model-deep-learning-keras.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 4 possibilidade em uma porta XOR\n",
    "training_data = np.array([[0,0],[0,1],[1,0],[1,1]], \"float32\")\n",
    "\n",
    "# 4 resultados esperados, na mesma ordem\n",
    "target_data = np.array([[0],[1],[1],[0]], \"float32\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "0s - loss: 0.2468 - binary_accuracy: 0.5000\n",
      "Epoch 2/500\n",
      "0s - loss: 0.2464 - binary_accuracy: 0.2500\n",
      "Epoch 3/500\n",
      "0s - loss: 0.2460 - binary_accuracy: 0.5000\n",
      "Epoch 4/500\n",
      "0s - loss: 0.2455 - binary_accuracy: 0.5000\n",
      "Epoch 5/500\n",
      "0s - loss: 0.2451 - binary_accuracy: 0.5000\n",
      "Epoch 6/500\n",
      "0s - loss: 0.2447 - binary_accuracy: 0.5000\n",
      "Epoch 7/500\n",
      "0s - loss: 0.2443 - binary_accuracy: 0.5000\n",
      "Epoch 8/500\n",
      "0s - loss: 0.2439 - binary_accuracy: 0.5000\n",
      "Epoch 9/500\n",
      "0s - loss: 0.2435 - binary_accuracy: 0.5000\n",
      "Epoch 10/500\n",
      "0s - loss: 0.2432 - binary_accuracy: 0.5000\n",
      "Epoch 11/500\n",
      "0s - loss: 0.2428 - binary_accuracy: 0.5000\n",
      "Epoch 12/500\n",
      "0s - loss: 0.2424 - binary_accuracy: 0.5000\n",
      "Epoch 13/500\n",
      "0s - loss: 0.2420 - binary_accuracy: 0.5000\n",
      "Epoch 14/500\n",
      "0s - loss: 0.2416 - binary_accuracy: 0.5000\n",
      "Epoch 15/500\n",
      "0s - loss: 0.2412 - binary_accuracy: 0.5000\n",
      "Epoch 16/500\n",
      "0s - loss: 0.2408 - binary_accuracy: 0.5000\n",
      "Epoch 17/500\n",
      "0s - loss: 0.2405 - binary_accuracy: 0.5000\n",
      "Epoch 18/500\n",
      "0s - loss: 0.2401 - binary_accuracy: 0.5000\n",
      "Epoch 19/500\n",
      "0s - loss: 0.2397 - binary_accuracy: 0.5000\n",
      "Epoch 20/500\n",
      "0s - loss: 0.2393 - binary_accuracy: 0.5000\n",
      "Epoch 21/500\n",
      "0s - loss: 0.2390 - binary_accuracy: 0.5000\n",
      "Epoch 22/500\n",
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      "Epoch 23/500\n",
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      "Epoch 24/500\n",
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      "Epoch 30/500\n",
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      "Epoch 31/500\n",
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      "Epoch 32/500\n",
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      "Epoch 33/500\n",
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      "Epoch 34/500\n",
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      "Epoch 35/500\n",
      "0s - loss: 0.2343 - binary_accuracy: 0.7500\n",
      "Epoch 36/500\n",
      "0s - loss: 0.2340 - binary_accuracy: 0.7500\n",
      "Epoch 37/500\n",
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      "Epoch 38/500\n",
      "0s - loss: 0.2333 - binary_accuracy: 0.7500\n",
      "Epoch 39/500\n",
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      "Epoch 40/500\n",
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      "Epoch 41/500\n",
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      "Epoch 42/500\n",
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      "Epoch 43/500\n",
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      "Epoch 44/500\n",
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      "Epoch 45/500\n",
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      "Epoch 46/500\n",
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      "Epoch 47/500\n",
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      "Epoch 48/500\n",
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      "Epoch 49/500\n",
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      "Epoch 50/500\n",
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      "Epoch 70/500\n",
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      "Epoch 71/500\n",
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      "Epoch 72/500\n",
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      "Epoch 73/500\n",
      "0s - loss: 0.2220 - binary_accuracy: 0.7500\n",
      "Epoch 74/500\n",
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      "Epoch 75/500\n",
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      "Epoch 76/500\n",
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      "Epoch 77/500\n",
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      "Epoch 78/500\n",
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      "Epoch 79/500\n",
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      "Epoch 80/500\n",
      "0s - loss: 0.2196 - binary_accuracy: 0.7500\n",
      "Epoch 81/500\n",
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      "Epoch 82/500\n",
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      "Epoch 83/500\n",
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      "Epoch 89/500\n",
      "0s - loss: 0.2167 - binary_accuracy: 1.0000\n",
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      "0s - loss: 0.0866 - binary_accuracy: 1.0000\n",
      "Epoch 431/500\n",
      "0s - loss: 0.0863 - binary_accuracy: 1.0000\n",
      "Epoch 432/500\n",
      "0s - loss: 0.0860 - binary_accuracy: 1.0000\n",
      "Epoch 433/500\n",
      "0s - loss: 0.0857 - binary_accuracy: 1.0000\n",
      "Epoch 434/500\n",
      "0s - loss: 0.0854 - binary_accuracy: 1.0000\n",
      "Epoch 435/500\n",
      "0s - loss: 0.0851 - binary_accuracy: 1.0000\n",
      "Epoch 436/500\n",
      "0s - loss: 0.0848 - binary_accuracy: 1.0000\n",
      "Epoch 437/500\n",
      "0s - loss: 0.0845 - binary_accuracy: 1.0000\n",
      "Epoch 438/500\n",
      "0s - loss: 0.0842 - binary_accuracy: 1.0000\n",
      "Epoch 439/500\n",
      "0s - loss: 0.0839 - binary_accuracy: 1.0000\n",
      "Epoch 440/500\n",
      "0s - loss: 0.0836 - binary_accuracy: 1.0000\n",
      "Epoch 441/500\n",
      "0s - loss: 0.0833 - binary_accuracy: 1.0000\n",
      "Epoch 442/500\n",
      "0s - loss: 0.0830 - binary_accuracy: 1.0000\n",
      "Epoch 443/500\n",
      "0s - loss: 0.0827 - binary_accuracy: 1.0000\n",
      "Epoch 444/500\n",
      "0s - loss: 0.0824 - binary_accuracy: 1.0000\n",
      "Epoch 445/500\n",
      "0s - loss: 0.0821 - binary_accuracy: 1.0000\n",
      "Epoch 446/500\n",
      "0s - loss: 0.0818 - binary_accuracy: 1.0000\n",
      "Epoch 447/500\n",
      "0s - loss: 0.0816 - binary_accuracy: 1.0000\n",
      "Epoch 448/500\n",
      "0s - loss: 0.0813 - binary_accuracy: 1.0000\n",
      "Epoch 449/500\n",
      "0s - loss: 0.0810 - binary_accuracy: 1.0000\n",
      "Epoch 450/500\n",
      "0s - loss: 0.0807 - binary_accuracy: 1.0000\n",
      "Epoch 451/500\n",
      "0s - loss: 0.0804 - binary_accuracy: 1.0000\n",
      "Epoch 452/500\n",
      "0s - loss: 0.0801 - binary_accuracy: 1.0000\n",
      "Epoch 453/500\n",
      "0s - loss: 0.0799 - binary_accuracy: 1.0000\n",
      "Epoch 454/500\n",
      "0s - loss: 0.0796 - binary_accuracy: 1.0000\n",
      "Epoch 455/500\n",
      "0s - loss: 0.0793 - binary_accuracy: 1.0000\n",
      "Epoch 456/500\n",
      "0s - loss: 0.0790 - binary_accuracy: 1.0000\n",
      "Epoch 457/500\n",
      "0s - loss: 0.0787 - binary_accuracy: 1.0000\n",
      "Epoch 458/500\n",
      "0s - loss: 0.0784 - binary_accuracy: 1.0000\n",
      "Epoch 459/500\n",
      "0s - loss: 0.0782 - binary_accuracy: 1.0000\n",
      "Epoch 460/500\n",
      "0s - loss: 0.0779 - binary_accuracy: 1.0000\n",
      "Epoch 461/500\n",
      "0s - loss: 0.0776 - binary_accuracy: 1.0000\n",
      "Epoch 462/500\n",
      "0s - loss: 0.0773 - binary_accuracy: 1.0000\n",
      "Epoch 463/500\n",
      "0s - loss: 0.0771 - binary_accuracy: 1.0000\n",
      "Epoch 464/500\n",
      "0s - loss: 0.0768 - binary_accuracy: 1.0000\n",
      "Epoch 465/500\n",
      "0s - loss: 0.0765 - binary_accuracy: 1.0000\n",
      "Epoch 466/500\n",
      "0s - loss: 0.0762 - binary_accuracy: 1.0000\n",
      "Epoch 467/500\n",
      "0s - loss: 0.0759 - binary_accuracy: 1.0000\n",
      "Epoch 468/500\n",
      "0s - loss: 0.0757 - binary_accuracy: 1.0000\n",
      "Epoch 469/500\n",
      "0s - loss: 0.0754 - binary_accuracy: 1.0000\n",
      "Epoch 470/500\n",
      "0s - loss: 0.0752 - binary_accuracy: 1.0000\n",
      "Epoch 471/500\n",
      "0s - loss: 0.0749 - binary_accuracy: 1.0000\n",
      "Epoch 472/500\n",
      "0s - loss: 0.0746 - binary_accuracy: 1.0000\n",
      "Epoch 473/500\n",
      "0s - loss: 0.0744 - binary_accuracy: 1.0000\n",
      "Epoch 474/500\n",
      "0s - loss: 0.0741 - binary_accuracy: 1.0000\n",
      "Epoch 475/500\n",
      "0s - loss: 0.0738 - binary_accuracy: 1.0000\n",
      "Epoch 476/500\n",
      "0s - loss: 0.0736 - binary_accuracy: 1.0000\n",
      "Epoch 477/500\n",
      "0s - loss: 0.0733 - binary_accuracy: 1.0000\n",
      "Epoch 478/500\n",
      "0s - loss: 0.0730 - binary_accuracy: 1.0000\n",
      "Epoch 479/500\n",
      "0s - loss: 0.0728 - binary_accuracy: 1.0000\n",
      "Epoch 480/500\n",
      "0s - loss: 0.0725 - binary_accuracy: 1.0000\n",
      "Epoch 481/500\n",
      "0s - loss: 0.0723 - binary_accuracy: 1.0000\n",
      "Epoch 482/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0s - loss: 0.0720 - binary_accuracy: 1.0000\n",
      "Epoch 483/500\n",
      "0s - loss: 0.0717 - binary_accuracy: 1.0000\n",
      "Epoch 484/500\n",
      "0s - loss: 0.0715 - binary_accuracy: 1.0000\n",
      "Epoch 485/500\n",
      "0s - loss: 0.0712 - binary_accuracy: 1.0000\n",
      "Epoch 486/500\n",
      "0s - loss: 0.0710 - binary_accuracy: 1.0000\n",
      "Epoch 487/500\n",
      "0s - loss: 0.0707 - binary_accuracy: 1.0000\n",
      "Epoch 488/500\n",
      "0s - loss: 0.0705 - binary_accuracy: 1.0000\n",
      "Epoch 489/500\n",
      "0s - loss: 0.0702 - binary_accuracy: 1.0000\n",
      "Epoch 490/500\n",
      "0s - loss: 0.0700 - binary_accuracy: 1.0000\n",
      "Epoch 491/500\n",
      "0s - loss: 0.0697 - binary_accuracy: 1.0000\n",
      "Epoch 492/500\n",
      "0s - loss: 0.0695 - binary_accuracy: 1.0000\n",
      "Epoch 493/500\n",
      "0s - loss: 0.0692 - binary_accuracy: 1.0000\n",
      "Epoch 494/500\n",
      "0s - loss: 0.0690 - binary_accuracy: 1.0000\n",
      "Epoch 495/500\n",
      "0s - loss: 0.0687 - binary_accuracy: 1.0000\n",
      "Epoch 496/500\n",
      "0s - loss: 0.0685 - binary_accuracy: 1.0000\n",
      "Epoch 497/500\n",
      "0s - loss: 0.0682 - binary_accuracy: 1.0000\n",
      "Epoch 498/500\n",
      "0s - loss: 0.0680 - binary_accuracy: 1.0000\n",
      "Epoch 499/500\n",
      "0s - loss: 0.0677 - binary_accuracy: 1.0000\n",
      "Epoch 500/500\n",
      "0s - loss: 0.0675 - binary_accuracy: 1.0000\n",
      "[[ 0.]\n",
      " [ 1.]\n",
      " [ 1.]\n",
      " [ 0.]]\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "l1 = Dense(16, input_dim=2, activation='relu')\n",
    "model.add(l1)\n",
    "l2 = Dense(1, activation='sigmoid')\n",
    "model.add(l2)\n",
    "model.compile(loss='mean_squared_error', optimizer='adam', metrics=['binary_accuracy'])\n",
    "model.fit(training_data, target_data, epochs=500, verbose=2)\n",
    "print (model.predict(training_data).round())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/1000\n",
      "4/4 [==============================] - 0s - loss: 0.8021     \n",
      "Epoch 2/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7848     \n",
      "Epoch 3/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7732     \n",
      "Epoch 4/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7666     \n",
      "Epoch 5/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7566     \n",
      "Epoch 6/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7545     \n",
      "Epoch 7/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7506     \n",
      "Epoch 8/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7467     \n",
      "Epoch 9/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7420     \n",
      "Epoch 10/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7371     \n",
      "Epoch 11/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7364     \n",
      "Epoch 12/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7320     \n",
      "Epoch 13/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7316     \n",
      "Epoch 14/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7296     \n",
      "Epoch 15/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7250     \n",
      "Epoch 16/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7248     \n",
      "Epoch 17/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7217     \n",
      "Epoch 18/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7219     \n",
      "Epoch 19/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7203     \n",
      "Epoch 20/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7192     \n",
      "Epoch 21/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7170     \n",
      "Epoch 22/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7158     \n",
      "Epoch 23/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7127     \n",
      "Epoch 24/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7135     \n",
      "Epoch 25/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7099     \n",
      "Epoch 26/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7107     \n",
      "Epoch 27/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7091     \n",
      "Epoch 28/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7056     \n",
      "Epoch 29/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7058     \n",
      "Epoch 30/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7049     \n",
      "Epoch 31/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7032     \n",
      "Epoch 32/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7013     \n",
      "Epoch 33/1000\n",
      "4/4 [==============================] - 0s - loss: 0.7005     \n",
      "Epoch 34/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6974     \n",
      "Epoch 35/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6955     \n",
      "Epoch 36/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6953     \n",
      "Epoch 37/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6937     \n",
      "Epoch 38/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6915     \n",
      "Epoch 39/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6899     \n",
      "Epoch 40/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6860     \n",
      "Epoch 41/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6861     \n",
      "Epoch 42/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6829     \n",
      "Epoch 43/1000\n",
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      "Epoch 44/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6799     \n",
      "Epoch 45/1000\n",
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      "Epoch 46/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6736     \n",
      "Epoch 47/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6728     \n",
      "Epoch 48/1000\n",
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      "Epoch 49/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6683     \n",
      "Epoch 50/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6657     \n",
      "Epoch 51/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6608     \n",
      "Epoch 52/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6600     \n",
      "Epoch 53/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6553     \n",
      "Epoch 54/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6545     \n",
      "Epoch 55/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6516     \n",
      "Epoch 56/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6483     \n",
      "Epoch 57/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6441     \n",
      "Epoch 58/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6416     \n",
      "Epoch 59/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6380     \n",
      "Epoch 60/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6343     \n",
      "Epoch 61/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6288     \n",
      "Epoch 62/1000\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.479 - 0s - loss: 0.6268     \n",
      "Epoch 63/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6229     \n",
      "Epoch 64/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6175     \n",
      "Epoch 65/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6153     \n",
      "Epoch 66/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6105     \n",
      "Epoch 67/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6039     \n",
      "Epoch 68/1000\n",
      "4/4 [==============================] - 0s - loss: 0.6003     \n",
      "Epoch 69/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5969     \n",
      "Epoch 70/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5924     \n",
      "Epoch 71/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5874     \n",
      "Epoch 72/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5825     \n",
      "Epoch 73/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5781     \n",
      "Epoch 74/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5723     \n",
      "Epoch 75/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5674     \n",
      "Epoch 76/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5610     \n",
      "Epoch 77/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5570     \n",
      "Epoch 78/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5494     \n",
      "Epoch 79/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5443     \n",
      "Epoch 80/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5392     \n",
      "Epoch 81/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5354     \n",
      "Epoch 82/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5288     \n",
      "Epoch 83/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5236     \n",
      "Epoch 84/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5172     \n",
      "Epoch 85/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5099     \n",
      "Epoch 86/1000\n",
      "4/4 [==============================] - 0s - loss: 0.5040     \n",
      "Epoch 87/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4987     \n",
      "Epoch 88/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4937     \n",
      "Epoch 89/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4851     \n",
      "Epoch 90/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4786     \n",
      "Epoch 91/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4737     \n",
      "Epoch 92/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4666     \n",
      "Epoch 93/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4618     \n",
      "Epoch 94/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4546     \n",
      "Epoch 95/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4465     \n",
      "Epoch 96/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4402     \n",
      "Epoch 97/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4352     \n",
      "Epoch 98/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4269     \n",
      "Epoch 99/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4220     \n",
      "Epoch 100/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4151     \n",
      "Epoch 101/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4076     \n",
      "Epoch 102/1000\n",
      "4/4 [==============================] - 0s - loss: 0.4025     \n",
      "Epoch 103/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3946     \n",
      "Epoch 104/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3881     \n",
      "Epoch 105/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3819     \n",
      "Epoch 106/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3766     \n",
      "Epoch 107/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3700     \n",
      "Epoch 108/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s - loss: 0.3617     \n",
      "Epoch 109/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3565     \n",
      "Epoch 110/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3489     \n",
      "Epoch 111/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3435     \n",
      "Epoch 112/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3375     \n",
      "Epoch 113/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3308     \n",
      "Epoch 114/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3249     \n",
      "Epoch 115/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3188     \n",
      "Epoch 116/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3126     \n",
      "Epoch 117/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3066     \n",
      "Epoch 118/1000\n",
      "4/4 [==============================] - 0s - loss: 0.3007     \n",
      "Epoch 119/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2952     \n",
      "Epoch 120/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2894     \n",
      "Epoch 121/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2834     \n",
      "Epoch 122/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2782     \n",
      "Epoch 123/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2729     \n",
      "Epoch 124/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2675     \n",
      "Epoch 125/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2615     \n",
      "Epoch 126/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2572     \n",
      "Epoch 127/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2513     \n",
      "Epoch 128/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2462     \n",
      "Epoch 129/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2422     \n",
      "Epoch 130/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2376     \n",
      "Epoch 131/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2327     \n",
      "Epoch 132/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2288     \n",
      "Epoch 133/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2239     \n",
      "Epoch 134/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2191     \n",
      "Epoch 135/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2149     \n",
      "Epoch 136/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2110     \n",
      "Epoch 137/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2073     \n",
      "Epoch 138/1000\n",
      "4/4 [==============================] - 0s - loss: 0.2038     \n",
      "Epoch 139/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1995     \n",
      "Epoch 140/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1954     \n",
      "Epoch 141/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1919     \n",
      "Epoch 142/1000\n",
      "4/4 [==============================] - 0s - loss: 0.1886     \n",
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      "Epoch 845/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "Epoch 951/1000\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4/4 [==============================] - 0s - loss: 0.0053     \n",
      "Epoch 952/1000\n",
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      "4/4 [==============================] - 0s - loss: 0.0052     \n",
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      "4/4 [==============================] - 0s - loss: 0.0052     \n",
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      "4/4 [==============================] - 0s - loss: 0.0052     \n",
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      "4/4 [==============================] - 0s - loss: 0.0052     \n",
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      "4/4 [==============================] - 0s - loss: 0.0052     \n",
      "Epoch 960/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0052     \n",
      "Epoch 961/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0052     \n",
      "Epoch 962/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0052     \n",
      "Epoch 963/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0052     \n",
      "Epoch 964/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0052     \n",
      "Epoch 965/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0052     \n",
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      "4/4 [==============================] - 0s - loss: 0.0052     \n",
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      "4/4 [==============================] - 0s - loss: 0.0052     \n",
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      "4/4 [==============================] - 0s - loss: 0.0051     \n",
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      "4/4 [==============================] - 0s - loss: 0.0051     \n",
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      "4/4 [==============================] - 0s - loss: 0.0051     \n",
      "Epoch 972/1000\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.007 - 0s - loss: 0.0051     \n",
      "Epoch 973/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0051     \n",
      "Epoch 974/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0051     \n",
      "Epoch 975/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0051     \n",
      "Epoch 976/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0051     \n",
      "Epoch 977/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0051     \n",
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      "4/4 [==============================] - 0s - loss: 0.0051     \n",
      "Epoch 979/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0051     \n",
      "Epoch 980/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0051     \n",
      "Epoch 981/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0050     \n",
      "Epoch 982/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0050     \n",
      "Epoch 983/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0050     \n",
      "Epoch 984/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0050     \n",
      "Epoch 985/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0050     \n",
      "Epoch 986/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0050     \n",
      "Epoch 987/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0050     \n",
      "Epoch 988/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0050     \n",
      "Epoch 989/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0050     \n",
      "Epoch 990/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0050     \n",
      "Epoch 991/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0050     \n",
      "Epoch 992/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0050     \n",
      "Epoch 993/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0050     \n",
      "Epoch 994/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0049     \n",
      "Epoch 995/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0049     \n",
      "Epoch 996/1000\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.005 - 0s - loss: 0.0049     \n",
      "Epoch 997/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0049     \n",
      "Epoch 998/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0049     \n",
      "Epoch 999/1000\n",
      "4/4 [==============================] - ETA: 0s - loss: 0.005 - 0s - loss: 0.0049     \n",
      "Epoch 1000/1000\n",
      "4/4 [==============================] - 0s - loss: 0.0049     \n",
      "4/4 [==============================] - 0s\n",
      "[[ 0.00198258]\n",
      " [ 0.99464333]\n",
      " [ 0.99465841]\n",
      " [ 0.00678403]]\n"
     ]
    }
   ],
   "source": [
    "#Executar e analisar:\n",
    "from keras.models import Sequential\n",
    "from keras.layers.core import Dense, Dropout, Activation\n",
    "from keras.optimizers import SGD\n",
    "import numpy as np\n",
    "X = np.array([[0,0],[0,1],[1,0],[1,1]])\n",
    "y = np.array([[0],[1],[1],[0]])\n",
    "\n",
    "model = Sequential()\n",
    "model.add(Dense(8, input_dim=2))\n",
    "model.add(Activation('tanh'))\n",
    "model.add(Dense(1))\n",
    "model.add(Activation('sigmoid'))\n",
    "sgd = SGD(lr=0.1)\n",
    "model.compile(loss='binary_crossentropy', optimizer=sgd)\n",
    "model.fit(X, y, batch_size=1, epochs=1000)\n",
    "print(model.predict_proba(X))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Para maiores detalhes: [Keras Examples Mnist](https://github.com/fchollet/keras/blob/master/examples/mnist_mlp.py)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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