Tensorflow1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Using TensorFlow backend.\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense\n",
    "import numpy\n",
    "\n",
    "# fix random seed for reproducibility\n",
    "numpy.random.seed(7)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load pima indians dataset\n",
    "dataset = numpy.loadtxt(\"pima-indians-diabetes.data.csv\", delimiter=\",\")\n",
    "X = dataset[:,0:8]\n",
    "Y = dataset[:,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# create model\n",
    "model = Sequential()\n",
    "model.add(Dense(12, input_dim=8, activation='relu'))\n",
    "model.add(Dense(8, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss='binary_crossentropy',\n",
    "              optimizer='adam',\n",
    "              metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/150\n",
      "768/768 [==============================] - 2s 3ms/step - loss: 3.6841 - acc: 0.5964\n",
      "Epoch 2/150\n",
      "768/768 [==============================] - 1s 732us/step - loss: 0.9283 - acc: 0.6003\n",
      "Epoch 3/150\n",
      "768/768 [==============================] - 0s 636us/step - loss: 0.7414 - acc: 0.6380\n",
      "Epoch 4/150\n",
      "768/768 [==============================] - 1s 681us/step - loss: 0.7065 - acc: 0.6615\n",
      "Epoch 5/150\n",
      "768/768 [==============================] - 1s 678us/step - loss: 0.6820 - acc: 0.6706\n",
      "Epoch 6/150\n",
      "768/768 [==============================] - 1s 669us/step - loss: 0.6509 - acc: 0.6849\n",
      "Epoch 7/150\n",
      "768/768 [==============================] - 1s 682us/step - loss: 0.6490 - acc: 0.6719\n",
      "Epoch 8/150\n",
      "768/768 [==============================] - 1s 703us/step - loss: 0.6359 - acc: 0.6836\n",
      "Epoch 9/150\n",
      "768/768 [==============================] - 1s 666us/step - loss: 0.6245 - acc: 0.6901\n",
      "Epoch 10/150\n",
      "768/768 [==============================] - 1s 682us/step - loss: 0.6294 - acc: 0.6810\n",
      "Epoch 11/150\n",
      "768/768 [==============================] - 1s 698us/step - loss: 0.6482 - acc: 0.6706\n",
      "Epoch 12/150\n",
      "768/768 [==============================] - 1s 678us/step - loss: 0.6405 - acc: 0.6758\n",
      "Epoch 13/150\n",
      "768/768 [==============================] - 1s 682us/step - loss: 0.6255 - acc: 0.6771\n",
      "Epoch 14/150\n",
      "768/768 [==============================] - 0s 622us/step - loss: 0.6175 - acc: 0.7018\n",
      "Epoch 15/150\n",
      "768/768 [==============================] - 1s 696us/step - loss: 0.6014 - acc: 0.6966\n",
      "Epoch 16/150\n",
      "768/768 [==============================] - 0s 622us/step - loss: 0.5878 - acc: 0.6992\n",
      "Epoch 17/150\n",
      "768/768 [==============================] - 0s 620us/step - loss: 0.5841 - acc: 0.6979\n",
      "Epoch 18/150\n",
      "768/768 [==============================] - 0s 577us/step - loss: 0.5988 - acc: 0.6888\n",
      "Epoch 19/150\n",
      "768/768 [==============================] - 1s 696us/step - loss: 0.5801 - acc: 0.7122\n",
      "Epoch 20/150\n",
      "768/768 [==============================] - 0s 634us/step - loss: 0.5793 - acc: 0.7253\n",
      "Epoch 21/150\n",
      "768/768 [==============================] - 0s 615us/step - loss: 0.5710 - acc: 0.7135\n",
      "Epoch 22/150\n",
      "768/768 [==============================] - 1s 685us/step - loss: 0.5824 - acc: 0.6992\n",
      "Epoch 23/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.5747 - acc: 0.7109\n",
      "Epoch 24/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.5697 - acc: 0.7266\n",
      "Epoch 25/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.5576 - acc: 0.7409\n",
      "Epoch 26/150\n",
      "768/768 [==============================] - 1s 909us/step - loss: 0.5709 - acc: 0.7044\n",
      "Epoch 27/150\n",
      "768/768 [==============================] - 1s 685us/step - loss: 0.5549 - acc: 0.7174\n",
      "Epoch 28/150\n",
      "768/768 [==============================] - 1s 695us/step - loss: 0.5547 - acc: 0.7318\n",
      "Epoch 29/150\n",
      "768/768 [==============================] - 0s 650us/step - loss: 0.5728 - acc: 0.7174\n",
      "Epoch 30/150\n",
      "768/768 [==============================] - 0s 642us/step - loss: 0.5607 - acc: 0.7227\n",
      "Epoch 31/150\n",
      "768/768 [==============================] - 1s 659us/step - loss: 0.5680 - acc: 0.7188\n",
      "Epoch 32/150\n",
      "768/768 [==============================] - 1s 750us/step - loss: 0.5652 - acc: 0.7109\n",
      "Epoch 33/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.5535 - acc: 0.7201\n",
      "Epoch 34/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.5513 - acc: 0.7318\n",
      "Epoch 35/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.5532 - acc: 0.7227\n",
      "Epoch 36/150\n",
      "768/768 [==============================] - 1s 694us/step - loss: 0.5623 - acc: 0.7096\n",
      "Epoch 37/150\n",
      "768/768 [==============================] - 1s 690us/step - loss: 0.5350 - acc: 0.7344\n",
      "Epoch 38/150\n",
      "768/768 [==============================] - 1s 772us/step - loss: 0.5447 - acc: 0.7122\n",
      "Epoch 39/150\n",
      "768/768 [==============================] - 1s 690us/step - loss: 0.5488 - acc: 0.7279\n",
      "Epoch 40/150\n",
      "768/768 [==============================] - 1s 673us/step - loss: 0.5480 - acc: 0.7174\n",
      "Epoch 41/150\n",
      "768/768 [==============================] - 1s 686us/step - loss: 0.5452 - acc: 0.7279\n",
      "Epoch 42/150\n",
      "768/768 [==============================] - 1s 724us/step - loss: 0.5372 - acc: 0.7409\n",
      "Epoch 43/150\n",
      "768/768 [==============================] - 1s 700us/step - loss: 0.5336 - acc: 0.7448\n",
      "Epoch 44/150\n",
      "768/768 [==============================] - 1s 716us/step - loss: 0.5345 - acc: 0.7461\n",
      "Epoch 45/150\n",
      "768/768 [==============================] - 1s 697us/step - loss: 0.5324 - acc: 0.7591\n",
      "Epoch 46/150\n",
      "768/768 [==============================] - 0s 595us/step - loss: 0.5300 - acc: 0.7539\n",
      "Epoch 47/150\n",
      "768/768 [==============================] - 1s 703us/step - loss: 0.5340 - acc: 0.7370\n",
      "Epoch 48/150\n",
      "768/768 [==============================] - 1s 667us/step - loss: 0.5343 - acc: 0.7422\n",
      "Epoch 49/150\n",
      "768/768 [==============================] - 0s 587us/step - loss: 0.5345 - acc: 0.7552\n",
      "Epoch 50/150\n",
      "768/768 [==============================] - 0s 636us/step - loss: 0.5273 - acc: 0.7383\n",
      "Epoch 51/150\n",
      "768/768 [==============================] - 0s 628us/step - loss: 0.5278 - acc: 0.7487\n",
      "Epoch 52/150\n",
      "768/768 [==============================] - 0s 592us/step - loss: 0.5294 - acc: 0.7448\n",
      "Epoch 53/150\n",
      "768/768 [==============================] - 1s 695us/step - loss: 0.5402 - acc: 0.7396\n",
      "Epoch 54/150\n",
      "768/768 [==============================] - 1s 682us/step - loss: 0.5385 - acc: 0.7266\n",
      "Epoch 55/150\n",
      "768/768 [==============================] - 1s 765us/step - loss: 0.5219 - acc: 0.7552\n",
      "Epoch 56/150\n",
      "768/768 [==============================] - 1s 693us/step - loss: 0.5286 - acc: 0.7396\n",
      "Epoch 57/150\n",
      "768/768 [==============================] - 1s 659us/step - loss: 0.5324 - acc: 0.7305\n",
      "Epoch 58/150\n",
      "768/768 [==============================] - 1s 680us/step - loss: 0.5218 - acc: 0.7513\n",
      "Epoch 59/150\n",
      "768/768 [==============================] - 0s 598us/step - loss: 0.5129 - acc: 0.7643\n",
      "Epoch 60/150\n",
      "768/768 [==============================] - 1s 657us/step - loss: 0.5343 - acc: 0.7292\n",
      "Epoch 61/150\n",
      "768/768 [==============================] - 1s 657us/step - loss: 0.5272 - acc: 0.7344\n",
      "Epoch 62/150\n",
      "768/768 [==============================] - 1s 746us/step - loss: 0.5171 - acc: 0.7552\n",
      "Epoch 63/150\n",
      "768/768 [==============================] - 0s 629us/step - loss: 0.5452 - acc: 0.7318\n",
      "Epoch 64/150\n",
      "768/768 [==============================] - 0s 651us/step - loss: 0.5328 - acc: 0.7383\n",
      "Epoch 65/150\n",
      "768/768 [==============================] - 1s 668us/step - loss: 0.5192 - acc: 0.7461\n",
      "Epoch 66/150\n",
      "768/768 [==============================] - 1s 692us/step - loss: 0.5064 - acc: 0.7487\n",
      "Epoch 67/150\n",
      "768/768 [==============================] - 1s 720us/step - loss: 0.5161 - acc: 0.7383\n",
      "Epoch 68/150\n",
      "768/768 [==============================] - 1s 675us/step - loss: 0.5127 - acc: 0.7500\n",
      "Epoch 69/150\n",
      "768/768 [==============================] - 1s 669us/step - loss: 0.5116 - acc: 0.7513\n",
      "Epoch 70/150\n",
      "768/768 [==============================] - 1s 690us/step - loss: 0.5338 - acc: 0.7174\n",
      "Epoch 71/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.5192 - acc: 0.7422\n",
      "Epoch 72/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.5175 - acc: 0.7513\n",
      "Epoch 73/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.5168 - acc: 0.7461\n",
      "Epoch 74/150\n",
      "768/768 [==============================] - 1s 738us/step - loss: 0.5105 - acc: 0.7617\n",
      "Epoch 75/150\n",
      "768/768 [==============================] - 0s 607us/step - loss: 0.5111 - acc: 0.7591\n",
      "Epoch 76/150\n",
      "768/768 [==============================] - 1s 775us/step - loss: 0.5134 - acc: 0.7513\n",
      "Epoch 77/150\n",
      "768/768 [==============================] - 1s 715us/step - loss: 0.5165 - acc: 0.7591\n",
      "Epoch 78/150\n",
      "768/768 [==============================] - 1s 679us/step - loss: 0.5144 - acc: 0.7500\n",
      "Epoch 79/150\n",
      "768/768 [==============================] - 1s 924us/step - loss: 0.5148 - acc: 0.7396\n",
      "Epoch 80/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.5106 - acc: 0.7539\n",
      "Epoch 81/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.5066 - acc: 0.7669\n",
      "Epoch 82/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.5016 - acc: 0.7513\n",
      "Epoch 83/150\n",
      "768/768 [==============================] - 1s 675us/step - loss: 0.5025 - acc: 0.7591\n",
      "Epoch 84/150\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "768/768 [==============================] - 1s 656us/step - loss: 0.4979 - acc: 0.7617\n",
      "Epoch 85/150\n",
      "768/768 [==============================] - 0s 600us/step - loss: 0.5070 - acc: 0.7461\n",
      "Epoch 86/150\n",
      "768/768 [==============================] - 0s 645us/step - loss: 0.5078 - acc: 0.7513\n",
      "Epoch 87/150\n",
      "768/768 [==============================] - 0s 594us/step - loss: 0.5001 - acc: 0.7604\n",
      "Epoch 88/150\n",
      "768/768 [==============================] - 0s 646us/step - loss: 0.5014 - acc: 0.7669\n",
      "Epoch 89/150\n",
      "768/768 [==============================] - 1s 671us/step - loss: 0.5064 - acc: 0.7669\n",
      "Epoch 90/150\n",
      "768/768 [==============================] - 1s 687us/step - loss: 0.5110 - acc: 0.7513\n",
      "Epoch 91/150\n",
      "768/768 [==============================] - 1s 697us/step - loss: 0.4997 - acc: 0.7526\n",
      "Epoch 92/150\n",
      "768/768 [==============================] - 1s 740us/step - loss: 0.5059 - acc: 0.7539\n",
      "Epoch 93/150\n",
      "768/768 [==============================] - 1s 675us/step - loss: 0.4985 - acc: 0.7617\n",
      "Epoch 94/150\n",
      "768/768 [==============================] - 0s 611us/step - loss: 0.4976 - acc: 0.7708\n",
      "Epoch 95/150\n",
      "768/768 [==============================] - 1s 899us/step - loss: 0.5039 - acc: 0.7526\n",
      "Epoch 96/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4909 - acc: 0.7734\n",
      "Epoch 97/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4959 - acc: 0.7708\n",
      "Epoch 98/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4884 - acc: 0.7669\n",
      "Epoch 99/150\n",
      "768/768 [==============================] - 1s 656us/step - loss: 0.4894 - acc: 0.7682\n",
      "Epoch 100/150\n",
      "768/768 [==============================] - 0s 642us/step - loss: 0.4850 - acc: 0.7826\n",
      "Epoch 101/150\n",
      "768/768 [==============================] - 1s 679us/step - loss: 0.4897 - acc: 0.7747\n",
      "Epoch 102/150\n",
      "768/768 [==============================] - 1s 743us/step - loss: 0.4978 - acc: 0.7539\n",
      "Epoch 103/150\n",
      "768/768 [==============================] - 1s 683us/step - loss: 0.4980 - acc: 0.7578\n",
      "Epoch 104/150\n",
      "768/768 [==============================] - 0s 631us/step - loss: 0.4920 - acc: 0.7852\n",
      "Epoch 105/150\n",
      "768/768 [==============================] - 1s 656us/step - loss: 0.5294 - acc: 0.7474\n",
      "Epoch 106/150\n",
      "768/768 [==============================] - 1s 658us/step - loss: 0.4912 - acc: 0.7747\n",
      "Epoch 107/150\n",
      "768/768 [==============================] - 0s 648us/step - loss: 0.4890 - acc: 0.7747\n",
      "Epoch 108/150\n",
      "768/768 [==============================] - 1s 675us/step - loss: 0.5037 - acc: 0.7617\n",
      "Epoch 109/150\n",
      "768/768 [==============================] - 1s 652us/step - loss: 0.4865 - acc: 0.7604\n",
      "Epoch 110/150\n",
      "768/768 [==============================] - 1s 681us/step - loss: 0.4871 - acc: 0.7630\n",
      "Epoch 111/150\n",
      "768/768 [==============================] - 1s 682us/step - loss: 0.4834 - acc: 0.7786\n",
      "Epoch 112/150\n",
      "768/768 [==============================] - 1s 699us/step - loss: 0.4915 - acc: 0.7773\n",
      "Epoch 113/150\n",
      "768/768 [==============================] - 1s 663us/step - loss: 0.4983 - acc: 0.7526\n",
      "Epoch 114/150\n",
      "768/768 [==============================] - 0s 607us/step - loss: 0.4908 - acc: 0.7552\n",
      "Epoch 115/150\n",
      "768/768 [==============================] - 1s 675us/step - loss: 0.4913 - acc: 0.7682\n",
      "Epoch 116/150\n",
      "768/768 [==============================] - 1s 988us/step - loss: 0.4907 - acc: 0.7695\n",
      "Epoch 117/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4916 - acc: 0.7604\n",
      "Epoch 118/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4874 - acc: 0.7799\n",
      "Epoch 119/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4837 - acc: 0.7682\n",
      "Epoch 120/150\n",
      "768/768 [==============================] - 1s 679us/step - loss: 0.4961 - acc: 0.7747\n",
      "Epoch 121/150\n",
      "768/768 [==============================] - 1s 790us/step - loss: 0.4918 - acc: 0.7747\n",
      "Epoch 122/150\n",
      "768/768 [==============================] - 1s 682us/step - loss: 0.4829 - acc: 0.7760\n",
      "Epoch 123/150\n",
      "768/768 [==============================] - 0s 597us/step - loss: 0.4844 - acc: 0.7630\n",
      "Epoch 124/150\n",
      "768/768 [==============================] - 0s 617us/step - loss: 0.4843 - acc: 0.7747\n",
      "Epoch 125/150\n",
      "768/768 [==============================] - 0s 620us/step - loss: 0.4869 - acc: 0.7799\n",
      "Epoch 126/150\n",
      "768/768 [==============================] - 1s 659us/step - loss: 0.4796 - acc: 0.7669\n",
      "Epoch 127/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4874 - acc: 0.7695\n",
      "Epoch 128/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4712 - acc: 0.7747\n",
      "Epoch 129/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4814 - acc: 0.7708\n",
      "Epoch 130/150\n",
      "768/768 [==============================] - 1s 860us/step - loss: 0.4708 - acc: 0.7904\n",
      "Epoch 131/150\n",
      "768/768 [==============================] - 1s 699us/step - loss: 0.4824 - acc: 0.7669\n",
      "Epoch 132/150\n",
      "768/768 [==============================] - 1s 747us/step - loss: 0.4809 - acc: 0.7799\n",
      "Epoch 133/150\n",
      "768/768 [==============================] - 1s 670us/step - loss: 0.4825 - acc: 0.7734\n",
      "Epoch 134/150\n",
      "768/768 [==============================] - 1s 686us/step - loss: 0.4871 - acc: 0.7734\n",
      "Epoch 135/150\n",
      "768/768 [==============================] - 1s 673us/step - loss: 0.4733 - acc: 0.7708\n",
      "Epoch 136/150\n",
      "768/768 [==============================] - 1s 679us/step - loss: 0.4720 - acc: 0.7695\n",
      "Epoch 137/150\n",
      "768/768 [==============================] - 1s 671us/step - loss: 0.4681 - acc: 0.7839\n",
      "Epoch 138/150\n",
      "768/768 [==============================] - 1s 692us/step - loss: 0.4775 - acc: 0.7852\n",
      "Epoch 139/150\n",
      "768/768 [==============================] - 1s 676us/step - loss: 0.4667 - acc: 0.7773\n",
      "Epoch 140/150\n",
      "768/768 [==============================] - 0s 627us/step - loss: 0.4791 - acc: 0.7852\n",
      "Epoch 141/150\n",
      "768/768 [==============================] - 1s 695us/step - loss: 0.4711 - acc: 0.7852\n",
      "Epoch 142/150\n",
      "768/768 [==============================] - 1s 902us/step - loss: 0.4806 - acc: 0.7760\n",
      "Epoch 143/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4716 - acc: 0.7760\n",
      "Epoch 144/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4731 - acc: 0.7839\n",
      "Epoch 145/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4868 - acc: 0.7617\n",
      "Epoch 146/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4913 - acc: 0.7695\n",
      "Epoch 147/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4821 - acc: 0.7852\n",
      "Epoch 148/150\n",
      "768/768 [==============================] - 1s 1ms/step - loss: 0.4682 - acc: 0.7773\n",
      "Epoch 149/150\n",
      "768/768 [==============================] - 1s 765us/step - loss: 0.4715 - acc: 0.7656\n",
      "Epoch 150/150\n",
      "768/768 [==============================] - 1s 657us/step - loss: 0.4774 - acc: 0.7799\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x768fb2d0>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Fit the model\n",
    "model.fit(X, Y, epochs=150, batch_size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "768/768 [==============================] - 0s 330us/step\n",
      "\n",
      "acc: 77.99479166666666\n"
     ]
    }
   ],
   "source": [
    "# evaluate the model\n",
    "scores = model.evaluate(X, Y)\n",
    "print(\"\\n{0}: {1}\".format(model.metrics_names[1], scores[1]*100))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calculate predictions\n",
    "predictions = model.predict(X)\n",
    "# rounded predictions\n",
    "rounded = [round(x[0]) for x in predictions]\n",
    "print(rounded)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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