acc: 0.9559\n",
      "Epoch 888/1000\n",
      "157/157 [==============================] - 0s 173us/step - loss: 0.0478 - acc: 0.9936 - val_loss: 0.0922 - val_acc: 0.9559\n",
      "Epoch 889/1000\n",
      "157/157 [==============================] - 0s 223us/step - loss: 0.0477 - acc: 0.9873 - val_loss: 0.0834 - val_acc: 0.9706\n",
      "Epoch 890/1000\n",
      "157/157 [==============================] - 0s 204us/step - loss: 0.0477 - acc: 0.9873 - val_loss: 0.0862 - val_acc: 0.9706\n",
      "Epoch 891/1000\n",
      "157/157 [==============================] - 0s 386us/step - loss: 0.0466 - acc: 0.9873 - val_loss: 0.0829 - val_acc: 0.9706\n",
      "Epoch 892/1000\n",
      "157/157 [==============================] - 0s 254us/step - loss: 0.0477 - acc: 0.9936 - val_loss: 0.0824 - val_acc: 0.9706\n",
      "Epoch 893/1000\n",
      "157/157 [==============================] - 0s 115us/step - loss: 0.0466 - acc: 0.9873 - val_loss: 0.0840 - val_acc: 0.9706\n",
      "Epoch 894/1000\n",
      "157/157 [==============================] - 0s 122us/step - loss: 0.0480 - acc: 0.9936 - val_loss: 0.0870 - val_acc: 0.9559\n",
      "Epoch 895/1000\n",
      "157/157 [==============================] - 0s 176us/step - loss: 0.0477 - acc: 0.9873 - val_loss: 0.0866 - val_acc: 0.9559\n",
      "Epoch 896/1000\n",
      "157/157 [==============================] - 0s 177us/step - loss: 0.0467 - acc: 0.9873 - val_loss: 0.0824 - val_acc: 0.9706\n",
      "Epoch 897/1000\n",
      "157/157 [==============================] - 0s 319us/step - loss: 0.0467 - acc: 0.9873 - val_loss: 0.0852 - val_acc: 0.9706\n",
      "Epoch 898/1000\n",
      "157/157 [==============================] - 0s 133us/step - loss: 0.0483 - acc: 0.9936 - val_loss: 0.0878 - val_acc: 0.9559\n",
      "Epoch 899/1000\n",
      "157/157 [==============================] - 0s 138us/step - loss: 0.0482 - acc: 0.9936 - val_loss: 0.0899 - val_acc: 0.9559\n",
      "Epoch 900/1000\n",
      "157/157 [==============================] - 0s 239us/step - loss: 0.0455 - acc: 0.9873 - val_loss: 0.0804 - val_acc: 0.9706\n",
      "Epoch 901/1000\n",
      "157/157 [==============================] - 0s 212us/step - loss: 0.0491 - acc: 0.9873 - val_loss: 0.0852 - val_acc: 0.9706\n",
      "Epoch 902/1000\n",
      "157/157 [==============================] - 0s 244us/step - loss: 0.0457 - acc: 0.9936 - val_loss: 0.0920 - val_acc: 0.9559\n",
      "Epoch 903/1000\n",
      "157/157 [==============================] - 0s 236us/step - loss: 0.0473 - acc: 0.9809 - val_loss: 0.0789 - val_acc: 0.9706\n",
      "Epoch 904/1000\n",
      "157/157 [==============================] - 0s 172us/step - loss: 0.0469 - acc: 0.9936 - val_loss: 0.0858 - val_acc: 0.9706\n",
      "Epoch 905/1000\n",
      "157/157 [==============================] - 0s 232us/step - loss: 0.0461 - acc: 0.9936 - val_loss: 0.0868 - val_acc: 0.9559\n",
      "Epoch 906/1000\n",
      "157/157 [==============================] - 0s 192us/step - loss: 0.0456 - acc: 0.9936 - val_loss: 0.0847 - val_acc: 0.9706\n",
      "Epoch 907/1000\n",
      "157/157 [==============================] - 0s 162us/step - loss: 0.0467 - acc: 0.9936 - val_loss: 0.0896 - val_acc: 0.9559\n",
      "Epoch 908/1000\n",
      "157/157 [==============================] - 0s 94us/step - loss: 0.0500 - acc: 0.9873 - val_loss: 0.0832 - val_acc: 0.9706\n",
      "Epoch 909/1000\n",
      "157/157 [==============================] - 0s 221us/step - loss: 0.0453 - acc: 0.9936 - val_loss: 0.0872 - val_acc: 0.9559\n",
      "Epoch 910/1000\n",
      "157/157 [==============================] - 0s 348us/step - loss: 0.0455 - acc: 0.9936 - val_loss: 0.0890 - val_acc: 0.9559\n",
      "Epoch 911/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.0463 - acc: 0.9873 - val_loss: 0.0857 - val_acc: 0.9706\n",
      "Epoch 912/1000\n",
      "157/157 [==============================] - 0s 144us/step - loss: 0.0452 - acc: 0.9936 - val_loss: 0.0939 - val_acc: 0.9559\n",
      "Epoch 913/1000\n",
      "157/157 [==============================] - 0s 158us/step - loss: 0.0465 - acc: 0.9873 - val_loss: 0.0809 - val_acc: 0.9706\n",
      "Epoch 914/1000\n",
      "157/157 [==============================] - 0s 123us/step - loss: 0.0448 - acc: 0.9936 - val_loss: 0.0851 - val_acc: 0.9706\n",
      "Epoch 915/1000\n",
      "157/157 [==============================] - 0s 148us/step - loss: 0.0480 - acc: 0.9873 - val_loss: 0.0852 - val_acc: 0.9706\n",
      "Epoch 916/1000\n",
      "157/157 [==============================] - 0s 179us/step - loss: 0.0450 - acc: 0.9936 - val_loss: 0.0950 - val_acc: 0.9559\n",
      "Epoch 917/1000\n",
      "157/157 [==============================] - 0s 141us/step - loss: 0.0466 - acc: 0.9936 - val_loss: 0.0868 - val_acc: 0.9559\n",
      "Epoch 918/1000\n",
      "157/157 [==============================] - 0s 184us/step - loss: 0.0452 - acc: 0.9873 - val_loss: 0.0825 - val_acc: 0.9706\n",
      "Epoch 919/1000\n",
      "157/157 [==============================] - 0s 107us/step - loss: 0.0457 - acc: 0.9936 - val_loss: 0.0792 - val_acc: 0.9706\n",
      "Epoch 920/1000\n",
      "157/157 [==============================] - 0s 104us/step - loss: 0.0446 - acc: 0.9936 - val_loss: 0.0843 - val_acc: 0.9706\n",
      "Epoch 921/1000\n",
      "157/157 [==============================] - 0s 102us/step - loss: 0.0462 - acc: 0.9873 - val_loss: 0.0818 - val_acc: 0.9706\n",
      "Epoch 922/1000\n",
      "157/157 [==============================] - ETA: 0s - loss: 0.0544 - acc: 1.000 - 0s 102us/step - loss: 0.0451 - acc: 0.9873 - val_loss: 0.0853 - val_acc: 0.9706\n",
      "Epoch 923/1000\n",
      "157/157 [==============================] - 0s 99us/step - loss: 0.0448 - acc: 0.9873 - val_loss: 0.0818 - val_acc: 0.9706\n",
      "Epoch 924/1000\n",
      "157/157 [==============================] - 0s 105us/step - loss: 0.0449 - acc: 0.9873 - val_loss: 0.0841 - val_acc: 0.9706\n",
      "Epoch 925/1000\n",
      "157/157 [==============================] - 0s 275us/step - loss: 0.0452 - acc: 0.9873 - val_loss: 0.0823 - val_acc: 0.9706\n",
      "Epoch 926/1000\n",
      "157/157 [==============================] - 0s 167us/step - loss: 0.0441 - acc: 0.9873 - val_loss: 0.0808 - val_acc: 0.9706\n",
      "Epoch 927/1000\n",
      "157/157 [==============================] - 0s 118us/step - loss: 0.0447 - acc: 0.9936 - val_loss: 0.0785 - val_acc: 0.9706\n",
      "Epoch 928/1000\n",
      "157/157 [==============================] - 0s 161us/step - loss: 0.0447 - acc: 0.9873 - val_loss: 0.0766 - val_acc: 0.9706\n",
      "Epoch 929/1000\n",
      "157/157 [==============================] - 0s 193us/step - loss: 0.0449 - acc: 0.9936 - val_loss: 0.0836 - val_acc: 0.9706\n",
      "Epoch 930/1000\n",
      "157/157 [==============================] - 0s 191us/step - loss: 0.0446 - acc: 0.9936 - val_loss: 0.0850 - val_acc: 0.9706\n",
      "Epoch 931/1000\n",
      "157/157 [==============================] - 0s 163us/step - loss: 0.0439 - acc: 0.9936 - val_loss: 0.0868 - val_acc: 0.9559\n",
      "Epoch 932/1000\n",
      "157/157 [==============================] - 0s 168us/step - loss: 0.0434 - acc: 0.9936 - val_loss: 0.0844 - val_acc: 0.9706\n",
      "Epoch 933/1000\n",
      "157/157 [==============================] - 0s 171us/step - loss: 0.0443 - acc: 0.9936 - val_loss: 0.0793 - val_acc: 0.9706\n",
      "Epoch 934/1000\n",
      "157/157 [==============================] - 0s 197us/step - loss: 0.0441 - acc: 0.9873 - val_loss: 0.0855 - val_acc: 0.9706\n",
      "Epoch 935/1000\n",
      "157/157 [==============================] - 0s 142us/step - loss: 0.0447 - acc: 0.9873 - val_loss: 0.0878 - val_acc: 0.9559\n",
      "Epoch 936/1000\n",
      "157/157 [==============================] - 0s 190us/step - loss: 0.0439 - acc: 0.9809 - val_loss: 0.0763 - val_acc: 0.9706\n",
      "Epoch 937/1000\n",
      "157/157 [==============================] - 0s 170us/step - loss: 0.0427 - acc: 0.9936 - val_loss: 0.0870 - val_acc: 0.9559\n",
      "Epoch 938/1000\n",
      "157/157 [==============================] - ETA: 0s - loss: 0.0191 - acc: 1.000 - 0s 138us/step - loss: 0.0439 - acc: 0.9873 - val_loss: 0.0760 - val_acc: 0.9706\n",
      "Epoch 939/1000\n",
      "157/157 [==============================] - 0s 188us/step - loss: 0.0442 - acc: 0.9936 - val_loss: 0.0777 - val_acc: 0.9706\n",
      "Epoch 940/1000\n",
      "157/157 [==============================] - 0s 186us/step - loss: 0.0445 - acc: 0.9936 - val_loss: 0.0877 - val_acc: 0.9559\n",
      "Epoch 941/1000\n",
      "157/157 [==============================] - 0s 170us/step - loss: 0.0441 - acc: 0.9873 - val_loss: 0.0819 - val_acc: 0.9706\n",
      "Epoch 942/1000\n",
      "157/157 [==============================] - 0s 204us/step - loss: 0.0433 - acc: 0.9936 - val_loss: 0.0863 - val_acc: 0.9559\n",
      "Epoch 943/1000\n",
      "157/157 [==============================] - 0s 235us/step - loss: 0.0430 - acc: 0.9873 - val_loss: 0.0843 - val_acc: 0.9706\n",
      "Epoch 944/1000\n",
      "157/157 [==============================] - 0s 207us/step - loss: 0.0436 - acc: 0.9873 - val_loss: 0.0817 - val_acc: 0.9706\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 945/1000\n",
      "157/157 [==============================] - 0s 178us/step - loss: 0.0470 - acc: 0.9873 - val_loss: 0.0836 - val_acc: 0.9706\n",
      "Epoch 946/1000\n",
      "157/157 [==============================] - 0s 170us/step - loss: 0.0432 - acc: 0.9936 - val_loss: 0.0858 - val_acc: 0.9559\n",
      "Epoch 947/1000\n",
      "157/157 [==============================] - 0s 327us/step - loss: 0.0428 - acc: 0.9873 - val_loss: 0.0818 - val_acc: 0.9706\n",
      "Epoch 948/1000\n",
      "157/157 [==============================] - 0s 219us/step - loss: 0.0433 - acc: 0.9873 - val_loss: 0.0807 - val_acc: 0.9706\n",
      "Epoch 949/1000\n",
      "157/157 [==============================] - 0s 253us/step - loss: 0.0438 - acc: 0.9936 - val_loss: 0.0792 - val_acc: 0.9706\n",
      "Epoch 950/1000\n",
      "157/157 [==============================] - 0s 369us/step - loss: 0.0430 - acc: 0.9936 - val_loss: 0.0869 - val_acc: 0.9559\n",
      "Epoch 951/1000\n",
      "157/157 [==============================] - 0s 241us/step - loss: 0.0435 - acc: 0.9936 - val_loss: 0.0822 - val_acc: 0.9706\n",
      "Epoch 952/1000\n",
      "157/157 [==============================] - 0s 82us/step - loss: 0.0430 - acc: 0.9873 - val_loss: 0.0797 - val_acc: 0.9706\n",
      "Epoch 953/1000\n",
      "157/157 [==============================] - 0s 86us/step - loss: 0.0431 - acc: 0.9873 - val_loss: 0.0845 - val_acc: 0.9706\n",
      "Epoch 954/1000\n",
      "157/157 [==============================] - 0s 112us/step - loss: 0.0433 - acc: 0.9873 - val_loss: 0.0792 - val_acc: 0.9706\n",
      "Epoch 955/1000\n",
      "157/157 [==============================] - 0s 152us/step - loss: 0.0437 - acc: 0.9936 - val_loss: 0.0863 - val_acc: 0.9559\n",
      "Epoch 956/1000\n",
      "157/157 [==============================] - 0s 323us/step - loss: 0.0438 - acc: 0.9873 - val_loss: 0.0850 - val_acc: 0.9706\n",
      "Epoch 957/1000\n",
      "157/157 [==============================] - 0s 229us/step - loss: 0.0424 - acc: 0.9936 - val_loss: 0.0862 - val_acc: 0.9559\n",
      "Epoch 958/1000\n",
      "157/157 [==============================] - 0s 332us/step - loss: 0.0422 - acc: 0.9873 - val_loss: 0.0763 - val_acc: 0.9706\n",
      "Epoch 959/1000\n",
      "157/157 [==============================] - 0s 369us/step - loss: 0.0434 - acc: 0.9873 - val_loss: 0.0755 - val_acc: 0.9706\n",
      "Epoch 960/1000\n",
      "157/157 [==============================] - 0s 314us/step - loss: 0.0421 - acc: 0.9936 - val_loss: 0.0840 - val_acc: 0.9706\n",
      "Epoch 961/1000\n",
      "157/157 [==============================] - 0s 358us/step - loss: 0.0423 - acc: 0.9873 - val_loss: 0.0861 - val_acc: 0.9559\n",
      "Epoch 962/1000\n",
      "157/157 [==============================] - 0s 363us/step - loss: 0.0416 - acc: 0.9936 - val_loss: 0.0824 - val_acc: 0.9706\n",
      "Epoch 963/1000\n",
      "157/157 [==============================] - 0s 227us/step - loss: 0.0443 - acc: 0.9873 - val_loss: 0.0831 - val_acc: 0.9706\n",
      "Epoch 964/1000\n",
      "157/157 [==============================] - 0s 347us/step - loss: 0.0440 - acc: 0.9873 - val_loss: 0.0839 - val_acc: 0.9706\n",
      "Epoch 965/1000\n",
      "157/157 [==============================] - 0s 256us/step - loss: 0.0411 - acc: 0.9936 - val_loss: 0.0864 - val_acc: 0.9559\n",
      "Epoch 966/1000\n",
      "157/157 [==============================] - 0s 331us/step - loss: 0.0416 - acc: 0.9873 - val_loss: 0.0843 - val_acc: 0.9706\n",
      "Epoch 967/1000\n",
      "157/157 [==============================] - 0s 286us/step - loss: 0.0419 - acc: 0.9873 - val_loss: 0.0754 - val_acc: 0.9706\n",
      "Epoch 968/1000\n",
      "157/157 [==============================] - 0s 299us/step - loss: 0.0425 - acc: 0.9936 - val_loss: 0.0783 - val_acc: 0.9706\n",
      "Epoch 969/1000\n",
      "157/157 [==============================] - 0s 314us/step - loss: 0.0417 - acc: 0.9936 - val_loss: 0.0784 - val_acc: 0.9706\n",
      "Epoch 970/1000\n",
      "157/157 [==============================] - 0s 348us/step - loss: 0.0418 - acc: 0.9936 - val_loss: 0.0836 - val_acc: 0.9706\n",
      "Epoch 971/1000\n",
      "157/157 [==============================] - 0s 310us/step - loss: 0.0428 - acc: 0.9873 - val_loss: 0.0837 - val_acc: 0.9706\n",
      "Epoch 972/1000\n",
      "157/157 [==============================] - 0s 357us/step - loss: 0.0416 - acc: 0.9936 - val_loss: 0.0867 - val_acc: 0.9559\n",
      "Epoch 973/1000\n",
      "157/157 [==============================] - 0s 317us/step - loss: 0.0430 - acc: 0.9936 - val_loss: 0.0851 - val_acc: 0.9559\n",
      "Epoch 974/1000\n",
      "157/157 [==============================] - 0s 296us/step - loss: 0.0413 - acc: 0.9873 - val_loss: 0.0855 - val_acc: 0.9559\n",
      "Epoch 975/1000\n",
      "157/157 [==============================] - 0s 249us/step - loss: 0.0414 - acc: 0.9936 - val_loss: 0.0873 - val_acc: 0.9559\n",
      "Epoch 976/1000\n",
      "157/157 [==============================] - 0s 279us/step - loss: 0.0416 - acc: 0.9873 - val_loss: 0.0879 - val_acc: 0.9559\n",
      "Epoch 977/1000\n",
      "157/157 [==============================] - 0s 98us/step - loss: 0.0414 - acc: 0.9936 - val_loss: 0.0799 - val_acc: 0.9706\n",
      "Epoch 978/1000\n",
      "157/157 [==============================] - 0s 90us/step - loss: 0.0418 - acc: 0.9936 - val_loss: 0.0793 - val_acc: 0.9706\n",
      "Epoch 979/1000\n",
      "157/157 [==============================] - 0s 149us/step - loss: 0.0414 - acc: 0.9873 - val_loss: 0.0807 - val_acc: 0.9706\n",
      "Epoch 980/1000\n",
      "157/157 [==============================] - 0s 120us/step - loss: 0.0405 - acc: 0.9873 - val_loss: 0.0741 - val_acc: 0.9706\n",
      "Epoch 981/1000\n",
      "157/157 [==============================] - 0s 129us/step - loss: 0.0413 - acc: 0.9936 - val_loss: 0.0755 - val_acc: 0.9706\n",
      "Epoch 982/1000\n",
      "157/157 [==============================] - 0s 252us/step - loss: 0.0409 - acc: 0.9936 - val_loss: 0.0803 - val_acc: 0.9706\n",
      "Epoch 983/1000\n",
      "157/157 [==============================] - 0s 263us/step - loss: 0.0404 - acc: 0.9873 - val_loss: 0.0769 - val_acc: 0.9706\n",
      "Epoch 984/1000\n",
      "157/157 [==============================] - 0s 158us/step - loss: 0.0419 - acc: 0.9936 - val_loss: 0.0744 - val_acc: 0.9706\n",
      "Epoch 985/1000\n",
      "157/157 [==============================] - 0s 220us/step - loss: 0.0410 - acc: 0.9936 - val_loss: 0.0833 - val_acc: 0.9706\n",
      "Epoch 986/1000\n",
      "157/157 [==============================] - 0s 115us/step - loss: 0.0417 - acc: 0.9873 - val_loss: 0.0915 - val_acc: 0.9559\n",
      "Epoch 987/1000\n",
      "157/157 [==============================] - 0s 141us/step - loss: 0.0403 - acc: 0.9873 - val_loss: 0.0797 - val_acc: 0.9706\n",
      "Epoch 988/1000\n",
      "157/157 [==============================] - 0s 115us/step - loss: 0.0405 - acc: 0.9873 - val_loss: 0.0821 - val_acc: 0.9706\n",
      "Epoch 989/1000\n",
      "157/157 [==============================] - 0s 102us/step - loss: 0.0397 - acc: 0.9936 - val_loss: 0.0813 - val_acc: 0.9706\n",
      "Epoch 990/1000\n",
      "157/157 [==============================] - 0s 161us/step - loss: 0.0402 - acc: 0.9936 - val_loss: 0.0899 - val_acc: 0.9559\n",
      "Epoch 991/1000\n",
      "157/157 [==============================] - 0s 299us/step - loss: 0.0421 - acc: 0.9809 - val_loss: 0.0819 - val_acc: 0.9706\n",
      "Epoch 992/1000\n",
      "157/157 [==============================] - 0s 218us/step - loss: 0.0400 - acc: 0.9873 - val_loss: 0.0787 - val_acc: 0.9706\n",
      "Epoch 993/1000\n",
      "157/157 [==============================] - 0s 195us/step - loss: 0.0410 - acc: 0.9936 - val_loss: 0.0817 - val_acc: 0.9706\n",
      "Epoch 994/1000\n",
      "157/157 [==============================] - 0s 114us/step - loss: 0.0392 - acc: 0.9936 - val_loss: 0.0889 - val_acc: 0.9559\n",
      "Epoch 995/1000\n",
      "157/157 [==============================] - 0s 186us/step - loss: 0.0399 - acc: 0.9873 - val_loss: 0.0750 - val_acc: 0.9706\n",
      "Epoch 996/1000\n",
      "157/157 [==============================] - 0s 474us/step - loss: 0.0406 - acc: 0.9936 - val_loss: 0.0791 - val_acc: 0.9706\n",
      "Epoch 997/1000\n",
      "157/157 [==============================] - 0s 267us/step - loss: 0.0396 - acc: 0.9936 - val_loss: 0.0862 - val_acc: 0.9559\n",
      "Epoch 998/1000\n",
      "157/157 [==============================] - 0s 310us/step - loss: 0.0395 - acc: 0.9873 - val_loss: 0.0734 - val_acc: 0.9706\n",
      "Epoch 999/1000\n",
      "157/157 [==============================] - 0s 457us/step - loss: 0.0398 - acc: 0.9936 - val_loss: 0.0901 - val_acc: 0.9559\n",
      "Epoch 1000/1000\n",
      "157/157 [==============================] - 0s 332us/step - loss: 0.0395 - acc: 0.9873 - val_loss: 0.0871 - val_acc: 0.9559\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "# Building a Keras model\n",
    "\n",
    "model = Sequential()\n",
    "\n",
    "model.add(Dense(8, input_shape = (4,), activation = \"relu\"))\n",
    "\n",
    "model.add(Dense(8, activation = \"relu\"))\n",
    "\n",
    "model.add(Dense(1, activation = \"sigmoid\"))\n",
    "\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=\"rmsprop\", metrics=[\"accuracy\"])\n",
    "\n",
    "num_epochs = 1000\n",
    "\n",
    "model_run = model.fit(X_train_scaled, y_train, epochs=num_epochs, validation_data = (X_test_scaled,y_test))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fe91c78a208>]"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe91c78a748>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 250,
       "width": 373
      },
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "history_model = model_run.history\n",
    "\n",
    "fig, ax = plt.subplot()\n",
    "\n",
    "plt.plot(np.arange(1,num_epochs+1), history_model[\"acc\"], \"--\")\n",
    "\n",
    "plt.plot(np.arange(1,num_epochs+1), history_model[\"val_acc\"])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## Network Architecture\n",
    "\n",
    "## CNN examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "TODO: \n",
    "\n",
    "- does keras support scikit-learn api ? (.fit and .predict methods) ?\n",
    "- if yes: we could use cross validation and hyper parameter optimzation for scikit-learn to evaluae / improve keras network.    \n",
    "      \n",
    "      "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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  "latex_envs": {
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