material/deep_learning/rnn_addition_text_classication.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "rnn_addition_text_classication.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.7.0"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ou-ygxNfo-Ec"
      },
      "source": [
        "## 셋업\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d8X6ncVagzaf"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras.layers import LSTM, Input, Dense, Flatten, Bidirectional"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M5OvBIiWo-Ec"
      },
      "source": [
        "\n",
        "TRAINING_SIZE = 50000   # 데이터의 갯수\n",
        "DIGITS = 3              # 덧셈에 사용될 숫자 자리수\n",
        "REVERSE = True          # 입력 문자열의 역순 여부. \n",
        "\n",
        "# 입력 문자열의 최대 길이.\n",
        "#      = 숫자길이 '+'   숫자길이 '='  정답숫자길이\n",
        "MAXLEN = DIGITS +  1  + DIGITS +  1 + (DIGITS+1)\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bYKxRgPOJHFo"
      },
      "source": [
        "# 데이터"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6dQt0G10o-Eg"
      },
      "source": [
        "## 입력 문제와 정답여부 생성\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COsdmY5vo-Eg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ed1c1e74-3752-4c82-9a9b-b4e872f5c11f"
      },
      "source": [
        "MAX_ERROR = 10\n",
        "chars = \"0123456789+= \"\n",
        "\n",
        "questions = []\n",
        "expecteds = []\n",
        "\n",
        "while len(questions) < TRAINING_SIZE:\n",
        "\n",
        "    a = np.random.randint(1000)\n",
        "    b = np.random.randint(1000)\n",
        "    # a = 123\n",
        "    # b = 45\n",
        "\n",
        "    error = np.random.randint(2)\n",
        "    # error = 1\n",
        "\n",
        "    s = a+b+error\n",
        "    # s = 123+45+1 = 169\n",
        "\n",
        "    q = \"{}+{}={}\".format(a, b, s)\n",
        "    # q = '123+45=169'\n",
        "\n",
        "    query = q.ljust(MAXLEN, ' ')\n",
        "    # query = '123+45=169  '\n",
        "\n",
        "    if REVERSE:\n",
        "        query = query[::-1]\n",
        "        # query = ' 961=54+321'\n",
        "\n",
        "    correct = 1 - error\n",
        "    questions.append(query)\n",
        "    expecteds.append(correct)\n",
        "\n",
        "print(\"Total questions:\", len(questions))\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Total questions: 50000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AfXrMCrKeRi1",
        "outputId": "a35de73f-55cd-401f-defa-d974bd7c8421"
      },
      "source": [
        "for i in range(10):\n",
        "  print(\"'\"+questions[i]+\"' --->\", expecteds[i])"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'  586=795+88' ---> 1\n",
            "'9401=052+997' ---> 1\n",
            "' 8401=35+499' ---> 0\n",
            "'6321=959+672' ---> 0\n",
            "'0911=228+763' ---> 0\n",
            "'  636=16+575' ---> 1\n",
            "' 297=791+495' ---> 0\n",
            "'9621=756+216' ---> 1\n",
            "'  922=802+12' ---> 1\n",
            "'8521=235+627' ---> 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S7eFX6jIyrnx"
      },
      "source": [
        "## 데이터를 벡터화 하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4jO8skDb7p3f"
      },
      "source": [
        "인코딩 디코딩을 위한 유틸 클래스 CharacterTable"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Sqn4LUtHyi46"
      },
      "source": [
        "class CharacterTable:\n",
        "\n",
        "    # ' ', '+', '0', '1', '2', '3', '4', '5', '6', '7', '8', '9', '='\n",
        "    #  0    1    2    3    4    5    6    7    8    9    10   11   12\n",
        "\n",
        "    def __init__(self, chars):\n",
        "        self.chars = sorted(set(chars))\n",
        "        self.char_indices = dict((c, i) for i, c in enumerate(self.chars)) # '+' ->  1\n",
        "        self.indices_char = dict((i, c) for i, c in enumerate(self.chars)) #  1  -> '+'\n",
        "\n",
        "    def encode(self, C, num_rows=MAXLEN):\n",
        "        x = np.zeros((num_rows, len(self.chars)))\n",
        "        for i, c in enumerate(C): # 각 자리 문자마다\n",
        "            x[i, self.char_indices[c]] = 1 # 각 줄의 index만 1로 만든다. one-hot encoding한다.\n",
        "        return x\n",
        "\n",
        "    def decode(self, x):\n",
        "        x = x.argmax(axis=-1)\n",
        "        return \"\".join(self.indices_char[x] for x in x)\n"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kVYzei1I7XZb"
      },
      "source": [
        "ctable = CharacterTable(chars)"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "I_fJZA7b7Y-e"
      },
      "source": [
        "다음은 인코딩, 디코딩 예이다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vwm40AA-r4tt",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bd980b6c-493b-4354-ba70-14984d0648c3"
      },
      "source": [
        "# 원 문자열\n",
        "org_str = '  655=123+45'\n",
        "print(f\"org_str='{org_str}'\")\n",
        "\n",
        "# 인코딩된 값\n",
        "encoded = ctable.encode(org_str)\n",
        "# [[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]   ' '\n",
        "#  [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]   ' '\n",
        "#  [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]   '6'\n",
        "#  [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]   '5'\n",
        "#  [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]   '5'\n",
        "#  [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]   '='\n",
        "#  [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]   '1'\n",
        "#  [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]   '2'\n",
        "#  [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]   '3'\n",
        "#  [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]   '+'\n",
        "#  [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]   '4'\n",
        "#  [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]]  '5'\n",
        "print(f\"encoded=\\n{encoded}\")\n",
        "print(f\"encoded.shape={encoded.shape}\")\n",
        "\n",
        "# 다시 디코딩해서 원복한 값\n",
        "decoded = ctable.decode(encoded)\n",
        "print(f\"decoded='{decoded}'\")"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "org_str='  655=123+45'\n",
            "encoded=\n",
            "[[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]]\n",
            "encoded.shape=(12, 13)\n",
            "decoded='  655=123+45'\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1QHj3MDo-Ej",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fb04d71e-be40-43f3-ce86-e261775eb713"
      },
      "source": [
        "# DIGITS = 3\n",
        "# MAXLEN = DIGITS + 1 + DIGITS + 1 + (DIGITS+1)\n",
        "# chars = '0123456789+='\n",
        "x = np.zeros((len(questions), MAXLEN, len(chars)))\n",
        "for i, sentence in enumerate(questions):\n",
        "    x[i] = ctable.encode(sentence)\n",
        "\n",
        "y = np.array(expecteds)\n",
        "print(x.shape)\n",
        "print(y.shape)"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(50000, 12, 13)\n",
            "(50000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJ9cKoW7OlWc",
        "outputId": "ccb92704-e8ba-4c29-c7e3-6271d1762826"
      },
      "source": [
        "split_at = int(len(x)*0.9)\n",
        "(train_x, test_x) = x[:split_at], x[split_at:]\n",
        "(train_y, test_y) = y[:split_at], y[split_at:]\n",
        "\n",
        "print(\"Training Data:\")\n",
        "print(train_x.shape)\n",
        "print(train_y.shape)\n",
        "\n",
        "print(\"Test Data:\")\n",
        "print(test_x.shape)\n",
        "print(test_y.shape)\n"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Training Data:\n",
            "(45000, 12, 13)\n",
            "(45000,)\n",
            "Test Data:\n",
            "(5000, 12, 13)\n",
            "(5000,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41UcKyn-q5Ez",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fd616d4-b1ff-4fb2-8dfb-ce32a92919d3"
      },
      "source": [
        "print(\"train_x[0]\\n\", train_x[0])\n",
        "print()\n",
        "print(\"train_y[0]\\n\", train_y[0])"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train_x[0]\n",
            " [[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0. 0.]\n",
            " [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 1. 0. 0.]]\n",
            "\n",
            "train_y[0]\n",
            " 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wa-gM3Nq84XK"
      },
      "source": [
        "준비된 데이터는 인코딩된 값들입니다. 디코딩에서 본 원 문자열은 다음과 같습니다."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zw_RFfJvrGFM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28714271-c619-4cae-a7f3-204ed14cc12b"
      },
      "source": [
        "for i in range(10):\n",
        "  print(\"'\"+ctable.decode(train_x[i])+\"' -> \"+str(train_y[i]))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'  586=795+88' -> 1\n",
            "'9401=052+997' -> 1\n",
            "' 8401=35+499' -> 0\n",
            "'6321=959+672' -> 0\n",
            "'0911=228+763' -> 0\n",
            "'  636=16+575' -> 1\n",
            "' 297=791+495' -> 0\n",
            "'9621=756+216' -> 1\n",
            "'  922=802+12' -> 1\n",
            "'8521=235+627' -> 1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i2liXf2Yo-En"
      },
      "source": [
        "# 학습\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LP8Vw2aNtzPn"
      },
      "source": [
        "## 모델 정의\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUr-sqkoo-En",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "6f113b9e-f899-461c-c93a-f42f64c9b440"
      },
      "source": [
        "input_shape = (MAXLEN, len(chars))\n",
        "model = keras.Sequential()\n",
        "model.add(Input(input_shape))\n",
        "model.add(LSTM(128))\n",
        "model.add(Dense(20))\n",
        "model.add(Dense(2, activation=\"softmax\"))\n",
        "model.compile(loss=\"sparse_categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "model.summary()\n"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_2 (LSTM)                (None, 128)               72704     \n",
            "_________________________________________________________________\n",
            "dense_4 (Dense)              (None, 20)                2580      \n",
            "_________________________________________________________________\n",
            "dense_5 (Dense)              (None, 2)                 42        \n",
            "=================================================================\n",
            "Total params: 75,326\n",
            "Trainable params: 75,326\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3vbuEEjVPGvX"
      },
      "source": [
        "## 학습 실행"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "noFAS9bNPJuu",
        "outputId": "efc5d2ac-c5f7-4eaf-f474-c9247ff172c2"
      },
      "source": [
        "history = model.fit(train_x, train_y, epochs=30, batch_size=128, validation_split=0.1)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "317/317 [==============================] - 3s 5ms/step - loss: 0.6938 - accuracy: 0.5027 - val_loss: 0.6938 - val_accuracy: 0.4887\n",
            "Epoch 2/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6934 - accuracy: 0.5049 - val_loss: 0.6937 - val_accuracy: 0.4971\n",
            "Epoch 3/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6932 - accuracy: 0.5045 - val_loss: 0.6939 - val_accuracy: 0.4936\n",
            "Epoch 4/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6930 - accuracy: 0.5083 - val_loss: 0.6937 - val_accuracy: 0.4971\n",
            "Epoch 5/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6931 - accuracy: 0.5101 - val_loss: 0.6939 - val_accuracy: 0.4916\n",
            "Epoch 6/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6931 - accuracy: 0.5072 - val_loss: 0.6937 - val_accuracy: 0.4933\n",
            "Epoch 7/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6930 - accuracy: 0.5079 - val_loss: 0.6938 - val_accuracy: 0.4929\n",
            "Epoch 8/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6930 - accuracy: 0.5083 - val_loss: 0.6939 - val_accuracy: 0.4940\n",
            "Epoch 9/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6929 - accuracy: 0.5124 - val_loss: 0.6947 - val_accuracy: 0.4862\n",
            "Epoch 10/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6929 - accuracy: 0.5111 - val_loss: 0.6940 - val_accuracy: 0.4858\n",
            "Epoch 11/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6928 - accuracy: 0.5123 - val_loss: 0.6941 - val_accuracy: 0.4933\n",
            "Epoch 12/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6927 - accuracy: 0.5114 - val_loss: 0.6948 - val_accuracy: 0.4940\n",
            "Epoch 13/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6927 - accuracy: 0.5154 - val_loss: 0.6944 - val_accuracy: 0.4933\n",
            "Epoch 14/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6927 - accuracy: 0.5142 - val_loss: 0.6947 - val_accuracy: 0.4911\n",
            "Epoch 15/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6924 - accuracy: 0.5172 - val_loss: 0.6945 - val_accuracy: 0.4936\n",
            "Epoch 16/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6917 - accuracy: 0.5178 - val_loss: 0.6932 - val_accuracy: 0.4929\n",
            "Epoch 17/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.6325 - accuracy: 0.5847 - val_loss: 0.2676 - val_accuracy: 0.8676\n",
            "Epoch 18/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.0387 - accuracy: 0.9835 - val_loss: 0.0117 - val_accuracy: 0.9956\n",
            "Epoch 19/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.0092 - accuracy: 0.9962 - val_loss: 0.0067 - val_accuracy: 0.9971\n",
            "Epoch 20/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.0061 - accuracy: 0.9975 - val_loss: 0.0084 - val_accuracy: 0.9971\n",
            "Epoch 21/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.0030 - accuracy: 0.9991 - val_loss: 0.0038 - val_accuracy: 0.9978\n",
            "Epoch 22/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 0.0017 - accuracy: 0.9995 - val_loss: 0.0011 - val_accuracy: 0.9998\n",
            "Epoch 23/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 9.7692e-04 - accuracy: 0.9997 - val_loss: 0.0012 - val_accuracy: 1.0000\n",
            "Epoch 24/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 4.0082e-04 - accuracy: 1.0000 - val_loss: 4.8447e-04 - val_accuracy: 1.0000\n",
            "Epoch 25/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 1.4950e-04 - accuracy: 1.0000 - val_loss: 2.1009e-04 - val_accuracy: 1.0000\n",
            "Epoch 26/30\n",
            "317/317 [==============================] - 1s 5ms/step - loss: 9.0826e-05 - accuracy: 1.0000 - val_loss: 1.8764e-04 - val_accuracy: 1.0000\n",
            "Epoch 27/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 6.1747e-05 - accuracy: 1.0000 - val_loss: 1.5984e-04 - val_accuracy: 1.0000\n",
            "Epoch 28/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 4.4513e-05 - accuracy: 1.0000 - val_loss: 1.1181e-04 - val_accuracy: 1.0000\n",
            "Epoch 29/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 3.3838e-05 - accuracy: 1.0000 - val_loss: 9.1176e-05 - val_accuracy: 1.0000\n",
            "Epoch 30/30\n",
            "317/317 [==============================] - 1s 4ms/step - loss: 2.5088e-05 - accuracy: 1.0000 - val_loss: 7.4166e-05 - val_accuracy: 1.0000\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "vRGy9SEnkYMj",
        "outputId": "6a53f890-e20f-4b4b-d0f7-7a3c6c108d27"
      },
      "source": [
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "iUSZGgdXkua3",
        "outputId": "d1212f89-e345-4060-ad0a-5c581fed0631"
      },
      "source": [
        "plt.plot(history.history['accuracy'])\n",
        "plt.plot(history.history['val_accuracy'])\n",
        "plt.show()"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1t4BgbHrPSqT"
      },
      "source": [
        "# 평가"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DglLNai3knXR",
        "outputId": "8b8bb2ea-cdd1-484b-9e99-18eed2e48b02"
      },
      "source": [
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss =\", loss)\n",
        "print(\"acc =\", acc)"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "157/157 [==============================] - 0s 2ms/step - loss: 5.7734e-04 - accuracy: 0.9996\n",
            "loss = 0.0005773439770564437\n",
            "acc = 0.9995999932289124\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wH3JDyPqPPj2",
        "outputId": "e36dca4f-5118-427c-a1d4-447fb07f7cc8"
      },
      "source": [
        "y_ = model.predict(test_x)\n",
        "predicted = np.argmax(y_, axis=-1)\n",
        "for i in range(10):\n",
        "  print(\"'\"+ctable.decode(test_x[i])+\"'  ----> \", predicted[i])\n",
        "\n",
        "\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "' 357=755+691'  ---->  1\n",
            "'1101=061+058'  ---->  0\n",
            "'7681=059+619'  ---->  0\n",
            "'8031=747+165'  ---->  1\n",
            "'  603=252+45'  ---->  1\n",
            "' 579=882+686'  ---->  0\n",
            "'  281=16+121'  ---->  1\n",
            "'3731=935+338'  ---->  0\n",
            "'0301=438+591'  ---->  0\n",
            "'2421=408+834'  ---->  1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YgSK50N0QWLG"
      },
      "source": [
        ""
      ],
      "execution_count": 21,
      "outputs": []
    }
  ]
}