11. 1D CNN Text Classification/11-3. imdb_review_sentiment_analysis_using_cnn.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "IMDB Review Sentiment Analysis using CNN.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "machine_shape": "hm"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6-OXsopEtDgV"
      },
      "source": [
        "2021년 10월 9일에 마지막으로 테스트 되었습니다.  \n",
        "\n",
        "이 자료는 위키독스 '딥 러닝을 이용한 자연어 처리 입문'의 1D CNN 학습 자료입니다.  \n",
        "\n",
        "링크 : https://wikidocs.net/80783"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 1. 데이터에 대한 전처리"
      ],
      "metadata": {
        "id": "zhuF7eTEeP85"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UZklPRG2nUp-"
      },
      "source": [
        "import tensorflow as tf\n",
        "from tensorflow.keras import datasets\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mWnsu6TO23Qf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "outputId": "a99cdbea-373d-410d-e0bd-66084a9483f7"
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'2.6.0'"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "drd-CDD1n9p-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1bcd40c-f65c-4d87-ca60-37a1d22245c7"
      },
      "source": [
        "vocab_size = 10000\n",
        "(X_train, y_train), (X_test, y_test) = datasets.imdb.load_data(num_words=vocab_size)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17465344/17464789 [==============================] - 0s 0us/step\n",
            "17473536/17464789 [==============================] - 0s 0us/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HfEREkFqoDh1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4d286a35-b560-4b03-89e3-0f09ee5c4921"
      },
      "source": [
        "print(X_train[:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[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, 5244, 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, 5952, 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, 7486, 18, 4, 226, 22, 21, 134, 476, 26, 480, 5, 144, 30, 5535, 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, 5345, 19, 178, 32])\n",
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            " list([1, 14, 47, 8, 30, 31, 7, 4, 249, 108, 7, 4, 5974, 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, 6905, 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",
            " list([1, 4, 2, 2, 33, 2804, 4, 2040, 432, 111, 153, 103, 4, 1494, 13, 70, 131, 67, 11, 61, 2, 744, 35, 3715, 761, 61, 5766, 452, 9214, 4, 985, 7, 2, 59, 166, 4, 105, 216, 1239, 41, 1797, 9, 15, 7, 35, 744, 2413, 31, 8, 4, 687, 23, 4, 2, 7339, 6, 3693, 42, 38, 39, 121, 59, 456, 10, 10, 7, 265, 12, 575, 111, 153, 159, 59, 16, 1447, 21, 25, 586, 482, 39, 4, 96, 59, 716, 12, 4, 172, 65, 9, 579, 11, 6004, 4, 1615, 5, 2, 7, 5168, 17, 13, 7064, 12, 19, 6, 464, 31, 314, 11, 2, 6, 719, 605, 11, 8, 202, 27, 310, 4, 3772, 3501, 8, 2722, 58, 10, 10, 537, 2116, 180, 40, 14, 413, 173, 7, 263, 112, 37, 152, 377, 4, 537, 263, 846, 579, 178, 54, 75, 71, 476, 36, 413, 263, 2504, 182, 5, 17, 75, 2306, 922, 36, 279, 131, 2895, 17, 2867, 42, 17, 35, 921, 2, 192, 5, 1219, 3890, 19, 2, 217, 4122, 1710, 537, 2, 1236, 5, 736, 10, 10, 61, 403, 9, 2, 40, 61, 4494, 5, 27, 4494, 159, 90, 263, 2311, 4319, 309, 8, 178, 5, 82, 4319, 4, 65, 15, 9225, 145, 143, 5122, 12, 7039, 537, 746, 537, 537, 15, 7979, 4, 2, 594, 7, 5168, 94, 9096, 3987, 2, 11, 2, 4, 538, 7, 1795, 246, 2, 9, 2, 11, 635, 14, 9, 51, 408, 12, 94, 318, 1382, 12, 47, 6, 2683, 936, 5, 6307, 2, 19, 49, 7, 4, 1885, 2, 1118, 25, 80, 126, 842, 10, 10, 2, 2, 4726, 27, 4494, 11, 1550, 3633, 159, 27, 341, 29, 2733, 19, 4185, 173, 7, 90, 2, 8, 30, 11, 4, 1784, 86, 1117, 8, 3261, 46, 11, 2, 21, 29, 9, 2841, 23, 4, 1010, 2, 793, 6, 2, 1386, 1830, 10, 10, 246, 50, 9, 6, 2750, 1944, 746, 90, 29, 2, 8, 124, 4, 882, 4, 882, 496, 27, 2, 2213, 537, 121, 127, 1219, 130, 5, 29, 494, 8, 124, 4, 882, 496, 4, 341, 7, 27, 846, 10, 10, 29, 9, 1906, 8, 97, 6, 236, 2, 1311, 8, 4, 2, 7, 31, 7, 2, 91, 2, 3987, 70, 4, 882, 30, 579, 42, 9, 12, 32, 11, 537, 10, 10, 11, 14, 65, 44, 537, 75, 2, 1775, 3353, 2, 1846, 4, 2, 7, 154, 5, 4, 518, 53, 2, 2, 7, 3211, 882, 11, 399, 38, 75, 257, 3807, 19, 2, 17, 29, 456, 4, 65, 7, 27, 205, 113, 10, 10, 2, 4, 2, 2, 9, 242, 4, 91, 1202, 2, 5, 2070, 307, 22, 7, 5168, 126, 93, 40, 2, 13, 188, 1076, 3222, 19, 4, 2, 7, 2348, 537, 23, 53, 537, 21, 82, 40, 2, 13, 2, 14, 280, 13, 219, 4, 2, 431, 758, 859, 4, 953, 1052, 2, 7, 5991, 5, 94, 40, 25, 238, 60, 2, 4, 2, 804, 2, 7, 4, 9941, 132, 8, 67, 6, 22, 15, 9, 283, 8, 5168, 14, 31, 9, 242, 955, 48, 25, 279, 2, 23, 12, 1685, 195, 25, 238, 60, 796, 2, 4, 671, 7, 2804, 5, 4, 559, 154, 888, 7, 726, 50, 26, 49, 7008, 15, 566, 30, 579, 21, 64, 2574])\n",
            " list([1, 249, 1323, 7, 61, 113, 10, 10, 13, 1637, 14, 20, 56, 33, 2401, 18, 457, 88, 13, 2626, 1400, 45, 3171, 13, 70, 79, 49, 706, 919, 13, 16, 355, 340, 355, 1696, 96, 143, 4, 22, 32, 289, 7, 61, 369, 71, 2359, 5, 13, 16, 131, 2073, 249, 114, 249, 229, 249, 20, 13, 28, 126, 110, 13, 473, 8, 569, 61, 419, 56, 429, 6, 1513, 18, 35, 534, 95, 474, 570, 5, 25, 124, 138, 88, 12, 421, 1543, 52, 725, 6397, 61, 419, 11, 13, 1571, 15, 1543, 20, 11, 4, 2, 5, 296, 12, 3524, 5, 15, 421, 128, 74, 233, 334, 207, 126, 224, 12, 562, 298, 2167, 1272, 7, 2601, 5, 516, 988, 43, 8, 79, 120, 15, 595, 13, 784, 25, 3171, 18, 165, 170, 143, 19, 14, 5, 7224, 6, 226, 251, 7, 61, 113])]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5ji7ZFEeoGGN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b68d458f-6b40-4793-fc75-185e8c4e2e17"
      },
      "source": [
        "print(y_train[:5])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 0 1 0]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L5zjSoW1oGnl"
      },
      "source": [
        "max_len = 200\n",
        "X_train = pad_sequences(X_train, maxlen=200)\n",
        "X_test = pad_sequences(X_test, maxlen=200)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYV4O7tGp5vf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "12364dee-e2bb-45c2-e3da-05c20a2c90a1"
      },
      "source": [
        "print('X_train의 크기(shape) :',X_train.shape)\n",
        "print('X_test의 크기(shape) :',X_test.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "X_train의 크기(shape) : (25000, 200)\n",
            "X_test의 크기(shape) : (25000, 200)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# 2. 1D CNN으로 IMDB 리뷰 분류하기"
      ],
      "metadata": {
        "id": "S0MMSanLeRlh"
      }
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K9PmfRG-oqAg"
      },
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Embedding, Dropout, Conv1D, GlobalMaxPooling1D, Dense\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "from tensorflow.keras.models import load_model"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UqtF4npfo5X2"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(Embedding(vocab_size, 256))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Conv1D(256, 3, padding='valid', activation='relu'))\n",
        "model.add(GlobalMaxPooling1D())\n",
        "model.add(Dense(128, activation='relu'))\n",
        "model.add(Dropout(0.5))\n",
        "model.add(Dense(1, activation='sigmoid'))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "l_NAcLE6pZoN",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "05f6ec78-2be3-4b41-f089-7d9a15d71f0c"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, None, 256)         2560000   \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, None, 256)         0         \n",
            "_________________________________________________________________\n",
            "conv1d (Conv1D)              (None, None, 256)         196864    \n",
            "_________________________________________________________________\n",
            "global_max_pooling1d (Global (None, 256)               0         \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 128)               32896     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 129       \n",
            "=================================================================\n",
            "Total params: 2,789,889\n",
            "Trainable params: 2,789,889\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fcQ-Rmic1Xzt"
      },
      "source": [
        "es = EarlyStopping(monitor = 'val_loss', mode = 'min', verbose = 1, patience = 3)\n",
        "mc = ModelCheckpoint('best_model.h5', monitor = 'val_acc', mode = 'max', verbose = 1, save_best_only = True)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nl--UYIKpbl1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "55781013-c9c4-4ba4-aee8-692afda8951e"
      },
      "source": [
        "model.compile(optimizer='adam', loss = 'binary_crossentropy', metrics = ['acc'])\n",
        "history = model.fit(X_train, y_train, epochs = 20, validation_data = (X_test, y_test), callbacks=[es, mc])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "782/782 [==============================] - 22s 7ms/step - loss: 0.4129 - acc: 0.7994 - val_loss: 0.2754 - val_acc: 0.8828\n",
            "\n",
            "Epoch 00001: val_acc improved from -inf to 0.88284, saving model to best_model.h5\n",
            "Epoch 2/20\n",
            "782/782 [==============================] - 5s 7ms/step - loss: 0.2182 - acc: 0.9155 - val_loss: 0.2672 - val_acc: 0.8893\n",
            "\n",
            "Epoch 00002: val_acc improved from 0.88284 to 0.88932, saving model to best_model.h5\n",
            "Epoch 3/20\n",
            "782/782 [==============================] - 5s 7ms/step - loss: 0.1142 - acc: 0.9600 - val_loss: 0.3220 - val_acc: 0.8865\n",
            "\n",
            "Epoch 00003: val_acc did not improve from 0.88932\n",
            "Epoch 4/20\n",
            "782/782 [==============================] - 5s 7ms/step - loss: 0.0474 - acc: 0.9842 - val_loss: 0.4095 - val_acc: 0.8845\n",
            "\n",
            "Epoch 00004: val_acc did not improve from 0.88932\n",
            "Epoch 5/20\n",
            "782/782 [==============================] - 5s 7ms/step - loss: 0.0292 - acc: 0.9901 - val_loss: 0.4845 - val_acc: 0.8836\n",
            "\n",
            "Epoch 00005: val_acc did not improve from 0.88932\n",
            "Epoch 00005: early stopping\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I_PQJ4u30-O0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "22ebbaf1-6440-428e-f8fc-1c9fe40b2a4a"
      },
      "source": [
        "loaded_model = load_model('best_model.h5')\n",
        "print(\"\\n 테스트 정확도: %.4f\" % (loaded_model.evaluate(X_test, y_test)[1]))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 [==============================] - 2s 2ms/step - loss: 0.2672 - acc: 0.8893\n",
            "\n",
            " 테스트 정확도: 0.8893\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pvh0jGH32K4D",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "7c16b512-09b9-4765-917d-6a992a072689"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "epochs = range(1, len(history.history['acc']) + 1)\n",
        "plt.plot(epochs, history.history['loss'])\n",
        "plt.plot(epochs, history.history['val_loss'])\n",
        "plt.title('model loss')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'test'], loc='upper left')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}