chapter10_dl-for-timeseries.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rLyx0NKfjbcR"
      },
      "source": [
        "이 노트북은 [케라스 창시자에게 배우는 딥러닝 2판](https://tensorflow.blog/kerasdl2/)의 예제 코드를 담고 있습니다.\n",
        "\n",
        "<table align=\"left\">\n",
        "    <tr>\n",
        "        <td>\n",
        "            <a href=\"https://colab.research.google.com/github/rickiepark/deep-learning-with-python-2nd/blob/main/chapter10_dl-for-timeseries.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>\n",
        "        </td>\n",
        "    </tr>\n",
        "</table>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-jmgSYTnjbcU"
      },
      "source": [
        "# 시계열을 위한 딥러닝"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yqfO6TQzjbcV"
      },
      "source": [
        "## 다양한 종류의 시계열 작업"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rMlIvey2jbcV"
      },
      "source": [
        "## 기온 예측 문제"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:26:55.453195Z",
          "iopub.status.busy": "2022-05-20T13:26:55.452690Z",
          "iopub.status.idle": "2022-05-20T13:26:58.879200Z",
          "shell.execute_reply": "2022-05-20T13:26:58.877667Z"
        },
        "id": "aUCXJRQgjbcV",
        "outputId": "9bfc1196-0468-429d-9f9a-e901bf054faf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-11-03 06:56:25--  https://s3.amazonaws.com/keras-datasets/jena_climate_2009_2016.csv.zip\n",
            "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.88.190, 52.217.234.152, 16.182.73.0, ...\n",
            "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.88.190|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 13565642 (13M) [application/zip]\n",
            "Saving to: ‘jena_climate_2009_2016.csv.zip’\n",
            "\n",
            "jena_climate_2009_2 100%[===================>]  12.94M  18.9MB/s    in 0.7s    \n",
            "\n",
            "2023-11-03 06:56:26 (18.9 MB/s) - ‘jena_climate_2009_2016.csv.zip’ saved [13565642/13565642]\n",
            "\n",
            "Archive:  jena_climate_2009_2016.csv.zip\n",
            "  inflating: jena_climate_2009_2016.csv  \n",
            "  inflating: __MACOSX/._jena_climate_2009_2016.csv  \n"
          ]
        }
      ],
      "source": [
        "!wget https://s3.amazonaws.com/keras-datasets/jena_climate_2009_2016.csv.zip\n",
        "!unzip jena_climate_2009_2016.csv.zip"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C93ZoFD3jbcX"
      },
      "source": [
        "**예나 날씨 데이터셋 조사하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:26:58.883391Z",
          "iopub.status.busy": "2022-05-20T13:26:58.883062Z",
          "iopub.status.idle": "2022-05-20T13:26:59.026206Z",
          "shell.execute_reply": "2022-05-20T13:26:59.025347Z"
        },
        "id": "Z-0eD31YjbcX",
        "outputId": "ab5774ca-e4b4-4974-bea0-3674b9fbfa21",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['\"Date Time\"', '\"p (mbar)\"', '\"T (degC)\"', '\"Tpot (K)\"', '\"Tdew (degC)\"', '\"rh (%)\"', '\"VPmax (mbar)\"', '\"VPact (mbar)\"', '\"VPdef (mbar)\"', '\"sh (g/kg)\"', '\"H2OC (mmol/mol)\"', '\"rho (g/m**3)\"', '\"wv (m/s)\"', '\"max. wv (m/s)\"', '\"wd (deg)\"']\n",
            "420451\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "fname = os.path.join(\"jena_climate_2009_2016.csv\")\n",
        "\n",
        "with open(fname) as f:\n",
        "    data = f.read()\n",
        "\n",
        "lines = data.split(\"\\n\")\n",
        "header = lines[0].split(\",\")\n",
        "lines = lines[1:]\n",
        "print(header)\n",
        "print(len(lines))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kRnwyaNijbcY"
      },
      "source": [
        "**데이터 파싱**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:26:59.030213Z",
          "iopub.status.busy": "2022-05-20T13:26:59.029492Z",
          "iopub.status.idle": "2022-05-20T13:27:00.936580Z",
          "shell.execute_reply": "2022-05-20T13:27:00.935499Z"
        },
        "id": "FiZFxhiCjbcY"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "temperature = np.zeros((len(lines),))\n",
        "raw_data = np.zeros((len(lines), len(header) - 1))\n",
        "for i, line in enumerate(lines):\n",
        "    values = [float(x) for x in line.split(\",\")[1:]]\n",
        "    temperature[i] = values[1]\n",
        "    raw_data[i, :] = values[:]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "b1MbsjsljbcZ"
      },
      "source": [
        "**전체 기온을 그래프로 그리기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:27:00.940623Z",
          "iopub.status.busy": "2022-05-20T13:27:00.940326Z",
          "iopub.status.idle": "2022-05-20T13:27:02.011631Z",
          "shell.execute_reply": "2022-05-20T13:27:02.010321Z"
        },
        "id": "o6gAh5ecjbcZ",
        "outputId": "ed573fee-c328-4375-e242-23d617c53d60",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "from matplotlib import pyplot as plt\n",
        "plt.plot(range(len(temperature)), temperature)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "aALKlaFJjbcZ"
      },
      "source": [
        "**처음 10일간의 기온을 그래프로 그리기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:27:02.015866Z",
          "iopub.status.busy": "2022-05-20T13:27:02.015374Z",
          "iopub.status.idle": "2022-05-20T13:27:02.123996Z",
          "shell.execute_reply": "2022-05-20T13:27:02.122754Z"
        },
        "id": "FGPdWjXujbca",
        "outputId": "c478e933-8aff-4a8f-8c01-1f3c8886c72f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "plt.plot(range(1440), temperature[:1440])\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QaZN550Xjbca"
      },
      "source": [
        "**각 분할에 사용할 샘플 수 계산하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:27:02.128070Z",
          "iopub.status.busy": "2022-05-20T13:27:02.127766Z",
          "iopub.status.idle": "2022-05-20T13:27:02.133998Z",
          "shell.execute_reply": "2022-05-20T13:27:02.132864Z"
        },
        "id": "YG7JjzJtjbca",
        "outputId": "827dba02-8953-4d91-d752-ca7b5aa269f9",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "num_train_samples: 210225\n",
            "num_val_samples: 105112\n",
            "num_test_samples: 105114\n"
          ]
        }
      ],
      "source": [
        "num_train_samples = int(0.5 * len(raw_data))\n",
        "num_val_samples = int(0.25 * len(raw_data))\n",
        "num_test_samples = len(raw_data) - num_train_samples - num_val_samples\n",
        "print(\"num_train_samples:\", num_train_samples)\n",
        "print(\"num_val_samples:\", num_val_samples)\n",
        "print(\"num_test_samples:\", num_test_samples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B-GzlCYCjbcb"
      },
      "source": [
        "### 데이터 준비"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gsmS_D67jbcb"
      },
      "source": [
        "**데이터 정규화**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:27:02.137634Z",
          "iopub.status.busy": "2022-05-20T13:27:02.137353Z",
          "iopub.status.idle": "2022-05-20T13:27:02.191157Z",
          "shell.execute_reply": "2022-05-20T13:27:02.189956Z"
        },
        "id": "5BhCfjkrjbcb"
      },
      "outputs": [],
      "source": [
        "mean = raw_data[:num_train_samples].mean(axis=0)\n",
        "raw_data -= mean\n",
        "std = raw_data[:num_train_samples].std(axis=0)\n",
        "raw_data /= std"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:27:02.195344Z",
          "iopub.status.busy": "2022-05-20T13:27:02.195011Z",
          "iopub.status.idle": "2022-05-20T13:27:02.314820Z",
          "shell.execute_reply": "2022-05-20T13:27:02.312523Z"
        },
        "id": "sgoxHRsPjbcb",
        "outputId": "b8a2eb7a-e517-4bec-b251-8d1c992216ae",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 1, 2] 3\n",
            "[1, 2, 3] 4\n",
            "[2, 3, 4] 5\n",
            "[3, 4, 5] 6\n",
            "[4, 5, 6] 7\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "int_sequence = np.arange(10)\n",
        "dummy_dataset = keras.utils.timeseries_dataset_from_array(\n",
        "    data=int_sequence[:-3],\n",
        "    targets=int_sequence[3:],\n",
        "    sequence_length=3,\n",
        "    batch_size=2,\n",
        ")\n",
        "\n",
        "for inputs, targets in dummy_dataset:\n",
        "    for i in range(inputs.shape[0]):\n",
        "        print([int(x) for x in inputs[i]], int(targets[i]))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "n_d8n6Yqjbcb"
      },
      "source": [
        "**훈련, 검증, 테스트 데이터셋 만들기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:27:02.318918Z",
          "iopub.status.busy": "2022-05-20T13:27:02.318531Z",
          "iopub.status.idle": "2022-05-20T13:27:02.483352Z",
          "shell.execute_reply": "2022-05-20T13:27:02.482263Z"
        },
        "id": "uts3EvXajbcc"
      },
      "outputs": [],
      "source": [
        "sampling_rate = 6\n",
        "sequence_length = 120\n",
        "delay = sampling_rate * (sequence_length + 24 - 1)\n",
        "batch_size = 256\n",
        "\n",
        "train_dataset = keras.utils.timeseries_dataset_from_array(\n",
        "    raw_data[:-delay],\n",
        "    targets=temperature[delay:],\n",
        "    sampling_rate=sampling_rate,\n",
        "    sequence_length=sequence_length,\n",
        "    shuffle=True,\n",
        "    batch_size=batch_size,\n",
        "    start_index=0,\n",
        "    end_index=num_train_samples)\n",
        "\n",
        "val_dataset = keras.utils.timeseries_dataset_from_array(\n",
        "    raw_data[:-delay],\n",
        "    targets=temperature[delay:],\n",
        "    sampling_rate=sampling_rate,\n",
        "    sequence_length=sequence_length,\n",
        "    shuffle=True,\n",
        "    batch_size=batch_size,\n",
        "    start_index=num_train_samples,\n",
        "    end_index=num_train_samples + num_val_samples)\n",
        "\n",
        "test_dataset = keras.utils.timeseries_dataset_from_array(\n",
        "    raw_data[:-delay],\n",
        "    targets=temperature[delay:],\n",
        "    sampling_rate=sampling_rate,\n",
        "    sequence_length=sequence_length,\n",
        "    shuffle=True,\n",
        "    batch_size=batch_size,\n",
        "    start_index=num_train_samples + num_val_samples)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dZNUb2s2jbcc"
      },
      "source": [
        "**훈련 데이터셋의 배치 크기 확인하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:27:02.487516Z",
          "iopub.status.busy": "2022-05-20T13:27:02.487209Z",
          "iopub.status.idle": "2022-05-20T13:27:02.708306Z",
          "shell.execute_reply": "2022-05-20T13:27:02.707147Z"
        },
        "id": "pC_5Co8Zjbcc",
        "outputId": "5ec9328c-7061-4943-ebd0-41ec507d5034",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "샘플 크기: (256, 120, 14)\n",
            "타깃 크기: (256,)\n"
          ]
        }
      ],
      "source": [
        "for samples, targets in train_dataset:\n",
        "    print(\"샘플 크기:\", samples.shape)\n",
        "    print(\"타깃 크기:\", targets.shape)\n",
        "    break"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vdYKKLDjjbcd"
      },
      "source": [
        "### 상식 수준의 기준점"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OABELWY3jbcd"
      },
      "source": [
        "**상식적인 기준 모델의 MAE 계산하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:27:02.712723Z",
          "iopub.status.busy": "2022-05-20T13:27:02.712259Z",
          "iopub.status.idle": "2022-05-20T13:27:12.831040Z",
          "shell.execute_reply": "2022-05-20T13:27:12.829972Z"
        },
        "id": "-ryWeRYljbcd",
        "outputId": "77d1b786-a953-4fe8-99a6-aa8495098b8e",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "검증 MAE: 2.44\n",
            "테스트 MAE: 2.62\n"
          ]
        }
      ],
      "source": [
        "def evaluate_naive_method(dataset):\n",
        "    total_abs_err = 0.\n",
        "    samples_seen = 0\n",
        "    for samples, targets in dataset:\n",
        "        preds = samples[:, -1, 1] * std[1] + mean[1]\n",
        "        total_abs_err += np.sum(np.abs(preds - targets))\n",
        "        samples_seen += samples.shape[0]\n",
        "    return total_abs_err / samples_seen\n",
        "\n",
        "print(f\"검증 MAE: {evaluate_naive_method(val_dataset):.2f}\")\n",
        "print(f\"테스트 MAE: {evaluate_naive_method(test_dataset):.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AwYpH902jbcd"
      },
      "source": [
        "### 기본적인 머신 러닝 모델 시도해 보기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "g8shk8fVjbcd"
      },
      "source": [
        "**밀집 연결 모델 훈련하고 평가하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:27:12.835455Z",
          "iopub.status.busy": "2022-05-20T13:27:12.834662Z",
          "iopub.status.idle": "2022-05-20T13:30:16.406591Z",
          "shell.execute_reply": "2022-05-20T13:30:16.405650Z"
        },
        "id": "AnkykZycjbcd",
        "outputId": "f1191cef-b0e8-428f-ad07-6f1672d5b084",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "819/819 [==============================] - 49s 58ms/step - loss: 11.6431 - mae: 2.6472 - val_loss: 11.9873 - val_mae: 2.7567\n",
            "Epoch 2/10\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3079: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "819/819 [==============================] - 43s 52ms/step - loss: 8.7153 - mae: 2.3233 - val_loss: 10.7710 - val_mae: 2.6029\n",
            "Epoch 3/10\n",
            "819/819 [==============================] - 49s 59ms/step - loss: 8.0518 - mae: 2.2346 - val_loss: 12.3365 - val_mae: 2.8000\n",
            "Epoch 4/10\n",
            "819/819 [==============================] - 38s 46ms/step - loss: 7.6995 - mae: 2.1847 - val_loss: 10.6241 - val_mae: 2.5909\n",
            "Epoch 5/10\n",
            "819/819 [==============================] - 39s 47ms/step - loss: 7.3863 - mae: 2.1393 - val_loss: 10.8965 - val_mae: 2.6158\n",
            "Epoch 6/10\n",
            "819/819 [==============================] - 38s 47ms/step - loss: 7.1509 - mae: 2.1041 - val_loss: 10.6415 - val_mae: 2.5882\n",
            "Epoch 7/10\n",
            "819/819 [==============================] - 41s 50ms/step - loss: 6.9747 - mae: 2.0763 - val_loss: 12.4100 - val_mae: 2.8011\n",
            "Epoch 8/10\n",
            "819/819 [==============================] - 48s 59ms/step - loss: 6.8377 - mae: 2.0559 - val_loss: 11.0738 - val_mae: 2.6403\n",
            "Epoch 9/10\n",
            "819/819 [==============================] - 47s 57ms/step - loss: 6.7188 - mae: 2.0401 - val_loss: 11.0344 - val_mae: 2.6293\n",
            "Epoch 10/10\n",
            "819/819 [==============================] - 38s 46ms/step - loss: 6.5949 - mae: 2.0211 - val_loss: 11.2066 - val_mae: 2.6507\n",
            "405/405 [==============================] - 14s 34ms/step - loss: 11.0903 - mae: 2.6144\n",
            "테스트 MAE: 2.61\n"
          ]
        }
      ],
      "source": [
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "\n",
        "inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\n",
        "x = layers.Flatten()(inputs)\n",
        "x = layers.Dense(16, activation=\"relu\")(x)\n",
        "outputs = layers.Dense(1)(x)\n",
        "model = keras.Model(inputs, outputs)\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\"jena_dense.h5\",\n",
        "                                    save_best_only=True)\n",
        "]\n",
        "model.compile(optimizer=\"rmsprop\", loss=\"mse\", metrics=[\"mae\"])\n",
        "history = model.fit(train_dataset,\n",
        "                    epochs=10,\n",
        "                    validation_data=val_dataset,\n",
        "                    callbacks=callbacks)\n",
        "\n",
        "model = keras.models.load_model(\"jena_dense.h5\")\n",
        "print(f\"테스트 MAE: {model.evaluate(test_dataset)[1]:.2f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lmTjKA_qjbce"
      },
      "source": [
        "**결과 그래프 그리기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:30:16.410386Z",
          "iopub.status.busy": "2022-05-20T13:30:16.410076Z",
          "iopub.status.idle": "2022-05-20T13:30:16.562302Z",
          "shell.execute_reply": "2022-05-20T13:30:16.561390Z"
        },
        "id": "GE_ngKAZjbce",
        "outputId": "6006f14f-4028-4ffe-9c1a-68af96192938",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "loss = history.history[\"mae\"]\n",
        "val_loss = history.history[\"val_mae\"]\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, \"bo\", label=\"Training MAE\")\n",
        "plt.plot(epochs, val_loss, \"b\", label=\"Validation MAE\")\n",
        "plt.title(\"Training and validation MAE\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nHd3-Y89jbce"
      },
      "source": [
        "### 1D 합성곱 모델 시도해 보기"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:30:16.566194Z",
          "iopub.status.busy": "2022-05-20T13:30:16.565904Z",
          "iopub.status.idle": "2022-05-20T13:35:49.724331Z",
          "shell.execute_reply": "2022-05-20T13:35:49.723349Z"
        },
        "id": "MIodgX8jjbce",
        "outputId": "544bb6f0-1c7f-414c-dfa4-485b430a5429",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "819/819 [==============================] - 45s 48ms/step - loss: 22.2081 - mae: 3.6986 - val_loss: 17.5946 - val_mae: 3.2966\n",
            "Epoch 2/10\n",
            "819/819 [==============================] - 41s 49ms/step - loss: 15.9101 - mae: 3.1617 - val_loss: 20.5424 - val_mae: 3.5932\n",
            "Epoch 3/10\n",
            "819/819 [==============================] - 48s 58ms/step - loss: 14.4073 - mae: 3.0054 - val_loss: 15.4583 - val_mae: 3.0871\n",
            "Epoch 4/10\n",
            "819/819 [==============================] - 40s 49ms/step - loss: 13.4702 - mae: 2.9036 - val_loss: 14.8965 - val_mae: 3.0534\n",
            "Epoch 5/10\n",
            "819/819 [==============================] - 40s 48ms/step - loss: 12.7056 - mae: 2.8179 - val_loss: 14.8444 - val_mae: 3.0447\n",
            "Epoch 6/10\n",
            "819/819 [==============================] - 39s 48ms/step - loss: 12.0952 - mae: 2.7489 - val_loss: 19.9825 - val_mae: 3.5700\n",
            "Epoch 7/10\n",
            "819/819 [==============================] - 38s 47ms/step - loss: 11.6504 - mae: 2.6981 - val_loss: 14.6754 - val_mae: 3.0367\n",
            "Epoch 8/10\n",
            "819/819 [==============================] - 42s 50ms/step - loss: 11.2289 - mae: 2.6506 - val_loss: 15.4748 - val_mae: 3.1188\n",
            "Epoch 9/10\n",
            "819/819 [==============================] - 49s 59ms/step - loss: 10.8513 - mae: 2.6073 - val_loss: 15.5818 - val_mae: 3.1455\n",
            "Epoch 10/10\n",
            "819/819 [==============================] - 41s 49ms/step - loss: 10.5683 - mae: 2.5734 - val_loss: 14.7543 - val_mae: 3.0433\n",
            "405/405 [==============================] - 13s 32ms/step - loss: 15.9907 - mae: 3.1695\n",
            "테스트 MAE: 3.17\n"
          ]
        }
      ],
      "source": [
        "inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\n",
        "x = layers.Conv1D(8, 24, activation=\"relu\")(inputs)\n",
        "x = layers.MaxPooling1D(2)(x)\n",
        "x = layers.Conv1D(8, 12, activation=\"relu\")(x)\n",
        "x = layers.MaxPooling1D(2)(x)\n",
        "x = layers.Conv1D(8, 6, activation=\"relu\")(x)\n",
        "x = layers.GlobalAveragePooling1D()(x)\n",
        "outputs = layers.Dense(1)(x)\n",
        "model = keras.Model(inputs, outputs)\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\"jena_conv.h5\",\n",
        "                                    save_best_only=True)\n",
        "]\n",
        "model.compile(optimizer=\"rmsprop\", loss=\"mse\", metrics=[\"mae\"])\n",
        "history = model.fit(train_dataset,\n",
        "                    epochs=10,\n",
        "                    validation_data=val_dataset,\n",
        "                    callbacks=callbacks)\n",
        "\n",
        "model = keras.models.load_model(\"jena_conv.h5\")\n",
        "print(f\"테스트 MAE: {model.evaluate(test_dataset)[1]:.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:35:49.728113Z",
          "iopub.status.busy": "2022-05-20T13:35:49.727783Z",
          "iopub.status.idle": "2022-05-20T13:35:49.850064Z",
          "shell.execute_reply": "2022-05-20T13:35:49.849311Z"
        },
        "id": "twXzOJXkjbce",
        "outputId": "29a01c0c-d6cb-4604-d0ec-71dddb174f98",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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CXl5eOHLkCMaPH4/vvvsOaWlpqFevHrZt25ZrYmhJ27FjB549e1Zgz0lISAjmzZuH9evXY9SoUQBEEjNw4MA82+f3fSAqjEIypo+CRFSo7t274/z583nOZyAiMkWcM0JkxF4t3X716lXs3LkTLVu2lCcgIiIDYM8IkRHz8fFR75dy69YtLFmyBOnp6Th79iyqVasmd3hERHrBOSNERqxDhw5Yt24dEhISYGdnh6CgIMyaNYuJCBGZFfaMEBERkaw4Z4SIiIhkxWSEiIiIZGUSc0aysrLwzz//wMXFRadSzERERCQfSZKQnJyM8uXLF1jkzySSkX/++Qf+/v5yh0FERERFcOfOHfj5+eV7u0kkIy4uLgDEi3F1dZU5GiIiItJGUlIS/P391e/j+TGJZEQ1NOPq6spkhIiIyMQUNsWCE1iJiIhIVkxGiIiISFZMRoiIiEhWJjFnhIiIik6SJGRmZkKpVModCpkZa2tr2NjYFLvsBpMRIiIzlpGRgfj4eKSmpsodCpkpR0dH+Pj4wNbWtsiPwWSEiMhMZWVlIS4uDtbW1ihfvjxsbW1ZOJL0RpIkZGRk4OHDh4iLi0O1atUKLGxWECYjRERmKiMjA1lZWfD394ejo6Pc4ZAZcnBwQKlSpXDr1i1kZGTA3t6+SI/DCaxERGauqJ9WibShj98vi+0ZUSqB6GggPh7w8QGCgwFra7mjIiIisjwWmYxERgKjRwN372af8/MDFi4EevaULy4iIiJLZHF9d5GRQK9emokIANy7J85HRsoTFxGRMVMqgagoYN068dUUVwkHBARgwYIFWrePioqCQqHAs2fPDBYTCRaVjCiVokdEknLfpjo3Zoxp/pERERlKZCQQEAC0agX06ye+BgQY7sObQqEo8DJ16tQiPe7Jkyfx4Ycfat2+adOmiI+Ph5ubW5GeT1uqpMfd3R1paWkat508eVL9uvNSs2ZN2NnZISEhIddtLVu2zPP79/HHHxvkdRSHRSUj0dG5e0RykiTgzh3RjoiI5OlNjo+PV18WLFgAV1dXjXPjxo1Tt1UVdNNGuXLldFpVZGtrC29v7xJbDu3i4oJNmzZpnFu2bBkqVKiQZ/tDhw7hxYsX6NWrF37++ec823zwwQca37v4+HjMmTNH77EXl0UlI/Hx+m1HRGTO5OpN9vb2Vl/c3NygUCjU1y9dugQXFxfs2rULDRs2hJ2dHQ4dOoTr16+jW7du8PLygrOzMxo3boy9e/dqPO6rwzQKhQL/93//hx49esDR0RHVqlXD1q1b1be/OkyzcuVKlC5dGrt370atWrXg7OyMDh06ID7Hm0ZmZiZGjRqF0qVLo0yZMhg/fjxCQ0PRvXv3Ql93aGgoli9frr7+4sULrF+/HqGhoXm2X7ZsGfr164eBAwdq3C8nR0dHje+nt7c3XF1dC42lpFlUMuLjo992RETmzJh7kydMmIDZs2fj4sWLqFevHp4/f45OnTph3759OHv2LDp06ICQkBDcvn27wMeZNm0a3nvvPfz111/o1KkT+vfvjydPnuTbPjU1Fd9++y1Wr16NgwcP4vbt2xo9Nd988w3Wrl2LFStW4PDhw0hKSsLmzZu1ek0DBw5EdHS0OubffvsNAQEBeP3113O1TU5OxoYNGzBgwAC0a9cOiYmJiDbhbn2LSkaCg8Wqmfx63BQKwN9ftCMisnTG3Js8ffp0tGvXDlWqVIGHhwfq16+Pjz76CIGBgahWrRpmzJiBKlWqaPR05GXw4MHo27cvqlatilmzZuH58+c4ceJEvu1fvnyJH374AY0aNcLrr7+OESNGYN++ferbv/vuO4SFhaFHjx6oWbMmFi9ejNKlS2v1mjw9PdGxY0esXLkSALB8+XIMGTIkz7br169HtWrVUKdOHVhbW6NPnz5YtmxZrnbff/89nJ2dNS5r167VKp6SZFHJiLW1WL4L5E5IVNcXLGC9ESIiwLh7kxs1aqRx/fnz5xg3bhxq1aqF0qVLw9nZGRcvXiy0Z6RevXrqYycnJ7i6uuLBgwf5tnd0dESVKlXU1318fNTtExMTcf/+fbzxxhvq262trdGwYUOtX9eQIUOwcuVK3LhxA0ePHkX//v3zbLd8+XIMGDBAfX3AgAHYsGEDkpOTNdr1798fMTExGpeuXbtqHU9JsahkBBB1RDZuBHx9Nc/7+YnzrDNCRCQYc2+yk5OTxvVx48Zh06ZNmDVrFqKjoxETE4O6desiIyOjwMcpVaqUxnWFQoGsrCyd2kt5Taopoo4dO+LFixcYOnQoQkJCUKZMmVxtLly4gGPHjuGLL76AjY0NbGxs0KRJE6SmpmL9+vUabd3c3FC1alWNi4uLi97i1ReLS0YAkXDcvAns3w9ERIivcXFMRIiIcjKl3uTDhw9j8ODB6NGjB+rWrQtvb2/cvHmzRGNwc3ODl5cXTp48qT6nVCpx5swZrR/DxsYGgwYNQlRUVL5DNMuWLUPz5s1x7tw5jR6PTz/9NM+hGlNgkRVYAfHH07Kl3FEQERk3VW9yXlWrFywwng9x1apVQ2RkJEJCQqBQKDB58uQCezgMZeTIkQgPD0fVqlVRs2ZNfPfdd3j69KlOy4NnzJiBzz//PM9ekZcvX2L16tWYPn06AgMDNW77z3/+g/nz5+P8+fOoU6cOADHh9tUaJHZ2dnB3dy/CqzMci+wZISIi7ZlCb/L8+fPh7u6Opk2bIiQkBO3bt89zFYqhjR8/Hn379sWgQYMQFBQEZ2dntG/fXqfdbG1tbVG2bNk8E5itW7fi8ePH6NGjR67batWqhVq1amn0jvz000/w8fHRuPTt27doL86AFJI+B7sMJCkpCW5ubkhMTDTK9dFERMYoLS0NcXFxqFSpUpG3dqfiycrKQq1atfDee+9hxowZcodjEAX9nmn7/m2xwzRERET6duvWLfzxxx9o0aIF0tPTsXjxYsTFxaFfv35yh2bUOExDRESkJ1ZWVli5ciUaN26MZs2aITY2Fnv37kWtWrXkDs2osWeEiIhIT/z9/XH48GG5wzA57BkhIiIiWTEZISIiIlkxGSEiIiJZMRkhIiIiWTEZISIiIlkxGSEiIiJZMRkhIiKz1LJlS4wZM0Z9PSAgAAsWLCjwPgqFAps3by72c+vrcSwFkxEiIjIqISEh6NChQ563RUdHQ6FQ4K+//tL5cU+ePIkPP/ywuOFpmDp1Kho0aJDrfHx8PDp27KjX53rVypUroVAo8iyotmHDBigUCgQEBOS67cWLF/Dw8EDZsmWRnp6e6/aAgAAoFIpcl9mzZxviZQBgMkJEREZm6NCh2LNnD+7m3Cb4XytWrECjRo1Qr149nR+3XLlycHR01EeIhfL29oadnZ3Bn8fJyQkPHjzA0aNHNc4vW7YMFSpUyPM+v/32G+rUqYOaNWvm23szffp0xMfHa1xGjhyp7/DVmIwQEVkQSQJSUuS5aLsta5cuXVCuXDmsXLlS4/zz58+xYcMGDB06FI8fP0bfvn3h6+sLR0dH1K1bF+vWrSvwcV8dprl69SqaN28Oe3t71K5dG3v27Ml1n/Hjx6N69epwdHRE5cqVMXnyZLx8+RKA6JmYNm0azp07p+49UMX86jBNbGwsWrduDQcHB5QpUwYffvghnj9/rr598ODB6N69O7799lv4+PigTJkyGD58uPq58mNjY4N+/fph+fLl6nN3795FVFRUvvvhLFu2DAMGDMCAAQM0dvjNycXFBd7e3hoXJyenAmMpDpaDJyKyIKmpgLOzPM/9/DmgzfuZjY0NBg0ahJUrV+LLL7+EQqEAIIYelEol+vbti+fPn6Nhw4YYP348XF1dsWPHDgwcOBBVqlTBG2+8UehzZGVloWfPnvDy8sLx48eRmJioMb9ExcXFBStXrkT58uURGxuLDz74AC4uLvjiiy/Qu3dv/P333/j999+xd+9eAICbm1uux0hJSUH79u0RFBSEkydP4sGDB/jPf/6DESNGaCRc+/fvh4+PD/bv349r166hd+/eaNCgAT744IMCX8uQIUPQsmVLLFy4EI6Ojli5ciU6dOgALy+vXG2vX7+Oo0ePIjIyEpIkYezYsbh16xYqVqxY6PfMkNgzQkRERmfIkCG4fv06Dhw4oD63YsUKvPPOO3Bzc4Ovry/GjRuHBg0aoHLlyhg5ciQ6dOiAX3/9VavH37t3Ly5duoRVq1ahfv36aN68OWbNmpWr3aRJk9C0aVMEBAQgJCQE48aNUz+Hg4MDnJ2dYWNjo+49cHBwyPUYERERSEtLw6pVqxAYGIjWrVtj8eLFWL16Ne7fv69u5+7ujsWLF6NmzZro0qULOnfujH379hX6Wl577TVUrlwZGzduhCRJWLlyJYYMGZJn2+XLl6Njx45wd3eHh4cH2rdvjxUrVuRqN378eDg7O2tcoqOjC42lqNgzQkRkQRwdRQ+FXM+trZo1a6Jp06ZYvnw5WrZsiWvXriE6OhrTp08HACiVSsyaNQu//vor7t27h4yMDKSnp2s9J+TixYvw9/dH+fLl1eeCgoJytfvll1+waNEiXL9+Hc+fP0dmZiZcXV21fyH/Plf9+vU1hjmaNWuGrKwsXL58Wd2DUadOHVhbW6vb+Pj4IDY2VqvnGDJkCFasWIEKFSogJSUFnTp1wuLFizXaKJVK/Pzzz1i4cKH63IABAzBu3DhMmTIFVlbZ/ROff/45Bg8erHF/X19frV+zrpiMEBFZEIVCu6ESYzB06FCMHDkS//vf/7BixQpUqVIFLVq0AADMnTsXCxcuxIIFC1C3bl04OTlhzJgxyMjI0NvzHz16FP3798e0adPQvn17uLm5Yf369Zg3b57eniOnUqVKaVxXKBTIysrS6r79+/fHF198galTp2LgwIGwscn99r57927cu3cPvXv31jivVCqxb98+tGvXTn2ubNmyqFq1ahFeRdHoNEyzZMkS1KtXD66urnB1dUVQUBB27dpV4H2ePXuG4cOHw8fHB3Z2dqhevTp27txZrKCJiMj8vffee7CyskJERARWrVqFIUOGqOePHD58GN26dcOAAQNQv359VK5cGVeuXNH6sWvVqoU7d+4gPj5efe7YsWMabY4cOYKKFSviyy+/RKNGjVCtWjXcunVLo42trS2USmWhz3Xu3DmkpKSozx0+fBhWVlaoUaOG1jEXxMPDA127dsWBAwfyHaJZtmwZ+vTpg5iYGI1Lnz598p3IWlJ0Skb8/Pwwe/ZsnD59GqdOnULr1q3RrVs3nD9/Ps/2GRkZaNeuHW7evImNGzfi8uXL+Omnnwza1UNERObB2dkZvXv3RlhYGOLj4zWGDapVq4Y9e/bgyJEjuHjxIj766CON+ReFadu2LapXr47Q0FCcO3cO0dHR+PLLLzXaVKtWDbdv38b69etx/fp1LFq0CJs2bdJoExAQgLi4OMTExODRo0d51u3o378/7O3tERoair///hv79+/HyJEjMXDgwDwnmRbVypUr8ejRI9SsWTPXbQ8fPsS2bdsQGhqKwMBAjcugQYOwefNmPHnyRN0+OTkZCQkJGpekpCS9xfoqnZKRkJAQdOrUCdWqVUP16tUxc+ZMODs758omVZYvX44nT55g8+bNaNasGQICAtCiRQvUr19fL8ETEZF5Gzp0KJ4+fYr27dtrzO+YNGkSXn/9dbRv3x4tW7aEt7c3unfvrvXjWllZYdOmTXjx4gXeeOMN/Oc//8HMmTM12nTt2hVjx47FiBEj0KBBAxw5cgSTJ0/WaPPOO++gQ4cOaNWqFcqVK5fn8mJHR0fs3r0bT548QePGjdGrVy+0adMm15yO4lItG87LqlWr4OTkhDZt2uS6rU2bNnBwcMCaNWvU56ZMmQIfHx+NyxdffKHXeHNSSJK2K781KZVKbNiwAaGhoTh79ixq166dq02nTp3g4eEBR0dHbNmyBeXKlUO/fv0wfvx4jUk6r0pPT9fILpOSkuDv74/ExESdJw4REVmqtLQ0xMXFoVKlSrC3t5c7HDJTBf2eJSUlwc3NrdD3b50nsMbGxiIoKAhpaWlwdnbGpk2b8kxEAODGjRv4888/0b9/f+zcuRPXrl3DJ598gpcvX+Krr77K9znCw8Mxbdo0XUOzWJIEvHwJ2NrKHQkREZHudK4zUqNGDcTExOD48eMYNmwYQkNDceHChTzbZmVlwdPTE0uXLkXDhg3Ru3dvfPnll/jhhx8KfI6wsDAkJiaqL3fu3NE1TIvy5ZeAgwNw8KDckRAREelO554RW1tb9XKfhg0b4uTJk1i4cCF+/PHHXG19fHxQqlQpjSGZWrVqISEhARkZGbDN56O8nZ1didT0NwfnzwPffANkZQFLlgDNm8sdERERkW6KXYE1Kysrz9nDgCjqcu3aNY110leuXIGPj0++iQjp5rPPRCICANu2AS9eyBsPERGRrnRKRsLCwnDw4EHcvHkTsbGxCAsLQ1RUFPr37w8AGDRoEMLCwtTthw0bhidPnmD06NG4cuUKduzYgVmzZmH48OH6fRUWatcuYPduoFQpwNNTbET1++9yR0VExqaI6xSItKKP3y+dhmkePHiAQYMGIT4+Hm5ubqhXrx52796trtp2+/ZtjXKy/v7+2L17N8aOHYt69erB19cXo0ePxvjx44sduKXLzBS9IgAwapSYxDp/PrBhA9Cjh7yxEZFxUFX0TE1NzXPPFCJ9SE1NBZC7gqwuiry0tyRpuzTIknz/PTB8OFC2LHD1KnDxItC0qdiN88EDMaGViCg+Ph7Pnj2Dp6cnHB0d1RVMiYpLkiSkpqbiwYMHKF26NHx8fHK1MdjSXpLfs2fAlCnieNo0oHRp4M03AT8/4O5dMXSjQ+0fIrOXmCg2aSvGBzeT5e3tDUD0bBMZQunSpdW/Z0XFZMQEff018PgxUKsW8OGH4pyVFdCrF7BggRiqYTJCJFy8CDRsCISEAL/8Inc0JU+hUMDHxweenp54+fKl3OGQmXl1xWxRMRkxMdevA4sWieN584CcGzO++65IRrZtA9LSABZcJAJWrxarzDZuBB49EkOblsja2lovbxpEhlDspb1Usr74QlRbbd8e6NhR87YmTQBfXyA5GfjjD3niIzImkgT89ps4zsoCtm+XNx4iyhuTERNy4AAQGSmGZObNy327aqgGEEM1RJbu4kUg567yr2y4SkRGgsmIicjKAj79VBx/+CFQp07e7d59V3zduhXIpxYdkcVQJR//Fo3GH3+IejxEZFyYjJiIVauAM2cAV1dg+vT82wUFAeXLA0lJHKohiowUX8ePBypXFnOpdu+WNyYiyo3JiAlISQEmThTHkyYB5crl39bKCnjnHXHMoRqyZDdvigTeygro1i17hdnmzTIGRUR5YjJiAubMAeLjxSe7UaMKb68aqtmyhUM1ZLlUQzTNm4sEXlWZeNs2MQmciIwHkxEjd/cuMHeuOJ4zB9BmM+NmzQAfHzFUs3evYeMjMlaqIRpVEhIUJJKSZ8+AgwdlC4uI8sBkxMiFhYkaCcHBQM+e2t2HQzVk6e7fBw4fFseqZMTaWgzXAFxVQ2RsmIwYsRMngDVrxPH8+YAuW0rkHKrJyNB/bETGbMsWUWOkcWPA3z/7fM55I8a/KxeR5WAyYqQkKXsp76BBQKNGut2/WTPA21t0SXOohiyNaojm1d7ENm3EZpL37gGnTpV8XESUNyYjRmrjRtHN7OgIzJql+/2trbP/EXOohizJs2fAvn3iWDVEo2Jvn125mKtqiIwHkxEjlJYmyr4D4quvb9EeRzVUs3kzh2rIcuzYAWRmArVrAzVq5L5dlaBw3giR8WAyYoQWLhQ1EsqXB8aNK/rjBAcDXl6anxSJzF1+QzQqnToBpUqJUvGXL5dcXESUPyYjRubBA2DmTHEcHg44ORX9sXIO1WzcWPzYiIxdaiqwa5c4zi8ZcXMDWrcWxxyqITIOTEaMzJQpYtfdRo2AAQOK/3g5h2pY6InM3e7dYil8xYpAgwb5t1OtquFQDZFxYDJiRGJjgZ9+Esfz54t6IcWlqj755Anw55/FfzwiY6ZKLnr2LHgpvKreyPHjwD//GD4uIioYkxEjIUnAZ5+J3XnfeUfM99AHrqohS5GRIUq9A4UXCPTxAZo0Ecdbthg2LiIqHJMRI7FzJ7BnD2BrK8q+65NqqGbTJg7VkPmKihKTtb28ROn3wqhW1XDeCJH8mIwYgZcvs1fNjB4tNsTTpxYtgLJlxVDN/v36fWwiY6FaRdOtm+gRLIxq3siff4okhojkw2TECPz4I3Dpkpjb8eWX+n98GxuuqiHzplRm93Bou4dT9eqiFklmpuiZJCL5MBmR2dOnwFdfiePp08WyQ0PIOVSTmWmY5yCSy7FjYnM8NzegVSvt78cCaETGgcmIzGbMEMMndeoA//mP4Z6nZUsxVPPokRhbJzInqiGakBAx70pbqqGaXbtE5WMikgeTERldvQosXiyO580TwymGYmOT/SmQq2rInEhSdjLy6l40hWnYEPDzA1JSuKEkkZyYjMjoiy/E5NWOHYH27Q3/fL16ia+RkRyqIfNx7pzYPsHBQfe/I4Uiu3eEq2qI5MNkRCb794t/ftbWwLfflsxztmoFeHiIoZoDB0rmOYkMTdUr0qFD0bZPUPWmbN0qJsISUcljMiIDpRL49FNx/NFHYkZ/SShVikM1ZH4K2xivMMHBgLs78PAhcOSI/uIiIu0xGZHBzz8DMTFi5v+0aSX73KpVNZGR/BRIpu/KFeD8eTEnqnPnoj1GqVJi4ivAVTVEcmEyUsKeP8+uJTJ5sljhUpJatxZDNQ8fAgcPluxzE+mbKnlo3Vr0bhRVzo3zJKnYYRGRjpiMlLBvvgESEoAqVYARI0r++UuVyv7Hy6EaMnXFHaJRad9eTIC9eRP4669ih0VEOmIyUoJu386erDp3LmBnJ08cHKohc3DnDnDihFgRo9qFt6gcHYG33xbHHKohKnlMRkpQWJgorNSiRXbvhBxatwZKlxYVK6Oj5YuDqDhUS3GbNgW8vYv/eNw4j0g+TEZKyPHjQESE+BQ3f774KhdbWw7VkOlT9WAUd4hGpUsXsdT+3DkgLk4/j0lE2mEyUgIkCRg7VhyHhgKvvy5vPED2UM1vv3GohkxPzlo5ulZdzU+ZMkDz5uKYvSNEJYvJSAn49Vfg6FExLj1zptzRCG3bZg/VHD4sdzREutm6FcjKAl57DahUSX+Pm3NVDRGVHCYjBpaWBowfL47HjwfKl5c3HhVb2+xJfxyqIVOjr1U0r1IlI4cPAw8e6PexiSh/TEYM7L//BW7dEptxjRsndzSacg7VZGXJGwuRtpKTgT17xLG+hmhUKlQQw6hZWcC2bfp9bCLKH5MRA7p/H5g1SxyHh4thGmPStq2oAhsfz6EaMh07dwIZGUD16obZSoGraohKHpMRA5o8WVRcbdwY6NdP7mhys7MDunYVxxyqIVORc4jGEKvSVEM1e/aIXhgiMjwmIwby11/AsmXi+L//BayM9DvNoRoyJWlpwI4d4ljf80VU6tQBqlYF0tOB3bsN8xxEpMlI3yJNmySJXXmzssSbfbNmckeUv7ffBlxdgX/+ESt+iIzZnj1ASoqYg9WokWGeQ6HIHqrhqhqiksFkxAC2bwf27RMrVr75Ru5oCsahGjIlquSgRw/DFg5UDdXs2CHmpxCRYTEZ0bOXL7NXzYwdq98aCIaiGqrZuJFDNWS8MjOBLVvEsaGGaFSaNAG8vIDERCAqyrDPRURMRvRuyRLgyhXA0xOYOFHuaLTz9tuAiwtw7x5w7Jjc0RDl7eBB4MkToGxZ4K23DPtcVlbZdXi4qobI8JiM6NGTJ8DUqeJ4xgwxF8MU2NsDISHimEM1ZKxUq2i6dgVsbAz/fDmX+LLHkMiwmIzo0fTpwNOnQGAgMGSI3NHohkM1ZMyysrJ7KAw9RKPSqpXoMYyPB06cKJnnJLJUTEb05PJl4H//E8fz55fMJzd9at8ecHYG7t4VOwwTGZOTJ8UwoosL0KZNyTynnR3QubM45lANkWHplIwsWbIE9erVg6urK1xdXREUFIRdu3Zpdd/169dDoVCgu2qaupn54gsxwa5zZ6BdO7mj0Z2DQ/ZQzcaN8sZC9CrVEE3nzmJYsaTk3DhPkkrueYksjU7JiJ+fH2bPno3Tp0/j1KlTaN26Nbp164bz588XeL+bN29i3LhxCA4OLlawxurPP8UuotbWwLffyh1N0eUcquE/XjIWkpSdjOh7L5rCdOwoluhfuQJculSyz01kSXRKRkJCQtCpUydUq1YN1atXx8yZM+Hs7IxjBSzBUCqV6N+/P6ZNm4bKlSsXO2Bjo1SKJbwAMGwYULOmvPEUR4cOYqjm9m2OkZPxOH8euHZNDJt07Fiyz+3qmj0sxAJoRIZT5DkjSqUS69evR0pKCoKCgvJtN336dHh6emLo0KFaP3Z6ejqSkpI0LsZqxQpR+r106eyVNKbKwQHo0kUcc1UNGQtVr4hqCXpJ48Z5RIanczISGxsLZ2dn2NnZ4eOPP8amTZtQO5+tMw8dOoRly5bhp59+0uk5wsPD4ebmpr74+/vrGmaJSE4GJk0Sx1OmAGXKyBuPPvTqJb5yqIaMRc6N8eTQtauo9nrypJjgTUT6p3MyUqNGDcTExOD48eMYNmwYQkNDceHChVztkpOTMXDgQPz0008oW7asTs8RFhaGxMRE9eXOnTu6hlkiZs8G7t8HqlUDhg+XOxr96NgRcHQEbt0S/3yJ5HTjBnDunJiPpZpgXdK8vICmTcWxqgIsEemXzsmIra0tqlatioYNGyI8PBz169fHwoULc7W7fv06bt68iZCQENjY2MDGxgarVq3C1q1bYWNjg+vXr+f7HHZ2duoVO6qLsbl1C5g3TxzPnSsmuZkDR0cO1ZDxUM3TaNFC3p7HnKtqiEj/il1nJCsrC+np6bnO16xZE7GxsYiJiVFfunbtilatWiEmJsZoh160NWGC2GK8ZcvsjebMBVfVkLGQe4hGRZWMREWJwoZEpF86leYKCwtDx44dUaFCBSQnJyMiIgJRUVHYvXs3AGDQoEHw9fVFeHg47O3tERgYqHH/0qVLA0Cu86bm6FFg/Xoxjvzf/xp291A5dOokekhu3gROnzbcVu1EBYmPB44cEcdylyeqWlVUVv77b7Er98CB8sZDZG506hl58OABBg0ahBo1aqBNmzY4efIkdu/ejXb/Vvm6ffs24uPjDRKosZCk7KW8778PNGggazgG4eiYXXmSQzUkF9XqlTffBHx9ZQ0FAFfVEBmSQpKMvyM+KSkJbm5uSExMlH3+yLp1QL9+gJMTcPUq4OMjazgGs2ED8N57QKVKwPXr5tf7Q8bv7beBPXuAb74RFY7ldvYs8PrrIll/9EgshSeigmn7/s29aXTw4gUwfrw4njDBfBMRQAzVODgAcXHAmTNyR0OW5skTYP9+cVzSVVfz06ABULEikJoqkiQi0h8mIzqYPx+4cwfw9wc++0zuaAzLyUkkJACHaqjkbd8u9nqqW1csnTcGCgVX1RAZCpMRLSUkAOHh4nj2bMvoouWqGpKLXHvRFEaVjGzbJpIlItIPJiNamjQJSEkRk+n69pU7mpKh2iH1+nUgJkbuaMhSpKQA/y7Qk31J76veekvUO3n8GDh0SO5oiMwHkxEtxMQAy5eLY3NcypsfZ2cO1VDJ+/13IC0NqFwZqFdP7mg02dhkV4LlUA2R/jAZKYQkAZ9+Kr727g0UsCegWVIN1WzYwKEaKhk5C50ZY+Kfc4kv/yaI9IPJSCG2bhWz+u3sxBJDS6Maqrl2TewRQmRIGRli8ipgfPNFVNq1E8t7b98Wy32JqPiYjBQgIwP4/HNx/OmnYlmfpXFxATp0EMccqiFD+/NPICkJ8PYGmjSRO5q8OThk/02wABqRfjAZKcD334vCZp6eQFiY3NHIh0M1VFJyrqKxMuL/TlziS6RfRvznLq/Hj4Fp08Tx11+LHgJLFRIihqmuXgViY+WOhsyVUpnd02Bsq2he1aULYG0t9qq5dk3uaIhMH5ORfEybBjx7JmbzDxkidzTy4lANlYTDh4GHDwF3d6BFC7mjKZi7u9ixG+BQDZE+MBnJw6VLYogGEFVXra3ljccYcKiGDE015BESApQqJW8s2uDGeUT6w2QkD59/LrqMQ0KANm3kjsY4hIQAtrbA5cuia5pInyRJc0mvKejWTXw9cgS4f1/eWIhMHZORV+zdK5YW2tgAc+fKHY3xcHUF2rcXxxyqIX07c0YslXV0FLv1mgI/P6BxY5FIbd0qdzREpo3JSA5KpVjCCwCffALUqCFvPMaGQzVkKKpekY4dTWvfJ66qIdIPJiM5LFsmVou4uwNffSV3NMana1cxVHPpEnD+vNzRkDlRvZmbyhCNimreyL59oj4KERUNk5F/JSUBkyeL46++Ajw85I3HGLm5ZXehb9wobyxkPi5eFJdSpUTFX1NSsyZQvbookLhrl9zREJkuJiP/Cg8HHjwQ/1g++UTuaIxXzqEaIn1Q9Yq0bSsSXlOiUGT3jnCohqjomIwAuHlT7MYLAN9+axrLCuXStav4/ly4IC5ExZWz6qopUsW9cyeQni5vLESmiskIgPHjxT+R1q1FZUXKX+nS2UM17B2h4rp1Czh9WvQwqJbKmprGjQEfHyA5WeytQ0S6s/hk5PBh4NdfxT/D+fONc8tyY9Orl/jKZISKS1UwLDhY7AFliqysslfVsAAaUdFYdDKSlZW9lHfoUKB+fXnjMRXduomhmvPnxcRDoqIytUJn+VElI1u2iBIBRKQbi05G1q0DTpwAnJ2BGTPkjsZ0uLuLyYYAe0eo6O7fB6KjxbHqzdxUtWwpJt/evw8cPy53NESmx2KTkbQ0YMIEcRwWBnh7yxuPqVGtquESXyqqrVtF8byGDYGKFeWOpnhsbbOXJXNVDZHuLDYZsbcHli4Vu9GOHSt3NKanWzdRMj82VuxXQ6QrUy10lp+cS3xZoZhINxabjACi9PSuXaZVftpYeHhwqIaKLjFR7AMFmE8y0qEDYGcHXL/OCsVEurLoZISKhwXQqKh27ABevhQVTGvWlDsa/XB2Btq1E8dcVUOkGyYjVGTdugHW1sBffwFXrsgdDZkSc1lF8ypunEdUNExGqMjKlAHatBHH7B0hbb14kb2Pi7klI127irojZ84At2/LHQ2R6WAyQsXCVTWkqz/+AFJTgQoVgNdflzsa/SpXDmjWTBxzqIZIe0xGqFi6dxdDNTExwLVrckdDpiDnXjTmWPFYtaqGyQiR9piMULGULSv29AE4VEOFe/lS1BcBzG+IRkU1b+TgQeDxY1lDITIZTEao2LiqhrR14ADw7JnmcIa5qVRJbC2hVALbtskdDZFpYDJCxdajhxiqOXtW1Fggyo9qiEY1vGeuuHEekW6YjFCxlS0r9uYA2DtC+cvKyl7yqppXYa5Ur2/3biAlRd5YiEwBkxHSCw7VUGGOHQMSEgBX1+x5RuaqXj0xXJOWJlYPEVHBmIyQXvTokV1f4cYNuaMhY6TqFenSRZRNN2cKBQugEemCyQjphadn9lANa47QqyTJfKuu5kc1VLN9u1hFRET5YzJCesOhGsrPX3+JHjN7e7GhnCVo2lSsGnr6VCzzJaL8MRkhvenZUwzVnDoFxMXJHQ0ZE1WvSPv2gJOTvLGUFGtrUR4e4KoaosIwGSG98fQEWrQQxxyqoZxU8yYsZYhGJecSX0mSMxIi48ZkhPSqVy/xlUM1pHL1KhAbC9jYiMmrlqRtW9ETdPcucPq03NEQGS8mI6RXPXuKlQQnTwI3b8odDRkDVa9Iq1aAh4e8sZQ0e3ugY0dxzFU1RPljMkJ65e0NNG8ujn/7Td5YyDjk3BjPEnHjPKLCMRkhveOqGlK5dw84flyz7oal6dRJDFFduABcuSJ3NETGickI6d0774g3n+PHgdu35Y6G5KTqDQgKAnx8ZA1FNqVLZ1ecZe8IUd6YjJDeeXsDwcHimKtqLJulFTrLD6uxEhWMyQgZBFfV0OPHwIED4thS54uodOsmvh47Bvzzj7yxEBkjJiNkEKqhmmPHgDt35I6G5LBtG6BUAvXrA5Uryx2NvMqXB958Uxxv3SpvLETGSKdkZMmSJahXrx5cXV3h6uqKoKAg7Nq1K9/2P/30E4KDg+Hu7g53d3e0bdsWJ06cKHbQZPzKlweaNRPHXFVjmThEo0nVO8ShGqLcdEpG/Pz8MHv2bJw+fRqnTp1C69at0a1bN5w/fz7P9lFRUejbty/279+Po0ePwt/fH2+//Tbu3bunl+DJuHFVjeVKTgb++EMcMxkRVPNG/vwTePZMzkiIjI9CkopXpNjDwwNz587F0KFDC22rVCrh7u6OxYsXY9CgQfm2S09PR3p6uvp6UlIS/P39kZiYCFdX1+KESyXo3j3Az08c37mTfUzm79dfgd69gapVxXJWhULuiIxD7drAxYvA2rVAv35yR0NkeElJSXBzcyv0/bvIc0aUSiXWr1+PlJQUBAUFaXWf1NRUvHz5Eh6FlGEMDw+Hm5ub+uLv71/UMElGvr4cqrFUOYdomIhky7lXDRFl0zkZiY2NhbOzM+zs7PDxxx9j06ZNqF27tlb3HT9+PMqXL4+2bdsW2C4sLAyJiYnqyx3OgDRZHKqxPGlpwI4d4phDNJpU80Z27RLfJyISdE5GatSogZiYGBw/fhzDhg1DaGgoLly4UOj9Zs+ejfXr12PTpk2wt7cvsK2dnZ16kqzqQqbpnXfE18OHxbANmb99+4Dnz0XPWOPGckdjXBo1Et+X58/F94mIBJ2TEVtbW1StWhUNGzZEeHg46tevj4ULFxZ4n2+//RazZ8/GH3/8gXr16hU5WDI9fn6i+ibAoRpLoRqi6d4dsGLxAA05y+JzVQ1RtmL/q8jKytKYbPqqOXPmYMaMGfj999/RqFGj4j4dmSDVUA2rsZq/zExgyxZxzCGavKmGarZuFXVYiEjHZCQsLAwHDx7EzZs3ERsbi7CwMERFRaF///4AgEGDBiEsLEzd/ptvvsHkyZOxfPlyBAQEICEhAQkJCXj+/Ll+XwUZNVU11kOHgPh4eWMhwzp0SFRe9fDI3r2ZNDVvDri7Aw8fAkeOyB0NkXHQKRl58OABBg0ahBo1aqBNmzY4efIkdu/ejXbt2gEAbt++jfgc7zZLlixBRkYGevXqBR8fH/Xl22+/1e+rIKPm7w80aQJIEodqzJ1qiKZbN7FTLeVWqhTQpYs45qoaIqHYdUZKgrbrlC2RUglER4seBx8fsUGdtbXcUeU2fz7w2WfiU6FqvxIyL1lZQIUKYqLy1q1ASIjcERmvyEgxubtSJeD6dS5/JvNl8DojJL/ISCAgAGjVShRQatVKXFd9OjUmqqEaVeJE5ufUKZGIODkB/3aWUj7atwfs7YG4OCA2Vu5oiOTHZMRERUaKN/i7dzXP37snzhtbQlKhAvDGG2KoxthiI/1QrQ7p3Fm80VL+nJyAt98Wx1xVQ8RkxCQplcDo0eKN/VWqc2PGGN9MfRZAM1855wNxFY12uHEeUTYmIyYoOjp3j0hOkiT2gomOLrmYtKEaqjl4ELh/X95YSL8uXACuXgVsbYGOHeWOxjR06SLqsJw7J4ZriCwZkxETpO2cC2ObmxEQICpycqjG/Kh+nu3aAZxjrp2yZbOXP3NVDVk6JiMmyMdHv+1KEodqzJNqqIFDNLrhxnlEApf2miClUvQy3LuX97wRhUKUYY+LM75lvnFxQOXKons6Ph7w9JQ7IiqunD/T+/fFJ37Szq1b4m/ZygpISADKlZM7IiL94tJeM2ZtDai2A3q1PoHq+oIFxpeIAKKuQqNGoiYFh2rMg6pXpHlzJiK6qlgReO018fewbZvc0ZC20tOB8+fFpO0DB/L+UEi6YTJionr2FHu9+PpqnvfzE+eNubtcNZGVQzXmQZVUGvPvnDHjqhrjlZgIHD8OrFwJTJggKgtXry6WZgcGiv9lLVsCnToB167JHa1p4zCNiTOVCqw53bgBVKnCrmlzkJAAlC8vPhnevi1K/5NuYmOBevUAOzvg0SPA2VnuiCyLJIn/n5cuARcvZl8uXQL++Sf/+7m6isTkr7+AjAzx8wsLA8aPZ52dnLR9/+buESbO2lpk5qakcmXg9deBM2fEp8EPP5Q7IiqqLVvEP/M33mAiUlSBgSI5v34d+P337J5D0q/MTDG/KWeyofqamJj//Xx8gFq1xKVmzexjHx8xLH7lCjB8OLB3LzB1KrB6NfC//4kqu6Q9JiMki3ffFcnIhg1MRkyZaohGNdRAulMoxKqaefPEqhomI8WTmioShJy9HBcvijo4GRl538fKSiSEOZONWrWAGjWA0qULfr7q1YE//hD/y8aOFUllhw7i5/jf/4qhcyoch2lIFtevA1Wrip6dhAROfDRFT5+K1VCZmcDly+KfMhXN4cPAW28Bbm7AgweieBwV7PHj3L0cFy+KFUr5vas5OIgE49VejmrVxDBLcSUlid6RRYvEELqzMzBtGjBypNit2RJp+/7NZIRk8/rrwNmzwNKlwAcfyB0N6WrNGmDgQKBOHeDvv+WOxrQplWIy+v374lM2NxoUsrJENem85nM8fJj//Tw8NHs4VMlHxYqiF8TQzp0DPvkEOHJEXK9bF/j+e5FwWhrOGSGj16uXSEY2bGAyYoq4ikZ/rK2Brl2Bn34S86gsLRnJyBCrUfKaz5Gamv/9KlTI3ctRq5boaX217EFJql9fLCxYuRL44gsxSTk4GHj/feCbbzhpPy/sGSHZXL0quvY5VGN6UlLEP9QXL8Tcn9dekzsi07drl1giWr686A0oiU/wcsjKEq/10KHshOPatfw39rSxEcMor/Zy1KhhGiuPHj8Wq2x++klcd3cHZs8G/vMf8/0Z58RhGjIJDRqILs3/+z9g6FC5oyFtRUYC77wjqofeuCHvp1BzkZ4uErzkZODYMeDNN+WOSL+USlED6euv8x7Wc3bO3cNRq5ZYfWcO8y2OHQOGDQNiYsT1N94AliwRw9XmjBVYySRwrxrTlHMvGiYi+mFnJ3pGAPMqgJaZKeYXBQYCffqIRMTFRfQMLFwo5sjcuSMmf548CaxaJXoSuncXvR/mkIgAQJMm4vUtXChe/4kTYuPQUaMKXlpsKdgzQrK6ckX8w7GxEZP3PDzkjogKk5EhVtEkJoqu9mbN5I7IfKxfD/TtK/4mLl2SO5rieflSJCGzZmVXJy1dGhgzRrwBu7vLGZ284uOBzz4D1q0T1728xNLufv3ML7lnzwiZhOrVRfXJzEzuXGoq9u8XiYiXFxAUJHc05qVTJ9ETcPmymE9hitLTgR9/FH/bQ4aIRKRMGWDmTLHs9quvLDsRAUTBtIgIUSitRg3xQWzAAKB1a9P9uRcXkxGSHYdqTItqFU337pYxAa8kuboCbdqIY1NLztPSgMWLRf2gjz8Gbt4UPWhz54rjiRPF66NsbdqIOXMzZ4oS8lFRYiVOWJiYJG5J+K+EZKeqOLl3L/DkibyxUMGUSlECHuCSXkMxtY3zUlNFpdFKlURxr7t3xYqgBQtE+fVx40xj1Ytc7OxEonbhAtClixjemj0bqF07+2/NEjAZIdnVrCkmt2VmWtYfnyk6elR0KZcubXp7IpmKrl3FvIGTJ8Ubu7FKThY1MwICgE8/Fcvz/f3FvizXrwOjRwOOjnJHaToqVQK2bRP/AytUEBtPdu8ufh/i4uSOzvCYjJBRUA3VbNwobxxUMNUQTZcuLFluKN7e2XNxjDE5T0wUy3MDAoAJE0Ql1EqVRB2Na9dE5VHuWlt0XbuKXpKwMDF/aNs2UeV41iwxH8dcMRkho6BKRvbsAZ49kzUUyocksepqSeneXXw1pnkjT54AU6aIkuqTJ4vr1auLKqOXL4ulukxQ9cPJSSQf584BrVqJ4oJffinmk+zbJ3d0hsGlvWQ0AgOB8+dFNc/gYKBRI6BhQzHb3Npa7ujo7FlRoMnBAXj0iF3whqSqTmxjIzbOk3P1ycOHwPz5YnLq8+fiXO3awKRJwHvv8W/T0CRJLAH+9FMxRAqI5d/z5olVOcaOS3vJ5Hz4ofh69qzY9XLQINE96eYmkpMxY4DVq0UXZn6lo8lwVL0iHTsyETG0atXE735mJrBjhzwxqGphBASICZXPn4tP5hs2iL1W+vZlIlISFApRf+TyZTFB2MpKJCc1aoj/k5mZckeoH+wZIaNy6ZKYuHfqFHD6tEhM8tooy8lJ9KA0bJjdg6La54YMo04dkQiuXi1qIpBhTZ4s5mb07An89lvJPe/du8CcOWI3bdUchUaNRDwhIeZXlMvUnDkjysqfOCGuN2ggyso3aSJrWPni3jRkFpRKkaCcPp19KShBef11kZioLkxQ9OPSJbFPiI2N6LYvXVruiMzf6dMiCXB0FMNiDg6Gfb6bN0UPyIoVosouICbSTp4MdOjAJMSYZGWJ/bwmTACePhXnPvgACA8XBeaMCZMRMluvJiinTonNp/JKUJyd8+5BYbEu3YSHi1oI7dsDv/8udzSWQZLEEMnt22JVTdeuhnmea9fEz3fVquwu/+bNxWTV1q2ZhBizhw+B8eNFAgmIROSbb4D33zee/3FMRsiiqBIU1fCOqgflxYvcbZ2dNXtQGjUSY/TG8sdrjN54Qwyf/fhj9tweMrzRo8W8gPffB5Yv1+9jX7okVmysXSs+aQNA27aiJ6R5c/0+FxnWoUNi6Ea1G3JQkBi6qV9f3rgAJiNEyMzMuwclrwTFxUX0oKh6Txo2ZIKicvu2WM6pUIhJjV5eckdkOfbvF70TZcqIomI2NsV/zL//FnNRfv1V9L4AYlLy5Mnca8iUvXwJfPed2Pvn+XMxPD1yJDBtmrxl+JmMEOVBlaC82oOSlpa7rYtL7h6UqlUtL0FZtEh8Qn/rLSA6Wu5oLEtmpkj+njwRiUlxqt6ePSuSENWqKADo1k0s0W3UqNihkpG4e1csA1bt9VW+vCjX/+678gy5MRkh0lJmptgp89UelLwSFFfX3D0o5p6gtGwJHDggak2MHSt3NJZn8GDg559FQrhgge73P3ECmDED2L5dXFcogHfeEUmIMXTjk2Hs3g2MGCHmBAFAu3aiVkz16iUbB5MRomJQJSg5e1AKSlBe7UGpUsU8EpSHD0V58qwssT9GQIDcEVmeLVtERdYKFcSKF20/3R4+LJKQ3bvFdSsroE8fUcmzdm1DRUvGJC1NLNNWlZK3tRUTXsPCDL86S4XJCJGeZWaKOhs5e1DOnct/iKdcOfHV1VV8zXms7Tk7u5J/nTktWybKfL/2mqhvQCUvNRUoW1bMdTpzRvws8iNJohdrxgzgzz/FOWtrURdm4sSS/1RMxuH6ddFLoloJV6mS6CXp1Mnwz63t+7cepkMRWQYbG6BePXF5/31x7uXL3D0o586JHU2Tk4v/nLa2uROUoiQ1rq4isdF1zJh70cjP0VHU+di0SVzySkYkCdi7F5g+XaysAMQma6Gh4lNw5colGzMZlypVgJ07xe/P6NGil7NzZ6BHDzH0V6GC3BGyZ4RI716+FOO0T5+KhCQpKTs5UR3ndS7ncV41U4rLxka3BMbJSfSKZGSIPYPYtS+fVatEYlG3LvDXX9nnJUm8ycyYARw/Ls7Z2oqf2/jxxvEmQ8bl+XORtP73v6K319FRrMAZM8YwGx1ymIbIhGVmin8aRU1mcp5TbW5WVDVqiN4fFr+Sz5MngKenqKdz7ZroZt+6VSQhquEze3vgo4+Azz8HfH3ljZeM399/A598kr1CrnZtUTztjTf0+zwcpiEyYTY2ouS6PsquZ2XlTmy0TWbS04Fx45iIyM3DA2jRQswDmThRJIexseI2JyfxpvLZZ6wBQ9oLDBTzi1avFn/jcn/gYM8IEZEJWLxYFLFScXER18eOFRNciYrq6VOx6qpPH/0/NodpiIjMSHy8mDOiVIrx/VGjAHd3uaMiKhiHaYiIzIiPj1gFUaqUmB9CZE6YjBARmQgXF7kjIDIMM6gRSURERKaMPSNkFJRKscQsPl50RwcHi8qRRERk/piMkOwiI0VVwLt3s8/5+QELF7LyJxGRJeAwDckqMhLo1UszEQGAe/fE+ZzbnRMRkXnSKRlZsmQJ6tWrB1dXV7i6uiIoKAi7du0q8D4bNmxAzZo1YW9vj7p162Lnzp3FCpjMh1IpekTyWlyuOjdmjGhHRETmS6dkxM/PD7Nnz8bp06dx6tQptG7dGt26dcP58+fzbH/kyBH07dsXQ4cOxdmzZ9G9e3d0794df//9t16CJ9MWHZ27RyQnSQLu3MkuV0xEROap2EXPPDw8MHfuXAwdOjTXbb1790ZKSgq2b9+uPtekSRM0aNAAP/zwg9bPwaJn5mndOqBfv8LbRUQAffsaPh4iItIvbd+/izxnRKlUYv369UhJSUFQUFCebY4ePYq2bdtqnGvfvj2OHj1a4GOnp6cjKSlJ40Lmx8dHv+2IiMg06ZyMxMbGwtnZGXZ2dvj444+xadMm1M5nb/GEhAR4vbJzk5eXFxISEgp8jvDwcLi5uakv/v7+uoZJJiA4WKyayW9zJoUC8PcX7YiIyHzpnIzUqFEDMTExOH78OIYNG4bQ0FBcuHBBr0GFhYUhMTFRfblz545eH5+Mg7W1WL4L5E5IVNcXLGC9ESIic6dzMmJra4uqVauiYcOGCA8PR/369bFQ9Y7yCm9vb9y/f1/j3P379+Ht7V3gc9jZ2alX7KguZJ569gQ2bgR8fTXP+/mJ86wzQkRk/opdZyQrKwvp6el53hYUFIR9+/ZpnNuzZ0++c0zIMvXsCdy8CezfLyar7t8vNgRjIkJEZBl0qsAaFhaGjh07okKFCkhOTkZERASioqKwe/duAMCgQYPg6+uL8PBwAMDo0aPRokULzJs3D507d8b69etx6tQpLF26VP+vhEyatTXQsqXcURARkRx0SkYePHiAQYMGIT4+Hm5ubqhXrx52796Ndu3aAQBu374NK6vszpamTZsiIiICkyZNwsSJE1GtWjVs3rwZgYGB+n0VREREZLKKXWekJLDOCBERkekxeJ0RIiIiIn1gMkJERESyYjJCREREsmIyQkRERLJiMkJERESyYjJCREREsmIyQkRERLJiMkJERESyYjJCREREsmIyQkRERLJiMkJERESyYjJCREREsmIyQkRERLJiMkJERESyYjJCREREsmIyQkRERLJiMkJERESyspE7ACJzo1QC0dFAfDzg4wMEBwPW1nJHRURkvJiMEOlRZCQwejRw9272OT8/YOFCoGdP+eIiIjJmHKYh0pPISKBXL81EBADu3RPnIyPliYuIyNgxGSHSA6VS9IhIUu7bVOfGjBHtiIhIE5MRIj2Ijs7dI5KTJAF37oh2RESkickIkR7Ex+u3HRGRJWEyQqQHPj76bUdEZEmYjBDpQXCwWDWjUOR9u0IB+PuLdkREpInJCJEeWFuL5btA7oREdX3BAtYbISLKC5MRIj3p2RPYuBHw9dU87+cnzrPOCBFR3lj0jEiPevYEunVjBVYiIl0wGSHSM2troGVLuaMgIjIdHKYhIiIiWTEZISIiIlkxGSEiIiJZMRkhIiIiWTEZISIiIlkxGSEiIiJZMRkhIiIiWTEZISIiIlkxGSEiIiJZMRkhIiIiWTEZISIiIlkxGSEiIiJZMRkhIiIiWXHXXiLKk1IJREcD8fGAjw8QHCx2JCYi0jcmI0SUS2QkMHo0cPdu9jk/P2DhQqBnT/niIiLzxGEaItIQGQn06qWZiADAvXvifGSkPHERkfliMkJEakql6BGRpNy3qc6NGSPaERHpC5MRIlKLjs7dI5KTJAF37oh2RET6wmSEiNTi4/XbjohIG0xGiEjNx0e/7YiItKFTMhIeHo7GjRvDxcUFnp6e6N69Oy5fvlzo/RYsWIAaNWrAwcEB/v7+GDt2LNLS0oocNBEZRnCwWDWjUOR9u0IB+PuLdkRE+qJTMnLgwAEMHz4cx44dw549e/Dy5Uu8/fbbSElJyfc+ERERmDBhAr766itcvHgRy5Ytwy+//IKJEycWO3gi0i9ra7F8F8idkKiuL1jAeiNEpF8KScpr3rx2Hj58CE9PTxw4cADNmzfPs82IESNw8eJF7Nu3T33us88+w/Hjx3Ho0CGtnicpKQlubm5ITEyEq6trUcMlIi3lVWfE318kIqwzQkTa0vb9u1hzRhITEwEAHh4e+bZp2rQpTp8+jRMnTgAAbty4gZ07d6JTp0753ic9PR1JSUkaFyIqOT17AjdvAvv3AxER4mtcHBMRIjKMIldgzcrKwpgxY9CsWTMEBgbm265fv3549OgR3nrrLUiShMzMTHz88ccFDtOEh4dj2rRpRQ2NiPTA2hpo2VLuKIjIEhS5Z2T48OH4+++/sX79+gLbRUVFYdasWfj+++9x5swZREZGYseOHZgxY0a+9wkLC0NiYqL6cufOnaKGSUREREauSHNGRowYgS1btuDgwYOoVKlSgW2Dg4PRpEkTzJ07V31uzZo1+PDDD/H8+XNYWRWeD3HOCBERkenR9v1bp2EaSZIwcuRIbNq0CVFRUYUmIgCQmpqaK+Gw/ncqfjHmzhIREZGZ0CkZGT58OCIiIrBlyxa4uLggISEBAODm5gYHBwcAwKBBg+Dr64vw8HAAQEhICObPn4/XXnsNb775Jq5du4bJkycjJCREnZQQERGR5dIpGVmyZAkAoOUrs9pWrFiBwYMHAwBu376t0RMyadIkKBQKTJo0Cffu3UO5cuUQEhKCmTNnFi9yIiIiMgvFqjNSUjhnhIiIyPSUSJ0RIiIiouJiMkJERESyYjJCREREsipyBVYiIlOgVALR0UB8PODjI3Yc5kI+IuPCZISIzFZeG/75+YmdibnPDpHx4DANEZmlyEigVy/NRAQA7t0T5yMj5YmLiHJjMkJEZkepFD0ieRUuUJ0bM0a0IyL5MRkhIrMTHZ27RyQnSQLu3BHtiEh+TEaIyOzEx+u3HREZFpMRIjI7Pj76bUdEhsVkhIjMTnCwWDWjUOR9u0IB+PuLdkQkPyYjRGR2rK3F8l0gd0Kiur5gAeuNEBkLJiNEZJZ69gQ2bgR8fTXP+/mJ86wzQmQ8WPSMiMxWz55At26swEpk7JiMEJFZs7YGWraUOwoiKgiHaYiIiEhWTEaIiIhIVkxGiIiISFacM0JEZAKUSk7EJfPFZISIyMhFRoqN/3Lut+PnJ2qpcIkymQMO0xARGbHISKBXr9wb/927J85HRsoTF5E+MRkhIjJSSqXoEZGk3Lepzo0ZI9oRmTImI0RERio6OnePSE6SBNy5I9oRmTImI0RERio+Xr/tiIwVkxEiIiPl46PfdkTGiskIEZGRCg4Wq2Ze3XlYRaEA/P1FOyJTxmSEiMhIWVuL5btA7oREdX3BAtYbIdPHZISIyIj17Als3Aj4+mqe9/MT51lnhMwBi54RERm5nj2Bbt1YgZXMF5MRIiITYG0NtGwpdxREhsFhGiIiIpIVkxEiIiKSFYdpiIioxHD3YcoLkxEiIioR3H2Y8sNhGiIiMjjuPkwFYTJCREQGxd2HqTBMRoiIyKC4+zAVhskIEREZFHcfpsIwGSEiIoPi7sNUGCYjRERkUNx9mArDZISIiAyKuw9TYZiMEBGRwXH3YSoIi54REVGJ4O7DlB8mI0REVGK4+zDlhcM0REREJCv2jBAREemIG/7pF5MRIiIiHXDDP/3jMA0REZGWuOGfYTAZISIi0gI3/DMcnZKR8PBwNG7cGC4uLvD09ET37t1x+fLlQu/37NkzDB8+HD4+PrCzs0P16tWxc+fOIgdNRERU0rjhn+HoNGfkwIEDGD58OBo3bozMzExMnDgRb7/9Ni5cuAAnJ6c875ORkYF27drB09MTGzduhK+vL27duoXSpUvrI34iIqISwQ3/DEenZOT333/XuL5y5Up4enri9OnTaN68eZ73Wb58OZ48eYIjR46gVKlSAICAgICiRUtERCQTbvhnOMWaM5KYmAgA8PDwyLfN1q1bERQUhOHDh8PLywuBgYGYNWsWlAUMqqWnpyMpKUnjQkREJCdu+Gc4RU5GsrKyMGbMGDRr1gyBgYH5trtx4wY2btwIpVKJnTt3YvLkyZg3bx6+/vrrfO8THh4ONzc39cXf37+oYRIREekFN/wzHIUk5TUvuHDDhg3Drl27cOjQIfj5+eXbrnr16khLS0NcXBys//0JzZ8/H3PnzkV8PgNr6enpSE9PV19PSkqCv78/EhMT4erqWpRwiYiI9CKvOiP+/iIRYZ0RTUlJSXBzcyv0/btIRc9GjBiB7du34+DBgwUmIgDg4+ODUqVKqRMRAKhVqxYSEhKQkZEBW1vbXPexs7ODnZ1dUUIjIiIyKHPa8M9YKsnqlIxIkoSRI0di06ZNiIqKQqVKlQq9T7NmzRAREYGsrCxYWYlRoStXrsDHxyfPRISIiMjYmcOGf8ZUSVanOSPDhw/HmjVrEBERARcXFyQkJCAhIQEvXrxQtxk0aBDCwsLU14cNG4YnT55g9OjRuHLlCnbs2IFZs2Zh+PDh+nsVREREpDVjqySr05wRRT5TiFesWIHBgwcDAFq2bImAgACsXLlSffvRo0cxduxYxMTEwNfXF0OHDsX48eM1hm4Kou2YExERERVMqQQCAvIv4KZQiB6SuLjiD9lo+/5d5AmsJYnJCBERkX5ERQGtWhXebv/+4g9Fafv+zb1piIiILIgxVpJlMkJERGRBjLGSLJMRIiIiC2KMlWSZjBAREVkQY6wky2SEiIjIwvTsCWzcCPj6ap738xPnS7rOSJEqsBIREZFpM6ZKskxGiIiILJSxVJLlMA0RERHJiskIERERyYrJCBEREcmKyQgRERHJiskIERERyYrJCBEREcmKyQgRERHJiskIERERyYrJCBEREcnKJCqwSpIEAEhKSpI5EiIiItKW6n1b9T6eH5NIRpKTkwEA/v7+MkdCREREukpOToabm1u+tyukwtIVI5CVlYV//vkHLi4uULy63zEhKSkJ/v7+uHPnDlxdXeUOh8CfibHhz8O48OdhXAz585AkCcnJyShfvjysrPKfGWISPSNWVlbw8/OTOwyj5+rqyj9sI8OfiXHhz8O48OdhXAz18yioR0SFE1iJiIhIVkxGiIiISFZMRsyAnZ0dvvrqK9jZ2ckdCv2LPxPjwp+HceHPw7gYw8/DJCawEhERkflizwgRERHJiskIERERyYrJCBEREcmKyQgRERHJiskIERERyYrJiAkLDw9H48aN4eLiAk9PT3Tv3h2XL1+WOyz61+zZs6FQKDBmzBi5Q7FY9+7dw4ABA1CmTBk4ODigbt26OHXqlNxhWSylUonJkyejUqVKcHBwQJUqVTBjxoxCN1Ej/Th48CBCQkJQvnx5KBQKbN68WeN2SZIwZcoU+Pj4wMHBAW3btsXVq1dLJDYmIybswIEDGD58OI4dO4Y9e/bg5cuXePvtt5GSkiJ3aBbv5MmT+PHHH1GvXj25Q7FYT58+RbNmzVCqVCns2rULFy5cwLx58+Du7i53aBbrm2++wZIlS7B48WJcvHgR33zzDebMmYPvvvtO7tAsQkpKCurXr4///e9/ed4+Z84cLFq0CD/88AOOHz8OJycntG/fHmlpaQaPjXVGzMjDhw/h6emJAwcOoHnz5nKHY7GeP3+O119/Hd9//z2+/vprNGjQAAsWLJA7LIszYcIEHD58GNHR0XKHQv/q0qULvLy8sGzZMvW5d955Bw4ODlizZo2MkVkehUKBTZs2oXv37gBEr0j58uXx2WefYdy4cQCAxMREeHl5YeXKlejTp49B42HPiBlJTEwEAHh4eMgciWUbPnw4OnfujLZt28odikXbunUrGjVqhHfffReenp547bXX8NNPP8kdlkVr2rQp9u3bhytXrgAAzp07h0OHDqFjx44yR0ZxcXFISEjQ+L/l5uaGN998E0ePHjX485vErr1UuKysLIwZMwbNmjVDYGCg3OFYrPXr1+PMmTM4efKk3KFYvBs3bmDJkiX49NNPMXHiRJw8eRKjRo2Cra0tQkND5Q7PIk2YMAFJSUmoWbMmrK2toVQqMXPmTPTv31/u0CxeQkICAMDLy0vjvJeXl/o2Q2IyYiaGDx+Ov//+G4cOHZI7FIt1584djB49Gnv27IG9vb3c4Vi8rKwsNGrUCLNmzQIAvPbaa/j777/xww8/MBmRya+//oq1a9ciIiICderUQUxMDMaMGYPy5cvzZ2LhOExjBkaMGIHt27dj//798PPzkzsci3X69Gk8ePAAr7/+OmxsbGBjY4MDBw5g0aJFsLGxgVKplDtEi+Lj44PatWtrnKtVqxZu374tU0T0+eefY8KECejTpw/q1q2LgQMHYuzYsQgPD5c7NIvn7e0NALh//77G+fv376tvMyQmIyZMkiSMGDECmzZtwp9//olKlSrJHZJFa9OmDWJjYxETE6O+NGrUCP3790dMTAysra3lDtGiNGvWLNdS9ytXrqBixYoyRUSpqamwstJ827G2tkZWVpZMEZFKpUqV4O3tjX379qnPJSUl4fjx4wgKCjL483OYxoQNHz4cERER2LJlC1xcXNTjem5ubnBwcJA5Osvj4uKSa76Ok5MTypQpw3k8Mhg7diyaNm2KWbNm4b333sOJEyewdOlSLF26VO7QLFZISAhmzpyJChUqoE6dOjh79izmz5+PIUOGyB2aRXj+/DmuXbumvh4XF4eYmBh4eHigQoUKGDNmDL7++mtUq1YNlSpVwuTJk1G+fHn1ihuDkshkAcjzsmLFCrlDo3+1aNFCGj16tNxhWKxt27ZJgYGBkp2dnVSzZk1p6dKlcodk0ZKSkqTRo0dLFSpUkOzt7aXKlStLX375pZSeni53aBZh//79eb5nhIaGSpIkSVlZWdLkyZMlLy8vyc7OTmrTpo10+fLlEomNdUaIiIhIVpwzQkRERLJiMkJERESyYjJCREREsmIyQkRERLJiMkJERESyYjJCREREsmIyQkRERLJiMkJERESyYjJCREREsmIyQkRERLJiMkJERESy+n+CO6zlt77qbgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "loss = history.history[\"mae\"]\n",
        "val_loss = history.history[\"val_mae\"]\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, \"bo\", label=\"Training MAE\")\n",
        "plt.plot(epochs, val_loss, \"b\", label=\"Validation MAE\")\n",
        "plt.title(\"Training and validation MAE\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SIVEXb7ojbcf"
      },
      "source": [
        "### 첫 번째 순환 신경망"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGsF8BjGjbcf"
      },
      "source": [
        "**간단한 LSTM 기반 모델**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:35:49.855589Z",
          "iopub.status.busy": "2022-05-20T13:35:49.855249Z",
          "iopub.status.idle": "2022-05-20T13:46:15.333207Z",
          "shell.execute_reply": "2022-05-20T13:46:15.332248Z"
        },
        "id": "ElIY09G2jbcg",
        "outputId": "e940d8b5-e5e3-4f13-a1c8-125d11218542",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "819/819 [==============================] - 46s 52ms/step - loss: 48.8885 - mae: 5.1263 - val_loss: 14.2664 - val_mae: 2.8398\n",
            "Epoch 2/10\n",
            "819/819 [==============================] - 54s 65ms/step - loss: 11.2802 - mae: 2.6053 - val_loss: 10.2443 - val_mae: 2.4610\n",
            "Epoch 3/10\n",
            "819/819 [==============================] - 43s 52ms/step - loss: 9.9043 - mae: 2.4533 - val_loss: 9.6731 - val_mae: 2.4112\n",
            "Epoch 4/10\n",
            "819/819 [==============================] - 44s 54ms/step - loss: 9.5824 - mae: 2.4103 - val_loss: 9.5505 - val_mae: 2.4027\n",
            "Epoch 5/10\n",
            "819/819 [==============================] - 45s 55ms/step - loss: 9.2994 - mae: 2.3731 - val_loss: 9.9079 - val_mae: 2.4361\n",
            "Epoch 6/10\n",
            "819/819 [==============================] - 44s 54ms/step - loss: 9.0046 - mae: 2.3327 - val_loss: 9.6714 - val_mae: 2.4089\n",
            "Epoch 7/10\n",
            "819/819 [==============================] - 52s 63ms/step - loss: 8.7308 - mae: 2.2986 - val_loss: 9.7696 - val_mae: 2.4314\n",
            "Epoch 8/10\n",
            "819/819 [==============================] - 52s 63ms/step - loss: 8.5207 - mae: 2.2718 - val_loss: 10.1050 - val_mae: 2.4742\n",
            "Epoch 9/10\n",
            "819/819 [==============================] - 45s 54ms/step - loss: 8.3483 - mae: 2.2507 - val_loss: 10.1452 - val_mae: 2.4756\n",
            "Epoch 10/10\n",
            "819/819 [==============================] - 45s 55ms/step - loss: 8.2053 - mae: 2.2310 - val_loss: 9.9607 - val_mae: 2.4624\n",
            "405/405 [==============================] - 15s 34ms/step - loss: 10.5307 - mae: 2.5450\n",
            "테스트 MAE: 2.55\n"
          ]
        }
      ],
      "source": [
        "inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\n",
        "x = layers.LSTM(16)(inputs)\n",
        "outputs = layers.Dense(1)(x)\n",
        "model = keras.Model(inputs, outputs)\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\"jena_lstm.h5\",\n",
        "                                    save_best_only=True)\n",
        "]\n",
        "model.compile(optimizer=\"rmsprop\", loss=\"mse\", metrics=[\"mae\"])\n",
        "history = model.fit(train_dataset,\n",
        "                    epochs=10,\n",
        "                    validation_data=val_dataset,\n",
        "                    callbacks=callbacks)\n",
        "\n",
        "model = keras.models.load_model(\"jena_lstm.h5\")\n",
        "print(f\"테스트 MAE: {model.evaluate(test_dataset)[1]:.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:46:15.336897Z",
          "iopub.status.busy": "2022-05-20T13:46:15.336598Z",
          "iopub.status.idle": "2022-05-20T13:46:15.462410Z",
          "shell.execute_reply": "2022-05-20T13:46:15.461466Z"
        },
        "id": "EX3qK89tjbcg",
        "outputId": "705756d4-902b-4675-fc33-399eb076ac84",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "loss = history.history[\"mae\"]\n",
        "val_loss = history.history[\"val_mae\"]\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, \"bo\", label=\"Training MAE\")\n",
        "plt.plot(epochs, val_loss, \"b\", label=\"Validation MAE\")\n",
        "plt.title(\"Training and validation MAE\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OjfiTkmOjbcg"
      },
      "source": [
        "## 순환 신경망 이해하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "11qaJzuKjbcg"
      },
      "source": [
        "**넘파이로 구현한 간단한 RNN**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:46:15.466402Z",
          "iopub.status.busy": "2022-05-20T13:46:15.466102Z",
          "iopub.status.idle": "2022-05-20T13:46:15.474183Z",
          "shell.execute_reply": "2022-05-20T13:46:15.473353Z"
        },
        "id": "LqYsbiAGjbcg"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "timesteps = 100\n",
        "input_features = 32\n",
        "output_features = 64\n",
        "inputs = np.random.random((timesteps, input_features))\n",
        "state_t = np.zeros((output_features,))\n",
        "W = np.random.random((output_features, input_features))\n",
        "U = np.random.random((output_features, output_features))\n",
        "b = np.random.random((output_features,))\n",
        "successive_outputs = []\n",
        "for input_t in inputs:\n",
        "    output_t = np.tanh(np.dot(W, input_t) + np.dot(U, state_t) + b)\n",
        "    successive_outputs.append(output_t)\n",
        "    state_t = output_t\n",
        "final_output_sequence = np.stack(successive_outputs, axis=0)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "m5_JDLW5jbch"
      },
      "source": [
        "### 케라스의 순환 층"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8scRPX8yjbch"
      },
      "source": [
        "**어떤 길이의 시퀀스도 처리할 수 있는 RNN 층**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:46:15.477895Z",
          "iopub.status.busy": "2022-05-20T13:46:15.477617Z",
          "iopub.status.idle": "2022-05-20T13:46:15.532690Z",
          "shell.execute_reply": "2022-05-20T13:46:15.531842Z"
        },
        "id": "RDRvy-SDjbch"
      },
      "outputs": [],
      "source": [
        "num_features = 14\n",
        "inputs = keras.Input(shape=(None, num_features))\n",
        "outputs = layers.SimpleRNN(16)(inputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "d_cZi_TVjbch"
      },
      "source": [
        "**마지막 출력 스텝만 반환하는 RNN 층**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 21,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:46:15.536872Z",
          "iopub.status.busy": "2022-05-20T13:46:15.536352Z",
          "iopub.status.idle": "2022-05-20T13:46:15.590222Z",
          "shell.execute_reply": "2022-05-20T13:46:15.589305Z"
        },
        "id": "7OpmWg1yjbch",
        "outputId": "13eb9a16-45d7-44bb-d7af-7cd0ca8e2986",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(None, 16)\n"
          ]
        }
      ],
      "source": [
        "num_features = 14\n",
        "steps = 120\n",
        "inputs = keras.Input(shape=(steps, num_features))\n",
        "outputs = layers.SimpleRNN(16, return_sequences=False)(inputs)\n",
        "print(outputs.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MvXPhL0qjbch"
      },
      "source": [
        "**전체 출력 시퀀스를 반환하는 RNN 층**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 22,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:46:15.594315Z",
          "iopub.status.busy": "2022-05-20T13:46:15.593481Z",
          "iopub.status.idle": "2022-05-20T13:46:15.647526Z",
          "shell.execute_reply": "2022-05-20T13:46:15.646394Z"
        },
        "id": "jDKDaTY9jbch",
        "outputId": "b50928d0-1f3d-46d4-e106-a3b92607392d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(None, 120, 16)\n"
          ]
        }
      ],
      "source": [
        "num_features = 14\n",
        "steps = 120\n",
        "inputs = keras.Input(shape=(steps, num_features))\n",
        "outputs = layers.SimpleRNN(16, return_sequences=True)(inputs)\n",
        "print(outputs.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Twps5rVojbch"
      },
      "source": [
        "**스태킹(stacking) RNN 층**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 23,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:46:15.651371Z",
          "iopub.status.busy": "2022-05-20T13:46:15.651069Z",
          "iopub.status.idle": "2022-05-20T13:46:15.801889Z",
          "shell.execute_reply": "2022-05-20T13:46:15.801117Z"
        },
        "id": "WCXNh9L6jbci"
      },
      "outputs": [],
      "source": [
        "inputs = keras.Input(shape=(steps, num_features))\n",
        "x = layers.SimpleRNN(16, return_sequences=True)(inputs)\n",
        "x = layers.SimpleRNN(16, return_sequences=True)(x)\n",
        "outputs = layers.SimpleRNN(16)(x)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5UTaHUSfjbci"
      },
      "source": [
        "## 순환 신경망의 고급 사용법"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HfvBqBgLjbci"
      },
      "source": [
        "### 과대적합을 감소하기 위해 순환 드롭아웃 사용하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tzGShMPQjbci"
      },
      "source": [
        "**드롭아웃 규제를 적용한 LSTM 모델 훈련하고 평가하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 24,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T13:46:15.806506Z",
          "iopub.status.busy": "2022-05-20T13:46:15.805817Z",
          "iopub.status.idle": "2022-05-20T15:04:54.368986Z",
          "shell.execute_reply": "2022-05-20T15:04:54.368048Z"
        },
        "id": "ZDfwJ1Osjbci",
        "outputId": "5448682c-47fe-4ee4-f44b-ce40d10d177f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "819/819 [==============================] - 57s 66ms/step - loss: 25.4986 - mae: 3.7522 - val_loss: 9.8706 - val_mae: 2.4336\n",
            "Epoch 2/50\n",
            "819/819 [==============================] - 45s 55ms/step - loss: 14.4447 - mae: 2.9416 - val_loss: 9.6151 - val_mae: 2.4041\n",
            "Epoch 3/50\n",
            "819/819 [==============================] - 45s 55ms/step - loss: 13.3854 - mae: 2.8292 - val_loss: 9.1080 - val_mae: 2.3482\n",
            "Epoch 4/50\n",
            "819/819 [==============================] - 45s 54ms/step - loss: 12.6074 - mae: 2.7476 - val_loss: 9.1785 - val_mae: 2.3614\n",
            "Epoch 5/50\n",
            "819/819 [==============================] - 46s 56ms/step - loss: 11.9744 - mae: 2.6818 - val_loss: 9.6602 - val_mae: 2.4325\n",
            "Epoch 6/50\n",
            "819/819 [==============================] - 52s 64ms/step - loss: 11.5510 - mae: 2.6302 - val_loss: 9.8566 - val_mae: 2.4397\n",
            "Epoch 7/50\n",
            "819/819 [==============================] - 45s 55ms/step - loss: 11.1829 - mae: 2.5894 - val_loss: 9.9830 - val_mae: 2.4565\n",
            "Epoch 8/50\n",
            "819/819 [==============================] - 52s 64ms/step - loss: 10.8488 - mae: 2.5491 - val_loss: 9.9855 - val_mae: 2.4540\n",
            "Epoch 9/50\n",
            "819/819 [==============================] - 52s 63ms/step - loss: 10.5122 - mae: 2.5074 - val_loss: 9.9750 - val_mae: 2.4552\n",
            "Epoch 10/50\n",
            "819/819 [==============================] - 52s 64ms/step - loss: 10.2381 - mae: 2.4736 - val_loss: 9.8188 - val_mae: 2.4285\n",
            "Epoch 11/50\n",
            "819/819 [==============================] - 52s 64ms/step - loss: 10.0666 - mae: 2.4539 - val_loss: 9.9175 - val_mae: 2.4407\n",
            "Epoch 12/50\n",
            "819/819 [==============================] - 53s 64ms/step - loss: 9.8885 - mae: 2.4292 - val_loss: 10.3388 - val_mae: 2.4796\n",
            "Epoch 13/50\n",
            "819/819 [==============================] - 43s 53ms/step - loss: 9.6407 - mae: 2.4019 - val_loss: 10.5940 - val_mae: 2.5119\n",
            "Epoch 14/50\n",
            "819/819 [==============================] - 51s 61ms/step - loss: 9.4883 - mae: 2.3839 - val_loss: 10.3215 - val_mae: 2.4897\n",
            "Epoch 15/50\n",
            "819/819 [==============================] - 43s 52ms/step - loss: 9.2910 - mae: 2.3578 - val_loss: 10.8644 - val_mae: 2.5587\n",
            "Epoch 16/50\n",
            "819/819 [==============================] - 45s 55ms/step - loss: 9.2247 - mae: 2.3478 - val_loss: 10.4439 - val_mae: 2.5084\n",
            "Epoch 17/50\n",
            "819/819 [==============================] - 43s 52ms/step - loss: 9.0260 - mae: 2.3257 - val_loss: 10.8093 - val_mae: 2.5439\n",
            "Epoch 18/50\n",
            "819/819 [==============================] - 42s 52ms/step - loss: 8.8336 - mae: 2.2995 - val_loss: 10.5174 - val_mae: 2.5241\n",
            "Epoch 19/50\n",
            "819/819 [==============================] - 51s 62ms/step - loss: 8.7315 - mae: 2.2882 - val_loss: 10.7891 - val_mae: 2.5436\n",
            "Epoch 20/50\n",
            "819/819 [==============================] - 45s 54ms/step - loss: 8.6459 - mae: 2.2758 - val_loss: 11.7115 - val_mae: 2.6362\n",
            "Epoch 21/50\n",
            "819/819 [==============================] - 52s 63ms/step - loss: 8.5328 - mae: 2.2556 - val_loss: 10.6754 - val_mae: 2.5366\n",
            "Epoch 22/50\n",
            "819/819 [==============================] - 50s 61ms/step - loss: 8.4481 - mae: 2.2449 - val_loss: 11.1854 - val_mae: 2.6017\n",
            "Epoch 23/50\n",
            "819/819 [==============================] - 41s 50ms/step - loss: 8.3170 - mae: 2.2281 - val_loss: 11.4942 - val_mae: 2.6337\n",
            "Epoch 24/50\n",
            "819/819 [==============================] - 44s 53ms/step - loss: 8.1969 - mae: 2.2113 - val_loss: 11.3965 - val_mae: 2.6321\n",
            "Epoch 25/50\n",
            "819/819 [==============================] - 44s 53ms/step - loss: 8.1032 - mae: 2.2016 - val_loss: 11.6903 - val_mae: 2.6496\n",
            "Epoch 26/50\n",
            "819/819 [==============================] - 52s 63ms/step - loss: 8.0902 - mae: 2.1973 - val_loss: 11.7693 - val_mae: 2.6579\n",
            "Epoch 27/50\n",
            "819/819 [==============================] - 44s 53ms/step - loss: 8.0150 - mae: 2.1830 - val_loss: 12.3008 - val_mae: 2.7247\n",
            "Epoch 28/50\n",
            "819/819 [==============================] - 52s 63ms/step - loss: 7.9218 - mae: 2.1727 - val_loss: 11.6535 - val_mae: 2.6532\n",
            "Epoch 29/50\n",
            "819/819 [==============================] - 44s 53ms/step - loss: 7.8722 - mae: 2.1669 - val_loss: 11.7176 - val_mae: 2.6710\n",
            "Epoch 30/50\n",
            "819/819 [==============================] - 43s 52ms/step - loss: 7.8223 - mae: 2.1607 - val_loss: 11.8862 - val_mae: 2.6703\n",
            "Epoch 31/50\n",
            "819/819 [==============================] - 54s 65ms/step - loss: 7.7822 - mae: 2.1542 - val_loss: 11.7702 - val_mae: 2.6802\n",
            "Epoch 32/50\n",
            "819/819 [==============================] - 44s 54ms/step - loss: 7.6700 - mae: 2.1390 - val_loss: 11.7326 - val_mae: 2.6548\n",
            "Epoch 33/50\n",
            "819/819 [==============================] - 53s 64ms/step - loss: 7.6104 - mae: 2.1305 - val_loss: 12.0417 - val_mae: 2.6959\n",
            "Epoch 34/50\n",
            "819/819 [==============================] - 52s 64ms/step - loss: 7.5418 - mae: 2.1213 - val_loss: 12.0713 - val_mae: 2.7156\n",
            "Epoch 35/50\n",
            "819/819 [==============================] - 46s 56ms/step - loss: 7.5991 - mae: 2.1265 - val_loss: 11.8309 - val_mae: 2.6712\n",
            "Epoch 36/50\n",
            "819/819 [==============================] - 51s 62ms/step - loss: 7.4671 - mae: 2.1101 - val_loss: 11.6795 - val_mae: 2.6549\n",
            "Epoch 37/50\n",
            "819/819 [==============================] - 53s 65ms/step - loss: 7.4418 - mae: 2.1002 - val_loss: 12.2726 - val_mae: 2.7131\n",
            "Epoch 38/50\n",
            "819/819 [==============================] - 52s 63ms/step - loss: 7.3467 - mae: 2.0913 - val_loss: 12.3240 - val_mae: 2.7344\n",
            "Epoch 39/50\n",
            "819/819 [==============================] - 53s 65ms/step - loss: 7.3312 - mae: 2.0872 - val_loss: 12.5737 - val_mae: 2.7530\n",
            "Epoch 40/50\n",
            "819/819 [==============================] - 53s 65ms/step - loss: 7.2847 - mae: 2.0797 - val_loss: 12.5659 - val_mae: 2.7555\n",
            "Epoch 41/50\n",
            "819/819 [==============================] - 52s 63ms/step - loss: 7.2394 - mae: 2.0739 - val_loss: 12.2820 - val_mae: 2.7487\n",
            "Epoch 42/50\n",
            "819/819 [==============================] - 43s 52ms/step - loss: 7.1787 - mae: 2.0670 - val_loss: 12.4972 - val_mae: 2.7671\n",
            "Epoch 43/50\n",
            "819/819 [==============================] - 45s 54ms/step - loss: 7.1801 - mae: 2.0662 - val_loss: 12.4279 - val_mae: 2.7455\n",
            "Epoch 44/50\n",
            "819/819 [==============================] - 44s 53ms/step - loss: 7.1909 - mae: 2.0621 - val_loss: 12.1659 - val_mae: 2.7240\n",
            "Epoch 45/50\n",
            "819/819 [==============================] - 45s 55ms/step - loss: 7.0875 - mae: 2.0499 - val_loss: 12.0019 - val_mae: 2.7073\n",
            "Epoch 46/50\n",
            "819/819 [==============================] - 42s 52ms/step - loss: 7.0072 - mae: 2.0370 - val_loss: 12.5955 - val_mae: 2.7657\n",
            "Epoch 47/50\n",
            "819/819 [==============================] - 43s 52ms/step - loss: 7.0623 - mae: 2.0469 - val_loss: 12.2096 - val_mae: 2.7393\n",
            "Epoch 48/50\n",
            "819/819 [==============================] - 44s 53ms/step - loss: 6.9894 - mae: 2.0378 - val_loss: 12.3516 - val_mae: 2.7411\n",
            "Epoch 49/50\n",
            "819/819 [==============================] - 44s 54ms/step - loss: 6.9863 - mae: 2.0351 - val_loss: 12.7507 - val_mae: 2.7650\n",
            "Epoch 50/50\n",
            "819/819 [==============================] - 42s 51ms/step - loss: 6.9292 - mae: 2.0246 - val_loss: 12.5219 - val_mae: 2.7645\n"
          ]
        }
      ],
      "source": [
        "inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\n",
        "# 훈련 속도를 놓이기 위해 순환 드롭아웃을 제외합니다.\n",
        "#x = layers.LSTM(32, recurrent_dropout=0.25)(inputs)\n",
        "x = layers.LSTM(32)(inputs)\n",
        "x = layers.Dropout(0.5)(x)\n",
        "outputs = layers.Dense(1)(x)\n",
        "model = keras.Model(inputs, outputs)\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\"jena_lstm_dropout.h5\",\n",
        "                                    save_best_only=True)\n",
        "]\n",
        "model.compile(optimizer=\"rmsprop\", loss=\"mse\", metrics=[\"mae\"])\n",
        "history = model.fit(train_dataset,\n",
        "                    epochs=50,\n",
        "                    validation_data=val_dataset,\n",
        "                    callbacks=callbacks)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 25,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T15:04:54.372845Z",
          "iopub.status.busy": "2022-05-20T15:04:54.372302Z",
          "iopub.status.idle": "2022-05-20T15:04:54.500136Z",
          "shell.execute_reply": "2022-05-20T15:04:54.499227Z"
        },
        "id": "TUqqYeRgjbci",
        "outputId": "1e87f3d2-07d2-4f42-e74a-961e592ee1f9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "loss = history.history[\"mae\"]\n",
        "val_loss = history.history[\"val_mae\"]\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, \"bo\", label=\"Training MAE\")\n",
        "plt.plot(epochs, val_loss, \"b\", label=\"Validation MAE\")\n",
        "plt.title(\"Training and validation MAE\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 26,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T15:04:54.505555Z",
          "iopub.status.busy": "2022-05-20T15:04:54.504713Z",
          "iopub.status.idle": "2022-05-20T15:04:57.937020Z",
          "shell.execute_reply": "2022-05-20T15:04:57.935916Z"
        },
        "id": "u_-Jap5vjbcj",
        "outputId": "1c020a6e-ccbd-4589-c0e1-ac9cf4ae48f5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
          ]
        }
      ],
      "source": [
        "inputs = keras.Input(shape=(sequence_length, num_features))\n",
        "x = layers.LSTM(32, recurrent_dropout=0.2, unroll=True)(inputs)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dIAEo0Ttjbcj"
      },
      "source": [
        "### 스태킹 순환 층"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9AfaRKy2jbcj"
      },
      "source": [
        "**드롭아웃 규제와 스태킹을 적용한 GRU 모델을 훈련하고 평가하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 27,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T15:04:57.941005Z",
          "iopub.status.busy": "2022-05-20T15:04:57.940685Z",
          "iopub.status.idle": "2022-05-20T17:21:17.499207Z",
          "shell.execute_reply": "2022-05-20T17:21:17.498103Z"
        },
        "id": "-v7IqHRsjbcj",
        "outputId": "33e7d895-4f9a-4745-e138-1083978290c0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "819/819 [==============================] - 51s 58ms/step - loss: 22.8440 - mae: 3.5376 - val_loss: 10.0216 - val_mae: 2.4642\n",
            "Epoch 2/50\n",
            "819/819 [==============================] - 48s 58ms/step - loss: 13.4467 - mae: 2.8365 - val_loss: 9.1210 - val_mae: 2.3532\n",
            "Epoch 3/50\n",
            "819/819 [==============================] - 45s 55ms/step - loss: 12.5574 - mae: 2.7434 - val_loss: 8.7419 - val_mae: 2.2949\n",
            "Epoch 4/50\n",
            "819/819 [==============================] - 46s 56ms/step - loss: 11.7473 - mae: 2.6557 - val_loss: 9.0236 - val_mae: 2.3442\n",
            "Epoch 5/50\n",
            "819/819 [==============================] - 54s 65ms/step - loss: 11.1289 - mae: 2.5857 - val_loss: 9.2370 - val_mae: 2.3689\n",
            "Epoch 6/50\n",
            "819/819 [==============================] - 45s 55ms/step - loss: 10.4581 - mae: 2.5057 - val_loss: 9.5454 - val_mae: 2.4172\n",
            "Epoch 7/50\n",
            "819/819 [==============================] - 47s 58ms/step - loss: 9.8363 - mae: 2.4268 - val_loss: 10.0694 - val_mae: 2.4758\n",
            "Epoch 8/50\n",
            "819/819 [==============================] - 47s 57ms/step - loss: 9.2070 - mae: 2.3489 - val_loss: 11.8144 - val_mae: 2.6840\n",
            "Epoch 9/50\n",
            "819/819 [==============================] - 45s 55ms/step - loss: 8.6518 - mae: 2.2719 - val_loss: 10.4207 - val_mae: 2.5372\n",
            "Epoch 10/50\n",
            "819/819 [==============================] - 45s 54ms/step - loss: 8.1494 - mae: 2.2023 - val_loss: 10.8794 - val_mae: 2.5728\n",
            "Epoch 11/50\n",
            "819/819 [==============================] - 56s 68ms/step - loss: 7.7268 - mae: 2.1437 - val_loss: 11.0731 - val_mae: 2.6115\n",
            "Epoch 12/50\n",
            "819/819 [==============================] - 48s 59ms/step - loss: 7.3437 - mae: 2.0845 - val_loss: 11.2584 - val_mae: 2.6359\n",
            "Epoch 13/50\n",
            "819/819 [==============================] - 45s 54ms/step - loss: 6.9795 - mae: 2.0310 - val_loss: 11.4462 - val_mae: 2.6616\n",
            "Epoch 14/50\n",
            "819/819 [==============================] - 46s 56ms/step - loss: 6.7458 - mae: 1.9899 - val_loss: 11.4366 - val_mae: 2.6633\n",
            "Epoch 15/50\n",
            "819/819 [==============================] - 47s 57ms/step - loss: 6.4761 - mae: 1.9494 - val_loss: 13.3479 - val_mae: 2.8778\n",
            "Epoch 16/50\n",
            "819/819 [==============================] - 48s 58ms/step - loss: 6.2376 - mae: 1.9104 - val_loss: 12.4025 - val_mae: 2.7766\n",
            "Epoch 17/50\n",
            "819/819 [==============================] - 53s 64ms/step - loss: 6.0623 - mae: 1.8815 - val_loss: 12.0828 - val_mae: 2.7340\n",
            "Epoch 18/50\n",
            "819/819 [==============================] - 47s 57ms/step - loss: 5.9401 - mae: 1.8583 - val_loss: 12.4278 - val_mae: 2.7732\n",
            "Epoch 19/50\n",
            "819/819 [==============================] - 46s 56ms/step - loss: 5.7513 - mae: 1.8275 - val_loss: 12.9756 - val_mae: 2.8379\n",
            "Epoch 20/50\n",
            "819/819 [==============================] - 46s 56ms/step - loss: 5.6277 - mae: 1.8082 - val_loss: 12.3878 - val_mae: 2.7626\n",
            "Epoch 21/50\n",
            "819/819 [==============================] - 47s 57ms/step - loss: 5.5174 - mae: 1.7923 - val_loss: 12.5262 - val_mae: 2.7857\n",
            "Epoch 22/50\n",
            "819/819 [==============================] - 45s 55ms/step - loss: 5.4402 - mae: 1.7751 - val_loss: 12.8304 - val_mae: 2.8223\n",
            "Epoch 23/50\n",
            "819/819 [==============================] - 47s 57ms/step - loss: 5.2961 - mae: 1.7504 - val_loss: 12.7924 - val_mae: 2.8134\n",
            "Epoch 24/50\n",
            "819/819 [==============================] - 48s 59ms/step - loss: 5.2390 - mae: 1.7407 - val_loss: 12.9842 - val_mae: 2.8528\n",
            "Epoch 25/50\n",
            "819/819 [==============================] - 54s 65ms/step - loss: 5.1705 - mae: 1.7261 - val_loss: 12.5561 - val_mae: 2.7865\n",
            "Epoch 26/50\n",
            "819/819 [==============================] - 49s 59ms/step - loss: 5.0919 - mae: 1.7113 - val_loss: 13.1329 - val_mae: 2.8616\n",
            "Epoch 27/50\n",
            "819/819 [==============================] - 49s 59ms/step - loss: 5.0037 - mae: 1.6965 - val_loss: 13.1333 - val_mae: 2.8493\n",
            "Epoch 28/50\n",
            "819/819 [==============================] - 48s 58ms/step - loss: 4.9409 - mae: 1.6856 - val_loss: 13.0040 - val_mae: 2.8309\n",
            "Epoch 29/50\n",
            "819/819 [==============================] - 47s 57ms/step - loss: 4.9046 - mae: 1.6768 - val_loss: 13.2298 - val_mae: 2.8646\n",
            "Epoch 30/50\n",
            "819/819 [==============================] - 48s 59ms/step - loss: 4.8342 - mae: 1.6648 - val_loss: 13.7260 - val_mae: 2.9129\n",
            "Epoch 31/50\n",
            "819/819 [==============================] - 48s 59ms/step - loss: 4.7925 - mae: 1.6568 - val_loss: 12.8225 - val_mae: 2.8170\n",
            "Epoch 32/50\n",
            "819/819 [==============================] - 50s 60ms/step - loss: 4.7432 - mae: 1.6477 - val_loss: 12.9575 - val_mae: 2.8242\n",
            "Epoch 33/50\n",
            "819/819 [==============================] - 47s 57ms/step - loss: 4.7277 - mae: 1.6438 - val_loss: 12.9113 - val_mae: 2.8201\n",
            "Epoch 34/50\n",
            "819/819 [==============================] - 45s 55ms/step - loss: 4.6971 - mae: 1.6360 - val_loss: 13.4627 - val_mae: 2.9010\n",
            "Epoch 35/50\n",
            "819/819 [==============================] - 49s 59ms/step - loss: 4.6079 - mae: 1.6224 - val_loss: 13.0904 - val_mae: 2.8473\n",
            "Epoch 36/50\n",
            "819/819 [==============================] - 50s 60ms/step - loss: 4.5908 - mae: 1.6170 - val_loss: 13.4342 - val_mae: 2.8787\n",
            "Epoch 37/50\n",
            "819/819 [==============================] - 45s 55ms/step - loss: 4.5897 - mae: 1.6144 - val_loss: 13.5889 - val_mae: 2.9002\n",
            "Epoch 38/50\n",
            "819/819 [==============================] - 56s 68ms/step - loss: 4.5562 - mae: 1.6073 - val_loss: 13.3792 - val_mae: 2.8734\n",
            "Epoch 39/50\n",
            "819/819 [==============================] - 48s 59ms/step - loss: 4.5062 - mae: 1.6011 - val_loss: 13.3669 - val_mae: 2.8576\n",
            "Epoch 40/50\n",
            "819/819 [==============================] - 49s 60ms/step - loss: 4.4988 - mae: 1.5970 - val_loss: 13.5926 - val_mae: 2.8983\n",
            "Epoch 41/50\n",
            "819/819 [==============================] - 49s 59ms/step - loss: 4.4507 - mae: 1.5877 - val_loss: 13.0201 - val_mae: 2.8481\n",
            "Epoch 42/50\n",
            "819/819 [==============================] - 56s 68ms/step - loss: 4.4428 - mae: 1.5861 - val_loss: 13.8423 - val_mae: 2.9251\n",
            "Epoch 43/50\n",
            "819/819 [==============================] - 48s 58ms/step - loss: 4.4128 - mae: 1.5829 - val_loss: 13.0376 - val_mae: 2.8410\n",
            "Epoch 44/50\n",
            "819/819 [==============================] - 48s 59ms/step - loss: 4.3728 - mae: 1.5722 - val_loss: 13.5246 - val_mae: 2.8782\n",
            "Epoch 45/50\n",
            "819/819 [==============================] - 48s 58ms/step - loss: 4.3393 - mae: 1.5665 - val_loss: 13.6383 - val_mae: 2.8924\n",
            "Epoch 46/50\n",
            "819/819 [==============================] - 47s 57ms/step - loss: 4.3161 - mae: 1.5642 - val_loss: 13.2954 - val_mae: 2.8655\n",
            "Epoch 47/50\n",
            "819/819 [==============================] - 49s 60ms/step - loss: 4.3288 - mae: 1.5648 - val_loss: 12.9048 - val_mae: 2.8169\n",
            "Epoch 48/50\n",
            "819/819 [==============================] - 47s 58ms/step - loss: 4.3075 - mae: 1.5599 - val_loss: 13.8102 - val_mae: 2.9179\n",
            "Epoch 49/50\n",
            "819/819 [==============================] - 49s 59ms/step - loss: 4.2367 - mae: 1.5503 - val_loss: 13.4424 - val_mae: 2.8812\n",
            "Epoch 50/50\n",
            "819/819 [==============================] - 47s 57ms/step - loss: 4.2598 - mae: 1.5501 - val_loss: 13.9578 - val_mae: 2.9307\n",
            "405/405 [==============================] - 16s 38ms/step - loss: 9.5284 - mae: 2.4201\n",
            "테스트 MAE: 2.42\n"
          ]
        }
      ],
      "source": [
        "inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\n",
        "# 훈련 속도를 놓이기 위해 순환 드롭아웃을 제외합니다.\n",
        "# x = layers.GRU(32, recurrent_dropout=0.5, return_sequences=True)(inputs)\n",
        "# x = layers.GRU(32, recurrent_dropout=0.5)(x)\n",
        "x = layers.GRU(32, return_sequences=True)(inputs)\n",
        "x = layers.GRU(32)(x)\n",
        "x = layers.Dropout(0.5)(x)\n",
        "outputs = layers.Dense(1)(x)\n",
        "model = keras.Model(inputs, outputs)\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\"jena_stacked_gru_dropout.h5\",\n",
        "                                    save_best_only=True)\n",
        "]\n",
        "model.compile(optimizer=\"rmsprop\", loss=\"mse\", metrics=[\"mae\"])\n",
        "history = model.fit(train_dataset,\n",
        "                    epochs=50,\n",
        "                    validation_data=val_dataset,\n",
        "                    callbacks=callbacks)\n",
        "model = keras.models.load_model(\"jena_stacked_gru_dropout.h5\")\n",
        "print(f\"테스트 MAE: {model.evaluate(test_dataset)[1]:.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T17:21:17.502935Z",
          "iopub.status.busy": "2022-05-20T17:21:17.502627Z",
          "iopub.status.idle": "2022-05-20T17:21:17.637424Z",
          "shell.execute_reply": "2022-05-20T17:21:17.636229Z"
        },
        "id": "ajG9EuXxjbck",
        "outputId": "2dccd493-f54c-4779-a2ee-c64e33c347ab",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "loss = history.history[\"mae\"]\n",
        "val_loss = history.history[\"val_mae\"]\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, \"bo\", label=\"Training MAE\")\n",
        "plt.plot(epochs, val_loss, \"b\", label=\"Validation MAE\")\n",
        "plt.title(\"Training and validation MAE\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yj_01R2Cjbck"
      },
      "source": [
        "### 양방향 RNN 사용하기"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HfYzAJbqjbck"
      },
      "source": [
        "시퀀스를 거꾸로 뒤집어 LSTM 모델 훈련하기 (책에는 없음)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T17:21:17.641176Z",
          "iopub.status.busy": "2022-05-20T17:21:17.640902Z",
          "iopub.status.idle": "2022-05-20T17:21:17.646145Z",
          "shell.execute_reply": "2022-05-20T17:21:17.645090Z"
        },
        "id": "P2Ns9cJajbck"
      },
      "outputs": [],
      "source": [
        "def train_generator():\n",
        "    while True:\n",
        "        for samples, targets in train_dataset:\n",
        "            yield samples[:, ::-1, :], targets\n",
        "\n",
        "def val_generator():\n",
        "    while True:\n",
        "        for samples, targets in val_dataset:\n",
        "            yield samples[:, ::-1, :], targets\n",
        "\n",
        "train_gen = train_generator()\n",
        "val_gen = val_generator()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T17:21:17.649630Z",
          "iopub.status.busy": "2022-05-20T17:21:17.649357Z",
          "iopub.status.idle": "2022-05-20T17:38:03.455221Z",
          "shell.execute_reply": "2022-05-20T17:38:03.454271Z"
        },
        "id": "4Yd7xpW3jbck",
        "outputId": "cba42d83-1c0c-4bd7-dea5-581c2965c011",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "819/819 [==============================] - 58s 68ms/step - loss: 34.4427 - mae: 4.4943 - val_loss: 17.2340 - val_mae: 3.2700\n",
            "Epoch 2/10\n",
            "819/819 [==============================] - 53s 65ms/step - loss: 21.8827 - mae: 3.6623 - val_loss: 16.7471 - val_mae: 3.2083\n",
            "Epoch 3/10\n",
            "819/819 [==============================] - 55s 67ms/step - loss: 20.3650 - mae: 3.5217 - val_loss: 16.0861 - val_mae: 3.1253\n",
            "Epoch 4/10\n",
            "819/819 [==============================] - 82s 92ms/step - loss: 17.7713 - mae: 3.2862 - val_loss: 13.3988 - val_mae: 2.8408\n",
            "Epoch 5/10\n",
            "819/819 [==============================] - 49s 60ms/step - loss: 15.6091 - mae: 3.0858 - val_loss: 12.8040 - val_mae: 2.7785\n",
            "Epoch 6/10\n",
            "819/819 [==============================] - 115s 140ms/step - loss: 14.6476 - mae: 2.9809 - val_loss: 12.5205 - val_mae: 2.7213\n",
            "Epoch 7/10\n",
            "819/819 [==============================] - 48s 58ms/step - loss: 13.9296 - mae: 2.9091 - val_loss: 12.2423 - val_mae: 2.6778\n",
            "Epoch 8/10\n",
            "819/819 [==============================] - 46s 56ms/step - loss: 13.3959 - mae: 2.8444 - val_loss: 12.2930 - val_mae: 2.7002\n",
            "Epoch 9/10\n",
            "819/819 [==============================] - 72s 62ms/step - loss: 13.0317 - mae: 2.8016 - val_loss: 12.1610 - val_mae: 2.6823\n",
            "Epoch 10/10\n",
            "819/819 [==============================] - 51s 63ms/step - loss: 12.6035 - mae: 2.7555 - val_loss: 12.3106 - val_mae: 2.7035\n"
          ]
        }
      ],
      "source": [
        "inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\n",
        "# 훈련 속도를 놓이기 위해 순환 드롭아웃을 제외합니다.\n",
        "# x = layers.LSTM(32, recurrent_dropout=0.25)(inputs)\n",
        "x = layers.LSTM(32)(inputs)\n",
        "x = layers.Dropout(0.5)(x)\n",
        "outputs = layers.Dense(1)(x)\n",
        "model = keras.Model(inputs, outputs)\n",
        "\n",
        "model.compile(optimizer=\"rmsprop\", loss=\"mse\", metrics=[\"mae\"])\n",
        "history = model.fit(train_gen,\n",
        "                    epochs=10,\n",
        "                    steps_per_epoch=819,\n",
        "                    validation_data=val_gen,\n",
        "                    validation_steps=410)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T17:38:03.459157Z",
          "iopub.status.busy": "2022-05-20T17:38:03.458872Z",
          "iopub.status.idle": "2022-05-20T17:38:03.588800Z",
          "shell.execute_reply": "2022-05-20T17:38:03.587843Z"
        },
        "id": "HjBY66fkjbck",
        "outputId": "1d4b18a3-c5c3-46c6-aabe-d79991418675",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "loss = history.history[\"mae\"]\n",
        "val_loss = history.history[\"val_mae\"]\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.figure()\n",
        "plt.plot(epochs, loss, \"bo\", label=\"Training MAE\")\n",
        "plt.plot(epochs, val_loss, \"b\", label=\"Validation MAE\")\n",
        "plt.title(\"Training and validation MAE\")\n",
        "plt.legend()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9mc9iUIKjbck"
      },
      "source": [
        "**양방향 LSTM 모델 훈련하고 평가하기**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "metadata": {
        "execution": {
          "iopub.execute_input": "2022-05-20T17:38:03.594401Z",
          "iopub.status.busy": "2022-05-20T17:38:03.593387Z",
          "iopub.status.idle": "2022-05-20T17:49:14.410464Z",
          "shell.execute_reply": "2022-05-20T17:49:14.409759Z"
        },
        "id": "eysu8EhMjbcl",
        "outputId": "79e7ce1c-f525-44ea-bd14-f595ba429570",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "819/819 [==============================] - 55s 62ms/step - loss: 26.2686 - mae: 3.6970 - val_loss: 11.1051 - val_mae: 2.5860\n",
            "Epoch 2/10\n",
            "819/819 [==============================] - 48s 58ms/step - loss: 9.6917 - mae: 2.4327 - val_loss: 9.7543 - val_mae: 2.4046\n",
            "Epoch 3/10\n",
            "819/819 [==============================] - 47s 57ms/step - loss: 8.9986 - mae: 2.3375 - val_loss: 9.4536 - val_mae: 2.3768\n",
            "Epoch 4/10\n",
            "819/819 [==============================] - 47s 57ms/step - loss: 8.4802 - mae: 2.2696 - val_loss: 9.7171 - val_mae: 2.4206\n",
            "Epoch 5/10\n",
            "819/819 [==============================] - 48s 58ms/step - loss: 8.0960 - mae: 2.2161 - val_loss: 9.4793 - val_mae: 2.3819\n",
            "Epoch 6/10\n",
            "819/819 [==============================] - 49s 59ms/step - loss: 7.7758 - mae: 2.1717 - val_loss: 9.8579 - val_mae: 2.4301\n",
            "Epoch 7/10\n",
            "819/819 [==============================] - 47s 58ms/step - loss: 7.5232 - mae: 2.1358 - val_loss: 9.7900 - val_mae: 2.4127\n",
            "Epoch 8/10\n",
            "819/819 [==============================] - 48s 59ms/step - loss: 7.3138 - mae: 2.1070 - val_loss: 9.8580 - val_mae: 2.4292\n",
            "Epoch 9/10\n",
            "819/819 [==============================] - 47s 57ms/step - loss: 7.1005 - mae: 2.0763 - val_loss: 10.1506 - val_mae: 2.4593\n",
            "Epoch 10/10\n",
            "819/819 [==============================] - 55s 67ms/step - loss: 6.9811 - mae: 2.0572 - val_loss: 10.7489 - val_mae: 2.5391\n"
          ]
        }
      ],
      "source": [
        "inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\n",
        "x = layers.Bidirectional(layers.LSTM(16))(inputs)\n",
        "outputs = layers.Dense(1)(x)\n",
        "model = keras.Model(inputs, outputs)\n",
        "\n",
        "model.compile(optimizer=\"rmsprop\", loss=\"mse\", metrics=[\"mae\"])\n",
        "history = model.fit(train_dataset,\n",
        "                    epochs=10,\n",
        "                    validation_data=val_dataset)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wrkE0wkWjbcl"
      },
      "source": [
        "### 더 나아가서"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "fi2t0VRNjbcl"
      },
      "source": [
        "## 요약"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "chapter10_dl-for-timeseries.i",
      "provenance": [],
      "gpuClass": "premium"
    },
    "kernelspec": {
      "display_name": "default:Python",
      "language": "python",
      "name": "conda-env-default-py"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
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