material/deep_learning/template_text_data_sequential_generation.ipynb

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  "metadata": {
    "colab": {
      "name": "template_text_data_sequential_generation.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
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      "file_extension": ".py",
      "mimetype": "text/x-python",
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      "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": "XKxToZdMYeEv"
      },
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "M5OvBIiWo-Ec"
      },
      "source": [
        "TRAINING_SIZE = 5000   # 데이터의 갯수\n",
        "DIGITS = 2              # 덧셈에 사용될 숫자 자리수\n",
        "REVERSE = False          # 입력 문자열의 역순 여부. \n",
        "\n",
        "# 입력 문자열의 최대 길이.\n",
        "#      = 숫자길이 '+'   숫자길이\n",
        "MAXLEN = DIGITS +  1  + DIGITS\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bYKxRgPOJHFo"
      },
      "source": [
        "# 데이터"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "DWfm3GkaX5yH"
      },
      "source": [
        "## 입력 문제와 정답여부 생성\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "COsdmY5vo-Eg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d01cd0e7-0310-4b6d-e6bc-302642343701"
      },
      "source": [
        "chars = \"0123456789+ \"\n",
        "\n",
        "questions = []\n",
        "expecteds = []\n",
        "\n",
        "while len(questions) < TRAINING_SIZE:\n",
        "\n",
        "    a = np.random.randint(10**DIGITS)\n",
        "    b = np.random.randint(10**DIGITS)\n",
        "    # a = 12\n",
        "    # b = 34\n",
        "\n",
        "    q = \"{}+{}\".format(a, b)\n",
        "    # q = '12+34'\n",
        "    ans = str(a+b)\n",
        "    # ans = '46' = 123+45\n",
        "\n",
        "\n",
        "    query = q.ljust(MAXLEN, ' ')\n",
        "    # query = '12+34 '\n",
        "    ans = ans.ljust(DIGITS+1, ' ')\n",
        "\n",
        "    if REVERSE:\n",
        "        query = query[::-1]\n",
        "        # query = ' 43+21'\n",
        "\n",
        "    questions.append(query)\n",
        "    expecteds.append(ans)\n",
        "\n",
        "print(\"Total questions:\", len(questions))\n"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Total questions: 5000\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t0CxfZv3YsRG",
        "outputId": "4bb1ca9d-1072-4db1-da06-6581e4034b19"
      },
      "source": [
        "for i in range(10):\n",
        "  print(\"'\"+questions[i]+\"' ---> '\"+expecteds[i]+\"'\")"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'98+85' ---> '183'\n",
            "'49+33' ---> '82 '\n",
            "'83+58' ---> '141'\n",
            "'72+89' ---> '161'\n",
            "'45+78' ---> '123'\n",
            "'28+81' ---> '109'\n",
            "'68+44' ---> '112'\n",
            "'39+33' ---> '72 '\n",
            "'14+93' ---> '107'\n",
            "'47+49' ---> '96 '\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "'78+69     183'"
      ],
      "metadata": {
        "id": "kFLrCVx3DMRo",
        "outputId": "da88f306-64c7-462e-82f4-1c7240d04d4b",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 36
        }
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'78+69     183'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "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",
        "    def __init__(self, chars):\n",
        "        self.chars = sorted(set(chars))\n",
        "        self.char_indices = dict((c, i) for i, c in enumerate(self.chars))\n",
        "        self.indices_char = dict((i, c) for i, c in enumerate(self.chars))\n",
        "\n",
        "    def encode(self, C, num_rows):\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": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kVYzei1I7XZb"
      },
      "source": [
        "ctable = CharacterTable(chars)"
      ],
      "execution_count": 7,
      "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": "b6c7768e-76b7-4b20-e75d-1e5b4fe3424f"
      },
      "source": [
        "# 원 문자열\n",
        "org_str = ' 12+5'\n",
        "print(f\"org_str='{org_str}'\")\n",
        "\n",
        "# 인코딩된 값\n",
        "encoded = ctable.encode(org_str, MAXLEN)\n",
        "# [[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]      <--- ' '\n",
        "#  [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]      <--- '1'\n",
        "#  [0. 0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0.]      <--- '2'\n",
        "#  [0. 1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]      <--- '+'\n",
        "#  [0. 0. 0. 0. 0. 0. 0. 1. 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": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "org_str=' 12+5'\n",
            "encoded=\n",
            "[[1. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [0. 0. 0. 1. 0. 0. 0. 0. 0. 0. 0. 0.]\n",
            " [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.]\n",
            " [0. 0. 0. 0. 0. 0. 0. 1. 0. 0. 0. 0.]]\n",
            "encoded.shape=(5, 12)\n",
            "decoded=' 12+5'\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "d1QHj3MDo-Ej",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ece46b3b-4b88-4bfa-a63e-b1cdb4f43739"
      },
      "source": [
        "print(\"Vectorization...\")\n",
        "\n",
        "x = np.zeros((len(questions), MAXLEN, len(chars)))\n",
        "y = np.zeros((len(questions), DIGITS + 1, len(chars)))\n",
        "for i, sentence in enumerate(questions):\n",
        "    x[i] = ctable.encode(sentence, MAXLEN)\n",
        "for i, sentence in enumerate(expecteds):\n",
        "    y[i] = ctable.encode(sentence, DIGITS + 1)\n",
        "\n",
        "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",
          "name": "stdout",
          "text": [
            "Vectorization...\n",
            "Training Data:\n",
            "(4500, 5, 12)\n",
            "(4500, 3, 12)\n",
            "Test Data:\n",
            "(500, 5, 12)\n",
            "(500, 3, 12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "41UcKyn-q5Ez",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1a375812-7508-4bbf-ebbe-9a602f9286cb"
      },
      "source": [
        "print(\"train_x[0]\\n\", train_x[0].astype(np.int8))\n",
        "print()\n",
        "print(\"train_y[0]\\n\", train_y[0].astype(np.int8))"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train_x[0]\n",
            " [[0 0 0 0 0 0 0 0 0 0 0 1]\n",
            " [0 0 0 0 0 0 0 0 0 0 1 0]\n",
            " [0 1 0 0 0 0 0 0 0 0 0 0]\n",
            " [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]]\n",
            "\n",
            "train_y[0]\n",
            " [[0 0 0 1 0 0 0 0 0 0 0 0]\n",
            " [0 0 0 0 0 0 0 0 0 0 1 0]\n",
            " [0 0 0 0 0 1 0 0 0 0 0 0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zw_RFfJvrGFM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a6d51c59-7d6a-4ecd-f01c-3fb6454ff4f3"
      },
      "source": [
        "for i in range(10):\n",
        "  print(\"'\"+ctable.decode(train_x[i])+\"' -> '\"+ctable.decode(train_y[i])+\"'\")"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'98+85' -> '183'\n",
            "'49+33' -> '82 '\n",
            "'83+58' -> '141'\n",
            "'72+89' -> '161'\n",
            "'45+78' -> '123'\n",
            "'28+81' -> '109'\n",
            "'68+44' -> '112'\n",
            "'39+33' -> '72 '\n",
            "'14+93' -> '107'\n",
            "'47+49' -> '96 '\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i2liXf2Yo-En"
      },
      "source": [
        "# Template\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yP4uppTDaJdU"
      },
      "source": [
        "## LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GUr-sqkoo-En",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "outputId": "6d02e79e-24a8-40b6-c594-8e78666579ca"
      },
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras.layers import Dense, RepeatVector, Flatten\n",
        "from tensorflow.keras.layers import Bidirectional, LSTM, GRU, Input\n",
        "\n",
        "input_shape = train_x.shape[1:]\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Input(input_shape))\n",
        "model.add(LSTM(128))\n",
        "model.add(RepeatVector(DIGITS + 1))\n",
        "model.add(LSTM(128, return_sequences=True))\n",
        "model.add(Dense(len(chars), activation=\"softmax\"))\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "model.summary()\n",
        "\n",
        "history = model.fit(train_x, train_y, epochs=100, verbose=0, validation_split=0.1, batch_size=128)\n",
        "\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"acc=\", acc)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "for i in range(10):\n",
        "  print(\"q: {}, a: {}, predicted: {}\".format( \n",
        "      ctable.decode(test_x[i]), \n",
        "      ctable.decode(test_y[i]),\n",
        "      ctable.decode(y_[i])\n",
        "      ))"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm (LSTM)                 (None, 128)               72192     \n",
            "                                                                 \n",
            " repeat_vector (RepeatVector  (None, 3, 128)           0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 3, 128)            131584    \n",
            "                                                                 \n",
            " dense (Dense)               (None, 3, 12)             1548      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 205,324\n",
            "Trainable params: 205,324\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 0s 4ms/step - loss: 0.3102 - accuracy: 0.9147\n",
            "loss= 0.3102341294288635\n",
            "acc= 0.9146666526794434\n",
            "q: 1+21 , a: 22 , predicted: 14 \n",
            "q: 47+15, a: 62 , predicted: 62 \n",
            "q: 73+25, a: 98 , predicted: 97 \n",
            "q: 83+34, a: 117, predicted: 117\n",
            "q: 92+94, a: 186, predicted: 186\n",
            "q: 11+50, a: 61 , predicted: 61 \n",
            "q: 47+99, a: 146, predicted: 146\n",
            "q: 78+11, a: 89 , predicted: 89 \n",
            "q: 61+52, a: 113, predicted: 113\n",
            "q: 32+86, a: 118, predicted: 118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1sV7tBtAWEz"
      },
      "source": [
        "## GRU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 819
        },
        "id": "Y3EEqdA7m4h3",
        "outputId": "d5041670-34f9-400a-ff91-156d9827edab"
      },
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras.layers import Dense, RepeatVector, Flatten\n",
        "from tensorflow.keras.layers import Bidirectional, LSTM, GRU, Input\n",
        "\n",
        "input_shape = train_x.shape[1:]\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Input(input_shape))\n",
        "model.add(GRU(128))\n",
        "model.add(RepeatVector(DIGITS + 1))\n",
        "model.add(GRU(128, return_sequences=True))\n",
        "model.add(Dense(len(chars), activation=\"softmax\"))\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "model.summary()\n",
        "\n",
        "history = model.fit(train_x, train_y, epochs=100, verbose=0, validation_split=0.1, batch_size=128)\n",
        "\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"acc=\", acc)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "for i in range(10):\n",
        "  print(\"q: {}, a: {}, predicted: {}\".format( \n",
        "      ctable.decode(test_x[i]), \n",
        "      ctable.decode(test_y[i]),\n",
        "      ctable.decode(y_[i])\n",
        "      ))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " gru (GRU)                   (None, 128)               54528     \n",
            "                                                                 \n",
            " repeat_vector_1 (RepeatVect  (None, 3, 128)           0         \n",
            " or)                                                             \n",
            "                                                                 \n",
            " gru_1 (GRU)                 (None, 3, 128)            99072     \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 3, 12)             1548      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 155,148\n",
            "Trainable params: 155,148\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 0s 4ms/step - loss: 0.1152 - accuracy: 0.9693\n",
            "loss= 0.11516711115837097\n",
            "acc= 0.9693333506584167\n",
            "q: 1+21 , a: 22 , predicted: 24 \n",
            "q: 47+15, a: 62 , predicted: 62 \n",
            "q: 73+25, a: 98 , predicted: 98 \n",
            "q: 83+34, a: 117, predicted: 117\n",
            "q: 92+94, a: 186, predicted: 186\n",
            "q: 11+50, a: 61 , predicted: 61 \n",
            "q: 47+99, a: 146, predicted: 146\n",
            "q: 78+11, a: 89 , predicted: 89 \n",
            "q: 61+52, a: 113, predicted: 113\n",
            "q: 32+86, a: 118, predicted: 118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "jCBiKCg9AeHT"
      },
      "source": [
        "## Bidirectional + LSTM"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 854
        },
        "id": "ER0YZAxnm6o4",
        "outputId": "a36cf109-aed0-4fce-bcc8-df2821274bb7"
      },
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras.layers import Dense, RepeatVector, Flatten\n",
        "from tensorflow.keras.layers import Bidirectional, LSTM, GRU, Input\n",
        "\n",
        "input_shape = train_x.shape[1:]\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Input(input_shape))\n",
        "model.add(Bidirectional(LSTM(128)))\n",
        "model.add(RepeatVector(DIGITS + 1))\n",
        "model.add(Bidirectional(LSTM(128, return_sequences=True)))\n",
        "model.add(Dense(len(chars), activation=\"softmax\"))\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "model.summary()\n",
        "\n",
        "history = model.fit(train_x, train_y, epochs=100, verbose=0, validation_split=0.1, batch_size=128)\n",
        "\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"acc=\", acc)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "for i in range(10):\n",
        "  print(\"q: {}, a: {}, predicted: {}\".format( \n",
        "      ctable.decode(test_x[i]), \n",
        "      ctable.decode(test_y[i]),\n",
        "      ctable.decode(y_[i])\n",
        "      ))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " bidirectional (Bidirectiona  (None, 256)              144384    \n",
            " l)                                                              \n",
            "                                                                 \n",
            " repeat_vector_2 (RepeatVect  (None, 3, 256)           0         \n",
            " or)                                                             \n",
            "                                                                 \n",
            " bidirectional_1 (Bidirectio  (None, 3, 256)           394240    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dense_2 (Dense)             (None, 3, 12)             3084      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 541,708\n",
            "Trainable params: 541,708\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 0s 5ms/step - loss: 0.0461 - accuracy: 0.9893\n",
            "loss= 0.046135902404785156\n",
            "acc= 0.9893333315849304\n",
            "q: 1+21 , a: 22 , predicted: 13 \n",
            "q: 47+15, a: 62 , predicted: 62 \n",
            "q: 73+25, a: 98 , predicted: 98 \n",
            "q: 83+34, a: 117, predicted: 117\n",
            "q: 92+94, a: 186, predicted: 186\n",
            "q: 11+50, a: 61 , predicted: 61 \n",
            "q: 47+99, a: 146, predicted: 146\n",
            "q: 78+11, a: 89 , predicted: 89 \n",
            "q: 61+52, a: 113, predicted: 113\n",
            "q: 32+86, a: 118, predicted: 118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TfY_p_SJA5Rf"
      },
      "source": [
        "## Bidirectional + GRU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "azGH_FDbwqHT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 854
        },
        "outputId": "3a309525-8593-4517-bdd2-1f7106ba776b"
      },
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras.layers import Dense, RepeatVector, Flatten\n",
        "from tensorflow.keras.layers import Bidirectional, LSTM, GRU, Input\n",
        "\n",
        "input_shape = train_x.shape[1:]\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Input(input_shape))\n",
        "model.add(Bidirectional(GRU(128)))\n",
        "model.add(RepeatVector(DIGITS + 1))\n",
        "model.add(Bidirectional(GRU(128, return_sequences=True)))\n",
        "model.add(Dense(len(chars), activation=\"softmax\"))\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "model.summary()\n",
        "\n",
        "history = model.fit(train_x, train_y, epochs=100, verbose=0, validation_split=0.1, batch_size=128)\n",
        "\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"acc=\", acc)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "for i in range(10):\n",
        "  print(\"q: {}, a: {}, predicted: {}\".format( \n",
        "      ctable.decode(test_x[i]), \n",
        "      ctable.decode(test_y[i]),\n",
        "      ctable.decode(y_[i])\n",
        "      ))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_3\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " bidirectional_2 (Bidirectio  (None, 256)              109056    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " repeat_vector_3 (RepeatVect  (None, 3, 256)           0         \n",
            " or)                                                             \n",
            "                                                                 \n",
            " bidirectional_3 (Bidirectio  (None, 3, 256)           296448    \n",
            " nal)                                                            \n",
            "                                                                 \n",
            " dense_3 (Dense)             (None, 3, 12)             3084      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 408,588\n",
            "Trainable params: 408,588\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 0s 5ms/step - loss: 0.0467 - accuracy: 0.9873\n",
            "loss= 0.046720489859580994\n",
            "acc= 0.987333357334137\n",
            "q: 1+21 , a: 22 , predicted: 12 \n",
            "q: 47+15, a: 62 , predicted: 62 \n",
            "q: 73+25, a: 98 , predicted: 98 \n",
            "q: 83+34, a: 117, predicted: 117\n",
            "q: 92+94, a: 186, predicted: 186\n",
            "q: 11+50, a: 61 , predicted: 61 \n",
            "q: 47+99, a: 146, predicted: 146\n",
            "q: 78+11, a: 89 , predicted: 89 \n",
            "q: 61+52, a: 113, predicted: 113\n",
            "q: 32+86, a: 118, predicted: 118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "P3aO3WEwA-lR"
      },
      "source": [
        "## stacked"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 890
        },
        "id": "kjlbPMfTm750",
        "outputId": "55e81c9f-cae9-4a68-e52f-7a057eb44996"
      },
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras.layers import Dense, RepeatVector, Flatten\n",
        "from tensorflow.keras.layers import Bidirectional, LSTM, GRU, Input\n",
        "\n",
        "input_shape = train_x.shape[1:]\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(Input(input_shape))\n",
        "model.add(LSTM(128, return_sequences=True))\n",
        "model.add(LSTM(128))\n",
        "model.add(RepeatVector(DIGITS + 1))\n",
        "model.add(LSTM(128, return_sequences=True))\n",
        "model.add(LSTM(128, return_sequences=True))\n",
        "model.add(Dense(len(chars), activation=\"softmax\"))\n",
        "model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
        "model.summary()\n",
        "\n",
        "history = model.fit(train_x, train_y, epochs=100, verbose=0, validation_split=0.1, batch_size=128)\n",
        "\n",
        "\n",
        "plt.plot(history.history['loss'])\n",
        "plt.plot(history.history['val_loss'])\n",
        "plt.show()\n",
        "\n",
        "\n",
        "loss, acc = model.evaluate(test_x, test_y)\n",
        "print(\"loss=\", loss)\n",
        "print(\"acc=\", acc)\n",
        "\n",
        "\n",
        "y_ = model.predict(test_x)\n",
        "\n",
        "for i in range(10):\n",
        "  print(\"q: {}, a: {}, predicted: {}\".format( \n",
        "      ctable.decode(test_x[i]), \n",
        "      ctable.decode(test_y[i]),\n",
        "      ctable.decode(y_[i])\n",
        "      ))"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_4\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_4 (LSTM)               (None, 5, 128)            72192     \n",
            "                                                                 \n",
            " lstm_5 (LSTM)               (None, 128)               131584    \n",
            "                                                                 \n",
            " repeat_vector_4 (RepeatVect  (None, 3, 128)           0         \n",
            " or)                                                             \n",
            "                                                                 \n",
            " lstm_6 (LSTM)               (None, 3, 128)            131584    \n",
            "                                                                 \n",
            " lstm_7 (LSTM)               (None, 3, 128)            131584    \n",
            "                                                                 \n",
            " dense_4 (Dense)             (None, 3, 12)             1548      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 468,492\n",
            "Trainable params: 468,492\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "16/16 [==============================] - 0s 5ms/step - loss: 0.3059 - accuracy: 0.9173\n",
            "loss= 0.3058969974517822\n",
            "acc= 0.9173333048820496\n",
            "q: 1+21 , a: 22 , predicted: 22 \n",
            "q: 47+15, a: 62 , predicted: 62 \n",
            "q: 73+25, a: 98 , predicted: 98 \n",
            "q: 83+34, a: 117, predicted: 117\n",
            "q: 92+94, a: 186, predicted: 186\n",
            "q: 11+50, a: 61 , predicted: 62 \n",
            "q: 47+99, a: 146, predicted: 146\n",
            "q: 78+11, a: 89 , predicted: 89 \n",
            "q: 61+52, a: 113, predicted: 113\n",
            "q: 32+86, a: 118, predicted: 118\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "emE8KhR9BF-Z"
      },
      "source": [
        ""
      ],
      "execution_count": 16,
      "outputs": []
    }
  ]
}