autoencodermodified2.ipynb

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        "<a href=\"https://colab.research.google.com/github/BrendanGramp/UMDAOSC650/blob/main/autoencodermodified2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import xarray as xr\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_squared_error\n",
        "import keras\n",
        "from keras import layers"
      ],
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        "id": "7IgRdMrZBxRF"
      },
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
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        "# Set the seed using keras.utils.set_random_seed. This will set:\n",
        "# 1) `numpy` seed\n",
        "# 2) backend random seed\n",
        "# 3) `python` random seed\n",
        "keras.utils.set_random_seed(812)"
      ],
      "metadata": {
        "id": "Y-RbDEG114eE"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "! wget https://zenodo.org/records/10576726/files/current_SP3hourly_20110427.nc"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "80ddcf75-b960-4699-83bf-92d7e3d62cf0"
      },
      "execution_count": 3,
      "outputs": [
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          "name": "stdout",
          "text": [
            "--2024-04-09 16:34:34--  https://zenodo.org/records/10576726/files/current_SP3hourly_20110427.nc\n",
            "Resolving zenodo.org (zenodo.org)... 188.184.103.159, 188.184.98.238, 188.185.79.172, ...\n",
            "Connecting to zenodo.org (zenodo.org)|188.184.103.159|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 109400556 (104M) [application/octet-stream]\n",
            "Saving to: ‘current_SP3hourly_20110427.nc’\n",
            "\n",
            "current_SP3hourly_2 100%[===================>] 104.33M  1.61MB/s    in 68s     \n",
            "\n",
            "2024-04-09 16:35:43 (1.53 MB/s) - ‘current_SP3hourly_20110427.nc’ saved [109400556/109400556]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import dataset (netcdf)\n",
        "ds = xr.open_dataset('current_SP3hourly_20110427.nc')"
      ],
      "metadata": {
        "id": "kvvqhZ8HB8Ot"
      },
      "execution_count": 4,
      "outputs": []
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        "ds"
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        "outputId": "dc287599-c381-4116-b90d-9f34fd6b6b57"
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      "execution_count": 5,
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              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.Dataset&gt;\n",
              "Dimensions:      (starttime: 2224, y: 32, x: 32)\n",
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              "    lats         (starttime, y, x) float32 ...\n",
              "    lons         (starttime, y, x) float32 ...</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.Dataset</div></div><ul class='xr-sections'><li class='xr-section-item'><input id='section-d61e0696-a61e-44f2-b558-6c384454baad' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-d61e0696-a61e-44f2-b558-6c384454baad' class='xr-section-summary'  title='Expand/collapse section'>Dimensions:</label><div class='xr-section-inline-details'><ul class='xr-dim-list'><li><span class='xr-has-index'>starttime</span>: 2224</li><li><span>y</span>: 32</li><li><span>x</span>: 32</li></ul></div><div class='xr-section-details'></div></li><li class='xr-section-item'><input id='section-3ba6a4c6-572b-44ab-84a4-7da79ca477c8' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3ba6a4c6-572b-44ab-84a4-7da79ca477c8' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>starttime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.264e+04 9.264e+04 ... 9.266e+04</div><input id='attrs-7e39e53d-f4db-4deb-ae2d-ff398f593b02' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-7e39e53d-f4db-4deb-ae2d-ff398f593b02' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-65425326-0796-4e34-9217-27ebbe7b6d92' class='xr-var-data-in' type='checkbox'><label for='data-65425326-0796-4e34-9217-27ebbe7b6d92' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([92640., 92640., 92640., ..., 92661., 92661., 92661.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>endtime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a86434f2-9506-42bd-bd93-6dffed61e19b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a86434f2-9506-42bd-bd93-6dffed61e19b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-8ae978fa-e4de-47d2-8315-ba26178d398b' class='xr-var-data-in' type='checkbox'><label for='data-8ae978fa-e4de-47d2-8315-ba26178d398b' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-622b4a87-c96e-47a4-8bfd-f6674927a9ad' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-622b4a87-c96e-47a4-8bfd-f6674927a9ad' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-e08463cb-2a8a-4524-9548-a6e93382c689' class='xr-var-data-in' type='checkbox'><label for='data-e08463cb-2a8a-4524-9548-a6e93382c689' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-9cc2a7fc-a8bf-4c7b-81c3-36cfe508dd11' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-9cc2a7fc-a8bf-4c7b-81c3-36cfe508dd11' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-7cad6a33-9d3f-4545-b0a2-634e183a2152' class='xr-var-data-in' type='checkbox'><label for='data-7cad6a33-9d3f-4545-b0a2-634e183a2152' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-ab3f8e39-985a-444f-9e99-cc683dc473b5' class='xr-section-summary-in' type='checkbox'  checked><label for='section-ab3f8e39-985a-444f-9e99-cc683dc473b5' class='xr-section-summary' >Data variables: <span>(9)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span>uh25_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e7f2237c-8bbd-47a8-9984-5ca93fd1ff86' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e7f2237c-8bbd-47a8-9984-5ca93fd1ff86' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-1b9ebda7-0c84-4214-bfd5-a5fe41bbdde7' class='xr-var-data-in' type='checkbox'><label for='data-1b9ebda7-0c84-4214-bfd5-a5fe41bbdde7' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>uh03_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-d4f514f6-60fb-4543-bf18-0e123e8dc80b' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-d4f514f6-60fb-4543-bf18-0e123e8dc80b' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-6e8dda40-afab-41fc-99b5-841c87c7ebbf' class='xr-var-data-in' type='checkbox'><label for='data-6e8dda40-afab-41fc-99b5-841c87c7ebbf' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>ctt_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-65435347-30c2-45c5-8ec8-a4f201c58ffb' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-65435347-30c2-45c5-8ec8-a4f201c58ffb' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-9c8fea3b-6c95-4a33-a63b-62c641ef9955' class='xr-var-data-in' type='checkbox'><label for='data-9c8fea3b-6c95-4a33-a63b-62c641ef9955' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>dbz_grid</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-2166ebd4-3040-480b-8da4-08c5413871a2' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-2166ebd4-3040-480b-8da4-08c5413871a2' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2e2ae2d3-bc73-41a8-b9cf-94a95a025c73' class='xr-var-data-in' type='checkbox'><label for='data-2e2ae2d3-bc73-41a8-b9cf-94a95a025c73' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>mask</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-e8a95464-95be-4d58-afe3-85eceff4ebb5' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-e8a95464-95be-4d58-afe3-85eceff4ebb5' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-cc9d126c-b6c8-4003-bec7-4fc894ce5a15' class='xr-var-data-in' type='checkbox'><label for='data-cc9d126c-b6c8-4003-bec7-4fc894ce5a15' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>row_indices</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-4acf7b32-5c0e-461d-a4de-c53e22c92279' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-4acf7b32-5c0e-461d-a4de-c53e22c92279' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-759abdc1-e8cd-4bf5-8a51-8c14a7efe33d' class='xr-var-data-in' type='checkbox'><label for='data-759abdc1-e8cd-4bf5-8a51-8c14a7efe33d' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>col_indices</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-1a31cc3b-f09e-4fea-8597-35e2e8ff465a' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-1a31cc3b-f09e-4fea-8597-35e2e8ff465a' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-948ee791-a7d5-4459-a55b-24a691219086' class='xr-var-data-in' type='checkbox'><label for='data-948ee791-a7d5-4459-a55b-24a691219086' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lats</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-ed05f0fb-bb4c-44a9-801d-d70850073bd7' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ed05f0fb-bb4c-44a9-801d-d70850073bd7' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-5bb4c4fb-2ae2-4b7d-9868-03b2bc20a600' class='xr-var-data-in' type='checkbox'><label for='data-5bb4c4fb-2ae2-4b7d-9868-03b2bc20a600' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>lons</span></div><div class='xr-var-dims'>(starttime, y, x)</div><div class='xr-var-dtype'>float32</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-05232bb1-9849-4c2d-85b9-0fa36141c364' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-05232bb1-9849-4c2d-85b9-0fa36141c364' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-0d76d875-e082-4777-8b3f-01eebf875056' class='xr-var-data-in' type='checkbox'><label for='data-0d76d875-e082-4777-8b3f-01eebf875056' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2277376 values with dtype=float32]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-4305cf4e-a3e2-4b82-b950-e2a6ea0f7bcc' class='xr-section-summary-in' type='checkbox'  ><label for='section-4305cf4e-a3e2-4b82-b950-e2a6ea0f7bcc' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>starttime</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-9511c6b2-3125-4e69-a2db-89fa7ac5bd4d' class='xr-index-data-in' type='checkbox'/><label for='index-9511c6b2-3125-4e69-a2db-89fa7ac5bd4d' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0,\n",
              "       92640.0, 92640.0,\n",
              "       ...\n",
              "       92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0,\n",
              "       92661.0, 92661.0],\n",
              "      dtype=&#x27;float64&#x27;, name=&#x27;starttime&#x27;, length=2224))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-76f36e6f-4af0-48a6-9caa-810f79c4af4e' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-76f36e6f-4af0-48a6-9caa-810f79c4af4e' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## ANN Autoencoder"
      ],
      "metadata": {
        "id": "wVklUrN4Ekw2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "We will again use the previous data, but this time we will focus on reconstructing one input variable (feature)."
      ],
      "metadata": {
        "id": "qqoNw_88CTV3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# selecting a variable\n",
        "ds['dbz_grid']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "x1tCi9N9_Fhu",
        "outputId": "7dcf2ae2-8905-4744-9d2e-598ad980f7c3"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<xarray.DataArray 'dbz_grid' (starttime: 2224, y: 32, x: 32)>\n",
              "[2277376 values with dtype=float32]\n",
              "Coordinates:\n",
              "  * starttime  (starttime) float64 9.264e+04 9.264e+04 ... 9.266e+04 9.266e+04\n",
              "    endtime    (starttime) float64 ...\n",
              "    x_speed    (starttime) int64 ...\n",
              "    y_speed    (starttime) int64 ...\n",
              "Dimensions without coordinates: y, x"
            ],
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              "  --xr-font-color2: rgba(255, 255, 255, 0.54);\n",
              "  --xr-font-color3: rgba(255, 255, 255, 0.38);\n",
              "  --xr-border-color: #1F1F1F;\n",
              "  --xr-disabled-color: #515151;\n",
              "  --xr-background-color: #111111;\n",
              "  --xr-background-color-row-even: #111111;\n",
              "  --xr-background-color-row-odd: #313131;\n",
              "}\n",
              "\n",
              ".xr-wrap {\n",
              "  display: block !important;\n",
              "  min-width: 300px;\n",
              "  max-width: 700px;\n",
              "}\n",
              "\n",
              ".xr-text-repr-fallback {\n",
              "  /* fallback to plain text repr when CSS is not injected (untrusted notebook) */\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-header {\n",
              "  padding-top: 6px;\n",
              "  padding-bottom: 6px;\n",
              "  margin-bottom: 4px;\n",
              "  border-bottom: solid 1px var(--xr-border-color);\n",
              "}\n",
              "\n",
              ".xr-header > div,\n",
              ".xr-header > ul {\n",
              "  display: inline;\n",
              "  margin-top: 0;\n",
              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-obj-type,\n",
              ".xr-array-name {\n",
              "  margin-left: 2px;\n",
              "  margin-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-obj-type {\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-sections {\n",
              "  padding-left: 0 !important;\n",
              "  display: grid;\n",
              "  grid-template-columns: 150px auto auto 1fr 20px 20px;\n",
              "}\n",
              "\n",
              ".xr-section-item {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-section-item input {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-section-item input + label {\n",
              "  color: var(--xr-disabled-color);\n",
              "}\n",
              "\n",
              ".xr-section-item input:enabled + label {\n",
              "  cursor: pointer;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-section-item input:enabled + label:hover {\n",
              "  color: var(--xr-font-color0);\n",
              "}\n",
              "\n",
              ".xr-section-summary {\n",
              "  grid-column: 1;\n",
              "  color: var(--xr-font-color2);\n",
              "  font-weight: 500;\n",
              "}\n",
              "\n",
              ".xr-section-summary > span {\n",
              "  display: inline-block;\n",
              "  padding-left: 0.5em;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:disabled + label {\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-section-summary-in + label:before {\n",
              "  display: inline-block;\n",
              "  content: '►';\n",
              "  font-size: 11px;\n",
              "  width: 15px;\n",
              "  text-align: center;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:disabled + label:before {\n",
              "  color: var(--xr-disabled-color);\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label:before {\n",
              "  content: '▼';\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked + label > span {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-section-summary,\n",
              ".xr-section-inline-details {\n",
              "  padding-top: 4px;\n",
              "  padding-bottom: 4px;\n",
              "}\n",
              "\n",
              ".xr-section-inline-details {\n",
              "  grid-column: 2 / -1;\n",
              "}\n",
              "\n",
              ".xr-section-details {\n",
              "  display: none;\n",
              "  grid-column: 1 / -1;\n",
              "  margin-bottom: 5px;\n",
              "}\n",
              "\n",
              ".xr-section-summary-in:checked ~ .xr-section-details {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-array-wrap {\n",
              "  grid-column: 1 / -1;\n",
              "  display: grid;\n",
              "  grid-template-columns: 20px auto;\n",
              "}\n",
              "\n",
              ".xr-array-wrap > label {\n",
              "  grid-column: 1;\n",
              "  vertical-align: top;\n",
              "}\n",
              "\n",
              ".xr-preview {\n",
              "  color: var(--xr-font-color3);\n",
              "}\n",
              "\n",
              ".xr-array-preview,\n",
              ".xr-array-data {\n",
              "  padding: 0 5px !important;\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-array-data,\n",
              ".xr-array-in:checked ~ .xr-array-preview {\n",
              "  display: none;\n",
              "}\n",
              "\n",
              ".xr-array-in:checked ~ .xr-array-data,\n",
              ".xr-array-preview {\n",
              "  display: inline-block;\n",
              "}\n",
              "\n",
              ".xr-dim-list {\n",
              "  display: inline-block !important;\n",
              "  list-style: none;\n",
              "  padding: 0 !important;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list li {\n",
              "  display: inline-block;\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "}\n",
              "\n",
              ".xr-dim-list:before {\n",
              "  content: '(';\n",
              "}\n",
              "\n",
              ".xr-dim-list:after {\n",
              "  content: ')';\n",
              "}\n",
              "\n",
              ".xr-dim-list li:not(:last-child):after {\n",
              "  content: ',';\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-has-index {\n",
              "  font-weight: bold;\n",
              "}\n",
              "\n",
              ".xr-var-list,\n",
              ".xr-var-item {\n",
              "  display: contents;\n",
              "}\n",
              "\n",
              ".xr-var-item > div,\n",
              ".xr-var-item label,\n",
              ".xr-var-item > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-even);\n",
              "  margin-bottom: 0;\n",
              "}\n",
              "\n",
              ".xr-var-item > .xr-var-name:hover span {\n",
              "  padding-right: 5px;\n",
              "}\n",
              "\n",
              ".xr-var-list > li:nth-child(odd) > div,\n",
              ".xr-var-list > li:nth-child(odd) > label,\n",
              ".xr-var-list > li:nth-child(odd) > .xr-var-name span {\n",
              "  background-color: var(--xr-background-color-row-odd);\n",
              "}\n",
              "\n",
              ".xr-var-name {\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-var-dims {\n",
              "  grid-column: 2;\n",
              "}\n",
              "\n",
              ".xr-var-dtype {\n",
              "  grid-column: 3;\n",
              "  text-align: right;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-preview {\n",
              "  grid-column: 4;\n",
              "}\n",
              "\n",
              ".xr-index-preview {\n",
              "  grid-column: 2 / 5;\n",
              "  color: var(--xr-font-color2);\n",
              "}\n",
              "\n",
              ".xr-var-name,\n",
              ".xr-var-dims,\n",
              ".xr-var-dtype,\n",
              ".xr-preview,\n",
              ".xr-attrs dt {\n",
              "  white-space: nowrap;\n",
              "  overflow: hidden;\n",
              "  text-overflow: ellipsis;\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-var-name:hover,\n",
              ".xr-var-dims:hover,\n",
              ".xr-var-dtype:hover,\n",
              ".xr-attrs dt:hover {\n",
              "  overflow: visible;\n",
              "  width: auto;\n",
              "  z-index: 1;\n",
              "}\n",
              "\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  display: none;\n",
              "  background-color: var(--xr-background-color) !important;\n",
              "  padding-bottom: 5px !important;\n",
              "}\n",
              "\n",
              ".xr-var-attrs-in:checked ~ .xr-var-attrs,\n",
              ".xr-var-data-in:checked ~ .xr-var-data,\n",
              ".xr-index-data-in:checked ~ .xr-index-data {\n",
              "  display: block;\n",
              "}\n",
              "\n",
              ".xr-var-data > table {\n",
              "  float: right;\n",
              "}\n",
              "\n",
              ".xr-var-name span,\n",
              ".xr-var-data,\n",
              ".xr-index-name div,\n",
              ".xr-index-data,\n",
              ".xr-attrs {\n",
              "  padding-left: 25px !important;\n",
              "}\n",
              "\n",
              ".xr-attrs,\n",
              ".xr-var-attrs,\n",
              ".xr-var-data,\n",
              ".xr-index-data {\n",
              "  grid-column: 1 / -1;\n",
              "}\n",
              "\n",
              "dl.xr-attrs {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  display: grid;\n",
              "  grid-template-columns: 125px auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt,\n",
              ".xr-attrs dd {\n",
              "  padding: 0;\n",
              "  margin: 0;\n",
              "  float: left;\n",
              "  padding-right: 10px;\n",
              "  width: auto;\n",
              "}\n",
              "\n",
              ".xr-attrs dt {\n",
              "  font-weight: normal;\n",
              "  grid-column: 1;\n",
              "}\n",
              "\n",
              ".xr-attrs dt:hover span {\n",
              "  display: inline-block;\n",
              "  background: var(--xr-background-color);\n",
              "  padding-right: 10px;\n",
              "}\n",
              "\n",
              ".xr-attrs dd {\n",
              "  grid-column: 2;\n",
              "  white-space: pre-wrap;\n",
              "  word-break: break-all;\n",
              "}\n",
              "\n",
              ".xr-icon-database,\n",
              ".xr-icon-file-text2,\n",
              ".xr-no-icon {\n",
              "  display: inline-block;\n",
              "  vertical-align: middle;\n",
              "  width: 1em;\n",
              "  height: 1.5em !important;\n",
              "  stroke-width: 0;\n",
              "  stroke: currentColor;\n",
              "  fill: currentColor;\n",
              "}\n",
              "</style><pre class='xr-text-repr-fallback'>&lt;xarray.DataArray &#x27;dbz_grid&#x27; (starttime: 2224, y: 32, x: 32)&gt;\n",
              "[2277376 values with dtype=float32]\n",
              "Coordinates:\n",
              "  * starttime  (starttime) float64 9.264e+04 9.264e+04 ... 9.266e+04 9.266e+04\n",
              "    endtime    (starttime) float64 ...\n",
              "    x_speed    (starttime) int64 ...\n",
              "    y_speed    (starttime) int64 ...\n",
              "Dimensions without coordinates: y, x</pre><div class='xr-wrap' style='display:none'><div class='xr-header'><div class='xr-obj-type'>xarray.DataArray</div><div class='xr-array-name'>'dbz_grid'</div><ul class='xr-dim-list'><li><span class='xr-has-index'>starttime</span>: 2224</li><li><span>y</span>: 32</li><li><span>x</span>: 32</li></ul></div><ul class='xr-sections'><li class='xr-section-item'><div class='xr-array-wrap'><input id='section-907b9070-ec04-4bf8-a141-7d69a225381c' class='xr-array-in' type='checkbox' checked><label for='section-907b9070-ec04-4bf8-a141-7d69a225381c' title='Show/hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-array-preview xr-preview'><span>...</span></div><div class='xr-array-data'><pre>[2277376 values with dtype=float32]</pre></div></div></li><li class='xr-section-item'><input id='section-3be44edd-299d-4d12-b31b-856a6d33a456' class='xr-section-summary-in' type='checkbox'  checked><label for='section-3be44edd-299d-4d12-b31b-856a6d33a456' class='xr-section-summary' >Coordinates: <span>(4)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-var-name'><span class='xr-has-index'>starttime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>9.264e+04 9.264e+04 ... 9.266e+04</div><input id='attrs-ad0a7920-6cc5-4260-a08e-9fce51dd2d54' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-ad0a7920-6cc5-4260-a08e-9fce51dd2d54' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-2c1732a1-ea29-47ed-95d0-1013d5bf48c8' class='xr-var-data-in' type='checkbox'><label for='data-2c1732a1-ea29-47ed-95d0-1013d5bf48c8' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>array([92640., 92640., 92640., ..., 92661., 92661., 92661.])</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>endtime</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>float64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-c3c0f6cb-48f0-49e1-b9df-f5e61495a54c' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-c3c0f6cb-48f0-49e1-b9df-f5e61495a54c' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-878e4d8e-4169-40f6-8af1-35990e247324' class='xr-var-data-in' type='checkbox'><label for='data-878e4d8e-4169-40f6-8af1-35990e247324' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=float64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>x_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-a6b192a2-2f96-4f04-a34e-cadee5ba36ef' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-a6b192a2-2f96-4f04-a34e-cadee5ba36ef' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-ec914e44-247f-4561-80a8-0bebf4c494ce' class='xr-var-data-in' type='checkbox'><label for='data-ec914e44-247f-4561-80a8-0bebf4c494ce' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=int64]</pre></div></li><li class='xr-var-item'><div class='xr-var-name'><span>y_speed</span></div><div class='xr-var-dims'>(starttime)</div><div class='xr-var-dtype'>int64</div><div class='xr-var-preview xr-preview'>...</div><input id='attrs-5c90738b-f543-4674-b478-bde3e62e3a04' class='xr-var-attrs-in' type='checkbox' disabled><label for='attrs-5c90738b-f543-4674-b478-bde3e62e3a04' title='Show/Hide attributes'><svg class='icon xr-icon-file-text2'><use xlink:href='#icon-file-text2'></use></svg></label><input id='data-df7c4fda-61ab-4c31-9c31-8d7c6087c816' class='xr-var-data-in' type='checkbox'><label for='data-df7c4fda-61ab-4c31-9c31-8d7c6087c816' title='Show/Hide data repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-var-attrs'><dl class='xr-attrs'></dl></div><div class='xr-var-data'><pre>[2224 values with dtype=int64]</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-dc109faa-2c45-4b79-92fd-0b977bdf0313' class='xr-section-summary-in' type='checkbox'  ><label for='section-dc109faa-2c45-4b79-92fd-0b977bdf0313' class='xr-section-summary' >Indexes: <span>(1)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><ul class='xr-var-list'><li class='xr-var-item'><div class='xr-index-name'><div>starttime</div></div><div class='xr-index-preview'>PandasIndex</div><div></div><input id='index-227bffbe-9319-4069-9b4a-9772647cb0fb' class='xr-index-data-in' type='checkbox'/><label for='index-227bffbe-9319-4069-9b4a-9772647cb0fb' title='Show/Hide index repr'><svg class='icon xr-icon-database'><use xlink:href='#icon-database'></use></svg></label><div class='xr-index-data'><pre>PandasIndex(Index([92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0, 92640.0,\n",
              "       92640.0, 92640.0,\n",
              "       ...\n",
              "       92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0, 92661.0,\n",
              "       92661.0, 92661.0],\n",
              "      dtype=&#x27;float64&#x27;, name=&#x27;starttime&#x27;, length=2224))</pre></div></li></ul></div></li><li class='xr-section-item'><input id='section-28988897-8a0e-490b-8409-f8fa63124535' class='xr-section-summary-in' type='checkbox' disabled ><label for='section-28988897-8a0e-490b-8409-f8fa63124535' class='xr-section-summary'  title='Expand/collapse section'>Attributes: <span>(0)</span></label><div class='xr-section-inline-details'></div><div class='xr-section-details'><dl class='xr-attrs'></dl></div></li></ul></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reshape data to be samples, features\n",
        "features_data = ds['dbz_grid'].stack(new_dim=([\"y\", \"x\"])).values\n",
        "print(features_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Dg1nX786FXrR",
        "outputId": "e3b51691-b453-4688-9366-e48fcd84050b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2224, 1024)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2vcr9t3cIsXN",
        "outputId": "465ab236-b7b8-491b-f935-afe8b6c5eaa8"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-35.      , -35.      , -35.      , ...,  13.949479,  12.911252,\n",
              "         10.474219],\n",
              "       [-35.      , -35.      , -35.      , ...,  15.252525,   9.551773,\n",
              "        -12.005707],\n",
              "       [-35.      , -35.      , -35.      , ..., -35.      , -35.      ,\n",
              "        -35.      ],\n",
              "       ...,\n",
              "       [-29.150763, -33.773006, -35.      , ..., -12.28549 , -21.292902,\n",
              "        -30.720982],\n",
              "       [ 24.57368 ,  13.765106,  14.084679, ...,  29.180717,  28.653072,\n",
              "         28.988672],\n",
              "       [ 13.142024,  16.35466 ,  17.247124, ...,  25.714262,  23.523518,\n",
              "         26.171114]], dtype=float32)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# split dataset into training and testing set\n",
        "X_train, X_test, y_train, y_test = train_test_split(\n",
        "    features_data,\n",
        "    features_data,\n",
        "    test_size=0.3,\n",
        "    shuffle=False,\n",
        "    random_state=42)"
      ],
      "metadata": {
        "id": "b7dfQyq4JIbF"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# now rescale your data\n",
        "scaler = StandardScaler()\n",
        "scaler.fit(X_train)\n",
        "X_train = scaler.transform(X_train) # input\n",
        "X_test = scaler.transform(X_test) # input\n",
        "y_train = scaler.transform(y_train) # label\n",
        "y_test = scaler.transform(y_test) # label"
      ],
      "metadata": {
        "id": "ffojuEJYJISv"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check shapes\n",
        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3WNN1oQYKvbq",
        "outputId": "1e6aecb5-71e3-4b7b-8445-e32874b56a8e"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1556, 1024) (668, 1024) (1556, 1024) (668, 1024)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# create fully connected autoencoder\n",
        "model = keras.Sequential()\n",
        "\n",
        "# input\n",
        "model.add(keras.Input(shape=(1024))) # features\n",
        "\n",
        "# encoder\n",
        "# hidden layers\n",
        "model.add(layers.Dense(32, activation=\"relu\"))\n",
        "model.add(layers.Dense(8, activation=\"relu\"))\n",
        "\n",
        "# desired dimensions\n",
        "model.add(layers.Dense(4, activation=\"relu\"))\n",
        "\n",
        "# decoder\n",
        "model.add(layers.Dense(8, activation=\"relu\"))\n",
        "model.add(layers.Dense(32, activation=\"relu\"))\n",
        "\n",
        "# Finally, we add last layer\n",
        "model.add(layers.Dense(1024, activation='linear')) # back to the original shape\n",
        "\n",
        "# print model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "n4JnAwI1J9Bk",
        "outputId": "49563243-1df2-482c-c394-4d2db0a82605"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_6 (Dense)             (None, 32)                32800     \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 8)                 264       \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 4)                 36        \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 8)                 40        \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 32)                288       \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 1024)              33792     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 67220 (262.58 KB)\n",
            "Trainable params: 67220 (262.58 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# define list of metrics\n",
        "METRICS = [\n",
        "    keras.metrics.MeanSquaredError(name='mean_squared_error'),\n",
        "    keras.metrics.RootMeanSquaredError(name='root_mean_squared_error'),\n",
        "    keras.metrics.MeanAbsoluteError(name='mean_absolute_error'),\n",
        "    keras.metrics.MeanAbsolutePercentageError(name='mean_absolute_percentage_error'),\n",
        "    keras.metrics.R2Score()\n",
        "]\n",
        "\n",
        "# compile your nn\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
        "    loss=\"mean_squared_error\",\n",
        "    metrics=METRICS\n",
        ")\n",
        "\n",
        "# set hyperparameters\n",
        "BATCH_SIZE = 32\n",
        "EPOCHS = 50"
      ],
      "metadata": {
        "id": "NTMiTP9ELYO9"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# train the NN now\n",
        "history = model.fit(\n",
        "    x=X_train,\n",
        "    y=y_train,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    validation_split=0.1,\n",
        "    shuffle=True,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "y5dS4BDILYLA",
        "outputId": "e8bb6155-aaea-46c5-ad42-ca29d5ced186"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/50\n",
            "44/44 [==============================] - 6s 31ms/step - loss: 0.8038 - mean_squared_error: 0.8038 - root_mean_squared_error: 0.8966 - mean_absolute_error: 0.7135 - mean_absolute_percentage_error: 1452.4873 - r2_score: 0.1908 - val_loss: 0.6727 - val_mean_squared_error: 0.6727 - val_root_mean_squared_error: 0.8202 - val_mean_absolute_error: 0.6562 - val_mean_absolute_percentage_error: 293.2098 - val_r2_score: 0.3559\n",
            "Epoch 2/50\n",
            "44/44 [==============================] - 1s 12ms/step - loss: 0.5503 - mean_squared_error: 0.5503 - root_mean_squared_error: 0.7418 - mean_absolute_error: 0.5572 - mean_absolute_percentage_error: 1752.6810 - r2_score: 0.4456 - val_loss: 0.5822 - val_mean_squared_error: 0.5822 - val_root_mean_squared_error: 0.7630 - val_mean_absolute_error: 0.5882 - val_mean_absolute_percentage_error: 373.6329 - val_r2_score: 0.4457\n",
            "Epoch 3/50\n",
            "44/44 [==============================] - 1s 14ms/step - loss: 0.4919 - mean_squared_error: 0.4919 - root_mean_squared_error: 0.7013 - mean_absolute_error: 0.5187 - mean_absolute_percentage_error: 1064.7892 - r2_score: 0.5041 - val_loss: 0.5541 - val_mean_squared_error: 0.5541 - val_root_mean_squared_error: 0.7444 - val_mean_absolute_error: 0.5668 - val_mean_absolute_percentage_error: 339.0057 - val_r2_score: 0.4751\n",
            "Epoch 4/50\n",
            "44/44 [==============================] - 1s 13ms/step - loss: 0.4648 - mean_squared_error: 0.4648 - root_mean_squared_error: 0.6818 - mean_absolute_error: 0.4987 - mean_absolute_percentage_error: 1557.6130 - r2_score: 0.5311 - val_loss: 0.5399 - val_mean_squared_error: 0.5399 - val_root_mean_squared_error: 0.7348 - val_mean_absolute_error: 0.5545 - val_mean_absolute_percentage_error: 367.5035 - val_r2_score: 0.4896\n",
            "Epoch 5/50\n",
            "44/44 [==============================] - 1s 16ms/step - loss: 0.4556 - mean_squared_error: 0.4556 - root_mean_squared_error: 0.6750 - mean_absolute_error: 0.4896 - mean_absolute_percentage_error: 1501.5663 - r2_score: 0.5403 - val_loss: 0.5300 - val_mean_squared_error: 0.5300 - val_root_mean_squared_error: 0.7280 - val_mean_absolute_error: 0.5477 - val_mean_absolute_percentage_error: 374.5008 - val_r2_score: 0.4996\n",
            "Epoch 6/50\n",
            "44/44 [==============================] - 0s 10ms/step - loss: 0.4505 - mean_squared_error: 0.4505 - root_mean_squared_error: 0.6712 - mean_absolute_error: 0.4853 - mean_absolute_percentage_error: 1443.8392 - r2_score: 0.5455 - val_loss: 0.5263 - val_mean_squared_error: 0.5263 - val_root_mean_squared_error: 0.7255 - val_mean_absolute_error: 0.5467 - val_mean_absolute_percentage_error: 378.5075 - val_r2_score: 0.5028\n",
            "Epoch 7/50\n",
            "44/44 [==============================] - 0s 9ms/step - loss: 0.4485 - mean_squared_error: 0.4485 - root_mean_squared_error: 0.6697 - mean_absolute_error: 0.4836 - mean_absolute_percentage_error: 1325.3066 - r2_score: 0.5474 - val_loss: 0.5200 - val_mean_squared_error: 0.5200 - val_root_mean_squared_error: 0.7211 - val_mean_absolute_error: 0.5411 - val_mean_absolute_percentage_error: 363.5587 - val_r2_score: 0.5076\n",
            "Epoch 8/50\n",
            "44/44 [==============================] - 0s 9ms/step - loss: 0.4446 - mean_squared_error: 0.4446 - root_mean_squared_error: 0.6668 - mean_absolute_error: 0.4810 - mean_absolute_percentage_error: 1309.3558 - r2_score: 0.5514 - val_loss: 0.5138 - val_mean_squared_error: 0.5138 - val_root_mean_squared_error: 0.7168 - val_mean_absolute_error: 0.5393 - val_mean_absolute_percentage_error: 362.2919 - val_r2_score: 0.5128\n",
            "Epoch 9/50\n",
            "44/44 [==============================] - 0s 10ms/step - loss: 0.4383 - mean_squared_error: 0.4383 - root_mean_squared_error: 0.6620 - mean_absolute_error: 0.4780 - mean_absolute_percentage_error: 1458.4099 - r2_score: 0.5578 - val_loss: 0.4977 - val_mean_squared_error: 0.4977 - val_root_mean_squared_error: 0.7055 - val_mean_absolute_error: 0.5296 - val_mean_absolute_percentage_error: 355.9711 - val_r2_score: 0.5270\n",
            "Epoch 10/50\n",
            "44/44 [==============================] - 0s 9ms/step - loss: 0.4194 - mean_squared_error: 0.4194 - root_mean_squared_error: 0.6476 - mean_absolute_error: 0.4687 - mean_absolute_percentage_error: 1194.7715 - r2_score: 0.5770 - val_loss: 0.4562 - val_mean_squared_error: 0.4562 - val_root_mean_squared_error: 0.6754 - val_mean_absolute_error: 0.5059 - val_mean_absolute_percentage_error: 358.3204 - val_r2_score: 0.5659\n",
            "Epoch 11/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.4004 - mean_squared_error: 0.4004 - root_mean_squared_error: 0.6328 - mean_absolute_error: 0.4573 - mean_absolute_percentage_error: 1463.4135 - r2_score: 0.5961 - val_loss: 0.4273 - val_mean_squared_error: 0.4273 - val_root_mean_squared_error: 0.6537 - val_mean_absolute_error: 0.4869 - val_mean_absolute_percentage_error: 362.8921 - val_r2_score: 0.5937\n",
            "Epoch 12/50\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.3836 - mean_squared_error: 0.3836 - root_mean_squared_error: 0.6194 - mean_absolute_error: 0.4458 - mean_absolute_percentage_error: 1437.9861 - r2_score: 0.6131 - val_loss: 0.4027 - val_mean_squared_error: 0.4027 - val_root_mean_squared_error: 0.6345 - val_mean_absolute_error: 0.4690 - val_mean_absolute_percentage_error: 363.7306 - val_r2_score: 0.6161\n",
            "Epoch 13/50\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.3746 - mean_squared_error: 0.3746 - root_mean_squared_error: 0.6120 - mean_absolute_error: 0.4388 - mean_absolute_percentage_error: 1353.5345 - r2_score: 0.6223 - val_loss: 0.3920 - val_mean_squared_error: 0.3920 - val_root_mean_squared_error: 0.6261 - val_mean_absolute_error: 0.4619 - val_mean_absolute_percentage_error: 341.3275 - val_r2_score: 0.6257\n",
            "Epoch 14/50\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.3688 - mean_squared_error: 0.3688 - root_mean_squared_error: 0.6073 - mean_absolute_error: 0.4350 - mean_absolute_percentage_error: 1139.2068 - r2_score: 0.6281 - val_loss: 0.3820 - val_mean_squared_error: 0.3820 - val_root_mean_squared_error: 0.6181 - val_mean_absolute_error: 0.4554 - val_mean_absolute_percentage_error: 317.1075 - val_r2_score: 0.6354\n",
            "Epoch 15/50\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.3668 - mean_squared_error: 0.3668 - root_mean_squared_error: 0.6057 - mean_absolute_error: 0.4330 - mean_absolute_percentage_error: 1487.2107 - r2_score: 0.6301 - val_loss: 0.3796 - val_mean_squared_error: 0.3796 - val_root_mean_squared_error: 0.6161 - val_mean_absolute_error: 0.4521 - val_mean_absolute_percentage_error: 331.4267 - val_r2_score: 0.6380\n",
            "Epoch 16/50\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.3642 - mean_squared_error: 0.3642 - root_mean_squared_error: 0.6035 - mean_absolute_error: 0.4319 - mean_absolute_percentage_error: 1390.6442 - r2_score: 0.6327 - val_loss: 0.3755 - val_mean_squared_error: 0.3755 - val_root_mean_squared_error: 0.6128 - val_mean_absolute_error: 0.4502 - val_mean_absolute_percentage_error: 347.0076 - val_r2_score: 0.6410\n",
            "Epoch 17/50\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.3612 - mean_squared_error: 0.3612 - root_mean_squared_error: 0.6010 - mean_absolute_error: 0.4296 - mean_absolute_percentage_error: 1195.7916 - r2_score: 0.6359 - val_loss: 0.3758 - val_mean_squared_error: 0.3758 - val_root_mean_squared_error: 0.6130 - val_mean_absolute_error: 0.4497 - val_mean_absolute_percentage_error: 342.8804 - val_r2_score: 0.6407\n",
            "Epoch 18/50\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.3596 - mean_squared_error: 0.3596 - root_mean_squared_error: 0.5997 - mean_absolute_error: 0.4286 - mean_absolute_percentage_error: 1460.2904 - r2_score: 0.6374 - val_loss: 0.3746 - val_mean_squared_error: 0.3746 - val_root_mean_squared_error: 0.6120 - val_mean_absolute_error: 0.4482 - val_mean_absolute_percentage_error: 334.4282 - val_r2_score: 0.6418\n",
            "Epoch 19/50\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.3585 - mean_squared_error: 0.3585 - root_mean_squared_error: 0.5987 - mean_absolute_error: 0.4276 - mean_absolute_percentage_error: 1239.6962 - r2_score: 0.6386 - val_loss: 0.3731 - val_mean_squared_error: 0.3731 - val_root_mean_squared_error: 0.6108 - val_mean_absolute_error: 0.4474 - val_mean_absolute_percentage_error: 334.8305 - val_r2_score: 0.6431\n",
            "Epoch 20/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3577 - mean_squared_error: 0.3577 - root_mean_squared_error: 0.5981 - mean_absolute_error: 0.4271 - mean_absolute_percentage_error: 1368.7661 - r2_score: 0.6393 - val_loss: 0.3733 - val_mean_squared_error: 0.3733 - val_root_mean_squared_error: 0.6110 - val_mean_absolute_error: 0.4467 - val_mean_absolute_percentage_error: 330.3598 - val_r2_score: 0.6433\n",
            "Epoch 21/50\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.3566 - mean_squared_error: 0.3566 - root_mean_squared_error: 0.5971 - mean_absolute_error: 0.4264 - mean_absolute_percentage_error: 1291.2457 - r2_score: 0.6405 - val_loss: 0.3725 - val_mean_squared_error: 0.3725 - val_root_mean_squared_error: 0.6103 - val_mean_absolute_error: 0.4466 - val_mean_absolute_percentage_error: 330.0425 - val_r2_score: 0.6441\n",
            "Epoch 22/50\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.3562 - mean_squared_error: 0.3562 - root_mean_squared_error: 0.5968 - mean_absolute_error: 0.4261 - mean_absolute_percentage_error: 1205.7397 - r2_score: 0.6409 - val_loss: 0.3716 - val_mean_squared_error: 0.3716 - val_root_mean_squared_error: 0.6096 - val_mean_absolute_error: 0.4459 - val_mean_absolute_percentage_error: 324.5832 - val_r2_score: 0.6448\n",
            "Epoch 23/50\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.3548 - mean_squared_error: 0.3548 - root_mean_squared_error: 0.5957 - mean_absolute_error: 0.4252 - mean_absolute_percentage_error: 1280.3743 - r2_score: 0.6422 - val_loss: 0.3720 - val_mean_squared_error: 0.3720 - val_root_mean_squared_error: 0.6099 - val_mean_absolute_error: 0.4459 - val_mean_absolute_percentage_error: 335.5073 - val_r2_score: 0.6443\n",
            "Epoch 24/50\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.3545 - mean_squared_error: 0.3545 - root_mean_squared_error: 0.5954 - mean_absolute_error: 0.4250 - mean_absolute_percentage_error: 1291.4491 - r2_score: 0.6426 - val_loss: 0.3731 - val_mean_squared_error: 0.3731 - val_root_mean_squared_error: 0.6108 - val_mean_absolute_error: 0.4465 - val_mean_absolute_percentage_error: 340.4568 - val_r2_score: 0.6425\n",
            "Epoch 25/50\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.3535 - mean_squared_error: 0.3535 - root_mean_squared_error: 0.5945 - mean_absolute_error: 0.4243 - mean_absolute_percentage_error: 1172.2338 - r2_score: 0.6436 - val_loss: 0.3713 - val_mean_squared_error: 0.3713 - val_root_mean_squared_error: 0.6094 - val_mean_absolute_error: 0.4452 - val_mean_absolute_percentage_error: 347.3269 - val_r2_score: 0.6446\n",
            "Epoch 26/50\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.3539 - mean_squared_error: 0.3539 - root_mean_squared_error: 0.5949 - mean_absolute_error: 0.4247 - mean_absolute_percentage_error: 1116.5134 - r2_score: 0.6432 - val_loss: 0.3725 - val_mean_squared_error: 0.3725 - val_root_mean_squared_error: 0.6103 - val_mean_absolute_error: 0.4467 - val_mean_absolute_percentage_error: 340.9958 - val_r2_score: 0.6435\n",
            "Epoch 27/50\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.3526 - mean_squared_error: 0.3526 - root_mean_squared_error: 0.5938 - mean_absolute_error: 0.4241 - mean_absolute_percentage_error: 1270.6472 - r2_score: 0.6444 - val_loss: 0.3686 - val_mean_squared_error: 0.3686 - val_root_mean_squared_error: 0.6071 - val_mean_absolute_error: 0.4437 - val_mean_absolute_percentage_error: 334.0159 - val_r2_score: 0.6477\n",
            "Epoch 28/50\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.3511 - mean_squared_error: 0.3511 - root_mean_squared_error: 0.5925 - mean_absolute_error: 0.4227 - mean_absolute_percentage_error: 1148.0643 - r2_score: 0.6460 - val_loss: 0.3700 - val_mean_squared_error: 0.3700 - val_root_mean_squared_error: 0.6082 - val_mean_absolute_error: 0.4440 - val_mean_absolute_percentage_error: 351.2470 - val_r2_score: 0.6460\n",
            "Epoch 29/50\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.3506 - mean_squared_error: 0.3506 - root_mean_squared_error: 0.5921 - mean_absolute_error: 0.4227 - mean_absolute_percentage_error: 1195.7357 - r2_score: 0.6465 - val_loss: 0.3663 - val_mean_squared_error: 0.3663 - val_root_mean_squared_error: 0.6052 - val_mean_absolute_error: 0.4424 - val_mean_absolute_percentage_error: 345.3177 - val_r2_score: 0.6492\n",
            "Epoch 30/50\n",
            "44/44 [==============================] - 0s 4ms/step - loss: 0.3498 - mean_squared_error: 0.3498 - root_mean_squared_error: 0.5915 - mean_absolute_error: 0.4218 - mean_absolute_percentage_error: 1191.9302 - r2_score: 0.6473 - val_loss: 0.3675 - val_mean_squared_error: 0.3675 - val_root_mean_squared_error: 0.6062 - val_mean_absolute_error: 0.4418 - val_mean_absolute_percentage_error: 342.1555 - val_r2_score: 0.6484\n",
            "Epoch 31/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3492 - mean_squared_error: 0.3492 - root_mean_squared_error: 0.5909 - mean_absolute_error: 0.4215 - mean_absolute_percentage_error: 1221.5092 - r2_score: 0.6479 - val_loss: 0.3682 - val_mean_squared_error: 0.3682 - val_root_mean_squared_error: 0.6068 - val_mean_absolute_error: 0.4425 - val_mean_absolute_percentage_error: 357.3474 - val_r2_score: 0.6476\n",
            "Epoch 32/50\n",
            "44/44 [==============================] - 0s 7ms/step - loss: 0.3476 - mean_squared_error: 0.3476 - root_mean_squared_error: 0.5896 - mean_absolute_error: 0.4201 - mean_absolute_percentage_error: 1293.4991 - r2_score: 0.6495 - val_loss: 0.3676 - val_mean_squared_error: 0.3676 - val_root_mean_squared_error: 0.6063 - val_mean_absolute_error: 0.4409 - val_mean_absolute_percentage_error: 370.4690 - val_r2_score: 0.6481\n",
            "Epoch 33/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3474 - mean_squared_error: 0.3474 - root_mean_squared_error: 0.5894 - mean_absolute_error: 0.4202 - mean_absolute_percentage_error: 1139.0006 - r2_score: 0.6497 - val_loss: 0.3856 - val_mean_squared_error: 0.3856 - val_root_mean_squared_error: 0.6210 - val_mean_absolute_error: 0.4512 - val_mean_absolute_percentage_error: 365.9625 - val_r2_score: 0.6305\n",
            "Epoch 34/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3488 - mean_squared_error: 0.3488 - root_mean_squared_error: 0.5906 - mean_absolute_error: 0.4215 - mean_absolute_percentage_error: 1080.1770 - r2_score: 0.6482 - val_loss: 0.3621 - val_mean_squared_error: 0.3621 - val_root_mean_squared_error: 0.6017 - val_mean_absolute_error: 0.4381 - val_mean_absolute_percentage_error: 357.4906 - val_r2_score: 0.6526\n",
            "Epoch 35/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3395 - mean_squared_error: 0.3395 - root_mean_squared_error: 0.5826 - mean_absolute_error: 0.4160 - mean_absolute_percentage_error: 1122.7133 - r2_score: 0.6577 - val_loss: 0.3563 - val_mean_squared_error: 0.3563 - val_root_mean_squared_error: 0.5969 - val_mean_absolute_error: 0.4342 - val_mean_absolute_percentage_error: 370.1422 - val_r2_score: 0.6594\n",
            "Epoch 36/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3345 - mean_squared_error: 0.3345 - root_mean_squared_error: 0.5783 - mean_absolute_error: 0.4136 - mean_absolute_percentage_error: 1211.7094 - r2_score: 0.6627 - val_loss: 0.3500 - val_mean_squared_error: 0.3500 - val_root_mean_squared_error: 0.5916 - val_mean_absolute_error: 0.4300 - val_mean_absolute_percentage_error: 342.8646 - val_r2_score: 0.6656\n",
            "Epoch 37/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3308 - mean_squared_error: 0.3308 - root_mean_squared_error: 0.5752 - mean_absolute_error: 0.4106 - mean_absolute_percentage_error: 1065.7291 - r2_score: 0.6664 - val_loss: 0.3484 - val_mean_squared_error: 0.3484 - val_root_mean_squared_error: 0.5903 - val_mean_absolute_error: 0.4286 - val_mean_absolute_percentage_error: 361.7993 - val_r2_score: 0.6670\n",
            "Epoch 38/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3290 - mean_squared_error: 0.3290 - root_mean_squared_error: 0.5736 - mean_absolute_error: 0.4087 - mean_absolute_percentage_error: 1315.9840 - r2_score: 0.6682 - val_loss: 0.3457 - val_mean_squared_error: 0.3457 - val_root_mean_squared_error: 0.5880 - val_mean_absolute_error: 0.4263 - val_mean_absolute_percentage_error: 325.0993 - val_r2_score: 0.6693\n",
            "Epoch 39/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3273 - mean_squared_error: 0.3273 - root_mean_squared_error: 0.5721 - mean_absolute_error: 0.4078 - mean_absolute_percentage_error: 1046.6840 - r2_score: 0.6699 - val_loss: 0.3497 - val_mean_squared_error: 0.3497 - val_root_mean_squared_error: 0.5913 - val_mean_absolute_error: 0.4281 - val_mean_absolute_percentage_error: 353.5066 - val_r2_score: 0.6656\n",
            "Epoch 40/50\n",
            "44/44 [==============================] - 0s 7ms/step - loss: 0.3268 - mean_squared_error: 0.3268 - root_mean_squared_error: 0.5717 - mean_absolute_error: 0.4073 - mean_absolute_percentage_error: 1067.1451 - r2_score: 0.6705 - val_loss: 0.3472 - val_mean_squared_error: 0.3472 - val_root_mean_squared_error: 0.5893 - val_mean_absolute_error: 0.4281 - val_mean_absolute_percentage_error: 350.1482 - val_r2_score: 0.6678\n",
            "Epoch 41/50\n",
            "44/44 [==============================] - 0s 7ms/step - loss: 0.3252 - mean_squared_error: 0.3252 - root_mean_squared_error: 0.5702 - mean_absolute_error: 0.4061 - mean_absolute_percentage_error: 1095.7719 - r2_score: 0.6721 - val_loss: 0.3458 - val_mean_squared_error: 0.3458 - val_root_mean_squared_error: 0.5881 - val_mean_absolute_error: 0.4255 - val_mean_absolute_percentage_error: 352.8716 - val_r2_score: 0.6700\n",
            "Epoch 42/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3254 - mean_squared_error: 0.3254 - root_mean_squared_error: 0.5705 - mean_absolute_error: 0.4065 - mean_absolute_percentage_error: 1040.8656 - r2_score: 0.6718 - val_loss: 0.3458 - val_mean_squared_error: 0.3458 - val_root_mean_squared_error: 0.5881 - val_mean_absolute_error: 0.4267 - val_mean_absolute_percentage_error: 349.3541 - val_r2_score: 0.6694\n",
            "Epoch 43/50\n",
            "44/44 [==============================] - 0s 5ms/step - loss: 0.3237 - mean_squared_error: 0.3237 - root_mean_squared_error: 0.5689 - mean_absolute_error: 0.4052 - mean_absolute_percentage_error: 1076.8762 - r2_score: 0.6736 - val_loss: 0.3438 - val_mean_squared_error: 0.3438 - val_root_mean_squared_error: 0.5864 - val_mean_absolute_error: 0.4240 - val_mean_absolute_percentage_error: 346.8205 - val_r2_score: 0.6709\n",
            "Epoch 44/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3230 - mean_squared_error: 0.3230 - root_mean_squared_error: 0.5684 - mean_absolute_error: 0.4049 - mean_absolute_percentage_error: 1222.8643 - r2_score: 0.6743 - val_loss: 0.3419 - val_mean_squared_error: 0.3419 - val_root_mean_squared_error: 0.5848 - val_mean_absolute_error: 0.4233 - val_mean_absolute_percentage_error: 361.6768 - val_r2_score: 0.6730\n",
            "Epoch 45/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3230 - mean_squared_error: 0.3230 - root_mean_squared_error: 0.5683 - mean_absolute_error: 0.4047 - mean_absolute_percentage_error: 1258.8732 - r2_score: 0.6743 - val_loss: 0.3431 - val_mean_squared_error: 0.3431 - val_root_mean_squared_error: 0.5857 - val_mean_absolute_error: 0.4231 - val_mean_absolute_percentage_error: 354.6769 - val_r2_score: 0.6721\n",
            "Epoch 46/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3217 - mean_squared_error: 0.3217 - root_mean_squared_error: 0.5672 - mean_absolute_error: 0.4037 - mean_absolute_percentage_error: 1029.9370 - r2_score: 0.6756 - val_loss: 0.3427 - val_mean_squared_error: 0.3427 - val_root_mean_squared_error: 0.5854 - val_mean_absolute_error: 0.4238 - val_mean_absolute_percentage_error: 346.7292 - val_r2_score: 0.6724\n",
            "Epoch 47/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3210 - mean_squared_error: 0.3210 - root_mean_squared_error: 0.5666 - mean_absolute_error: 0.4033 - mean_absolute_percentage_error: 1176.5065 - r2_score: 0.6763 - val_loss: 0.3429 - val_mean_squared_error: 0.3429 - val_root_mean_squared_error: 0.5856 - val_mean_absolute_error: 0.4245 - val_mean_absolute_percentage_error: 364.4020 - val_r2_score: 0.6720\n",
            "Epoch 48/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3203 - mean_squared_error: 0.3203 - root_mean_squared_error: 0.5660 - mean_absolute_error: 0.4028 - mean_absolute_percentage_error: 1128.7419 - r2_score: 0.6770 - val_loss: 0.3414 - val_mean_squared_error: 0.3414 - val_root_mean_squared_error: 0.5843 - val_mean_absolute_error: 0.4223 - val_mean_absolute_percentage_error: 333.2863 - val_r2_score: 0.6737\n",
            "Epoch 49/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3203 - mean_squared_error: 0.3203 - root_mean_squared_error: 0.5660 - mean_absolute_error: 0.4027 - mean_absolute_percentage_error: 1099.5601 - r2_score: 0.6770 - val_loss: 0.3426 - val_mean_squared_error: 0.3426 - val_root_mean_squared_error: 0.5853 - val_mean_absolute_error: 0.4235 - val_mean_absolute_percentage_error: 355.1969 - val_r2_score: 0.6724\n",
            "Epoch 50/50\n",
            "44/44 [==============================] - 0s 6ms/step - loss: 0.3196 - mean_squared_error: 0.3196 - root_mean_squared_error: 0.5654 - mean_absolute_error: 0.4021 - mean_absolute_percentage_error: 1036.5424 - r2_score: 0.6777 - val_loss: 0.3439 - val_mean_squared_error: 0.3439 - val_root_mean_squared_error: 0.5865 - val_mean_absolute_error: 0.4243 - val_mean_absolute_percentage_error: 345.4810 - val_r2_score: 0.6716\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize training and validation curves\n",
        "plt.plot(history.history[\"mean_squared_error\"], c='k')\n",
        "plt.plot(history.history[\"val_mean_squared_error\"], c='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "KIo2XzMzX7xU",
        "outputId": "282b0c0d-2051-4ae5-a224-ebc9555aa855"
      },
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history[\"mean_absolute_percentage_error\"], c='k')\n",
        "plt.plot(history.history[\"val_mean_absolute_percentage_error\"], c='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "gZcbAGAYxTW0",
        "outputId": "0de6f870-5cbc-4f15-f063-3e6841852ded",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluation mode\n",
        "y_pred = model.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wuwjUjxQFpH0",
        "outputId": "782c8c4f-cc01-46f9-d79a-5f178e0a5a4e"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "21/21 [==============================] - 0s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# check prediction shape matches test data shape\n",
        "y_pred.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bP_iZHp-FpE8",
        "outputId": "b5338935-8da0-4f94-91c3-6b323f80f045"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(668, 1024)"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "y_test.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G05BTQjzFpB3",
        "outputId": "b4c5bfd5-d82a-4db2-cb33-42d55baa4cd1"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(668, 1024)"
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# visualize obs (x-axis) to nn predictions (y-axis)\n",
        "plt.scatter(y_test, y_pred)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "8Gqwmol2Fo9Z",
        "outputId": "f7969d18-190d-4e37-df2a-391e0e6fd9a4"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluations (MSE)\n",
        "mean_squared_error(y_test, y_pred)"
      ],
      "metadata": {
        "id": "YF-NMyDDGLw0",
        "outputId": "5025a021-bdd4-4cf8-ca33-5fa41e36ce06",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.58079916"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# to extract model layers, we can first print out model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "id": "tOlStrPvwcRG",
        "outputId": "5b2953af-640f-4ec0-bb3c-4d8c55d40efb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " dense_6 (Dense)             (None, 32)                32800     \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 8)                 264       \n",
            "                                                                 \n",
            " dense_8 (Dense)             (None, 4)                 36        \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 8)                 40        \n",
            "                                                                 \n",
            " dense_10 (Dense)            (None, 32)                288       \n",
            "                                                                 \n",
            " dense_11 (Dense)            (None, 1024)              33792     \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 67220 (262.58 KB)\n",
            "Trainable params: 67220 (262.58 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# model layers are contained as a list\n",
        "model.layers"
      ],
      "metadata": {
        "id": "wYrVfb4Nw2W7",
        "outputId": "2d33780b-5aa6-4b14-c15a-1cbc74564bbe",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.src.layers.core.dense.Dense at 0x786dbb4c4a00>,\n",
              " <keras.src.layers.core.dense.Dense at 0x786db3272bc0>,\n",
              " <keras.src.layers.core.dense.Dense at 0x786db3034490>,\n",
              " <keras.src.layers.core.dense.Dense at 0x786db3035780>,\n",
              " <keras.src.layers.core.dense.Dense at 0x786db3034580>,\n",
              " <keras.src.layers.core.dense.Dense at 0x786db3035930>]"
            ]
          },
          "metadata": {},
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# lets select the first layer\n",
        "model.layers[0].input"
      ],
      "metadata": {
        "id": "t9WTz86DwZ3_",
        "outputId": "8e9feee6-b831-451c-c705-89ba42a54d0b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<KerasTensor: shape=(None, 1024) dtype=float32 (created by layer 'input_2')>"
            ]
          },
          "metadata": {},
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# and the bottleneck layer\n",
        "model.layers[2].output"
      ],
      "metadata": {
        "id": "7evftiBgwoko",
        "outputId": "6395fad0-28b0-4e9a-ca98-78b8b83358b5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<KerasTensor: shape=(None, 4) dtype=float32 (created by layer 'dense_8')>"
            ]
          },
          "metadata": {},
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we define the input layer to the ANN and the output of the bottleneck layer\n",
        "bottleneck = keras.backend.function([model.layers[0].input],[model.layers[2].output])"
      ],
      "metadata": {
        "id": "xKcPKNXfIK1d"
      },
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# input test data into the bottleneck function\n",
        "bottleneck_output = bottleneck(X_test)"
      ],
      "metadata": {
        "id": "z5wKXdZZI6T4"
      },
      "execution_count": 52,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# we can visualize what these two nodes in the bottleneck output\n",
        "plt.scatter(bottleneck_output[0][:,0],bottleneck_output[0][:,1])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "2UdtAmxdw9Ch",
        "outputId": "9bf908cf-814b-4943-ddb0-6b5daf022e36",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# we can visualize what these two nodes in the bottleneck output\n",
        "plt.scatter(bottleneck_output[0][:,2],bottleneck_output[0][:,3])\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "d_j8O5ykynB9",
        "outputId": "6e0bf5b6-0949-40b3-c360-2feec25037fc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        }
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# saving model\n",
        "model.save('autoencodersample.keras')"
      ],
      "metadata": {
        "id": "FP6Vu34_LYD_"
      },
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# loading model\n",
        "model2 = keras.models.load_model('autoencodersample.keras')"
      ],
      "metadata": {
        "id": "uunVHVJcKEMt"
      },
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# inference with loaded model\n",
        "ypred2 = model2.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jupJauZbKNZg",
        "outputId": "a976252d-bef0-45e3-eaec-4455926c9559"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "21/21 [==============================] - 0s 2ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "We can use autoencoders for the task of anomaly detection. For anomaly detection, we would note instances where reconstruction error for an image is higher, indicating that the sample was likely not similar to those in the training set distribution."
      ],
      "metadata": {
        "id": "E0t93MSi1QW1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# for get the mean absolute error between actual and reconstruction/prediction\n",
        "prediction_loss = keras.losses.mae(ypred2, X_test)\n",
        "\n",
        "# for check the prediction loss threshold for 2% of outliers\n",
        "loss_threshold = np.percentile(prediction_loss, 98)\n",
        "\n",
        "print(f'The prediction loss threshold for 2% of outliers is {loss_threshold:.2f}')\n",
        "# for visualize the threshold\n",
        "plt.hist(prediction_loss, bins=30, alpha=0.8)\n",
        "plt.axvline(x=loss_threshold, color='orange')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "WGap0EHv2EV-",
        "outputId": "0a6c5bc6-cd75-4579-f199-677cb29bb6be"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The prediction loss threshold for 2% of outliers is 0.97\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAGdCAYAAACyzRGfAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAgPUlEQVR4nO3df1RUdR7/8ReKDJoMBsavdSh/lGZKnaho0syUItzD6sp317Jj2HH1WMhZ4exm9MuwH3g6e8ryILWtaXuOxG4dtbU13aTA4wnKSI72i020lY6CW7swiDmi3O8ffZvvTqI5w8wHZnw+zrnnMHfu3Hlzde25d2buRFiWZQkAAMCQAX09AAAAuLAQHwAAwCjiAwAAGEV8AAAAo4gPAABgFPEBAACMIj4AAIBRxAcAADAqsq8H+LHu7m4dPnxYMTExioiI6OtxAADAebAsSx0dHUpJSdGAAec+t9Hv4uPw4cNyOBx9PQYAAPBDc3OzRowYcc5t+l18xMTESPp+eLvd3sfTAABC0qlOaWPK9z/PPixFXtS381wAXC6XHA6H57/j59Lv4uOHl1rsdjvxAQDwz6mB0pD/97PdTnwYdD5vmeANpwAAwCjiAwAAGEV8AAAAo4gPAABgFPEBAACMIj4AAIBRxAcAADCK+AAAAEYRHwAAwCjiAwAAGEV8AAAAo4gPAABgFPEBAACMIj4AAIBRkX09AIIrZ/Uuvx+7pWByACcBAOB7nPkAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKN6FR8rV65URESEli5d6ll34sQJ5efnKz4+XkOHDlVubq5aW1t7OycAAAgTfsfH7t279dJLLyktLc1rfWFhobZs2aLXX39dNTU1Onz4sGbPnt3rQQEAQHjwKz6OHTumu+++Wy+//LIuvvhiz/r29natXbtWzz77rKZNm6b09HStW7dO77//vurq6gI2NAAACF1+xUd+fr5+/vOfKzMz02t9fX29urq6vNaPGzdOqampqq2t7XFfbrdbLpfLawEAAOEr0tcHVFZW6uOPP9bu3bvPuK+lpUVRUVEaNmyY1/rExES1tLT0uL/S0lKVlJT4OgYA+CRn9S6/H7ulYHIAJwHg05mP5uZm/fa3v9WGDRsUHR0dkAGKi4vV3t7uWZqbmwOyXwAA0D/5FB/19fU6evSorr32WkVGRioyMlI1NTV64YUXFBkZqcTERJ08eVJtbW1ej2ttbVVSUlKP+7TZbLLb7V4LAAAIXz697DJ9+nTt27fPa929996rcePGadmyZXI4HBo0aJCqqqqUm5srSWpsbNShQ4fkdDoDNzUAAAhZPsVHTEyMJkyY4LXuoosuUnx8vGf9ggULVFRUpLi4ONntdhUUFMjpdOrGG28M3NQAACBk+fyG05/y3HPPacCAAcrNzZXb7VZWVpbWrFkT6KcBAAAhqtfxUV1d7XU7OjpaZWVlKisr6+2uAQBAGOK7XQAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGCUT/FRXl6utLQ02e122e12OZ1Ovf322577p06dqoiICK9l8eLFAR8aAACErkhfNh4xYoRWrlypyy+/XJZl6dVXX9XMmTO1Z88eXXXVVZKkhQsXasWKFZ7HDBkyJLATAwCAkOZTfOTk5Hjdfuqpp1ReXq66ujpPfAwZMkRJSUmBmxAAAIQVv9/zcfr0aVVWVqqzs1NOp9OzfsOGDRo+fLgmTJig4uJiHT9+/Jz7cbvdcrlcXgsAAAhfPp35kKR9+/bJ6XTqxIkTGjp0qDZt2qTx48dLkubOnatLL71UKSkp2rt3r5YtW6bGxkZt3LjxrPsrLS1VSUmJ/78BAPRjOat3+f3YLQWTAzgJ0H/4HB9jx45VQ0OD2tvb9cYbbygvL081NTUaP368Fi1a5Nlu4sSJSk5O1vTp09XU1KTRo0f3uL/i4mIVFRV5brtcLjkcDj9+FQAAEAp8jo+oqCiNGTNGkpSenq7du3fr+eef10svvXTGthkZGZKk/fv3nzU+bDabbDabr2MAAIAQ1evrfHR3d8vtdvd4X0NDgyQpOTm5t08DAADChE9nPoqLi5Wdna3U1FR1dHSooqJC1dXV2r59u5qamlRRUaEZM2YoPj5ee/fuVWFhoaZMmaK0tLRgzQ8AAEKMT/Fx9OhR3XPPPTpy5IhiY2OVlpam7du367bbblNzc7N27NihVatWqbOzUw6HQ7m5uXrkkUeCNTsAAAhBPsXH2rVrz3qfw+FQTU1NrwcCAADhje92AQAARhEfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMIr4AAAARhEfAADAKJ8urw4ACB05q3f5/dgtBZMDOAngjTMfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMIrrfBjC5+2B0NWb//2G4vMCwcaZDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwyqf4KC8vV1pamux2u+x2u5xOp95++23P/SdOnFB+fr7i4+M1dOhQ5ebmqrW1NeBDAwCA0OVTfIwYMUIrV65UfX29PvroI02bNk0zZ87Up59+KkkqLCzUli1b9Prrr6umpkaHDx/W7NmzgzI4AAAITZG+bJyTk+N1+6mnnlJ5ebnq6uo0YsQIrV27VhUVFZo2bZokad26dbryyitVV1enG2+8MXBTAwCAkOX3ez5Onz6tyspKdXZ2yul0qr6+Xl1dXcrMzPRsM27cOKWmpqq2tvas+3G73XK5XF4LAAAIXz7Hx759+zR06FDZbDYtXrxYmzZt0vjx49XS0qKoqCgNGzbMa/vExES1tLScdX+lpaWKjY31LA6Hw+dfAgAAhA6f42Ps2LFqaGjQBx98oPvuu095eXn67LPP/B6guLhY7e3tnqW5udnvfQEAgP7Pp/d8SFJUVJTGjBkjSUpPT9fu3bv1/PPPa86cOTp58qTa2tq8zn60trYqKSnprPuz2Wyy2Wy+Tw4AAEJSr6/z0d3dLbfbrfT0dA0aNEhVVVWe+xobG3Xo0CE5nc7ePg0AAAgTPp35KC4uVnZ2tlJTU9XR0aGKigpVV1dr+/btio2N1YIFC1RUVKS4uDjZ7XYVFBTI6XTySRcAAODhU3wcPXpU99xzj44cOaLY2FilpaVp+/btuu222yRJzz33nAYMGKDc3Fy53W5lZWVpzZo1QRkcAACEJp/iY+3atee8Pzo6WmVlZSorK+vVUAAAIHzx3S4AAMAonz/tAvNyVu/q6xGM6s3vu6VgcgAnAQAEA2c+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjOIiYwiKULwwGhc3M4PjDIAzHwAAwCjiAwAAGEV8AAAAo4gPAABgFPEBAACMIj4AAIBRxAcAADCK63wAIYxrZgAIRZz5AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFFc5wNAyOjNdU0A9B+c+QAAAEYRHwAAwCjiAwAAGEV8AAAAo3yKj9LSUl1//fWKiYlRQkKCZs2apcbGRq9tpk6dqoiICK9l8eLFAR0aAACELp/io6amRvn5+aqrq9M777yjrq4u3X777ers7PTabuHChTpy5IhneeaZZwI6NAAACF0+fdR227ZtXrfXr1+vhIQE1dfXa8qUKZ71Q4YMUVJSUmAmBAAAYaVX7/lob2+XJMXFxXmt37Bhg4YPH64JEyaouLhYx48fP+s+3G63XC6X1wIAAMKX3xcZ6+7u1tKlSzVp0iRNmDDBs37u3Lm69NJLlZKSor1792rZsmVqbGzUxo0be9xPaWmpSkpK/B0DQcQFnQAAweB3fOTn5+uTTz7Rrl3e/4FatGiR5+eJEycqOTlZ06dPV1NTk0aPHn3GfoqLi1VUVOS57XK55HA4/B0LAAD0c37Fx5IlS/TWW29p586dGjFixDm3zcjIkCTt37+/x/iw2Wyy2Wz+jAEAAEKQT/FhWZYKCgq0adMmVVdXa+TIkT/5mIaGBklScnKyXwMCAIDw4lN85Ofnq6KiQm+++aZiYmLU0tIiSYqNjdXgwYPV1NSkiooKzZgxQ/Hx8dq7d68KCws1ZcoUpaWlBeUXAAAAocWn+CgvL5f0/YXE/te6des0f/58RUVFaceOHVq1apU6OzvlcDiUm5urRx55JGADAwCA0Obzyy7n4nA4VFNT06uBAABAeOO7XQAAgFF+f9QW6I+4NgkA9H+c+QAAAEYRHwAAwCjiAwAAGEV8AAAAo4gPAABgFPEBAACMIj4AAIBRxAcAADCKi4wBfYwLowG40HDmAwAAGEV8AAAAo4gPAABgFPEBAACMIj4AAIBRxAcAADCK+AAAAEZxnQ8gAC60a3VcaL8vgMDizAcAADCK+AAAAEYRHwAAwCjiAwAAGEV8AAAAo4gPAABgFPEBAACM4jofwAWKa3UA6Cuc+QAAAEYRHwAAwCjiAwAAGOVTfJSWlur6669XTEyMEhISNGvWLDU2Nnptc+LECeXn5ys+Pl5Dhw5Vbm6uWltbAzo0AAAIXT7FR01NjfLz81VXV6d33nlHXV1duv3229XZ2enZprCwUFu2bNHrr7+umpoaHT58WLNnzw744AAAIDT59GmXbdu2ed1ev369EhISVF9frylTpqi9vV1r165VRUWFpk2bJklat26drrzyStXV1enGG28M3OQAACAk9eo9H+3t7ZKkuLg4SVJ9fb26urqUmZnp2WbcuHFKTU1VbW1tb54KAACECb+v89Hd3a2lS5dq0qRJmjBhgiSppaVFUVFRGjZsmNe2iYmJamlp6XE/brdbbrfbc9vlcvk7EgAACAF+x0d+fr4++eQT7drVuwsVlZaWqqSkpFf7AAAEVm8uQrelYHIAJ0E48utllyVLluitt97Se++9pxEjRnjWJyUl6eTJk2pra/PavrW1VUlJST3uq7i4WO3t7Z6lubnZn5EAAECI8Ck+LMvSkiVLtGnTJr377rsaOXKk1/3p6ekaNGiQqqqqPOsaGxt16NAhOZ3OHvdps9lkt9u9FgAAEL58etklPz9fFRUVevPNNxUTE+N5H0dsbKwGDx6s2NhYLViwQEVFRYqLi5PdbldBQYGcTiefdAEAAJJ8jI/y8nJJ0tSpU73Wr1u3TvPnz5ckPffccxowYIByc3PldruVlZWlNWvWBGRYAAAQ+nyKD8uyfnKb6OholZWVqayszO+hAABA+OK7XQAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYRXwAAACjiA8AAGAU8QEAAIwiPgAAgFHEBwAAMIr4AAAARkX29QChJGf1rr4eAQCAkMeZDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUT7Hx86dO5WTk6OUlBRFRERo8+bNXvfPnz9fERERXssdd9wRqHkBAECI8zk+Ojs7dfXVV6usrOys29xxxx06cuSIZ3nttdd6NSQAAAgfPn+rbXZ2trKzs8+5jc1mU1JSkt9DAQCA8BWU93xUV1crISFBY8eO1X333advv/02GE8DAABCkM9nPn7KHXfcodmzZ2vkyJFqamrSQw89pOzsbNXW1mrgwIFnbO92u+V2uz23XS5XoEcCAAD9SMDj48477/T8PHHiRKWlpWn06NGqrq7W9OnTz9i+tLRUJSUlgR4DAAD0U0H/qO2oUaM0fPhw7d+/v8f7i4uL1d7e7lmam5uDPRIAAOhDAT/z8WNff/21vv32WyUnJ/d4v81mk81mC/YYAACgn/A5Po4dO+Z1FuPgwYNqaGhQXFyc4uLiVFJSotzcXCUlJampqUkPPPCAxowZo6ysrIAODgAAQpPP8fHRRx/p1ltv9dwuKiqSJOXl5am8vFx79+7Vq6++qra2NqWkpOj222/XE088wdkNAAAgyY/4mDp1qizLOuv927dv79VAAAAgvPHdLgAAwCjiAwAAGBX0T7sAAC4sOat39cnzbimY3CfPC99x5gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjPI5Pnbu3KmcnBylpKQoIiJCmzdv9rrfsiw99thjSk5O1uDBg5WZmakvv/wyUPMCAIAQ53N8dHZ26uqrr1ZZWVmP9z/zzDN64YUX9OKLL+qDDz7QRRddpKysLJ04caLXwwIAgNAX6esDsrOzlZ2d3eN9lmVp1apVeuSRRzRz5kxJ0p///GclJiZq8+bNuvPOO3s3LQAACHkBfc/HwYMH1dLSoszMTM+62NhYZWRkqLa2tsfHuN1uuVwurwUAAISvgMZHS0uLJCkxMdFrfWJioue+HystLVVsbKxncTgcgRwJAAD0M33+aZfi4mK1t7d7lubm5r4eCQAABFFA4yMpKUmS1Nra6rW+tbXVc9+P2Ww22e12rwUAAISvgMbHyJEjlZSUpKqqKs86l8ulDz74QE6nM5BPBQAAQpTPn3Y5duyY9u/f77l98OBBNTQ0KC4uTqmpqVq6dKmefPJJXX755Ro5cqQeffRRpaSkaNasWYGcGwAAhCif4+Ojjz7Srbfe6rldVFQkScrLy9P69ev1wAMPqLOzU4sWLVJbW5smT56sbdu2KTo6OnBTAwCAkOVzfEydOlWWZZ31/oiICK1YsUIrVqzo1WAAACA89fmnXQAAwIWF+AAAAEb5/LJLqMtZvauvRwAA4ILGmQ8AAGAU8QEAAIwiPgAAgFHEBwAAMIr4AAAARhEfAADAKOIDAAAYdcFd5wMAEJ7+9zpONn2nN+K///n/lL8vtwaf87FbCiYHczT8CGc+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMCogMfH448/roiICK9l3LhxgX4aAAAQoiKDsdOrrrpKO3bs+P9PEhmUpwEAACEoKFUQGRmppKSkYOwaAACEuKC85+PLL79USkqKRo0apbvvvluHDh0667Zut1sul8trAQAA4SvgZz4yMjK0fv16jR07VkeOHFFJSYluvvlmffLJJ4qJiTlj+9LSUpWUlAR6DAAAzlvO6l1+P3ZLweQATnJhCPiZj+zsbP3qV79SWlqasrKytHXrVrW1temvf/1rj9sXFxervb3dszQ3Nwd6JAAA0I8E/Z2gw4YN0xVXXKH9+/f3eL/NZpPNZgv2GAAAoJ8I+nU+jh07pqamJiUnJwf7qQAAQAgIeHz87ne/U01Njb766iu9//77+uUvf6mBAwfqrrvuCvRTAQCAEBTwl12+/vpr3XXXXfr22291ySWXaPLkyaqrq9Mll1wS6KcCAAAhKODxUVlZGehdAgCAMMJ3uwAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjIrs6wEAAAhlOat39fUIPttSMLlPn58zHwAAwCjiAwAAGEV8AAAAo4IWH2VlZbrssssUHR2tjIwMffjhh8F6KgAAEEKCEh9/+ctfVFRUpOXLl+vjjz/W1VdfraysLB09ejQYTwcAAEJIUOLj2Wef1cKFC3Xvvfdq/PjxevHFFzVkyBC98sorwXg6AAAQQgL+UduTJ0+qvr5excXFnnUDBgxQZmamamtrz9je7XbL7XZ7bre3t0uSXC5XoEeTJHV91xmU/QIA+o8B+k6u49//3PVdp7rU3bcD9TPB+G/sD/u0LOsntw14fHzzzTc6ffq0EhMTvdYnJibqiy++OGP70tJSlZSUnLHe4XAEejQAwAUk1vPTL/twiv4pdlnw9t3R0aHY2NhzbtPnFxkrLi5WUVGR53Z3d7f+85//KD4+XhEREX04WeC4XC45HA41NzfLbrf39ThhjWNtDsfaHI61ORxr/1mWpY6ODqWkpPzktgGPj+HDh2vgwIFqbW31Wt/a2qqkpKQztrfZbLLZbF7rhg0bFuix+gW73c5fZkM41uZwrM3hWJvDsfbPT53x+EHA33AaFRWl9PR0VVVVedZ1d3erqqpKTqcz0E8HAABCTFBedikqKlJeXp6uu+463XDDDVq1apU6Ozt17733BuPpAABACAlKfMyZM0f//ve/9dhjj6mlpUXXXHONtm3bdsabUC8UNptNy5cvP+PlJQQex9ocjrU5HGtzONZmRFjn85kYAACAAOG7XQAAgFHEBwAAMIr4AAAARhEfAADAKOIjQMrKynTZZZcpOjpaGRkZ+vDDD8+67csvv6ybb75ZF198sS6++GJlZmaec3t48+VY/6/KykpFRERo1qxZwR0wjPh6rNva2pSfn6/k5GTZbDZdccUV2rp1q6FpQ5uvx3rVqlUaO3asBg8eLIfDocLCQp04ccLQtKFr586dysnJUUpKiiIiIrR58+affEx1dbWuvfZa2Ww2jRkzRuvXrw/6nGHPQq9VVlZaUVFR1iuvvGJ9+umn1sKFC61hw4ZZra2tPW4/d+5cq6yszNqzZ4/1+eefW/Pnz7diY2Otr7/+2vDkocfXY/2DgwcPWj/72c+sm2++2Zo5c6aZYUOcr8fa7XZb1113nTVjxgxr165d1sGDB63q6mqroaHB8OShx9djvWHDBstms1kbNmywDh48aG3fvt1KTk62CgsLDU8eerZu3Wo9/PDD1saNGy1J1qZNm865/YEDB6whQ4ZYRUVF1meffWatXr3aGjhwoLVt2zYzA4cp4iMAbrjhBis/P99z+/Tp01ZKSopVWlp6Xo8/deqUFRMTY7366qvBGjFs+HOsT506Zd10003Wn/70JysvL4/4OE++Huvy8nJr1KhR1smTJ02NGDZ8Pdb5+fnWtGnTvNYVFRVZkyZNCuqc4eZ84uOBBx6wrrrqKq91c+bMsbKysoI4WfjjZZdeOnnypOrr65WZmelZN2DAAGVmZqq2tva89nH8+HF1dXUpLi4uWGOGBX+P9YoVK5SQkKAFCxaYGDMs+HOs//a3v8npdCo/P1+JiYmaMGGCnn76aZ0+fdrU2CHJn2N90003qb6+3vPSzIEDB7R161bNmDHDyMwXktraWq8/G0nKyso673/f0bM+/1bbUPfNN9/o9OnTZ1y9NTExUV988cV57WPZsmVKSUk54y84vPlzrHft2qW1a9eqoaHBwIThw59jfeDAAb377ru6++67tXXrVu3fv1/333+/urq6tHz5chNjhyR/jvXcuXP1zTffaPLkybIsS6dOndLixYv10EMPmRj5gtLS0tLjn43L5dJ3332nwYMH99FkoY0zH31s5cqVqqys1KZNmxQdHd3X44SVjo4OzZs3Ty+//LKGDx/e1+OEve7ubiUkJOiPf/yj0tPTNWfOHD388MN68cUX+3q0sFNdXa2nn35aa9as0ccff6yNGzfq73//u5544om+Hg04L5z56KXhw4dr4MCBam1t9Vrf2tqqpKSkcz72D3/4g1auXKkdO3YoLS0tmGOGBV+PdVNTk7766ivl5OR41nV3d0uSIiMj1djYqNGjRwd36BDlz9/r5ORkDRo0SAMHDvSsu/LKK9XS0qKTJ08qKioqqDOHKn+O9aOPPqp58+bpN7/5jSRp4sSJ6uzs1KJFi/Twww9rwAD+f2WgJCUl9fhnY7fbOevRC/wN7aWoqCilp6erqqrKs667u1tVVVVyOp1nfdwzzzyjJ554Qtu2bdN1111nYtSQ5+uxHjdunPbt26eGhgbP8otf/EK33nqrGhoa5HA4TI4fUvz5ez1p0iTt37/fE3iS9M9//lPJycmExzn4c6yPHz9+RmD8EH0WX9cVUE6n0+vPRpLeeeedc/77jvPQ1+94DQeVlZWWzWaz1q9fb3322WfWokWLrGHDhlktLS2WZVnWvHnzrAcffNCz/cqVK62oqCjrjTfesI4cOeJZOjo6+upXCBm+Husf49Mu58/XY33o0CErJibGWrJkidXY2Gi99dZbVkJCgvXkk0/21a8QMnw91suXL7diYmKs1157zTpw4ID1j3/8wxo9erT161//uq9+hZDR0dFh7dmzx9qzZ48lyXr22WetPXv2WP/6178sy7KsBx980Jo3b55n+x8+avv73//e+vzzz62ysjI+ahsAxEeArF692kpNTbWioqKsG264waqrq/Pcd8stt1h5eXme25deeqkl6Yxl+fLl5gcPQb4c6x8jPnzj67F+//33rYyMDMtms1mjRo2ynnrqKevUqVOGpw5Nvhzrrq4u6/HHH7dGjx5tRUdHWw6Hw7r//vut//73v+YHDzHvvfdej//+/nB88/LyrFtuueWMx1xzzTVWVFSUNWrUKGvdunXG5w43EZbFOToAAGAO7/kAAABGER8AAMAo4gMAABhFfAAAAKOIDwAAYBTxAQAAjCI+AACAUcQHAAAwivgAAABGER8AAMAo4gMAABhFfAAAAKP+LzFAuJJh72gFAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# same as above but for inverse transformed data\n",
        "# for get the mean absolute error between actual and reconstruction/prediction\n",
        "prediction_loss = keras.losses.mae(\n",
        "    scaler.inverse_transform(ypred2),\n",
        "    scaler.inverse_transform(X_test)\n",
        "    )\n",
        "\n",
        "# for check the prediction loss threshold for 2% of outliers\n",
        "loss_threshold = np.percentile(prediction_loss, 98)\n",
        "\n",
        "print(f'The prediction loss threshold for 2% of outliers is {loss_threshold:.2f}')\n",
        "# for visualize the threshold\n",
        "plt.hist(prediction_loss, bins=30, alpha=0.8)\n",
        "plt.axvline(x=loss_threshold, color='orange')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 447
        },
        "id": "bQMnms3FHR2i",
        "outputId": "2354306c-e77d-4f40-a0ba-d9be9447aa00"
      },
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The prediction loss threshold for 2% of outliers is 18.60\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CNN Autoencoder"
      ],
      "metadata": {
        "id": "CnJOrJ4WErKd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def z_score(da):\n",
        "    \"\"\"\n",
        "    Compute z-score for training data.\n",
        "    Normalizing data for ML model training.\n",
        "    Args:\n",
        "        da (array): Array of training variable.\n",
        "    \"\"\"\n",
        "    return (da - np.mean(da)) / np.std(da)\n",
        "\n",
        "def z_score_testing(da, testdata):\n",
        "    \"\"\"\n",
        "    Compute z-score for testing data.\n",
        "    Args:\n",
        "        da (array): Array of training variable.\n",
        "    \"\"\"\n",
        "    return (testdata - np.mean(da)) / np.std(da)"
      ],
      "metadata": {
        "id": "8tBktm3K4JNl"
      },
      "execution_count": 60,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# reshape data to be samples, features\n",
        "features_data = ds['dbz_grid'].values\n",
        "print(features_data.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aVRljSszBQIe",
        "outputId": "53f8ff52-0da9-4b29-9eae-d53d34ac9143"
      },
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2224, 32, 32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = features_data[:1800], features_data[1800:], features_data[:1800], features_data[1800:]"
      ],
      "metadata": {
        "id": "Whxd2_cZ3k8W"
      },
      "execution_count": 62,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_test = z_score_testing(X_train, X_test) # input\n",
        "y_test = z_score_testing(y_train, y_test) # label\n",
        "\n",
        "X_train = z_score(X_train) # input\n",
        "y_train = z_score(y_train) # label"
      ],
      "metadata": {
        "id": "6xmsBq_k3k2r"
      },
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check shapes\n",
        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dXCWZMpj3kw6",
        "outputId": "7b0a6162-08a4-4d86-b24f-cecec01771b3"
      },
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(1800, 32, 32) (424, 32, 32) (1800, 32, 32) (424, 32, 32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = keras.Sequential()\n",
        "\n",
        "# input\n",
        "model.add(keras.Input(shape=(32, 32, 1)))  # 32x32 images\n",
        "\n",
        "# encoder\n",
        "# hidden layers\n",
        "model.add(layers.Conv2D(16, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "\n",
        "model.add(layers.Conv2D(8, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "\n",
        "model.add(layers.Conv2D(4, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.MaxPooling2D(2))\n",
        "\n",
        "# decoder\n",
        "model.add(layers.Conv2D(4, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.UpSampling2D(2))\n",
        "\n",
        "model.add(layers.Conv2D(8, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.UpSampling2D(2))\n",
        "\n",
        "model.add(layers.Conv2D(16, 3, strides=1, padding=\"same\", activation=\"relu\"))\n",
        "model.add(layers.UpSampling2D(2))\n",
        "\n",
        "# Finally, we add last layer\n",
        "model.add(layers.Conv2D(1, (3, 3), activation='linear', padding='same'))\n",
        "\n",
        "# print model summary\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AzQMwzPt3kpH",
        "outputId": "66b3bc86-fdf1-432d-9c35-887e4499ce29"
      },
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_2\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 32, 32, 16)        160       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2  (None, 16, 16, 16)        0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 16, 16, 8)         1160      \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPoolin  (None, 8, 8, 8)           0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 8, 8, 4)           292       \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPoolin  (None, 4, 4, 4)           0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 4, 4, 4)           148       \n",
            "                                                                 \n",
            " up_sampling2d (UpSampling2  (None, 8, 8, 4)           0         \n",
            " D)                                                              \n",
            "                                                                 \n",
            " conv2d_4 (Conv2D)           (None, 8, 8, 8)           296       \n",
            "                                                                 \n",
            " up_sampling2d_1 (UpSamplin  (None, 16, 16, 8)         0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_5 (Conv2D)           (None, 16, 16, 16)        1168      \n",
            "                                                                 \n",
            " up_sampling2d_2 (UpSamplin  (None, 32, 32, 16)        0         \n",
            " g2D)                                                            \n",
            "                                                                 \n",
            " conv2d_6 (Conv2D)           (None, 32, 32, 1)         145       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3369 (13.16 KB)\n",
            "Trainable params: 3369 (13.16 KB)\n",
            "Non-trainable params: 0 (0.00 Byte)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "METRICS = [\n",
        "    keras.metrics.MeanSquaredError(name='mean_squared_error'),\n",
        "    keras.metrics.RootMeanSquaredError(name='root_mean_squared_error'),\n",
        "]\n",
        "\n",
        "model.compile(\n",
        "    optimizer=keras.optimizers.Adam(learning_rate=0.001),\n",
        "    loss=\"mean_squared_error\",\n",
        "    metrics=METRICS\n",
        ")\n",
        "\n",
        "BATCH_SIZE = 32\n",
        "EPOCHS = 10"
      ],
      "metadata": {
        "id": "teBOl4-45KCx"
      },
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    x=X_train,\n",
        "    y=y_train,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    validation_split=0.1,\n",
        "    shuffle=True,\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vkufMFbc5J9C",
        "outputId": "a11f7077-aaad-4a13-9c9c-57afa8ba1576"
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "51/51 [==============================] - 5s 51ms/step - loss: 0.6572 - mean_squared_error: 0.6572 - root_mean_squared_error: 0.8107 - val_loss: 0.5431 - val_mean_squared_error: 0.5431 - val_root_mean_squared_error: 0.7370\n",
            "Epoch 2/10\n",
            "51/51 [==============================] - 2s 46ms/step - loss: 0.3308 - mean_squared_error: 0.3308 - root_mean_squared_error: 0.5752 - val_loss: 0.3983 - val_mean_squared_error: 0.3983 - val_root_mean_squared_error: 0.6311\n",
            "Epoch 3/10\n",
            "51/51 [==============================] - 2s 46ms/step - loss: 0.2610 - mean_squared_error: 0.2610 - root_mean_squared_error: 0.5109 - val_loss: 0.3565 - val_mean_squared_error: 0.3565 - val_root_mean_squared_error: 0.5971\n",
            "Epoch 4/10\n",
            "51/51 [==============================] - 4s 69ms/step - loss: 0.2315 - mean_squared_error: 0.2315 - root_mean_squared_error: 0.4811 - val_loss: 0.3303 - val_mean_squared_error: 0.3303 - val_root_mean_squared_error: 0.5747\n",
            "Epoch 5/10\n",
            "51/51 [==============================] - 2s 44ms/step - loss: 0.2160 - mean_squared_error: 0.2160 - root_mean_squared_error: 0.4648 - val_loss: 0.3161 - val_mean_squared_error: 0.3161 - val_root_mean_squared_error: 0.5622\n",
            "Epoch 6/10\n",
            "51/51 [==============================] - 2s 42ms/step - loss: 0.2056 - mean_squared_error: 0.2056 - root_mean_squared_error: 0.4535 - val_loss: 0.3064 - val_mean_squared_error: 0.3064 - val_root_mean_squared_error: 0.5536\n",
            "Epoch 7/10\n",
            "51/51 [==============================] - 2s 45ms/step - loss: 0.1971 - mean_squared_error: 0.1971 - root_mean_squared_error: 0.4440 - val_loss: 0.2991 - val_mean_squared_error: 0.2991 - val_root_mean_squared_error: 0.5469\n",
            "Epoch 8/10\n",
            "51/51 [==============================] - 3s 50ms/step - loss: 0.1921 - mean_squared_error: 0.1921 - root_mean_squared_error: 0.4383 - val_loss: 0.2932 - val_mean_squared_error: 0.2932 - val_root_mean_squared_error: 0.5415\n",
            "Epoch 9/10\n",
            "51/51 [==============================] - 4s 69ms/step - loss: 0.1850 - mean_squared_error: 0.1850 - root_mean_squared_error: 0.4301 - val_loss: 0.2843 - val_mean_squared_error: 0.2843 - val_root_mean_squared_error: 0.5332\n",
            "Epoch 10/10\n",
            "51/51 [==============================] - 2s 46ms/step - loss: 0.1790 - mean_squared_error: 0.1790 - root_mean_squared_error: 0.4231 - val_loss: 0.2846 - val_mean_squared_error: 0.2846 - val_root_mean_squared_error: 0.5335\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history[\"mean_squared_error\"], c='k')\n",
        "plt.plot(history.history[\"val_mean_squared_error\"], c='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "sr2Dr77c5J05",
        "outputId": "28b76fc1-3235-4a37-d2ac-e6e580c02e83"
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# saving model\n",
        "model.save('autoencodercnn.keras')\n",
        "\n",
        "# loading model\n",
        "model2 = keras.models.load_model('autoencodercnn.keras')"
      ],
      "metadata": {
        "id": "KMa7dgXE5WpG"
      },
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# inference with loaded model\n",
        "ypred2 = model2.predict(X_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jmdp8MjZoHze",
        "outputId": "b44fff3f-b812-4a78-94bd-47d602e52f56"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14/14 [==============================] - 0s 12ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(X_test.shape, ypred2[:,:,:,0].shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cmxkhle1obQ0",
        "outputId": "121d295c-5ff8-4786-872a-da0bccad952a"
      },
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(424, 32, 32) (424, 32, 32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluations\n",
        "plt.scatter(np.ndarray.flatten(y_test), np.ndarray.flatten(ypred2[:,:,:,0]))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 430
        },
        "id": "W9flZbSJoHs-",
        "outputId": "5d3d178b-0f70-4aac-d331-77728c722ee0"
      },
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluations\n",
        "sample_indx = 10\n",
        "\n",
        "plt.imshow(y_test[sample_indx,:,:], vmin=-1, vmax=1)\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(ypred2[sample_indx,:,:,0], vmin=-1, vmax=1)\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(ypred2[sample_indx,:,:,0] - y_test[sample_indx,:,:], cmap='seismic')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EqkYWhIWo1zD",
        "outputId": "f997e794-c431-466c-bb28-97a0a5b3e939"
      },
      "execution_count": 75,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluations (MSE)\n",
        "mean_squared_error(np.ndarray.flatten(y_test), np.ndarray.flatten(ypred2[:,:,:,0]))"
      ],
      "metadata": {
        "id": "rtT5yvYC5mdS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0fbd51c-cfbc-490d-e680-5276e925b199"
      },
      "execution_count": 76,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.37860158"
            ]
          },
          "metadata": {},
          "execution_count": 76
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## CNN Autoencoder Noise Removal"
      ],
      "metadata": {
        "id": "It_XWynZEtsc"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 77,
      "metadata": {
        "id": "4d682kwu-tLe"
      },
      "outputs": [],
      "source": [
        "def noise(array):\n",
        "    \"\"\"Adds random noise to each image in the supplied array.\"\"\"\n",
        "    noise_factor = 0.4\n",
        "    noisy_array = array + noise_factor * np.random.normal(\n",
        "        loc=0.0, scale=1.0, size=array.shape\n",
        "    )\n",
        "    return noisy_array"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# reshape data to be samples, features\n",
        "features_data = ds['dbz_grid'].values\n",
        "print(features_data.shape)\n",
        "\n",
        "X_train, X_test, y_train, y_test = features_data[:1800], features_data[1800:], features_data[:1800], features_data[1800:]\n",
        "\n",
        "X_test = z_score_testing(X_train, X_test) # input\n",
        "y_test = z_score_testing(y_train, y_test) # label\n",
        "\n",
        "X_train = z_score(X_train) # input\n",
        "y_train = z_score(y_train) # label\n",
        "\n",
        "# check shapes\n",
        "print(X_train.shape, X_test.shape, y_train.shape, y_test.shape)"
      ],
      "metadata": {
        "id": "SfUSdJXiBP9T",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "88769eae-09c6-4b7b-e2b6-0dde91b4cdf2"
      },
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(2224, 32, 32)\n",
            "(1800, 32, 32) (424, 32, 32) (1800, 32, 32) (424, 32, 32)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a copy of the data with added noise\n",
        "noisy_train_data = noise(X_train)\n",
        "noisy_test_data = noise(X_test)"
      ],
      "metadata": {
        "id": "CEXwYXwZBP2h"
      },
      "execution_count": 79,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.imshow(noisy_train_data[0,:,:], vmin=-1, vmax=1)\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(X_train[0,:,:], vmin=-1, vmax=1)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ApqtvKmTBZ1j",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 843
        },
        "outputId": "37648a40-9a2f-4d5a-f013-8251b2d229f7"
      },
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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1//4WePMz42x7uEWv7FqqYDCopKQjr58zIACAExQQAMAJ6wLasGGDZsyYoczMTPl8Pq1Zs6bL7VdffbV8Pl+Xy/Tp0yO1XgBAL2FdQE1NTRo3bpyWLFlyxMz06dO1e/fuzsuTTz55XIsEAPQ+1u8HVFhYqMLCwu/N+P1+paenH/OiAAC9X1SeAyorK1NqaqpGjBihhQsXau/evUfMtrS0KBQKdbkAAHq/iBfQ9OnT9fjjj2v9+vX6zW9+o/LychUWFqqj4/AvxS0tLVUgEOi8ZGdnR3pJAIBuKOJvyX355Zd3/nnMmDEaO3ashg0bprKyMk2ZMuU7+ZKSEhUXF3d+HAqFKCEAOAlE/WXYQ4cOVUpKinbs2HHY2/1+v5KSkrpcAAC9X9QLaNeuXdq7d68yMjKifVcAgB7E+kdwBw4c6HI2U1tbqy1btig5OVnJycm65557NGfOHKWnp6umpka33HKLhg8frmnTpkV04QCAns26gDZt2qRLLrmk8+Nvnr+ZN2+eli5dqq1bt+oPf/iD9u3bp8zMTE2dOlW//OUv5feb79vUnWw9x3yrvJhxg6xmr9yaZZwdlHzAarZvr/meUI9eutZqdmpsP6s8jl+L12ac9VvuofiHHXnG2WXNF1rNPje7zji7/Su7vRT1YX/j6F0jzrIaXf+R5VrODhtHE9403zdOkk5/8gvjrO/Ql1az378z1Th7ypfm2fb2ZmnX0XPWBTR58mR93/6lL774ou1IAMBJiL3gAABOUEAAACcoIACAExQQAMAJCggA4AQFBABwggICADhBAQEAnKCAAABOUEAAACci/n5A3V1c1qlW+fZdnxlnw+98YDX7lOrzjbOtTadYzS5e8Jxxdsqm+VazF499yio/pc/h34zwcH71xUir2TbO7VtrlZ/etyVKK5Emb5tllS/Ofck4m+Az/3xL0jsTn7TK27jkvZnG2bfOedpq9ujWq4yza5ZfbDU7ZuJBq3z4C/O9LuMP+KxmNw0baJytnxRrNVtt7cbR9lPMZ7e3mWU5AwIAOEEBAQCcoIAAAE5QQAAAJyggAIATFBAAwAkKCADgBAUEAHCCAgIAOEEBAQCc6BVb8YQv/oFxtr18cxRXYif7129Ebfay+BnG2dRpu6xm17SmWeWn9Kk3zn7V1tdq9j9nvG2V7y7KRq+xyn/Qar41zJkJdp/D3DULjLNLp/7BavbMzHeMs5MWmq9DknwjzL99pbxrt61SWz+7z2Frctg4e2Co+fY3ktSaaL4FTsKwkNXswAtJxtm9C4LG2Y6DLdLLR89xBgQAcIICAgA4QQEBAJyggAAATlBAAAAnKCAAgBMUEADACQoIAOAEBQQAcIICAgA4QQEBAJzweZ7nuV7EXwqFQgoEApqsmYrzxbteTo8VlzvEONte+6nV7E+fHmOVvyCn1jhbE0qxmh0f22GcXX76k1azs+L6W+VtfNVhvrebJA2MNd+b7K7Pz7Ka3eGZ/z/0tpS3rGaPef4fzcMxdt+KYvuZ76kWv72P1eyEfVZxNWWb7wU3/Ad1VrN3vWj+bzntLbs97/adnmCevajZOBs+2Kyd83+pYDCopKQj7zfHGRAAwAkKCADgBAUEAHCCAgIAOEEBAQCcoIAAAE5QQAAAJyggAIATFBAAwAkKCADgRJzrBZxowavOs8onv73XOHvwtAFWs/0v2G1rYqP6V8nG2WFX2W3Fk/xsP6v8liTzrXuu+scXrWb/btV04+zUr66zmh0XZ77Nz9aJdtv89I2x22bqrIqrjLPv5T9hNXt42dXG2XWvXWg1e8b/3mScffm/JljNPn2K+ZY27zZnWc1uO8N8mx9J6tvXfAucpkfs1nJwlvkWOB1TvrSaPbDU/PtE4k7zumhvi9NOgxxnQAAAJ6wKqLS0VBMmTFBiYqJSU1M1a9YsVVdXd8k0NzerqKhIgwYNUv/+/TVnzhw1NjZGdNEAgJ7PqoDKy8tVVFSkyspKvfzyy2pra9PUqVPV1NTUmbnxxhv13HPP6ZlnnlF5ebnq6+s1e/bsiC8cANCzWT0HtG7dui4fr1ixQqmpqaqqqtKkSZMUDAb12GOPaeXKlbr00kslScuXL9eZZ56pyspKnXee3fMvAIDe67ieAwoGg5Kk5OSvn8iqqqpSW1ubCgoKOjMjR45UTk6OKioqDjujpaVFoVCoywUA0PsdcwGFw2HdcMMNuuCCCzR69GhJUkNDgxISEjRgwIAu2bS0NDU0NBx2TmlpqQKBQOclOzv7WJcEAOhBjrmAioqKtG3bNq1ateq4FlBSUqJgMNh5qauze7dAAEDPdEy/B7Ro0SI9//zz2rBhg7Ky/uc17enp6WptbdW+ffu6nAU1NjYqPT39sLP8fr/8fv+xLAMA0INZnQF5nqdFixZp9erVevXVV5Wbm9vl9vHjxys+Pl7r16/vvK66ulo7d+5Ufn5+ZFYMAOgVrM6AioqKtHLlSq1du1aJiYmdz+sEAgH16dNHgUBA1157rYqLi5WcnKykpCRdf/31ys/P5xVwAIAurApo6dKlkqTJkyd3uX758uW6+uqrJUkPPfSQYmJiNGfOHLW0tGjatGl69NFHI7JYAEDv4fM8z3O9iL8UCoUUCAQ0WTMV5zPbL2vPovON5/u/ClutZ+DqrcbZ8MGDVrOjKibWOOobNzKKC5E+/ZuAcTbtgnqr2f2uaTXO7p2cYzX7y/91yDg7MtNut48PK3KPHvoLH/14qXE2d91PrGb/7uLlxtn/U32Z1ey2DvOf8n/1ZX+r2Vmrzf//fGig+b8HScq99iOrfM1/nGGc3TfC7ltu8hnm+7u9/gO7fQB/FxxqnP3n135onA0falbdTXcoGAwqKSnpiDn2ggMAOEEBAQCcoIAAAE5QQAAAJyggAIATFBAAwAkKCADgBAUEAHCCAgIAOEEBAQCcOKa3Y+huUv/lDeNsXMbh3xbiSNq70/Y6NsIdxtGY+s+tRn92xXCrfNZ688/hF3szrWbHjmgxzh5Ms/v/Vu4V7xhnWy3fUmTgXLuteMY89FPjbMypdttNLXxqgXE2ttVnNXvajDeNs+vLJ1rN7vvZfuPsoZ+Yf51I0lctfa3y8QfMt9dJ2Wz3OfTeGWScnTdwmtXsd14034ZrYIP5Y+xojZHJO7txBgQAcIICAgA4QQEBAJyggAAATlBAAAAnKCAAgBMUEADACQoIAOAEBQQAcIICAgA4QQEBAJzoFXvB2Wjf3eB6Cd1OR+Meq3z6Yru8jcEd46zy+0/rY5yNtdsOTHuKzjfOZjzxntXsmHbzfbUkKfOBCuPs/rnnWc0O5ZrvTXYo3W6fuc13nWOcPXj5IavZMf9lvsdgn383309NkmLWfmSVv3Tzp8bZJzfb7XmXN+Jj4+yOFSOsZicHzY9n/6crjbPtXptRjjMgAIATFBAAwAkKCADgBAUEAHCCAgIAOEEBAQCcoIAAAE5QQAAAJyggAIATFBAAwAmf53l2e4JEWSgUUiAQ0GTNVJwv3vVy0IuEL/6BVf5gaoJx1mf5r6jfHzfa/QULsWeebpXv+GC7cTYuPc1q9s6/H2aczXrhS6vZX0wcaJ4932xrmG/44uwO6KlrzXc1S3qn0Wp282nm2wh9+kPL75me+TZMKVvMPycdrc16++nbFQwGlZSUdMQcZ0AAACcoIACAExQQAMAJCggA4AQFBABwggICADhBAQEAnKCAAABOUEAAACcoIACAExQQAMAJ8w2MgB4upnyzVb5/lNYRbTZ7u9lqb7DbxyzzAfN82HIt+36cb5wdVGm3R9qhweZ7pElS0tt1xtmms9KtZnf4zc8T+jTYnVPEdJhnkz45ZJxtb282u3/zuwcAIHKsCqi0tFQTJkxQYmKiUlNTNWvWLFVXV3fJTJ48WT6fr8vluuuui+iiAQA9n1UBlZeXq6ioSJWVlXr55ZfV1tamqVOnqqmpqUtu/vz52r17d+fl/vvvj+iiAQA9n9VzQOvWrevy8YoVK5SamqqqqipNmjSp8/q+ffsqPd3u55wAgJPLcT0HFAwGJUnJycldrn/iiSeUkpKi0aNHq6SkRAcPHjzijJaWFoVCoS4XAEDvd8yvgguHw7rhhht0wQUXaPTo0Z3XX3nllRoyZIgyMzO1detW3Xrrraqurtazzz572DmlpaW65557jnUZAIAe6pgLqKioSNu2bdPrr7/e5foFCxZ0/nnMmDHKyMjQlClTVFNTo2HDvvv2vCUlJSouLu78OBQKKTs7+1iXBQDoIY6pgBYtWqTnn39eGzZsUFZW1vdm8/LyJEk7duw4bAH5/X75/f5jWQYAoAezKiDP83T99ddr9erVKisrU25u7lH/zpYtWyRJGRkZx7RAAEDvZFVARUVFWrlypdauXavExEQ1NDRIkgKBgPr06aOamhqtXLlSP/zhDzVo0CBt3bpVN954oyZNmqSxY8dG5QEAAHomqwJaunSppK9/2fQvLV++XFdffbUSEhL0yiuvaPHixWpqalJ2drbmzJmj22+/PWILBgD0DtY/gvs+2dnZKi8vP64FATix4oaYv+in/VPzPc+ibegtFcZZX5zd091ee7tV3ibdfm6m1ey4Q+a75GX+00ar2Tbapp5rnG1vN/t8sxccAMAJCggA4AQFBABwggICADhBAQEAnKCAAABOUEAAACcoIACAExQQAMAJCggA4MQxvx8QgN6hO22vEy22W+tEU7//jN52OXGn5VjlO3Y3GmdPqfzIONvutRrlOAMCADhBAQEAnKCAAABOUEAAACcoIACAExQQAMAJCggA4AQFBABwggICADhBAQEAnKCAAABOsBccAPQS7Z/sjNrsjpYW86zXZpTjDAgA4AQFBABwggICADhBAQEAnKCAAABOUEAAACcoIACAExQQAMAJCggA4AQFBABwgq14AOB4TRxjHG3K6Ws1ut+zm4yzcakpVrPbGxqt8pHGGRAAwAkKCADgBAUEAHCCAgIAOEEBAQCcoIAAAE5QQAAAJyggAIATFBAAwAkKCADgBAUEAHCCveAA4Hi9+a5xtOnc861G97lorHH2w59Yjdbwv2cvOADASciqgJYuXaqxY8cqKSlJSUlJys/P1wsvvNB5e3Nzs4qKijRo0CD1799fc+bMUWOj24YFAHRPVgWUlZWl++67T1VVVdq0aZMuvfRSzZw5U++9954k6cYbb9Rzzz2nZ555RuXl5aqvr9fs2bOjsnAAQM9m9RzQjBkzunz861//WkuXLlVlZaWysrL02GOPaeXKlbr00kslScuXL9eZZ56pyspKnXfeeZFbNQCgxzvm54A6Ojq0atUqNTU1KT8/X1VVVWpra1NBQUFnZuTIkcrJyVFFRcUR57S0tCgUCnW5AAB6P+sCevfdd9W/f3/5/X5dd911Wr16tUaNGqWGhgYlJCRowIABXfJpaWlqaGg44rzS0lIFAoHOS3Z2tvWDAAD0PNYFNGLECG3ZskUbN27UwoULNW/ePL3//vvHvICSkhIFg8HOS11d3THPAgD0HNa/B5SQkKDhw4dLksaPH6+33npLv/3tbzV37ly1trZq3759Xc6CGhsblZ6efsR5fr9ffr/ffuUAgB7tuH8PKBwOq6WlRePHj1d8fLzWr1/feVt1dbV27typ/Pz8470bAEAvY3UGVFJSosLCQuXk5Gj//v1auXKlysrK9OKLLyoQCOjaa69VcXGxkpOTlZSUpOuvv175+fm8Ag4A8B1WBbRnzx79+Mc/1u7duxUIBDR27Fi9+OKL+qu/+itJ0kMPPaSYmBjNmTNHLS0tmjZtmh599NGoLBzAyS3m7FHGWd9nn9sNHzzQKh7um2Cc7dfYYTU7fs8B4+yIX4atZtutJPJ8nud5jtfQRSgUUiAQ0GTNVJwv3vVyAHRTPbWADgzpZzU76f0vzcMdlgX0UY1V3lS716YyrVUwGFRSUtIRc+wFBwBwggICADhBAQEAnKCAAABOUEAAACcoIACAExQQAMAJCggA4AQFBABwwno37Gj7ZmOGdrVJ3WqPBgDdSUxHi3HWF261G24xW5LCFjsQtLfFWs1ut1mL7U4IXptV3lS7vp57tI12ut1WPLt27eJN6QCgF6irq1NWVtYRb+92BRQOh1VfX6/ExET5fL7O60OhkLKzs1VXV/e9ewv1dDzO3uNkeIwSj7O3icTj9DxP+/fvV2ZmpmJijvxMT7f7EVxMTMz3NmZSUlKvPvjf4HH2HifDY5R4nL3N8T7OQCBw1AwvQgAAOEEBAQCc6DEF5Pf7ddddd8nv97teSlTxOHuPk+ExSjzO3uZEPs5u9yIEAMDJocecAQEAehcKCADgBAUEAHCCAgIAONFjCmjJkiU67bTTdMoppygvL09vvvmm6yVF1N133y2fz9flMnLkSNfLOi4bNmzQjBkzlJmZKZ/PpzVr1nS53fM83XnnncrIyFCfPn1UUFCg7du3u1nscTja47z66qu/c2ynT5/uZrHHqLS0VBMmTFBiYqJSU1M1a9YsVVdXd8k0NzerqKhIgwYNUv/+/TVnzhw1NjY6WvGxMXmckydP/s7xvO666xyt+NgsXbpUY8eO7fxl0/z8fL3wwgudt5+oY9kjCuipp55ScXGx7rrrLr399tsaN26cpk2bpj179rheWkSdddZZ2r17d+fl9ddfd72k49LU1KRx48ZpyZIlh739/vvv18MPP6xly5Zp48aN6tevn6ZNm6bm5uYTvNLjc7THKUnTp0/vcmyffPLJE7jC41deXq6ioiJVVlbq5ZdfVltbm6ZOnaqmpqbOzI033qjnnntOzzzzjMrLy1VfX6/Zs2c7XLU9k8cpSfPnz+9yPO+//35HKz42WVlZuu+++1RVVaVNmzbp0ksv1cyZM/Xee+9JOoHH0usBJk6c6BUVFXV+3NHR4WVmZnqlpaUOVxVZd911lzdu3DjXy4gaSd7q1as7Pw6Hw156err3wAMPdF63b98+z+/3e08++aSDFUbGtx+n53nevHnzvJkzZzpZT7Ts2bPHk+SVl5d7nvf1sYuPj/eeeeaZzswHH3zgSfIqKipcLfO4fftxep7nXXzxxd7PfvYzd4uKkoEDB3q/+93vTuix7PZnQK2traqqqlJBQUHndTExMSooKFBFRYXDlUXe9u3blZmZqaFDh+qqq67Szp07XS8pampra9XQ0NDluAYCAeXl5fW64ypJZWVlSk1N1YgRI7Rw4ULt3bvX9ZKOSzAYlCQlJydLkqqqqtTW1tbleI4cOVI5OTk9+nh++3F+44knnlBKSopGjx6tkpISHTx40MXyIqKjo0OrVq1SU1OT8vPzT+ix7HabkX7bF198oY6ODqWlpXW5Pi0tTR9++KGjVUVeXl6eVqxYoREjRmj37t265557dNFFF2nbtm1KTEx0vbyIa2hokKTDHtdvbustpk+frtmzZys3N1c1NTX6xS9+ocLCQlVUVCg21u69YbqDcDisG264QRdccIFGjx4t6evjmZCQoAEDBnTJ9uTjebjHKUlXXnmlhgwZoszMTG3dulW33nqrqqur9eyzzzpcrb13331X+fn5am5uVv/+/bV69WqNGjVKW7ZsOWHHstsX0MmisLCw889jx45VXl6ehgwZoqefflrXXnutw5XheF1++eWdfx4zZozGjh2rYcOGqaysTFOmTHG4smNTVFSkbdu29fjnKI/mSI9zwYIFnX8eM2aMMjIyNGXKFNXU1GjYsGEnepnHbMSIEdqyZYuCwaD++Mc/at68eSovLz+ha+j2P4JLSUlRbGzsd16B0djYqPT0dEerir4BAwbojDPO0I4dO1wvJSq+OXYn23GVpKFDhyolJaVHHttFixbp+eef12uvvdblbVPS09PV2tqqffv2dcn31ON5pMd5OHl5eZLU445nQkKChg8frvHjx6u0tFTjxo3Tb3/72xN6LLt9ASUkJGj8+PFav35953XhcFjr169Xfn6+w5VF14EDB1RTU6OMjAzXS4mK3NxcpaendzmuoVBIGzdu7NXHVfr6XX/37t3bo46t53latGiRVq9erVdffVW5ubldbh8/frzi4+O7HM/q6mrt3LmzRx3Poz3Ow9myZYsk9ajjeTjhcFgtLS0n9lhG9CUNUbJq1SrP7/d7K1as8N5//31vwYIF3oABA7yGhgbXS4uYm266ySsrK/Nqa2u9P//5z15BQYGXkpLi7dmzx/XSjtn+/fu9zZs3e5s3b/YkeQ8++KC3efNm79NPP/U8z/Puu+8+b8CAAd7atWu9rVu3ejNnzvRyc3O9Q4cOOV65ne97nPv37/duvvlmr6KiwqutrfVeeeUV75xzzvFOP/10r7m52fXSjS1cuNALBAJeWVmZt3v37s7LwYMHOzPXXXedl5OT47366qvepk2bvPz8fC8/P9/hqu0d7XHu2LHDu/fee71NmzZ5tbW13tq1a72hQ4d6kyZNcrxyO7fddptXXl7u1dbWelu3bvVuu+02z+fzeS+99JLneSfuWPaIAvI8z3vkkUe8nJwcLyEhwZs4caJXWVnpekkRNXfuXC8jI8NLSEjwTj31VG/u3Lnejh07XC/ruLz22muepO9c5s2b53ne1y/FvuOOO7y0tDTP7/d7U6ZM8aqrq90u+hh83+M8ePCgN3XqVG/w4MFefHy8N2TIEG/+/Pk97j9Ph3t8krzly5d3Zg4dOuT99Kc/9QYOHOj17dvXu+yyy7zdu3e7W/QxONrj3Llzpzdp0iQvOTnZ8/v93vDhw72f//znXjAYdLtwS//wD//gDRkyxEtISPAGDx7sTZkypbN8PO/EHUvejgEA4ES3fw4IANA7UUAAACcoIACAExQQAMAJCggA4AQFBABwggICADhBAQEAnKCAAABOUEAAACcoIACAExQQAMCJ/w8s4mV+aAe8jQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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3d6u6uloLFy7U3/3d3ykcHh5zuEa99Gun+kkv2dcmFl7rtPax4lHWtb/WF5zWvmum/XZmc7Ybzi5pHAZ8SfqLUT3Wte8PuM0D+0NyrHVtymRz6FkWufY94PbLoWDCfv1gv1svAZeHiuNmpvLs/8OA/dOVkiG7dZ0DaP78+TJm6Af4K6+84rokAOASxCw4AIAXBBAAwAsCCADgBQEEAPCCAAIAeEEAAQC8IIAAAF4QQAAALwggAIAXBBAAwIuMfx7QcBe68nKnepMfsq7N/+U+p7XLC+da134UK3Ra+7nYDda1tyx+02ntLw6TuX6Xku7UgHXtB0m3jzQZFUhY115R2Om0djBgP5fuP3pLnNbuPWP/OEycKnBaO3TK7bV56Iz9TLXA2T+bc0jGoZVUvtswuIHPGav2mT4C9msng3a1HAEBALwggAAAXhBAAAAvCCAAgBcEEADACwIIAOAFAQQA8IIAAgB4QQABALwggAAAXlxyo3jU+aFTeeqjj7LUiFS4/df2tY5rx+683rr24SuXOa29YcoWp/qJeWOd6i8FJ1NnnOr/34D9fXgiWeS0dtLhdWhP0u2R+IfEGOvaD0/Z10pSPGY/iifQ5/ha222ijVL59iNtAinHxbPIZbyOy0igZIhRPACAYYwAAgB4QQABALwggAAAXhBAAAAvCCAAgBcEEADACwIIAOAFAQQA8IIAAgB4QQABALy45GbBJbM42204Kf3Vf1jXJp4/7rT20v/+baf6+KKYde1/m/qW09pLiv+Pde3leQmntYuDo6xrz5h+p7U/SLq99utL5VvXnk7Zz0iTpGP946xr3+8rdVr79z3jnepd5I0esK4dcHypnQy5vja3rw/Yj437mBkes+Nc+jZJuzqOgAAAXhBAAAAvCCAAgBcEEADACwIIAOAFAQQA8IIAAgB4QQABALwggAAAXhBAAAAvLrlRPOYvZjvVn6m0H8cy9l/tx8JIUqqvz6neae1xRda1ptrtPjGOL1vyd0Wsa1968ctOa2/+2hzr2m9e+SuntaeH7UcUhQIpp7V7Hcfl9CQL7WtT9o9ZSYq6rJ1wWzsUtL9fxo6KO62dTNk/EJOOs3hMIHuvzV1/frJ5mODysA3YTz5SgFE8AIDhzCmAmpqadN1116moqEjl5eVatmyZ2tvbB9X09fWpsbFR48eP19ixY7V8+XJ1dXVltGkAQO5zCqCWlhY1NjZqz549evXVV5VIJLRw4UL19vamax566CG9+OKL2rJli1paWnTs2DHdeuutGW8cAJDbnN4D2rFjx6CvN27cqPLycrW1tWnevHmKRqP6p3/6J23atEk33XSTJOnZZ5/VlVdeqT179uj666/PXOcAgJx2Qe8BRaNRSVJp6cefEdLW1qZEIqGGhoZ0zYwZMzRp0iS1traedY14PK5YLDboAgAY+c47gFKplB588EHdcMMNmjlzpiSps7NTBQUFKikpGVRbUVGhzs7Os67T1NSkSCSSvtTU1JxvSwCAHHLeAdTY2Ki3335bmzdvvqAG1qxZo2g0mr4cOXLkgtYDAOSG8/o7oFWrVumll17S7t27NXHixPT1lZWV6u/vV3d396CjoK6uLlVWVp51rXA4rHDY7W8iAAC5z+kIyBijVatWaevWrdq1a5dqa2sH3T5nzhzl5+dr586d6eva29t1+PBh1dfXZ6ZjAMCI4HQE1NjYqE2bNmn79u0qKipKv68TiURUWFioSCSie+65R6tXr1ZpaamKi4v1wAMPqL6+njPgAACDOAXQ+vXrJUnz588fdP2zzz6ru+++W5L0wx/+UMFgUMuXL1c8HteiRYv0k5/8JCPNAgBGjoAxxvhu4k/FYjFFIhEtGLdCeYECq/+T/Ogj+/XvdDsSG3O837o21Pym09rDRaLBfp6aJHVen7337FznZFXtsd8//zEv32nt5JQz1rUTy7qd1i4f3eNUPyqUsK4dSIWc1j41YL8/Pzg9xmntk1H7+oGT9jPpJCl42v7BEnIdu+j4ODQud7nb2ECF4gH7WsftzDvtUmsfFcn+Ph147n8oGo2quLh4yDpmwQEAvCCAAABeEEAAAC8IIACAFwQQAMALAggA4AUBBADwggACAHhBAAEAvCCAAABenNfHMVwMyY+6FQi4jU6xUfz8noyvmevyX2tzqq8ybqN7+iP2D7NTlW5jZHous3+M1P5vx0/bfeegdWlw0mVOS7f/l+lO9X0T7Meg9I9POq0dKrIf8+M6uCvVa7/vwx+67ftx7fYzbc6Md3utnShyKtdAof0dE3C8DwMDDrVuu96pPujQh7Gs5QgIAOAFAQQA8IIAAgB4QQABALwggAAAXhBAAAAvCCAAgBcEEADACwIIAOAFAQQA8IIAAgB4MWxnwWH4ytvpNjvO5UE2tshtCFfi2sud6l0kr7vSujYQ63Nau3Ltr5zqQ8XF9r2MGe20dufNtda1feMDTmu7KOhxqy/6/RnrWhMsdFrb5Lm+Nre/X4zj0kGXeW2Os+CCA/aD6YJJ+1pjWcsREADACwIIAOAFAQQA8IIAAgB4QQABALwggAAAXhBAAAAvCCAAgBcEEADACwIIAOAFo3gwrKR63OaxhF5/07rWfpDIxwom11jXpiJjHFd3k4zF7ItdaiWV/a9O69q8molOaw9UjbOuDTjuoOBHp6xrQ5eNcls77tZLyGFCUTZH8YT63O7EYMJh7bjDKJ4Eo3gAAMMYAQQA8IIAAgB4QQABALwggAAAXhBAAAAvCCAAgBcEEADACwIIAOAFAQQA8IIAAgB4wSw4YAgD7x/x3cKwM3DkqFN94MQH1rUm7jaALXXtTOvacNRhoJqkgphbfaIoZF872u11f8rhWdpltpskhfrt57vln0pZ1wYSdrUcAQEAvHAKoKamJl133XUqKipSeXm5li1bpvb29kE18+fPVyAQGHS5//77M9o0ACD3OQVQS0uLGhsbtWfPHr366qtKJBJauHChent7B9Xde++9On78ePry5JNPZrRpAEDuc3oPaMeOHYO+3rhxo8rLy9XW1qZ58+alrx89erQqKysz0yEAYES6oPeAotGoJKm0tHTQ9T/72c9UVlammTNnas2aNTp9+vSQa8TjccVisUEXAMDId95nwaVSKT344IO64YYbNHPmf56N8vWvf12TJ09WdXW1Dhw4oO9+97tqb2/XCy+8cNZ1mpqa9Nhjj51vGwCAHBUwxrh+UrEkaeXKlXr55Zf1xhtvaOLEoT+md9euXVqwYIEOHjyoqVOnfub2eDyu+J+cfhmLxVRTU6P5Wqq8QP75tAZgmAiEw9a1rqdhBxxOw+4vdftIbuPwEdvSpXEatsup6QOJPrW++qii0aiKi4uHrDuvI6BVq1bppZde0u7duz83fCSprq5OkoYMoHA4rLDDgxQAMDI4BZAxRg888IC2bt2q5uZm1dbWnvP/7N+/X5JUVVV1Xg0CAEYmpwBqbGzUpk2btH37dhUVFamzs1OSFIlEVFhYqEOHDmnTpk362te+pvHjx+vAgQN66KGHNG/ePM2aNSsrGwAAyE1OAbR+/XpJH/+x6Z969tlndffdd6ugoECvvfaa1q5dq97eXtXU1Gj58uV6+OGHM9YwAGBkcP4V3OepqalRS0vLBTUEYORwPbHAae19b1vXZvt0pgKH2rzayU5r908sPXfRH6UK3E5wCPbbz3cLneq3rh1I2u13ZsEBALwggAAAXhBAAAAvCCAAgBcEEADACwIIAOAFAQQA8IIAAgB4QQABALwggAAAXpz3B9IBANwNdLzvVB90qM/mEYXLB8cZY/fBRBwBAQC8IIAAAF4QQAAALwggAIAXBBAAwAsCCADgBQEEAPCCAAIAeEEAAQC8IIAAAF4QQAAALwggAIAXBBAAwAsCCADgBQEEAPCCAAIAeEEAAQC8IIAAAF4QQAAALwggAIAXBBAAwAsCCADgBQEEAPCCAAIAeEEAAQC8IIAAAF4QQAAALwggAIAXBBAAwAsCCADgBQEEAPCCAAIAeEEAAQC8IIAAAF7k+W4AAHJdXu1k69pk6VintZOF+a7tWAudSdjXnojaL5yKS0fPXcYREADAC6cAWr9+vWbNmqXi4mIVFxervr5eL7/8cvr2vr4+NTY2avz48Ro7dqyWL1+urq6ujDcNAMh9TgE0ceJEPfHEE2pra9O+fft00003aenSpfrd734nSXrooYf04osvasuWLWppadGxY8d06623ZqVxAEBuCxhjzIUsUFpaqqeeekq33XabJkyYoE2bNum2226TJL377ru68sor1draquuvv95qvVgspkgkovlaqrxA9n73CQCZwntAgw2k4nrt6HpFo1EVFxcPWXfe7wElk0lt3rxZvb29qq+vV1tbmxKJhBoaGtI1M2bM0KRJk9Ta2jrkOvF4XLFYbNAFADDyOQfQb3/7W40dO1bhcFj333+/tm7dqquuukqdnZ0qKChQSUnJoPqKigp1dnYOuV5TU5MikUj6UlNT47wRAIDc4xxA06dP1/79+7V3716tXLlSK1as0DvvvHPeDaxZs0bRaDR9OXLkyHmvBQDIHc5/B1RQUKBp06ZJkubMmaPf/OY3euaZZ3T77berv79f3d3dg46Curq6VFlZOeR64XBY4XDYvXMAQE674L8DSqVSisfjmjNnjvLz87Vz5870be3t7Tp8+LDq6+sv9NsAAEYYpyOgNWvWaMmSJZo0aZJ6enq0adMmNTc365VXXlEkEtE999yj1atXq7S0VMXFxXrggQdUX19vfQYcAODS4RRAJ06c0F/+5V/q+PHjikQimjVrll555RV99atflST98Ic/VDAY1PLlyxWPx7Vo0SL95Cc/yUrjAGArNH2aU318YsSpvrfE/qk0MdrtF0/JAqdyJ3nxUda14aox1rUDiT6rUTwX/HdAmcbfAQHItGwHUDxnA8j+6T/cnbKuHUj0ac+OR7L3d0AAAFwIAggA4AUBBADwggACAHhBAAEAvCCAAABeEEAAAC8IIACAFwQQAMAL52nY2fbJYIYBJaRhNaMBQK4yybhT/cBAn1t9wv6pNNnvOAnBqdpNoN/+SXYg4TAJ4Y/337kG7Qy7AOrp6ZEkvaF/9dwJgBHjvSzX46x6enoUiQw91mjYzYJLpVI6duyYioqKFAgE0tfHYjHV1NToyJEjnztbKNexnSPHpbCNEts50mRiO40x6unpUXV1tYLBoY/4ht0RUDAY1MSJE4e8vbi4eETv/E+wnSPHpbCNEts50lzodn7ekc8nOAkBAOAFAQQA8CJnAigcDuvRRx9VOBz23UpWsZ0jx6WwjRLbOdJczO0cdichAAAuDTlzBAQAGFkIIACAFwQQAMALAggA4EXOBNC6dev0hS98QaNGjVJdXZ1+/etf+24po37wgx8oEAgMusyYMcN3Wxdk9+7duvnmm1VdXa1AIKBt27YNut0Yo0ceeURVVVUqLCxUQ0OD3nsv92agnGs777777s/s28WLF/tp9jw1NTXpuuuuU1FRkcrLy7Vs2TK1t7cPqunr61NjY6PGjx+vsWPHavny5erq6vLU8fmx2c758+d/Zn/ef//9njo+P+vXr9esWbPSf2xaX1+vl19+OX37xdqXORFAP//5z7V69Wo9+uijevPNNzV79mwtWrRIJ06c8N1aRl199dU6fvx4+vLGG2/4bumC9Pb2avbs2Vq3bt1Zb3/yySf1ox/9SBs2bNDevXs1ZswYLVq0SH19boMgfTvXdkrS4sWLB+3b559//iJ2eOFaWlrU2NioPXv26NVXX1UikdDChQvV29ubrnnooYf04osvasuWLWppadGxY8d06623euzanc12StK99947aH8++eSTnjo+PxMnTtQTTzyhtrY27du3TzfddJOWLl2q3/3ud5Iu4r40OWDu3LmmsbEx/XUymTTV1dWmqanJY1eZ9eijj5rZs2f7biNrJJmtW7emv06lUqaystI89dRT6eu6u7tNOBw2zz//vIcOM+PT22mMMStWrDBLly710k+2nDhxwkgyLS0txpiP911+fr7ZsmVLuubf//3fjSTT2trqq80L9untNMaYL3/5y+av//qv/TWVJePGjTP/+I//eFH35bA/Aurv71dbW5saGhrS1wWDQTU0NKi1tdVjZ5n33nvvqbq6WlOmTNFdd92lw4cP+24pazo6OtTZ2Tlov0YiEdXV1Y24/SpJzc3NKi8v1/Tp07Vy5UqdPHnSd0sXJBqNSpJKS0slSW1tbUokEoP254wZMzRp0qSc3p+f3s5P/OxnP1NZWZlmzpypNWvW6PTp0z7ay4hkMqnNmzert7dX9fX1F3VfDrthpJ/24YcfKplMqqKiYtD1FRUVevfddz11lXl1dXXauHGjpk+fruPHj+uxxx7Tl770Jb399tsqKiry3V7GdXZ2StJZ9+snt40Uixcv1q233qra2lodOnRIf/u3f6slS5aotbVVoVDId3vOUqmUHnzwQd1www2aOXOmpI/3Z0FBgUpKSgbV5vL+PNt2StLXv/51TZ48WdXV1Tpw4IC++93vqr29XS+88ILHbt399re/VX19vfr6+jR27Fht3bpVV111lfbv33/R9uWwD6BLxZIlS9L/njVrlurq6jR58mT94he/0D333OOxM1yoO+64I/3va665RrNmzdLUqVPV3NysBQsWeOzs/DQ2Nurtt9/O+fcoz2Wo7bzvvvvS/77mmmtUVVWlBQsW6NChQ5o6derFbvO8TZ8+Xfv371c0GtW//Mu/aMWKFWppabmoPQz7X8GVlZUpFAp95gyMrq4uVVZWeuoq+0pKSnTFFVfo4MGDvlvJik/23aW2XyVpypQpKisry8l9u2rVKr300kt6/fXXB31sSmVlpfr7+9Xd3T2oPlf351DbeTZ1dXWSlHP7s6CgQNOmTdOcOXPU1NSk2bNn65lnnrmo+3LYB1BBQYHmzJmjnTt3pq9LpVLauXOn6uvrPXaWXadOndKhQ4dUVVXlu5WsqK2tVWVl5aD9GovFtHfv3hG9XyXp6NGjOnnyZE7tW2OMVq1apa1bt2rXrl2qra0ddPucOXOUn58/aH+2t7fr8OHDObU/z7WdZ7N//35Jyqn9eTapVErxePzi7suMntKQJZs3bzbhcNhs3LjRvPPOO+a+++4zJSUlprOz03drGfM3f/M3prm52XR0dJh/+7d/Mw0NDaasrMycOHHCd2vnraenx7z11lvmrbfeMpLM008/bd566y3z/vvvG2OMeeKJJ0xJSYnZvn27OXDggFm6dKmpra01Z86c8dy5m8/bzp6eHvOtb33LtLa2mo6ODvPaa6+ZP//zPzeXX3656evr8926tZUrV5pIJGKam5vN8ePH05fTp0+na+6//34zadIks2vXLrNv3z5TX19v6uvrPXbt7lzbefDgQfP444+bffv2mY6ODrN9+3YzZcoUM2/ePM+du/ne975nWlpaTEdHhzlw4ID53ve+ZwKBgPnlL39pjLl4+zInAsgYY3784x+bSZMmmYKCAjN37lyzZ88e3y1l1O23326qqqpMQUGBueyyy8ztt99uDh486LutC/L6668bSZ+5rFixwhjz8anY3//+901FRYUJh8NmwYIFpr293W/T5+HztvP06dNm4cKFZsKECSY/P99MnjzZ3HvvvTn34uls2yfJPPvss+maM2fOmL/6q78y48aNM6NHjza33HKLOX78uL+mz8O5tvPw4cNm3rx5prS01ITDYTNt2jTz7W9/20SjUb+NO/rmN79pJk+ebAoKCsyECRPMggUL0uFjzMXbl3wcAwDAi2H/HhAAYGQigAAAXhBAAAAvCCAAgBcEEADACwIIAOAFAQQA8IIAAgB4QQABALwggAAAXhBAAAAvCCAAgBf/H7sIyF1Cp1A0AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_transfer = keras.models.load_model('autoencodercnn.keras')"
      ],
      "metadata": {
        "id": "LyqSzP956bIQ"
      },
      "execution_count": 85,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history = model_transfer.fit(\n",
        "    x=noisy_train_data,\n",
        "    y=y_train,\n",
        "    batch_size=BATCH_SIZE,\n",
        "    epochs=EPOCHS,\n",
        "    validation_split=0.1,\n",
        "    shuffle=True,\n",
        ")"
      ],
      "metadata": {
        "id": "Lyv_iWs2BZny",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "33eb0783-8021-4192-b6b9-54e55014d998"
      },
      "execution_count": 87,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "51/51 [==============================] - 7s 61ms/step - loss: 0.1805 - mean_squared_error: 0.1805 - root_mean_squared_error: 0.4249 - val_loss: 0.2740 - val_mean_squared_error: 0.2740 - val_root_mean_squared_error: 0.5235\n",
            "Epoch 2/10\n",
            "51/51 [==============================] - 2s 42ms/step - loss: 0.1737 - mean_squared_error: 0.1737 - root_mean_squared_error: 0.4168 - val_loss: 0.2674 - val_mean_squared_error: 0.2674 - val_root_mean_squared_error: 0.5171\n",
            "Epoch 3/10\n",
            "51/51 [==============================] - 2s 42ms/step - loss: 0.1685 - mean_squared_error: 0.1685 - root_mean_squared_error: 0.4105 - val_loss: 0.2619 - val_mean_squared_error: 0.2619 - val_root_mean_squared_error: 0.5118\n",
            "Epoch 4/10\n",
            "51/51 [==============================] - 2s 45ms/step - loss: 0.1649 - mean_squared_error: 0.1649 - root_mean_squared_error: 0.4061 - val_loss: 0.2554 - val_mean_squared_error: 0.2554 - val_root_mean_squared_error: 0.5053\n",
            "Epoch 5/10\n",
            "51/51 [==============================] - 3s 67ms/step - loss: 0.1608 - mean_squared_error: 0.1608 - root_mean_squared_error: 0.4010 - val_loss: 0.2516 - val_mean_squared_error: 0.2516 - val_root_mean_squared_error: 0.5016\n",
            "Epoch 6/10\n",
            "51/51 [==============================] - 2s 46ms/step - loss: 0.1572 - mean_squared_error: 0.1572 - root_mean_squared_error: 0.3964 - val_loss: 0.2451 - val_mean_squared_error: 0.2451 - val_root_mean_squared_error: 0.4950\n",
            "Epoch 7/10\n",
            "51/51 [==============================] - 2s 44ms/step - loss: 0.1537 - mean_squared_error: 0.1537 - root_mean_squared_error: 0.3921 - val_loss: 0.2417 - val_mean_squared_error: 0.2417 - val_root_mean_squared_error: 0.4916\n",
            "Epoch 8/10\n",
            "51/51 [==============================] - 2s 41ms/step - loss: 0.1514 - mean_squared_error: 0.1514 - root_mean_squared_error: 0.3891 - val_loss: 0.2392 - val_mean_squared_error: 0.2392 - val_root_mean_squared_error: 0.4891\n",
            "Epoch 9/10\n",
            "51/51 [==============================] - 2s 40ms/step - loss: 0.1487 - mean_squared_error: 0.1487 - root_mean_squared_error: 0.3857 - val_loss: 0.2352 - val_mean_squared_error: 0.2352 - val_root_mean_squared_error: 0.4850\n",
            "Epoch 10/10\n",
            "51/51 [==============================] - 3s 58ms/step - loss: 0.1470 - mean_squared_error: 0.1470 - root_mean_squared_error: 0.3834 - val_loss: 0.2326 - val_mean_squared_error: 0.2326 - val_root_mean_squared_error: 0.4823\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.plot(history.history[\"mean_squared_error\"], c='k')\n",
        "plt.plot(history.history[\"val_mean_squared_error\"], c='r')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 434
        },
        "id": "QZGBAhGzrD2g",
        "outputId": "7f4ae0f2-6a47-41ab-bd2a-c62881ae5945"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# inference with model\n",
        "ypred = model.predict(noisy_test_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cm0_RjscrDuL",
        "outputId": "be439285-0dae-4f34-df59-dcd76a0239bd"
      },
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "14/14 [==============================] - 0s 21ms/step\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# evaluations\n",
        "sample_indx = 10\n",
        "\n",
        "plt.imshow(noisy_test_data[sample_indx, :, :])\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(y_test[sample_indx,:,:])\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(ypred[sample_indx,:,:,0])\n",
        "plt.show()\n",
        "\n",
        "plt.imshow(ypred[sample_indx,:,:,0] - y_test[sample_indx,:,:], cmap='seismic')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6fTN2v2YrizE",
        "outputId": "6ab6b02a-680e-4ca5-ae5a-d72b1ca04e69"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "LCcZfhT0ruF2"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}