assignment11_002.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "assignment11_002.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMCGBb9RlzC54ugsTTZ8z8L",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dlguswn3659/class-MachineLearning/blob/master/assignment11_002.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vQn0xfmgWDzR",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "bb682b35-2af5-4f70-bcfa-c0ee3510c129"
      },
      "source": [
        "import numpy\n",
        "from keras.datasets import imdb\n",
        "from matplotlib import pyplot\n",
        "# load the dataset\n",
        "(X_train, y_train), (X_test, y_test) = imdb.load_data()\n",
        "X = numpy.concatenate((X_train, X_test), axis=0)\n",
        "y = numpy.concatenate((y_train, y_test), axis=0)\n",
        "..."
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ellipsis"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cu0DhMswWcBV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 70
        },
        "outputId": "c6ca0634-4512-4687-ca7f-779bd2e57cd9"
      },
      "source": [
        "...\n",
        "# summarize size\n",
        "print(\"Training data: \")\n",
        "print(X.shape)\n",
        "print(y.shape)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training data: \n",
            "(50000,)\n",
            "(50000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HpPJWD1mWd-Z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "0404168a-1ea6-4721-ab5a-224c25d4b591"
      },
      "source": [
        "...\n",
        "# Summarize number of classes\n",
        "print(\"Classes: \")\n",
        "print(numpy.unique(y))"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Classes: \n",
            "[0 1]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xfTy0-IIWicp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "outputId": "f1495f69-d1df-4f25-e81b-dd21089f0738"
      },
      "source": [
        "...\n",
        "# Summarize number of words\n",
        "print(\"Number of words: \")\n",
        "print(len(numpy.unique(numpy.hstack(X))))"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Number of words: \n",
            "88585\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iFiOmwGHWl2g",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "c6abd5d9-1888-43c6-cf00-f8a3c29ed79f"
      },
      "source": [
        "...\n",
        "# Summarize review length\n",
        "print(\"Review length: \")\n",
        "result = [len(x) for x in X]\n",
        "print(\"Mean %.2f words (%f)\" % (numpy.mean(result), numpy.std(result)))\n",
        "# plot review length\n",
        "pyplot.boxplot(result)\n",
        "pyplot.show()"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Review length: \n",
            "Mean 234.76 words (172.911495)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R0M82UHxWoTy",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 374
        },
        "outputId": "51e362a2-9d74-4a15-8225-48e32a66ab7e"
      },
      "source": [
        "...\n",
        "imdb.load_data(nb_words=5000)"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/keras/datasets/imdb.py:49: UserWarning: The `nb_words` argument in `load_data` has been renamed `num_words`.\n",
            "  warnings.warn('The `nb_words` argument in `load_data` '\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((array([list([1, 14, 22, 16, 43, 530, 973, 1622, 1385, 65, 458, 4468, 66, 3941, 4, 173, 36, 256, 5, 25, 100, 43, 838, 112, 50, 670, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 4536, 1111, 17, 546, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2025, 19, 14, 22, 4, 1920, 4613, 469, 4, 22, 71, 87, 12, 16, 43, 530, 38, 76, 15, 13, 1247, 4, 22, 17, 515, 17, 12, 16, 626, 18, 2, 5, 62, 386, 12, 8, 316, 8, 106, 5, 4, 2223, 2, 16, 480, 66, 3785, 33, 4, 130, 12, 16, 38, 619, 5, 25, 124, 51, 36, 135, 48, 25, 1415, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 3766, 5, 723, 36, 71, 43, 530, 476, 26, 400, 317, 46, 7, 4, 2, 1029, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2071, 56, 26, 141, 6, 194, 2, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 1334, 88, 12, 16, 283, 5, 16, 4472, 113, 103, 32, 15, 16, 2, 19, 178, 32]),\n",
              "         list([1, 194, 1153, 194, 2, 78, 228, 5, 6, 1463, 4369, 2, 134, 26, 4, 715, 8, 118, 1634, 14, 394, 20, 13, 119, 954, 189, 102, 5, 207, 110, 3103, 21, 14, 69, 188, 8, 30, 23, 7, 4, 249, 126, 93, 4, 114, 9, 2300, 1523, 5, 647, 4, 116, 9, 35, 2, 4, 229, 9, 340, 1322, 4, 118, 9, 4, 130, 4901, 19, 4, 1002, 5, 89, 29, 952, 46, 37, 4, 455, 9, 45, 43, 38, 1543, 1905, 398, 4, 1649, 26, 2, 5, 163, 11, 3215, 2, 4, 1153, 9, 194, 775, 7, 2, 2, 349, 2637, 148, 605, 2, 2, 15, 123, 125, 68, 2, 2, 15, 349, 165, 4362, 98, 5, 4, 228, 9, 43, 2, 1157, 15, 299, 120, 5, 120, 174, 11, 220, 175, 136, 50, 9, 4373, 228, 2, 5, 2, 656, 245, 2350, 5, 4, 2, 131, 152, 491, 18, 2, 32, 2, 1212, 14, 9, 6, 371, 78, 22, 625, 64, 1382, 9, 8, 168, 145, 23, 4, 1690, 15, 16, 4, 1355, 5, 28, 6, 52, 154, 462, 33, 89, 78, 285, 16, 145, 95]),\n",
              "         list([1, 14, 47, 8, 30, 31, 7, 4, 249, 108, 7, 4, 2, 54, 61, 369, 13, 71, 149, 14, 22, 112, 4, 2401, 311, 12, 16, 3711, 33, 75, 43, 1829, 296, 4, 86, 320, 35, 534, 19, 263, 4821, 1301, 4, 1873, 33, 89, 78, 12, 66, 16, 4, 360, 7, 4, 58, 316, 334, 11, 4, 1716, 43, 645, 662, 8, 257, 85, 1200, 42, 1228, 2578, 83, 68, 3912, 15, 36, 165, 1539, 278, 36, 69, 2, 780, 8, 106, 14, 2, 1338, 18, 6, 22, 12, 215, 28, 610, 40, 6, 87, 326, 23, 2300, 21, 23, 22, 12, 272, 40, 57, 31, 11, 4, 22, 47, 6, 2307, 51, 9, 170, 23, 595, 116, 595, 1352, 13, 191, 79, 638, 89, 2, 14, 9, 8, 106, 607, 624, 35, 534, 6, 227, 7, 129, 113]),\n",
              "         ...,\n",
              "         list([1, 11, 6, 230, 245, 2, 9, 6, 1225, 446, 2, 45, 2174, 84, 2, 4007, 21, 4, 912, 84, 2, 325, 725, 134, 2, 1715, 84, 5, 36, 28, 57, 1099, 21, 8, 140, 8, 703, 5, 2, 84, 56, 18, 1644, 14, 9, 31, 7, 4, 2, 1209, 2295, 2, 1008, 18, 6, 20, 207, 110, 563, 12, 8, 2901, 2, 8, 97, 6, 20, 53, 4767, 74, 4, 460, 364, 1273, 29, 270, 11, 960, 108, 45, 40, 29, 2961, 395, 11, 6, 4065, 500, 7, 2, 89, 364, 70, 29, 140, 4, 64, 4780, 11, 4, 2678, 26, 178, 4, 529, 443, 2, 5, 27, 710, 117, 2, 2, 165, 47, 84, 37, 131, 818, 14, 595, 10, 10, 61, 1242, 1209, 10, 10, 288, 2260, 1702, 34, 2901, 2, 4, 65, 496, 4, 231, 7, 790, 5, 6, 320, 234, 2766, 234, 1119, 1574, 7, 496, 4, 139, 929, 2901, 2, 2, 5, 4241, 18, 4, 2, 2, 250, 11, 1818, 2, 4, 4217, 2, 747, 1115, 372, 1890, 1006, 541, 2, 7, 4, 59, 2, 4, 3586, 2]),\n",
              "         list([1, 1446, 2, 69, 72, 3305, 13, 610, 930, 8, 12, 582, 23, 5, 16, 484, 685, 54, 349, 11, 4120, 2959, 45, 58, 1466, 13, 197, 12, 16, 43, 23, 2, 5, 62, 30, 145, 402, 11, 4131, 51, 575, 32, 61, 369, 71, 66, 770, 12, 1054, 75, 100, 2198, 8, 4, 105, 37, 69, 147, 712, 75, 3543, 44, 257, 390, 5, 69, 263, 514, 105, 50, 286, 1814, 23, 4, 123, 13, 161, 40, 5, 421, 4, 116, 16, 897, 13, 2, 40, 319, 2, 112, 2, 11, 4803, 121, 25, 70, 3468, 4, 719, 3798, 13, 18, 31, 62, 40, 8, 2, 4, 2, 7, 14, 123, 5, 942, 25, 8, 721, 12, 145, 5, 202, 12, 160, 580, 202, 12, 6, 52, 58, 2, 92, 401, 728, 12, 39, 14, 251, 8, 15, 251, 5, 2, 12, 38, 84, 80, 124, 12, 9, 23]),\n",
              "         list([1, 17, 6, 194, 337, 7, 4, 204, 22, 45, 254, 8, 106, 14, 123, 4, 2, 270, 2, 5, 2, 2, 732, 2098, 101, 405, 39, 14, 1034, 4, 1310, 9, 115, 50, 305, 12, 47, 4, 168, 5, 235, 7, 38, 111, 699, 102, 7, 4, 4039, 2, 9, 24, 6, 78, 1099, 17, 2345, 2, 21, 27, 2, 2, 5, 2, 1603, 92, 1183, 4, 1310, 7, 4, 204, 42, 97, 90, 35, 221, 109, 29, 127, 27, 118, 8, 97, 12, 157, 21, 2, 2, 9, 6, 66, 78, 1099, 4, 631, 1191, 5, 2642, 272, 191, 1070, 6, 2, 8, 2197, 2, 2, 544, 5, 383, 1271, 848, 1468, 2, 497, 2, 8, 1597, 2, 2, 21, 60, 27, 239, 9, 43, 2, 209, 405, 10, 10, 12, 764, 40, 4, 248, 20, 12, 16, 5, 174, 1791, 72, 7, 51, 6, 1739, 22, 4, 204, 131, 9])],\n",
              "        dtype=object), array([1, 0, 0, ..., 0, 1, 0])),\n",
              " (array([list([1, 591, 202, 14, 31, 6, 717, 10, 10, 2, 2, 5, 4, 360, 7, 4, 177, 2, 394, 354, 4, 123, 9, 1035, 1035, 1035, 10, 10, 13, 92, 124, 89, 488, 2, 100, 28, 1668, 14, 31, 23, 27, 2, 29, 220, 468, 8, 124, 14, 286, 170, 8, 157, 46, 5, 27, 239, 16, 179, 2, 38, 32, 25, 2, 451, 202, 14, 6, 717]),\n",
              "         list([1, 14, 22, 3443, 6, 176, 7, 2, 88, 12, 2679, 23, 1310, 5, 109, 943, 4, 114, 9, 55, 606, 5, 111, 7, 4, 139, 193, 273, 23, 4, 172, 270, 11, 2, 2, 4, 2, 2801, 109, 1603, 21, 4, 22, 3861, 8, 6, 1193, 1330, 10, 10, 4, 105, 987, 35, 841, 2, 19, 861, 1074, 5, 1987, 2, 45, 55, 221, 15, 670, 2, 526, 14, 1069, 4, 405, 5, 2438, 7, 27, 85, 108, 131, 4, 2, 2, 3884, 405, 9, 3523, 133, 5, 50, 13, 104, 51, 66, 166, 14, 22, 157, 9, 4, 530, 239, 34, 2, 2801, 45, 407, 31, 7, 41, 3778, 105, 21, 59, 299, 12, 38, 950, 5, 4521, 15, 45, 629, 488, 2733, 127, 6, 52, 292, 17, 4, 2, 185, 132, 1988, 2, 1799, 488, 2693, 47, 6, 392, 173, 4, 2, 4378, 270, 2352, 4, 1500, 7, 4, 65, 55, 73, 11, 346, 14, 20, 9, 6, 976, 2078, 7, 2, 861, 2, 5, 4182, 30, 3127, 2, 56, 4, 841, 5, 990, 692, 8, 4, 1669, 398, 229, 10, 10, 13, 2822, 670, 2, 14, 9, 31, 7, 27, 111, 108, 15, 2033, 19, 2, 1429, 875, 551, 14, 22, 9, 1193, 21, 45, 4829, 5, 45, 252, 8, 2, 6, 565, 921, 3639, 39, 4, 529, 48, 25, 181, 8, 67, 35, 1732, 22, 49, 238, 60, 135, 1162, 14, 9, 290, 4, 58, 10, 10, 472, 45, 55, 878, 8, 169, 11, 374, 2, 25, 203, 28, 8, 818, 12, 125, 4, 3077]),\n",
              "         list([1, 111, 748, 4368, 1133, 2, 2, 4, 87, 1551, 1262, 7, 31, 318, 2, 7, 4, 498, 2, 748, 63, 29, 2, 220, 686, 2, 5, 17, 12, 575, 220, 2507, 17, 6, 185, 132, 2, 16, 53, 928, 11, 2, 74, 4, 438, 21, 27, 2, 589, 8, 22, 107, 2, 2, 997, 1638, 8, 35, 2076, 2, 11, 22, 231, 54, 29, 1706, 29, 100, 2, 2425, 34, 2, 2, 2, 5, 2, 98, 31, 2122, 33, 6, 58, 14, 3808, 1638, 8, 4, 365, 7, 2789, 3761, 356, 346, 4, 2, 1060, 63, 29, 93, 11, 2, 11, 2, 33, 6, 58, 54, 1270, 431, 748, 7, 32, 2580, 16, 11, 94, 2, 10, 10, 4, 993, 2, 7, 4, 1766, 2634, 2164, 2, 8, 847, 8, 1450, 121, 31, 7, 27, 86, 2663, 2, 16, 6, 465, 993, 2006, 2, 573, 17, 2, 42, 4, 2, 37, 473, 6, 711, 6, 2, 7, 328, 212, 70, 30, 258, 11, 220, 32, 7, 108, 21, 133, 12, 9, 55, 465, 849, 3711, 53, 33, 2071, 1969, 37, 70, 1144, 4, 2, 1409, 74, 476, 37, 62, 91, 1329, 169, 4, 1330, 2, 146, 655, 2212, 5, 258, 12, 184, 2, 546, 5, 849, 2, 7, 4, 22, 1436, 18, 631, 1386, 797, 7, 4, 2, 71, 348, 425, 4320, 1061, 19, 2, 5, 2, 11, 661, 8, 339, 2, 4, 2455, 2, 7, 4, 1962, 10, 10, 263, 787, 9, 270, 11, 6, 2, 4, 2, 2, 121, 4, 2, 26, 4434, 19, 68, 1372, 5, 28, 446, 6, 318, 2, 8, 67, 51, 36, 70, 81, 8, 4392, 2294, 36, 1197, 8, 2, 2, 18, 6, 711, 4, 2, 26, 2, 1125, 11, 14, 636, 720, 12, 426, 28, 77, 776, 8, 97, 38, 111, 2, 2, 168, 1239, 2, 137, 2, 18, 27, 173, 9, 2399, 17, 6, 2, 428, 2, 232, 11, 4, 2, 37, 272, 40, 2708, 247, 30, 656, 6, 2, 54, 2, 3292, 98, 6, 2840, 40, 558, 37, 2, 98, 4, 2, 1197, 15, 14, 9, 57, 4893, 5, 4659, 6, 275, 711, 2, 2, 3292, 98, 6, 2, 10, 10, 2, 19, 14, 2, 267, 162, 711, 37, 2, 752, 98, 4, 2, 2378, 90, 19, 6, 2, 7, 2, 1810, 2, 4, 4770, 3183, 930, 8, 508, 90, 4, 1317, 8, 4, 2, 17, 2, 3965, 1853, 4, 1494, 8, 4468, 189, 4, 2, 2, 2, 4, 4770, 5, 95, 271, 23, 6, 2, 2, 2, 2, 33, 1526, 6, 425, 3155, 2, 4535, 1636, 7, 4, 4669, 2, 469, 4, 4552, 54, 4, 150, 2, 2, 280, 53, 2, 2, 18, 339, 29, 1978, 27, 2, 5, 2, 68, 1830, 19, 2, 2, 4, 1515, 7, 263, 65, 2132, 34, 6, 2, 2, 43, 159, 29, 9, 4706, 9, 387, 73, 195, 584, 10, 10, 1069, 4, 58, 810, 54, 14, 2, 117, 22, 16, 93, 5, 1069, 4, 192, 15, 12, 16, 93, 34, 6, 1766, 2, 33, 4, 2, 7, 15, 2, 2, 3286, 325, 12, 62, 30, 776, 8, 67, 14, 17, 6, 2, 44, 148, 687, 2, 203, 42, 203, 24, 28, 69, 2, 2, 11, 330, 54, 29, 93, 2, 21, 845, 2, 27, 1099, 7, 819, 4, 22, 1407, 17, 6, 2, 787, 7, 2460, 2, 2, 100, 30, 4, 3737, 3617, 3169, 2321, 42, 1898, 11, 4, 3814, 42, 101, 704, 7, 101, 999, 15, 1625, 94, 2926, 180, 5, 9, 2, 34, 2, 45, 6, 1429, 22, 60, 6, 1220, 31, 11, 94, 2, 96, 21, 94, 749, 9, 57, 975]),\n",
              "         ...,\n",
              "         list([1, 13, 1408, 15, 8, 135, 14, 9, 35, 32, 46, 394, 20, 62, 30, 2, 21, 45, 184, 78, 4, 1492, 910, 769, 2290, 2515, 395, 4257, 5, 1454, 11, 119, 2, 89, 1036, 4, 116, 218, 78, 21, 407, 100, 30, 128, 262, 15, 7, 185, 2280, 284, 1842, 2, 37, 315, 4, 226, 20, 272, 2942, 40, 29, 152, 60, 181, 8, 30, 50, 553, 362, 80, 119, 12, 21, 846, 2]),\n",
              "         list([1, 11, 119, 241, 9, 4, 840, 20, 12, 468, 15, 94, 3684, 562, 791, 39, 4, 86, 107, 8, 97, 14, 31, 33, 4, 2960, 7, 743, 46, 1028, 9, 3531, 5, 4, 768, 47, 8, 79, 90, 145, 164, 162, 50, 6, 501, 119, 7, 9, 4, 78, 232, 15, 16, 224, 11, 4, 333, 20, 4, 985, 200, 5, 2, 5, 9, 1861, 8, 79, 357, 4, 20, 47, 220, 57, 206, 139, 11, 12, 5, 55, 117, 212, 13, 1276, 92, 124, 51, 45, 1188, 71, 536, 13, 520, 14, 20, 6, 2302, 7, 470]),\n",
              "         list([1, 6, 52, 2, 430, 22, 9, 220, 2594, 8, 28, 2, 519, 3227, 6, 769, 15, 47, 6, 3482, 4067, 8, 114, 5, 33, 222, 31, 55, 184, 704, 2, 2, 19, 346, 3153, 5, 6, 364, 350, 4, 184, 2, 9, 133, 1810, 11, 2, 2, 21, 4, 2, 2, 570, 50, 2005, 2643, 9, 6, 1249, 17, 6, 2, 2, 21, 17, 6, 1211, 232, 1138, 2249, 29, 266, 56, 96, 346, 194, 308, 9, 194, 21, 29, 218, 1078, 19, 4, 78, 173, 7, 27, 2, 2, 3406, 718, 2, 9, 6, 2, 17, 210, 5, 3281, 2, 47, 77, 395, 14, 172, 173, 18, 2740, 2931, 4517, 82, 127, 27, 173, 11, 6, 392, 217, 21, 50, 9, 57, 65, 12, 2, 53, 40, 35, 390, 7, 11, 4, 3567, 7, 4, 314, 74, 6, 792, 22, 2, 19, 714, 727, 2, 382, 4, 91, 2, 439, 19, 14, 20, 9, 1441, 2, 1118, 4, 756, 25, 124, 4, 31, 12, 16, 93, 804, 34, 2005, 2643])],\n",
              "        dtype=object),\n",
              "  array([0, 1, 1, ..., 0, 0, 0])))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EsVBPuB1Xb_J",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "8f71a0b2-7e01-4996-c919-34b30765bfed"
      },
      "source": [
        "# MLP for the IMDB problem\n",
        "from keras.datasets import imdb\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.layers.embeddings import Embedding\n",
        "from keras.preprocessing import sequence\n",
        " \n",
        "..."
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ellipsis"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5g7tlL7EWrLA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "...\n",
        "X_train = sequence.pad_sequences(X_train, maxlen=500)\n",
        "X_test = sequence.pad_sequences(X_test, maxlen=500)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tP-uVMsfWsup",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "94b1664c-3c07-4bd9-acef-f3eda1c7b016"
      },
      "source": [
        "...\n",
        "Embedding(5000, 32, input_length=500)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.layers.embeddings.Embedding at 0x7f644db827b8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tSytxBq4WujJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "341b1250-419a-4be6-841f-b8b412542f4c"
      },
      "source": [
        "# MLP for the IMDB problem\n",
        "from keras.datasets import imdb\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.layers.embeddings import Embedding\n",
        "from keras.preprocessing import sequence\n",
        " \n",
        "..."
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Ellipsis"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5zW7yueZWwt4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "...\n",
        "# load the dataset but only keep the top n words, zero the rest\n",
        "top_words = 5000\n",
        "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xySQZVqhWyp5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "...\n",
        "max_words = 500\n",
        "X_train = sequence.pad_sequences(X_train, maxlen=max_words)\n",
        "X_test = sequence.pad_sequences(X_test, maxlen=max_words)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aEZ4MbxvW0QA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 318
        },
        "outputId": "81372d39-3a73-4589-d266-43de708fd73a"
      },
      "source": [
        "...\n",
        "# create the model\n",
        "model = Sequential()\n",
        "model.add(Embedding(top_words, 32, input_length=max_words))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(250, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "print(model.summary())"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_2 (Embedding)      (None, 500, 32)           160000    \n",
            "_________________________________________________________________\n",
            "flatten_1 (Flatten)          (None, 16000)             0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 250)               4000250   \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 251       \n",
            "=================================================================\n",
            "Total params: 4,160,501\n",
            "Trainable params: 4,160,501\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n",
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MhNEomDjW2QA",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 178
        },
        "outputId": "f5e9fc0e-599c-43b5-d19f-2bd575abfdf9"
      },
      "source": [
        "...\n",
        "# Fit the model\n",
        "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch_size=128, verbose=2)\n",
        "# Final evaluation of the model\n",
        "scores = model.evaluate(X_test, y_test, verbose=0)\n",
        "print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/indexed_slices.py:434: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
            "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 25000 samples, validate on 25000 samples\n",
            "Epoch 1/2\n",
            " - 27s - loss: 0.4862 - accuracy: 0.7374 - val_loss: 0.2908 - val_accuracy: 0.8786\n",
            "Epoch 2/2\n",
            " - 27s - loss: 0.1789 - accuracy: 0.9332 - val_loss: 0.3053 - val_accuracy: 0.8758\n",
            "Accuracy: 87.58%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8lQzZZ_bW4zJ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 463
        },
        "outputId": "269f8b96-b7a6-467c-afdd-b956b142da18"
      },
      "source": [
        "# MLP for the IMDB problem\n",
        "from keras.datasets import imdb\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense\n",
        "from keras.layers import Flatten\n",
        "from keras.layers.embeddings import Embedding\n",
        "from keras.preprocessing import sequence\n",
        "# load the dataset but only keep the top n words, zero the rest\n",
        "top_words = 5000\n",
        "(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)\n",
        "max_words = 500\n",
        "X_train = sequence.pad_sequences(X_train, maxlen=max_words)\n",
        "X_test = sequence.pad_sequences(X_test, maxlen=max_words)\n",
        "# create the model\n",
        "model = Sequential()\n",
        "model.add(Embedding(top_words, 32, input_length=max_words))\n",
        "model.add(Flatten())\n",
        "model.add(Dense(250, activation='relu'))\n",
        "model.add(Dense(1, activation='sigmoid'))\n",
        "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
        "model.summary()\n",
        "# Fit the model\n",
        "model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=2, batch_size=128, verbose=2)\n",
        "# Final evaluation of the model\n",
        "scores = model.evaluate(X_test, y_test, verbose=0)\n",
        "print(\"Accuracy: %.2f%%\" % (scores[1]*100))"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_3 (Embedding)      (None, 500, 32)           160000    \n",
            "_________________________________________________________________\n",
            "flatten_2 (Flatten)          (None, 16000)             0         \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 250)               4000250   \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 1)                 251       \n",
            "=================================================================\n",
            "Total params: 4,160,501\n",
            "Trainable params: 4,160,501\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/indexed_slices.py:434: UserWarning: Converting sparse IndexedSlices to a dense Tensor of unknown shape. This may consume a large amount of memory.\n",
            "  \"Converting sparse IndexedSlices to a dense Tensor of unknown shape. \"\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Train on 25000 samples, validate on 25000 samples\n",
            "Epoch 1/2\n",
            " - 26s - loss: 0.4496 - accuracy: 0.7690 - val_loss: 0.2849 - val_accuracy: 0.8794\n",
            "Epoch 2/2\n",
            " - 26s - loss: 0.1734 - accuracy: 0.9352 - val_loss: 0.3098 - val_accuracy: 0.8732\n",
            "Accuracy: 87.32%\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xwlr8cNHYVv0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 372
        },
        "outputId": "c661de2d-dc26-4609-b443-f05475425eb8"
      },
      "source": [
        "print(X_train)\n",
        "print(X_test)\n",
        "print(y_train)\n",
        "print(y_test)\n",
        "\n",
        "print(X_train.shape)\n",
        "print(X_test.shape)\n",
        "print(y_train.shape)\n",
        "print(y_test.shape)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[   0    0    0 ...   19  178   32]\n",
            " [   0    0    0 ...   16  145   95]\n",
            " [   0    0    0 ...    7  129  113]\n",
            " ...\n",
            " [   0    0    0 ...    4 3586    2]\n",
            " [   0    0    0 ...   12    9   23]\n",
            " [   0    0    0 ...  204  131    9]]\n",
            "[[   0    0    0 ...   14    6  717]\n",
            " [   0    0    0 ...  125    4 3077]\n",
            " [  33    6   58 ...    9   57  975]\n",
            " ...\n",
            " [   0    0    0 ...   21  846    2]\n",
            " [   0    0    0 ... 2302    7  470]\n",
            " [   0    0    0 ...   34 2005 2643]]\n",
            "[1 0 0 ... 0 1 0]\n",
            "[0 1 1 ... 0 0 0]\n",
            "(25000, 500)\n",
            "(25000, 500)\n",
            "(25000,)\n",
            "(25000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XisO0kkGzlQW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score\n",
        "\n",
        "print(confusion_matrix(y_train,l_list_tmp))\n",
        "print(classification_report(y_train,l_list_tmp))\n",
        "print(accuracy_score(y_train, l_list_tmp))\n",
        "\n",
        "print(confusion_matrix(y_test,l_list2_tmp))\n",
        "print(classification_report(y_test,l_list2_tmp))\n",
        "print(accuracy_score(y_test, l_list2_tmp))"
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}