9-2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MusV5YYT6kQF"
      },
      "source": [
        "# 순환 신경망으로 IMDB 리뷰 분류하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9uYfRb5-6kQQ"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/9-2.ipynb\"><img src=\"https://www.tensorflow.org/images/colab_logo_32px.png\" />구글 코랩에서 실행하기</a>\n",
        "  </td>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "oBxL32NvSEVb"
      },
      "outputs": [],
      "source": [
        "# 실행마다 동일한 결과를 얻기 위해 케라스에 랜덤 시드를 사용하고 텐서플로 연산을 결정적으로 만듭니다.\n",
        "import tensorflow as tf\n",
        "\n",
        "tf.keras.utils.set_random_seed(42)\n",
        "tf.config.experimental.enable_op_determinism()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S1A-DDIQ6kQQ"
      },
      "source": [
        "## IMDB 리뷰 데이터셋"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sZoZm1ZO6kQR",
        "outputId": "8f162cf4-1a39-4080-9d9f-1345fa691739"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/imdb.npz\n",
            "17464789/17464789 [==============================] - 0s 0us/step\n"
          ]
        }
      ],
      "source": [
        "from tensorflow.keras.datasets import imdb\n",
        "\n",
        "(train_input, train_target), (test_input, test_target) = imdb.load_data(\n",
        "    num_words=300)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HTFjz6k06kQR",
        "outputId": "ae36d403-7f58-4006-b4f1-c593ce5eb7c5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(25000,) (25000,)\n"
          ]
        }
      ],
      "source": [
        "print(train_input.shape, test_input.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4jMLURzw6kQS",
        "outputId": "345bd2ab-f9f9-4930-bba4-a0c20ab6de7a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "218\n"
          ]
        }
      ],
      "source": [
        "print(len(train_input[0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdO834PL6kQT",
        "outputId": "e12cf580-35f8-481d-8b66-47b4de43b2fd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "189\n"
          ]
        }
      ],
      "source": [
        "print(len(train_input[1]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QNuzGQ2c6kQT",
        "outputId": "eb35125e-61fb-479b-fbb9-cec36e7494cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 14, 22, 16, 43, 2, 2, 2, 2, 65, 2, 2, 66, 2, 4, 173, 36, 256, 5, 25, 100, 43, 2, 112, 50, 2, 2, 9, 35, 2, 284, 5, 150, 4, 172, 112, 167, 2, 2, 2, 39, 4, 172, 2, 2, 17, 2, 38, 13, 2, 4, 192, 50, 16, 6, 147, 2, 19, 14, 22, 4, 2, 2, 2, 4, 22, 71, 87, 12, 16, 43, 2, 38, 76, 15, 13, 2, 4, 22, 17, 2, 17, 12, 16, 2, 18, 2, 5, 62, 2, 12, 8, 2, 8, 106, 5, 4, 2, 2, 16, 2, 66, 2, 33, 4, 130, 12, 16, 38, 2, 5, 25, 124, 51, 36, 135, 48, 25, 2, 33, 6, 22, 12, 215, 28, 77, 52, 5, 14, 2, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 2, 5, 2, 36, 71, 43, 2, 2, 26, 2, 2, 46, 7, 4, 2, 2, 13, 104, 88, 4, 2, 15, 297, 98, 32, 2, 56, 26, 141, 6, 194, 2, 18, 4, 226, 22, 21, 134, 2, 26, 2, 5, 144, 30, 2, 18, 51, 36, 28, 224, 92, 25, 104, 4, 226, 65, 16, 38, 2, 88, 12, 16, 283, 5, 16, 2, 113, 103, 32, 15, 16, 2, 19, 178, 32]\n"
          ]
        }
      ],
      "source": [
        "print(train_input[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "In8Qzltz6kQT",
        "outputId": "df64e3c3-b476-47ea-ca33-b679fbc4d0ee"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1 0 0 1 0 0 1 0 1 0 1 0 0 0 0 0 1 1 0 1]\n"
          ]
        }
      ],
      "source": [
        "print(train_target[:20])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "0-zjNgBw6kQU"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_input, val_input, train_target, val_target = train_test_split(\n",
        "    train_input, train_target, test_size=0.2, random_state=42)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "id": "sRVvgntx6kQU"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "\n",
        "lengths = np.array([len(x) for x in train_input])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p76m_61w6kQU",
        "outputId": "5ab14954-5b6b-452a-f7e3-8d39dd0b4745"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "239.00925 178.0\n"
          ]
        }
      ],
      "source": [
        "print(np.mean(lengths), np.median(lengths))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "TVDrsRYF6kQV",
        "outputId": "dc109dc5-812f-4c9c-f161-7996aa6e32db"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.hist(lengths)\n",
        "plt.xlabel('length')\n",
        "plt.ylabel('frequency')\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "id": "fbLSFToP6kQV"
      },
      "outputs": [],
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "train_seq = pad_sequences(train_input, maxlen=100)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3c86x0Yj6kQV",
        "outputId": "41c5c2ed-c692-414f-c002-672957c60899"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20000, 100)\n"
          ]
        }
      ],
      "source": [
        "print(train_seq.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JuP0QJaU6kQW",
        "outputId": "aa7ad51e-3f78-4da7-de0a-c4a467ff3a68"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[ 10   4  20   9   2   2   2   5  45   6   2   2  33 269   8   2 142   2\n",
            "   5   2  17  73  17 204   5   2  19  55   2   2  92  66 104  14  20  93\n",
            "  76   2 151  33   4  58  12 188   2 151  12 215  69 224 142  73 237   6\n",
            "   2   7   2   2 188   2 103  14  31  10  10   2   7   2   5   2  80  91\n",
            "   2  30   2  34  14  20 151  50  26 131  49   2  84  46  50  37  80  79\n",
            "   6   2  46   7  14  20  10  10   2 158]\n"
          ]
        }
      ],
      "source": [
        "print(train_seq[0])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6t8kaDji6kQW",
        "outputId": "e8de311c-e14c-4b11-ed7e-e8097cc9e75d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[6, 2, 46, 7, 14, 20, 10, 10, 2, 158]\n"
          ]
        }
      ],
      "source": [
        "print(train_input[0][-10:])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Emmoztwa6kQX",
        "outputId": "b02ee754-9a12-420b-b7ef-dba4b1326409",
        "scrolled": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[  0   0   0   0   1   2 195  19  49   2   2 190   4   2   2   2 183  10\n",
            "  10  13  82  79   4   2  36  71 269   8   2  25  19  49   7   4   2   2\n",
            "   2   2   2  10  10  48  25  40   2  11   2   2  40   2   2   5   4   2\n",
            "   2  95  14 238  56 129   2  10  10  21   2  94   2   2   2   2  11 190\n",
            "  24   2   2   7  94 205   2  10  10  87   2  34  49   2   7   2   2   2\n",
            "   2   2 290   2  46  48  64  18   4   2]\n"
          ]
        }
      ],
      "source": [
        "print(train_seq[5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "id": "ZeFmsjOd6kQX"
      },
      "outputs": [],
      "source": [
        "val_seq = pad_sequences(val_input, maxlen=100)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9xab0nu_6kQX"
      },
      "source": [
        "## 순환 신경망 만들기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "45BtHGKX6kQX"
      },
      "outputs": [],
      "source": [
        "from tensorflow import keras\n",
        "\n",
        "model = keras.Sequential()\n",
        "\n",
        "model.add(keras.layers.SimpleRNN(8, input_shape=(100, 300)))\n",
        "model.add(keras.layers.Dense(1, activation='sigmoid'))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "id": "oXIWyOuw6kQY"
      },
      "outputs": [],
      "source": [
        "train_oh = keras.utils.to_categorical(train_seq)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VD94WiRT6kQY",
        "outputId": "96179a4e-cb8c-469b-953b-21d404330738"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(20000, 100, 300)\n"
          ]
        }
      ],
      "source": [
        "print(train_oh.shape)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VIVSHwJg6kQY",
        "outputId": "3578f832-5291-41ec-84ba-795df5122dcf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n"
          ]
        }
      ],
      "source": [
        "print(train_oh[0][0][:12])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CP3ivii46kQZ",
        "outputId": "bb4321c3-abb6-4d2d-9533-ca700943e40c"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.0\n"
          ]
        }
      ],
      "source": [
        "print(np.sum(train_oh[0][0]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "id": "vgijH6m26kQZ"
      },
      "outputs": [],
      "source": [
        "val_oh = keras.utils.to_categorical(val_seq)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0V0Svpuf6kQZ",
        "outputId": "94fbe518-a5e5-414b-a7d8-947b33c12093"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " simple_rnn (SimpleRNN)      (None, 8)                 2472      \n",
            "                                                                 \n",
            " dense (Dense)               (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2481 (9.69 KB)\n",
            "Trainable params: 2481 (9.69 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model.summary()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S22Hc-7L6kQZ"
      },
      "source": [
        "## 순환 신경망 훈련하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YcwM299t6kQZ",
        "outputId": "90a632f2-f42d-4867-80fb-cff61682eac8"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "313/313 [==============================] - 31s 80ms/step - loss: 0.7003 - accuracy: 0.5002 - val_loss: 0.6970 - val_accuracy: 0.5058\n",
            "Epoch 2/100\n",
            "  2/313 [..............................] - ETA: 17s - loss: 0.7103 - accuracy: 0.5078"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "313/313 [==============================] - 22s 70ms/step - loss: 0.6956 - accuracy: 0.5123 - val_loss: 0.6946 - val_accuracy: 0.5124\n",
            "Epoch 3/100\n",
            "313/313 [==============================] - 23s 74ms/step - loss: 0.6917 - accuracy: 0.5282 - val_loss: 0.6909 - val_accuracy: 0.5318\n",
            "Epoch 4/100\n",
            "313/313 [==============================] - 23s 74ms/step - loss: 0.6844 - accuracy: 0.5549 - val_loss: 0.6833 - val_accuracy: 0.5690\n",
            "Epoch 5/100\n",
            "313/313 [==============================] - 24s 75ms/step - loss: 0.6784 - accuracy: 0.5778 - val_loss: 0.6797 - val_accuracy: 0.5770\n",
            "Epoch 6/100\n",
            "313/313 [==============================] - 23s 73ms/step - loss: 0.6734 - accuracy: 0.5890 - val_loss: 0.6743 - val_accuracy: 0.5890\n",
            "Epoch 7/100\n",
            "313/313 [==============================] - 26s 83ms/step - loss: 0.6676 - accuracy: 0.6068 - val_loss: 0.6695 - val_accuracy: 0.5982\n",
            "Epoch 8/100\n",
            "313/313 [==============================] - 21s 68ms/step - loss: 0.6614 - accuracy: 0.6197 - val_loss: 0.6628 - val_accuracy: 0.6112\n",
            "Epoch 9/100\n",
            "313/313 [==============================] - 23s 73ms/step - loss: 0.6539 - accuracy: 0.6309 - val_loss: 0.6567 - val_accuracy: 0.6228\n",
            "Epoch 10/100\n",
            "313/313 [==============================] - 22s 71ms/step - loss: 0.6461 - accuracy: 0.6445 - val_loss: 0.6481 - val_accuracy: 0.6366\n",
            "Epoch 11/100\n",
            "313/313 [==============================] - 22s 70ms/step - loss: 0.6377 - accuracy: 0.6564 - val_loss: 0.6409 - val_accuracy: 0.6486\n",
            "Epoch 12/100\n",
            "313/313 [==============================] - 23s 74ms/step - loss: 0.6285 - accuracy: 0.6684 - val_loss: 0.6302 - val_accuracy: 0.6642\n",
            "Epoch 13/100\n",
            "313/313 [==============================] - 22s 69ms/step - loss: 0.6191 - accuracy: 0.6804 - val_loss: 0.6211 - val_accuracy: 0.6740\n",
            "Epoch 14/100\n",
            "313/313 [==============================] - 23s 73ms/step - loss: 0.6092 - accuracy: 0.6884 - val_loss: 0.6122 - val_accuracy: 0.6824\n",
            "Epoch 15/100\n",
            "313/313 [==============================] - 24s 78ms/step - loss: 0.5992 - accuracy: 0.6998 - val_loss: 0.6032 - val_accuracy: 0.6896\n",
            "Epoch 16/100\n",
            "313/313 [==============================] - 23s 74ms/step - loss: 0.5900 - accuracy: 0.7071 - val_loss: 0.5935 - val_accuracy: 0.6972\n",
            "Epoch 17/100\n",
            "313/313 [==============================] - 23s 73ms/step - loss: 0.5816 - accuracy: 0.7142 - val_loss: 0.5855 - val_accuracy: 0.7028\n",
            "Epoch 18/100\n",
            "313/313 [==============================] - 22s 70ms/step - loss: 0.5731 - accuracy: 0.7209 - val_loss: 0.5785 - val_accuracy: 0.7094\n",
            "Epoch 19/100\n",
            "313/313 [==============================] - 22s 70ms/step - loss: 0.5648 - accuracy: 0.7260 - val_loss: 0.5691 - val_accuracy: 0.7174\n",
            "Epoch 20/100\n",
            "313/313 [==============================] - 23s 73ms/step - loss: 0.5569 - accuracy: 0.7308 - val_loss: 0.5620 - val_accuracy: 0.7214\n",
            "Epoch 21/100\n",
            "313/313 [==============================] - 22s 70ms/step - loss: 0.5503 - accuracy: 0.7351 - val_loss: 0.5560 - val_accuracy: 0.7234\n",
            "Epoch 22/100\n",
            "313/313 [==============================] - 23s 72ms/step - loss: 0.5443 - accuracy: 0.7391 - val_loss: 0.5515 - val_accuracy: 0.7266\n",
            "Epoch 23/100\n",
            "313/313 [==============================] - 22s 72ms/step - loss: 0.5381 - accuracy: 0.7427 - val_loss: 0.5473 - val_accuracy: 0.7300\n",
            "Epoch 24/100\n",
            "313/313 [==============================] - 23s 75ms/step - loss: 0.5339 - accuracy: 0.7456 - val_loss: 0.5423 - val_accuracy: 0.7344\n",
            "Epoch 25/100\n",
            "313/313 [==============================] - 23s 74ms/step - loss: 0.5297 - accuracy: 0.7477 - val_loss: 0.5385 - val_accuracy: 0.7342\n",
            "Epoch 26/100\n",
            "313/313 [==============================] - 23s 73ms/step - loss: 0.5257 - accuracy: 0.7510 - val_loss: 0.5360 - val_accuracy: 0.7376\n",
            "Epoch 27/100\n",
            "313/313 [==============================] - 22s 71ms/step - loss: 0.5223 - accuracy: 0.7534 - val_loss: 0.5330 - val_accuracy: 0.7386\n",
            "Epoch 28/100\n",
            "313/313 [==============================] - 23s 73ms/step - loss: 0.5192 - accuracy: 0.7556 - val_loss: 0.5315 - val_accuracy: 0.7352\n",
            "Epoch 29/100\n",
            "313/313 [==============================] - 21s 68ms/step - loss: 0.5159 - accuracy: 0.7567 - val_loss: 0.5394 - val_accuracy: 0.7338\n",
            "Epoch 30/100\n",
            "313/313 [==============================] - 23s 73ms/step - loss: 0.5133 - accuracy: 0.7573 - val_loss: 0.5275 - val_accuracy: 0.7386\n",
            "Epoch 31/100\n",
            "313/313 [==============================] - 22s 70ms/step - loss: 0.5116 - accuracy: 0.7596 - val_loss: 0.5254 - val_accuracy: 0.7428\n",
            "Epoch 32/100\n",
            "313/313 [==============================] - 22s 71ms/step - loss: 0.5090 - accuracy: 0.7609 - val_loss: 0.5244 - val_accuracy: 0.7450\n",
            "Epoch 33/100\n",
            "313/313 [==============================] - 25s 79ms/step - loss: 0.5071 - accuracy: 0.7610 - val_loss: 0.5243 - val_accuracy: 0.7438\n",
            "Epoch 34/100\n",
            "313/313 [==============================] - 21s 68ms/step - loss: 0.5047 - accuracy: 0.7625 - val_loss: 0.5242 - val_accuracy: 0.7446\n",
            "Epoch 35/100\n",
            "313/313 [==============================] - 24s 78ms/step - loss: 0.5036 - accuracy: 0.7634 - val_loss: 0.5218 - val_accuracy: 0.7460\n",
            "Epoch 36/100\n",
            "313/313 [==============================] - 23s 73ms/step - loss: 0.5022 - accuracy: 0.7638 - val_loss: 0.5202 - val_accuracy: 0.7462\n",
            "Epoch 37/100\n",
            "313/313 [==============================] - 21s 68ms/step - loss: 0.5006 - accuracy: 0.7654 - val_loss: 0.5208 - val_accuracy: 0.7482\n",
            "Epoch 38/100\n",
            "313/313 [==============================] - 23s 74ms/step - loss: 0.4992 - accuracy: 0.7649 - val_loss: 0.5210 - val_accuracy: 0.7468\n",
            "Epoch 39/100\n",
            "313/313 [==============================] - 23s 73ms/step - loss: 0.4982 - accuracy: 0.7659 - val_loss: 0.5201 - val_accuracy: 0.7442\n",
            "Epoch 40/100\n",
            "313/313 [==============================] - 22s 70ms/step - loss: 0.4959 - accuracy: 0.7671 - val_loss: 0.5180 - val_accuracy: 0.7446\n",
            "Epoch 41/100\n",
            "313/313 [==============================] - 23s 73ms/step - loss: 0.4950 - accuracy: 0.7684 - val_loss: 0.5162 - val_accuracy: 0.7470\n",
            "Epoch 42/100\n",
            "313/313 [==============================] - 31s 99ms/step - loss: 0.4942 - accuracy: 0.7686 - val_loss: 0.5164 - val_accuracy: 0.7480\n",
            "Epoch 43/100\n",
            "313/313 [==============================] - 24s 78ms/step - loss: 0.4931 - accuracy: 0.7696 - val_loss: 0.5152 - val_accuracy: 0.7484\n",
            "Epoch 44/100\n",
            "313/313 [==============================] - 23s 74ms/step - loss: 0.4919 - accuracy: 0.7692 - val_loss: 0.5139 - val_accuracy: 0.7460\n",
            "Epoch 45/100\n",
            "313/313 [==============================] - 23s 73ms/step - loss: 0.4916 - accuracy: 0.7699 - val_loss: 0.5148 - val_accuracy: 0.7478\n",
            "Epoch 46/100\n",
            "313/313 [==============================] - 24s 77ms/step - loss: 0.4902 - accuracy: 0.7703 - val_loss: 0.5157 - val_accuracy: 0.7458\n",
            "Epoch 47/100\n",
            "313/313 [==============================] - 22s 69ms/step - loss: 0.4892 - accuracy: 0.7714 - val_loss: 0.5147 - val_accuracy: 0.7496\n"
          ]
        }
      ],
      "source": [
        "rmsprop = keras.optimizers.RMSprop(learning_rate=1e-4)\n",
        "model.compile(optimizer=rmsprop, loss='binary_crossentropy',\n",
        "              metrics=['accuracy'])\n",
        "\n",
        "checkpoint_cb = keras.callbacks.ModelCheckpoint('best-simplernn-model.h5',\n",
        "                                                save_best_only=True)\n",
        "early_stopping_cb = keras.callbacks.EarlyStopping(patience=3,\n",
        "                                                  restore_best_weights=True)\n",
        "\n",
        "history = model.fit(train_oh, train_target, epochs=100, batch_size=64,\n",
        "                    validation_data=(val_oh, val_target),\n",
        "                    callbacks=[checkpoint_cb, early_stopping_cb])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "YS_5tIG26kQa",
        "outputId": "be13fad5-017a-4aa1-9143-29abb7a0815b"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'val'])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K_U_lVie6kQa"
      },
      "source": [
        "## 단어 임베딩을 사용하기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ofl-Ifev6kQa",
        "outputId": "690f697b-aea5-4b7a-d8fd-ab76ea2521cd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " embedding (Embedding)       (None, 100, 16)           4800      \n",
            "                                                                 \n",
            " simple_rnn_1 (SimpleRNN)    (None, 8)                 200       \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 9         \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 5009 (19.57 KB)\n",
            "Trainable params: 5009 (19.57 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ],
      "source": [
        "model2 = keras.Sequential()\n",
        "\n",
        "model2.add(keras.layers.Embedding(300, 16, input_length=100))\n",
        "model2.add(keras.layers.SimpleRNN(8))\n",
        "model2.add(keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model2.summary()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "78TUrJAs6kQa",
        "outputId": "201fcba8-e210-4179-d841-c29e5b8f3974"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "313/313 [==============================] - 39s 119ms/step - loss: 0.6893 - accuracy: 0.5351 - val_loss: 0.6706 - val_accuracy: 0.5872\n",
            "Epoch 2/100\n",
            "313/313 [==============================] - 35s 112ms/step - loss: 0.6399 - accuracy: 0.6467 - val_loss: 0.6234 - val_accuracy: 0.6664\n",
            "Epoch 3/100\n",
            "313/313 [==============================] - 35s 112ms/step - loss: 0.6051 - accuracy: 0.6941 - val_loss: 0.6003 - val_accuracy: 0.6948\n",
            "Epoch 4/100\n",
            "313/313 [==============================] - 36s 114ms/step - loss: 0.5831 - accuracy: 0.7172 - val_loss: 0.5888 - val_accuracy: 0.7026\n",
            "Epoch 5/100\n",
            "313/313 [==============================] - 34s 110ms/step - loss: 0.5663 - accuracy: 0.7305 - val_loss: 0.5669 - val_accuracy: 0.7300\n",
            "Epoch 6/100\n",
            "313/313 [==============================] - 33s 107ms/step - loss: 0.5527 - accuracy: 0.7408 - val_loss: 0.5536 - val_accuracy: 0.7356\n",
            "Epoch 7/100\n",
            "313/313 [==============================] - 35s 112ms/step - loss: 0.5410 - accuracy: 0.7475 - val_loss: 0.5422 - val_accuracy: 0.7404\n",
            "Epoch 8/100\n",
            "313/313 [==============================] - 33s 105ms/step - loss: 0.5313 - accuracy: 0.7509 - val_loss: 0.5443 - val_accuracy: 0.7352\n",
            "Epoch 9/100\n",
            "313/313 [==============================] - 38s 120ms/step - loss: 0.5236 - accuracy: 0.7549 - val_loss: 0.5463 - val_accuracy: 0.7238\n",
            "Epoch 10/100\n",
            "313/313 [==============================] - 35s 112ms/step - loss: 0.5177 - accuracy: 0.7578 - val_loss: 0.5377 - val_accuracy: 0.7382\n",
            "Epoch 11/100\n",
            "313/313 [==============================] - 35s 111ms/step - loss: 0.5135 - accuracy: 0.7590 - val_loss: 0.5264 - val_accuracy: 0.7454\n",
            "Epoch 12/100\n",
            "313/313 [==============================] - 34s 108ms/step - loss: 0.5094 - accuracy: 0.7618 - val_loss: 0.5265 - val_accuracy: 0.7422\n",
            "Epoch 13/100\n",
            "313/313 [==============================] - 34s 109ms/step - loss: 0.5056 - accuracy: 0.7640 - val_loss: 0.5207 - val_accuracy: 0.7468\n",
            "Epoch 14/100\n",
            "313/313 [==============================] - 34s 107ms/step - loss: 0.5027 - accuracy: 0.7659 - val_loss: 0.5226 - val_accuracy: 0.7434\n",
            "Epoch 15/100\n",
            "313/313 [==============================] - 34s 110ms/step - loss: 0.4998 - accuracy: 0.7689 - val_loss: 0.5678 - val_accuracy: 0.6992\n",
            "Epoch 16/100\n",
            "313/313 [==============================] - 38s 121ms/step - loss: 0.4977 - accuracy: 0.7677 - val_loss: 0.5155 - val_accuracy: 0.7500\n",
            "Epoch 17/100\n",
            "313/313 [==============================] - 34s 110ms/step - loss: 0.4944 - accuracy: 0.7713 - val_loss: 0.5222 - val_accuracy: 0.7450\n",
            "Epoch 18/100\n",
            "313/313 [==============================] - 34s 108ms/step - loss: 0.4927 - accuracy: 0.7712 - val_loss: 0.5149 - val_accuracy: 0.7486\n",
            "Epoch 19/100\n",
            "313/313 [==============================] - 33s 106ms/step - loss: 0.4905 - accuracy: 0.7730 - val_loss: 0.5138 - val_accuracy: 0.7464\n",
            "Epoch 20/100\n",
            "313/313 [==============================] - 36s 114ms/step - loss: 0.4880 - accuracy: 0.7740 - val_loss: 0.5124 - val_accuracy: 0.7500\n",
            "Epoch 21/100\n",
            "313/313 [==============================] - 34s 108ms/step - loss: 0.4871 - accuracy: 0.7749 - val_loss: 0.5143 - val_accuracy: 0.7488\n",
            "Epoch 22/100\n",
            "313/313 [==============================] - 39s 125ms/step - loss: 0.4847 - accuracy: 0.7776 - val_loss: 0.5148 - val_accuracy: 0.7486\n",
            "Epoch 23/100\n",
            "313/313 [==============================] - 35s 112ms/step - loss: 0.4830 - accuracy: 0.7776 - val_loss: 0.5135 - val_accuracy: 0.7500\n"
          ]
        }
      ],
      "source": [
        "rmsprop = keras.optimizers.RMSprop(learning_rate=1e-4)\n",
        "model2.compile(optimizer=rmsprop, loss='binary_crossentropy',\n",
        "               metrics=['accuracy'])\n",
        "\n",
        "checkpoint_cb = keras.callbacks.ModelCheckpoint('best-embedding-model.h5',\n",
        "                                                save_best_only=True)\n",
        "early_stopping_cb = keras.callbacks.EarlyStopping(patience=3,\n",
        "                                                  restore_best_weights=True)\n",
        "\n",
        "history = model2.fit(train_seq, train_target, epochs=100, batch_size=64,\n",
        "                     validation_data=(val_seq, val_target),\n",
        "                     callbacks=[checkpoint_cb, early_stopping_cb])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        },
        "id": "brUvKwLt6kQb",
        "outputId": "70152d22-4cac-4483-bfac-19c7bc5a27d2",
        "tags": []
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.xlabel('epoch')\n",
        "plt.ylabel('loss')\n",
        "plt.legend(['train', 'val'])\n",
        "plt.show()"
      ]
    }
  ],
  "metadata": {
    "accelerator": "GPU",
    "colab": {
      "name": "9-2.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "default:Python",
      "language": "python",
      "name": "conda-env-default-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}