LSTM_Explanation__Week_12_Class_11.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "E8cy0OkfIlCO"
      },
      "source": [
        "## Week 12 - Class 11"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oCJ3Yj41Iiip"
      },
      "source": [
        "This notebook have the most important exercises for theoretical class 11, which covers chapter 10 and 11 from the book. The labs from https://github.com/fchollet/deep-learning-with-python-notebooks are a guide for this notebook, mainly the labs 10,11\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Dataset source:\n",
        "https://keras.io/examples/timeseries/timeseries_weather_forecasting/"
      ],
      "metadata": {
        "id": "PqQ5WSMEe1Q1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "-HzPRitYI3wA"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jvIe5n5LIHU_",
        "outputId": "f2122ae3-087f-4da4-af0a-6b63fdb5f208"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "--2023-06-22 09:45:20--  https://s3.amazonaws.com/keras-datasets/jena_climate_2009_2016.csv.zip\n",
            "Resolving s3.amazonaws.com (s3.amazonaws.com)... 54.231.135.208, 54.231.225.72, 52.217.101.102, ...\n",
            "Connecting to s3.amazonaws.com (s3.amazonaws.com)|54.231.135.208|: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  --.-KB/s    in 0.1s    \n",
            "\n",
            "2023-06-22 09:45:21 (132 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": [
        "#get the time series data, it is weather data\n",
        "!wget https://s3.amazonaws.com/keras-datasets/jena_climate_2009_2016.csv.zip\n",
        "!unzip jena_climate_2009_2016.csv.zip"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "SaYC_f-sIf5h"
      },
      "outputs": [],
      "source": [
        "df=pd.read_csv('jena_climate_2009_2016.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "qTCpPMLBI1qs",
        "outputId": "996b3ce8-dd87-43e5-d944-e31207bfbce5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             Date Time  p (mbar)  T (degC)  Tpot (K)  Tdew (degC)  rh (%)  \\\n",
              "0  01.01.2009 00:10:00    996.52     -8.02    265.40        -8.90    93.3   \n",
              "1  01.01.2009 00:20:00    996.57     -8.41    265.01        -9.28    93.4   \n",
              "2  01.01.2009 00:30:00    996.53     -8.51    264.91        -9.31    93.9   \n",
              "3  01.01.2009 00:40:00    996.51     -8.31    265.12        -9.07    94.2   \n",
              "4  01.01.2009 00:50:00    996.51     -8.27    265.15        -9.04    94.1   \n",
              "\n",
              "   VPmax (mbar)  VPact (mbar)  VPdef (mbar)  sh (g/kg)  H2OC (mmol/mol)  \\\n",
              "0          3.33          3.11          0.22       1.94             3.12   \n",
              "1          3.23          3.02          0.21       1.89             3.03   \n",
              "2          3.21          3.01          0.20       1.88             3.02   \n",
              "3          3.26          3.07          0.19       1.92             3.08   \n",
              "4          3.27          3.08          0.19       1.92             3.09   \n",
              "\n",
              "   rho (g/m**3)  wv (m/s)  max. wv (m/s)  wd (deg)  \n",
              "0       1307.75      1.03           1.75     152.3  \n",
              "1       1309.80      0.72           1.50     136.1  \n",
              "2       1310.24      0.19           0.63     171.6  \n",
              "3       1309.19      0.34           0.50     198.0  \n",
              "4       1309.00      0.32           0.63     214.3  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date Time</th>\n",
              "      <th>p (mbar)</th>\n",
              "      <th>T (degC)</th>\n",
              "      <th>Tpot (K)</th>\n",
              "      <th>Tdew (degC)</th>\n",
              "      <th>rh (%)</th>\n",
              "      <th>VPmax (mbar)</th>\n",
              "      <th>VPact (mbar)</th>\n",
              "      <th>VPdef (mbar)</th>\n",
              "      <th>sh (g/kg)</th>\n",
              "      <th>H2OC (mmol/mol)</th>\n",
              "      <th>rho (g/m**3)</th>\n",
              "      <th>wv (m/s)</th>\n",
              "      <th>max. wv (m/s)</th>\n",
              "      <th>wd (deg)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>01.01.2009 00:10:00</td>\n",
              "      <td>996.52</td>\n",
              "      <td>-8.02</td>\n",
              "      <td>265.40</td>\n",
              "      <td>-8.90</td>\n",
              "      <td>93.3</td>\n",
              "      <td>3.33</td>\n",
              "      <td>3.11</td>\n",
              "      <td>0.22</td>\n",
              "      <td>1.94</td>\n",
              "      <td>3.12</td>\n",
              "      <td>1307.75</td>\n",
              "      <td>1.03</td>\n",
              "      <td>1.75</td>\n",
              "      <td>152.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01.01.2009 00:20:00</td>\n",
              "      <td>996.57</td>\n",
              "      <td>-8.41</td>\n",
              "      <td>265.01</td>\n",
              "      <td>-9.28</td>\n",
              "      <td>93.4</td>\n",
              "      <td>3.23</td>\n",
              "      <td>3.02</td>\n",
              "      <td>0.21</td>\n",
              "      <td>1.89</td>\n",
              "      <td>3.03</td>\n",
              "      <td>1309.80</td>\n",
              "      <td>0.72</td>\n",
              "      <td>1.50</td>\n",
              "      <td>136.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01.01.2009 00:30:00</td>\n",
              "      <td>996.53</td>\n",
              "      <td>-8.51</td>\n",
              "      <td>264.91</td>\n",
              "      <td>-9.31</td>\n",
              "      <td>93.9</td>\n",
              "      <td>3.21</td>\n",
              "      <td>3.01</td>\n",
              "      <td>0.20</td>\n",
              "      <td>1.88</td>\n",
              "      <td>3.02</td>\n",
              "      <td>1310.24</td>\n",
              "      <td>0.19</td>\n",
              "      <td>0.63</td>\n",
              "      <td>171.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01.01.2009 00:40:00</td>\n",
              "      <td>996.51</td>\n",
              "      <td>-8.31</td>\n",
              "      <td>265.12</td>\n",
              "      <td>-9.07</td>\n",
              "      <td>94.2</td>\n",
              "      <td>3.26</td>\n",
              "      <td>3.07</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.92</td>\n",
              "      <td>3.08</td>\n",
              "      <td>1309.19</td>\n",
              "      <td>0.34</td>\n",
              "      <td>0.50</td>\n",
              "      <td>198.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01.01.2009 00:50:00</td>\n",
              "      <td>996.51</td>\n",
              "      <td>-8.27</td>\n",
              "      <td>265.15</td>\n",
              "      <td>-9.04</td>\n",
              "      <td>94.1</td>\n",
              "      <td>3.27</td>\n",
              "      <td>3.08</td>\n",
              "      <td>0.19</td>\n",
              "      <td>1.92</td>\n",
              "      <td>3.09</td>\n",
              "      <td>1309.00</td>\n",
              "      <td>0.32</td>\n",
              "      <td>0.63</td>\n",
              "      <td>214.3</td>\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
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            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ],
      "source": [
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VkDt-h6YKq2g",
        "outputId": "28caec41-8360-404e-d8f8-63f608dd6b18"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Date Time', 'p (mbar)', 'T (degC)', 'Tpot (K)', 'Tdew (degC)',\n",
              "       'rh (%)', 'VPmax (mbar)', 'VPact (mbar)', 'VPdef (mbar)', 'sh (g/kg)',\n",
              "       'H2OC (mmol/mol)', 'rho (g/m**3)', 'wv (m/s)', 'max. wv (m/s)',\n",
              "       'wd (deg)'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9SnfZVAqKzVP",
        "outputId": "6cd90e37-a945-41f7-a91b-8acfbf02dace"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(420451, 15)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bkGAB6kOK1qc",
        "outputId": "92735f09-21bb-4cce-afc1-55e2c106b3fe"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1440"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "24*6*10\n",
        "#every 10min"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 451
        },
        "id": "CpxND5VUI7ID",
        "outputId": "fca9b9d7-bd67-42cf-c7b2-409a0bf63586"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7faa9df82a40>]"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "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(df['T (degC)'])), df['T (degC)'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "NNkT0flzKwEm",
        "outputId": "54b1e55a-17f1-4a01-a1e2-0dfbab0a797a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7faa9d08b010>]"
            ]
          },
          "metadata": {},
          "execution_count": 8
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "#plot first 10 days\n",
        "\n",
        "plt.plot(range(1440),  df['T (degC)'][:1440])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y3e8oQP-N4dR",
        "outputId": "8f135935-c98c-46ad-e33b-ff3a442d79b5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Date Time', 'p (mbar)', 'T (degC)', 'Tpot (K)', 'Tdew (degC)',\n",
              "       'rh (%)', 'VPmax (mbar)', 'VPact (mbar)', 'VPdef (mbar)', 'sh (g/kg)',\n",
              "       'H2OC (mmol/mol)', 'rho (g/m**3)', 'wv (m/s)', 'max. wv (m/s)',\n",
              "       'wd (deg)'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "df.columns"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Aditional step, remove temperature columns ('Tpot (K)', 'Tdew (degC)'), leave just the one we want to predict: 'T (degC)'"
      ],
      "metadata": {
        "id": "Wae8BLH3hxqp"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "metadata": {
        "id": "HO0F8g0TNzZg"
      },
      "outputs": [],
      "source": [
        "#remove first column (we don't input timestamps)\n",
        "#delete the column name in this selection:\n",
        "df_raw=df[['p (mbar)', 'T (degC)',\n",
        "       'rh (%)', 'VPmax (mbar)', 'VPact (mbar)', 'VPdef (mbar)', 'sh (g/kg)',\n",
        "       'H2OC (mmol/mol)', 'rho (g/m**3)', 'wv (m/s)', 'max. wv (m/s)',\n",
        "       'wd (deg)']]\n",
        "\n",
        "#or:\n",
        "#df_raw = df.drop('Date Time', axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 34,
      "metadata": {
        "id": "rHGjvh8RNk11"
      },
      "outputs": [],
      "source": [
        "raw_data=df_raw.to_numpy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 35,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fozYVK2lNvmP",
        "outputId": "48524cb6-cb72-426a-d9b8-b183293edf07"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(420451, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ],
      "source": [
        "raw_data.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "IAnQXVLkNx1o"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": 36,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hdUHBBl0K-wO",
        "outputId": "650d4899-9f2e-4173-e4f0-757907b0f581"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "num_train_samples: 210225\n",
            "num_val_samples: 105112\n",
            "num_test_samples: 105114\n"
          ]
        }
      ],
      "source": [
        "# define indexes of training, validation, testing datasets\n",
        "\n",
        "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",
      "source": [
        "Since every feature has values with varying ranges, we do normalization to confine feature values to a range of [0, 1] before training a neural network."
      ],
      "metadata": {
        "id": "7LhE501wf7o1"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 37,
      "metadata": {
        "id": "-LpvnISbMCQc"
      },
      "outputs": [],
      "source": [
        "#normalize the data\n",
        "\n",
        "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",
      "source": [
        "raw_data.shape"
      ],
      "metadata": {
        "id": "v0RufkJAQkXU",
        "outputId": "09c2659f-6d0c-4282-c137-1f80b668ff06",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(420451, 12)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "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], targets=int_sequence[3:], sequence_length=3, batch_size=2,\n",
        ")\n",
        "for inputs, targets in dummy_dataset:\n",
        "  #print(inputs.shape)\n",
        "  for i in range(inputs.shape[0]):\n",
        "    print([int(x) for x in inputs[i]], int(targets[i]))\n",
        "    #break"
      ],
      "metadata": {
        "id": "v1l_irkiXSs1",
        "outputId": "c96f7a37-ab03-4116-b766-de5e3d8daff1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "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"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "int_sequence"
      ],
      "metadata": {
        "id": "9jrkneMnXZaS",
        "outputId": "2f35411a-e14a-4a15-b1eb-1a7da22a6013",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "int_sequence[:-3]"
      ],
      "metadata": {
        "id": "ck5K4MPIXiKp",
        "outputId": "4c45b37e-63be-488d-f183-5bbda0a48b93",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4, 5, 6])"
            ]
          },
          "metadata": {},
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "int_sequence[3:]"
      ],
      "metadata": {
        "id": "HwV8ypC7XluM",
        "outputId": "b64eba51-04a4-40a1-ed6e-33b0c3bed712",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3, 4, 5, 6, 7, 8, 9])"
            ]
          },
          "metadata": {},
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#SOL\n",
        "sampling_rate = 6 #every hour!\n",
        "sequence_length = 120 #use last 120 hours to predict\n",
        "delay = sampling_rate * (sequence_length + 24 - 1)\n",
        "batch_size = 256"
      ],
      "metadata": {
        "id": "0KieoGGpnMOs"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#choose the column 1 (starting on zero), which is the temperature column for label/target\n",
        "targets0=pd.DataFrame(raw_data)[1].to_numpy()"
      ],
      "metadata": {
        "id": "cOgXDqmRiyge"
      },
      "execution_count": 57,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 58,
      "metadata": {
        "id": "Ekg63XcuMKhG"
      },
      "outputs": [],
      "source": [
        "\n",
        "#SOL\n",
        "train_dataset = keras.utils.timeseries_dataset_from_array(\n",
        "    raw_data[:-delay],\n",
        "    targets=targets0[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=targets0[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=targets0[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": "code",
      "source": [
        "train_dataset"
      ],
      "metadata": {
        "id": "iY6yxVXY_YsX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": 59,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VlhK0HwcMPD1",
        "outputId": "bf441198-628d-4215-b352-8bc8f6752ea1"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "samples shape: (256, 120, 12)\n",
            "targets shape: (256,)\n"
          ]
        }
      ],
      "source": [
        "for samples, targets in train_dataset:\n",
        "    print(\"samples shape:\", samples.shape)\n",
        "    print(\"targets shape:\", targets.shape)\n",
        "    break"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "3gBZjJBPM3kn"
      },
      "outputs": [],
      "source": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "81aJWI-_MdGb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "929759eb-737b-490b-e24c-366dbdc72ec5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "819/819 [==============================] - 87s 104ms/step - loss: 0.1441 - val_loss: 0.1191\n",
            "Epoch 2/10\n",
            "819/819 [==============================] - 94s 115ms/step - loss: 0.1001 - val_loss: 0.1360\n",
            "Epoch 3/10\n",
            "819/819 [==============================] - 88s 107ms/step - loss: 0.0834 - val_loss: 0.1422\n",
            "Epoch 4/10\n",
            "819/819 [==============================] - 87s 105ms/step - loss: 0.0719 - val_loss: 0.1512\n",
            "Epoch 5/10\n",
            "819/819 [==============================] - 87s 106ms/step - loss: 0.0646 - val_loss: 0.1544\n",
            "Epoch 6/10\n",
            "819/819 [==============================] - 87s 106ms/step - loss: 0.0579 - val_loss: 0.1600\n",
            "Epoch 7/10\n",
            "648/819 [======================>.......] - ETA: 14s - loss: 0.0537"
          ]
        }
      ],
      "source": [
        "inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\n",
        "x = layers.LSTM(32)(inputs)\n",
        "outputs = layers.Dense(1)(x)\n",
        "model = keras.Model(inputs, outputs)\n",
        "\n",
        "callbacks = [\n",
        "    keras.callbacks.ModelCheckpoint(\"jena_lstm.keras\",\n",
        "                                    save_best_only=True)\n",
        "]\n",
        "model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.001), loss=\"mse\")\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.keras\")\n",
        "print(f\"Test MAE: {model.evaluate(test_dataset)[1]:.2f}\")"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()\n"
      ],
      "metadata": {
        "id": "gpDi8FkQqcw6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "08b2f421-642a-4ba1-ae34-fa84a0c88dbd"
      },
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_2 (InputLayer)        [(None, 120, 12)]         0         \n",
            "                                                                 \n",
            " lstm_1 (LSTM)               (None, 16)                1856      \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 1)                 17        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 1,873\n",
            "Trainable params: 1,873\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 64,
      "metadata": {
        "id": "uB7HLyOxMhMY",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "outputId": "5d336aac-4972-4556-a607-4b5aa9d33aab"
      },
      "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": "code",
      "execution_count": null,
      "metadata": {
        "id": "cN3npbbBQp_l"
      },
      "outputs": [],
      "source": []
    }
  ],
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}