5_6_timeseries.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/minyoung347/python101/blob/main/5_6_timeseries.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rMlIvey2jbcV"
      },
      "source": [
        "## 기온 예측 문제"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": "5831cc8f-cdae-4ce3-9515-d420526c5f71",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2024-06-13 16:48:44--  https://s3.amazonaws.com/keras-datasets/jena_climate_2009_2016.csv.zip\n",
            "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.216.25.134, 52.217.47.118, 52.217.132.240, ...\n",
            "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.216.25.134|: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  21.1MB/s    in 0.6s    \n",
            "\n",
            "2024-06-13 16:48:45 (21.1 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": null,
      "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": "1542aa26-94b9-4c26-ee0b-b1e62f74e674",
        "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": null,
      "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": null,
      "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": "9e4e0886-da2c-47e4-8e4d-0a5ada4c652a",
        "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": null,
      "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": "762d0eea-c884-46fd-fb72-800aa7ae97e1",
        "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": null,
      "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": "5428c1aa-7a40-4eb7-d2ba-337d6866d519",
        "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": null,
      "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": null,
      "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": "8acf0f8f-4e77-467a-845e-d264b326e855",
        "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": null,
      "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": null,
      "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": "8c418829-1c39-40b6-91e7-23084c7bef68",
        "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": null,
      "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": "cda18c4c-9db9-40ee-ab18-55cfdbdcfc45",
        "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": null,
      "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": "6945dcdd-7fc8-4f92-e530-829ad2686bdc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "819/819 [==============================] - 10s 11ms/step - loss: 12.5112 - mae: 2.7394 - val_loss: 11.6077 - val_mae: 2.6940\n",
            "Epoch 2/10\n",
            "  9/819 [..............................] - ETA: 5s - loss: 10.5443 - mae: 2.5720 "
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3103: 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 [==============================] - 9s 10ms/step - loss: 9.1552 - mae: 2.3790 - val_loss: 10.5309 - val_mae: 2.5596\n",
            "Epoch 3/10\n",
            "819/819 [==============================] - 8s 10ms/step - loss: 8.4326 - mae: 2.2857 - val_loss: 10.5142 - val_mae: 2.5600\n",
            "Epoch 4/10\n",
            "819/819 [==============================] - 8s 10ms/step - loss: 7.9514 - mae: 2.2214 - val_loss: 13.4457 - val_mae: 2.9175\n",
            "Epoch 5/10\n",
            "819/819 [==============================] - 9s 10ms/step - loss: 7.6262 - mae: 2.1754 - val_loss: 12.4853 - val_mae: 2.7990\n",
            "Epoch 6/10\n",
            "819/819 [==============================] - 8s 10ms/step - loss: 7.3565 - mae: 2.1373 - val_loss: 11.2123 - val_mae: 2.6457\n",
            "Epoch 7/10\n",
            "819/819 [==============================] - 8s 10ms/step - loss: 7.1674 - mae: 2.1113 - val_loss: 11.2171 - val_mae: 2.6473\n",
            "Epoch 8/10\n",
            "819/819 [==============================] - 9s 11ms/step - loss: 6.9974 - mae: 2.0873 - val_loss: 10.4293 - val_mae: 2.5524\n",
            "Epoch 9/10\n",
            "819/819 [==============================] - 8s 10ms/step - loss: 6.8474 - mae: 2.0656 - val_loss: 11.1296 - val_mae: 2.6345\n",
            "Epoch 10/10\n",
            "819/819 [==============================] - 8s 10ms/step - loss: 6.7016 - mae: 2.0428 - val_loss: 12.5579 - val_mae: 2.7930\n",
            "405/405 [==============================] - 3s 7ms/step - loss: 11.4375 - mae: 2.6531\n",
            "테스트 MAE: 2.65\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": null,
      "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": "07df536d-d645-4186-c5f3-8158b0c394c5",
        "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": null,
      "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": "8b745bdd-bc88-4fcc-f872-745e79ae5385",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "819/819 [==============================] - 12s 11ms/step - loss: 21.6470 - mae: 3.6515 - val_loss: 14.7916 - val_mae: 3.0325\n",
            "Epoch 2/10\n",
            "819/819 [==============================] - 9s 10ms/step - loss: 14.9033 - mae: 3.0633 - val_loss: 14.4621 - val_mae: 3.0321\n",
            "Epoch 3/10\n",
            "819/819 [==============================] - 9s 11ms/step - loss: 13.3890 - mae: 2.9018 - val_loss: 14.6207 - val_mae: 3.0057\n",
            "Epoch 4/10\n",
            "819/819 [==============================] - 9s 10ms/step - loss: 12.5756 - mae: 2.8059 - val_loss: 17.4610 - val_mae: 3.2999\n",
            "Epoch 5/10\n",
            "819/819 [==============================] - 9s 10ms/step - loss: 11.9429 - mae: 2.7295 - val_loss: 16.2755 - val_mae: 3.1905\n",
            "Epoch 6/10\n",
            "819/819 [==============================] - 9s 11ms/step - loss: 11.4360 - mae: 2.6700 - val_loss: 15.3896 - val_mae: 3.1053\n",
            "Epoch 7/10\n",
            "819/819 [==============================] - 9s 11ms/step - loss: 11.0527 - mae: 2.6247 - val_loss: 15.4962 - val_mae: 3.0874\n",
            "Epoch 8/10\n",
            "819/819 [==============================] - 9s 10ms/step - loss: 10.6982 - mae: 2.5821 - val_loss: 14.2828 - val_mae: 2.9642\n",
            "Epoch 9/10\n",
            "819/819 [==============================] - 9s 10ms/step - loss: 10.3793 - mae: 2.5448 - val_loss: 14.7305 - val_mae: 3.0063\n",
            "Epoch 10/10\n",
            "819/819 [==============================] - 9s 10ms/step - loss: 10.1013 - mae: 2.5110 - val_loss: 15.4623 - val_mae: 3.0877\n",
            "405/405 [==============================] - 3s 7ms/step - loss: 15.5322 - mae: 3.0961\n",
            "테스트 MAE: 3.10\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": null,
      "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": "78cdd3cb-313c-4b8d-bad7-c7d2e06e9242",
        "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": "SIVEXb7ojbcf"
      },
      "source": [
        "### 첫 번째 순환 신경망"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KGsF8BjGjbcf"
      },
      "source": [
        "**간단한 LSTM 기반 모델**"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "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": "8d08dbba-43e6-4a65-9d30-295b305d9a65",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "819/819 [==============================] - 13s 13ms/step - loss: 36.9951 - mae: 4.4044 - val_loss: 11.7005 - val_mae: 2.6129\n",
            "Epoch 2/10\n",
            "819/819 [==============================] - 11s 13ms/step - loss: 10.8761 - mae: 2.5685 - val_loss: 9.3253 - val_mae: 2.3835\n",
            "Epoch 3/10\n",
            "819/819 [==============================] - 11s 13ms/step - loss: 9.8482 - mae: 2.4532 - val_loss: 9.4003 - val_mae: 2.3838\n",
            "Epoch 4/10\n",
            "803/819 [============================>.] - ETA: 0s - loss: 9.4365 - mae: 2.4005"
          ]
        }
      ],
      "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": null,
      "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"
      },
      "outputs": [],
      "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": null,
      "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": null,
      "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": null,
      "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"
      },
      "outputs": [],
      "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": null,
      "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"
      },
      "outputs": [],
      "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": null,
      "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": null,
      "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"
      },
      "outputs": [],
      "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": null,
      "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"
      },
      "outputs": [],
      "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": null,
      "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"
      },
      "outputs": [],
      "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": null,
      "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"
      },
      "outputs": [],
      "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": null,
      "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"
      },
      "outputs": [],
      "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": null,
      "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": null,
      "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"
      },
      "outputs": [],
      "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": null,
      "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"
      },
      "outputs": [],
      "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": null,
      "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"
      },
      "outputs": [],
      "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": "code",
      "source": [],
      "metadata": {
        "id": "4ITtRpLDgPGe"
      },
      "execution_count": null,
      "outputs": []
    }
  ],
  "metadata": {
    "colab": {
      "name": "chapter10_dl-for-timeseries.i",
      "provenance": [],
      "gpuClass": "premium",
      "include_colab_link": true
    },
    "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",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.9.10"
    },
    "accelerator": "GPU",
    "gpuClass": "premium"
  },
  "nbformat": 4,
  "nbformat_minor": 0
}