9-3.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "kernelspec": {
      "display_name": "TensorFlow 2.3 on Python 3.6 (CUDA 10.1)",
      "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": "3.6.9"
    },
    "colab": {
      "name": "9-3.ipynb",
      "provenance": []
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AlaMdWF26k2H"
      },
      "source": [
        "# LSTM과 GRU 셀"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o_Csb7v96k2M"
      },
      "source": [
        "<table align=\"left\">\n",
        "  <td>\n",
        "    <a target=\"_blank\" href=\"https://colab.research.google.com/github/rickiepark/hg-mldl/blob/master/9-3.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": "KWi9yl0q6k2N"
      },
      "source": [
        "## LSTM 신경망 훈련하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T09:05:30.590337Z",
          "iopub.status.busy": "2020-10-12T09:05:30.589154Z",
          "iopub.status.idle": "2020-10-12T09:05:40.880700Z",
          "shell.execute_reply": "2020-10-12T09:05:40.879133Z"
        },
        "id": "XL7x0NCx6k2N",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d5c9f55e-6a51-49b7-e739-d2242adddcae"
      },
      "source": [
        "from tensorflow.keras.datasets import imdb\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "(train_input, train_target), (test_input, test_target) = imdb.load_data(\n",
        "    num_words=500)\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)"
      ],
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T09:05:40.904623Z",
          "iopub.status.busy": "2020-10-12T09:05:40.902896Z",
          "iopub.status.idle": "2020-10-12T09:05:41.543511Z",
          "shell.execute_reply": "2020-10-12T09:05:41.542251Z"
        },
        "id": "5bjJ14QJ6k2O"
      },
      "source": [
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "\n",
        "train_seq = pad_sequences(train_input, maxlen=100)\n",
        "val_seq = pad_sequences(val_input, maxlen=100)"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T09:05:41.551517Z",
          "iopub.status.busy": "2020-10-12T09:05:41.550145Z",
          "iopub.status.idle": "2020-10-12T09:05:43.982644Z",
          "shell.execute_reply": "2020-10-12T09:05:43.983878Z"
        },
        "id": "uMlTD4zv6k2O",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dc7c0080-f72c-4032-caf4-1a8e63202989"
      },
      "source": [
        "from tensorflow import keras\n",
        "\n",
        "model = keras.Sequential()\n",
        "\n",
        "model.add(keras.layers.Embedding(500, 16, input_length=100))\n",
        "model.add(keras.layers.LSTM(8))\n",
        "model.add(keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model.summary()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding (Embedding)        (None, 100, 16)           8000      \n",
            "_________________________________________________________________\n",
            "lstm (LSTM)                  (None, 8)                 800       \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 8,809\n",
            "Trainable params: 8,809\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T09:05:44.007411Z",
          "iopub.status.busy": "2020-10-12T09:05:44.005663Z",
          "iopub.status.idle": "2020-10-12T09:09:36.703760Z",
          "shell.execute_reply": "2020-10-12T09:09:36.702162Z"
        },
        "id": "nCmqOkjI6k2P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5057828c-5f90-48a5-fafa-c31f7407cc4c"
      },
      "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-lstm-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_seq, train_target, epochs=100, batch_size=64,\n",
        "                    validation_data=(val_seq, val_target),\n",
        "                    callbacks=[checkpoint_cb, early_stopping_cb])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "313/313 [==============================] - 13s 21ms/step - loss: 0.6922 - accuracy: 0.5435 - val_loss: 0.6906 - val_accuracy: 0.5886\n",
            "Epoch 2/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.6877 - accuracy: 0.6327 - val_loss: 0.6829 - val_accuracy: 0.6708\n",
            "Epoch 3/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.6647 - accuracy: 0.6944 - val_loss: 0.6343 - val_accuracy: 0.7098\n",
            "Epoch 4/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.6053 - accuracy: 0.7117 - val_loss: 0.5918 - val_accuracy: 0.7134\n",
            "Epoch 5/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.5763 - accuracy: 0.7304 - val_loss: 0.5709 - val_accuracy: 0.7320\n",
            "Epoch 6/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.5540 - accuracy: 0.7467 - val_loss: 0.5487 - val_accuracy: 0.7470\n",
            "Epoch 7/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.5326 - accuracy: 0.7625 - val_loss: 0.5285 - val_accuracy: 0.7602\n",
            "Epoch 8/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.5124 - accuracy: 0.7727 - val_loss: 0.5122 - val_accuracy: 0.7666\n",
            "Epoch 9/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4955 - accuracy: 0.7830 - val_loss: 0.5002 - val_accuracy: 0.7724\n",
            "Epoch 10/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4817 - accuracy: 0.7896 - val_loss: 0.4953 - val_accuracy: 0.7698\n",
            "Epoch 11/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4716 - accuracy: 0.7940 - val_loss: 0.4785 - val_accuracy: 0.7818\n",
            "Epoch 12/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4636 - accuracy: 0.7986 - val_loss: 0.4729 - val_accuracy: 0.7868\n",
            "Epoch 13/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4569 - accuracy: 0.7992 - val_loss: 0.4746 - val_accuracy: 0.7836\n",
            "Epoch 14/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4512 - accuracy: 0.8023 - val_loss: 0.4707 - val_accuracy: 0.7886\n",
            "Epoch 15/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4471 - accuracy: 0.8023 - val_loss: 0.4614 - val_accuracy: 0.7908\n",
            "Epoch 16/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4426 - accuracy: 0.8040 - val_loss: 0.4588 - val_accuracy: 0.7916\n",
            "Epoch 17/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4390 - accuracy: 0.8051 - val_loss: 0.4577 - val_accuracy: 0.7938\n",
            "Epoch 18/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4358 - accuracy: 0.8058 - val_loss: 0.4535 - val_accuracy: 0.7948\n",
            "Epoch 19/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4329 - accuracy: 0.8074 - val_loss: 0.4557 - val_accuracy: 0.7938\n",
            "Epoch 20/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4308 - accuracy: 0.8069 - val_loss: 0.4503 - val_accuracy: 0.7946\n",
            "Epoch 21/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4282 - accuracy: 0.8092 - val_loss: 0.4488 - val_accuracy: 0.7954\n",
            "Epoch 22/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4261 - accuracy: 0.8102 - val_loss: 0.4462 - val_accuracy: 0.7948\n",
            "Epoch 23/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4242 - accuracy: 0.8101 - val_loss: 0.4458 - val_accuracy: 0.7936\n",
            "Epoch 24/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4222 - accuracy: 0.8106 - val_loss: 0.4441 - val_accuracy: 0.7944\n",
            "Epoch 25/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4208 - accuracy: 0.8100 - val_loss: 0.4426 - val_accuracy: 0.7980\n",
            "Epoch 26/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4194 - accuracy: 0.8119 - val_loss: 0.4421 - val_accuracy: 0.7936\n",
            "Epoch 27/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4178 - accuracy: 0.8127 - val_loss: 0.4409 - val_accuracy: 0.7952\n",
            "Epoch 28/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4169 - accuracy: 0.8127 - val_loss: 0.4392 - val_accuracy: 0.7988\n",
            "Epoch 29/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4154 - accuracy: 0.8135 - val_loss: 0.4402 - val_accuracy: 0.8000\n",
            "Epoch 30/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4140 - accuracy: 0.8140 - val_loss: 0.4370 - val_accuracy: 0.7988\n",
            "Epoch 31/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4134 - accuracy: 0.8134 - val_loss: 0.4365 - val_accuracy: 0.7976\n",
            "Epoch 32/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4120 - accuracy: 0.8145 - val_loss: 0.4390 - val_accuracy: 0.8030\n",
            "Epoch 33/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4115 - accuracy: 0.8145 - val_loss: 0.4365 - val_accuracy: 0.7934\n",
            "Epoch 34/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4106 - accuracy: 0.8165 - val_loss: 0.4390 - val_accuracy: 0.8008\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T09:09:36.725150Z",
          "iopub.status.busy": "2020-10-12T09:09:36.723949Z",
          "iopub.status.idle": "2020-10-12T09:09:37.618677Z",
          "shell.execute_reply": "2020-10-12T09:09:37.620018Z"
        },
        "id": "Xl1bliNr6k2Q",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "4e6792a9-2118-4498-a4cb-f0b39d1609c0"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "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()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ob79h7Kc6k2Q"
      },
      "source": [
        "## 순환 층에 드롭아웃 적용하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T09:09:37.633391Z",
          "iopub.status.busy": "2020-10-12T09:09:37.631822Z",
          "iopub.status.idle": "2020-10-12T09:09:37.849640Z",
          "shell.execute_reply": "2020-10-12T09:09:37.850929Z"
        },
        "id": "Fr0DUR-Y6k2R"
      },
      "source": [
        "model2 = keras.Sequential()\n",
        "\n",
        "model2.add(keras.layers.Embedding(500, 16, input_length=100))\n",
        "model2.add(keras.layers.LSTM(8, dropout=0.3))\n",
        "model2.add(keras.layers.Dense(1, activation='sigmoid'))"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T09:09:37.868353Z",
          "iopub.status.busy": "2020-10-12T09:09:37.866777Z",
          "iopub.status.idle": "2020-10-12T09:13:59.438760Z",
          "shell.execute_reply": "2020-10-12T09:13:59.440154Z"
        },
        "id": "Ap8XxUO46k2R",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5953909f-5c48-43e9-b321-8b5f3ed0d52a"
      },
      "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-dropout-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])"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "313/313 [==============================] - 9s 21ms/step - loss: 0.6929 - accuracy: 0.5147 - val_loss: 0.6927 - val_accuracy: 0.5338\n",
            "Epoch 2/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.6918 - accuracy: 0.5716 - val_loss: 0.6911 - val_accuracy: 0.5994\n",
            "Epoch 3/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.6887 - accuracy: 0.6428 - val_loss: 0.6851 - val_accuracy: 0.6766\n",
            "Epoch 4/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.6666 - accuracy: 0.6772 - val_loss: 0.6351 - val_accuracy: 0.7054\n",
            "Epoch 5/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.6195 - accuracy: 0.6981 - val_loss: 0.6059 - val_accuracy: 0.7084\n",
            "Epoch 6/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.5951 - accuracy: 0.7171 - val_loss: 0.5846 - val_accuracy: 0.7296\n",
            "Epoch 7/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.5743 - accuracy: 0.7350 - val_loss: 0.5613 - val_accuracy: 0.7536\n",
            "Epoch 8/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.5547 - accuracy: 0.7497 - val_loss: 0.5425 - val_accuracy: 0.7654\n",
            "Epoch 9/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.5372 - accuracy: 0.7598 - val_loss: 0.5290 - val_accuracy: 0.7686\n",
            "Epoch 10/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.5233 - accuracy: 0.7668 - val_loss: 0.5154 - val_accuracy: 0.7754\n",
            "Epoch 11/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.5081 - accuracy: 0.7761 - val_loss: 0.5050 - val_accuracy: 0.7744\n",
            "Epoch 12/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4987 - accuracy: 0.7776 - val_loss: 0.4984 - val_accuracy: 0.7770\n",
            "Epoch 13/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4915 - accuracy: 0.7801 - val_loss: 0.4893 - val_accuracy: 0.7818\n",
            "Epoch 14/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4838 - accuracy: 0.7839 - val_loss: 0.4855 - val_accuracy: 0.7772\n",
            "Epoch 15/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4756 - accuracy: 0.7878 - val_loss: 0.4775 - val_accuracy: 0.7850\n",
            "Epoch 16/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4694 - accuracy: 0.7896 - val_loss: 0.4735 - val_accuracy: 0.7848\n",
            "Epoch 17/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4650 - accuracy: 0.7925 - val_loss: 0.4677 - val_accuracy: 0.7870\n",
            "Epoch 18/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4597 - accuracy: 0.7929 - val_loss: 0.4669 - val_accuracy: 0.7844\n",
            "Epoch 19/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4513 - accuracy: 0.7996 - val_loss: 0.4606 - val_accuracy: 0.7886\n",
            "Epoch 20/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4498 - accuracy: 0.7994 - val_loss: 0.4576 - val_accuracy: 0.7904\n",
            "Epoch 21/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4450 - accuracy: 0.8013 - val_loss: 0.4552 - val_accuracy: 0.7910\n",
            "Epoch 22/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4435 - accuracy: 0.8000 - val_loss: 0.4512 - val_accuracy: 0.7932\n",
            "Epoch 23/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4398 - accuracy: 0.8022 - val_loss: 0.4486 - val_accuracy: 0.7956\n",
            "Epoch 24/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4385 - accuracy: 0.8027 - val_loss: 0.4479 - val_accuracy: 0.7956\n",
            "Epoch 25/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4348 - accuracy: 0.8035 - val_loss: 0.4462 - val_accuracy: 0.7918\n",
            "Epoch 26/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4332 - accuracy: 0.8019 - val_loss: 0.4435 - val_accuracy: 0.7976\n",
            "Epoch 27/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4279 - accuracy: 0.8079 - val_loss: 0.4417 - val_accuracy: 0.7946\n",
            "Epoch 28/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4280 - accuracy: 0.8065 - val_loss: 0.4425 - val_accuracy: 0.7930\n",
            "Epoch 29/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4271 - accuracy: 0.8069 - val_loss: 0.4382 - val_accuracy: 0.8004\n",
            "Epoch 30/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4247 - accuracy: 0.8076 - val_loss: 0.4379 - val_accuracy: 0.7984\n",
            "Epoch 31/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4247 - accuracy: 0.8080 - val_loss: 0.4353 - val_accuracy: 0.8004\n",
            "Epoch 32/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4242 - accuracy: 0.8091 - val_loss: 0.4347 - val_accuracy: 0.7988\n",
            "Epoch 33/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4210 - accuracy: 0.8094 - val_loss: 0.4340 - val_accuracy: 0.7972\n",
            "Epoch 34/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4188 - accuracy: 0.8111 - val_loss: 0.4346 - val_accuracy: 0.8018\n",
            "Epoch 35/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4192 - accuracy: 0.8102 - val_loss: 0.4341 - val_accuracy: 0.8022\n",
            "Epoch 36/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4163 - accuracy: 0.8099 - val_loss: 0.4342 - val_accuracy: 0.7970\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T09:13:59.466740Z",
          "iopub.status.busy": "2020-10-12T09:13:59.465154Z",
          "iopub.status.idle": "2020-10-12T09:14:00.055101Z",
          "shell.execute_reply": "2020-10-12T09:14:00.056561Z"
        },
        "id": "Qzwsp3pl6k2R",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "97305973-3c11-4c55-c466-a608273094cb"
      },
      "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()"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DKtQByM96k2S"
      },
      "source": [
        "## 2개의 층을 연결하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T09:14:00.069924Z",
          "iopub.status.busy": "2020-10-12T09:14:00.068581Z",
          "iopub.status.idle": "2020-10-12T09:14:00.459073Z",
          "shell.execute_reply": "2020-10-12T09:14:00.460006Z"
        },
        "id": "YkQboCpD6k2S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5f30a5ec-16a9-476a-95b6-3d55b36b64b4"
      },
      "source": [
        "model3 = keras.Sequential()\n",
        "\n",
        "model3.add(keras.layers.Embedding(500, 16, input_length=100))\n",
        "model3.add(keras.layers.LSTM(8, dropout=0.3, return_sequences=True))\n",
        "model3.add(keras.layers.LSTM(8, dropout=0.3))\n",
        "model3.add(keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model3.summary()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_2 (Embedding)      (None, 100, 16)           8000      \n",
            "_________________________________________________________________\n",
            "lstm_2 (LSTM)                (None, 100, 8)            800       \n",
            "_________________________________________________________________\n",
            "lstm_3 (LSTM)                (None, 8)                 544       \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 9,353\n",
            "Trainable params: 9,353\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T09:14:00.485178Z",
          "iopub.status.busy": "2020-10-12T09:14:00.483959Z",
          "iopub.status.idle": "2020-10-12T09:20:24.570047Z",
          "shell.execute_reply": "2020-10-12T09:20:24.571421Z"
        },
        "id": "hDk_ffAo6k2S",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f7439e48-073f-4124-b10e-dea60f18f69d"
      },
      "source": [
        "rmsprop = keras.optimizers.RMSprop(learning_rate=1e-4)\n",
        "model3.compile(optimizer=rmsprop, loss='binary_crossentropy', \n",
        "               metrics=['accuracy'])\n",
        "\n",
        "checkpoint_cb = keras.callbacks.ModelCheckpoint('best-2rnn-model.h5', \n",
        "                                                save_best_only=True)\n",
        "early_stopping_cb = keras.callbacks.EarlyStopping(patience=3,\n",
        "                                                  restore_best_weights=True)\n",
        "\n",
        "history = model3.fit(train_seq, train_target, epochs=100, batch_size=64,\n",
        "                     validation_data=(val_seq, val_target),\n",
        "                     callbacks=[checkpoint_cb, early_stopping_cb])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "313/313 [==============================] - 17s 43ms/step - loss: 0.6927 - accuracy: 0.5375 - val_loss: 0.6921 - val_accuracy: 0.5916\n",
            "Epoch 2/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.6900 - accuracy: 0.5748 - val_loss: 0.6862 - val_accuracy: 0.6402\n",
            "Epoch 3/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.6643 - accuracy: 0.6443 - val_loss: 0.6234 - val_accuracy: 0.6786\n",
            "Epoch 4/100\n",
            "313/313 [==============================] - 13s 40ms/step - loss: 0.5833 - accuracy: 0.7151 - val_loss: 0.5522 - val_accuracy: 0.7318\n",
            "Epoch 5/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.5374 - accuracy: 0.7417 - val_loss: 0.5187 - val_accuracy: 0.7554\n",
            "Epoch 6/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.5097 - accuracy: 0.7574 - val_loss: 0.4940 - val_accuracy: 0.7682\n",
            "Epoch 7/100\n",
            "313/313 [==============================] - 13s 40ms/step - loss: 0.4920 - accuracy: 0.7671 - val_loss: 0.4819 - val_accuracy: 0.7702\n",
            "Epoch 8/100\n",
            "313/313 [==============================] - 13s 40ms/step - loss: 0.4755 - accuracy: 0.7766 - val_loss: 0.4670 - val_accuracy: 0.7842\n",
            "Epoch 9/100\n",
            "313/313 [==============================] - 13s 40ms/step - loss: 0.4687 - accuracy: 0.7817 - val_loss: 0.4599 - val_accuracy: 0.7884\n",
            "Epoch 10/100\n",
            "313/313 [==============================] - 13s 40ms/step - loss: 0.4610 - accuracy: 0.7872 - val_loss: 0.4567 - val_accuracy: 0.7892\n",
            "Epoch 11/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4567 - accuracy: 0.7897 - val_loss: 0.4517 - val_accuracy: 0.7910\n",
            "Epoch 12/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4525 - accuracy: 0.7911 - val_loss: 0.4527 - val_accuracy: 0.7866\n",
            "Epoch 13/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4482 - accuracy: 0.7917 - val_loss: 0.4503 - val_accuracy: 0.7922\n",
            "Epoch 14/100\n",
            "313/313 [==============================] - 13s 40ms/step - loss: 0.4463 - accuracy: 0.7938 - val_loss: 0.4472 - val_accuracy: 0.7926\n",
            "Epoch 15/100\n",
            "313/313 [==============================] - 13s 40ms/step - loss: 0.4458 - accuracy: 0.7936 - val_loss: 0.4470 - val_accuracy: 0.7914\n",
            "Epoch 16/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4421 - accuracy: 0.7921 - val_loss: 0.4480 - val_accuracy: 0.7908\n",
            "Epoch 17/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4399 - accuracy: 0.7969 - val_loss: 0.4429 - val_accuracy: 0.7944\n",
            "Epoch 18/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4392 - accuracy: 0.7976 - val_loss: 0.4415 - val_accuracy: 0.7954\n",
            "Epoch 19/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4383 - accuracy: 0.7965 - val_loss: 0.4503 - val_accuracy: 0.7870\n",
            "Epoch 20/100\n",
            "313/313 [==============================] - 13s 40ms/step - loss: 0.4340 - accuracy: 0.8012 - val_loss: 0.4407 - val_accuracy: 0.7914\n",
            "Epoch 21/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4355 - accuracy: 0.7967 - val_loss: 0.4406 - val_accuracy: 0.7918\n",
            "Epoch 22/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4327 - accuracy: 0.8001 - val_loss: 0.4396 - val_accuracy: 0.7918\n",
            "Epoch 23/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4306 - accuracy: 0.8014 - val_loss: 0.4448 - val_accuracy: 0.7878\n",
            "Epoch 24/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4308 - accuracy: 0.8009 - val_loss: 0.4407 - val_accuracy: 0.7986\n",
            "Epoch 25/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4292 - accuracy: 0.8022 - val_loss: 0.4375 - val_accuracy: 0.7972\n",
            "Epoch 26/100\n",
            "313/313 [==============================] - 13s 40ms/step - loss: 0.4274 - accuracy: 0.8044 - val_loss: 0.4375 - val_accuracy: 0.7988\n",
            "Epoch 27/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4283 - accuracy: 0.8022 - val_loss: 0.4369 - val_accuracy: 0.8002\n",
            "Epoch 28/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4271 - accuracy: 0.8026 - val_loss: 0.4360 - val_accuracy: 0.7952\n",
            "Epoch 29/100\n",
            "313/313 [==============================] - 13s 40ms/step - loss: 0.4263 - accuracy: 0.8048 - val_loss: 0.4380 - val_accuracy: 0.8014\n",
            "Epoch 30/100\n",
            "313/313 [==============================] - 13s 40ms/step - loss: 0.4259 - accuracy: 0.8056 - val_loss: 0.4356 - val_accuracy: 0.7980\n",
            "Epoch 31/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4244 - accuracy: 0.8043 - val_loss: 0.4365 - val_accuracy: 0.8016\n",
            "Epoch 32/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4233 - accuracy: 0.8050 - val_loss: 0.4351 - val_accuracy: 0.7978\n",
            "Epoch 33/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4216 - accuracy: 0.8068 - val_loss: 0.4350 - val_accuracy: 0.7976\n",
            "Epoch 34/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4232 - accuracy: 0.8043 - val_loss: 0.4375 - val_accuracy: 0.7996\n",
            "Epoch 35/100\n",
            "313/313 [==============================] - 13s 41ms/step - loss: 0.4213 - accuracy: 0.8059 - val_loss: 0.4378 - val_accuracy: 0.7994\n",
            "Epoch 36/100\n",
            "313/313 [==============================] - 13s 40ms/step - loss: 0.4209 - accuracy: 0.8080 - val_loss: 0.4421 - val_accuracy: 0.7996\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T09:20:24.599872Z",
          "iopub.status.busy": "2020-10-12T09:20:24.598716Z",
          "iopub.status.idle": "2020-10-12T09:20:25.172291Z",
          "shell.execute_reply": "2020-10-12T09:20:25.173681Z"
        },
        "id": "1paBqczf6k2S",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "8dec69f9-2aa3-4b2f-f5c9-c0d2713ab4f6"
      },
      "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()"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Jisn0P9h6k2T"
      },
      "source": [
        "## GRU 신경망 훈련하기"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T09:20:25.189832Z",
          "iopub.status.busy": "2020-10-12T09:20:25.188004Z",
          "iopub.status.idle": "2020-10-12T09:20:25.402624Z",
          "shell.execute_reply": "2020-10-12T09:20:25.401128Z"
        },
        "id": "dwYF578d6k2T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "72677151-359c-4d9c-9fd0-026c778f4ad5"
      },
      "source": [
        "model4 = keras.Sequential()\n",
        "\n",
        "model4.add(keras.layers.Embedding(500, 16, input_length=100))\n",
        "model4.add(keras.layers.GRU(8))\n",
        "model4.add(keras.layers.Dense(1, activation='sigmoid'))\n",
        "\n",
        "model4.summary()"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_3 (Embedding)      (None, 100, 16)           8000      \n",
            "_________________________________________________________________\n",
            "gru (GRU)                    (None, 8)                 624       \n",
            "_________________________________________________________________\n",
            "dense_3 (Dense)              (None, 1)                 9         \n",
            "=================================================================\n",
            "Total params: 8,633\n",
            "Trainable params: 8,633\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T09:20:25.420177Z",
          "iopub.status.busy": "2020-10-12T09:20:25.419630Z",
          "iopub.status.idle": "2020-10-12T09:25:10.574200Z",
          "shell.execute_reply": "2020-10-12T09:25:10.575704Z"
        },
        "id": "j2SfOZ3v6k2U",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "80bc83ae-1472-4c03-f372-92a6572e71b8"
      },
      "source": [
        "rmsprop = keras.optimizers.RMSprop(learning_rate=1e-4)\n",
        "model4.compile(optimizer=rmsprop, loss='binary_crossentropy', \n",
        "               metrics=['accuracy'])\n",
        "\n",
        "checkpoint_cb = keras.callbacks.ModelCheckpoint('best-gru-model.h5', \n",
        "                                                save_best_only=True)\n",
        "early_stopping_cb = keras.callbacks.EarlyStopping(patience=3,\n",
        "                                                  restore_best_weights=True)\n",
        "\n",
        "history = model4.fit(train_seq, train_target, epochs=100, batch_size=64,\n",
        "                     validation_data=(val_seq, val_target),\n",
        "                     callbacks=[checkpoint_cb, early_stopping_cb])"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "313/313 [==============================] - 9s 21ms/step - loss: 0.6924 - accuracy: 0.5174 - val_loss: 0.6914 - val_accuracy: 0.5478\n",
            "Epoch 2/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.6903 - accuracy: 0.5601 - val_loss: 0.6892 - val_accuracy: 0.5740\n",
            "Epoch 3/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.6871 - accuracy: 0.5849 - val_loss: 0.6855 - val_accuracy: 0.5888\n",
            "Epoch 4/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.6820 - accuracy: 0.6039 - val_loss: 0.6799 - val_accuracy: 0.5948\n",
            "Epoch 5/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.6744 - accuracy: 0.6191 - val_loss: 0.6713 - val_accuracy: 0.6132\n",
            "Epoch 6/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.6629 - accuracy: 0.6321 - val_loss: 0.6589 - val_accuracy: 0.6248\n",
            "Epoch 7/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.6457 - accuracy: 0.6491 - val_loss: 0.6391 - val_accuracy: 0.6458\n",
            "Epoch 8/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.6188 - accuracy: 0.6726 - val_loss: 0.6072 - val_accuracy: 0.6786\n",
            "Epoch 9/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.5720 - accuracy: 0.7079 - val_loss: 0.5599 - val_accuracy: 0.7192\n",
            "Epoch 10/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.5303 - accuracy: 0.7366 - val_loss: 0.5295 - val_accuracy: 0.7416\n",
            "Epoch 11/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.5077 - accuracy: 0.7544 - val_loss: 0.5158 - val_accuracy: 0.7490\n",
            "Epoch 12/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4949 - accuracy: 0.7640 - val_loss: 0.5056 - val_accuracy: 0.7594\n",
            "Epoch 13/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4849 - accuracy: 0.7713 - val_loss: 0.4966 - val_accuracy: 0.7642\n",
            "Epoch 14/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4763 - accuracy: 0.7759 - val_loss: 0.4909 - val_accuracy: 0.7634\n",
            "Epoch 15/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4683 - accuracy: 0.7814 - val_loss: 0.4846 - val_accuracy: 0.7686\n",
            "Epoch 16/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4618 - accuracy: 0.7863 - val_loss: 0.4752 - val_accuracy: 0.7768\n",
            "Epoch 17/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4560 - accuracy: 0.7898 - val_loss: 0.4714 - val_accuracy: 0.7828\n",
            "Epoch 18/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4509 - accuracy: 0.7929 - val_loss: 0.4661 - val_accuracy: 0.7834\n",
            "Epoch 19/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4461 - accuracy: 0.7973 - val_loss: 0.4634 - val_accuracy: 0.7838\n",
            "Epoch 20/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4424 - accuracy: 0.7978 - val_loss: 0.4599 - val_accuracy: 0.7812\n",
            "Epoch 21/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4391 - accuracy: 0.8008 - val_loss: 0.4563 - val_accuracy: 0.7866\n",
            "Epoch 22/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4360 - accuracy: 0.8035 - val_loss: 0.4549 - val_accuracy: 0.7844\n",
            "Epoch 23/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4332 - accuracy: 0.8047 - val_loss: 0.4545 - val_accuracy: 0.7842\n",
            "Epoch 24/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4315 - accuracy: 0.8056 - val_loss: 0.4513 - val_accuracy: 0.7882\n",
            "Epoch 25/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4293 - accuracy: 0.8059 - val_loss: 0.4574 - val_accuracy: 0.7802\n",
            "Epoch 26/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4275 - accuracy: 0.8064 - val_loss: 0.4491 - val_accuracy: 0.7914\n",
            "Epoch 27/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4259 - accuracy: 0.8074 - val_loss: 0.4501 - val_accuracy: 0.7926\n",
            "Epoch 28/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4240 - accuracy: 0.8106 - val_loss: 0.4528 - val_accuracy: 0.7934\n",
            "Epoch 29/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4227 - accuracy: 0.8102 - val_loss: 0.4474 - val_accuracy: 0.7926\n",
            "Epoch 30/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4220 - accuracy: 0.8119 - val_loss: 0.4472 - val_accuracy: 0.7902\n",
            "Epoch 31/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4204 - accuracy: 0.8122 - val_loss: 0.4464 - val_accuracy: 0.7910\n",
            "Epoch 32/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4196 - accuracy: 0.8124 - val_loss: 0.4450 - val_accuracy: 0.7930\n",
            "Epoch 33/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4188 - accuracy: 0.8135 - val_loss: 0.4449 - val_accuracy: 0.7924\n",
            "Epoch 34/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4179 - accuracy: 0.8131 - val_loss: 0.4445 - val_accuracy: 0.7922\n",
            "Epoch 35/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4173 - accuracy: 0.8159 - val_loss: 0.4433 - val_accuracy: 0.7928\n",
            "Epoch 36/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4164 - accuracy: 0.8155 - val_loss: 0.4424 - val_accuracy: 0.7970\n",
            "Epoch 37/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4157 - accuracy: 0.8155 - val_loss: 0.4422 - val_accuracy: 0.7958\n",
            "Epoch 38/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4154 - accuracy: 0.8149 - val_loss: 0.4423 - val_accuracy: 0.7966\n",
            "Epoch 39/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4148 - accuracy: 0.8156 - val_loss: 0.4416 - val_accuracy: 0.7970\n",
            "Epoch 40/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4142 - accuracy: 0.8163 - val_loss: 0.4440 - val_accuracy: 0.7914\n",
            "Epoch 41/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4136 - accuracy: 0.8173 - val_loss: 0.4409 - val_accuracy: 0.7948\n",
            "Epoch 42/100\n",
            "313/313 [==============================] - 6s 20ms/step - loss: 0.4132 - accuracy: 0.8148 - val_loss: 0.4407 - val_accuracy: 0.7954\n",
            "Epoch 43/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4128 - accuracy: 0.8162 - val_loss: 0.4399 - val_accuracy: 0.7956\n",
            "Epoch 44/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4121 - accuracy: 0.8170 - val_loss: 0.4437 - val_accuracy: 0.7946\n",
            "Epoch 45/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4116 - accuracy: 0.8180 - val_loss: 0.4397 - val_accuracy: 0.7954\n",
            "Epoch 46/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4116 - accuracy: 0.8166 - val_loss: 0.4387 - val_accuracy: 0.7976\n",
            "Epoch 47/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4110 - accuracy: 0.8171 - val_loss: 0.4406 - val_accuracy: 0.7966\n",
            "Epoch 48/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4104 - accuracy: 0.8174 - val_loss: 0.4491 - val_accuracy: 0.7920\n",
            "Epoch 49/100\n",
            "313/313 [==============================] - 6s 19ms/step - loss: 0.4103 - accuracy: 0.8183 - val_loss: 0.4427 - val_accuracy: 0.7924\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2020-10-12T09:25:10.602797Z",
          "iopub.status.busy": "2020-10-12T09:25:10.601459Z",
          "iopub.status.idle": "2020-10-12T09:25:11.203520Z",
          "shell.execute_reply": "2020-10-12T09:25:11.202193Z"
        },
        "id": "3yUJM4nA6k2U",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "outputId": "cf86c235-1135-4d0f-93c2-08b7cb98ead7"
      },
      "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()"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ja3-116b6k2V"
      },
      "source": [
        "## 마무리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-5WtFRN-6k2V",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "dbbf3f1d-3cd9-49cf-bf90-233ecac13134"
      },
      "source": [
        "test_seq = pad_sequences(test_input, maxlen=100)\n",
        "\n",
        "rnn_model = keras.models.load_model('best-2rnn-model.h5')\n",
        "\n",
        "rnn_model.evaluate(test_seq, test_target)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "782/782 [==============================] - 12s 15ms/step - loss: 0.4291 - accuracy: 0.8004\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.4291330873966217, 0.8004400134086609]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    }
  ]
}