9_Convolution_Neural_Network.ipynb

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      "name": "9_Convolution_Neural_Network.ipynb",
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "86l7I5SQ_u9J"
      },
      "source": [
        "# 합성곱 신경망(Convolution Neural Network)\n",
        "\n",
        "- 이미지 인식, 음성 인식 등에 자주 사용되는데,  \n",
        "  특히, 이미지 인식 분야에서 거의 모든 딥러닝 기술에 사용\n",
        "\n",
        "<br>\n",
        "\n",
        "## 컨볼루션 신경망의 등장\n",
        "- 1989년 얀 르쿤(Yann LeCun) 교수의 논문에서 발표\n",
        "\n",
        "  - 필기체 인식에서 의미가 있었지만 범용화하는데에는 무리\n",
        "\n",
        "- 1998년, \"Gradient-Based Learning Applied to Document Recognition\"이라는 논문에서 LeNet-5 제시\n",
        "\n",
        "- 합성곱 층(convolution layer), 풀링 층(pooling layer) 소개\n",
        "  <br>\n",
        "\n",
        "  <img src=\"https://miro.medium.com/max/4308/1*1TI1aGBZ4dybR6__DI9dzA.png\">\n",
        "  \n",
        "  <center>[LeNet-5 구조]</center>\n",
        "\n",
        "  <sub>출처: https://medium.com/@pechyonkin/key-deep-learning-architectures-lenet-5-6fc3c59e6f4</sub>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Y46evOQHNukF"
      },
      "source": [
        "## 컨볼루션 신경망 구조 예시\n",
        "\n",
        "<img src=\"https://www.oreilly.com/library/view/neural-network-projects/9781789138900/assets/c38754ca-f2ea-425a-b7a6-1fe0f2f5074e.png\" width=\"600\">\n",
        "\n",
        "<sub>출처: https://www.oreilly.com/library/view/neural-network-projects/9781789138900/8e87ad66-6de3-4275-81a4-62b54436bf16.xhtml</sub>\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wtb8UWyTNxp_"
      },
      "source": [
        "## 합성곱 연산\n",
        "- 필터(filter) 연산\n",
        "  - 입력 데이터에 필터를 통한 어떠한 연산을 진행\n",
        "  \n",
        "  - **필터에 대응하는 원소끼리 곱하고, 그 합을 구함**\n",
        "\n",
        "  - 연산이 완료된 결과 데이터를 **특징 맵(feature map)**이라 부름\n",
        "\n",
        "- 필터(filter)\n",
        "  - 커널(kernel)이라고도 칭함\n",
        "  \n",
        "  - 흔히 사진 어플에서 사용하는 '이미지 필터'와 비슷한 개념\n",
        "\n",
        "  - 필터의 사이즈는 \"거의 항상 홀수\"\n",
        "    - 짝수이면 패딩이 비대칭이 되어버림\n",
        "  \n",
        "    - 왼쪽, 오른쪽을 다르게 주어야함\n",
        "  \n",
        "    - 중심위치가 존재, 즉 구별된 하나의 픽셀(중심 픽셀)이 존재\n",
        "\n",
        "  - 필터의 학습 파라미터 개수는 입력 데이터의 크기와 상관없이 일정  \n",
        "    따라서, 과적합을 방지할 수 있음\n",
        "\n",
        "  <br>\n",
        "  <img src=\"http://deeplearning.net/software/theano_versions/dev/_images/numerical_no_padding_no_strides.gif\">\n",
        "\n",
        "  <sub>출처: http://deeplearning.net/software/theano_versions/dev/tutorial/conv_arithmetic.html</sub>\n",
        "\n",
        "\n",
        "- 연산 시각화\n",
        "  <img src=\"https://www.researchgate.net/profile/Ihab_S_Mohamed/publication/324165524/figure/fig3/AS:611103423860736@1522709818959/An-example-of-convolution-operation-in-2D-2.png\" width=\"500\">\n",
        "\n",
        "  <sub>출처: https://www.researchgate.net/figure/An-example-of-convolution-operation-in-2D-2_fig3_324165524</sub>\n",
        "\n",
        "\n",
        "- 일반적으로, 합성곱 연산을 한 후의 데이터 사이즈는  \n",
        "  ### $\\quad (n-f+1) \\times (n-f+1)$\n",
        "    $n$: 입력 데이터의 크기  \n",
        "    $f$: 필터(커널)의 크기\n",
        "\n",
        "\n",
        "  <img src=\"https://miro.medium.com/max/1400/1*Fw-ehcNBR9byHtho-Rxbtw.gif\" width=\"400\">\n",
        "\n",
        "  <sub>출처: https://towardsdatascience.com/intuitively-understanding-convolutions-for-deep-learning-1f6f42faee1</sub>\n",
        "  \n",
        "  위 예에서 입력 데이터 크기($n$)는 5, 필터의 크기($k$)는 3이므로  \n",
        "  출력 데이터의 크기는 $(5 - 3 + 1) = 3$\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5-ekDsJwN2Y-"
      },
      "source": [
        "## 패딩(padding)과 스트라이드(stride)\n",
        "- 필터(커널) 사이즈과 함께 **입력 이미지와 출력 이미지의 사이즈를 결정**하기 위해 사용\n",
        "\n",
        "- 사용자가 결정할 수 있음\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "alV1bgcjN4Kc"
      },
      "source": [
        "\n",
        "### 패딩\n",
        "- 입력 데이터의 주변을 특정 값으로 채우는 기법\n",
        "  - 주로 0으로 많이 채움\n",
        "\n",
        "  <img src=\"http://deeplearning.net/software/theano_versions/dev/_images/arbitrary_padding_no_strides.gif\" width=\"300\">\n",
        "\n",
        "- 출력 데이터의 크기\n",
        "  ### $\\quad (n+2p-f+1) \\times (n+2p-f+1)$\n",
        "\n",
        "  위 그림에서, 입력 데이터의 크기($n$)는 5, 필터의 크기($f$)는 4, 패딩값($p$)은 2이므로    \n",
        "  출력 데이터의 크기는 ($5 + 2\\times 2 - 4 + 1) = 6$\n",
        "\n",
        "### 'valid' 와 'same'\n",
        "- 'valid'\n",
        "  - 패딩을 주지 않음\n",
        "  - padding=0 (0으로 채워진 테두리가 아니라 패딩을 주지 않는다는 의미)\n",
        "\n",
        "- 'same'\n",
        "  - 패딩을 주어 입력 이미지의 크기와 연산 후의 이미지 크기를 같게함\n",
        "\n",
        "  - 만약, 필터(커널)의 크기가 $k$ 이면,  \n",
        "    패딩의 크기는 $p = \\frac{k-1}{2}$ (단, <u>stride=1)</u>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zlZ7zG6ON85J"
      },
      "source": [
        "\n",
        "\n",
        "### 스트라이드\n",
        "- 필터를 적용하는 간격을 의미\n",
        "\n",
        "- 아래는 그림의 간격 2\n",
        "\n",
        "  <img src=\"http://deeplearning.net/software/theano_versions/dev/_images/no_padding_strides.gif\">\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LPcsND-0OCNm"
      },
      "source": [
        "## 출력 데이터의 크기\n",
        "\n",
        "## $\\qquad OH = \\frac{H + 2P - FH}{S} + 1 $ \n",
        "## $\\qquad OW = \\frac{W + 2P - FW}{S} + 1 $ \n",
        "\n",
        "- 입력 크기 : $(H, W)$\n",
        "\n",
        "- 필터 크기 : $(FH, FW)$\n",
        "\n",
        "- 출력 크기 : $(OH, OW)$\n",
        "\n",
        "- 패딩, 스트라이드 : $P, S$\n",
        "\n",
        "- (주의) \n",
        "  - 위 식의 값에서 $\\frac{H + 2P - FH}{S}$ 또는 $\\frac{W + 2P - FW}{S}$가 정수로 나누어 떨어지는 값이어야 함\n",
        "  - 만약, 정수로 나누어 떨어지지 않으면  \n",
        "    패딩, 스트라이드값을 조정하여 정수로 나누어 떨어지게 해야함\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1x4UoMbF8jJ9"
      },
      "source": [
        "## 풀링(Pooling)\n",
        "\n",
        "- 필터(커널) 사이즈 내에서 특정 값을 추출하는 과정"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lDiaO3XF8oC_"
      },
      "source": [
        "### 맥스 풀링(Max Pooling)\n",
        "- 가장 많이 사용되는 방법\n",
        "\n",
        "- 출력 데이터의 사이즈 계산은 컨볼루션 연산과 동일\n",
        "## $\\quad OH = \\frac{H + 2P - FH}{S} + 1 $ \n",
        "## $\\quad OW = \\frac{W + 2P - FW}{S} + 1 $ \n",
        "\n",
        "- 일반적으로 stride=2, kernel_size=2 를 통해  \n",
        "  **특징맵의 크기를 <u>절반으로 줄이는 역할</u>**\n",
        "\n",
        "- 모델이 물체의 주요한 특징을 학습할 수 있도록 해주며,  \n",
        "  컨볼루션 신경망이 이동 불변성 특성을 가지게 해줌\n",
        "  - 예를 들어, 아래의 그림에서 초록색 사각형 안에 있는  \n",
        "    2와 8의 위치를 바꾼다해도 맥스 풀링 연산은 8을 추출\n",
        "\n",
        "- 모델의 파라미터 개수를 줄여주고, 연산 속도를 빠르게 해줌\n",
        "\n",
        "  <br>\n",
        "\n",
        "  <img src=\"https://cs231n.github.io/assets/cnn/maxpool.jpeg\" width=\"600\">\n",
        "\n",
        "  <sub>출처: https://cs231n.github.io/convolutional-networks/</sub>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CrvtX4LrkhPI"
      },
      "source": [
        "## Conv1D \n",
        "\n",
        "- 텍스트 분류나 시계열 예측 같은 간단한 문제, 오디오 생성, 기계 번역 등의 문제에서 좋은 성능\n",
        "\n",
        "- 타임스텝의 순서에 민감하지 X\n",
        "\n",
        "\n",
        "- 2D Convolution\n",
        "  - 지역적 특징을 인식\n",
        "\n",
        "  <img src=\"https://missinglink.ai/wp-content/uploads/2019/03/2D-convolutional-example_2x-700x584.png\" width=\"500\">\n",
        "\n",
        "- 1D Convolution\n",
        "  - 문맥을 인식\n",
        "\n",
        "  <img src=\"https://missinglink.ai/wp-content/uploads/2019/03/1D-convolutional-example_2x.png\" width=\"500\">\n",
        "\n",
        "  <sub>출처: https://missinglink.ai/guides/keras/keras-conv1d-working-1d-convolutional-neural-networks-keras/</sub>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rGh9JuYaCN49"
      },
      "source": [
        "# 자연어 처리는 Con1D를 사용하게 된다. 공간적인 요소는 2D사용"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aJNAI-aYnDuc"
      },
      "source": [
        "### Conv1D Layer\n",
        "- 입력: `(batch_size, timesteps, channels)`\n",
        "\n",
        "- 출력: `(batch_size, timesteps, filters)`\n",
        "\n",
        "- 필터의 사이즈가 커져도 모델이 급격히 증가하지 않기 때문에 다양한 크기를 사용할 수 있음\n",
        "- 데이터의 품질이 좋으면 굳이 크기를 달리하여 여러 개를 사용하지 않아도 될 수도 있음"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ADJZHPLJqTZa"
      },
      "source": [
        "### MaxPooling1D Layer\n",
        "- 다운 샘플링 효과\n",
        "\n",
        "- 단지 1차원형태로 바뀐 것 뿐"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wirZzwYJrNll"
      },
      "source": [
        "### GlobalMaxPooling\n",
        "- 배치차원을 제외하고 2차원 형태를 1차원 형태로 바꾸어주는 레이어\n",
        "\n",
        "- Flatten layer로 대신 사용가능"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YQY_1IL9nQs2"
      },
      "source": [
        "## IMDB 데이터"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "pl4Eu32qnUES"
      },
      "source": [
        "### 데이터 로드 및 전처리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WcG-Hiltklbe"
      },
      "source": [
        "from tensorflow.keras.datasets import imdb\n",
        "from tensorflow.keras.preprocessing.sequence import pad_sequences\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.layers import Dense, Embedding, Conv1D, MaxPooling1D, GlobalMaxPooling1D"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GGYaNT8hnec5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "135ba55d-5e5e-4b1f-b8ac-68500179c194"
      },
      "source": [
        "num_words = 10000\n",
        "max_len = 500\n",
        "batch_size = 32\n",
        "\n",
        "(input_train, y_train), (input_test, y_test) = imdb.load_data(num_words=num_words)\n",
        "\n",
        "print(len(input_train))\n",
        "print(len(input_test))\n"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "25000\n",
            "25000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nQBzp3ownpEK",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "39755a66-6db1-4dcf-9b2a-5827964f6d70"
      },
      "source": [
        "pad_x_train = pad_sequences(input_train, maxlen=max_len)\n",
        "pad_x_test = pad_sequences(input_test, maxlen=max_len)\n",
        "\n",
        "print(pad_x_train.shape)\n",
        "print(pad_x_test.shape)"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(25000, 500)\n",
            "(25000, 500)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "j3E0hYiHoBO0"
      },
      "source": [
        "### 모델 구성"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PvKPr7N6nuHr"
      },
      "source": [
        "def build_model():\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Embedding(input_dim=num_words, output_dim=32, input_length=max_len))\n",
        "  model.add(Conv1D(32, 7, activation='relu'))\n",
        "  model.add(MaxPooling1D(7))\n",
        "  model.add(Conv1D(32, 5, activation='relu'))\n",
        "  model.add(MaxPooling1D(5))\n",
        "  model.add(GlobalMaxPooling1D())\n",
        "  model.add(Dense(1, activation='sigmoid'))\n",
        "\n",
        "  model.compile(optimizer=RMSprop(learning_rate=1e-4),\n",
        "                loss='binary_crossentropy',\n",
        "                metrics=['accuracy'])\n",
        "  \n",
        "  return model"
      ],
      "execution_count": 39,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wwWZ47NHolB6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8770f59a-a0a9-4eaf-8641-0faf83cb7067"
      },
      "source": [
        "model = build_model()\n",
        "model.summary()"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_5 (Embedding)      (None, 500, 32)           320000    \n",
            "_________________________________________________________________\n",
            "conv1d_10 (Conv1D)           (None, 494, 32)           7200      \n",
            "_________________________________________________________________\n",
            "max_pooling1d_10 (MaxPooling (None, 70, 32)            0         \n",
            "_________________________________________________________________\n",
            "conv1d_11 (Conv1D)           (None, 66, 32)            5152      \n",
            "_________________________________________________________________\n",
            "max_pooling1d_11 (MaxPooling (None, 13, 32)            0         \n",
            "_________________________________________________________________\n",
            "global_max_pooling1d_2 (Glob (None, 32)                0         \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 1)                 33        \n",
            "=================================================================\n",
            "Total params: 332,385\n",
            "Trainable params: 332,385\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0qUbPGdmpHpQ"
      },
      "source": [
        "### 모델 학습"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xcCgjLHvonWB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b2499db4-5f28-4c26-cd0d-cdc49a7a6aa0"
      },
      "source": [
        "history = model.fit(pad_x_train, y_train,\n",
        "                    batch_size=128, epochs=30,\n",
        "                    validation_split=0.2)"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "157/157 [==============================] - 3s 16ms/step - loss: 0.6927 - accuracy: 0.5134 - val_loss: 0.6925 - val_accuracy: 0.5126\n",
            "Epoch 2/30\n",
            "157/157 [==============================] - 2s 15ms/step - loss: 0.6899 - accuracy: 0.6187 - val_loss: 0.6905 - val_accuracy: 0.6018\n",
            "Epoch 3/30\n",
            "157/157 [==============================] - 2s 13ms/step - loss: 0.6860 - accuracy: 0.6844 - val_loss: 0.6862 - val_accuracy: 0.6610\n",
            "Epoch 4/30\n",
            "157/157 [==============================] - 2s 15ms/step - loss: 0.6780 - accuracy: 0.7246 - val_loss: 0.6750 - val_accuracy: 0.7072\n",
            "Epoch 5/30\n",
            "157/157 [==============================] - 2s 15ms/step - loss: 0.6589 - accuracy: 0.7565 - val_loss: 0.6463 - val_accuracy: 0.7528\n",
            "Epoch 6/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.6166 - accuracy: 0.7806 - val_loss: 0.5921 - val_accuracy: 0.7756\n",
            "Epoch 7/30\n",
            "157/157 [==============================] - 2s 15ms/step - loss: 0.5464 - accuracy: 0.8065 - val_loss: 0.5152 - val_accuracy: 0.8098\n",
            "Epoch 8/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.4630 - accuracy: 0.8339 - val_loss: 0.4440 - val_accuracy: 0.8262\n",
            "Epoch 9/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.3941 - accuracy: 0.8508 - val_loss: 0.3958 - val_accuracy: 0.8382\n",
            "Epoch 10/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.3459 - accuracy: 0.8651 - val_loss: 0.3680 - val_accuracy: 0.8496\n",
            "Epoch 11/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.3137 - accuracy: 0.8761 - val_loss: 0.3530 - val_accuracy: 0.8510\n",
            "Epoch 12/30\n",
            "157/157 [==============================] - 2s 13ms/step - loss: 0.2901 - accuracy: 0.8853 - val_loss: 0.3432 - val_accuracy: 0.8558\n",
            "Epoch 13/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.2711 - accuracy: 0.8945 - val_loss: 0.3365 - val_accuracy: 0.8584\n",
            "Epoch 14/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.2549 - accuracy: 0.9016 - val_loss: 0.3326 - val_accuracy: 0.8614\n",
            "Epoch 15/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.2411 - accuracy: 0.9079 - val_loss: 0.3302 - val_accuracy: 0.8626\n",
            "Epoch 16/30\n",
            "157/157 [==============================] - 2s 13ms/step - loss: 0.2288 - accuracy: 0.9146 - val_loss: 0.3290 - val_accuracy: 0.8616\n",
            "Epoch 17/30\n",
            "157/157 [==============================] - 2s 15ms/step - loss: 0.2175 - accuracy: 0.9186 - val_loss: 0.3294 - val_accuracy: 0.8658\n",
            "Epoch 18/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.2075 - accuracy: 0.9230 - val_loss: 0.3290 - val_accuracy: 0.8660\n",
            "Epoch 19/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.1981 - accuracy: 0.9269 - val_loss: 0.3313 - val_accuracy: 0.8658\n",
            "Epoch 20/30\n",
            "157/157 [==============================] - 2s 15ms/step - loss: 0.1897 - accuracy: 0.9308 - val_loss: 0.3330 - val_accuracy: 0.8674\n",
            "Epoch 21/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.1818 - accuracy: 0.9340 - val_loss: 0.3354 - val_accuracy: 0.8678\n",
            "Epoch 22/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.1744 - accuracy: 0.9380 - val_loss: 0.3373 - val_accuracy: 0.8670\n",
            "Epoch 23/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.1673 - accuracy: 0.9404 - val_loss: 0.3404 - val_accuracy: 0.8652\n",
            "Epoch 24/30\n",
            "157/157 [==============================] - 2s 15ms/step - loss: 0.1610 - accuracy: 0.9446 - val_loss: 0.3431 - val_accuracy: 0.8646\n",
            "Epoch 25/30\n",
            "157/157 [==============================] - 2s 13ms/step - loss: 0.1549 - accuracy: 0.9464 - val_loss: 0.3476 - val_accuracy: 0.8652\n",
            "Epoch 26/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.1486 - accuracy: 0.9492 - val_loss: 0.3531 - val_accuracy: 0.8610\n",
            "Epoch 27/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.1436 - accuracy: 0.9520 - val_loss: 0.3555 - val_accuracy: 0.8610\n",
            "Epoch 28/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.1381 - accuracy: 0.9539 - val_loss: 0.3603 - val_accuracy: 0.8598\n",
            "Epoch 29/30\n",
            "157/157 [==============================] - 2s 13ms/step - loss: 0.1332 - accuracy: 0.9563 - val_loss: 0.3718 - val_accuracy: 0.8622\n",
            "Epoch 30/30\n",
            "157/157 [==============================] - 2s 14ms/step - loss: 0.1284 - accuracy: 0.9587 - val_loss: 0.3677 - val_accuracy: 0.8622\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n2yEkawIph4C"
      },
      "source": [
        "### 시각화"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BfKC0UOkpPmP"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('seaborn-white')"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "id": "zVga7NoCEubj",
        "outputId": "473cbd8d-d5c9-4279-9dea-5ec7ba873e34"
      },
      "source": [
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "epochs = range(1, len(loss)+1)\n",
        "\n",
        "plt.plot(epochs, loss, 'b--', label='training loss')\n",
        "plt.plot(epochs, loss, 'r:', label='validation loss')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(epochs, acc, 'b--', label='training acc')\n",
        "plt.plot(epochs, val_acc , 'r:', label='validation acc')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jYWHBggULiI2NpWvXrsyaNavM7xMfH09WVhYAeXl5xMXF1VAJ+qEqNVl21bDFZzZW3dvp7c+hrp8jQ2Bs9YDx1VSdelJSUsrdVuXbATV3zJSl0WhISUlh3LhxtGzZkueff549e/bQp0+fu/ZzcXHB0bHkvYxxcXHVf1Zfz1S1po5H3tJhmuqTc6T/jK0eML6aqlPP7TlVylLhUImdnR3p6ena5dTUVGxtbYGSCV1atGiBk5MTJiYm9OzZk3Pnzt1XyLro7KaT7B29QukYQggDU2Hj9vX11b5kMzY2Fjs7OywtLQEwNTWlVatWXLx4Ubu9bdu2uktrZK68s5ZO37zG5T+zlI4ihDAgFQ6VeHt74+HhQWBgICqVitDQUCIjI7GysmLAgAGEhIQwe/ZsNBoN7du3r/BNy+J/2q54jbZdQ5iyzoq331Y6jRDCUFRqjDs4OLjUspubm/br1q1b8803cnfE/WjdpRkPPQorV8LcuVDNtxkJIeoIeQpEYTOfuc6atIc5+uIapaMIIQyENG6F+Q1vinXjInKz5RF4IUTlyOyAClOpVfjc2MG/3jsqhBDlkituPaBSgaZYQ8JW43nwQAihO9K49cSvQ97BaZgXV/+QuV6EEPcmjVtPtHn1aSbxKas2NVU6ihBCz0nj1hNt+rUj5ZFn+eiLhtyS11IKIe5BGrcemT6liP4pX7N//nalowgh9Jg0bj0yYKCKt+u9ienaL5SOIoTQY3I7oB5Rm6op3rGTHt1kTnMhRPmkcesZZ/+SqW81xRpUarm5WwhxNxkq0UN/vLuTi2ZupJ4qfyJ1IUTdJY1bDzX1asXFghZs+vSa0lGEEHpIhkr0UNtB7ZnYfzcXfobJGuRxeCFEKXLFrafGjoWU89n89t9UpaMIIfSMNG499fjQAi7QlpzX3lA6ihBCz8hQiZ5qZFOPHSMXYNndU+koQgg9I41bjw3Y8JzSEYQQekiGSvRc4q4EomduVDqGEEKPSOPWcxdfWorX+8+QmZyjdBQhhJ6Qxq3nGr39Km78SeQ2c6WjCCH0hDRuPffgsFaYubRi3Tqlkwgh9IU0bj2nUsGLg88xbtd4ko/+pXQcIYQekMZtAB4druJRtnB+S4zSUYQQekBuBzQArfu78HdaCr7N6isdRQihB+SK20A0+qdpFxcWK5xECKG0Sl1xh4eHc+LECVQqFSEhIXh5eWm39evXDwcHB0xMTABYvHgx9vb2uklbhxXlF3HSPoDMDt3pc2ih0nGEEAqqsHEfOXKExMREIiIiSEhIICQkhIiIiFKfWbVqFRYWFjoLKcCkvgmXmnbi4Jl2+BWDWv6uJESdVeEf/+joaAICAgBwdnYmMzOT7OxsnQcTd8tbsJRFGc/z669KJxFCKKnCK+709HQ8PDy0y9bW1qSlpWFpaaldFxoayuXLl+nSpQuzZs1CVcYE0vHx8WRlZQGQl5dHXFxcTeTXG7VRk6urCivzdnwXfgwHh0Y6PZacI/1nbPWA8dVUnXpSUsp/A1aV7yrRaDSlll988UUeeughGjduzNSpU4mKiuLhhx++az8XFxccHUvepxgXF4e7u3tVD63Xaqumza1n0O2Xr6hvf5WG1g11dhw5R/rP2OoB46upOvVYWVmVu63CoRI7OzvS09O1y6mpqdja2mqXH3vsMWxsbDA1NcXPz4+zZ8/eV0hROS1CnuHQs6vQqE2UjiKEUEiFjdvX15eoqCgAYmNjsbOz0w6TZGVlMWHCBPLz8wE4evQorq6uOowrPMZ0ZtDnT2PeRO7pFqKuqnCoxNvbGw8PDwIDA1GpVISGhhIZGYmVlRUDBgzAz8+PkSNH0qBBAzp27FjmMImoWZmXMjkx91s8543A2tVG6ThCiFpWqTHu4ODgUstubm7ar8ePH8/48eNrNpW4p6uHLuK39nl2qhrQf/U4peMIIWqZ3A1sgNo/1YkRLieZf26s0lGEEAqQxm2AVCroMfEBDkarOH9e6TRCiNomjdtAjQrUEMbr/DnjY6WjCCFqmTRuA9XKScXApkcwOXNa6ShCiFom07oaMI9L22hoKfdzC1HXyBW3AbvdtAtyChROIoSoTdK4Ddyex94ntZGzNG8h6hBp3AbOrHNHIouGs3VjjtJRhBC1RBq3gfOZO4il7ZaxZFVjpaMIIWqJNG4DZ2IC06dDxoEYTn0Xr3QcIUQtkMZtBP4zMoeD9CIj+G2lowghaoHcDmgEGjc3Z//8zTg92lnpKEKIWiCN20j0fmOA0hGEELVEhkqMSNy63znQJoi8jDylowghdEgatxHJvXIDl8Rf2P7hn0pHEULokDRuI9I5uD+DO14i9LsH+derQYUQRkQatxFRqVVMfqkBx49r2L8tW+k4QggdkcZtZMaMgZ31HqbeBHkzjhDGSu4qMTING4Jm2HCua+qj0ZS8dEEIYVykcRuh/punKB1BCKFDMlRipArzCjn48rdkX5WxbiGMjTRuI/Xn17/Ta+lIjgR/q3QUIUQNk8ZtpDwndGeK2y6mHB5PcbHSaYQQNUkatxHrPa8vZ+JN2LZN6SRCiJokjduIPfkkzGj8FZoXXlA6ihCiBknjNmL168NjXf/C/tppstNl/hIhjEWlGnd4eDgjR44kMDCQkydPlvmZJUuWMHbs2BoNJ6qv55Y5eGfvw7KZmdJRhBA1pMLGfeTIERITE4mIiCAsLIywsLC7PhMfH8/Ro0d1ElBUTwNzE9RquJl6k5upN5WOI4SoARU27ujoaAICAgBwdnYmMzOT7OzS9wYvXLiQl19+WTcJRbVdO5NOvkMrjj7zkdJRhBA1oMInJ9PT0/Hw8NAuW1tbk5aWhqWlJQCRkZF069aNli1b3vP7xMfHk5WVBUBeXh5xcXHVya139L2mfS2m8N3h3ticisO0Es/L6ns998PYajK2esD4aqpOPSkpKeVuq/Ij75o75gvNyMggMjKSL7/88p4HAXBxccHR0RGAuLg43N3dq3povabvNZ396G22PQbP/glPPVXx5/W9nvthbDUZWz1gfDVVpx4rK6tyt1U4VGJnZ0d6erp2OTU1FVtbWwAOHTrE9evXCQoKYtq0acTGxhIeHn5fIYVuDR0K3m1vkDo5lFt/31I6jhCiGips3L6+vkRFRQEQGxuLnZ2ddpjk4Ycf5r///S/ffvsty5cvx8PDg5CQEN0mFvfFxARWPHOISdfe5uiSvUrHEUJUQ4VDJd7e3nh4eBAYGIhKpSI0NJTIyEisrKwYMEBeUGtIfOYP5nSXeHo/0lbpKEKIaqjUGHdwcHCpZTc3t7s+4+joyNq1a2smldCZjv807cSd8Tj1dUallgm7hTA08uRkHXRqZTQtAtw5OCNC6ShCiPsgjbsO6vhMNz5vOZ8JGwZw44bSaYQQVSWNuw4yqW9C9x/nce66DXPmKJ1GCFFV0rjrqM6dYe5zV3n00yGcWnlQ6ThCiCqQxl2HvfKGJS71LnH18CWlowghqkBeFlyHWTpY0jrjBO3NTZSOIoSoArniruMa/NO0f3t7G0l7LyicRghRGdK4BTcSrtNh3lOcHLuIO6aiEULoKWncgqbO1mx9cQcjLi1l82al0wghKiKNWwDw1JIeeHRuwMzpBWQm5ygdRwhxD9K4BQCmprBy2S22XPXh+KDXlI4jhLgHadxCq6tvA9L9nyS/j0weJoQ+k9sBRSkBe+YCJRPAa4o1MgmVEHpIrrhFmeLnbeNguyCK8ouUjiKE+Bdp3KJMqmuZ5CSmMWPSLblFUAg9I41blMn545Hsm/MzH31pTshrctUthD6Rxi3K9UaYKdMn5vHQu8PYM/gdpeMIIf4hjVuUS6WCpR/Xp1Fra7LqWSsdRwjxD7mrRNyT2lRNr4S1qE1K7i7JSc/BvJm5wqmEqNvkiltU6HbTPvPtCbLsnTkatl3hRELUbdK4RaU5dG9NjFUvxr/lwoEDSqcRou6Sxi0qrXHrJnie2UyhUzseeQRidqcpHUmIOkkat6gSe3vYsQOCeRe7gAdIiv5L6UhC1DnSuEWVtW4No9cP4/eO47D1aq50HCHqHGnc4r60G+LG4FOLMLMwIfm3y8R+eUTpSELUGdK4RbVdGDELh2cH8+WybKWjCFEnVOo+7vDwcE6cOIFKpSIkJAQvLy/ttm+//ZZNmzahVqtxc3MjNDQUlUpmlKtL3HZ+xMKgU7z7oiUHT8KypUWYWcgLiIXQlQqvuI8cOUJiYiIRERGEhYURFham3Zabm8tPP/3E119/zYYNGzh//jzHjh3TaWChf6xdbVgQ3YfXX4f0z74jwb4nV0+kKB1LCKNV4RV3dHQ0AQEBADg7O5OZmUl2djaWlpY0bNiQ1atXAyVNPDs7G1tbW90mFnrJxATefhsOFZuQvbwxjs2bKB1JCKNVYeNOT0/Hw8NDu2xtbU1aWhqWlpbadStXrmTNmjWMGzeOVq1alfl94uPjycrKAiAvL4+4uLjqZtcrxlbT/dbTeKwrxaM/4Mq181y8kE/Su7to98YgvXghg5wj/WdsNVWnnpSU8v/WWuW5SjRlTM78/PPPM27cOCZOnEiXLl3o0qXLXZ9xcXHB0dERKHm7iru7e1UPrdeMraaaqGfv6BUM3TyTkIzDhHzfjTv+X68IOUf6z9hqqk49VlZW5W6rcIzbzs6O9PR07XJqaqp2OCQjI4OjR48CYGZmhp+fH3/88cd9hRTG56F1L/D1pH28s7sbPXrAuVN5SkcSwihU2Lh9fX2JiooCIDY2Fjs7O+0wSWFhIbNnz+bmzZsAnDp1irZt2+owrjAkKrWKoE96ExUFTf86hWWndhx6a4fSsYQweBUOlXh7e+Ph4UFgYCAqlYrQ0FAiIyOxsrJiwIABTJ06lXHjxmFqakqHDh3o379/beQWBiQgANx+asSFJ3rg0P8BAHkRsRDVUKkx7uDg4FLLbm5u2q9HjBjBiBEjajaVMDqOvq1xvBqpXY5uM4oc10489N85NGigYDAhDJA8OSlqXX52PhoTU7bvMqFTJ9i7V+lEQhgWadyi1tW3rI/vhXX0//kVbt2C+f572Ov+PBlJWUpHE8IgyKvLhGIGPawiJgZ2DTtGi72/UoQ8Ji9EZcgVt1CUhQUM2/UyDldPYNPKnKL8IvZ0DSZpf6LS0YTQW9K4hV6wbGYGwNmNJ/D5/ROC+/5GcDBcu6ZwMCH0kDRuoVfcg7zJOHIO86ARvPcehLRay57B75CfU6h0NCH0hjRuoXda+rTgy69UnDgBTzT7Fatft6KuJ+PfQtwmjVvorQcegIGXPsMlfhum9VT8/dffHG/Sh/0L91PGlDlC1BnSuIXea9zCAoCUo5dolHOVl+c0wM8Pog8UK5xMCGVI4xYGw/VxT1plnea5FT7Ex8Oe3q9zsOVT3MqVBi7qFmncwqDUa6DmhRcgPh58BtmQ1cCGBg1L/jM+u/kUmmIZQxHGTxq3MEgWFhCwLZhB51cAkHz0L9o+6c3yFuGsXQsFBQoHFEKHpHELo2DjbsfhiZ/zY6Mgxo2Dh51Os2f4e2Sn3FQ6mhA1Thq3MAr1LevTe+U4os604aefYGTDH+i6ZT7Xk28ByBCKMCrSuIVRUalgyBB4/vwcru3/E6cHrQE47DiC08NXcuQIciuhMHjSuIXRau1b8o7Tovwibtm04PeLzeneHbp20bBr4jfcTJVhFGGYpHELo2dS3wT/Ux/x+IHhfPwxuP99mH6fjWbf9G8BGUYRhkcat6gzLC2LmTwZ1p7tzsmP9uGzeCQAB6et53iTPny/Ko38fIVDClEJ0rhFnaNSq/Ca0hubVuYlyyZq/s5vwIjnbXByglVjfiVxZ7zCKYUonzRuUef1WjaK3tlR/PRfNd18NPT9egJXH5uk3V6YJzMTCv0ijVsIQK2GwYNhy48qLI7+isWqpQBkX83muoUjn/l+ya+/yh0pQj9I4xbiX5p3bYlnoCcAOanZ/Ok6jPXHO9KnD/RpfYE9/d4k9XS6siFFnSaNW4h7sPNywO/PVfyY2p01a2CYxS56736Ta3/lAnDlcBKZiRkKpxR1jTRuISrBwgLGjoXguAmk/nEZ94GtADg/+nVy27gxdEgxX30FN67JTIVC96RxC1FFLTrba7+2eWMGOx75gJjTav7zHzhp2zTIhwcAAA1kSURBVI8dD7ysYDpRF0jjFqIa3Md0YezWkVy4AIejiyn26UFR+45AyRObB50C+e+ru7l+XeGgwqhUqnGHh4czcuRIAgMDOXnyZKlthw4d4umnnyYwMJA5c+ZQXCx/VRR1j0oF3Xqo6Xt4IQ9vngjAlUOXaJV8mC/fTcfODp7wS+PXpz8i7U95db2ongob95EjR0hMTCQiIoKwsDDCwsJKbZ8/fz4ffvghGzZs4ObNm+zbt09nYYUwJK382uJ46zyzD4/glVegTcJO/DdOI2FPEgCXoy+RsDVOHrkXVWZa0Qeio6MJCAgAwNnZmczMTLKzs7G0tAQgMjJS+7W1tTU3btzQYVwhDItKraJLNxO6dAMWBHL+58506d8egHMvfYTvkffo6pzOgCcb80Tf63Tp3wS1qYxginursHGnp6fj4eGhXba2tiYtLU3brG//OzU1lQMHDjBjxowyv098fDxZWVkA5OXlERcXV+3w+sTYajK2ekBPamoD8Ql/AtDg1WFs3OiGWZIJS5Zo6P7OBOrX/4t6x9agUpU8sWlqVv4fUb2op4YZW03VqSclJaXcbRU27n/TlPHo2LVr15g0aRKhoaE0bdq0zP1cXFxwdCyZZjMuLg53d/eqHlqvGVtNxlYP6GFN7tDzid6MBjIy4GRoEKkpmQzoWJLxbEMv/rAOIH7yezz8MHh31qA2UWl317t6aoCx1VSdeqysrMrdVuHfyezs7EhP/99TYqmpqdja2mqXs7OzmThxIi+99BK9e/e+r4BC1HVNmoDfB08yYMMEoOSOlCtdHuWkWTfmzYPePnlcrt+GnWO/Ujao0AsVNm5fX1+ioqIAiI2Nxc7OTjs8ArBw4ULGjx+Pn5+f7lIKUceY1Dehz/63CU8IJDUV1n+cSWIbf+o5OwGQuDMeTedRLA+KJioKcnIUDixqVYVDJd7e3nh4eBAYGIhKpSI0NJTIyEisrKzo3bs333//PYmJiWzatAmAoUOHMnLkSJ0HF6KusLWFEZPtYfIa7bqc5Exy6zfik43NOL0ehppu4y2LhVhsXoNrfycF04raUKkx7uDg4FLLbm5u2q9jYmJqNpEQokLuY7oQ1+VTjrZ2Zf9+uLIiH5OoPJp2sANg7+gVNNi9jd9fjaDf4AZ06FByr7kwDlX+5aQQQn+Ym8PAgcDAR4FHtes1+QXkZt5i6swGMBM+tJhDx5YZ9PvzE2ngRkAatxBGyH/TdGA6Cedh505o/n4RxblF2qZ9qOUTXG7sTsq0t/H3h44d5YrckEjjFsKItWtX8g8TF2nXaYo1FFg2Je5KY+ZNBdBw2uQBkga/wMAfpwNQXFgsDwLpMWncQtQxKrWKh858Rm8NjL4AB7Zlc+29LqgdmwNw/dw1TNo780mnFRQ/HUjvnkV061yAWRMzhZOL26RxC1FHqVT/XJFPsYIpq7Xrb2Xmca5jIH9kubLxdejBEfbQh31v/MxD8/uRfTWbwtwCmrQt+2E7oXvSuIUQpTTv2pLmsSvwA65dg+MbmxH9xQzaPFzyOrfj8zbT67P/8LDzWRx6u9C/7Xm8ndJpP6oL9cxMlA1fR0jjFkKUy8YG+k9yhUn/GyNvPrwbey6G0cCsHdu2QfuUzwjkXa73/Rv7Ng35Y9Ev3Dx7GZuXx9HBTYWJ9PIaJ41bCFElzkPdcR7qTj9K3np/+bfp/PZzX3q2aQhA7sdf0iLxEC6fj8fSEt6xW4KrYy4Dfp2rbHAjIo1bCHHfVCpw9GmOo09z7bqe8WtJOHCVry7C0aPQZsNxGmRkabcfbj6cy/XacjRoKT4+0K1dOi29bFCp5X7EypLGLYSoUWpTNa7+LXD1h/HjgeVrS70sIre5MxdTWrJ4MRQWakjGk19cRjDg3McAHHtvN60e8aJZBxuFKtB/cqOmEELn7rya7vPHe8y8PIusLDh8sJgzI+djPvYJAG5ezqbzrH4sdfuEdu0g6Kl89jz2Pud/Oa9UdL0kV9xCCEWYmUG3nibQc4p2Xf0m9Tm+dA9OV53oEg/X9p2mT8pMomwdaRfQjr8OJHJ+4gJSg17GsX8H3N00NG5S94ZYpHELIfRGPYv6uM/w50HgeQAeJOX4FbrZlLxUIPVgPJ3iNuA/dxIn5sJQtrJSPZmUr3fwYKA7Vw4nkf7HJVqN8KGpfX0FK9EtGSoRQug1+web07RVyTsAvF/pj1XBdTaf9WLLFnh6ii0JbfrR7IEWAJxbuBmvKb3p4JBB8+bweqet/Nr5Ja7/ZVwTlssVtxDCoKhN1Ti7grMrMKwH0EO7reNbozjauQOvNrTldBw4/BJH+7++paH1+wDs6RWC7bHthD9+lE4Pquhhdx43b3PsvByUKeY+SeMWQhgNW097bD0H46Nd8wrFRcHad3WaenQg/moO+/arWP8NRDKLTHUctoV/olLB/ufXcEtlhtm4p/HwKHmlnD6Sxi2EMGp3vmC596rxwHiGA9evw4UNs0m9fA3Xfz7SZP1HXLxpS8DKpwHY3mAYuLsz4FjJk6NnN53ErksrxedpkcYthKiTrK3Bekr3Uus6ZkRjdTqLrUlw+jSov2hNgV3Jw0WaYg3Nnu5LhOZJQu0/xd0dQjJexeqJgfSYG6D9TG08SCSNWwgh/qE2VdPaqzGtveCRR4BXlmu3aYo1JISupeF1e4ZkwYWYm/gcX8WBRs1gbgA3U29SaN+CZa2XcNbvOTxc8+npeAV3d/cazymNWwghKkFtqsYndAg+wDgALNAUX2dAbiFQMh3uqQfGca1ee3btgj/WnsVr5lWdZJHGLYQQ90mlVlHfoh4A1q42+J9chj/wPvB3qgsXE4t1cly5j1sIIXSgkZ0Z9Szr6eR7S+MWQggDI41bCCEMjDRuIYQwMNK4hRDCwEjjFkIIAyONWwghDIzO7+MuKioC4OrV/92InpKSgpWVla4PXauMrSZjqweMryZjqweMr6bq1HO7Z97uoXfSeeNOS0sDICgoSNeHEkIIo5OWlkbr1q1LrVNpNBpNOZ+vEXl5ecTExGBra4uJiYkuDyWEEEajqKiItLQ0PD09MTMzK7VN541bCCFEzZJfTgohhIGp9UmmwsPDOXHiBCqVipCQELy8vGo7Qo06fPgwM2bMwNXVFYD27dszb948hVNV3dmzZ5kyZQrPPPMMY8aMITk5mVdffZWioiJsbW159913qV/fsF6++u+aZs+eTWxsLE3+ea3JhAkT6NOnj7Ihq2DRokX8/vvvFBYW8sILL/DAAw8Y/Dn6d027du0y2HOUm5vL7NmzuXbtGrdu3WLKlCm4ubnp5BzVauM+cuQIiYmJREREkJCQQEhICBEREbUZQSe6devGhx9+qHSM+5aTk8Nbb71Fz549tes+/PBDRo8ezeDBg3nvvffYtGkTo0ePVjBl1ZRVE8DMmTPp27evQqnu36FDhzh37hwRERHcuHGDxx9/nJ49exr0OSqrph49ehjsOdq9ezeenp5MnDiRy5cv8+yzz+Lt7a2Tc1SrQyXR0dEEBJS8KcLZ2ZnMzEyys7NrM4IoQ/369Vm1ahV2dnbadYcPH6Z///4A9O3bl+joaKXi3ZeyajJkPj4+fPDBBwA0atSI3Nxcgz9HZdVU1q1vhmLIkCFMnDgRgOTkZOzt7XV2jmq1caenp9O06f/e1WZtba29XdCQxcfHM2nSJEaNGsWBAweUjlNlpqamd/3WOjc3V/tXOhsbG4M7T2XVBLBu3TrGjRvHyy+/zPXr1xVIdn9MTEwwNzcHYNOmTfj5+Rn8OSqrJhMTE4M9R7cFBgYSHBxMSEiIzs6Roi9SMIYbWtq0acO0adMYPHgwSUlJjBs3ju3btxvcWOO9GMN5Ahg+fDhNmjTB3d2dlStXsnz5cubPn690rCr55Zdf2LRpE1988QUDBw7Urjfkc3RnTTExMQZ/jjZs2EBcXByvvPJKqfNSk+eoVq+47ezsSE9P1y6npqZia2tbmxFqnL29PUOGDEGlUuHk5ESzZs1ISUlROla1mZubk5eXB5Q8/WUMQw49e/bUvv+vX79+nD17VuFEVbNv3z5WrFjBqlWrsLKyMopz9O+aDPkcxcTEkJycDIC7uztFRUVYWFjo5BzVauP29fUlKioKgNjYWOzs7LC0tKzNCDVuy5YtfP7550DJE07Xrl3D3t5e4VTV16tXL+252r59Ow899JDCiapv+vTpJCUlASVj+LfvBDIEWVlZLFq0iE8//VR7x4Whn6OyajLkc/Tbb7/xxRdfACXDwjk5OTo7R7X+AM7ixYv57bffUKlUhIaG4ubmVpuHr3HZ2dkEBwfz999/U1BQwLRp0/D391c6VpXExMTwzjvvcPnyZUxNTbG3t2fx4sXMnj2bW7du0aJFCxYsWEC9erp5DZMulFXTmDFjWLlyJQ0bNsTc3JwFCxZgY2OjdNRKiYiIYNmyZbRt21a7buHChcydO9dgz1FZNY0YMYJ169YZ5DnKy8vj9ddfJzk5mby8PKZNm4anpyevvfZajZ8jeXJSCCEMjDw5KYQQBkYatxBCGBhp3EIIYWCkcQshhIGRxi2EEAZGGrcQQhgYadxCCGFgpHELIYSB+X/aAl1I/LDFGgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "09QEtpzVFbNw",
        "outputId": "f54d5ff3-8796-4aa9-b073-26800f0ca790"
      },
      "source": [
        "model.evaluate(pad_x_test, y_test)"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "782/782 [==============================] - 2s 3ms/step - loss: 0.3916 - accuracy: 0.8457\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.39156457781791687, 0.8457199931144714]"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Oz_Iu_VHV7Z5"
      },
      "source": [
        "## Reuters 데이터"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4_Q6p0nhV7Z6"
      },
      "source": [
        "### 데이터 로드 및 전처리"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "waEmkCowV7Z7"
      },
      "source": [
        "from tensorflow.keras.datasets import reuters"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dggcmeJ4V7Z_",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6d492177-7e39-4a13-bec5-8073c7b0c7b6"
      },
      "source": [
        "num_words = 10000\n",
        "max_len = 500\n",
        "\n",
        "(input_train, y_train), (input_test, y_test) = reuters.load_data(num_words=num_words)\n",
        "\n",
        "print(len(input_train))\n",
        "print(len(input_test))\n"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "8982\n",
            "2246\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "EBOJIgbgV7aB",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "405435b0-282a-45b5-ab4f-40f10f4c0cee"
      },
      "source": [
        "pad_x_train = pad_sequences(input_train, maxlen=max_len)\n",
        "pad_x_test = pad_sequences(input_test, maxlen=max_len)\n",
        "\n",
        "print(pad_x_train.shape)\n",
        "print(pad_x_test.shape)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(8982, 500)\n",
            "(2246, 500)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xehgNec0V7aE"
      },
      "source": [
        "### 모델 구성"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RTV2qF8_V7aE"
      },
      "source": [
        "def build_model():\n",
        "  model = Sequential()\n",
        "\n",
        "  model.add(Embedding(input_dim=num_words, output_dim=64, input_length=max_len))\n",
        "  model.add(Conv1D(64, 7, activation='relu'))\n",
        "  model.add(MaxPooling1D(7))\n",
        "  model.add(Conv1D(64, 5, activation='relu'))\n",
        "  model.add(MaxPooling1D(5))\n",
        "  model.add(GlobalMaxPooling1D())\n",
        "  model.add(Dense(46, activation='softmax'))\n",
        "\n",
        "  model.compile(optimizer='adam',\n",
        "                loss='sparse_categorical_crossentropy',\n",
        "                metrics=['accuracy'])\n",
        "  \n",
        "  return model"
      ],
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "48n2W3xpV7aG",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6a29c40e-8cd2-48cb-9877-999efea99d2e"
      },
      "source": [
        "model = build_model()\n",
        "model.summary()"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "embedding_4 (Embedding)      (None, 500, 64)           640000    \n",
            "_________________________________________________________________\n",
            "conv1d_8 (Conv1D)            (None, 494, 64)           28736     \n",
            "_________________________________________________________________\n",
            "max_pooling1d_8 (MaxPooling1 (None, 70, 64)            0         \n",
            "_________________________________________________________________\n",
            "conv1d_9 (Conv1D)            (None, 66, 64)            20544     \n",
            "_________________________________________________________________\n",
            "max_pooling1d_9 (MaxPooling1 (None, 13, 64)            0         \n",
            "_________________________________________________________________\n",
            "global_max_pooling1d_1 (Glob (None, 64)                0         \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 46)                2990      \n",
            "=================================================================\n",
            "Total params: 692,270\n",
            "Trainable params: 692,270\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WeRmu5WMV7aI"
      },
      "source": [
        "### 모델 학습"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BGVdYyIrV7aJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8a42296-5184-45a1-dc00-328779e174ff"
      },
      "source": [
        "history = model.fit(pad_x_train, y_train,\n",
        "                    batch_size=128, epochs=30,\n",
        "                    validation_split=0.2)"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/30\n",
            "57/57 [==============================] - 31s 28ms/step - loss: 2.7627 - accuracy: 0.3389 - val_loss: 2.0996 - val_accuracy: 0.5064\n",
            "Epoch 2/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 1.8988 - accuracy: 0.5219 - val_loss: 1.7370 - val_accuracy: 0.5498\n",
            "Epoch 3/30\n",
            "57/57 [==============================] - 1s 21ms/step - loss: 1.6440 - accuracy: 0.5907 - val_loss: 1.5911 - val_accuracy: 0.6105\n",
            "Epoch 4/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 1.4485 - accuracy: 0.6512 - val_loss: 1.4517 - val_accuracy: 0.6516\n",
            "Epoch 5/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 1.2338 - accuracy: 0.6937 - val_loss: 1.3408 - val_accuracy: 0.6850\n",
            "Epoch 6/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 1.0482 - accuracy: 0.7364 - val_loss: 1.2909 - val_accuracy: 0.6945\n",
            "Epoch 7/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 0.8789 - accuracy: 0.7791 - val_loss: 1.2827 - val_accuracy: 0.7117\n",
            "Epoch 8/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 0.7284 - accuracy: 0.8202 - val_loss: 1.2985 - val_accuracy: 0.7212\n",
            "Epoch 9/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 0.6071 - accuracy: 0.8470 - val_loss: 1.3408 - val_accuracy: 0.7262\n",
            "Epoch 10/30\n",
            "57/57 [==============================] - 1s 21ms/step - loss: 0.4959 - accuracy: 0.8800 - val_loss: 1.3797 - val_accuracy: 0.7295\n",
            "Epoch 11/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 0.4079 - accuracy: 0.9026 - val_loss: 1.4544 - val_accuracy: 0.7245\n",
            "Epoch 12/30\n",
            "57/57 [==============================] - 1s 21ms/step - loss: 0.3324 - accuracy: 0.9189 - val_loss: 1.5302 - val_accuracy: 0.7279\n",
            "Epoch 13/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 0.2724 - accuracy: 0.9372 - val_loss: 1.5580 - val_accuracy: 0.7284\n",
            "Epoch 14/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 0.2369 - accuracy: 0.9467 - val_loss: 1.6104 - val_accuracy: 0.7323\n",
            "Epoch 15/30\n",
            "57/57 [==============================] - 1s 21ms/step - loss: 0.2126 - accuracy: 0.9488 - val_loss: 1.6645 - val_accuracy: 0.7257\n",
            "Epoch 16/30\n",
            "57/57 [==============================] - 1s 21ms/step - loss: 0.1849 - accuracy: 0.9567 - val_loss: 1.6846 - val_accuracy: 0.7323\n",
            "Epoch 17/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 0.1608 - accuracy: 0.9602 - val_loss: 1.7655 - val_accuracy: 0.7273\n",
            "Epoch 18/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 0.1572 - accuracy: 0.9624 - val_loss: 1.7581 - val_accuracy: 0.7307\n",
            "Epoch 19/30\n",
            "57/57 [==============================] - 1s 22ms/step - loss: 0.1532 - accuracy: 0.9612 - val_loss: 1.8316 - val_accuracy: 0.7234\n",
            "Epoch 20/30\n",
            "57/57 [==============================] - 1s 21ms/step - loss: 0.1353 - accuracy: 0.9623 - val_loss: 1.8054 - val_accuracy: 0.7234\n",
            "Epoch 21/30\n",
            "57/57 [==============================] - 1s 21ms/step - loss: 0.1224 - accuracy: 0.9665 - val_loss: 1.8028 - val_accuracy: 0.7357\n",
            "Epoch 22/30\n",
            "57/57 [==============================] - 1s 21ms/step - loss: 0.1224 - accuracy: 0.9640 - val_loss: 1.8219 - val_accuracy: 0.7307\n",
            "Epoch 23/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 0.1175 - accuracy: 0.9662 - val_loss: 1.8733 - val_accuracy: 0.7251\n",
            "Epoch 24/30\n",
            "57/57 [==============================] - 1s 21ms/step - loss: 0.1115 - accuracy: 0.9646 - val_loss: 1.8286 - val_accuracy: 0.7290\n",
            "Epoch 25/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 0.1148 - accuracy: 0.9635 - val_loss: 1.9624 - val_accuracy: 0.7156\n",
            "Epoch 26/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 0.1071 - accuracy: 0.9656 - val_loss: 1.8426 - val_accuracy: 0.7251\n",
            "Epoch 27/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 0.1176 - accuracy: 0.9613 - val_loss: 1.8492 - val_accuracy: 0.7368\n",
            "Epoch 28/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 0.1089 - accuracy: 0.9651 - val_loss: 1.9157 - val_accuracy: 0.7240\n",
            "Epoch 29/30\n",
            "57/57 [==============================] - 1s 20ms/step - loss: 0.1122 - accuracy: 0.9642 - val_loss: 1.8854 - val_accuracy: 0.7334\n",
            "Epoch 30/30\n",
            "57/57 [==============================] - 1s 21ms/step - loss: 0.1127 - accuracy: 0.9652 - val_loss: 1.8470 - val_accuracy: 0.7312\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ao8NNj2SV7aL"
      },
      "source": [
        "### 시각화"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "D_CisVtyV7aL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 507
        },
        "outputId": "ad5c7ae4-96ab-4c7d-a900-40df301a374e"
      },
      "source": [
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "epochs = range(1, len(loss)+1)\n",
        "\n",
        "plt.plot(epochs, loss, 'b--', label='training loss')\n",
        "plt.plot(epochs, loss, 'r:', label='validation loss')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "\n",
        "plt.figure()\n",
        "plt.plot(epochs, acc, 'b--', label='training acc')\n",
        "plt.plot(epochs, val_acc , 'r:', label='validation acc')\n",
        "plt.grid()\n",
        "plt.legend()\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NCsNsoiUV7aP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd6badbd-0b08-457b-e1d6-0a16930d9e5b"
      },
      "source": [
        "model.evaluate(pad_x_test, y_test)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "71/71 [==============================] - 0s 5ms/step - loss: 1.8340 - accuracy: 0.7364\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1.8339585065841675, 0.7364202737808228]"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Mqi0fcPXI4Gv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}