CNN_transfer_learning.ipynb

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      "name": "python"
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  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# CNN"
      ],
      "metadata": {
        "id": "vEv53BZ7Gyvh"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Importing libraries"
      ],
      "metadata": {
        "id": "v-g6rdyLG0kw"
      }
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "K8JTir6CF71Y"
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      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt\n",
        "plt.style.use('ggplot')\n",
        "plt.rcParams['figure.figsize'] = (15,4)\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import plotly\n",
        "import plotly.graph_objs as go\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.preprocessing import sequence\n",
        "from tensorflow.keras.layers import Dense\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.optimizers import RMSprop\n",
        "from tensorflow.keras.metrics import mean_squared_error, mean_absolute_error\n",
        "from plotly.offline import init_notebook_mode, iplot"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Reading data"
      ],
      "metadata": {
        "id": "pk4VtkPPG3g4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X_train = pd.read_csv('X_train_complete.csv', index_col=['date'])\n",
        "y_train = pd.read_csv('y_train_complete.csv', index_col=['date'])\n",
        "\n",
        "X_test = pd.read_csv('X_test.csv', index_col=['date'])\n",
        "y_test = pd.read_csv('y_test.csv', index_col=['date'])"
      ],
      "metadata": {
        "id": "8W06miZZGHKQ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_data = pd.concat([X_train, y_train], axis=1)\n",
        "test_data = pd.concat([X_test, y_test], axis=1)"
      ],
      "metadata": {
        "id": "t53Tt9XpHjfy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_data = pd.concat([train_data, test_data], axis=0)"
      ],
      "metadata": {
        "id": "uxvLVvKZt65w"
      },
      "execution_count": null,
      "outputs": []
    },
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      "source": [
        "full_data"
      ],
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        "outputId": "bb0a531f-3d68-4ca2-eb38-922523b29ca1"
      },
      "execution_count": null,
      "outputs": [
        {
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              "            maximum_atmospheric_pressure  minimum_atmospheric_pressure  \\\n",
              "date                                                                     \n",
              "2017-01-01                    617.799658                    599.803362   \n",
              "2017-01-02                    620.147296                    599.795100   \n",
              "2017-01-03                    620.212662                    599.817608   \n",
              "2017-01-04                    616.165437                    599.787975   \n",
              "2017-01-05                    619.378317                    599.802992   \n",
              "...                                  ...                           ...   \n",
              "2017-11-25                    617.598033                    599.774778   \n",
              "2017-11-26                    618.306160                    599.760990   \n",
              "2017-11-27                    618.387980                    599.769210   \n",
              "2017-11-28                    615.830622                    599.787011   \n",
              "2017-11-29                    617.115633                    599.775633   \n",
              "\n",
              "            precipitation  maximum_air_temperature  minimum_air_temperature  \\\n",
              "date                                                                          \n",
              "2017-01-01       0.000000                30.318042                28.722792   \n",
              "2017-01-02       0.000000                29.630875                28.269417   \n",
              "2017-01-03       0.000000                29.409917                28.005583   \n",
              "2017-01-04       0.105833                29.755875                28.265958   \n",
              "2017-01-05       0.000000                28.977792                27.437292   \n",
              "...                   ...                      ...                      ...   \n",
              "2017-11-25       0.000000                26.162222                25.260667   \n",
              "2017-11-26       0.000000                25.160600                24.471200   \n",
              "2017-11-27       0.000000                26.168600                25.124500   \n",
              "2017-11-28       0.000000                26.670000                25.588444   \n",
              "2017-11-29       0.000000                27.488417                26.432167   \n",
              "\n",
              "            maximum_relative_humidity  minimum_relative_humidity  \\\n",
              "date                                                               \n",
              "2017-01-01                  66.477167                  60.174625   \n",
              "2017-01-02                  67.488500                  61.223083   \n",
              "2017-01-03                  70.001792                  64.124500   \n",
              "2017-01-04                  69.879833                  63.227792   \n",
              "2017-01-05                  70.608750                  64.299542   \n",
              "...                               ...                        ...   \n",
              "2017-11-25                  77.043111                  72.860000   \n",
              "2017-11-26                  80.193500                  75.632100   \n",
              "2017-11-27                  79.476900                  73.358900   \n",
              "2017-11-28                  74.379444                  68.880667   \n",
              "2017-11-29                  72.656833                  67.606917   \n",
              "\n",
              "            total_solar_radiation  maximum_temperature  minimum_temperature  \\\n",
              "date                                                                          \n",
              "2017-01-01            2145.823333            21.408342            20.312011   \n",
              "2017-01-02            1951.849667            21.264383            20.061859   \n",
              "2017-01-03            2104.909208            21.635286            20.695279   \n",
              "2017-01-04            1963.737875            21.848865            20.813229   \n",
              "2017-01-05            2001.953125            21.344600            20.438310   \n",
              "...                           ...                  ...                  ...   \n",
              "2017-11-25            2500.997667            21.037083            20.542369   \n",
              "2017-11-26            2459.780530            20.800224            20.227300   \n",
              "2017-11-27            2507.158000            21.240160            20.642043   \n",
              "2017-11-28            2315.237867            20.622838            20.113804   \n",
              "2017-11-29            2247.963342            21.044991            20.470040   \n",
              "\n",
              "            average_air_temperature  average_relative_humidity  wind_speed  \\\n",
              "date                                                                         \n",
              "2017-01-01                29.543242                  63.174958    1.132543   \n",
              "2017-01-02                28.910676                  64.436735    1.087624   \n",
              "2017-01-03                28.650169                  67.002696    1.630787   \n",
              "2017-01-04                28.881102                  66.914866    1.921033   \n",
              "2017-01-05                28.167198                  67.434008    1.197141   \n",
              "...                             ...                        ...         ...   \n",
              "2017-11-25                25.666700                  75.260879    1.739529   \n",
              "2017-11-26                24.812676                  77.932516    1.118328   \n",
              "2017-11-27                25.678424                  76.354913    1.606195   \n",
              "2017-11-28                26.121304                  71.750116    1.845503   \n",
              "2017-11-29                26.989442                  70.110090    1.822953   \n",
              "\n",
              "            total_solar_radiation.1  reference_evapotranspiration  \n",
              "date                                                               \n",
              "2017-01-01                 5.960620                      0.058537  \n",
              "2017-01-02                 5.421805                      0.048532  \n",
              "2017-01-03                 5.846970                      0.064639  \n",
              "2017-01-04                 5.454827                      0.072362  \n",
              "2017-01-05                 5.560981                      0.048342  \n",
              "...                             ...                           ...  \n",
              "2017-11-25                 6.947216                      0.035565  \n",
              "2017-11-26                 7.850408                      0.025799  \n",
              "2017-11-27                 7.510718                      0.034958  \n",
              "2017-11-28                 8.251289                      0.042553  \n",
              "2017-11-29                 7.229652                      0.049749  \n",
              "\n",
              "[334 rows x 15 columns]"
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              "      <th>maximum_atmospheric_pressure</th>\n",
              "      <th>minimum_atmospheric_pressure</th>\n",
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              "      <th>maximum_air_temperature</th>\n",
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              "      <th>average_relative_humidity</th>\n",
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              "      <th>2017-01-02</th>\n",
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              "      <th>2017-01-03</th>\n",
              "      <td>620.212662</td>\n",
              "      <td>599.817608</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>29.409917</td>\n",
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              "      <td>70.001792</td>\n",
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              "      <td>2104.909208</td>\n",
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              "      <td>5.846970</td>\n",
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              "      <th>2017-01-04</th>\n",
              "      <td>616.165437</td>\n",
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              "      <td>0.105833</td>\n",
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              "      <th>2017-01-05</th>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-58e54f18-e047-4e75-bfdc-3838f68cf171 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-58e54f18-e047-4e75-bfdc-3838f68cf171');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "BATCH_SIZE = 32\n",
        "N_INPUT = len(X_train)\n",
        "N_FEATURES = len(X_train.columns)"
      ],
      "metadata": {
        "id": "xYj4qPQvIA_y"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "trace0 = go.Scatter(\n",
        "    x = train_data.index,\n",
        "    y = train_data['reference_evapotranspiration'],\n",
        "    name = 'Train'\n",
        ")\n",
        "\n",
        "trace1 = go.Scatter(\n",
        "    x = test_data.index,\n",
        "    y = test_data['reference_evapotranspiration'],\n",
        "    name = 'Validation'\n",
        ")\n",
        "\n",
        "data = [trace0, trace1]\n",
        "\n",
        "iplot(data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "id": "VM9rb2UVIKYW",
        "outputId": "4c9c2b94-b46c-49c5-f11c-bc5d3a7d4799"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# MASE implemented courtesy of sktime - https://github.com/alan-turing-institute/sktime/blob/ee7a06843a44f4aaec7582d847e36073a9ab0566/sktime/performance_metrics/forecasting/_functions.py#L16\n",
        "def mean_absolute_scaled_error(y_true, y_pred):\n",
        "  \"\"\"\n",
        "  Implement MASE (assuming no seasonality of data).\n",
        "  \"\"\"\n",
        "  mae = tf.reduce_mean(tf.abs(y_true - y_pred))\n",
        "\n",
        "  # Find MAE of naive forecast (no seasonality)\n",
        "  mae_naive_no_season = tf.reduce_mean(tf.abs(y_true[1:] - y_true[:-1])) # our seasonality is 1 day (hence the shifting of 1 day)\n",
        "\n",
        "  return mae / mae_naive_no_season"
      ],
      "metadata": {
        "id": "bQL4XrseThXh"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def evaluate_preds(y_true, y_pred):\n",
        "  # Make sure float32 (for metric calculations)\n",
        "  y_true = tf.cast(y_true, dtype=tf.float32)\n",
        "  y_pred = tf.cast(y_pred, dtype=tf.float32)\n",
        "\n",
        "  # Calculate various metrics\n",
        "  mae = tf.keras.metrics.mean_absolute_error(y_true, y_pred)\n",
        "  mse = tf.keras.metrics.mean_squared_error(y_true, y_pred) # puts and emphasis on outliers (all errors get squared)\n",
        "  rmse = tf.sqrt(mse)\n",
        "  mape = tf.keras.metrics.mean_absolute_percentage_error(y_true, y_pred)\n",
        "  mase = mean_absolute_scaled_error(y_true, y_pred)\n",
        "  \n",
        "  return {\"mae\": mae.numpy(),\n",
        "          \"mse\": mse.numpy(),\n",
        "          \"rmse\": rmse.numpy(),\n",
        "          \"mape\": mape.numpy(),\n",
        "          \"mase\": mase.numpy()}"
      ],
      "metadata": {
        "id": "liGXvTgCTqqX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "HORIZON = 1\n",
        "WINDOW_SIZE = 7"
      ],
      "metadata": {
        "id": "sxcnaKy3IytN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create function to label windowed data\n",
        "def get_labelled_windows(x, horizon=1):\n",
        "  \"\"\"\n",
        "  Creates labels for windowed dataset.\n",
        "\n",
        "  E.g. if horizon=1 (default)\n",
        "  Input: [1, 2, 3, 4, 5, 6] -> Output: ([1, 2, 3, 4, 5], [6])\n",
        "  \"\"\"\n",
        "  return x[:, :-horizon], x[:, -horizon:]"
      ],
      "metadata": {
        "id": "ofnz0UGAT9Gq"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Test out the window labelling function\n",
        "test_window, test_label = get_labelled_windows(tf.expand_dims(tf.range(8)+1, axis=0), horizon=HORIZON)\n",
        "print(f\"Window: {tf.squeeze(test_window).numpy()} -> Label: {tf.squeeze(test_label).numpy()}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g3wSzsGMUGIn",
        "outputId": "e8a856e4-a21f-4433-9cab-d367884e6433"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Window: [1 2 3 4 5 6 7] -> Label: 8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create function to view NumPy arrays as windows \n",
        "def make_windows(x, window_size=7, horizon=1):\n",
        "  \"\"\"\n",
        "  Turns a 1D array into a 2D array of sequential windows of window_size.\n",
        "  \"\"\"\n",
        "  # 1. Create a window of specific window_size (add the horizon on the end for later labelling)\n",
        "  window_step = np.expand_dims(np.arange(window_size+horizon), axis=0)\n",
        "  # print(f\"Window step:\\n {window_step}\")\n",
        "\n",
        "  # 2. Create a 2D array of multiple window steps (minus 1 to account for 0 indexing)\n",
        "  window_indexes = window_step + np.expand_dims(np.arange(len(x)-(window_size+horizon-1)), axis=0).T # create 2D array of windows of size window_size\n",
        "  # print(f\"Window indexes:\\n {window_indexes[:3], window_indexes[-3:], window_indexes.shape}\")\n",
        "\n",
        "  # 3. Index on the target array (time series) with 2D array of multiple window steps\n",
        "  windowed_array = x[window_indexes]\n",
        "\n",
        "  # 4. Get the labelled windows\n",
        "  windows, labels = get_labelled_windows(windowed_array, horizon=horizon)\n",
        "\n",
        "  return windows, labels"
      ],
      "metadata": {
        "id": "sBhd44llUOr1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "timesteps = full_data.index.to_numpy()\n",
        "re = full_data[\"reference_evapotranspiration\"].to_numpy()"
      ],
      "metadata": {
        "id": "Ih3CbJQhthFg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "full_windows, full_labels = make_windows(re, window_size=WINDOW_SIZE, horizon=HORIZON)\n",
        "len(full_windows), len(full_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xODqe4yoUiYs",
        "outputId": "b6d799fc-7d03-46eb-a6a3-0aaf529d2fdc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(327, 327)"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# View the first 3 windows/labels\n",
        "for i in range(3):\n",
        "  print(f\"Window: {full_windows[i]} -> Label: {full_labels[i]}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4zDOooXSwI6c",
        "outputId": "a20f8e1e-ba04-4c7f-c2e2-f0d3797ee885"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Window: [0.05853737 0.04853203 0.06463882 0.0723622  0.04834153 0.0520071\n",
            " 0.04217207] -> Label: [0.05636294]\n",
            "Window: [0.04853203 0.06463882 0.0723622  0.04834153 0.0520071  0.04217207\n",
            " 0.05636294] -> Label: [0.04979837]\n",
            "Window: [0.06463882 0.0723622  0.04834153 0.0520071  0.04217207 0.05636294\n",
            " 0.04979837] -> Label: [0.04917493]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Make the train/test splits\n",
        "def make_train_test_splits(windows, labels, test_split=0.2):\n",
        "  \"\"\"\n",
        "  Splits matching pairs of windows and labels into train and test splits.\n",
        "  \"\"\"\n",
        "  split_size = int(len(windows) * (1-test_split)) # this will default to 80% train/20% test\n",
        "  train_windows = windows[:split_size]\n",
        "  train_labels = labels[:split_size]\n",
        "  test_windows = windows[split_size:]\n",
        "  test_labels = labels[split_size:]\n",
        "  return train_windows, test_windows, train_labels, test_labels"
      ],
      "metadata": {
        "id": "9T0JL3ufwcfi"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_windows, test_windows, train_labels, test_labels = make_train_test_splits(full_windows, full_labels)\n",
        "len(train_windows), len(test_windows), len(train_labels), len(test_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VS7G_Geuwj6A",
        "outputId": "a11eda19-96b9-4047-fe52-77fb9da360d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(261, 66, 261, 66)"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import os\n",
        "\n",
        "# Create a function to implement a ModelCheckpoint callback with a specific filename \n",
        "def create_model_checkpoint(model_name, save_path=\"model_experiments\"):\n",
        "  return tf.keras.callbacks.ModelCheckpoint(filepath=os.path.join(save_path, model_name), # create filepath to save model\n",
        "                                            verbose=0, # only output a limited amount of text\n",
        "                                            save_best_only=True) # save only the best model to file"
      ],
      "metadata": {
        "id": "ibvLN0ltx3hT"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_windows.shape, train_labels.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lDY4n05n2lrG",
        "outputId": "abef980c-1609-4987-fa26-bfb5ce4e334e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((261, 7), (261, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "early_stopping = tf.keras.callbacks.EarlyStopping(monitor = 'val_loss', mode = 'min',\n",
        "                                                  verbose = 1, patience = 20)\n",
        "\n",
        "reduce_lr = tf.keras.callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2,\n",
        "                                                 patience=5, min_lr=0.00001)"
      ],
      "metadata": {
        "id": "dlMoXRsJHEFW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.layers import Dense, LSTM, Dropout, Bidirectional, Conv1D\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import layers"
      ],
      "metadata": {
        "id": "FYQ6HeQHsAwR"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "# Create model\n",
        "model = tf.keras.Sequential([\n",
        "  # Create Lambda layer to reshape inputs, without this layer, the model will error\n",
        "  layers.Lambda(lambda x: tf.expand_dims(x, axis=1)), # resize the inputs to adjust for window size / Conv1D 3D input requirements\n",
        "  layers.Conv1D(filters=64, kernel_size=5, padding=\"causal\", activation=\"relu\"),\n",
        "  layers.Dense(HORIZON)\n",
        "], name=\"model_conv1D\")\n",
        "\n",
        "rmse = tf.keras.metrics.RootMeanSquaredError()\n",
        "\n",
        "# Compile model\n",
        "model.compile(loss=\"mean_squared_error\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[rmse,'mae'])\n",
        "\n",
        "history_1 = model.fit(train_windows,\n",
        "            train_labels,\n",
        "            epochs=100,\n",
        "            verbose=1,\n",
        "            batch_size=128,\n",
        "            validation_data=(test_windows, test_labels),\n",
        "            callbacks=[create_model_checkpoint(model_name=model.name)])"
      ],
      "metadata": {
        "id": "Jbd4_J3XI1jq",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1fd95cbe-bdf3-42fd-edb9-3174b84b678f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/3 [=========>....................] - ETA: 1s - loss: 0.0019 - root_mean_squared_error: 0.0433 - mae: 0.0388INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 439ms/step - loss: 0.0016 - root_mean_squared_error: 0.0401 - mae: 0.0354 - val_loss: 0.0031 - val_root_mean_squared_error: 0.0553 - val_mae: 0.0535\n",
            "Epoch 2/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 7.6929e-04 - root_mean_squared_error: 0.0277 - mae: 0.0222INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 348ms/step - loss: 6.2377e-04 - root_mean_squared_error: 0.0250 - mae: 0.0196 - val_loss: 0.0013 - val_root_mean_squared_error: 0.0364 - val_mae: 0.0342\n",
            "Epoch 3/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 2.8430e-04 - root_mean_squared_error: 0.0169 - mae: 0.0137INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 359ms/step - loss: 2.7012e-04 - root_mean_squared_error: 0.0164 - mae: 0.0135 - val_loss: 5.6635e-04 - val_root_mean_squared_error: 0.0238 - val_mae: 0.0218\n",
            "Epoch 4/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 3.0629e-04 - root_mean_squared_error: 0.0175 - mae: 0.0149INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 364ms/step - loss: 2.8138e-04 - root_mean_squared_error: 0.0168 - mae: 0.0140 - val_loss: 3.7687e-04 - val_root_mean_squared_error: 0.0194 - val_mae: 0.0174\n",
            "Epoch 5/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 3.6055e-04 - root_mean_squared_error: 0.0190 - mae: 0.0161INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 373ms/step - loss: 3.0947e-04 - root_mean_squared_error: 0.0176 - mae: 0.0147 - val_loss: 3.7537e-04 - val_root_mean_squared_error: 0.0194 - val_mae: 0.0174\n",
            "Epoch 6/100\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 2.7110e-04 - root_mean_squared_error: 0.0165 - mae: 0.0137 - val_loss: 4.9706e-04 - val_root_mean_squared_error: 0.0223 - val_mae: 0.0203\n",
            "Epoch 7/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 2.1824e-04 - root_mean_squared_error: 0.0148 - mae: 0.0121 - val_loss: 6.4359e-04 - val_root_mean_squared_error: 0.0254 - val_mae: 0.0233\n",
            "Epoch 8/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 2.0142e-04 - root_mean_squared_error: 0.0142 - mae: 0.0114 - val_loss: 7.3800e-04 - val_root_mean_squared_error: 0.0272 - val_mae: 0.0251\n",
            "Epoch 9/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 2.0363e-04 - root_mean_squared_error: 0.0143 - mae: 0.0114 - val_loss: 7.5911e-04 - val_root_mean_squared_error: 0.0276 - val_mae: 0.0255\n",
            "Epoch 10/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 2.0301e-04 - root_mean_squared_error: 0.0142 - mae: 0.0113 - val_loss: 6.8546e-04 - val_root_mean_squared_error: 0.0262 - val_mae: 0.0242\n",
            "Epoch 11/100\n",
            "3/3 [==============================] - 0s 26ms/step - loss: 1.9263e-04 - root_mean_squared_error: 0.0139 - mae: 0.0110 - val_loss: 5.4754e-04 - val_root_mean_squared_error: 0.0234 - val_mae: 0.0214\n",
            "Epoch 12/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.8364e-04 - root_mean_squared_error: 0.0136 - mae: 0.0108 - val_loss: 4.5254e-04 - val_root_mean_squared_error: 0.0213 - val_mae: 0.0193\n",
            "Epoch 13/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.8257e-04 - root_mean_squared_error: 0.0135 - mae: 0.0108 - val_loss: 4.0998e-04 - val_root_mean_squared_error: 0.0202 - val_mae: 0.0183\n",
            "Epoch 14/100\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 1.7976e-04 - root_mean_squared_error: 0.0134 - mae: 0.0107 - val_loss: 4.0414e-04 - val_root_mean_squared_error: 0.0201 - val_mae: 0.0181\n",
            "Epoch 15/100\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.7373e-04 - root_mean_squared_error: 0.0132 - mae: 0.0105 - val_loss: 4.1534e-04 - val_root_mean_squared_error: 0.0204 - val_mae: 0.0184\n",
            "Epoch 16/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.6802e-04 - root_mean_squared_error: 0.0130 - mae: 0.0103 - val_loss: 4.3198e-04 - val_root_mean_squared_error: 0.0208 - val_mae: 0.0188\n",
            "Epoch 17/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.6343e-04 - root_mean_squared_error: 0.0128 - mae: 0.0101 - val_loss: 4.6556e-04 - val_root_mean_squared_error: 0.0216 - val_mae: 0.0196\n",
            "Epoch 18/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.6350e-04 - root_mean_squared_error: 0.0128 - mae: 0.0100 - val_loss: 4.4733e-04 - val_root_mean_squared_error: 0.0212 - val_mae: 0.0192\n",
            "Epoch 19/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.6511e-04 - root_mean_squared_error: 0.0128 - mae: 0.0103INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 387ms/step - loss: 1.5776e-04 - root_mean_squared_error: 0.0126 - mae: 0.0099 - val_loss: 3.4781e-04 - val_root_mean_squared_error: 0.0186 - val_mae: 0.0166\n",
            "Epoch 20/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.6119e-04 - root_mean_squared_error: 0.0127 - mae: 0.0103INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 614ms/step - loss: 1.5612e-04 - root_mean_squared_error: 0.0125 - mae: 0.0099 - val_loss: 2.8229e-04 - val_root_mean_squared_error: 0.0168 - val_mae: 0.0146\n",
            "Epoch 21/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.4296e-04 - root_mean_squared_error: 0.0120 - mae: 0.0095INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 383ms/step - loss: 1.6054e-04 - root_mean_squared_error: 0.0127 - mae: 0.0100 - val_loss: 2.5235e-04 - val_root_mean_squared_error: 0.0159 - val_mae: 0.0137\n",
            "Epoch 22/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.6136e-04 - root_mean_squared_error: 0.0127 - mae: 0.0100 - val_loss: 2.5624e-04 - val_root_mean_squared_error: 0.0160 - val_mae: 0.0138\n",
            "Epoch 23/100\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.5409e-04 - root_mean_squared_error: 0.0124 - mae: 0.0098 - val_loss: 2.9854e-04 - val_root_mean_squared_error: 0.0173 - val_mae: 0.0152\n",
            "Epoch 24/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.4470e-04 - root_mean_squared_error: 0.0120 - mae: 0.0094 - val_loss: 3.5960e-04 - val_root_mean_squared_error: 0.0190 - val_mae: 0.0169\n",
            "Epoch 25/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.4812e-04 - root_mean_squared_error: 0.0122 - mae: 0.0096 - val_loss: 4.0900e-04 - val_root_mean_squared_error: 0.0202 - val_mae: 0.0182\n",
            "Epoch 26/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.5307e-04 - root_mean_squared_error: 0.0124 - mae: 0.0097 - val_loss: 3.5331e-04 - val_root_mean_squared_error: 0.0188 - val_mae: 0.0167\n",
            "Epoch 27/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.7086e-04 - root_mean_squared_error: 0.0131 - mae: 0.0099INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 380ms/step - loss: 1.4250e-04 - root_mean_squared_error: 0.0119 - mae: 0.0093 - val_loss: 2.4458e-04 - val_root_mean_squared_error: 0.0156 - val_mae: 0.0134\n",
            "Epoch 28/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.1123e-04 - root_mean_squared_error: 0.0105 - mae: 0.0083INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 394ms/step - loss: 1.4007e-04 - root_mean_squared_error: 0.0118 - mae: 0.0092 - val_loss: 1.7708e-04 - val_root_mean_squared_error: 0.0133 - val_mae: 0.0109\n",
            "Epoch 29/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.3355e-04 - root_mean_squared_error: 0.0116 - mae: 0.0091INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 369ms/step - loss: 1.5696e-04 - root_mean_squared_error: 0.0125 - mae: 0.0097 - val_loss: 1.6015e-04 - val_root_mean_squared_error: 0.0127 - val_mae: 0.0100\n",
            "Epoch 30/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.6537e-04 - root_mean_squared_error: 0.0129 - mae: 0.0101 - val_loss: 1.7148e-04 - val_root_mean_squared_error: 0.0131 - val_mae: 0.0106\n",
            "Epoch 31/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.4543e-04 - root_mean_squared_error: 0.0121 - mae: 0.0093 - val_loss: 2.2925e-04 - val_root_mean_squared_error: 0.0151 - val_mae: 0.0129\n",
            "Epoch 32/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.3297e-04 - root_mean_squared_error: 0.0115 - mae: 0.0090 - val_loss: 3.0017e-04 - val_root_mean_squared_error: 0.0173 - val_mae: 0.0152\n",
            "Epoch 33/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.4096e-04 - root_mean_squared_error: 0.0119 - mae: 0.0093 - val_loss: 3.2847e-04 - val_root_mean_squared_error: 0.0181 - val_mae: 0.0160\n",
            "Epoch 34/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.4646e-04 - root_mean_squared_error: 0.0121 - mae: 0.0095 - val_loss: 2.9354e-04 - val_root_mean_squared_error: 0.0171 - val_mae: 0.0150\n",
            "Epoch 35/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.3784e-04 - root_mean_squared_error: 0.0117 - mae: 0.0091 - val_loss: 2.2756e-04 - val_root_mean_squared_error: 0.0151 - val_mae: 0.0129\n",
            "Epoch 36/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.2854e-04 - root_mean_squared_error: 0.0113 - mae: 0.0088 - val_loss: 2.0605e-04 - val_root_mean_squared_error: 0.0144 - val_mae: 0.0121\n",
            "Epoch 37/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.2818e-04 - root_mean_squared_error: 0.0113 - mae: 0.0088 - val_loss: 2.1165e-04 - val_root_mean_squared_error: 0.0145 - val_mae: 0.0123\n",
            "Epoch 38/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.2711e-04 - root_mean_squared_error: 0.0113 - mae: 0.0087 - val_loss: 2.2675e-04 - val_root_mean_squared_error: 0.0151 - val_mae: 0.0128\n",
            "Epoch 39/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.2802e-04 - root_mean_squared_error: 0.0113 - mae: 0.0088 - val_loss: 2.3712e-04 - val_root_mean_squared_error: 0.0154 - val_mae: 0.0132\n",
            "Epoch 40/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.2857e-04 - root_mean_squared_error: 0.0113 - mae: 0.0088 - val_loss: 2.2670e-04 - val_root_mean_squared_error: 0.0151 - val_mae: 0.0128\n",
            "Epoch 41/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.2713e-04 - root_mean_squared_error: 0.0113 - mae: 0.0087 - val_loss: 1.9928e-04 - val_root_mean_squared_error: 0.0141 - val_mae: 0.0119\n",
            "Epoch 42/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.2470e-04 - root_mean_squared_error: 0.0112 - mae: 0.0086 - val_loss: 1.7170e-04 - val_root_mean_squared_error: 0.0131 - val_mae: 0.0107\n",
            "Epoch 43/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.2753e-04 - root_mean_squared_error: 0.0113 - mae: 0.0087 - val_loss: 1.6650e-04 - val_root_mean_squared_error: 0.0129 - val_mae: 0.0104\n",
            "Epoch 44/100\n",
            "3/3 [==============================] - 0s 21ms/step - loss: 1.2818e-04 - root_mean_squared_error: 0.0113 - mae: 0.0087 - val_loss: 1.6670e-04 - val_root_mean_squared_error: 0.0129 - val_mae: 0.0104\n",
            "Epoch 45/100\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.2640e-04 - root_mean_squared_error: 0.0112 - mae: 0.0086 - val_loss: 1.7761e-04 - val_root_mean_squared_error: 0.0133 - val_mae: 0.0110\n",
            "Epoch 46/100\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 1.2305e-04 - root_mean_squared_error: 0.0111 - mae: 0.0085 - val_loss: 2.0293e-04 - val_root_mean_squared_error: 0.0142 - val_mae: 0.0120\n",
            "Epoch 47/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.2416e-04 - root_mean_squared_error: 0.0111 - mae: 0.0086 - val_loss: 2.2465e-04 - val_root_mean_squared_error: 0.0150 - val_mae: 0.0128\n",
            "Epoch 48/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.2800e-04 - root_mean_squared_error: 0.0113 - mae: 0.0088 - val_loss: 2.2715e-04 - val_root_mean_squared_error: 0.0151 - val_mae: 0.0129\n",
            "Epoch 49/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.2771e-04 - root_mean_squared_error: 0.0113 - mae: 0.0088 - val_loss: 2.0145e-04 - val_root_mean_squared_error: 0.0142 - val_mae: 0.0120\n",
            "Epoch 50/100\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 1.2202e-04 - root_mean_squared_error: 0.0110 - mae: 0.0085 - val_loss: 1.7246e-04 - val_root_mean_squared_error: 0.0131 - val_mae: 0.0107\n",
            "Epoch 51/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.2119e-04 - root_mean_squared_error: 0.0110 - mae: 0.0084 - val_loss: 1.6281e-04 - val_root_mean_squared_error: 0.0128 - val_mae: 0.0103\n",
            "Epoch 52/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.0094e-04 - root_mean_squared_error: 0.0100 - mae: 0.0079INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 373ms/step - loss: 1.2208e-04 - root_mean_squared_error: 0.0110 - mae: 0.0085 - val_loss: 1.5999e-04 - val_root_mean_squared_error: 0.0126 - val_mae: 0.0102\n",
            "Epoch 53/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.2279e-04 - root_mean_squared_error: 0.0111 - mae: 0.0085 - val_loss: 1.6125e-04 - val_root_mean_squared_error: 0.0127 - val_mae: 0.0102\n",
            "Epoch 54/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.2125e-04 - root_mean_squared_error: 0.0110 - mae: 0.0084 - val_loss: 1.6676e-04 - val_root_mean_squared_error: 0.0129 - val_mae: 0.0105\n",
            "Epoch 55/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.3250e-04 - root_mean_squared_error: 0.0115 - mae: 0.0085INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 356ms/step - loss: 1.2040e-04 - root_mean_squared_error: 0.0110 - mae: 0.0084 - val_loss: 1.5833e-04 - val_root_mean_squared_error: 0.0126 - val_mae: 0.0101\n",
            "Epoch 56/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.2794e-04 - root_mean_squared_error: 0.0113 - mae: 0.0090INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 357ms/step - loss: 1.2130e-04 - root_mean_squared_error: 0.0110 - mae: 0.0084 - val_loss: 1.5571e-04 - val_root_mean_squared_error: 0.0125 - val_mae: 0.0099\n",
            "Epoch 57/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.1283e-04 - root_mean_squared_error: 0.0106 - mae: 0.0083INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 374ms/step - loss: 1.2148e-04 - root_mean_squared_error: 0.0110 - mae: 0.0084 - val_loss: 1.5554e-04 - val_root_mean_squared_error: 0.0125 - val_mae: 0.0099\n",
            "Epoch 58/100\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.2071e-04 - root_mean_squared_error: 0.0110 - mae: 0.0084 - val_loss: 1.6097e-04 - val_root_mean_squared_error: 0.0127 - val_mae: 0.0102\n",
            "Epoch 59/100\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 1.1891e-04 - root_mean_squared_error: 0.0109 - mae: 0.0083 - val_loss: 1.6473e-04 - val_root_mean_squared_error: 0.0128 - val_mae: 0.0104\n",
            "Epoch 60/100\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 1.1801e-04 - root_mean_squared_error: 0.0109 - mae: 0.0083 - val_loss: 1.6960e-04 - val_root_mean_squared_error: 0.0130 - val_mae: 0.0106\n",
            "Epoch 61/100\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 1.1760e-04 - root_mean_squared_error: 0.0108 - mae: 0.0083 - val_loss: 1.7337e-04 - val_root_mean_squared_error: 0.0132 - val_mae: 0.0108\n",
            "Epoch 62/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1730e-04 - root_mean_squared_error: 0.0108 - mae: 0.0083 - val_loss: 1.7659e-04 - val_root_mean_squared_error: 0.0133 - val_mae: 0.0110\n",
            "Epoch 63/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1799e-04 - root_mean_squared_error: 0.0109 - mae: 0.0083 - val_loss: 1.7191e-04 - val_root_mean_squared_error: 0.0131 - val_mae: 0.0108\n",
            "Epoch 64/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.1098e-04 - root_mean_squared_error: 0.0105 - mae: 0.0078INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 387ms/step - loss: 1.1680e-04 - root_mean_squared_error: 0.0108 - mae: 0.0083 - val_loss: 1.5424e-04 - val_root_mean_squared_error: 0.0124 - val_mae: 0.0099\n",
            "Epoch 65/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.1958e-04 - root_mean_squared_error: 0.0109 - mae: 0.0088INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 369ms/step - loss: 1.1763e-04 - root_mean_squared_error: 0.0108 - mae: 0.0083 - val_loss: 1.4877e-04 - val_root_mean_squared_error: 0.0122 - val_mae: 0.0096\n",
            "Epoch 66/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.1875e-04 - root_mean_squared_error: 0.0109 - mae: 0.0083 - val_loss: 1.5078e-04 - val_root_mean_squared_error: 0.0123 - val_mae: 0.0097\n",
            "Epoch 67/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1721e-04 - root_mean_squared_error: 0.0108 - mae: 0.0083 - val_loss: 1.5723e-04 - val_root_mean_squared_error: 0.0125 - val_mae: 0.0101\n",
            "Epoch 68/100\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.1660e-04 - root_mean_squared_error: 0.0108 - mae: 0.0082 - val_loss: 1.5919e-04 - val_root_mean_squared_error: 0.0126 - val_mae: 0.0102\n",
            "Epoch 69/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.1551e-04 - root_mean_squared_error: 0.0107 - mae: 0.0082 - val_loss: 1.8760e-04 - val_root_mean_squared_error: 0.0137 - val_mae: 0.0114\n",
            "Epoch 70/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.2014e-04 - root_mean_squared_error: 0.0110 - mae: 0.0085 - val_loss: 2.0717e-04 - val_root_mean_squared_error: 0.0144 - val_mae: 0.0122\n",
            "Epoch 71/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.2307e-04 - root_mean_squared_error: 0.0111 - mae: 0.0087 - val_loss: 1.8817e-04 - val_root_mean_squared_error: 0.0137 - val_mae: 0.0115\n",
            "Epoch 72/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1721e-04 - root_mean_squared_error: 0.0108 - mae: 0.0083 - val_loss: 1.6261e-04 - val_root_mean_squared_error: 0.0128 - val_mae: 0.0104\n",
            "Epoch 73/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.1401e-04 - root_mean_squared_error: 0.0107 - mae: 0.0082 - val_loss: 1.5030e-04 - val_root_mean_squared_error: 0.0123 - val_mae: 0.0097\n",
            "Epoch 74/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 9.7861e-05 - root_mean_squared_error: 0.0099 - mae: 0.0075INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 368ms/step - loss: 1.1420e-04 - root_mean_squared_error: 0.0107 - mae: 0.0081 - val_loss: 1.4298e-04 - val_root_mean_squared_error: 0.0120 - val_mae: 0.0093\n",
            "Epoch 75/100\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 1.1634e-04 - root_mean_squared_error: 0.0108 - mae: 0.0082 - val_loss: 1.4317e-04 - val_root_mean_squared_error: 0.0120 - val_mae: 0.0093\n",
            "Epoch 76/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.1465e-04 - root_mean_squared_error: 0.0107 - mae: 0.0082 - val_loss: 1.5212e-04 - val_root_mean_squared_error: 0.0123 - val_mae: 0.0099\n",
            "Epoch 77/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.1281e-04 - root_mean_squared_error: 0.0106 - mae: 0.0081 - val_loss: 1.5471e-04 - val_root_mean_squared_error: 0.0124 - val_mae: 0.0100\n",
            "Epoch 78/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.1256e-04 - root_mean_squared_error: 0.0106 - mae: 0.0081 - val_loss: 1.4869e-04 - val_root_mean_squared_error: 0.0122 - val_mae: 0.0097\n",
            "Epoch 79/100\n",
            "3/3 [==============================] - 0s 28ms/step - loss: 1.1247e-04 - root_mean_squared_error: 0.0106 - mae: 0.0081 - val_loss: 1.4381e-04 - val_root_mean_squared_error: 0.0120 - val_mae: 0.0094\n",
            "Epoch 80/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.0624e-04 - root_mean_squared_error: 0.0103 - mae: 0.0081INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 361ms/step - loss: 1.1363e-04 - root_mean_squared_error: 0.0107 - mae: 0.0081 - val_loss: 1.3847e-04 - val_root_mean_squared_error: 0.0118 - val_mae: 0.0090\n",
            "Epoch 81/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1523e-04 - root_mean_squared_error: 0.0107 - mae: 0.0082 - val_loss: 1.4403e-04 - val_root_mean_squared_error: 0.0120 - val_mae: 0.0094\n",
            "Epoch 82/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.1220e-04 - root_mean_squared_error: 0.0106 - mae: 0.0081 - val_loss: 1.7690e-04 - val_root_mean_squared_error: 0.0133 - val_mae: 0.0110\n",
            "Epoch 83/100\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 1.1969e-04 - root_mean_squared_error: 0.0109 - mae: 0.0086 - val_loss: 2.2482e-04 - val_root_mean_squared_error: 0.0150 - val_mae: 0.0129\n",
            "Epoch 84/100\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.3506e-04 - root_mean_squared_error: 0.0116 - mae: 0.0092 - val_loss: 1.9677e-04 - val_root_mean_squared_error: 0.0140 - val_mae: 0.0118\n",
            "Epoch 85/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.3168e-04 - root_mean_squared_error: 0.0115 - mae: 0.0090INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 375ms/step - loss: 1.1769e-04 - root_mean_squared_error: 0.0108 - mae: 0.0084 - val_loss: 1.3794e-04 - val_root_mean_squared_error: 0.0117 - val_mae: 0.0091\n",
            "Epoch 86/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.0630e-04 - root_mean_squared_error: 0.0103 - mae: 0.0079INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 355ms/step - loss: 1.1835e-04 - root_mean_squared_error: 0.0109 - mae: 0.0083 - val_loss: 1.3709e-04 - val_root_mean_squared_error: 0.0117 - val_mae: 0.0088\n",
            "Epoch 87/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.2727e-04 - root_mean_squared_error: 0.0113 - mae: 0.0086 - val_loss: 1.4427e-04 - val_root_mean_squared_error: 0.0120 - val_mae: 0.0095\n",
            "Epoch 88/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.1076e-04 - root_mean_squared_error: 0.0105 - mae: 0.0081 - val_loss: 2.0516e-04 - val_root_mean_squared_error: 0.0143 - val_mae: 0.0122\n",
            "Epoch 89/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.2490e-04 - root_mean_squared_error: 0.0112 - mae: 0.0088 - val_loss: 1.9262e-04 - val_root_mean_squared_error: 0.0139 - val_mae: 0.0117\n",
            "Epoch 90/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.1545e-04 - root_mean_squared_error: 0.0107 - mae: 0.0083 - val_loss: 1.4271e-04 - val_root_mean_squared_error: 0.0119 - val_mae: 0.0094\n",
            "Epoch 91/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.2420e-04 - root_mean_squared_error: 0.0111 - mae: 0.0083INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 646ms/step - loss: 1.1035e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.3440e-04 - val_root_mean_squared_error: 0.0116 - val_mae: 0.0087\n",
            "Epoch 92/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.3194e-04 - root_mean_squared_error: 0.0115 - mae: 0.0091INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 366ms/step - loss: 1.2673e-04 - root_mean_squared_error: 0.0113 - mae: 0.0086 - val_loss: 1.3353e-04 - val_root_mean_squared_error: 0.0116 - val_mae: 0.0087\n",
            "Epoch 93/100\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.1804e-04 - root_mean_squared_error: 0.0109 - mae: 0.0083 - val_loss: 1.4494e-04 - val_root_mean_squared_error: 0.0120 - val_mae: 0.0095\n",
            "Epoch 94/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1046e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.7321e-04 - val_root_mean_squared_error: 0.0132 - val_mae: 0.0109\n",
            "Epoch 95/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.1395e-04 - root_mean_squared_error: 0.0107 - mae: 0.0083 - val_loss: 1.5953e-04 - val_root_mean_squared_error: 0.0126 - val_mae: 0.0102\n",
            "Epoch 96/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.0938e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.3490e-04 - val_root_mean_squared_error: 0.0116 - val_mae: 0.0089\n",
            "Epoch 97/100\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 1.1539e-04 - root_mean_squared_error: 0.0107 - mae: 0.0082 - val_loss: 1.3601e-04 - val_root_mean_squared_error: 0.0117 - val_mae: 0.0088\n",
            "Epoch 98/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.0343e-04 - root_mean_squared_error: 0.0102 - mae: 0.0081INFO:tensorflow:Assets written to: model_experiments/model_conv1D/assets\n",
            "3/3 [==============================] - 1s 373ms/step - loss: 1.2451e-04 - root_mean_squared_error: 0.0112 - mae: 0.0086 - val_loss: 1.3296e-04 - val_root_mean_squared_error: 0.0115 - val_mae: 0.0087\n",
            "Epoch 99/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1443e-04 - root_mean_squared_error: 0.0107 - mae: 0.0082 - val_loss: 1.4738e-04 - val_root_mean_squared_error: 0.0121 - val_mae: 0.0097\n",
            "Epoch 100/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.0877e-04 - root_mean_squared_error: 0.0104 - mae: 0.0080 - val_loss: 1.6141e-04 - val_root_mean_squared_error: 0.0127 - val_mae: 0.0103\n",
            "CPU times: user 22.9 s, sys: 607 ms, total: 23.5 s\n",
            "Wall time: 23.6 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sSJWzjkI5ssR",
        "outputId": "4e36f307-ee33-495b-cb2f-a279f85a1289"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"model_conv1D\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lambda_7 (Lambda)           (None, 1, 7)              0         \n",
            "                                                                 \n",
            " conv1d_14 (Conv1D)          (None, 1, 64)             2304      \n",
            "                                                                 \n",
            " dense_16 (Dense)            (None, 1, 1)              65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 2,369\n",
            "Trainable params: 2,369\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(history_1.history['loss'])\n",
        "plt.plot(history_1.history['val_loss'])\n",
        "plt.title('CNN model')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "BnWQyJrvGZwa",
        "outputId": "31e45fcf-31da-4f76-fea3-c00060af8c99"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(history_1.history['mae'])\n",
        "plt.plot(history_1.history['val_mae'])\n",
        "plt.title('CNN model')\n",
        "plt.ylabel('mae')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "eu4JjyIZGasD",
        "outputId": "9edc6ef1-fc2e-49d8-cce7-f854b8d999dd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(history_1.history['root_mean_squared_error'])\n",
        "plt.plot(history_1.history['val_root_mean_squared_error'])\n",
        "plt.title('CNN model')\n",
        "plt.ylabel('rmse')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "kFgbQN1OLSpT",
        "outputId": "3ae57e94-fa62-4c4a-8318-2d9822f90a21"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(test_windows, test_labels)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_lDSzkfGxTdU",
        "outputId": "97a5890a-16f7-47ef-f228-f1877c913937"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 4ms/step - loss: 1.6141e-04 - root_mean_squared_error: 0.0127 - mae: 0.0103\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.00016141239029821008, 0.012704817578196526, 0.010324354283511639]"
            ]
          },
          "metadata": {},
          "execution_count": 178
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.load_model(\"model_experiments/model_conv1D\")\n",
        "model.evaluate(test_windows, test_labels)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zA1b3YmpyWdX",
        "outputId": "2522c1c1-a092-4fa4-df00-196e063c8cfa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 5ms/step - loss: 1.3296e-04 - root_mean_squared_error: 0.0115 - mae: 0.0087\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.00013296424003783613, 0.011531012132763863, 0.008690427988767624]"
            ]
          },
          "metadata": {},
          "execution_count": 179
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def make_preds(model, input_data):\n",
        "  \"\"\"\n",
        "  Uses model to make predictions on input_data.\n",
        "\n",
        "  Parameters\n",
        "  ----------\n",
        "  model: trained model \n",
        "  input_data: windowed input data (same kind of data model was trained on)\n",
        "\n",
        "  Returns model predictions on input_data.\n",
        "  \"\"\"\n",
        "  forecast = model.predict(input_data)\n",
        "  return tf.squeeze(forecast) # return 1D array of predictions"
      ],
      "metadata": {
        "id": "IwRis47syw0P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Make predictions using model_dnn on the test dataset and view the results\n",
        "model_preds = make_preds(model, test_windows)\n",
        "len(model_preds), model_preds[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_QeO-bwNy1VI",
        "outputId": "17ed766f-6530-4657-c3a7-45fc4ebe3bdf"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(66, <tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              " array([0.04908531, 0.05142915, 0.05771179, 0.05900952, 0.0627019 ,\n",
              "        0.06583834, 0.06407106, 0.06565234, 0.06960471, 0.06487384],\n",
              "       dtype=float32)>)"
            ]
          },
          "metadata": {},
          "execution_count": 181
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate preds\n",
        "model_results = evaluate_preds(y_true=tf.squeeze(test_labels), # reduce to right shape\n",
        "                                 y_pred=model_preds)\n",
        "model_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WC2raBeyyeSQ",
        "outputId": "7f12ac87-9973-4a12-bac3-9ef122e9bab0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 0.008690428,\n",
              " 'mape': 15.141067,\n",
              " 'mase': 0.9583155,\n",
              " 'mse': 0.00013296425,\n",
              " 'rmse': 0.011531013}"
            ]
          },
          "metadata": {},
          "execution_count": 182
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,5))  \n",
        "plt.plot(tf.squeeze(test_labels), color='blue', label='ET0 true')  \n",
        "plt.plot(model_preds, color='red', label='ET0 predicted')  \n",
        "plt.title('ET0 Forecasting')  \n",
        "plt.xlabel('Date')  \n",
        "plt.ylabel('ET0')  \n",
        "plt.legend()\n",
        "#plt.savefig(\"eto.png\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "KapesSOWJ3et",
        "outputId": "0ed1aafd-3a98-4319-de0f-fae94fc12766"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving cnn model and applying transfer learning"
      ],
      "metadata": {
        "id": "4ezuiY6YJmY0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('cnn_model.h5')"
      ],
      "metadata": {
        "id": "5BC73ADvKXu8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model=tf.keras.models.load_model('cnn_model.h5')"
      ],
      "metadata": {
        "id": "xLtD8wLLKoys"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "loaded_model.layers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "976235fGK1mN",
        "outputId": "06d0d4c2-c08f-4d0e-9dcd-330ff42ee982"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.layers.core.lambda_layer.Lambda at 0x7fc5cf7aa650>,\n",
              " <keras.layers.convolutional.Conv1D at 0x7fc5d19e63d0>,\n",
              " <keras.layers.core.dense.Dense at 0x7fc5d24576d0>]"
            ]
          },
          "metadata": {},
          "execution_count": 186
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model1=Sequential()\n",
        "for layer in loaded_model.layers[:-1]:\n",
        " model1.add(layer)\n",
        " \n",
        "# Freeze all the layers of base model\n",
        "for layer in loaded_model.layers:\n",
        " layer.trainable=False\n",
        " \n",
        "# adding new layers\n",
        "\n",
        "model1.add(Conv1D(filters=64, kernel_size=5, padding=\"causal\", activation=\"relu\"))\n",
        "\n",
        "#model1.add(Dropout(0.2))\n",
        "model1.add(Dense(1))"
      ],
      "metadata": {
        "id": "1Z6K1zSkLG4E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1.build(input_shape=(1,WINDOW_SIZE))"
      ],
      "metadata": {
        "id": "xz1YPvIHSCwI"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model1.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rB4WmXmkLooa",
        "outputId": "1a427a9a-a028-45b4-b386-7937b2f5053e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_7\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lambda_7 (Lambda)           multiple                  0         \n",
            "                                                                 \n",
            " conv1d_14 (Conv1D)          multiple                  2304      \n",
            "                                                                 \n",
            " conv1d_15 (Conv1D)          (1, 1, 64)                20544     \n",
            "                                                                 \n",
            " dense_17 (Dense)            (1, 1, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 22,913\n",
            "Trainable params: 20,609\n",
            "Non-trainable params: 2,304\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rmse = tf.keras.metrics.RootMeanSquaredError()\n",
        "\n",
        "# Compile model\n",
        "model1.compile(loss=\"mean_squared_error\",\n",
        "                optimizer=tf.keras.optimizers.Adam(),\n",
        "                metrics=[rmse,'mae'])"
      ],
      "metadata": {
        "id": "XZBB5I0MMhQ5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "history_2 = model1.fit(x=train_windows, # train windows of 7 timesteps of Bitcoin prices\n",
        "              y=train_labels, # horizon value of 1 (using the previous 7 timesteps to predict next day)\n",
        "              epochs=100,\n",
        "              verbose=1,\n",
        "              batch_size=128,\n",
        "              validation_data=(test_windows, test_labels),\n",
        "              callbacks=[create_model_checkpoint(model_name='loaded_model')])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P2H6J7BfMFVk",
        "outputId": "f656de23-2ba1-4ed6-c397-46463cec5972"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "1/3 [=========>....................] - ETA: 2s - loss: 0.0014 - root_mean_squared_error: 0.0371 - mae: 0.0329INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 3s 863ms/step - loss: 0.0012 - root_mean_squared_error: 0.0339 - mae: 0.0294 - val_loss: 0.0018 - val_root_mean_squared_error: 0.0427 - val_mae: 0.0407\n",
            "Epoch 2/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 4.2730e-04 - root_mean_squared_error: 0.0207 - mae: 0.0161INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 2s 792ms/step - loss: 3.4539e-04 - root_mean_squared_error: 0.0186 - mae: 0.0147 - val_loss: 6.1846e-04 - val_root_mean_squared_error: 0.0249 - val_mae: 0.0228\n",
            "Epoch 3/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 2.2470e-04 - root_mean_squared_error: 0.0150 - mae: 0.0124INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 1s 732ms/step - loss: 2.4677e-04 - root_mean_squared_error: 0.0157 - mae: 0.0130 - val_loss: 3.4104e-04 - val_root_mean_squared_error: 0.0185 - val_mae: 0.0164\n",
            "Epoch 4/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 2.9932e-04 - root_mean_squared_error: 0.0173 - mae: 0.0145 - val_loss: 3.7031e-04 - val_root_mean_squared_error: 0.0192 - val_mae: 0.0172\n",
            "Epoch 5/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 2.6304e-04 - root_mean_squared_error: 0.0162 - mae: 0.0134 - val_loss: 5.0144e-04 - val_root_mean_squared_error: 0.0224 - val_mae: 0.0204\n",
            "Epoch 6/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 2.1018e-04 - root_mean_squared_error: 0.0145 - mae: 0.0118 - val_loss: 6.7672e-04 - val_root_mean_squared_error: 0.0260 - val_mae: 0.0240\n",
            "Epoch 7/100\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 1.9676e-04 - root_mean_squared_error: 0.0140 - mae: 0.0113 - val_loss: 7.7607e-04 - val_root_mean_squared_error: 0.0279 - val_mae: 0.0258\n",
            "Epoch 8/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 2.0091e-04 - root_mean_squared_error: 0.0142 - mae: 0.0113 - val_loss: 7.3808e-04 - val_root_mean_squared_error: 0.0272 - val_mae: 0.0251\n",
            "Epoch 9/100\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.9296e-04 - root_mean_squared_error: 0.0139 - mae: 0.0110 - val_loss: 6.2606e-04 - val_root_mean_squared_error: 0.0250 - val_mae: 0.0230\n",
            "Epoch 10/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.8011e-04 - root_mean_squared_error: 0.0134 - mae: 0.0107 - val_loss: 4.8237e-04 - val_root_mean_squared_error: 0.0220 - val_mae: 0.0200\n",
            "Epoch 11/100\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 1.7527e-04 - root_mean_squared_error: 0.0132 - mae: 0.0106 - val_loss: 3.6218e-04 - val_root_mean_squared_error: 0.0190 - val_mae: 0.0170\n",
            "Epoch 12/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.6778e-04 - root_mean_squared_error: 0.0130 - mae: 0.0103INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 1s 412ms/step - loss: 1.7858e-04 - root_mean_squared_error: 0.0134 - mae: 0.0107 - val_loss: 3.4011e-04 - val_root_mean_squared_error: 0.0184 - val_mae: 0.0164\n",
            "Epoch 13/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.7028e-04 - root_mean_squared_error: 0.0130 - mae: 0.0104 - val_loss: 3.8174e-04 - val_root_mean_squared_error: 0.0195 - val_mae: 0.0176\n",
            "Epoch 14/100\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.5758e-04 - root_mean_squared_error: 0.0126 - mae: 0.0100 - val_loss: 4.3522e-04 - val_root_mean_squared_error: 0.0209 - val_mae: 0.0189\n",
            "Epoch 15/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.5440e-04 - root_mean_squared_error: 0.0124 - mae: 0.0098 - val_loss: 4.4555e-04 - val_root_mean_squared_error: 0.0211 - val_mae: 0.0191\n",
            "Epoch 16/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.5196e-04 - root_mean_squared_error: 0.0123 - mae: 0.0097 - val_loss: 4.0301e-04 - val_root_mean_squared_error: 0.0201 - val_mae: 0.0181\n",
            "Epoch 17/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.4687e-04 - root_mean_squared_error: 0.0121 - mae: 0.0095 - val_loss: 3.6503e-04 - val_root_mean_squared_error: 0.0191 - val_mae: 0.0171\n",
            "Epoch 18/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.4645e-04 - root_mean_squared_error: 0.0121 - mae: 0.0095INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 1s 407ms/step - loss: 1.4221e-04 - root_mean_squared_error: 0.0119 - mae: 0.0093 - val_loss: 3.0530e-04 - val_root_mean_squared_error: 0.0175 - val_mae: 0.0154\n",
            "Epoch 19/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.4654e-04 - root_mean_squared_error: 0.0121 - mae: 0.0097INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 1s 415ms/step - loss: 1.3864e-04 - root_mean_squared_error: 0.0118 - mae: 0.0092 - val_loss: 2.2450e-04 - val_root_mean_squared_error: 0.0150 - val_mae: 0.0129\n",
            "Epoch 20/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.4510e-04 - root_mean_squared_error: 0.0120 - mae: 0.0097INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 1s 657ms/step - loss: 1.4543e-04 - root_mean_squared_error: 0.0121 - mae: 0.0095 - val_loss: 2.0559e-04 - val_root_mean_squared_error: 0.0143 - val_mae: 0.0122\n",
            "Epoch 21/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.4290e-04 - root_mean_squared_error: 0.0120 - mae: 0.0094 - val_loss: 2.1873e-04 - val_root_mean_squared_error: 0.0148 - val_mae: 0.0127\n",
            "Epoch 22/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.3421e-04 - root_mean_squared_error: 0.0116 - mae: 0.0090 - val_loss: 2.4860e-04 - val_root_mean_squared_error: 0.0158 - val_mae: 0.0137\n",
            "Epoch 23/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.2893e-04 - root_mean_squared_error: 0.0114 - mae: 0.0088 - val_loss: 2.8667e-04 - val_root_mean_squared_error: 0.0169 - val_mae: 0.0148\n",
            "Epoch 24/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.2903e-04 - root_mean_squared_error: 0.0114 - mae: 0.0088 - val_loss: 2.9989e-04 - val_root_mean_squared_error: 0.0173 - val_mae: 0.0152\n",
            "Epoch 25/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.3193e-04 - root_mean_squared_error: 0.0115 - mae: 0.0090 - val_loss: 2.8447e-04 - val_root_mean_squared_error: 0.0169 - val_mae: 0.0148\n",
            "Epoch 26/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.2752e-04 - root_mean_squared_error: 0.0113 - mae: 0.0088 - val_loss: 2.1108e-04 - val_root_mean_squared_error: 0.0145 - val_mae: 0.0124\n",
            "Epoch 27/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.4402e-04 - root_mean_squared_error: 0.0120 - mae: 0.0090INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 1s 417ms/step - loss: 1.2067e-04 - root_mean_squared_error: 0.0110 - mae: 0.0084 - val_loss: 1.5061e-04 - val_root_mean_squared_error: 0.0123 - val_mae: 0.0099\n",
            "Epoch 28/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.0476e-04 - root_mean_squared_error: 0.0102 - mae: 0.0079INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 1s 421ms/step - loss: 1.3383e-04 - root_mean_squared_error: 0.0116 - mae: 0.0089 - val_loss: 1.3509e-04 - val_root_mean_squared_error: 0.0116 - val_mae: 0.0090\n",
            "Epoch 29/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.4281e-04 - root_mean_squared_error: 0.0120 - mae: 0.0093 - val_loss: 1.3780e-04 - val_root_mean_squared_error: 0.0117 - val_mae: 0.0092\n",
            "Epoch 30/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.3042e-04 - root_mean_squared_error: 0.0114 - mae: 0.0088 - val_loss: 1.5896e-04 - val_root_mean_squared_error: 0.0126 - val_mae: 0.0103\n",
            "Epoch 31/100\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 1.1690e-04 - root_mean_squared_error: 0.0108 - mae: 0.0083 - val_loss: 2.1186e-04 - val_root_mean_squared_error: 0.0146 - val_mae: 0.0124\n",
            "Epoch 32/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.2252e-04 - root_mean_squared_error: 0.0111 - mae: 0.0086 - val_loss: 2.2751e-04 - val_root_mean_squared_error: 0.0151 - val_mae: 0.0130\n",
            "Epoch 33/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.2417e-04 - root_mean_squared_error: 0.0111 - mae: 0.0087 - val_loss: 1.9858e-04 - val_root_mean_squared_error: 0.0141 - val_mae: 0.0120\n",
            "Epoch 34/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1698e-04 - root_mean_squared_error: 0.0108 - mae: 0.0083 - val_loss: 1.6576e-04 - val_root_mean_squared_error: 0.0129 - val_mae: 0.0106\n",
            "Epoch 35/100\n",
            "3/3 [==============================] - 0s 25ms/step - loss: 1.1530e-04 - root_mean_squared_error: 0.0107 - mae: 0.0082 - val_loss: 1.4798e-04 - val_root_mean_squared_error: 0.0122 - val_mae: 0.0098\n",
            "Epoch 36/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1395e-04 - root_mean_squared_error: 0.0107 - mae: 0.0082 - val_loss: 1.6636e-04 - val_root_mean_squared_error: 0.0129 - val_mae: 0.0107\n",
            "Epoch 37/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1458e-04 - root_mean_squared_error: 0.0107 - mae: 0.0082 - val_loss: 1.9662e-04 - val_root_mean_squared_error: 0.0140 - val_mae: 0.0119\n",
            "Epoch 38/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.1856e-04 - root_mean_squared_error: 0.0109 - mae: 0.0084 - val_loss: 1.9720e-04 - val_root_mean_squared_error: 0.0140 - val_mae: 0.0119\n",
            "Epoch 39/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1848e-04 - root_mean_squared_error: 0.0109 - mae: 0.0084 - val_loss: 1.7277e-04 - val_root_mean_squared_error: 0.0131 - val_mae: 0.0109\n",
            "Epoch 40/100\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.1298e-04 - root_mean_squared_error: 0.0106 - mae: 0.0081 - val_loss: 1.4787e-04 - val_root_mean_squared_error: 0.0122 - val_mae: 0.0098\n",
            "Epoch 41/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.1262e-04 - root_mean_squared_error: 0.0106 - mae: 0.0081 - val_loss: 1.3914e-04 - val_root_mean_squared_error: 0.0118 - val_mae: 0.0093\n",
            "Epoch 42/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.1449e-04 - root_mean_squared_error: 0.0107 - mae: 0.0081 - val_loss: 1.3730e-04 - val_root_mean_squared_error: 0.0117 - val_mae: 0.0092\n",
            "Epoch 43/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1412e-04 - root_mean_squared_error: 0.0107 - mae: 0.0081 - val_loss: 1.4578e-04 - val_root_mean_squared_error: 0.0121 - val_mae: 0.0097\n",
            "Epoch 44/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.1112e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.4795e-04 - val_root_mean_squared_error: 0.0122 - val_mae: 0.0098\n",
            "Epoch 45/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.1089e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.4644e-04 - val_root_mean_squared_error: 0.0121 - val_mae: 0.0097\n",
            "Epoch 46/100\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 1.1047e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.5034e-04 - val_root_mean_squared_error: 0.0123 - val_mae: 0.0099\n",
            "Epoch 47/100\n",
            "3/3 [==============================] - 0s 14ms/step - loss: 1.1051e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.5776e-04 - val_root_mean_squared_error: 0.0126 - val_mae: 0.0103\n",
            "Epoch 48/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.1191e-04 - root_mean_squared_error: 0.0106 - mae: 0.0081 - val_loss: 1.6319e-04 - val_root_mean_squared_error: 0.0128 - val_mae: 0.0105\n",
            "Epoch 49/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.1268e-04 - root_mean_squared_error: 0.0106 - mae: 0.0081 - val_loss: 1.5262e-04 - val_root_mean_squared_error: 0.0124 - val_mae: 0.0100\n",
            "Epoch 50/100\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 1.0992e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.4085e-04 - val_root_mean_squared_error: 0.0119 - val_mae: 0.0094\n",
            "Epoch 51/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.0992e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.3965e-04 - val_root_mean_squared_error: 0.0118 - val_mae: 0.0093\n",
            "Epoch 52/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1002e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.3813e-04 - val_root_mean_squared_error: 0.0118 - val_mae: 0.0092\n",
            "Epoch 53/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.1097e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.3749e-04 - val_root_mean_squared_error: 0.0117 - val_mae: 0.0092\n",
            "Epoch 54/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1032e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.3915e-04 - val_root_mean_squared_error: 0.0118 - val_mae: 0.0093\n",
            "Epoch 55/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.2030e-04 - root_mean_squared_error: 0.0110 - mae: 0.0080INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 1s 430ms/step - loss: 1.1053e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.3186e-04 - val_root_mean_squared_error: 0.0115 - val_mae: 0.0088\n",
            "Epoch 56/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.1642e-04 - root_mean_squared_error: 0.0108 - mae: 0.0086INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 1s 425ms/step - loss: 1.1388e-04 - root_mean_squared_error: 0.0107 - mae: 0.0081 - val_loss: 1.3115e-04 - val_root_mean_squared_error: 0.0115 - val_mae: 0.0088\n",
            "Epoch 57/100\n",
            "3/3 [==============================] - 0s 19ms/step - loss: 1.1420e-04 - root_mean_squared_error: 0.0107 - mae: 0.0081 - val_loss: 1.3277e-04 - val_root_mean_squared_error: 0.0115 - val_mae: 0.0089\n",
            "Epoch 58/100\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.1159e-04 - root_mean_squared_error: 0.0106 - mae: 0.0080 - val_loss: 1.3885e-04 - val_root_mean_squared_error: 0.0118 - val_mae: 0.0093\n",
            "Epoch 59/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.0916e-04 - root_mean_squared_error: 0.0104 - mae: 0.0079 - val_loss: 1.4033e-04 - val_root_mean_squared_error: 0.0118 - val_mae: 0.0094\n",
            "Epoch 60/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.0893e-04 - root_mean_squared_error: 0.0104 - mae: 0.0079 - val_loss: 1.3960e-04 - val_root_mean_squared_error: 0.0118 - val_mae: 0.0094\n",
            "Epoch 61/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.0890e-04 - root_mean_squared_error: 0.0104 - mae: 0.0079 - val_loss: 1.3926e-04 - val_root_mean_squared_error: 0.0118 - val_mae: 0.0093\n",
            "Epoch 62/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.0871e-04 - root_mean_squared_error: 0.0104 - mae: 0.0079 - val_loss: 1.4544e-04 - val_root_mean_squared_error: 0.0121 - val_mae: 0.0097\n",
            "Epoch 63/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.1002e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.4709e-04 - val_root_mean_squared_error: 0.0121 - val_mae: 0.0098\n",
            "Epoch 64/100\n",
            "3/3 [==============================] - 0s 24ms/step - loss: 1.0883e-04 - root_mean_squared_error: 0.0104 - mae: 0.0080 - val_loss: 1.3420e-04 - val_root_mean_squared_error: 0.0116 - val_mae: 0.0090\n",
            "Epoch 65/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.0956e-04 - root_mean_squared_error: 0.0105 - mae: 0.0079 - val_loss: 1.3191e-04 - val_root_mean_squared_error: 0.0115 - val_mae: 0.0089\n",
            "Epoch 66/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.1012e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.3634e-04 - val_root_mean_squared_error: 0.0117 - val_mae: 0.0092\n",
            "Epoch 67/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.0877e-04 - root_mean_squared_error: 0.0104 - mae: 0.0079 - val_loss: 1.3960e-04 - val_root_mean_squared_error: 0.0118 - val_mae: 0.0094\n",
            "Epoch 68/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.0985e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.3492e-04 - val_root_mean_squared_error: 0.0116 - val_mae: 0.0091\n",
            "Epoch 69/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.0853e-04 - root_mean_squared_error: 0.0104 - mae: 0.0079 - val_loss: 1.5638e-04 - val_root_mean_squared_error: 0.0125 - val_mae: 0.0102\n",
            "Epoch 70/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1277e-04 - root_mean_squared_error: 0.0106 - mae: 0.0082 - val_loss: 1.7671e-04 - val_root_mean_squared_error: 0.0133 - val_mae: 0.0111\n",
            "Epoch 71/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.1706e-04 - root_mean_squared_error: 0.0108 - mae: 0.0084 - val_loss: 1.5864e-04 - val_root_mean_squared_error: 0.0126 - val_mae: 0.0103\n",
            "Epoch 72/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.1009e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.3756e-04 - val_root_mean_squared_error: 0.0117 - val_mae: 0.0093\n",
            "Epoch 73/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.0769e-04 - root_mean_squared_error: 0.0104 - mae: 0.0079 - val_loss: 1.3177e-04 - val_root_mean_squared_error: 0.0115 - val_mae: 0.0089\n",
            "Epoch 74/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 9.4023e-05 - root_mean_squared_error: 0.0097 - mae: 0.0074INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 1s 428ms/step - loss: 1.0853e-04 - root_mean_squared_error: 0.0104 - mae: 0.0079 - val_loss: 1.2963e-04 - val_root_mean_squared_error: 0.0114 - val_mae: 0.0087\n",
            "Epoch 75/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.0984e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.3058e-04 - val_root_mean_squared_error: 0.0114 - val_mae: 0.0088\n",
            "Epoch 76/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.0799e-04 - root_mean_squared_error: 0.0104 - mae: 0.0079 - val_loss: 1.3713e-04 - val_root_mean_squared_error: 0.0117 - val_mae: 0.0092\n",
            "Epoch 77/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.0722e-04 - root_mean_squared_error: 0.0104 - mae: 0.0079 - val_loss: 1.3450e-04 - val_root_mean_squared_error: 0.0116 - val_mae: 0.0091\n",
            "Epoch 78/100\n",
            "3/3 [==============================] - 0s 27ms/step - loss: 1.0740e-04 - root_mean_squared_error: 0.0104 - mae: 0.0079 - val_loss: 1.2996e-04 - val_root_mean_squared_error: 0.0114 - val_mae: 0.0088\n",
            "Epoch 79/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.0907e-04 - root_mean_squared_error: 0.0104 - mae: 0.0082INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 1s 425ms/step - loss: 1.0817e-04 - root_mean_squared_error: 0.0104 - mae: 0.0079 - val_loss: 1.2927e-04 - val_root_mean_squared_error: 0.0114 - val_mae: 0.0087\n",
            "Epoch 80/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.0182e-04 - root_mean_squared_error: 0.0101 - mae: 0.0079INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 1s 435ms/step - loss: 1.0925e-04 - root_mean_squared_error: 0.0105 - mae: 0.0079 - val_loss: 1.2808e-04 - val_root_mean_squared_error: 0.0113 - val_mae: 0.0086\n",
            "Epoch 81/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.0926e-04 - root_mean_squared_error: 0.0105 - mae: 0.0079 - val_loss: 1.3536e-04 - val_root_mean_squared_error: 0.0116 - val_mae: 0.0091\n",
            "Epoch 82/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.0829e-04 - root_mean_squared_error: 0.0104 - mae: 0.0080 - val_loss: 1.6898e-04 - val_root_mean_squared_error: 0.0130 - val_mae: 0.0108\n",
            "Epoch 83/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.2185e-04 - root_mean_squared_error: 0.0110 - mae: 0.0087 - val_loss: 1.9285e-04 - val_root_mean_squared_error: 0.0139 - val_mae: 0.0118\n",
            "Epoch 84/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.2764e-04 - root_mean_squared_error: 0.0113 - mae: 0.0089 - val_loss: 1.4757e-04 - val_root_mean_squared_error: 0.0121 - val_mae: 0.0098\n",
            "Epoch 85/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.0762e-04 - root_mean_squared_error: 0.0104 - mae: 0.0079 - val_loss: 1.2985e-04 - val_root_mean_squared_error: 0.0114 - val_mae: 0.0086\n",
            "Epoch 86/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.2581e-04 - root_mean_squared_error: 0.0112 - mae: 0.0086 - val_loss: 1.2954e-04 - val_root_mean_squared_error: 0.0114 - val_mae: 0.0086\n",
            "Epoch 87/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.1717e-04 - root_mean_squared_error: 0.0108 - mae: 0.0082 - val_loss: 1.6744e-04 - val_root_mean_squared_error: 0.0129 - val_mae: 0.0107\n",
            "Epoch 88/100\n",
            "3/3 [==============================] - 0s 18ms/step - loss: 1.2536e-04 - root_mean_squared_error: 0.0112 - mae: 0.0088 - val_loss: 2.2388e-04 - val_root_mean_squared_error: 0.0150 - val_mae: 0.0129\n",
            "Epoch 89/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.3713e-04 - root_mean_squared_error: 0.0117 - mae: 0.0093 - val_loss: 1.4360e-04 - val_root_mean_squared_error: 0.0120 - val_mae: 0.0096\n",
            "Epoch 90/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.0501e-04 - root_mean_squared_error: 0.0102 - mae: 0.0078 - val_loss: 1.3097e-04 - val_root_mean_squared_error: 0.0114 - val_mae: 0.0085\n",
            "Epoch 91/100\n",
            "3/3 [==============================] - 0s 20ms/step - loss: 1.3020e-04 - root_mean_squared_error: 0.0114 - mae: 0.0088 - val_loss: 1.3222e-04 - val_root_mean_squared_error: 0.0115 - val_mae: 0.0086\n",
            "Epoch 92/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.2440e-04 - root_mean_squared_error: 0.0112 - mae: 0.0086 - val_loss: 1.3446e-04 - val_root_mean_squared_error: 0.0116 - val_mae: 0.0091\n",
            "Epoch 93/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.0629e-04 - root_mean_squared_error: 0.0103 - mae: 0.0079 - val_loss: 1.7367e-04 - val_root_mean_squared_error: 0.0132 - val_mae: 0.0110\n",
            "Epoch 94/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1724e-04 - root_mean_squared_error: 0.0108 - mae: 0.0084 - val_loss: 1.6143e-04 - val_root_mean_squared_error: 0.0127 - val_mae: 0.0105\n",
            "Epoch 95/100\n",
            "1/3 [=========>....................] - ETA: 0s - loss: 1.0329e-04 - root_mean_squared_error: 0.0102 - mae: 0.0079INFO:tensorflow:Assets written to: model_experiments/loaded_model/assets\n",
            "3/3 [==============================] - 1s 421ms/step - loss: 1.0916e-04 - root_mean_squared_error: 0.0104 - mae: 0.0080 - val_loss: 1.2727e-04 - val_root_mean_squared_error: 0.0113 - val_mae: 0.0086\n",
            "Epoch 96/100\n",
            "3/3 [==============================] - 0s 22ms/step - loss: 1.1146e-04 - root_mean_squared_error: 0.0106 - mae: 0.0081 - val_loss: 1.3104e-04 - val_root_mean_squared_error: 0.0114 - val_mae: 0.0085\n",
            "Epoch 97/100\n",
            "3/3 [==============================] - 0s 17ms/step - loss: 1.2863e-04 - root_mean_squared_error: 0.0113 - mae: 0.0087 - val_loss: 1.2814e-04 - val_root_mean_squared_error: 0.0113 - val_mae: 0.0085\n",
            "Epoch 98/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1573e-04 - root_mean_squared_error: 0.0108 - mae: 0.0082 - val_loss: 1.3574e-04 - val_root_mean_squared_error: 0.0117 - val_mae: 0.0092\n",
            "Epoch 99/100\n",
            "3/3 [==============================] - 0s 15ms/step - loss: 1.0964e-04 - root_mean_squared_error: 0.0105 - mae: 0.0080 - val_loss: 1.6059e-04 - val_root_mean_squared_error: 0.0127 - val_mae: 0.0104\n",
            "Epoch 100/100\n",
            "3/3 [==============================] - 0s 16ms/step - loss: 1.1188e-04 - root_mean_squared_error: 0.0106 - mae: 0.0082 - val_loss: 1.3913e-04 - val_root_mean_squared_error: 0.0118 - val_mae: 0.0094\n",
            "CPU times: user 19 s, sys: 510 ms, total: 19.5 s\n",
            "Wall time: 20.8 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(history_2.history['loss'])\n",
        "plt.plot(history_2.history['val_loss'])\n",
        "plt.title('pretrained CNN model')\n",
        "plt.ylabel('loss')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "kde6JxPCIgol",
        "outputId": "eab0a339-7cbd-40d7-e075-974094a0edcd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(history_2.history['mae'])\n",
        "plt.plot(history_2.history['val_mae'])\n",
        "plt.title('pretrained CNN model')\n",
        "plt.ylabel('mae')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "oTS0hrGTIjqQ",
        "outputId": "06439915-d93d-48cd-96d3-2d6e45c31490"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(10,6))\n",
        "plt.plot(history_2.history['root_mean_squared_error'])\n",
        "plt.plot(history_2.history['val_root_mean_squared_error'])\n",
        "plt.title('pretrained CNN model')\n",
        "plt.ylabel('rmse')\n",
        "plt.xlabel('epoch')\n",
        "plt.legend(['train', 'validation'], loc='upper left')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 408
        },
        "id": "FTiDasNBL2X2",
        "outputId": "1f6f40da-13bd-4888-c1c3-1de3fdf7fabe"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ],
            "image/png": 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WDfITiYiISFTUAhey3gA3yAR38MEH09raysSJE5kwYQL/8A//wCWXXMLixYs5/PDDmTt37l7P/9CHPsQVV1zBiSeeyLx58zj88MMBOPTQQ1m4cCEnnHACkydP5uijj+4958ILL+TCCy9kwoQJ3Hnnnb3HDzvsMM477zzOOOMMAC644AIWLlyo7lIREZEiYQ7APTu9DRs27HKgra2td7zXcHV251i7LcP4MUkqSw7cfBzmdxqmuro6GhoaRroakkfPpXjp2RQnPZfiFMVzmTx5MuxcZraXulBDpr1QRUREJGoKcCHbuRPDiFZDRERE9mMF6+Oz1p4G/BCIAzc7567Nez8N3A4cBTQC5zvn1vR5fzqwArjaOffdgVxzJPSMgcuNbDVERERkP1aQFjhrbRy4HjgdWABcYK3NX4jsUqDJOTcX+D7w7bz3vwfcP8hrDkiY4wALsRfqaHCgf34REZEoFaoL9RhgpXNulXMuC/wCODuvzNnAbcHvdwKLrbUGwFp7DrAaWD7Iaw5ILBajq6trKKfuxhiDMeaAHgPX1dVFLKbeeRERkagUqgt1CtB3DYp6IH9hsd4yzrkua20zMNZa2wF8ETgF+OdBXnNASkpK6OjoIJPJ7HGrqsF4Y1MLE8YkKasrHfa1RhvP84jFYpSUlIx0VURERPZbo2Gdi6uB7zvndlhrh3QBa+1lwGUAzjnq6urCq10/fvGHpzhuVg2Lj5we6X1k8BKJROTPXwZPz6V46dkUJz2X4lTI51KoALcemNbn9dTgWH9l6q21CaAKfzLDImCJtfbfgWogF7TKPTuAawLgnLsRuDF46UW9dk4qbmhpbdcaPUVIaycVJz2X4qVnU5z0XIpThOvA7aZQAe5pYJ61dhZ+yPoA8MG8MncDlwB/BZYADzrnPODdPQWstVcDO5xzPwpC3r6uOSJS8Rid3QfyKDgRERGJUkFGmjvnuoDLgQeAl/1Dbrm19hpr7VlBsZ/gj3lbCVwBXDmUa0b1GQYjlYiRVYATERGRiGgrrQh8+c/rSdLN1xZrDFyxUbdDcdJzKV56NsVJz6U4aSutUU4tcCIiIhIlBbgIpOMxOnMKcCIiIhINBbgIpBJGLXAiIiISGQW4CKTjcTq7tRuqiIiIREMBLgJqgRMREZEoKcBFQOvAiYiISJQU4CKgWagiIiISJQW4CKTiMTpzGgMnIiIi0VCAi0AqEaMrB91aSkREREQioAAXgXTc/1q7FOBEREQkAgpwEUgl/K9V4+BEREQkCgpwEUjFewKcxsGJiIhI+BTgIpBK+HvOqgVOREREoqAAF4GeFjitBSciIiJRUICLgMbAiYiISJQU4CKwswVOY+BEREQkfApwEUj3tMBpGRERERGJgAJcBDQGTkRERKKkABcBLSMiIiIiUVKAi4AmMYiIiEiUFOAi0DMGTl2oIiIiEgUFuAjs7EJVgBMREZHwKcBFoGcnhs6cxsCJiIhI+BTgIpBWC5yIiIhESAEuAvGYIWY0Bk5ERESioQAXAWMMyZhRC5yIiIhEQgEuIqm40VZaIiIiEgkFuIgk4zG1wImIiEgkFOAikoqrC1VERESioQAXkaQCnIiIiEREAS4iGgMnIiIiUVGAi0gyFiObUwuciIiIhE8BLiJ+C5wCnIiIiIRPAS4iGgMnIiIiUVGAi4jGwImIiEhUFOAionXgREREJCoKcBHRGDgRERGJSqJQN7LWngb8EIgDNzvnrs17Pw3cDhwFNALnO+fWWGuPAW4MihngaufcXcE5a4DtQDfQ5Zx7RyE+y0Ck4kazUEVERCQSBWmBs9bGgeuB04EFwAXW2gV5xS4Fmpxzc4HvA98Ojr8EvMM5dwRwGnCDtbZv8DzJOXdEMYU3gFQ8pjFwIiIiEolCtcAdA6x0zq0CsNb+AjgbWNGnzNnA1cHvdwI/stYa51xbnzIlwKho1krGNAtVREREolGoMXBTgHV9XtcHx/ot45zrApqBsQDW2kXW2uXAi8Ang/fBD3N/sNY+a629LML6D1oqbsh50K1uVBEREQlZwcbADYdz7kngUGvtIcBt1tr7nXMdwPHOufXW2vHAH621rzjnHsk/Pwh3lwXXoq6uLtL6JhIJqivLgQYqqmspS8UjvZ8MXCKRiPz5y+DpuRQvPZvipOdSnAr5XAoV4NYD0/q8nhoc669MfTDGrQp/MkMv59zL1todwELgGefc+uD4ZmvtXfhdtbsFOOfcjeycCOE1NDQM/xPtRV1dHZ0d7QBs3LyFypJRkZMPCHV1dUT9/GXw9FyKl55NcdJzKU5RPJfJkyf3e7xQyeJpYJ61dhZ+UPsA8MG8MncDlwB/BZYADzrnvOCcdc65LmvtDGA+sMZaOwaIOee2B7+fClxToM+zT8m4AdBMVBEREQldQcbABWPWLgceAF72D7nl1tprrLVnBcV+Aoy11q4ErgCuDI4fDyy11r4A3AV82jnXAEwAHrPWLgWeAu51zv2+EJ9nIFI9Aa5LAU5ERETCZTzvgAsY3oYNGyK9QV1dHb99bhX//ugGfvi+mcysKYn0fjJw6nYoTnouxUvPpjjpuRSnCLtQTf5x7cQQkVTM/2o71YUqIiIiIVOAi0jvGDitBSciIiIhU4CLSM8YOO2HKiIiImHT+hYh89a/SWfTZpLxCgCy2k5LREREQqYWuJDl7rqdlv/6Nql4MAZOLXAiIiISMgW4kJlUGi+b2bmMiAKciIiIhEwBLmypFF6mo3cSg2ahioiISNgU4MKWDFrgYj0tcBoDJyIiIuFSgAtbKuUHuIT/1aoLVURERMKmABe2VBoyHSSCNZM1iUFERETCpgAXtmQKgHiui7hRC5yIiIiETwEubCk/wJHNkIzH6NQYOBEREQmZAlzYUmn/z2yWVNyoBU5ERERCpwAXtmQQ4DozJONGy4iIiIhI6BTgQmb6dKGqBU5ERESioAAXtr5dqLGY1oETERGR0CnAhS2YhUpn1u9CVQuciIiIhEwBLmy9LXDqQhUREZFoKMCFrXcMnN8CpwAnIiIiYVOAC1swC9Xr9FvgtA6ciIiIhE0BLmx9ulCT8Zha4ERERCR0CnBh69OFmoppHTgREREJnwJc2PJmoaoFTkRERMKmABe25K4L+WoMnIiIiIRNAS5kxhh/HFw2qzFwIiIiEgkFuAiYVBp6Z6F6eJ5CnIiIiIRHAS4CJl3S24XqAV3qRRUREZEQKcBFwARdqKm4//V25pTgREREJDwKcBEw6TReNkMybgA0Dk5ERERCpQAXAX8MXJZUEOC0ob2IiIiESQEuCj2zUGNqgRMREZHwKcBFYOckBv/rzWotOBEREQmRAlwEerpQNQZOREREoqAAFwGTTvcuIwIaAyciIiLhUoCLgEn5Xag7W+DUhSoiIiLhUYCLgEn3zEIN1oFTC5yIiIiESAEuChoDJyIiIhFKFOpG1trTgB8CceBm59y1ee+ngduBo4BG4Hzn3Bpr7THAjUExA1ztnLtrINccKSadhu5ukp7fddqZU4ATERGR8BSkBc5aGweuB04HFgAXWGsX5BW7FGhyzs0Fvg98Ozj+EvAO59wRwGnADdbaxACvOSJMKg1AKtcJaAyciIiIhKtQLXDHACudc6sArLW/AM4GVvQpczZwdfD7ncCPrLXGOdfWp0wJ0NOcNZBrjoieAJcMApzGwImIiEiYChXgpgDr+ryuBxbtqYxzrsta2wyMBRqstYuAW4AZwMXB+wO5JgDW2suAy4JrU1dXN/xPtBeZ0jEATKgYA2whWVIW+T1lYBKJhJ5FEdJzKV56NsVJz6U4FfK5FGwM3HA4554EDrXWHgLcZq29f5Dn38jOcXReQ0ND2FXcRXnC/1p3NGwCYFvLDqK+pwxMXV2dnkUR0nMpXno2xUnPpThF8VwmT57c7/FCzUJdD0zr83pqcKzfMtbaBFCFP5mhl3PuZWAHsHCA1xwZQRdqrKuTRMxoDJyIiIiEqlAtcE8D86y1s/BD1geAD+aVuRu4BPgrsAR40DnnBeesC7pNZwDzgTXAtgFcc0SYdIn/SyZDKp4kq1moIiIiEqKCtMA557qAy4EHgJf9Q265tfYaa+1ZQbGfAGOttSuBK4Arg+PHA0uttS8AdwGfds417Omahfg8+9IziaFnLThNYhAREZEwGc874MKFt2HDhkhvUNXSyNbPf4TYp7/Mx9fUcdjEMXz22EmR3lMGRuNGipOeS/HSsylOei7FKcIxcCb/uHZiiEBPC5yXzZCMx+jUGDgREREJkQJcBPp2oabiRltpiYiISKgU4CLQO4khmyGpACciIiIhU4CLQl4LnLpQRUREJEwKcBHo7ULNZknGY2qBExERkVApwEXAxOOQSEA247fAaR04ERERCZECXFSSaX8duJjGwImIiEi4FOCikkrtbIHTGDgREREJkQJcVFLpIMBpDJyIiIiESwEuKskUnrbSEhERkQgowEUlmYKsFvIVERGR8CnARSWd7l3ItzPncQDuOSsiIiIRUYCLSjLlL+Qb879iLSUiIiIiYVGAi0rPJIaEAYi8G9XzPL73+Ab+sHJbpPcRERGRkZcY6Qrsr0wyjZfNkIz5AS7qiQzrW7I8vKaFR9e2MH5MkiMmjYn0fiIiIjJyBhXgrLXzgfOAic65fwxep5xzyyKp3WiWSvXuhQqQjXgtuKfqdwAwoTzJdx5bz3dPm8mkilSk9xQREZGRMeAuVGvtecAjwBTg4uBwOfC9COo1+qV6JjEEY+AiboF7av0O5tSm+epJ0wD45sP1tHV2R3pPERERGRmDGQN3DXCKc+6TQE8yWAq8LfRa7Q/6LCMC0Y6Ba+7o4pUt7Rw9pZxJFSm+cPwU6luy/PCvb5HT7FcREZH9zmAC3Higp6vU6/OnEkJ/Uino6iTp57dIA9yzG1rxgGOmVgBwxKQxfOTI8fxt3Q7ci42R3VdERERGxmAC3LPs7Drt8QHgqfCqsx9JpQFIel1AtGPgnqrfztjSBLNr0r3H3n9wDSfPruTnLzbw5Lrtkd1bRERECm8wkxg+A/zBWnspMMZa+wBwEHBqJDUb7ZJ+mErl/N7mqMbAZbtzPP9WKyfNqsIY03vcGMOnjpnIaw0d/GpFI4umVURyfxERESm8AbfAOedeAeYD1wP/AvwPcJhz7vWI6ja6pfwZoEmvE4BsRAv5vrixjY4uj6OnlO9ehXiMY6dV8HpjhyY0iIiI7EcGtYyIc64NcADW2tlAHbAjgnqNfkEXaqrbD3BRtcA9tX4HJQnDYRPL+n3/8Ill/HJ5Iy9tausdIyciIiKj22CWEfm5tfZdwe8fAZYDy4MuVcljkkELXBDgohgD53keT9fv4O2TxpCK9/8o548rJRU3LNvUFvr9RUREZGQMZhLDYuCZ4PcrgPcCxwBXhl2p/ULQhRplC9yqpgyN7V39dp/2ViMe45BxpSzbqAAnIiKyvxhMgEs557LW2ilArXPucefccmBCRHUb3XpmoXZlgGiWEXmqfjsGeMdeAhzA4RPHsHZbhm0dXaHXQURERApvMAHuBWvtl4B/Be4FCMJcSxQVG/V6ZqF2ZYFoWuCeqt/B/HGlVJXsfSjj4RP88XFqhRMREdk/DCbAXQocBpTiz0IFOBb4WdiV2i/kt8Dlwh0Dt6W1k1VNGY7ZR+sbwJzaEsYkYyzb2BpqHURERGRkDHgWqnPuDeCDecfuBO4Mu1L7hWAMHJ0ZkjETegvcM+v9yb9HT913gIvHDAsnlGkig4iIyH5iUMuIWGvfDbwdfxP7Xs65b4ZZqf1CMAu1Zz/UsMfAPVW/g0kVSaZWpgZU/vCJZTxZv4NNO7JMKB/YOSIiIlKcBhzgrLX/CVjgUaC9z1vaC7U/QRcq2QzJeLgtcJmuHMs2tfG+g6p32X1hbw6fOAbwx8GdMlcBTkREZDQbTAvchcBC59yGqCqzX+ntQs2SisdCXQdufUuWrpzH/LrSAZ8zrTJFTUk8CHDVodVFRERECm8wkxjWAZmoKrLfiSfAxCLpQq1v8We2Thlg9yn4e6MeNnEMyza14nlqNBURERnNBtMCdylwk7X258Cmvm845x4JtVb7AWOM342azZBMGzpD3At1fUsGA0weRIADeNvEMh5Z08KbzVlmVKdDq4+IiOQoc3cAACAASURBVIgU1mAC3FHA6cAJ7D4GbnqYldpvpFLQmYmkBW58eXKP22ftyeETesbBtSrAiYiIjGKDCXDfBN7vnPtTVJXZ7yRTkM2SjMfIdoU7Bm6gs0/7Gl+eZGJ5kqUb23j//NrQ6iMiIiKFNZgmnFZAXaWDEXShpmLhdaHmPI/1LdlBd5/2eNvEMSzf3EZ3iF26IiIiUliDaYG7CviBtfYaYHPfN5xz+2xestaeBvwQiAM3O+euzXs/DdyO31XbCJzvnFtjrT0FuBZIAVngC865B4NzHgImsbNL91Tn3C51G1GpFF5nlmSIXaiNbV1ku70htcCBvx7cAyu3sXJrBwcPYhariIiIFI/BtMDdAnwSWA90Bj9dwZ97Za2NA9fjj6FbAFxgrV2QV+xSoMk5Nxf4PvDt4HgDftftYcAlwB15513onDsi+Cme8AZ+F2qnPwu1M6RlRHpmoE6tHNoYtsN690XVtloiIiKj1YACXBDAsvjha3afn1nBn/tyDLDSObfKOZcFfgGcnVfmbOC24Pc7gcXWWuOce77P2nPLgdKgta749cxCjcdCa4Grb/ZXchnMEiJ9VZUkmFmd1rZaIiIio9iAulCdc93W2leBliEu5DsFfx25HvXAoj2Vcc51WWubgbH4LXA9zgWec871XY/uf6y13cCvgK8753ZLStbay4DLgmtTV1c3hI8wcIlEgrq6OprKK8g1tFE5ppQury2U+27tbKY8FWfu1AkD3oUh3xHTtvGnV7cwduzYIV9jtOp5NlJc9FyKl55NcdJzKU6FfC6DGQP3M+B31tof4gew3qDUMyYtStbaQ/G7VU/tc/hC59x6a20FfoC7GH8c3S6cczcCNwYvvYaGhvwioaqrq6OhoYEcBq+9DbqytGW72LJly7AD08pNzUyqSNLY2Djka0ws8diR7ebltRsZX54cVn1Gm55nI8VFz6V46dkUJz2X4hTFc5k8eXK/xwczBu5TQA1wNXAz8JPg5+YBnLsemNbn9dTgWL9lrLUJoAp/MgPW2qnAXcCHnHNv9JzgnFsf/Lkd+F/8rtrikUxBNkNVOk6226M9hKVEhrqESF8za/we6NXbOoZdHxERESm8AbfAOedmDeM+TwPzrLWz8IPaB4AP5pW5G3+Swl+BJcCDzjnPWlsN3Atc6Zx7vKdwEPKqnXMN1tokcCZQXGvUpfx14KpL/a+5uaObsmR8yJdr6+ymsb1ryBMYevQs4ru2KcOiqRXDupaIiIgU3mC6UIcsGNN2OfAA/jIitzjnlgdLkjzjnLsbvzXvDmvtSmArfsgDuByYC1xlrb0qOHYq/rp0DwThLY4f3m4qxOcZsFQaOrNUpf3Q1tzRzaRh5KX1Q9gDtT9lyTgTy5Os3qatbUVEREajggQ4AOfcfcB9eceu6vN7B3BeP+d9Hfj6Hi57VJh1DF1PF2qJH+C2dXQN63K9Aa5qeAEOYFZNmjVN6kIVEREZjQa3maYMTjIFnkdVEJObO7qHdbn1LVliBiaFMPFgZk0Jb23vpCPELb5ERESkMBTgopT2x5pVGb/lLYwWuAnlSZKD3MS+P7Oq03jAWnWjioiIjDoKcFFK+gEumcsyJhWjeZgBrj6EGag9emeiqhtVRERk1FGAi1IyCFvZLFXpBNuG0YXanfPY0JJlyjBnoPYYPyZJWTLGmia1wImIiIw2CnARMkEXKtkM1SXxYbXANbR10pnzhj0DtbduxjCzOs1qBTgREZFRRwEuSj0tcJ1ZqkqG1wJX39yziX04AQ78btQ12zLkvHD2aRUREZHCUICLUp8u1OG2wK3fHs4acH3NqimhoyvHph2doV1TREREoqcAF6VU3y7UBNuzObpyQ2vtqm/OUpGKUZke+k4O+WYGOzKs0UxUERGRUUUBLko9Aa5z52K+LZmhdaOub8kwuTKNMSas2jGjOk3MoAV9RURERhkFuCgFAc7LZqku8Vfz3dY+tG7UMDaxz5dOxJhUkdJEBhERkVFGAS5KvWPgdrbANQ+hBa41201TR3foAQ78blR1oYqIiIwuCnBRSu06CxWG1gIX1ib2/ZlZk2bTjk7aOoe3zZeIiIgUjgJclHonMfizUAGaM4MPcPUhbmKfb1Z1CYAW9BURERlFFOCi1KcLtSwZIxEzQ9rQfn1LlriBieXRtMABGgcnIiIyiijARcgY44e4zgzGGKpL4kPa0H59S4aJFSkSsfBmoPaoK0tQnoqxZptmooqIiIwWCnBRS6Uh63eBVpUkhtQCF+Ym9vmMMcysKVELnIiIyCiiABe1ZAqyfjgaSgtcd87jre2dkUxg6DGrOs3abRm6h7jIsIiIiBSWAlzUUino3NkCN9j9UDe3dtIV4ib2/ZlZkybb7fHWjmxk9xAREZHwKMBFLZXG69MC19zRjTeIzePXBmu0TatKR1I98PdEBVirblQREZFRQQEuaslU7xi46pIEXTmP1s7cgE9fsy2DAaZHGOCmVaWIGc1EFRERGS0U4KKWSkOnH4x6d2MYRDfq2m0ZJpQnKU1G96hS8RhTKlOaiSoiIjJKKMBFLW8WKjCoiQxrmjK9a7VFaVa1ZqKKiIiMFgpwUcubhQrQPMAAl+nKsXFHlhnVBQhwNWka2rpoGcJerSIiIlJYCnARM3mzUGHgXahvNmfIef6G81GbXetPZFi1Vd2oIiIixU4BLmqpdG8LXFXab4EbaBdqzwzUmcF+pVGaHXTTrmpSgBMRESl2CnBRS+5sgYvHDBXp+IBb4NZsy5CKGyaUJ6OsIQCVJQnGliU0Dk5ERGQUUICLWp9JDOC3wg2mBW5GdZp4BHug9md2TYm6UEVEREYBBbiopVLQ3YXX7be6VZcOfD/UtU2Zgkxg6DG7Ns2G7VkyXQNfp05EREQKTwEuaskggHXuHAc3kO20trV30ZzpLsgEhh6za0rIeX7XbVSy3Tlyg9iJQkRERHanABe1VBDAenZjKE0MaBmRnhBV0Ba4muhmoq7dluE///YWH3Svc9vzW0K/voiIyIEkMdIV2O+lgk3oe9aCS8dp7czR2Z0jGd9zfu7ZFaGQAW7cmATlqVhoM1FznsdzG1q5+5WtLN3YRipumFyR4nevbuW0edVMqkiFch8REZEDjQJc1JJBSOnM342hm3Fj9hzg1m7LUFMS7y1fCMaYYCLD8LtQO7pyfOkPa1nVlKG2NMHFbxvHqfOq6cp5fPK3b/DTpVv4wvFTQqi1iIjIgUddqBEz+V2oA9wPdU1Thhk10a//lm9WTZq12zJ05YY3Tu3h1S2sasrwyaMncNM5c1iycCyV6Ti1pQnOPqSWx9Zu5/XG9pBqLSIicmBRgItaXhfqzt0Y9jwOrjvnsa45W9AJDD1m15bQmfOobx56K5znedz7WhOzatKcNq+aRN4yKH+/oJaqdJzbnt+CpwkNIiIig6YAF7W8Wag9LXB7Wwtuw/YsnTmvoOPfevROZBjGgr7LN7ezdluGMw6qwZjd17ArS8axh43lxU1tPLehdcj3EREROVApwEWttws1vwVuz12oO7fQKnyAm1KZIhU3w5rIcO9rTVSkYpwws3KPZf5ubg0Ty5Pc9sIWuofZXSsiInKgKdgIeWvtacAPgThws3Pu2rz308DtwFFAI3C+c26NtfYU4FogBWSBLzjnHgzOOQq4FSgF7gM+65wrrjQQdKF62SwGKEkYUnGz1xa4NU0ZYgamVhV+lmY8ZphRnR7ylloNbZ38bd12zp5fSzqx5/8/SMYNF71tHN99fAMPr2nh5NlVQ62yiIjIAacgLXDW2jhwPXA6sAC4wFq7IK/YpUCTc24u8H3g28HxBuD9zrnDgEuAO/qc82Pg48C84Oe0yD7EUOXNQjXGUF2y990Y1jZngpawkWkgnV1TwuqmjiGNT/v9a9vwPDj9oOp9lj1uRgVza0v42dItZLu1+4OIiMhAFSohHAOsdM6tcs5lgV8AZ+eVORu4Lfj9TmCxtdY45553zm0Iji8HSq21aWvtJKDSOfe3oNXtduCc6D/KIOV1oQJUlex9P9Q1Bd5CK9/s2jSt2RybWzsHdV5nd44/rNzG0VPLmVC+79bDmDFc8vZxNLR18btXm4ZaXRERkQNOoQLcFGBdn9f1wbF+yzjnuoBmYGxemXOB55xzmaB8/T6uOfJ6Z6Hu3NC+uiRBc6b/Fri2zm42t3aOyPi3Hjt3ZBhcN+rjb26nOdPNGQfVDPicwyeO4R2Tx/DLlxr3GmpFRERkp1GzkK+19lD8btVTh3DuZcBlAM456urqQq7drhKJRO89vO4uNgNlyQTlwbEJ1U2s2tbUbz1e3NACwGHTx1FXl59fC6Oiupv4H9byViY2qO/qgT/VM72mlJMXTifWz+zTPbnivWVc/NPn+dWr2/ni4nlDqfKA9X02Ujz0XIqXnk1x0nMpToV8LoUKcOuBaX1eTw2O9Vem3lqbAKrwJzNgrZ0K3AV8yDn3Rp/yU/dxTQCcczcCNwYvvYaGhqF/kgGoq6tjl3vEE7Rt20pHcKyELprasmzesmW3oPPCGr8rsTaWJep67s2UyhTL67fS0DBmQOVfa2hnxaYdXPaOCWxtbBzUvcYAZx5Uzd0vbeKkaaXMro1uAePdno0UBT2X4qVnU5z0XIpTFM9l8uTJ/R4vVBfq08A8a+0sa20K+ABwd16Zu/EnKQAsAR50znnW2mrgXuBK59zjPYWdc28BLdbad1prDfAh4LdRf5AhSaXyulDj5DzYkd194P7abRnKkjHGjRnZxtHZNSWDWgvuvteaKEnEOGn2npcO2Rt7WB2V6Tg3PbNJi/uKiIjsQ0ECXDCm7XLgAeBl/5Bbbq29xlp7VlDsJ8BYa+1K4ArgyuD45cBc4Cpr7QvBz/jgvU8DNwMrgTeA+wvxeQYtle6dhQp990PdfczX2m3+BIb+FsAtpNm1JWxt7xrQuLRt7V08unY7J8+upCwZH9L9ylNxLjpiHCu2tPP4m9uHdA0REZEDRcGaeZxz9+Gv1db32FV9fu8AzuvnvK8DX9/DNZ8BFoZb0wgkU7vMQt25H2oXVO2crOB5Hmu3ZXj3XhbALZRZNX69Vm3t4MjJ5Xst++sVjeQ8jzMPrh3WPRfPruK+15q49bnNHD2lfK/ryImIiBzI9F/IQkim8LK7t8DlrwXX0NZFa2duRGeg9hjollqNbZ3c//o2TpxZyZTK4S08HI8ZPn7UBLa0dXHXy1uHdS0REZH9mQJcIezWhdr/fqjPv+XvC1oMAa48HWf8mASrtu59S61frdhKV87j/MPCmXVz6IQyjptewa+WN7JlkOvQiYiIHCgU4AohtWsXakUqTszs2gK3cXuWW57dzPy6Ug6qKx2JWu5mVrAjw55sae3kgde3sXh2FZMqwtv268Nv94c4/nTpltCuKSIisj9RgCuEZHqXABePGSrSO3dj6Mp5fO+JDcQMfP64ycRjIzuBocfc2hI2bO/kpU1t/b7/y5caAQ+7MNw1b8aXJzllbjWPrd1Oa3bPW46JiIgcqBTgCiGvCxXYZT/UXyxr4NWGDj69aCLjy5MjUcN+nXZQDVMrU/zbQ/W81tC+y3ubdmT50xvbOGVOdSR1PnFmJV05jyfrd4R+bRERkdFOAa4ATF4XKvTsh9rNi5tauXN5I++dU8XxM0Z+9mlflek41yyeRnVJnK/9ZR1r+nSn/t+LjcSM4byF0ewWcdDYEsaPSfDY2pZIri8iIjKaKcAVQnLXhXwBqtMJNu/I8v3H32JSRYqPHTVhhCq3d2PLklyzeBrpRIyrHlxHfUuGDS1Z/rK6mdMOqmZsWTQthsYYjp9RyQtvtdKiPVJFRER2oQBXCOkSyLTvssNAVWmcpo5umjNd/PPxkylNFu+jmFCe4prF/k5oV/1pHTc/u4lkzLBkQbR7tb57RiXdHvx1nbpRRURE+ire1LA/GT8Z2tugeefaZtXBWnAXHzGOORHu/RmWqZVpvnbyNDq6czy7oZUzDq6hujTadaBn1aSZUplSN6qIiEiekd1w8wBhZszGA1i7Cqr9VquTZlUyJhnj7+ZVj2jdBmNWTQlfO3kad7/SxN9H3PoGfjfqu2dU8H8vNrK1vYvaiAOjiIjIaKEWuEKYOguMwVv3Ru+hsWVJTj+ohtgI73k6WPPGlvL54yZTmR7anqeDdfyMSjzgiTfVCiciItJDAa4ATEkpTJiCt3bVSFdl1JlWlWZmdZpH12iDexERkR4KcAVips+GN9/Yd0HZzbtnVPJKQ7u21hIREQkowBXK9DmwdQvednUFDtbxMyoANJlBREQkoABXIGb6bP+XdWqFG6yJFSnmjS3h0bXqRhUREQEFuMKZPgdA4+CG6N0zKnljawcbWrL7LiwiIrKfU4ArEDOmHOomaBzcEB2nblQREZFeCnCFNH0OngLckNSVJVkwrpTH1I0qIiKiAFdIZvps2PwWXntbv+97noe3bnWBazV6HD2lnLXNmRHZG3XpxlbWNHUU/L4iIiL9UYArIBOMg2Nd/+PgvL89RO6az+Ite7qAtRo9Dh5XCsArDe0Fu2drtpsfPLGBq/68jn/+/Vr+/Ma2gt1bRERkTxTgCmmGPxN1T92o3t/+AkDurp/i5XIFq9ZoMbe2hLiBVxsK0xK2bGMrn7l3NQ+vaWHJoWM5ZFwp//G3jdz0zCa6cl5B6iAiItIfbS5ZQKayBqpr/T1R83jNTfDyMpg6E+pXw3NPwDuOL3wli1g6EWN2bQmvbOm/Czosma4cdyzdwj2vNDG5Ism1p87g4LpSunMetz6/mbtfaWLNtgz/3/GTqSrRv0IiIlJ4aoErtD1MZPCeeQy8HLGPfR4mTSP32//Fy3WPQAWL2/y6Ul5r7IisBaw75/GlP77JPa80ccZB1fzgfbM4uM7vuo3HDJceNYHPHTuJ1xra+fz9a1i1VePiRESk8BTgCsxMnwNv1eNlMrsc9558GKbNwkyZQeycC2FjPd7fHh6hWhav+eNKyXZ7rGnK7LvwEDyypoU3tnbw2WMncdnRE0kndv9X5KTZVXzrlBl0e/Aff3srknqIiIjsjQJcgZkZs8HL+d2kAW/zBlj9GmbRif6Btx/rt9Td83O8Lu3/2VdPa9grDeF3o+Y8jzuXNzKjOs17ZlXutezcsSWcflA1q5sytGTUUioiIoWlAFdo04IdGd7cOQ7Oe+oRMAZz9AkAGGOInXMRNGzCe/zPI1LNYjVuTJKxZQle2RL+TNS/rdtOfUuWJYeOJWbMPssfOr4MgJcjHpMnIiKSTwGu0GrroLyid0cGz/P87tN5h2Jq63aWW3gkzJmP97v/w+vU9lF9za8r5dWQlxLxPI9fvtTI5Iokx02vGNA588aWkIgZVmwu3LImIiIioABXcMaYYCJD0AK3bhVsXI9ZdMJu5WJ/fzFsa8R7+P4RqGnxmj+ulM2tXTS2hde9/NyGVlY1ZTj30LHEY/tufQNIxWMcNLaE5ZvVAiciIoWlADcCzPQ5sH4tXlcn3pOPQDyBOfJdu5c7+DA45G14992J16FWnh7zg3FwYbXCeZ7HL5c3UleW4MSZVYM6d8H4MlZt7aCjS+v2iYhI4SjAjYTpc6C7yw9xTz0CC4/ElPc/aD521gdhe7NfTgCYVVNCKm5CGwe3fHM7L29p5x8WjCUZH1jrW49Dx5fS7YUXJkVERAZCAW4EmJ4dGf50D2xrxBxzwp4Lz5kP4ybiPfdEgWpX/JJxw9zaktC21PrlSw1Ul8R575zBtb6B350bM6gbVURECkoBbiTUTYTSMrwnH4J0CeZtx+yxqDHG7159ZRle647C1bHIHVxXyhtbM2S7h9d1+VpDOy9sbOPsQ2r7XfNtX8qScWZWpzWRQURECkoBbgSYWAymzQLPwxyxCJMu2Xv5o94F3d14S58sUA2L3/xxpXTlPN4Y5k4Iv1zeSHkqxmnzqod8jUPHl/FqQzud3dofVURECkMBboSY6f56cL2L9+7NzHlQW4f33F8jrtXoEcZEhtca2nmqfgfvP7iWsmR8yNdZMN7fHWK4YVJERGSgFOBGiHnnezDvWgyHHLHvsj3dqMufx2vXWCuA6tIEE8uTQ57I4HkeNz+7meqSOGcdUjOsuiwY5y/ou0Lj4EREpEAU4EaImTGX2Ec+i0kkBlb+yHdBVyfesqcjrtnoMb+ulFe2tON5g++6fGRNC682tHPxEeOG1foGfpicUplihXZkEBGRAlGAGy3mzIeqGnWj9nHwuFKaOrrZ3Dq4BX07unLc9sIW5tSmOXn24Gee9mfBuFJWbGknN4QwKSIiMlgDa/4JgbX2NOCHQBy42Tl3bd77aeB24CigETjfObfGWjsWuBM4GrjVOXd5n3MeAiYBPf1opzrnNkf9WUaCicUwbz8W74k/42U69jnx4UDQMw7ulS3tTChPDfi836zYSmNbF58/bvKA9jwdiAXjy/jjG828uS3DzBo9GxERiVZBWuCstXHgeuB0YAFwgbV2QV6xS4Em59xc4PvAt4PjHcC/Av+8h8tf6Jw7IvjZL8NbD3PksZDNwEvPjXRVisKM6jQlCTOoiQybtmf41YpGjpte0bsZfRgOHe+HyeVaTkRERAqgUF2oxwArnXOrnHNZ4BfA2XllzgZuC36/E1hsrTXOuVbn3GP4Qe7AdtBCKK/Qor6BeMxw0NjSQS3o++PH1+B5cMnbx4Val/FjkowtS4zYgr6NbZ18/v413PjMphG5v4iIFFahAtwUYF2f1/XBsX7LOOe6gGZg7ACu/T/W2hestf9qrQ2nP6xImXgcc8Q78ZY9jdeZHenqFIX540pZ3ZRhZeO+8/0rW9r546tbOOeQ2kF1uQ6EMYZDx5WxYoiTKoZj4/YsX/rjm6zc2sG9rzbxeqNaAUWkcK57fAN/XLltpKtxwCnYGLiIXOicW2+trQB+BVyMP45uF9bay4DLAJxz1NXVRVqpRCIR2T0yJ53Otsf+SGX9KtJHHx/JPUaTi99ZySNrd/DNR9Zz4/lvY2Jl/+PPcp7HbX9aSt2YFJedcBBlqeHNPO3PMbM7eWTtG2SS5UytLg39+v1Z3djGV/68imy3xw/+fiFX//5V7ljWxPVLpmJCGt9XCFH+OyPDo2dTnIrluWzanuGRNS20dRsueOfcka7OiCvkcylUgFsPTOvzempwrL8y9dbaBFCFP5lhj5xz64M/t1tr/xe/q3a3AOecuxG4MXjpNTQ0DOUzDFhdXR1R3cObPANKx9D8l98TmzU/knuMNl85cTJXPrCWf/r1Mr516gzK88JZW2c333/iLVZs2sG/nDqPtpYmoujonFHmb+v12Kvree+coe/sMFArGzu4+i/rSBj4+uLpzCjr4oLDavnxU5u45/nVvGt6ZeR1CEuU/87I8OjZFKdieS5/DlreXt28gy1btoyq/3GMQhTPZfLkyf0eL1QX6tPAPGvtLGttCvgAcHdembuBS4LflwAPOuf22BdlrU1Ya+uC35PAmcBLode8yJhEEvO2Y/CWPonXNbjlM/ZX06vSXHnCFDZsz/LtR9bvsqXV+pYsX/j9Wp5Zv4OPHTWe0+aPj6weU6tSVKRiBdkXdfnmNv71z29SmjB869QZzKhOA3DKnGpmVKW57fktdA5zn1gRkX15/q1WALZnumls7xrh2hxYChLggjFtlwMPAC/7h9xya+011tqzgmI/AcZaa1cCVwBX9pxvrV0DfA/4sLW2PpjBmgYesNYuA17Ab8G7qRCfZ6SZo94Fba2w9KmRrkrROHziGP5x0SSWbWrjv556C8/zeGb9Dr7w+zW0ZLr52snTeP/82kj/7zBmDAvGl0U+kaGzO8c3Hq6npjTBt06dwaSKneP54jHDR48az8YdndzzalOk9RCRA1t3zmPpxlamVvp/B63emhnhGh1YCjYGzjl3H3Bf3rGr+vzeAZy3h3Nn7uGyR4VVv1Hl0LfDxCnkbr4O09lJ7J3vGekaFYWTZ1exubWTny9rYGtbF0s3tjGrJs2XTpjK+PJkQepw6PgynqzfQWNbJ2PLornn8s3ttGZzXPGu8dT1c48jJo3hqMlj+OVLjSyeXUVVyWgf6ioixej1xg5aszk+8vZafvTkRlY3dXD01PKRrtYBQzsxjEImmSL2xW/DnEPwfvI9cnf9FC+n7jKA8xeO5eTZlbywsY13z6jk2lNnFCy8ASyc4K8t99Km6Frhnt2wg2TMcNiEPa9j95Ejx9PRlePny0Z+jIyI7J+ef2sHBnjntAomVSRZ1aQWuELS/5qPUqa8ktjnrsb73xvw7nN4m+qJfeSfMGl/LJTXsg3vlWXw6ktQW4c56QxM2ZgRrnX0jDFcvmgS7zuohrm1JQUfUDuzOs2YZIyXNrdx4qxwtunK9+yGVhZOKCOd2PP/f02rSnP6vGruf30b7zuohunBGDkRkbA8/1Yrc8eWUJGOM6umhFVbtVxrISnAjWImkYSL/xEmTsW783/INWzGzFuA9/JSWL/WL1RSCh3teH+4C3PKOZjF78eUhrcDQTGKxwzzxhZmGY/+7r1gfCkvbYpmIsPG7VnWt2Q5fd6+Z7l+4PBxPLSmhdue38y/njRtn+UH6uUtbTS0dpHpzpHt9sh25zAYFs+uojwd/vIsIlJ8dmS6eb2xgyWH+su1zqpJ88Sb22nr7KYsqb8HCkEBbpQzxmBOPQdvwhRyN30Xb8ObMPcQzDEnYA45AmbMhvo15O7+Od5vf4b3p7sxp5yNWXwmpmT/DnIjZeGEMp5ev4Wt7V3Ulob7r9izG/wZX0dN3vc4k8p0nHMOqeVnSxtY09QRyh6tj69t4d8f29Dveyu2tPGlE6YO+x4iEg7P88h5/v9Yhm3pxlZyHhw5ye/ZmR38/bK6KRPqNoWyZwpw+wnztqOJXXc7xAwmmbfLwPQ5xC//F7w1r/tB7jc/xXv0D8Qu/xfM1JkjUt/9Wc9fXi9tauOEmeGuxfbshh1MqkgyuXJgO0mcPq+GfDNHPwAAIABJREFUXy1v5K6Xt/JP7+p/LaGBynkeP3+xgamVKb54whTScUM6HiOVMNz36jbuWLqFJ+u3s2hqxbDuIyLh+NnSBv6yupn/PmsOyXi4Ie65t1oZk4xxUJ3f2zGrxh+msbqpQwGuQDSJYT9i0undw1vf92fOI/6Zq4h98Vro7iJ37Rfxlj1duAoeIGbXlFCWjIU+kSHTlePFTW0Dan3rUZGOc8rcah5d08KW1uGtG/jEm9tZ15zl/MPqmF6VZkJ5iurSBGXJOOcsqGVGVZobn95Ee6cm1IiMtDe2dvCrFY00tHXx4qbWUK/teR7Pv9XK4RPLelv3aksTVKXjrNZEhoJRgDsAmbkLiH35OpgwmdyPvkHuj78t+P6d+7N4zHDIuFJeCnk9uOWb28h2exw1eXCTUc6eX4sH3PPK1iHfO+d5/F/Q+nbc9N1b2BKx/7+9846Po7j7/3v27tROvVqSZUuuuOCKjcHYpgUwNZQsLaGHkISEJ6GFPMkPAjyhJNQEAoQSSAjOht5xKMYQXHDv3bIkq/dy0rWd3x9zsmVbstWLNe/X62zd3ZbZnZvdz37bCH58fBrlngCvrSvr9H40Gk3XCdqSp5YVExvuIMJpsCS/rlu3X1Dro8ITYGr6/odJIQQ5CeHsrtKJDL2FFnCDFJGQhHHHAzBlJtJ6Afn3p5ABXUW7u5iYFsXeWh9V3ViZfEVhA2EO0WH3RIrbxdzhsXyyo4Z6b7BT+16SV0deyPrWVjzNuJQozhwVz3tbq3Q2mkbTh3ywrYqdlU3cMD2NGZluluXXE7S77yG9efaFKekHXotyEiLYU+0j0I370rSNFnCDGBEegXHTrxDzL0F+tRD7wTuwF32IrGm7gr+srULmbtcWuyNwbA/Ug1tVWM+kI5QPaYvvjk+kKWDz8fbqDq9rS8mCw1jfWnLVlBRiwh08vby4W28YGo2mfZQ1+Hl1bRnTM9ycNDyGE7JiqPEG2VLWfZnxqwsbyIgJIy36wJCdnIRwArakoEa7UXsDncQwyBGGgbjoKuyMYcj3/4V89RnkP5+FkeMQ005AZI9G5u2CXVuQO7dARalaMWcMhnk9YtS4Pm1/f2VEQgSRToONpR7mdEMiQ2Gtj6I6P+eNTezU+jkJEUxNd/Pe1krOH5dAmKP9InBJvrK+3To744jZbNHhDm6YnsYj/y3k4+3VnDM2oVPt1Wg6izdg8+amCk7JiWNITPuSfY4WpJQ8s7wYKeFHM9IQQjAtI5owh+Cb/DomHKb4d3vxBW02lHr4zqhDSxnlJO7PRO2OrHfN4dEWOA0AxqyTMe57GuOePyPOuxyaPEjrBeyHf4Vc8Bxy20bIHoX43rWIK26CynLsh+7EfuYhZFlxXze/39FcD259N1ngVhbWA3Q4/q0lF41PpLopyKLdte1ex5aSf62raJf1rZk5w2OYku7m72vKqPB0LXFCo+koL68uZcH6Cu5bVIDH37mQgYHKN3l1rChs4MrJKfusY5Eug6npbpbk13WL52RTaSO+oNxXPqQlmTFhhDlEj8TBefxB3thYQU2TDvVpRlvgNPsQQkDmMETmMDjvMmRpIRTmwbCRiMSUA5aVJ56K/ORN5CdvIdcuUwWCz7ti30wQGlVOZGVhGdWNAeK7WA9uZaGaMLorFoVj06IYmRjBW5sqOX1kHEY7ZqlYkl/Hnhovvzwxvd21pIQQ3DQjjZ9/sJu/rSrj1pO6Vr5Eo2kvK/bW88G2aqZnuFld1MDj3xTxq7mZ7fqtD3TqfUH+uqKEkYnhnHuQ5fuErBiWFdSzvaJpX9mPzrK6qAGnIfZNG9gShyEYHh/eI1NqLdxRzStryvhkRzW/OXkow+L0vUZb4DRtIlIzEFNmHSLeIBQ/d/4VGPc/g5g5D7nwbexHf4Osa79152in+QK3sYvZqN6AzYYSD9O6YH0DJawuGp9IYZ2P5QX1R1xeZZ5WkBkbxknDO+YGTo8J47vjElm8p7ZbY280mraobgzw5NIisuPDuWtuJtdNS2VZQT3Whoq+blqvsGBdOTXeID+ZeejD1ozMaByCbslGXV3YwPiUSCLaiMUdkRDB7qqmbo+TXpxbS3qMC2/A5s5P9rCq8MjXsKMdLeA0XUIkJGFcewvGTXdC/m7sB+/QLtUQIxMjiHCKLrtR15d48NuyQ/Xf2uKErBiGRLuwNlQcMcng81017Kn2Yk5M6lQl94vGJ5EQ6eSFlSXYOulF04NIKXlyaRGNfptbZ2fgchicOzaBU3JieW1dOcsKureMRn/D4w/yn501zM2OZVTSobFn0eEOjh3SdTdqcZ2PPTVeph7mYTInIZx6n025p/tcnQU1XnZWejl7TAJ/PCubVLeL+xYV8OG2thPuBgNawGm6BTHtRIxf3gsNddgP3I7M3d7XTepznIZgXEpUly1wK/bWE+EUTEjt+vyuDkNw5eQUdlY28cqatuu15VY18ey3JUxMi2JOB61vzUS6DK6aksK2iiYW5/Z/y2y9N8iH26q4a+EenllejDegCxIPFD7cVs3KwgaumZrKsHjlWhNC8OOZQxiZGMFj/y06qjMjv9xdS1PA5uwxbScNnZgVQ1Gdnz3VnT8PX4bG8eGuCTmh5IVd3RgH92VuLYaAk4bHkuJ28cAZw5ie4ebZb0t4bkXJoM141wJO022IUeMxfvUQhIVj/+HXyPUr+rpJfc7EtCjyanydDryVUrKysIFJQ9y4OpA5ejjmZsdyzph43t5cyVetCCuPP8hDXxXidhnc1o7M08Nxck4sIxMjeGV1GU39UBDZUrKmqIFHvi7kmjd38Oy3JdR4g3y8vZrbPs4l7yi+6R8t5FV7+dvqUqZnuDl7zIGZkeFOg7vmZhLmEPzfl3tp8B19SQ1SSj7eXk1OQjhjWrG+NXP80GgEnXejSilZtLuWiamRpLhdbS43PD4cAd02I4OUksW5tRybFrVvbukol4O75g7lgmMS+GBrFZ/tqumWfbXFx9vVg11/u4ZpAafpVsSQoRh3/QGGDMX+8/0E/3Qf9of/Rm7dgPQOvpvhxNSuxcEtK6intMHPzMyuu09bcu20NMalRPKnpUUHPJFLKfnz0mKK633cdlImCV1MvjCE4PrpqVQ0Bnh7U+dnguhubClZtLuGH7+7i7s/z2dVUT1njIrj0fnZPHVuDvecmkVNU5DbPsrl8x6+OWg6jz9o88h/C4l0Gfx8VrpKxDqIFLeLO+dkUlLv47lvS/qglT3LlvJGcqu9zB+d0OrxNxMf6WR8aiRL8jsXO7ajsonCOh8n58QddrlIl0F6TFi3ZaJuq2iiuN7PvIPKMTkMwbXTUhkeH84nnahv2V6qmwK8tKqMTWWN/HNt/5plRgs4Tbcj4hIwbv89Yt58KC1CvvV37D/+GvuWywje/0vsvz+N/cUHyG0bkA1HdyDqqKQIwh2iUwV9631Bnvm2hJyEcE4ZcfiLZkdxOQR3zMkkymXwwOIC6kOWiQ+2VfHfvDq+Pzml1SyzzjAhNYrZw2J4c1MF5X1cVkRKyYq99fziw1we+6aIKJfBrbMzeOmiUdw4Q7nbhBBMSXfz2NnZjE6K4IklRTy5pEi7VPsh1oYKcqu9/HxW+mEzvSekRXHxhCQW5dYedcHvH2+rJtJpMLcd9SZPyIphT7WXwlpfh/ezaHctLkNwQjvKCakptbrngf3L3Lb3K4TgjFFx7Khs6rHZXxasK8cXtJmR6ea9rVVsr+g/SVlawGl6BBERiXHFj3Dc9zTGY//AuPm3iDMuhPAI5IqvkP98FvsPv8b+nysI3n4t9guPITetQdpHl4vD2TwvaknHB/3Lq0upaQrw0+OH4OyCG7MtEiOd3Dknk9J6P49/U8iWskZeWlXKjEw3F47vXMHgtrh6agq2hL8fJu6uJ5FSsrHUw6//k8d9iwpoCqhg90fmZzM3O7bVwsZJUS7uPW0Y5sQkPt9Vwx2f7OlzAarZT16Nlzc3VXBydizHtcNCbU5MYmhsGE8vKz5q6sPVNgX4Oq+OU0bEEuk68u18VpYSQR11owZsyVe5tcwYGk10mOOIy49IiKCk3r/vwbCzBG3J13vUfqNcre/35Ow4XIZg4Y7ut8IV1Hr5ZEc1Z46K5xcnZhAf4eSpZcX9ZqowXQdO0+OI6FiYPAMxeQagbqZUVcDeXGTBHijYjVy7HLn0C0hIRsw6GXHiqYghQ/u45d3D5HQ3L68u4795tcwe1r6EgPUlDSzcUcN3xyUyOqnryQttMS41iuunp/HcihLWFntIjHRxywkZ3V43Ky06jAvGJfL6xgrOHpPA2C7WogLlPius81Ph8RMf4STZ7SImzNjnRqr3BVlb1MDKwgZWFTVQ1RggPsLBTTPSOH1kPC7HkY+xOeljXEokD39VyO0f7+G3Jw9lRKKuMt+X2FLyl2XFRDoNrpue2q51XA6Dm2cN4a6FefxjTRk3zhjSw63seT7dVUPAlswf3b4ZT1LcLkYnRbAkv46LJyS1ez9rixqo8QY5uZ2zyoxIVIkkuVXeLlny1xY3UNMUPMR92pLocAcnDothcW4t105L7dRUg23xjzVlhDkMLjs2GXeYgx/NSOOBxXt5e3Mll3Tg/PUUWsBpeh0hBCQmQ2Iy4tjjAJA+rxJx33yO/PhN5EevQ84YxMw5iONOQsS3f7DIRg9s2wipQyAtA2Ec+YmxJzlnTAJL8+t5/JsikqNcRxQv3oDNU8uKGRLt4opJyT3evrPHxLOjsomv99Ryx5wMYsJ75nxdPCGRz3ZW87sv8rl+Wiqnjog7bMzOweTXePliVw35tT4KarwU1/s5+EE4zCFIinIS5TLYXeXFluAOM5gyxM30DDezh8e2Wb/qcEzLiObBM4Zx76IC7vpPHnfOyWBaN5R1GWj4g5IVhfXsrfVR1uCnrMFPaYOf6sYAZ45O4PuTkzvUp53l0501bCpr5GezhhAX0f7b2LiUKM4em8CHW6uYMzyWcandEybQF9hS8sn2asanRO7LvG0PJ2bF8PKaMjaWepjQzuNflFtLTJjR7t98cybq7qqmLgm4L3NrcYcZR5yB5oxR8XyZW8t/8+o4tZvCTTaXeliSX88Vk5L3uednZcVwQlY0/1pfzolZMWTE9u1UbWIQTkouCwsLe3QHycnJlJeX9+g+jmZkdSVy2ZfIZYsgfzcIAWMmImbMQUw7ERHT+tOYbGpEfvEB8pO3oCHkIggLg8xsRFYODB9JytkXU1Hf0HsHE6KmKcAdn+yh0W/z8JnDW51RQQYCsG0DLzcO4a0t1dx3WhaThnSteG97kVJS77N7TLw1U1jr409Li9hU1siUdDc/mZlGWnTYYceMP2jz+sYKXt+oCrKmx4QxNDacrLgwhsaGkex2UdMUoMIToNwToNzjp84bZExSJNMz3IxJjuxSJm1LKjx+7ltUwJ5qLzfNGMKZow+dD/JoIzk5mbyiEhbuqObdzVVUNKqM6pgwgxS3ixS3i4CtsqXPGRPPDcel9ejMB1WNAX763i5yEiO4/7SsDgvGRr/Nz97fRbjT4LGzszs0L3B/YmeDg1++vZFbZ2e0K/6tGY8/yC8/ysUXkDx+djaxRxDAHn+Qq9/Ywakj4vjxzPZbLa9+YzsT06K4/aTMdq/TEm/A5qo3djBneAw3z0o/7LJSSn7y3m7iIxw8cMbwTu3v4O3duTCP0gY/z5w/4oCHvgqPn5+9v5sRiRHc18rvryfu/xkZGQCH/NAd99xzT7fuaABwT11dzxZ1jIqKwuPpnjkwByMiIhIxahzGvPmIGXMgOgZ2boUlnyMXvoVc+y2UFwMC4pMgEEB+9h7y2Ydg7XI4ZhLG5TfChKmI+GQl5rasg5X/penrT5EZwxDJab16TBFOg6kZbhbuqGZ5QT3zsmMJa3FRkEX52H+6jx1fL+XPrkmcPiKG88b1vPWtGSFEt7oe2iIm3MGpI+KIj3Dy2c4aPtpeRaTLYFJWIo2Nh8YJbi71cO+iAr7Jr+ekYbHcc1oWF41P4qThsUwa4iY7IYJUt4usuHDGJkcyNd3N7GGxnDIijklD3KS4Xd0qJqJcDuZmx7K7ysu7W6ooDFkD99b5qPD4afDZ2FL1d3+avsmWkqqmIAW1XraVN7G1vJGiOh8VjQFqvUE8fhtfUOIPSgJSIqW6W9Q0BbE2lPPwojyW721gVFIEP5qRxo9npnHpsSmcNTqBOdmxzM2OpdFv897WaqoaAxyXGd0lS1zQljQF7FZL5/x5WRF51T7+3ylZRxQfreFyCIbGhfHe1ioE9NpDUnfz4soSajw+fnr8kA49oLgcBuNSonh/axV7qr3MyY49bF99taeOb/LquGF66mHLhxxMSb2fz3epBITxnbB0LsmrY/GeWq6bnrpvXte2EELgC9p8urOGk4bHdMgq2xpL8+t5Z0sl109PPcRjEuVyEBPu4P2tVaS4XYeEU/TE/T8mJgbgdwd/ri1wPYC2wHU/UkrI26XcrJvXwu6tEAwqC1tYONTXwfipGOdfjhh5TOvrb9uA+MfTBIv3IuadhbjkGkRE77pQNpR4uPvzPI5JieKeU7JwCon89F2a3nmN3IThPDvOpMZr82TVh8T89I5eb19vUlrv5+nlxawuaiDZHUZGtJP0mDCGRLtIjwljbXEDH22vJtXt5KYZQ5jezaVUukLQlry4qpQvdtXQ4D80O9UQkBDpJDnKRXKUc59bN9JlEOE0iHQaOB2CSk+AkgY/pfX7XZEnDo/lsmOTie2iNbSgxstbmytZV9xAhSdAsJOXegGcMCyGi8YfPh5TSsk/1pbz+sYKTh0Ry83Ht3/+3IAt2VnZxMYSDxtKPWwqbcQbtJk8xM287FhmZcUQ6TJYsbee+xYVcMWkZC49tmsPOI99U8hXubU8dObwbosztaWkqjFAQqSzRwV8WYOfG9/ZyYXjErlqavtiAA/mg61VPLeihGumpnDh+LZDVO7+LI+iej/Pnj+iQ6I84PPz5LISvsyrx5yYxBWTOuZev39RAbsqm3j+wpHtOpfVjQGue2sH545N4LrpnX9AD9iSn72/C6chePzsnFZ/w7aU/ObTPHKrvTx17ogDyi31pgVOC7geQAu4nkfFuW1AbloDNVWIU89FjJlwxPWSYqIpe/4J5GfvQkIyxlU3IyZM7YUW72fR7hoe+6aIGSlO4nM3scN2kxedTlAYGALuTKtihvUQDB+FccvdCPeR0/YHKlJKvtpTx8YKP7vL6ymu81HjVZlrhoBzxyZwxaSUdmXY9RW+oE1NU5DqpgDVjUGqmgKUNfgp9/gpbwhQ5vFT6QngbUNBhTsEadEuUt0uXA6DZQV1RDoNvjcxiXPHJnS4gPPW8kbe2FjB8oJ6XA7BjMxohkS7SHYrMZkc5cIdZtAUkHj8QRr9Ng0+m8aATcAOWeFs9RLAeVOGExlsf9jBv9aX88915cwZHsP/nJjRZgZ1U8Dm24J6vs6rZU2RZ1+R1KGxYUxMiyLKZfD1nlpKGwKEOQSzhsawucxDuNPg8bNz2pWEcjhqvUF+9v4uar1BZmXFcMExiRyT0jkhJ6VkWUE9C9aXs7vKi9tlMCIxgpGJEYxKjGB0UkSrYROd3dffVpfxzuZKnr1gxBGtU4fbzkNf7WV5QT0PnDG81djcCo+fG97eySUTkrhy8qFzYre57aIC7MfvJjg0m+dm3MB/dtZwwTEJXDsttV0irtYb5Jo3tnPeMYlcO639AvXBxXvZWOrhxQtHdqrwuS0lC9aX86/1Ffz25KGHzW4uqPXy3pYqfjAl5YDMXC3gehYt4AYxzX0jd27B/tuTUFwA4yZjnDwfJh+PcLTf6iH9qqSEcLXfrdCM9dk6Xi0Owx1oZFS0YPTIDEYnRTImOZLESCdy9VLs5x6GIUMxfvE7RGz7sswGKi3HTIMvSHG9n0in0edBwt1J0JZ4gzaNfiWW/EFJYqST2HDHATe15pkFVhY2kBbt4qopKczIjCbMIQ65+QVsSXkokaC43s+Xu2vYUNpIdJjB2WMSOGdsAvFddCd15nr25sYKXl5TRly4g+yEcIbH73+VewJ8vaeWbwvq8QYlCZFOjh8azaS0KCakRh1Qz82Wki1ljSpAfU8tDX6b+08f1u7g+yNR4fHzwdYqPt5RTYPPZmxyBBcck8isrJh2WQ+llHy7t57X1pWzq8pLeoyL74yMp6zBz47KJnZXefeVnMhJCGfu8FjmZMd2yBXZkt1VTby4spR1JR5OHZ3MLTO7ZoWs9wX5xYe5gOSx+TlEH2T1fWdzJS+uKuWp83IYGtu+RAm5exv2k7+DhnqQEv7vWV7IM/hgaxXzR8dz4wwVIymlpMFvU90YoKTez54aL3nVXvZUeymo9eELSh6bn92hjO9VhfX87osCbj8pg5M6OAXg1vJG/rqihO0VTZyQFc2dczI7FQagBVzPogXcIKZl30i/D/mfd5BffgSV5RCfiJhzBmLOmYiEQ10KsroSdm5B7tyM3LkF9uwEpxMx+XjEzDkq5s555Auz3LIO+8l7acjIIfqm2zGSW3/ClJtWYz/1e2Up/OV9iMTei4nrbfSYOZTVRQ28tKp030wZhoAolxFyxTrw+IJUNAYOyMRNinRywbhEzhgV321Wy872zZK8OlYU1pNb5SWvxouvhQUyNlT64aThMYxPiWqXWPIHlXsyNbpz4udwNPptPt9Vw3tbKymq8xPmEGTFhZEVG05WfDjD4sKIDXdS5w1S5wtS5w1S6w2ypqiBHZVNDIl2cemxyczLjj3gWPxBSX6Nl42lHhbn1rKtQhWbHZ8SyZzsWNJjwva51vf1rdM4RDhUNQZ4dW0Zn+6sITrM4PJJKVw5ayTVVV2f3WRbeSN3/WcPU9PdnH9MIi6HwGkIXIbg8SVFOA3BH8/Kbte25KbV2E8/ADFxGNf8HPuR3yDOuRRx/uW8sqaMNzdVMjQ2DL+t+tJ3kFU6KdLJsJDQn5QW1eGwiaAt+dE7O8mIDePe04YBSqSvLGxgdVEDEU7BMclRjEuJZGhcGIYQVDcGeGVNGZ/tqiEh0sk1U1OYd4S4wMOhBVzPogXcIKa1vpHBIKxfgf3lR7Bxtcp6dceop0cpQdpg29AUCrJ3uiB7NGLkWGj0IFd+oxIlotwqS/bE0xCjx7e6f7ljM/bjd0NiCsbtD7SZUbt/+U3YT94LcYkYdzx4xOUHKnrMtE7QlizNr6O43o/Hb9PoVwkHHr9NpMsg1e3a535Ni3aRHOXqtozbZrqjb4K2pLjez57qJqJcDo5Na59o622CtmRlYT0bSjzk1fjIr/FS7ml9HmNDqIzoi8YncnJOXLuKbRfV+fgqt5Yvc2spaGM2BKchSIhwEB/pJCHSidtlsCS/Hn/Q5pyxCVw6MZnocEen+0VWV0JhHoybvE+kvLulkhdWlra6/A+PS+XcsUcu7G1/+zXyhUchfSjGLfcg4hMJPn43FOVjPPBXEAbvba3i2731xEc4SYx0khDpIG7x+6RsX0mWy0/MKaEH6MjOW1gXrC/ntXXlXDgukXUlHnaGZmhIinISCMp9IRruMIPRwWq2Bd34DAfnH5PI9yYmtVkwuL1oAdezaAE3iDlS38iyYuQ3n0F9LSCUmDNClozEFMSocTBsxAGWNhkIwOY1yOVfIdcsVUJv4nSMS65BZO5PaZd7dmA/8huIiVdTjcW3b7YDuW0D9uP3wNBsZYmL6LnCvn2FHjP9l8HeNx5/kPwaH/XeIDHhjn2vKFfnM42llBTV+anxBmgMCXL1Cu6Lp6xqDFLVGKC6KcAxKZFcPSX1gJCCjvaLLC5ALnwbueRzCAQwfvJrxNRZ+77PrWqi3rc/DtLv8yFXfs2M48YRljP6sMciP/8A+a+/wqhxGDf/BhGlLGf2t18jn3tYhYGMPzTWWBbsxv7dLYiZ85A1lbB1PUS6ESefhTj1vHZfI1tS7vHzo3d2ErRhbHIkMzKjOS7TzfBQrbyiOj9byhvZvGkXW/dWk9pUydVDbbIu/l6H99UaWsD1LFrADWJ6um+kz4v84kPkhxY0NiJmn4Y4/wpoqMP+4/9CRCTGHQ8gEtsfEAwg1yzD/ssDqkTKzb9tNe5O7s1D5m6DsmIoL0GWl6i/k1Ixvnddm1bB/oAeM/0X3Tf9k/b2i9y5BfuTN2HNMnC6ELNPQ27bCE2NGPc+hQhvPcbMXvBX5GfvgcOBOO9yxPyLDymKLivLsF/5s/JcTJ6JcePtiLD9sXLS78e+/RrE+CkYN95+6D6e+wNy/QqMB19AuKORu7cjP3kTuWoJhIVj/O8jiPSOz8hTUOslJszRZjkRWVGK/btbIH0oInM48quFiO9+H+Mcs8P7OpjeFHB6JgaNphsRYeGIMy9Ezj4N+cG/VWHh5V+CKxxcLoxb7++weAMQU45HXP0z5EtPKDfFjbchDIcqj7J5LfYnb8Gm1aGFDTXTRcoQxLHHITetwX74V4iZcxEXX3NUx9JpNBqFlBJpvYj89B2Iikac/T2VrR8bj9y2EfsPdyE/sBAXXXXouts2Ij9/HzH7NPD5kG//Qwmt63+JSBmitv31f5DWC2DbiMtvRJx8NsI4MO5SuFyI4+chF3+CbKg7IKNeFhcgV3yNOPMihFtZ7ETOaMRNdyKL92I/eAf2357AuPPBDs+mc7iEC2kHsZ9/FKSNccOtkJQCfnWMdng4xukXdGhffYkWcBpNDyCiYxGXXo889RzkW39H7tmhLGcpnZ9/0TjxNOz6WuS/X4JXY5BjJiAXvgV5uyAuAXHRVYjpJ0JiKsK5f2hLbxPy4zfUFGVrlqkL+RnfRbi6nuEp/T7YuBq5fiX4mpTLGaEChAwHjD0WMX32Ae3RaDQ9j1z4NvLTd0I1L689IPRCjJmAOOEUtcwJpx5g5ZJeL/bLT0JSKuKyGyE8AibPRL76jHJ3Xvh95LoV6oFx7LEYV//ssNc1Mft05OfvI5cvRpxyzv79fPQGuFyI7xw36nDlAAAS1ElEQVQqmMSQTMTlNyKffwT56buIMy5s+zgD/nYlj+1b/sN/w45NiOt/sb/d19yC9PmQ/3oB2xWOMe+sdm+vL9FXVY2mBxEpQxCtuA46i3HGhUrEffQGLP4YhgxFXHUzYtYpbZYzEeERiAuuRJ54GvbrL6mn6Y/fgNQMRGo6pKYra11KurLcxScdtjSK9Hlhwyrkym+Q65armL9It5oxww4VtZU2+Hzw1ULkm68gTj8fMec7R3VhYk3/Q9pB2LoBuWyREh0jx2GY13XpQWogYC/7Evn6S2oe6StuOsQyBiAuuQa5djn2P59RsbWheD759j+gtEh5C0KiTxw/DzlqPPZLjyMX/BXCI9R2553V6rYP2M+wETBsBPLrTyEk4GRFKXLZImW1i219Ojoxcy5yxdfIt19FTpqJGHLolFz20i+QL/9JTbN42Q/3xd61hdy5BfneAsTMeRizTtm/L4cD44e3Yj/tQ776F2wpEXPPPOKx9TU6Bq4H0DEj/ZejoW+klMhFH6lSJ5NmdPgiIzevRa5egiwrhtIiqChVs1q0JDYeEpIhJg78PiXSGj3gbVQZt4EARMcgpp6AmD5bWdoOsrJJ24b1K7EXvgnbNqos3XlnIcZNAVdY6OUCVxjxYU6qNm+A4gJkcQEU74Xa6kOWwx2DGD0eMfZYGD6qxy17Ukp1zJ4GiIhCRPXttEvS2wRb1yM3rkZWV4DfD4HmVwCiYxEjxiJGjIWcMV3K5mtmoI0ZKSXk71aibfliqK6EiEgYNxk2rYFgUFmg518yoBOC2uoXuWWdSnoaORbjf353WEu7/cWHyH8+g/jhbRgz56qs94fvQsybj3HlTYdu27aRK79Rrs4OTEdof/4+8rXnMH77OGLYCOxXn0F+tRDj988dNqRDVldi330zZGSpxK8WrlT7s/eUmEzPgpK9EJugrIETp7W+rUYP9r23gJQY/++JVsey9Hmx/3w/bF4LQ3MwLrgCJs/sUEkRncTQs2gBN4jRfXMoMhiEyjIoK0ZWlauaeFXlyMoyqKtVU5VFRqmbXUSkiqeZMBXGTGx34WO5ayv2wrdg1VJlnWsLw4CUdBiSqZ7MAwEI+JWrNuBXbSvMU8uGR8LocYgRxyg3j8MJDiP0vxNcYYiwkAAMC1NxiM2XQBn6x5ZQW62OtaIUKstCx10Dnnol3JqtikJA5nDEqPEwahxi1HhEUvviGaUdVIVN62pUhnNdLdJTD2HhSmRFREFklDq/UoIdVKI6GAC/X9Ue3LAKtm9U5yQsHJLTwOlUZW2aX1XlUJS/v70ZwxDZo1XZm5zRqv0dcDdBLyT+2DYgOxzndMh2KsqQy79ELl2kfiMOB0ycjjh+HmLSTER4OLKqAvnmy2qZ+CQ1nd7MuV2at7WvaLUkUkEu9sN3QXwixp0P7YstawtpB7F/fztUV2L8v8ewH7oLggGMe/7UreJWNtRh33Y1Yt58xFkXY9/1Q8QJp2BcdfMR17WXfoF84THEpddjnH6BeoB99zXk+wtgyiyMG2+Dgj3YLz0ORfnKcva9axERUeraVpSPzNuJXPYlbF6Hccfv1Rg+zDmRyxYj33tNJYFlj1ZCbsI0NXbzdiHzd6n/C3IxfvPoAYkbWsD1LFrADWJ03/QtsqIMykuUVS/gU7NZ+H3EpqRRFx2nXLlHEBiyrkZNo7ZlPXLr+v2Cpas4ncrqmJiixKM7WrmGm/+vrUJu3ww7tyirHCjh2NrNv2X9QCkPtXB2hszhiAnTlIVh1Pg23dzSUw+7tyN3bUXu2gK7tyurafMxDs1BpGcpYetwKLHrdKqYRcH+OEbU31ExMXiavEocGw71fW11SOSXQ1UFVFcoURmfqApix6n/EUKJYE8DsrFBieLGRnX+mhrB2wQ+rxLu8Unq3CeqPiAySgnemipkbTXUVKl14hIgMVklAyUkgdOlsha3bVBtHjVOibbpJ7VZN1Hu2Iy94K+wZ4eyNmePRgwfpUTu8FGqDqSnXp23hnqor0PWVimBXFmOrKpQf/u8kJapYsiGDFXnNS0d3LEdmqFF+ryh/TVAXTWypkqd4+bjDvgPWSc8JhavK1xZyWPiEFFR2P98Dmwb464/tP/hYvc27Aduh7hEqK5Q7tRxk9vd9vZiP/cH5KY1yjW66COM+/+iQjiO1D4psZ/6P9i8BuO3j6uSJV98oDL8f3DzvodI6fcpd+t/3la/n9h4KMhV1xpQD0sXfr/dSQoyEEAu+Rz5gaUe7twx+8cRQFIqZOVgfP8niLj9M+VoAdezaAE3iNF90z/pSr8o61xAWauarVYBZbnC71WxeD4vBHzK8rbvMhiq8xcTt++C3x53tAwGYW+uEnPlJa0toQSJECojWAglkqJjlds5dMMlyq1uLo0e9WryIJsaVbscDuUeNhxKZA3N7lT2MoRciuUlyNwdkLsduWcHlBQeeL6CAQgqSxjtuSc0C66EJNWu+ETweVWR2OZXbbXaXmSUEsBRboiKhohIRHjImhsRoSypwSBUlSlBWFmmxFEgoL6Li1c349gERHiEElKV5Uo0NnpUe9IyEbNOVsKtnfFt0g4il38Fm1arc1NccORjF2J/eEFiMsLpQhbvVev6vAcuGxamjjcqWp0DUMfZ0rra6FECsRWBBihhHRuvxPEBjQcj4MOuqVLnqZmISFXwOyunXeegGfvvTyMXf4w4eT7GlT/u0LrtRW5crYqYo2LqjBtubf+61RXKlRoqqC7O+K5KzGjl4Unu2IT975eUBX7YCBg2EjF8JKRldMrKKwN+Fb+3extkDkNkqZi+tuaoPioFnGmaZwFPAA7gecuyHjzo+3DgFWA6UAFcallWrmmaScDrwAzgb5Zl3dxinenA34BI4EPgFsuyjnRAWsANYnTf9E90v/RPpG2TnJhIeWlJSHiErIpu9xFvhtIOAqJTgeDStiHgP8A11epyjR5luUpM6bIbVDZ5lFssd7uy9LlDgtsdrf6OiVXWxVYsxNK2lSWyKF/FlnrqQxa1emURbWoMFQUPCXLDUOcvKiRs3TH7xJ6IjYPYBCXcotxtHldycjJlZWVq23XVKtwhMaXVaQCPeOyNHuQ3nyFmn95jcYHSDmLf9UOoLMe458+IzGEdWt9e+gXyxccRF/4AcdbF/dbtfdTVgTNN0wE8BXwHKAC+NU3zXcuyNrVY7HqgyrKsUaZpXgY8BFwKNAG/BSaGXi35C/BDYBlKwJ0FfNSTx6LRaDSDBWEYCKfziEKq9XU7H9MmDONQq1Nry0VG7bdudREREaXiOsccfJtpx7qGoeqJJaUcepftQYQQIQtnFKRmdH47kVGI087rxpa1sg/DgfG965BlJR0WbwDGrFOQk4/vlsSco4XeypGdCeywLGuXZVk+YAFwsCP6AuDl0N+vA6eZpiksy2qwLOtrlJDbh2ma6UCsZVlLQ1a3V4Dv9uhRaDQajUaj6RTiuJMw5l/c+fW1eDuA3qoDlwm0jDQuAI5vaxnLsgKmadYASUBbtsjM0HZabvPQQjGAaZo3AjeGtk1ycs9Wonc6nT2+D03n0H3TP9H90n/RfdM/0f3SP+nNfhkUhXwty3oOeC70VvZ0rI2O5+m/6L7pn+h+6b/ovumf6H7pn/RgDNwh9JYLdS+Q1eL90NBnrS5jmqYTiEMlMxxumy1nuW1tmxqNRqPRaDRHHb1lgfsWGG2aZg5KZF0GXHHQMu8CVwNLgEuAzw+XUWpZVpFpmrWmac5CJTFcBfypJxqv0Wg0Go1G05/oFQucZVkB4GbgE2Cz+sjaaJrmvaZpnh9a7AUgyTTNHcAvgV81r2+aZi7wKHCNaZoFpmk2l1H+CfA8sAPYic5A1Wg0Go1GMwjQhXx7AB2b0H/RfdM/0f3Sf9F90z/R/dI/6c06cL0VA6fRaDQajUaj6Sa0gNNoNBqNRqMZYGgBp9FoNBqNRjPA0AJOo9FoNBqNZoChBZxGo9FoNBrNAEMLOI1Go9FoNJoBhhZwGo1Go9FoNAOMQVkHrq8boNFoNBqNRtMBdB041Eno0Zdpmit7Yz/6pfvmaHnpfum/L903/fOl+6V/vnqwXw5hMAo4jUaj0Wg0mgGNFnAajUaj0Wg0Awwt4HqG5/q6AZo20X3TP9H90n/RfdM/0f3SP+m1fhmMSQwajUaj0Wg0AxptgdNoNBqNRqMZYDj7ugFHG6ZpngU8ATiA5y3LerCPmzQoMU0zC3gFSEOVjnnOsqwnTNNMBP4FZAO5gGlZVlVftXOwYpqmA1gB7LUs61zTNHOABUASsBL4gWVZvr5s42DENM144HlgImrcXAdsRY+ZPsU0zV8AN6D6ZD1wLZCOHjO9jmmaLwLnAqWWZU0MfdbqfcU0TYHSA2cDHuAay7JWdVdbtAWuGwndlJ4C5gPjgctN0xzft60atASAWy3LGg/MAn4a6otfAZ9ZljUa+Cz0XtP73AJsbvH+IeAxy7JGAVXA9X3SKs0TwMeWZR0DTEb1kR4zfYhpmpnAz4HjQoLBAVyGHjN9xd+Asw76rK0xMh8YHXrdCPylOxuiBVz3MhPYYVnWrtCT0ALggj5u06DEsqyi5icdy7LqUDeiTFR/vBxa7GXgu33TwsGLaZpDgXNQlh5CT6mnAq+HFtH90geYphkHzAVeALAsy2dZVjV6zPQHnECkaZpOIAooQo+ZPsGyrMVA5UEftzVGLgBesSxLWpa1FIg3TTO9u9qiXajdSyaQ3+J9AXB8H7VFE8I0zWxgKrAMSLMsqyj0VTHKxarpXR4H7gBiQu+TgGrLsgKh9wWosaTpXXKAMuAl0zQno9xyt6DHTJ9iWdZe0zT/COQBjcBCVN/oMdN/aGuMtKYJMlECvMtoC5zmqMY0zWjgDeB/LMuqbfmdZVkSPbVar2KaZnPsyMq+bovmEJzANOAvlmVNBRo4yF2qx0zvY5pmAsqSkwNkAG4OdeFp+gm9OUa0gOte9gJZLd4PDX2m6QNM03ShxNurlmW9Gfq4pNmEHfq/tK/aN0iZDZxvmmYuKsTgVFTcVXzIPQR63PQVBUCBZVnLQu9fRwk6PWb6ltOB3ZZllVmW5QfeRI0jPWb6D22NkR7VBFrAdS/fAqNN08wxTTMMFWj6bh+3aVASiqt6AdhsWdajLb56F7g69PfVwDu93bbBjGVZd1mWNdSyrGzU+PjcsqwrgS+AS0KL6X7pAyzLKgbyTdMcG/roNGATesz0NXnALNM0o0LXteZ+0WOm/9DWGHkXuMo0TWGa5iygpoWrtcvoGLhuxLKsgGmaNwOfoDKFXrQsa2MfN2uwMhv4AbDeNM01oc9+DTwIWKZpXg/sAcw+ap/mQO4EFpimeT+wmlAgvabX+RnwaugBdBeqXIWBHjN9hmVZy0zTfB1YhcquX42q9v8Besz0OqZpvgacDCSbplkA3E3b95UPUSVEdqDKiFzbnW3RMzFoNBqNRqPRDDC0C1Wj0Wg0Go1mgKEFnEaj0Wg0Gs0AQws4jUaj0Wg0mgGGFnAajUaj0Wg0Awwt4DQajUaj0WgGGLqMiEaj0fQioanddgOuFlMhaTQaTYfQFjiNRqPRaDSaAYYWcBqNRqPRaDQDDF3IV6PRDHpM08wA/gTMBeqBxyzLetI0zXuAiUAQVVF9O3CtZVlrQ+uNA/4CTEHNcXiXZVnvhr6LBO5HTXcUD6wHvgOkoVyo1wD3AVGh/f1fbxyrRqM5OtACTqPRDGpM0zRQ8xi/g5oSZyjwKfBj4ATgf4HLQ9/fAvwUGBNafTPwIvBH4KTQMsdZlrXVNM2ngAnAlUAxcDywEkhHCbjngZ+HtrUcmGJZ1uYePlyNRnOUoAWcRqMZ1JimeTzwb8uyhrX47C6UsNoDnGVZ1qzQ5wbK0tY81+G/gQzLsuzQ968BW4F7gQZgVrO1rsW2s1ECLsuyrILQZ8uBRy3LWtBTx6nRaI4udBaqRqMZ7AwHMkzTrG7xmQP4CiXg8ps/tCzLDk1gnRH6KL9ZvIXYA2QCyUAEsPMw+y1u8bcHiO70EWg0mkGHFnAajWawkw/stixr9MFfhGLgslq8N1Au1sLQR1mmaRotRNwwYBtQDjQBI4EDLHAajUbTHWgBp9FoBjvLgTrTNO8EngR8wDggMvT9dNM0LwLeRcWseYGlgEBZzu4wTfMRYDZwHjAjZKl7EXjUNM0fACXATGBV7x2WRqM5mtFlRDQazaDGsqwgcC4qk3Q3ynr2PBAXWuQd4FKgCvgBcJFlWX7LsnwowTY/tM7TwFWWZW0JrXcbKvP0W6ASeAh9zdVoNN2ETmLQaDSaNgi5UEdZlvX9vm6LRqPRtEQ/DWo0Go1Go9EMMLSA02g0Go1GoxlgaBeqRqPRaDQazQBDW+A0Go1Go9FoBhhawGk0Go1Go9EMMLSA02g0Go1GoxlgaAGn0Wg0Go1GM8DQAk6j0Wg0Go1mgKEFnEaj0Wg0Gs0A4/8Dx/i1d+07AYQAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_cnn_transfer = tf.keras.models.load_model(\"model_experiments/loaded_model\")\n",
        "model_cnn_transfer.evaluate(test_windows, test_labels)"
      ],
      "metadata": {
        "id": "O6oTaTqnNVLP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d39d9741-369d-47a0-bfbe-cac56cdf4ece"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "3/3 [==============================] - 0s 4ms/step - loss: 1.2727e-04 - root_mean_squared_error: 0.0113 - mae: 0.0086\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.00012726533168461174, 0.011281194165349007, 0.008647440932691097]"
            ]
          },
          "metadata": {},
          "execution_count": 195
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_cnn_preds = make_preds(model_cnn_transfer, test_windows)\n",
        "len(model_cnn_preds), model_cnn_preds[:10]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gx4JtVYw9ZYb",
        "outputId": "4dc4234a-bb0a-436a-fedc-128d7ef990a9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(66, <tf.Tensor: shape=(10,), dtype=float32, numpy=\n",
              " array([0.04899385, 0.05292315, 0.05794185, 0.05821697, 0.06042644,\n",
              "        0.0637238 , 0.06255518, 0.06449168, 0.06891129, 0.06394944],\n",
              "       dtype=float32)>)"
            ]
          },
          "metadata": {},
          "execution_count": 196
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_cnn_results = evaluate_preds(y_true=tf.squeeze(test_labels), # reduce to right shape\n",
        "                                 y_pred=model_cnn_preds)\n",
        "model_cnn_results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zbvpgRLz9JAf",
        "outputId": "ed69d937-5265-4b35-9202-b86c4aff9c6b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'mae': 0.008647441,\n",
              " 'mape': 14.754489,\n",
              " 'mase': 0.9535752,\n",
              " 'mse': 0.00012726533,\n",
              " 'rmse': 0.011281194}"
            ]
          },
          "metadata": {},
          "execution_count": 197
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(12,5))  \n",
        "plt.plot(tf.squeeze(test_labels), color='blue', label='ET0 true')  \n",
        "plt.plot(model_cnn_preds, color='red', label='ET0 predicted')  \n",
        "plt.title('ET0 Forecasting')  \n",
        "plt.xlabel('Date')  \n",
        "plt.ylabel('ET0')  \n",
        "plt.legend()\n",
        "#plt.savefig(\"eto.png\")\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "MoVucZaO9edq",
        "outputId": "35b77376-21d3-40d1-de62-f1697b503565"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "barWidth = 0.20\n",
        "fig = plt.subplots(figsize =(8, 10))\n",
        " \n",
        "# set height of bar\n",
        "total_time = [23.5, 19.5]\n",
        "wall_time = [23.6, 20.8]\n",
        "\n",
        " \n",
        "# Set position of bar on X axis\n",
        "br1 = np.arange(len(total_time))\n",
        "br2 = [x + barWidth for x in br1]\n",
        " \n",
        "# Make the plot\n",
        "plt.bar(br1, total_time, color ='r', width = barWidth,\n",
        "        edgecolor ='grey', label ='Total Time')\n",
        "plt.bar(br2, wall_time, color ='g', width = barWidth,\n",
        "        edgecolor ='grey', label ='Wall Time')\n",
        " \n",
        "# Adding Xticks\n",
        "plt.xlabel('models', fontweight ='bold', fontsize = 15)\n",
        "plt.ylabel('time', fontweight ='bold', fontsize = 15)\n",
        "plt.xticks([r + barWidth for r in range(len(total_time))],\n",
        "        ['CNN model', 'pretrained CNN model'])\n",
        " \n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "TPVvpMNw9kiQ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 611
        },
        "outputId": "a0c19656-8274-4e88-cd57-10a7d45cc599"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x720 with 1 Axes>"
            ],
            "image/png": 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ANErkAaBRIg8AjRJ5AGiUyANAo0QeABol8gDQKJEHgEaJPAA0SuQBoFEiDwCNEnkAaJTIA0CjRB4AGiXyANAokQeARok8ADRK5AGgUSIPAI0SeQBolMgDQKNEHgAaJfIA0CiRB4BGLdyUmUspeybZP8niWusJMzIiAGAgph35Usp7krw2yVCSsSQnlFL+M8neSQ6rtX54ZoYIAGyOaW2uL6W8Nsnr+vmH+n9J8vf95UNmZHQAwGab7nvyf5Ru7X3FpOmf7L8+aWAjAgAGYrqR36P/esyk6T/svy4dzHAAgEGZbuTv7r8+aNL05f3XkcEMBwAYlOlG/sr+6z+MTyilvD7Jv6TbjL9ywOMCALbQdCP/V+l2sPu9dFFPkr9L8ov95b8Z8LgAgC00rcjXWj+R5Igkd+S+veuH+uuvqbV+cgN3BwDmwLSPeFdrPSPJI5M8J8lLkjw3ySNrrafO0NgAgC2wSUe8q7XeleSSGRoLADBAm3LEu2enO+jNbkkeMOnmsVrrswY5MABgy0wr8qWUNyc5cYqbxw9zCwDMI9Ndk39d7ov5zUnumbERAQADMd3I75Iu8PvWWv9zBscDAAzIdPeu/1D/de1MDQQAGKzprsm/KckvJ/lMKeX8JDcmWTNxhlrrcQMeGwCwBaYb+QOS/Ea6vepfOsU8Ig8A88h0I/+3SXbYwO32rgeAeWa6kd8tXchfn+TSOOscAMx70438J5IcnOS8WustMzgeAGBAphv5f06yX5KLSyknJflOfn7Hu8sHPDYAYAtMN/I13eb6hyQ5fT23j23CYwEAs2BTwjw0Y6MAAAZuupH/gxkdBQAwcNOKfK317JkeCAAwWFNGvpTyqCSptd44fnlDaq03DnJgAMCW2dCa/A3pjlW/sL+8oQPe2PEOAOaZjYV5aIrLAMA8t6HIH5f71t6P3cjjOKwtAMwzU0a+1vqOCVdfkWRtrfUxk+crpXwgIg8A885030d/dKYO+eH9bS8byIgAgIHY0N71OyfZZdK03XL/9+b36r+uHfzQAIAtsaE1+TcleXt/eXwt/ob1zDeW5KYBjgkAGIDp7l0/Nun6ZCcPZjgAwKBsKPKXTbh8TLrQT9zLfizJj5J8udb6xcEPDQDYEhvau/6zST6bJKWUA5KM1Vo39lE6AGCemO6x6/ef4XEAAAO2YK4HAADMDJEHgEaJPAA0SuQBoFEiDwCNEnkAaJTIA0CjRB4AGiXyANAokQeARok8ADRK5AGgUSIPAI0SeQBolMgDQKNEHgAaJfIA0CiRB4BGiTwANErkAaBRIg8AjRJ5AGiUyANAo0QeABol8gDQKJEHgEaJPAA0auFsPlkp5cwkv5Xk1lrrXv20xUk+nGT3JDckKbXWH83muACgRbO9Jv/+JM+dNO3oJJfWWvdIcml/HQDYQrMa+Vrr5UlumzT54CRn95fPTvI7szkmAGjVfHhPftda6/f7yzcn2XUuBwMArZjV9+Q3ptY6VkoZm+r2UsqRSY7s582SJUsG9tzDw8MDe6z5bMHQ0FwPYVYMDw8P9PcDZoPlUFvmw3JoPkT+llLKI2qt3y+lPCLJrVPNWGs9Ncmp/dWx1atXD2wQo6OjA3us+Wzt2FiyDby+RkdHM8jfD5gNlkNtGfRyaOnSpZt8n/mwuf6jSV7eX355kvPncCwA0IzZ/gjdh5Lsn2RJKeW7SY5J8q4ktZTyqiTfSVJmc0wA0KpZjXyt9dApbnrWbI4DALYF82FzPQAwA0QeABol8gDQKJEHgEaJPAA0SuQBoFEiDwCNEnkAaJTIA0CjRB4AGiXyANAokQeARok8ADRK5AGgUSIPAI0SeQBolMgDQKNEHgAaJfIA0CiRB4BGiTwANErkAaBRIg8AjRJ5AGiUyANAo0QeABol8gDQKJEHgEaJPAA0SuQBoFEiDwCNEnkAaJTIA0CjRB4AGiXyANAokQeARok8ADRK5AGgUSIPAI0SeQBolMgDQKNEHgAaJfIA0CiRB4BGiTwANErkAaBRIg8AjRJ5AGiUyANAo0QeABol8gDQKJEHgEaJPAA0SuQBoFEiDwCNEnkAaJTIA0CjRB4AGiXyANAokQeARok8ADRK5AGgUSIPAI0SeQBolMgDQKNEHgAaJfIA0CiRB4BGiTwANErkAaBRIg8AjRJ5AGiUyANAo0QeABol8gDQKJEHgEaJPAA0SuQBoFEiDwCNEnkAaJTIA0CjRB4AGiXyANAokQeARok8ADRK5AGgUSIPAI0SeQBolMgDQKNEHgAaJfIA0CiRB4BGiTwANErkAaBRIg8AjRJ5AGiUyANAo0QeABol8gDQKJEHgEaJPAA0auFcD2BcKeWGJHckuTfJmlrr8rkdEQBs3eZN5HsH1FpXz/UgAKAFNtcDQKPmU+THknyylHJlKeXIuR4MAGzt5tPm+l+vtd5USvmFJJeUUq6rtV4+cYY+/kcmSa01S5YsGdiTDw8PD+yx5rMFQ0NzPYRZMTw8PNDfD5gNlkNtmQ/LoXkT+VrrTf3XW0spH0nylCSXT5rn1CSn9lfHVq8e3Nv3o6OjA3us+Wzt2FiyDby+RkdHM8jfD5gNlkNtGfRyaOnSpZt8n3mxub6UsmMpZafxy0meneSauR0VAGzd5sua/K5JPlJKSbox/XOt9eNzOyQA2LrNi8jXWr+VZO+5HgcAtGRebK4HAAZP5AGgUSIPAI0SeQBolMgDQKNEHgAaJfIA0CiRB4BGiTwANErkAaBRIg8AjRJ5AGiUyANAo0QeABol8gDQKJEHgEaJPAA0SuQBoFEiDwCNEnkAaJTIA0CjRB4AGiXyANAokQeARok8ADRK5AGgUSIPAI0SeQBolMgDQKNEHgAaJfIA0CiRB4BGiTwANErkAaBRIg8AjRJ5AGiUyANAo0QeABol8gDQKJEHgEaJPAA0SuQBoFEiDwCNEnkAaJTIA0CjRB4AGiXyANAokQeARok8ADRK5AGgUSIPAI0SeQBolMgDQKNEHgAaJfIA0CiRB4BGiTwANErkAaBRIg8AjRJ5AGiUyANAo0QeABol8gDQKJEHgEaJPAA0SuQBoFEiDwCNEnkAaJTIA0CjRB4AGiXyANAokQeARok8ADRK5AGgUSIPAI0SeQBolMgDQKNEHgAaJfIA0CiRB4BGiTwANErkAaBRIg8AjRJ5AGiUyANAo0QeABol8gDQKJEHgEaJPAA0SuQBoFEiDwCNEnkAaJTIA0CjRB4AGiXyANAokQeARok8ADRK5AGgUSIPAI0SeQBolMgDQKMWzvUAxpVSnpvk75Nsl+T0Wuu75nhIALBVmxdr8qWU7ZL8Q5LnJXl8kkNLKY+f21EBwNZtXkQ+yVOSfLPW+q1a6z1J/iXJwXM8JgDYqs2XyD8yyaoJ17/bTwMANtPQ2NjYXI8hpZRDkjy31vqH/fWXJnlqrfV1k+Y7MsmRSVJr3WfWBwoAc2toU2aeLzve3ZRktwnXf7Gfdj+11lOTnDpbg2IwSikra63L53ocwLZrW10OzZfIfznJHqWUX0oX999PctjcDgkAtm7z4j35WuuaJK9L8okkX+sm1a/O7agAYOs2X9bkU2u9OMnFcz0OZoS3WIC5tk0uh+bFjncAwODNi831AMDgzZvN9cyuUsrDk/xdkn2T3J7kliRHJbknybeTvKHW+t5+3pOSrKy1vr+U8v4kByX55Vrr3aWUJf1tu8/weG9IsrzWunpL5gFmTinlqCSn1lp/uon3Oy7J5bXWTw1gDJcleXOtdeWk6cNJjk/ywiR3JLk7yXG11o/1y44ra60v7Oc9JMlv1VpfUUp5RZIzkzyx1vqV/vZr+ttv2NLxbuD7eH+SC2ut527JPNbkt0GllKEkH0lyWa31Mf0xB96aZNd+lluTvLGUsv0UD3FvklfO/EiB+aY/DPlUjkrywE29X6317YMI/EYcn+QRSfaqtT45ye8k2WnC7fts4HDq303ypzM8vhlhTX7bdECS0Vrr+8Yn1FqvSpJSyu5JfpDkC0lenuS09dz/75K8qZSyvtsy4XE+nuSLSZ6e7mOSZyU5NskvJDm81vofpZTF6f5K/uUkP01yZK31K6WUhyb5ULojH/57JhwAopTykiRvSLJ9ki8leW2t9d5N/ikA60x4zV6Z5MlJvprkZbXWn/Zruh9OtxXvr0opt6V7LT8gyf8k+YN0f/gvTfKZUsrqWusBpZQ7k5yS5MAkf1xK+c0kL0iyKMkVSV5dax2buEbaP9fZ/XzDSV5Ua72ulLJjkvcm2auf/o5a6/mllEXpli17J7muf+zJ39sDkxyR5JdqrXcnSa31liR1wmwr0oX88PX8eC5Msl8pZc9a69c38DO8Id1y63lJ1qQ7eNsJSR6b5K9rre/rV7L+qp9nLMk7a60f7qe/t/8Zr0q3VXX8cfdJ8u4kD0qyOskraq3fn2ocE1mT3zbtle6FvCEnJnnzFH9935jk80leupHHeGy6F86y/t9hSX49yZuTvK2f59gk/1Vr/dV+2jn99GOSfL7W+ivptjo8KklKKY9L8uIkz6i1PjHdVoX1vSiBTbdnkn+stT4uyU+SvHbCbT/s14A/leTPkhzYX1+Z5H/XWt+T5HtJDqi1HtDfZ8ckX6q17l1r/XySk2qt+9Za90oX49+aYhyr+8c+Od3yIukC/Ola61PSraj8dR/+1yT5aT/mY5Ks72ioj01yY631Jxv43muSJ5dSHrue29amC/Pb1nPbZDf2y6bPJXl/kkOSPC3dsi5Jfi/JE9P9UXJg/308Isnvpvv5Pz7Jy9KtHI2/zfDeJIf0W13PTPIX0xhHEpFnCrXWb6VbS57qoEQnJPk/2fDv0LdrrVfXWtemWyu4tNY6luTqJLv38/x6kg/0z/npJA8tpeycZL8k/9RPvyjJj/r5n5XuRfzlUsp/99d/eXO+R+DnrKq1fqG//E/pXp/jPtx/fVq6EH2hfw2+PMmjp3i8e5OcN+H6AaWUL5VSrk7ym0l+ZYr7/Vv/9crct6x4dpKj++e8LMkO6f74n7is+EqSr2z4W5zSvUn+Ot1bl+vzz0me1h+0bUM+2n+9Ot0fOHfUWn+Q5O5Syi7pfqYfqrXe229N+Gy6faP2mzD9e0k+3T/OnulWzC7pv/c/S3dU2GmxuX7b9NV0f11uzF8mOTfdL+H91Fqv73/hygbuf/eEy2snXF+bzf/dG0pydq11qhcisPkmf6Z64vW7+q9DSS6ptR46jccbGX8rrZSyQ5J/TLdz7KpSyjvShXp9xpcV9+a+ZcVQkhdO3lxeyoYWQet8M8mjSik7b2Rt/gPpIn/N5BtqrWtKKSuSvGUjzzVxOTd5Gbg5y72hJF+ttf7aZtzXmvw26tNJHtCf8CdJUkr51VLKb0ycqdZ6XZJr0703tj5/kfs2pW2uz6Xf3F5K2T/dZrqfJLk8/VaEUsrzkjykn//SJIeUUn6hv21xKWWqtQhg0zyqlDIek8PSvS032ReTPGN8s3YpZcdSyv/qb7sj99+ZbaLxoK8upTwo01vRmOgTSV7fv3edUsqT+ukTlxV7JfnVyXfs9/Y/I8nfj+9QXEp5WCnlRZPmG03yt0neNMUY3p9uE/vDNnHsE30uyYtLKduVUh6Wbg3+P/rvY3z6I9K9JZEkX0/ysPH/l1LKcCllqi0gP0fkt0H9JvPfTXJgKeV/SilfTbf5/eb1zP4XmWLTUH/o4f/cwuG8I91erV9J8q50m/6S7v2r/fqx/V66/QBSa7023eaqT/b3uSTdHrPAlvt6uh3kvpbuD+uTJ8/Qb3p+RZIP9a/Bf0+3z03SHVXu46WUz6znfren25H3mnTB/vImju34dDvcfaVfLhzfTz85yYP6MR+Xqfc3+rN0OxVf238E7sJ0+x1MdkamWOOutd6T5D3pdh7eXB9J95bCVelWuP5vrfXmfvr16Vaszkn3cx1/zkOSnFhKuSrJf6d/v346HPEOgPG96y/sd4qjEdbkAaBR1uQBoFHW5AGgUSIPAI0SeQBolIPhAJusPzPXWf3VA2qtl23i/cd3Bjq71vqKwY0MmMiaPAA0SuQBoFE218NWrJRyWZJnJvlOklenOyTn7umOpDV+6s+T0x3q85p0p+W9sr/vonRn1Sr9fX6a7qRE786MJ+8AAANNSURBVOzPGDb+HA9Md3rhF6c7feY5Sb42xXh2SfLn6c7VvVuSH6c7FPGf11qv38j3cmC6M43tlWTndKfUvDrJ+2qt/2+6PxPgPiIPbViS5Px05/dOkuenO5PXsiQP7ac9JclHSimP6a9/Mvc/y9j2SZ6T7nDHv1NrvbCf/r7c/7TCR2U9h0AupeyU7ljnE4+rvSTdHwfPLqU8darQ9+cfuCD3P2HJ0v7fN5OIPGwGm+uhDTsmOTHJLrnvpCLPSHfii4emWxNPurXrp6Y7KdB44D+YZHG6LQJ3JtkuyXtLKUP9iUcO7+f7r/7+e6Vbo5/sqHSBvyfJc9MF+wlJbk13HPTj13OfcctzX+D3TffHyqMz9UlSgGkQeWjDPUneVWv9cZIvTJj+N7XW25J8bMK03dKtsY/701rrj2qtl6c7tXDSbb7fI925w8eXE39ba/1uf2KiM9czhuf1X7dP8vEkI+k2t4+fzOOA9dxn3HcmXH5bkj9OtxXiwlrrv2zgfsAGiDy04Qe11p/1l0cmTL+x/3rPhGkPSLcZfdx3p7j8sNz/DH83Tbj8vfWMYWOn31w81Q211pXpznj4s3RnSHx3ujOV3VJK+cONPC4wBZGHNqxv8/mGpq+ecPmREy7/4qR5vj/FfEs38Ji3Jdmu1jo08V+6Nfwp1Vr/LN0fH89IdyrTLyZZlOQ9pRT7D8Fm8MKBbdMnk/x+f/mdpZQ3pHuv/YX9tBuSfCPJ2v7fgiRvKqV8OsmD0+25P9nH023eX5zk3aWUdya5K92e/S9Jt5XgxPUNppTyhHR75F+Q5KvpzrX91P7xFiXZKcmPNvu7hW2UNXnYNv1Tkn/vL780XUA/ly6m9yY5qtY61u8N/8F+vielC/VX04V3sr9Lcl1/+Y1JfpDuY3lfTPK6Ke4z7qFJjku3c9/t6XYAfE1/25dqrQIPm0HkYRtUax1NcmCSv0z3EbXRdJ9p/2SS36y1nj9h9tckOT3JHen+GPjHdJ+Fn/yYP07ya0lWJPmfdPsB3JbkynTvt5+zgSF9M8mp6f6A+HGSu9NtTTg13Ro+sBmcTx4AGmVNHgAaJfIA0CiRB4BGiTwANErkAaBRIg8AjRJ5AGiUyANAo0QeABr1/wEKhlntxiA3sQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "x = ['Total Time', 'Wall Time']\n",
        "time = [23.5, 23.6]\n",
        "ax.bar(x,time,color='r')\n",
        "plt.title('CNN model')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "_lycMKErXuqA",
        "outputId": "fcb83be9-8560-42a7-be49-908e777c1d62"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig = plt.figure()\n",
        "ax = fig.add_axes([0,0,1,1])\n",
        "x = ['Total Time', 'Wall Time']\n",
        "time = [19.5, 20.8]\n",
        "ax.bar(x,time,color='g')\n",
        "plt.title('Pretrained CNN model')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 353
        },
        "id": "74977irXY5Xf",
        "outputId": "5fd2acff-c620-48cd-c3c3-97bf404cbbe5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "kuwHtg2UaL7y"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}