9-2_Recurrent_Neural_Network.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": "markdown",
   "metadata": {
    "id": "S1A-DDIQ6kQQ"
   },
   "source": [
    "## IMDB 리뷰 데이터셋"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:23.566702Z",
     "iopub.status.busy": "2022-01-08T05:20:23.565530Z",
     "iopub.status.idle": "2022-01-08T05:20:29.491800Z",
     "shell.execute_reply": "2022-01-08T05:20:29.492380Z"
    },
    "id": "sZoZm1ZO6kQR",
    "outputId": "c340dd55-629f-4615-db76-9c06bc46e2c2"
   },
   "outputs": [],
   "source": [
    "from tensorflow.keras.datasets import imdb\n",
    "\n",
    "(train_input, train_target), (test_input, test_target) = imdb.load_data(\n",
    "    num_words=500)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:29.498778Z",
     "iopub.status.busy": "2022-01-08T05:20:29.497993Z",
     "iopub.status.idle": "2022-01-08T05:20:29.501675Z",
     "shell.execute_reply": "2022-01-08T05:20:29.502173Z"
    },
    "id": "HTFjz6k06kQR",
    "outputId": "1d4929d7-0a93-4bb8-cf03-682ada3d6ac9"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(25000,) (25000,)\n"
     ]
    }
   ],
   "source": [
    "print(train_input.shape, test_input.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:29.507450Z",
     "iopub.status.busy": "2022-01-08T05:20:29.506813Z",
     "iopub.status.idle": "2022-01-08T05:20:29.509552Z",
     "shell.execute_reply": "2022-01-08T05:20:29.508923Z"
    },
    "id": "4jMLURzw6kQS",
    "outputId": "ffb635f3-a3bd-42ed-b6da-fecaa0ede408"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "218\n"
     ]
    }
   ],
   "source": [
    "print(len(train_input[0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:29.514339Z",
     "iopub.status.busy": "2022-01-08T05:20:29.513627Z",
     "iopub.status.idle": "2022-01-08T05:20:29.516831Z",
     "shell.execute_reply": "2022-01-08T05:20:29.516284Z"
    },
    "id": "xdO834PL6kQT",
    "outputId": "28ba2580-b6b8-49b0-e07e-4df8026a5626"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "189\n"
     ]
    }
   ],
   "source": [
    "print(len(train_input[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:29.520851Z",
     "iopub.status.busy": "2022-01-08T05:20:29.519321Z",
     "iopub.status.idle": "2022-01-08T05:20:29.523542Z",
     "shell.execute_reply": "2022-01-08T05:20:29.523955Z"
    },
    "id": "QNuzGQ2c6kQT",
    "outputId": "88f7dd8c-188e-4ad9-edb2-f4b59c498d40"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 14, 22, 16, 43, 2, 2, 2, 2, 65, 458, 2, 66, 2, 4, 173, 36, 256, 5, 25, 100, 43, 2, 112, 50, 2, 2, 9, 35, 480, 284, 5, 150, 4, 172, 112, 167, 2, 336, 385, 39, 4, 172, 2, 2, 17, 2, 38, 13, 447, 4, 192, 50, 16, 6, 147, 2, 19, 14, 22, 4, 2, 2, 469, 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, 386, 12, 8, 316, 8, 106, 5, 4, 2, 2, 16, 480, 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, 407, 16, 82, 2, 8, 4, 107, 117, 2, 15, 256, 4, 2, 7, 2, 5, 2, 36, 71, 43, 2, 476, 26, 400, 317, 46, 7, 4, 2, 2, 13, 104, 88, 4, 381, 15, 297, 98, 32, 2, 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, 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": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:29.528303Z",
     "iopub.status.busy": "2022-01-08T05:20:29.527701Z",
     "iopub.status.idle": "2022-01-08T05:20:29.530858Z",
     "shell.execute_reply": "2022-01-08T05:20:29.530314Z"
    },
    "id": "In8Qzltz6kQT",
    "outputId": "78b5ddc6-d634-4755-9435-25cc8f2c9462"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:29.535982Z",
     "iopub.status.busy": "2022-01-08T05:20:29.535314Z",
     "iopub.status.idle": "2022-01-08T05:20:30.003976Z",
     "shell.execute_reply": "2022-01-08T05:20:30.003182Z"
    },
    "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": 8,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:30.014693Z",
     "iopub.status.busy": "2022-01-08T05:20:30.013910Z",
     "iopub.status.idle": "2022-01-08T05:20:30.017935Z",
     "shell.execute_reply": "2022-01-08T05:20:30.017351Z"
    },
    "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": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:30.023039Z",
     "iopub.status.busy": "2022-01-08T05:20:30.022404Z",
     "iopub.status.idle": "2022-01-08T05:20:30.025564Z",
     "shell.execute_reply": "2022-01-08T05:20:30.026023Z"
    },
    "id": "p76m_61w6kQU",
    "outputId": "04b533f6-45ef-4514-d87d-b484a89afde6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "239.00925 178.0\n"
     ]
    }
   ],
   "source": [
    "print(np.mean(lengths), np.median(lengths))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:30.031474Z",
     "iopub.status.busy": "2022-01-08T05:20:30.030669Z",
     "iopub.status.idle": "2022-01-08T05:20:30.732010Z",
     "shell.execute_reply": "2022-01-08T05:20:30.731361Z"
    },
    "id": "TVDrsRYF6kQV",
    "outputId": "2610932c-ac31-480b-8a6c-fd5ac2793a34"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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": 11,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:30.741785Z",
     "iopub.status.busy": "2022-01-08T05:20:30.736613Z",
     "iopub.status.idle": "2022-01-08T05:20:30.889977Z",
     "shell.execute_reply": "2022-01-08T05:20:30.890504Z"
    },
    "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": 12,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:30.895174Z",
     "iopub.status.busy": "2022-01-08T05:20:30.894526Z",
     "iopub.status.idle": "2022-01-08T05:20:30.897720Z",
     "shell.execute_reply": "2022-01-08T05:20:30.897208Z"
    },
    "id": "3c86x0Yj6kQV",
    "outputId": "52d4ff3a-9aa5-4ebd-e086-d9b2d490a29f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20000, 100)\n"
     ]
    }
   ],
   "source": [
    "print(train_seq.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:30.902380Z",
     "iopub.status.busy": "2022-01-08T05:20:30.901721Z",
     "iopub.status.idle": "2022-01-08T05:20:30.904481Z",
     "shell.execute_reply": "2022-01-08T05:20:30.904931Z"
    },
    "id": "JuP0QJaU6kQW",
    "outputId": "95e6521e-b200-4733-f5e8-65320ff2a81f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 10   4  20   9   2 364 352   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  10 451   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  10 470 158]\n"
     ]
    }
   ],
   "source": [
    "print(train_seq[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:30.909820Z",
     "iopub.status.busy": "2022-01-08T05:20:30.909091Z",
     "iopub.status.idle": "2022-01-08T05:20:30.911694Z",
     "shell.execute_reply": "2022-01-08T05:20:30.912135Z"
    },
    "id": "6t8kaDji6kQW",
    "outputId": "a9d5ba15-838d-4cac-c23c-18c2fa4b709f"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[6, 2, 46, 7, 14, 20, 10, 10, 470, 158]\n"
     ]
    }
   ],
   "source": [
    "print(train_input[0][-10:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:30.916735Z",
     "iopub.status.busy": "2022-01-08T05:20:30.916072Z",
     "iopub.status.idle": "2022-01-08T05:20:30.919117Z",
     "shell.execute_reply": "2022-01-08T05:20:30.918545Z"
    },
    "id": "Emmoztwa6kQX",
    "outputId": "d72b6139-e876-41a1-b331-313d3199548a",
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[  0   0   0   0   1   2 195  19  49   2   2 190   4   2 352   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  94 364 352   2   2  11 190\n",
      "  24 484   2   7  94 205 405  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": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:30.924446Z",
     "iopub.status.busy": "2022-01-08T05:20:30.923826Z",
     "iopub.status.idle": "2022-01-08T05:20:30.964050Z",
     "shell.execute_reply": "2022-01-08T05:20:30.964515Z"
    },
    "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": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:30.971560Z",
     "iopub.status.busy": "2022-01-08T05:20:30.969365Z",
     "iopub.status.idle": "2022-01-08T05:20:31.069269Z",
     "shell.execute_reply": "2022-01-08T05:20:31.069797Z"
    },
    "id": "45BtHGKX6kQX"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-08 05:20:30.972213: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations:  AVX2 FMA\n",
      "To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
     ]
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "\n",
    "model = keras.Sequential()\n",
    "\n",
    "model.add(keras.layers.SimpleRNN(8, input_shape=(100, 500)))\n",
    "model.add(keras.layers.Dense(1, activation='sigmoid'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:31.074464Z",
     "iopub.status.busy": "2022-01-08T05:20:31.073755Z",
     "iopub.status.idle": "2022-01-08T05:20:31.801381Z",
     "shell.execute_reply": "2022-01-08T05:20:31.800237Z"
    },
    "id": "oXIWyOuw6kQY"
   },
   "outputs": [],
   "source": [
    "train_oh = keras.utils.to_categorical(train_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:31.806214Z",
     "iopub.status.busy": "2022-01-08T05:20:31.805511Z",
     "iopub.status.idle": "2022-01-08T05:20:31.808892Z",
     "shell.execute_reply": "2022-01-08T05:20:31.808347Z"
    },
    "id": "VD94WiRT6kQY",
    "outputId": "d2074cc9-cdb3-4c73-e9ca-1318fdd34034"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(20000, 100, 500)\n"
     ]
    }
   ],
   "source": [
    "print(train_oh.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:31.814272Z",
     "iopub.status.busy": "2022-01-08T05:20:31.813567Z",
     "iopub.status.idle": "2022-01-08T05:20:31.816686Z",
     "shell.execute_reply": "2022-01-08T05:20:31.816011Z"
    },
    "id": "VIVSHwJg6kQY",
    "outputId": "972653fa-6acd-405d-d1ee-2422708f9863"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "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": 21,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:31.821685Z",
     "iopub.status.busy": "2022-01-08T05:20:31.821048Z",
     "iopub.status.idle": "2022-01-08T05:20:31.823968Z",
     "shell.execute_reply": "2022-01-08T05:20:31.823398Z"
    },
    "id": "CP3ivii46kQZ",
    "outputId": "6a669f1a-41fe-486a-e0e1-bd812d2b7aeb"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.0\n"
     ]
    }
   ],
   "source": [
    "print(np.sum(train_oh[0][0]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:31.828083Z",
     "iopub.status.busy": "2022-01-08T05:20:31.827427Z",
     "iopub.status.idle": "2022-01-08T05:20:32.012623Z",
     "shell.execute_reply": "2022-01-08T05:20:32.013109Z"
    },
    "id": "vgijH6m26kQZ"
   },
   "outputs": [],
   "source": [
    "val_oh = keras.utils.to_categorical(val_seq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:32.017927Z",
     "iopub.status.busy": "2022-01-08T05:20:32.017215Z",
     "iopub.status.idle": "2022-01-08T05:20:32.029398Z",
     "shell.execute_reply": "2022-01-08T05:20:32.028840Z"
    },
    "id": "0V0Svpuf6kQZ",
    "outputId": "34f0dd62-57da-4907-8032-9bb2655a986e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " simple_rnn (SimpleRNN)      (None, 8)                 4072      \n",
      "                                                                 \n",
      " dense (Dense)               (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 4,081\n",
      "Trainable params: 4,081\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S22Hc-7L6kQZ"
   },
   "source": [
    "## 순환 신경망 훈련하기"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:20:32.039845Z",
     "iopub.status.busy": "2022-01-08T05:20:32.036890Z",
     "iopub.status.idle": "2022-01-08T05:27:13.437874Z",
     "shell.execute_reply": "2022-01-08T05:27:13.437279Z"
    },
    "id": "YcwM299t6kQZ",
    "outputId": "257518bf-52ab-49d8-aa91-3c2e606ffd74"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2022-01-08 05:20:32.043116: W tensorflow/core/framework/cpu_allocator_impl.cc:82] Allocation of 4000000000 exceeds 10% of free system memory.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "313/313 [==============================] - 9s 27ms/step - loss: 0.6988 - accuracy: 0.5006 - val_loss: 0.6939 - val_accuracy: 0.5174\n",
      "Epoch 2/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6942 - accuracy: 0.5140 - val_loss: 0.6920 - val_accuracy: 0.5196\n",
      "Epoch 3/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6919 - accuracy: 0.5252 - val_loss: 0.6908 - val_accuracy: 0.5294\n",
      "Epoch 4/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6902 - accuracy: 0.5344 - val_loss: 0.6898 - val_accuracy: 0.5372\n",
      "Epoch 5/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6886 - accuracy: 0.5447 - val_loss: 0.6890 - val_accuracy: 0.5410\n",
      "Epoch 6/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6870 - accuracy: 0.5526 - val_loss: 0.6879 - val_accuracy: 0.5500\n",
      "Epoch 7/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6855 - accuracy: 0.5598 - val_loss: 0.6871 - val_accuracy: 0.5550\n",
      "Epoch 8/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6840 - accuracy: 0.5642 - val_loss: 0.6861 - val_accuracy: 0.5558\n",
      "Epoch 9/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6824 - accuracy: 0.5697 - val_loss: 0.6854 - val_accuracy: 0.5522\n",
      "Epoch 10/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.6808 - accuracy: 0.5745 - val_loss: 0.6845 - val_accuracy: 0.5586\n",
      "Epoch 11/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6792 - accuracy: 0.5757 - val_loss: 0.6837 - val_accuracy: 0.5618\n",
      "Epoch 12/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6776 - accuracy: 0.5807 - val_loss: 0.6832 - val_accuracy: 0.5630\n",
      "Epoch 13/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.6760 - accuracy: 0.5820 - val_loss: 0.6823 - val_accuracy: 0.5640\n",
      "Epoch 14/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.6743 - accuracy: 0.5849 - val_loss: 0.6815 - val_accuracy: 0.5630\n",
      "Epoch 15/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6727 - accuracy: 0.5908 - val_loss: 0.6810 - val_accuracy: 0.5604\n",
      "Epoch 16/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.6710 - accuracy: 0.5933 - val_loss: 0.6801 - val_accuracy: 0.5612\n",
      "Epoch 17/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6692 - accuracy: 0.5961 - val_loss: 0.6795 - val_accuracy: 0.5602\n",
      "Epoch 18/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.6674 - accuracy: 0.6005 - val_loss: 0.6788 - val_accuracy: 0.5572\n",
      "Epoch 19/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6654 - accuracy: 0.6033 - val_loss: 0.6780 - val_accuracy: 0.5604\n",
      "Epoch 20/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6633 - accuracy: 0.6043 - val_loss: 0.6767 - val_accuracy: 0.5620\n",
      "Epoch 21/100\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.6595 - accuracy: 0.6113 - val_loss: 0.6706 - val_accuracy: 0.5826\n",
      "Epoch 22/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6461 - accuracy: 0.6402 - val_loss: 0.6557 - val_accuracy: 0.6254\n",
      "Epoch 23/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6324 - accuracy: 0.6613 - val_loss: 0.6423 - val_accuracy: 0.6480\n",
      "Epoch 24/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6177 - accuracy: 0.6801 - val_loss: 0.6270 - val_accuracy: 0.6676\n",
      "Epoch 25/100\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.6014 - accuracy: 0.6980 - val_loss: 0.6121 - val_accuracy: 0.6834\n",
      "Epoch 26/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.5842 - accuracy: 0.7140 - val_loss: 0.5946 - val_accuracy: 0.7018\n",
      "Epoch 27/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.5673 - accuracy: 0.7301 - val_loss: 0.5791 - val_accuracy: 0.7156\n",
      "Epoch 28/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.5509 - accuracy: 0.7405 - val_loss: 0.5638 - val_accuracy: 0.7256\n",
      "Epoch 29/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.5347 - accuracy: 0.7536 - val_loss: 0.5499 - val_accuracy: 0.7400\n",
      "Epoch 30/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.5199 - accuracy: 0.7635 - val_loss: 0.5393 - val_accuracy: 0.7434\n",
      "Epoch 31/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.5061 - accuracy: 0.7726 - val_loss: 0.5261 - val_accuracy: 0.7540\n",
      "Epoch 32/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4943 - accuracy: 0.7793 - val_loss: 0.5148 - val_accuracy: 0.7594\n",
      "Epoch 33/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4838 - accuracy: 0.7854 - val_loss: 0.5076 - val_accuracy: 0.7606\n",
      "Epoch 34/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4744 - accuracy: 0.7910 - val_loss: 0.4985 - val_accuracy: 0.7700\n",
      "Epoch 35/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4662 - accuracy: 0.7945 - val_loss: 0.4923 - val_accuracy: 0.7730\n",
      "Epoch 36/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4595 - accuracy: 0.7964 - val_loss: 0.4872 - val_accuracy: 0.7774\n",
      "Epoch 37/100\n",
      "313/313 [==============================] - 8s 26ms/step - loss: 0.4533 - accuracy: 0.8009 - val_loss: 0.4831 - val_accuracy: 0.7798\n",
      "Epoch 38/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4478 - accuracy: 0.8027 - val_loss: 0.4792 - val_accuracy: 0.7782\n",
      "Epoch 39/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4424 - accuracy: 0.8065 - val_loss: 0.4765 - val_accuracy: 0.7790\n",
      "Epoch 40/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4377 - accuracy: 0.8094 - val_loss: 0.4750 - val_accuracy: 0.7772\n",
      "Epoch 41/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4332 - accuracy: 0.8120 - val_loss: 0.4733 - val_accuracy: 0.7800\n",
      "Epoch 42/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4293 - accuracy: 0.8120 - val_loss: 0.4782 - val_accuracy: 0.7790\n",
      "Epoch 43/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4259 - accuracy: 0.8154 - val_loss: 0.4684 - val_accuracy: 0.7818\n",
      "Epoch 44/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4230 - accuracy: 0.8140 - val_loss: 0.4674 - val_accuracy: 0.7812\n",
      "Epoch 45/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4201 - accuracy: 0.8167 - val_loss: 0.4701 - val_accuracy: 0.7810\n",
      "Epoch 46/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4175 - accuracy: 0.8178 - val_loss: 0.4699 - val_accuracy: 0.7812\n",
      "Epoch 47/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4154 - accuracy: 0.8193 - val_loss: 0.4649 - val_accuracy: 0.7826\n",
      "Epoch 48/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4135 - accuracy: 0.8208 - val_loss: 0.4641 - val_accuracy: 0.7826\n",
      "Epoch 49/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4115 - accuracy: 0.8211 - val_loss: 0.4641 - val_accuracy: 0.7844\n",
      "Epoch 50/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4093 - accuracy: 0.8227 - val_loss: 0.4658 - val_accuracy: 0.7818\n",
      "Epoch 51/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4083 - accuracy: 0.8218 - val_loss: 0.4658 - val_accuracy: 0.7850\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": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 279
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:27:13.450495Z",
     "iopub.status.busy": "2022-01-08T05:27:13.449785Z",
     "iopub.status.idle": "2022-01-08T05:27:13.568727Z",
     "shell.execute_reply": "2022-01-08T05:27:13.569535Z"
    },
    "id": "YS_5tIG26kQa",
    "outputId": "7affeec2-15fb-4217-daa5-2c8ef2f8a74b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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": 26,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:27:13.578088Z",
     "iopub.status.busy": "2022-01-08T05:27:13.576979Z",
     "iopub.status.idle": "2022-01-08T05:27:13.920651Z",
     "shell.execute_reply": "2022-01-08T05:27:13.921082Z"
    },
    "id": "Ofl-Ifev6kQa",
    "outputId": "5446f8dd-845c-4fea-eb3a-a8887f010da0"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"sequential_1\"\n",
      "_________________________________________________________________\n",
      " Layer (type)                Output Shape              Param #   \n",
      "=================================================================\n",
      " embedding (Embedding)       (None, 100, 16)           8000      \n",
      "                                                                 \n",
      " simple_rnn_1 (SimpleRNN)    (None, 8)                 200       \n",
      "                                                                 \n",
      " dense_1 (Dense)             (None, 1)                 9         \n",
      "                                                                 \n",
      "=================================================================\n",
      "Total params: 8,209\n",
      "Trainable params: 8,209\n",
      "Non-trainable params: 0\n",
      "_________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "model2 = keras.Sequential()\n",
    "\n",
    "model2.add(keras.layers.Embedding(500, 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": 27,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:27:13.930484Z",
     "iopub.status.busy": "2022-01-08T05:27:13.929775Z",
     "iopub.status.idle": "2022-01-08T05:31:21.300019Z",
     "shell.execute_reply": "2022-01-08T05:31:21.299400Z"
    },
    "id": "78TUrJAs6kQa",
    "outputId": "ee782c94-cf81-4add-e0a9-5290b91bd2bf"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "313/313 [==============================] - 9s 25ms/step - loss: 0.6942 - accuracy: 0.5133 - val_loss: 0.6917 - val_accuracy: 0.5270\n",
      "Epoch 2/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6821 - accuracy: 0.5755 - val_loss: 0.6761 - val_accuracy: 0.6048\n",
      "Epoch 3/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6620 - accuracy: 0.6469 - val_loss: 0.6559 - val_accuracy: 0.6596\n",
      "Epoch 4/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6389 - accuracy: 0.6995 - val_loss: 0.6354 - val_accuracy: 0.7026\n",
      "Epoch 5/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.6178 - accuracy: 0.7278 - val_loss: 0.6194 - val_accuracy: 0.7186\n",
      "Epoch 6/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.5994 - accuracy: 0.7462 - val_loss: 0.6013 - val_accuracy: 0.7364\n",
      "Epoch 7/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.5828 - accuracy: 0.7564 - val_loss: 0.5892 - val_accuracy: 0.7454\n",
      "Epoch 8/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.5676 - accuracy: 0.7667 - val_loss: 0.5790 - val_accuracy: 0.7512\n",
      "Epoch 9/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.5531 - accuracy: 0.7759 - val_loss: 0.5635 - val_accuracy: 0.7478\n",
      "Epoch 10/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.5390 - accuracy: 0.7805 - val_loss: 0.5539 - val_accuracy: 0.7554\n",
      "Epoch 11/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.5266 - accuracy: 0.7855 - val_loss: 0.5466 - val_accuracy: 0.7580\n",
      "Epoch 12/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.5153 - accuracy: 0.7901 - val_loss: 0.5344 - val_accuracy: 0.7674\n",
      "Epoch 13/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.5040 - accuracy: 0.7940 - val_loss: 0.5239 - val_accuracy: 0.7722\n",
      "Epoch 14/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4934 - accuracy: 0.7992 - val_loss: 0.5233 - val_accuracy: 0.7664\n",
      "Epoch 15/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4840 - accuracy: 0.8029 - val_loss: 0.5098 - val_accuracy: 0.7724\n",
      "Epoch 16/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.4743 - accuracy: 0.8055 - val_loss: 0.5040 - val_accuracy: 0.7748\n",
      "Epoch 17/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.4654 - accuracy: 0.8080 - val_loss: 0.4988 - val_accuracy: 0.7796\n",
      "Epoch 18/100\n",
      "313/313 [==============================] - 7s 24ms/step - loss: 0.4579 - accuracy: 0.8109 - val_loss: 0.4939 - val_accuracy: 0.7798\n",
      "Epoch 19/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.4508 - accuracy: 0.8136 - val_loss: 0.4932 - val_accuracy: 0.7734\n",
      "Epoch 20/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4441 - accuracy: 0.8148 - val_loss: 0.4896 - val_accuracy: 0.7766\n",
      "Epoch 21/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.4379 - accuracy: 0.8185 - val_loss: 0.4852 - val_accuracy: 0.7784\n",
      "Epoch 22/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4326 - accuracy: 0.8206 - val_loss: 0.4813 - val_accuracy: 0.7818\n",
      "Epoch 23/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4276 - accuracy: 0.8213 - val_loss: 0.4811 - val_accuracy: 0.7830\n",
      "Epoch 24/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4227 - accuracy: 0.8226 - val_loss: 0.4786 - val_accuracy: 0.7842\n",
      "Epoch 25/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4186 - accuracy: 0.8237 - val_loss: 0.4770 - val_accuracy: 0.7810\n",
      "Epoch 26/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.4147 - accuracy: 0.8268 - val_loss: 0.4750 - val_accuracy: 0.7834\n",
      "Epoch 27/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.4111 - accuracy: 0.8268 - val_loss: 0.4762 - val_accuracy: 0.7828\n",
      "Epoch 28/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.4076 - accuracy: 0.8288 - val_loss: 0.4796 - val_accuracy: 0.7804\n",
      "Epoch 29/100\n",
      "313/313 [==============================] - 8s 25ms/step - loss: 0.4043 - accuracy: 0.8300 - val_loss: 0.4746 - val_accuracy: 0.7822\n",
      "Epoch 30/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.4016 - accuracy: 0.8315 - val_loss: 0.4759 - val_accuracy: 0.7826\n",
      "Epoch 31/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.3984 - accuracy: 0.8335 - val_loss: 0.4776 - val_accuracy: 0.7810\n",
      "Epoch 32/100\n",
      "313/313 [==============================] - 8s 24ms/step - loss: 0.3960 - accuracy: 0.8341 - val_loss: 0.4764 - val_accuracy: 0.7820\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": 28,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 282
    },
    "execution": {
     "iopub.execute_input": "2022-01-08T05:31:21.309740Z",
     "iopub.status.busy": "2022-01-08T05:31:21.305105Z",
     "iopub.status.idle": "2022-01-08T05:31:21.449709Z",
     "shell.execute_reply": "2022-01-08T05:31:21.450221Z"
    },
    "id": "brUvKwLt6kQb",
    "outputId": "c9907bb0-000c-4c7f-fe2c-ce8128bac52b"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "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": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}