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      "execution_count": 3,
      "metadata": {
        "id": "OZCyQ0aetEuX"
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Dropout\n",
        "from tensorflow.keras.optimizers import Adam\n",
        "from tensorflow.keras import initializers\n",
        "from tensorflow.keras.callbacks import ModelCheckpoint"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "import os\n",
        "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
        "  for filename in filenames:\n",
        "    print(os.path.join(dirname, filename))\n"
      ],
      "metadata": {
        "id": "K5r_oQn8t-u4"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "missing_values=[\"N/a\", \"na\", np.nan]\n",
        "Data = pd.read_csv('/content/dataset1.csv', na_values=(missing_values))\n",
        "Data.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 206
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        "outputId": "0da1c881-47a0-43ac-cbba-ce2c52f92520"
      },
      "execution_count": 5,
      "outputs": [
        {
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              "     N    P    K    pH    EC    OC     S    Zn    Fe    Cu    Mn     B  Output\n",
              "0  138  8.6  560  7.46  0.62  0.70   5.9  0.24  0.31  0.77  8.71  0.11       0\n",
              "1  213  7.5  338  7.62  0.75  1.06  25.4  0.30  0.86  1.54  2.89  2.29       0\n",
              "2  163  9.6  718  7.59  0.51  1.11  14.3  0.30  0.86  1.57  2.70  2.03       0\n",
              "3  157  6.8  475  7.64  0.58  0.94  26.0  0.34  0.54  1.53  2.65  1.82       0\n",
              "4  270  9.9  444  7.63  0.40  0.86  11.8  0.25  0.76  1.69  2.43  2.26       1"
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              "      <th>N</th>\n",
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              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-3e5461d4-3a75-417e-b069-fe9ec81b2724\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3e5461d4-3a75-417e-b069-fe9ec81b2724')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3e5461d4-3a75-417e-b069-fe9ec81b2724 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Data",
              "summary": "{\n  \"name\": \"Data\",\n  \"rows\": 880,\n  \"fields\": [\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 77,\n        \"min\": 6,\n        \"max\": 383,\n        \"num_unique_values\": 61,\n        \"samples\": [\n          138,\n          220,\n          257\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21.96775509286144,\n        \"min\": 2.9,\n        \"max\": 125.0,\n        \"num_unique_values\": 92,\n        \"samples\": [\n          13.4,\n          6.6,\n          15.6\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 124,\n        \"min\": 11,\n        \"max\": 887,\n        \"num_unique_values\": 62,\n        \"samples\": [\n          528,\n          876,\n          560\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4649121658515394,\n        \"min\": 0.9,\n        \"max\": 11.15,\n        \"num_unique_values\": 107,\n        \"samples\": [\n          7.25,\n          7.45,\n          7.63\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.1415966877712754,\n        \"min\": 0.1,\n        \"max\": 0.95,\n        \"num_unique_values\": 71,\n        \"samples\": [\n          0.61,\n          0.62,\n          0.81\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.8429861461771505,\n        \"min\": 0.1,\n        \"max\": 24.0,\n        \"num_unique_values\": 68,\n        \"samples\": [\n          0.41,\n          1.0,\n          0.86\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.424183642638536,\n        \"min\": 0.64,\n        \"max\": 31.0,\n        \"num_unique_values\": 153,\n        \"samples\": [\n          7.5,\n          6.4,\n          3.1\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.8942340912435867,\n        \"min\": 0.07,\n        \"max\": 42.0,\n        \"num_unique_values\": 70,\n        \"samples\": [\n          0.83,\n          0.24,\n          0.79\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.1100108753080082,\n        \"min\": 0.21,\n        \"max\": 44.0,\n        \"num_unique_values\": 387,\n        \"samples\": [\n          1.56,\n          2.64,\n          1.98\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.4658998011958893,\n        \"min\": 0.09,\n        \"max\": 3.02,\n        \"num_unique_values\": 166,\n        \"samples\": [\n          0.12,\n          1.86,\n          0.74\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4.29882792449984,\n        \"min\": 0.11,\n        \"max\": 31.0,\n        \"num_unique_values\": 429,\n        \"samples\": [\n          4.02,\n          4.85,\n          8.84\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.5708004655532873,\n        \"min\": 0.06,\n        \"max\": 2.82,\n        \"num_unique_values\": 127,\n        \"samples\": [\n          0.88,\n          1.24,\n          0.22\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Output\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 2,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0,\n          1,\n          2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mtAngMfRuszz",
        "outputId": "f5e049e0-25d6-489e-ca6f-14dfbb7553e6"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 880 entries, 0 to 879\n",
            "Data columns (total 13 columns):\n",
            " #   Column  Non-Null Count  Dtype  \n",
            "---  ------  --------------  -----  \n",
            " 0   N       880 non-null    int64  \n",
            " 1   P       880 non-null    float64\n",
            " 2   K       880 non-null    int64  \n",
            " 3   pH      880 non-null    float64\n",
            " 4   EC      880 non-null    float64\n",
            " 5   OC      880 non-null    float64\n",
            " 6   S       880 non-null    float64\n",
            " 7   Zn      880 non-null    float64\n",
            " 8   Fe      880 non-null    float64\n",
            " 9   Cu      880 non-null    float64\n",
            " 10  Mn      880 non-null    float64\n",
            " 11  B       880 non-null    float64\n",
            " 12  Output  880 non-null    int64  \n",
            "dtypes: float64(10), int64(3)\n",
            "memory usage: 89.5 KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(Data.isnull(), yticklabels=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "jSI876Owuu32",
        "outputId": "1dec1c37-11e8-4cfe-adc1-34c1849d586c"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 7
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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SBAgAAFviUcvWYg4CAAAwIYMAALAnMgiWIkAAANgTT1q2FCUGAABgQgYBAGBLTFK0FgECAMCeCBAsRYkBAACYkEEAANgTkxQtRYAAALAl5iBYixIDAAAwIYMAALAnSgyWIkAAANgSJQZrUWIAANiTJ0hbIyxatEidO3dWeHi4kpOTtWXLlnrbr169Wj179lR4eLj69Omj9evXe4/V1dXp/vvvV58+fdSiRQu1b99eI0eO1JdffunTR+fOnRUSEuKzzZ49u3E3EAACBAAAGmDVqlXKy8tTfn6+tm/frn79+iktLU1Hjhzx237Tpk0aMWKEsrOztWPHDmVkZCgjI0OffPKJJOnUqVPavn27HnzwQW3fvl2vvfaa9u7dq5/+9KemvmbNmqXDhw97t9zcXMvuM8QwjIByNNuuybDsIgAA3y+JB9dYPsZXPxkQlH5avvJn1dTU+OxzOBxyOBx+2ycnJ+vGG2/UwoULJUkej0dxcXHKzc3VlClTTO2HDRumqqoqrV271rsvJSVF8fHxWrx4sd8xtm7dqqSkJH3xxRfq2LGjpG8yCBMnTtTEiRMbc5sNRgYBAGBPQSoxFBQUKDIy0mcrKCjwO2Rtba1KS0uVmprq3RcaGqrU1FSVlJT4PaekpMSnvSSlpaWdt70kHT9+XCEhIYqKivLZP3v2bLVu3VrXX3+95syZo9OnT5+3j4vFJEUAwBVt6tSpysvL89l3vuzBsWPHdObMGcXGxvrsj42N1Z49e/ye43a7/bZ3u91+21dXV+v+++/XiBEjFBER4d3/q1/9SjfccIOio6O1adMmTZ06VYcPH9Zvf/vbC95jYxAgAABsyQjSMsf6ygmXWl1dnf7rv/5LhmHod7/7nc+xc4OYvn37KiwsTHfffbcKCgosuX5KDAAAe7oMqxjatGmjJk2aqLy83Gd/eXm5nE6n33OcTmdA7c8GB1988YWKiop8sgf+JCcn6/Tp09q/f3/DbiJABAgAAAQoLCxMCQkJKi4u9u7zeDwqLi6Wy+Xye47L5fJpL0lFRUU+7c8GB/v27dPbb7+t1q1bX/Badu7cqdDQULVt27aRd1M/SgwAAFsKVomhofLy8jRq1CglJiYqKSlJ8+fPV1VVlcaMGSNJGjlypDp06OCd6DhhwgQNGDBAc+fO1dChQ7Vy5Upt27ZNzz77rKRvgoM77rhD27dv19q1a3XmzBnv/ITo6GiFhYWppKREmzdv1qBBg9SqVSuVlJRo0qRJuvPOO3X11Vdbcp8ECAAAW7pcAcKwYcN09OhRzZgxQ263W/Hx8SosLPRORDxw4IBCQ/+doO/fv79WrFih6dOna9q0aerevbvWrFmj6667TpJ06NAhvfHGG5Kk+Ph4n7HeeecdDRw4UA6HQytXrtTMmTNVU1OjLl26aNKkSabJlcHEcxAAAEF3KZ6DUD4oOM9BiH3n3aD0833DHAQAAGBCiQEAYE9GyOW+gu81AgQAgC1drjkIVwpKDAAAwIQMAgDAlgwPJQYrESAAAGyJEoO1KDEAAAATMggAAFsyWMVgKQIEAIAtUWKwFiUGAABgQgYBAGBLrGKwFgECAMCWAnuTEBqLAAEAYEtkEKzFHAQAAGBCBgEAYEtkEKxFgAAAsCXmIFiLEgMAADAhgwAAsCVKDNYiQAAA2BKPWrYWJQYAAGBCBgEAYEu8i8FaBAgAAFvyUGKwFCUGAABgQgYBAGBLTFK0FgECAMCWWOZoLQIEAIAt8SRFazEHAQAAmJBBAADYEiUGaxEgAABsiWWO1qLEAAAATMggAABsiWWO1iJAAADYEqsYrEWJAQAAmJBBAADYEpMUrUWAAACwJeYgWIsSAwAADbRo0SJ17txZ4eHhSk5O1pYtW+ptv3r1avXs2VPh4eHq06eP1q9f73PcMAzNmDFD7dq1U/PmzZWamqp9+/b5tKmoqFBWVpYiIiIUFRWl7OxsnTx5Muj3dhYBAgDAlgwjOFtDrVq1Snl5ecrPz9f27dvVr18/paWl6ciRI37bb9q0SSNGjFB2drZ27NihjIwMZWRk6JNPPvG2efzxx7VgwQItXrxYmzdvVosWLZSWlqbq6mpvm6ysLO3atUtFRUVau3at3nvvPY0bN67hNxCgEMMI7OPZdk2GZRcBAPh+STy4xvIxgvX3Up+/rVJNTY3PPofDIYfD4bd9cnKybrzxRi1cuFCS5PF4FBcXp9zcXE2ZMsXUftiwYaqqqtLatWu9+1JSUhQfH6/FixfLMAy1b99ekydP1q9//WtJ0vHjxxUbG6tly5Zp+PDhKisrU+/evbV161YlJiZKkgoLC3Xrrbfq4MGDat++fVA+i3ORQQAA2JJhhARlKygoUGRkpM9WUFDgd8za2lqVlpYqNTXVuy80NFSpqakqKSnxe05JSYlPe0lKS0vztv/888/ldrt92kRGRio5OdnbpqSkRFFRUd7gQJJSU1MVGhqqzZs3N+4DvAAmKQIArmhTp05VXl6ez77zZQ+OHTumM2fOKDY21md/bGys9uzZ4/cct9vtt73b7fYeP7uvvjZt27b1Od60aVNFR0d72wQbAQIAwJaCtcyxvnLClYwSAwDAlowgbQ3Rpk0bNWnSROXl5T77y8vL5XQ6/Z7jdDrrbX/2vy/U5tuTIE+fPq2KiorzjnuxCBAAAAhQWFiYEhISVFxc7N3n8XhUXFwsl8vl9xyXy+XTXpKKioq87bt06SKn0+nT5sSJE9q8ebO3jcvlUmVlpUpLS71tNmzYII/Ho+Tk5KDd37koMQAAbOlyPUkxLy9Po0aNUmJiopKSkjR//nxVVVVpzJgxkqSRI0eqQ4cO3omOEyZM0IABAzR37lwNHTpUK1eu1LZt2/Tss89KkkJCQjRx4kT97//+r7p3764uXbrowQcfVPv27ZWRkSFJ6tWrl9LT0zV27FgtXrxYdXV1ysnJ0fDhwy1ZwSARIAAAbOpyPUlx2LBhOnr0qGbMmCG32634+HgVFhZ6JxkeOHBAoaH/TtD3799fK1as0PTp0zVt2jR1795da9as0XXXXedtc99996mqqkrjxo1TZWWlfvjDH6qwsFDh4eHeNsuXL1dOTo4GDx6s0NBQZWZmasGCBZbdJ89BAAAE3aV4DsIHzjuC0s9/ul8JSj/fN2QQAAC25LncF/A9R4AAALAlQ7ysyUqsYgAAACZkEAAAtuRpxIuWEDgCBACALXkoMViKAAEAYEvMQbAWcxAAAIAJGQQAgC2xzNFaBAgAAFuixGAtSgwAAMCEDAIAwJYoMViLAAEAYEsECNaixAAAAEzIIAAAbIlJitYiQAAA2JKH+MBSlBgAAIAJGQQAgC3xLgZrESAAAGyJlzlaiwABAGBLLHO0FnMQAACACRkEAIAteUKYg2AlAgQAgC0xB8FalBgAAIAJGQQAgC0xSdFaBAgAAFviSYrWosQAAABMyCAAAGyJJylaiwABAGBLrGKwFiUGAABgQgYBAGBLTFK0FgECAMCWWOZoLQIEAIAtMQfBWsxBAAAAJmQQAAC2xBwEaxEgAABsiTkI1qLEAACABSoqKpSVlaWIiAhFRUUpOztbJ0+erPec6upqjR8/Xq1bt1bLli2VmZmp8vJy7/EPP/xQI0aMUFxcnJo3b65evXrpySef9Olj48aNCgkJMW1ut7tB108GAQBgS9/1DEJWVpYOHz6soqIi1dXVacyYMRo3bpxWrFhx3nMmTZqkdevWafXq1YqMjFROTo5uv/12ffDBB5Kk0tJStW3bVi+99JLi4uK0adMmjRs3Tk2aNFFOTo5PX3v37lVERIT357Zt2zbo+kMMwwhoIui2azIa1DEA4MqVeHCN5WMsjrszKP3c84+XgtLPucrKytS7d29t3bpViYmJkqTCwkLdeuutOnjwoNq3b2865/jx44qJidGKFSt0xx13SJL27NmjXr16qaSkRCkpKX7HGj9+vMrKyrRhwwZJ32QQBg0apK+//lpRUVGNvgdKDACAK1pNTY1OnDjhs9XU1FxUnyUlJYqKivIGB5KUmpqq0NBQbd682e85paWlqqurU2pqqndfz5491bFjR5WUlJx3rOPHjys6Otq0Pz4+Xu3atdOQIUO8GYiGIEAAANiSJ0hbQUGBIiMjfbaCgoKLuja3221K6Tdt2lTR0dHnnQvgdrsVFhZm+ld/bGzsec/ZtGmTVq1apXHjxnn3tWvXTosXL9arr76qV199VXFxcRo4cKC2b9/eoHtgDgIAwJaCNQdh6tSpysvL89nncDj8tp0yZYoee+yxevsrKysL0pXV75NPPtFtt92m/Px83Xzzzd79PXr0UI8ePbw/9+/fX3/72980b948/eEPfwi4fwIEAMAVzeFwnDcg+LbJkydr9OjR9bbp2rWrnE6njhw54rP/9OnTqqiokNPp9Hue0+lUbW2tKisrfbII5eXlpnN2796twYMHa9y4cZo+ffoFrzspKUl/+ctfLtjuXAQIAABbuhyPWo6JiVFMTMwF27lcLlVWVqq0tFQJCQmSpA0bNsjj8Sg5OdnvOQkJCWrWrJmKi4uVmZkp6ZuVCAcOHJDL5fK227Vrl2666SaNGjVKjzzySEDXvXPnTrVr1y6gtmcRIAAAbOm7/CTFXr16KT09XWPHjtXixYtVV1ennJwcDR8+3LuC4dChQxo8eLBefPFFJSUlKTIyUtnZ2crLy1N0dLQiIiKUm5srl8vlXcHwySef6KabblJaWpry8vK8cxOaNGniDVzmz5+vLl266Nprr1V1dbWWLFmiDRs26M9//nOD7oEAAQBgS9/15yAsX75cOTk5Gjx4sEJDQ5WZmakFCxZ4j9fV1Wnv3r06deqUd9+8efO8bWtqapSWlqann37ae/yVV17R0aNH9dJLL+mll/69PLNTp07av3+/JKm2tlaTJ0/WoUOHdNVVV6lv3756++23NWjQoAZdP89BAAAE3aV4DsK8jsF5DsKkA8F/DsL3ARkEAIAtfdczCHZHgAAAsKXLMUnxSsKDkgAAgAkZBACALX2XVzF8HxAgAABsiTkI1qLEAAAATMggAABsiUmK1iJAAADYkocQwVKUGAAAgAkZBACALTFJ0VoECAAAW6LAYC0CBACALZFBsBZzEAAAgAkZBACALfEkRWsRIAAAbIlljtaixAAAAEzIIAAAbIn8gbUIEAAAtsQqBmtRYgAAACZkEAAAtsQkRWsRIAAAbInwwFqUGAAAgAkZBACALTFJ0VoECAAAW2IOgrUIEAAAtkR4YC3mIAAAABMyCAAAW2IOgrUIEAAAtmRQZLAUJQYAAGBCBgEAYEuUGKxFgAAAsCWWOVqLEgMAADAhgwAAsCXyB9YiQAAA2BIlBmtRYgAAACYECAAAW/IEabNKRUWFsrKyFBERoaioKGVnZ+vkyZP1nlNdXa3x48erdevWatmypTIzM1VeXu7TJiQkxLStXLnSp83GjRt1ww03yOFwqFu3blq2bFmDr58AAQBgS0aQ/mOVrKws7dq1S0VFRVq7dq3ee+89jRs3rt5zJk2apDfffFOrV6/Wu+++qy+//FK33367qd3SpUt1+PBh75aRkeE99vnnn2vo0KEaNGiQdu7cqYkTJ+p//ud/9NZbbzXo+kMMwwjo09l2TcYF2wAAIEmJB9dYPsZdne8ISj/P738lKP2cq6ysTL1799bWrVuVmJgoSSosLNStt96qgwcPqn379qZzjh8/rpiYGK1YsUJ33PHNve3Zs0e9evVSSUmJUlJSJH2TQXj99dd9goJz3X///Vq3bp0++eQT777hw4ersrJShYWFAd8DGQQAwBWtpqZGJ06c8Nlqamouqs+SkhJFRUV5gwNJSk1NVWhoqDZv3uz3nNLSUtXV1Sk1NdW7r2fPnurYsaNKSkp82o4fP15t2rRRUlKSnn/+eZ37b/2SkhKfPiQpLS3N1MeFECAAAGwpWCWGgoICRUZG+mwFBQUXdW1ut1tt27b12de0aVNFR0fL7Xaf95ywsDBFRUX57I+NjfU5Z9asWfrjH/+ooqIiZWZm6t5779VTTz3l009sbKypjxMnTuhf//pXwPfAMkcAgC0Fa4Lh1KlTlZeX57PP4XD4bTtlyhQ99thj9fZXVlYWpCvz78EHH/T++frrr1dVVZXmzJmjX/3qV0EdhwABAHBFczgc5w0Ivm3y5MkaPXp0vW26du0qp9OpI0eO+Ow/ffq0Kioq5HQ6/Z7ndDpVW1uryspKnyxCeXn5ec+RpOTkZD388MOqqamRw+GQ0+k0rXwoLy9XRESEmjdvXv8NnoMAAQBgS57A5tgHVUxMjGJiYi7YzuVyqbKyUqWlpUpISJAkbdiwQR6PR8nJyX7PSUhIULNmzVRcXKzMzExJ0t69e3XgwAG5XK7zjrVz505dffXV3iDH5XJp/fr1Pm2Kiorq7cMfAgQAgC19l5+j2KtXL6Wnp2vs2LFavHix6urqlJOTo+HDh3tXMBw6dEiDBw/Wiy++qKSkJEVGRio7O1t5eXmKjo5WRESEcnNz5XK5vCsY3nzzTZWXlyslJUXh4eEqKirSo48+ql//+tfese+55x4tXLhQ9913n+666y5t2LBBf/zjH7Vu3boG3QMBAgAAFli+fLlycnI0ePBghYaGKjMzUwsWLPAer6ur0969e3Xq1Cnvvnnz5nnb1tTUKC0tTU8//bT3eLNmzbRo0SJNmjRJhmGoW7du+u1vf6uxY8d623Tp0kXr1q3TpEmT9OSTT+qaa67RkiVLlJaW1qDr5zkIAICguxTPQfhFp58FpZ8VX7welH6+b8ggAABsycqnIILnIAAAAD/IIAAAbMnKFy2BAAEAYFMeSgyWIkAAANgScxCsxRwEAABgQgYBAGBLzEGwFgECAMCWAnyMDxqJEgMAADAhgwAAsCVWMViLAAEAYEvMQbAWJQYAAGBCBgEAYEs8B8FaBAgAAFtiDoK1KDEAAAATMggAAFviOQjWIkAAANgSqxisRYAAALAlJilaizkIAADAhAwCAMCWWMVgLQIEAIAtMUnRWpQYAACACRkEAIAtUWKwFgECAMCWWMVgLUoMAADAhAwCAMCWPExStBQBAgDAlggPrEWJAQAAmJBBAADYEqsYrEWAAACwJQIEaxEgAABsiScpWos5CAAAwIQMAgDAligxWIsAAQBgSzxJ0VqUGAAAgAkBAgDAlgzDCMpmlYqKCmVlZSkiIkJRUVHKzs7WyZMn6z2nurpa48ePV+vWrdWyZUtlZmaqvLzce3zZsmUKCQnxux05ckSStHHjRr/H3W53g66fEgMAwJa+63MQsrKydPjwYRUVFamurk5jxozRuHHjtGLFivOeM2nSJK1bt06rV69WZGSkcnJydPvtt+uDDz6QJA0bNkzp6ek+54wePVrV1dVq27atz/69e/cqIiLC+/O3j18IAQIAAEFWVlamwsJCbd26VYmJiZKkp556SrfeequeeOIJtW/f3nTO8ePH9dxzz2nFihW66aabJElLly5Vr1699Ne//lUpKSlq3ry5mjdv7j3n6NGj2rBhg5577jlTf23btlVUVFSj74ESAwDAloJVYqipqdGJEyd8tpqamou6tpKSEkVFRXmDA0lKTU1VaGioNm/e7Pec0tJS1dXVKTU11buvZ8+e6tixo0pKSvye8+KLL+qqq67SHXfcYToWHx+vdu3aaciQId4MREMQIAAAbMkjIyhbQUGBIiMjfbaCgoKLuja3221K6Tdt2lTR0dHnnQvgdrsVFhZm+ld/bGzsec957rnn9Itf/MInq9CuXTstXrxYr776ql599VXFxcVp4MCB2r59e4PugRIDAOCKNnXqVOXl5fnsczgcfttOmTJFjz32WL39lZWVBe3a6lNSUqKysjL94Q9/8Nnfo0cP9ejRw/tz//799be//U3z5s0zta0PAQIAwJaC9RwEh8Nx3oDg2yZPnqzRo0fX26Zr165yOp3eVQVnnT59WhUVFXI6nX7Pczqdqq2tVWVlpU8Woby83O85S5YsUXx8vBISEi543UlJSfrLX/5ywXbnIkAAANiS5zK8iyEmJkYxMTEXbOdyuVRZWanS0lLvX+AbNmyQx+NRcnKy33MSEhLUrFkzFRcXKzMzU9I3KxEOHDggl8vl0/bkyZP64x//GHApZOfOnWrXrl1Abc8iQAAA2NJ3+UmKvXr1Unp6usaOHavFixerrq5OOTk5Gj58uHcFw6FDhzR48GC9+OKLSkpKUmRkpLKzs5WXl6fo6GhFREQoNzdXLpdLKSkpPv2vWrVKp0+f1p133mkae/78+erSpYuuvfZaVVdXa8mSJdqwYYP+/Oc/N+geCBAAALDA8uXLlZOTo8GDBys0NFSZmZlasGCB93hdXZ327t2rU6dOeffNmzfP27ampkZpaWl6+umnTX0/99xzuv322/0uY6ytrdXkyZN16NAhXXXVVerbt6/efvttDRo0qEHXH2IE+BipbddkNKhjAMCVK/HgGsvH6NU2KSj9lB3ZEpR+vm/IIAAAbOm7XGL4PuA5CAAAwIQMAgDAli7HKoYrCQECAMCWKDFYixIDAAAwIYMAALAlSgzWIkAAANgSJQZrUWIAAAAmZBAAALZkGJ7LfQnfawQIAABb8lBisBQBAgDAlgJ8UwAaiTkIAADAhAwCAMCWKDFYiwABAGBLlBisRYkBAACYkEEAANgST1K0FgECAMCWeJKitSgxAAAAEzIIAABbYpKitQgQAAC2xDJHa1FiAAAAJmQQAAC2RInBWgQIAABbYpmjtQgQAAC2RAbBWsxBAAAAJmQQAAC2xCoGaxEgAABsiRKDtSgxAAAAEzIIAABbYhWDtQgQAAC2xMuarEWJAQAAmJBBAADYEiUGaxEgAABsiVUM1qLEAAAATMggAABsiUmK1iJAAADYEiUGa1FiAADYkmEYQdmsUlFRoaysLEVERCgqKkrZ2dk6efJkvec8++yzGjhwoCIiIhQSEqLKyspG9fvRRx/pRz/6kcLDwxUXF6fHH3+8wddPgAAAgAWysrK0a9cuFRUVae3atXrvvfc0bty4es85deqU0tPTNW3atEb3e+LECd18883q1KmTSktLNWfOHM2cOVPPPvtsg64/xAgwfNp2TUaDOgYAXLkSD66xfIymYR2C0k/VP/+umpoan30Oh0MOh6PRfZaVlal3797aunWrEhMTJUmFhYW69dZbdfDgQbVv377e8zdu3KhBgwbp66+/VlRUVIP6/d3vfqcHHnhAbrdbYWFhkqQpU6ZozZo12rNnT+A3YVisurrayM/PN6qrq60e6jszNvd8aXHP3/9xL+fYV+I9X+6xL7X8/HxDks+Wn59/UX0+99xzRlRUlM++uro6o0mTJsZrr712wfPfeecdQ5Lx9ddfN7jf//7v/zZuu+02nzYbNmwwJBkVFRUB34PlAcLx48cNScbx48etHuo7Mzb3fGlxz9//cS/n2FfiPV/usS+16upq4/jx4z7bxQZGjzzyiPGDH/zAtD8mJsZ4+umnL3j++QKEQPodMmSIMW7cOJ/ju3btMiQZu3fvDvgemIMAALiiORwORURE+GznKy9MmTJFISEh9W4NSuN/h7HMEQCAAE2ePFmjR4+ut03Xrl3ldDp15MgRn/2nT59WRUWFnE5no8cPpF+n06ny8nKfNmd/bsjYBAgAAAQoJiZGMTExF2zncrlUWVmp0tJSJSQkSJI2bNggj8ej5OTkRo8fSL8ul0sPPPCA6urq1KxZM0lSUVGRevTooauvvjrgsSwvMTgcDuXn51/UbFC7jc09X1rc8/d/3Ms59pV4z5d77O+DXr16KT09XWPHjtWWLVv0wQcfKCcnR8OHD/euYDh06JB69uypLVu2eM9zu93auXOnPvvsM0nSxx9/rJ07d6qioiLgfn/xi18oLCxM2dnZ2rVrl1atWqUnn3xSeXl5DbuJgGcrAACAgH311VfGiBEjjJYtWxoRERHGmDFjjH/+85/e459//rkhyXjnnXe8+/ytqJBkLF26NOB+DcMwPvzwQ+OHP/yh4XA4jA4dOhizZ89u8PUH/BwEAABw5WAVAwAAMCFAAAAAJgQIAADAhAABAACYWBIgjB49WiEhIZo9e7bP/jVr1igkJMSKIf2OHxISorCwMHXr1k2zZs3S6dOnLR83IyPDZ98rr7yi8PBwzZ0719Kxzxo4cKAmTpxo2r9s2TKfF340xrmf67lbenq6t82OHTv085//XLGxsQoPD1f37t01duxYffrppxc19j/+8Q/dddddat++vcLCwtSpUydNmDBBX331lU+7zz77TGPGjNE111wjh8OhLl26aMSIEdq2bdtFjf9tR48e1S9/+Ut17NhRDodDTqdTaWlp+uCDD4LS/8aNG+t9UtugQYOCMk59zvf7Prv8yiput1u5ubnq2rWrHA6H4uLi9JOf/ETFxcWWjHf2Pu+55x7TsfHjxyskJOSCD8ax6prObq1bt1Z6ero++ugjv+0D/X7UZ//+/QoJCdHOnTuDdBe+QkJCtGbNGkv6hjUsyyCEh4frscce09dff23VEPVKT0/X4cOHtW/fPk2ePFkzZ87UnDlzLuk1LFmyRFlZWfrd736nyZMnX9KxrXL2cz13e/nllyVJa9euVUpKimpqarR8+XKVlZXppZdeUmRkpB588MFGj/n3v/9diYmJ2rdvn15++WV99tlnWrx4sYqLi+Vyubzrg7dt26aEhAR9+umneuaZZ7R79269/vrr6tmzZ9A//8zMTO3YsUMvvPCCPv30U73xxhsaOHBgg/4PuT79+/c3fc6HDx/WM888o5CQEN17771BGedC/P2+u3TpYtl4+/fvV0JCgjZs2KA5c+bo448/VmFhoQYNGqTx48dbNm5cXJxWrlypf/3rX9591dXVWrFihTp27GjZuPU597MvLi5W06ZN9eMf/9jULtDvB9BgDV4YGYBRo0YZP/7xj42ePXsav/nNb7z7X3/9dcOiIU3jf/tNVkOGDDFSUlIu2biPPfaYER4eHtBbuxpiwIABxvjx443x48cbERERRuvWrY3p06cbHo/He3zChAmm85YuXWpERkZe1Nj+PtezqqqqjDZt2hgZGRl+j3/7hSMNkZ6eblxzzTXGqVOnfPYfPnzYuOqqq4x77rnH8Hg8xrXXXmskJCQYZ86cCer4/vqSZGzcuDFofQZi9+7dRqtWrYwHHnjAMIx/v8zl7bffNhISEozmzZsbLpfL2LNnT1DGq+/3vWbNGuP66683HA6H0aVLF2PmzJlGXV3dRY95yy23GB06dDBOnjxpOvb11197143v2LHDZ7++tZa8Ic7e53XXXWe89NJL3v3Lly83+vbta9x2223GqFGjDMP45vuVm5tr/OY3vzGuvvpqIzY29qLf+lffNZ3r/fffNyQZR44c8dkfyPfDMAxDkvH666/7tImMjPSur9e31t0PGDDA51pmzpxptGnTxmjVqpVx9913GzU1Nd5+OnXqZMybN8+n7379+nk/m06dOvn03alTpwZ/Jrj0LMsgNGnSRI8++qieeuopHTx40KphAta8eXPV1tZekrHuv/9+Pfzww1q7dq1+9rOfBb3/F154QU2bNtWWLVv05JNP6re//a2WLFkS9HEa4q233tKxY8d03333+T3e2PJGRUWF3nrrLd17771q3ry5zzGn06msrCytWrVKO3fu1K5duzR58mSFhpr/Z32x5ZVztWzZUi1bttSaNWtM75C3SmVlpW677TYNHDhQDz/8sM+xBx54QHPnztW2bdvUtGlT3XXXXZZey/vvv6+RI0dqwoQJ2r17t5555hktW7ZMjzzyyEX1W1FRocLCQo0fP14tWrQwHQ/m79Cfu+66S0uXLvX+/Pzzz2vMmDGmdi+88IJatGihzZs36/HHH9esWbNUVFRk6bWdPHlSL730krp166bWrVt79wf6/TACeNzN2af5vf322zp8+LBee+0177Hi4mKVlZVp48aNevnll/Xaa6/poYceCvj6t27dKklaunSpDh8+7P0Z322WTlL82c9+pvj4eOXn51s5TL0Mw9Dbb7+tt956SzfddJPl4/3f//2fHn/8cf3pT3/S4MGDLRkjLi5O8+bNU48ePZSVlaXc3FzNmzfPe/zpp5/2/iV2dvNXX22MtWvXmvp+9NFHtW/fPklSz549gzLOWfv27ZNhGOrVq5ff47169dLXX39t2fj+NG3aVMuWLdMLL7ygqKgo/ed//qemTZt23vrwxfJ4PPrFL36hpk2bavny5aZ5PI888ogGDBig3r17a8qUKdq0aZOqq6uDMva3f98///nP9dBDD2nKlCkaNWqUunbtqiFDhujhhx/WM888c1FjffbZZzIM45L8Dv2588479Ze//EVffPGFvvjiC33wwQe68847Te369u2r/Px8de/eXSNHjlRiYqIl8yPO/exbtWqlN954Q6tWrfIJgAP9fhw9evSC4519v0Dr1q3ldDoVHR3tPRYWFqbnn39e1157rYYOHapZs2ZpwYIF8ng8Ad3L2b6joqLkdDoDepcBLj/LVzE89thjeuGFF1RWVmb1UD7OfrnCw8N1yy23aNiwYZo5c6bl4/bt21edO3dWfn6+Tp48ackYKSkpPn9JuFwu7du3T2fOnJEkZWVlaefOnT7brFmzgjL2oEGDTH3fc889Af0L5WJcqH+rx/+2zMxMffnll3rjjTeUnp6ujRs36oYbbtCyZcuCPta0adNUUlKiP/3pT2rVqpXpeN++fb1/bteunSSZ3vbWWN/+fS9YsEAffvihZs2a5RM4jB07VocPH9apU6caPdal/h1+W0xMjIYOHaply5Zp6dKlGjp0qNq0aWNqd+7nLX3zmQfr8z7XuZ/9li1blJaWpltuuUVffPGFqa3Vn12/fv101VVXeX92uVw6efKk/vGPf1g6Li4vywOE//f//p/S0tI0depUq4fycfbLtW/fPv3rX//ypgWt1qFDB23cuFGHDh1Senq6/vnPf1o+5rdFRkaqW7duPlvbtm2D0neLFi1MfUdHR+sHP/iBJAX9PejdunVTSEjIeQPMsrIyXX311ZaNX5/w8HANGTJEDz74oDZt2qTRo0cHPVu2cuVKPfHEE1q5cqW6d+/ut83Zt7VJ8gaOgf7L7kK+/ftu166dTp48qYceesgncPj444+1b98+hYeHN3qs7t27KyQkpN7f4dl/PZ/7F2JdXV2jx/y2u+66y5sdOl+p5tzPW/rmMw/W532ucz/7G2+8UUuWLFFVVZV+//vfe9sE+v2IiYlRSEiIKZAI1mcXGhpqWd+4fC7JcxBmz56tN998UyUlJZdiOEn//nJ17NhRTZte2rdad+rUSe+++67cbrclQcLmzZt9fv7rX/+q7t27q0mTJkEdpyFuvvlmtWnTRo8//rjf45WVlY3qt3Xr1hoyZIiefvppnxnm0jfL4ZYvX65hw4YpPj5evXv31ty5c/3+n3Vjx2+I3r17q6qqKmj97dy5U9nZ2Zo9e7bS0tKC1u/FuuGGG7R3715ToNitWze/8z8CFR0drbS0NC1atMjv51hZWelNTR8+fNi7P5jL8tLT01VbW6u6urrv1GcufROIhIaG+nwPAv1+hISEKCYmxudz27dvn0/GJywsTJK8mchzffjhhz79//Wvf1XLli0VFxcnSaa+T5w4oc8//9ynj2bNmvntG99dlyRA6NOnj7KysrRgwYJLMdx3QlxcnDZu3KgjR44oLS1NJ06cCFrfBw4cUF5envbu3auXX35ZTz31lCZMmBC0/utTU1Mjt9vtsx07dkwtWrTQkiVLtG7dOv30pz/V22+/rf3792vbtm267777LmoOxMKFC1VTU6O0tDS99957+sc//qHCwkINGTJEHTp00COPPKKQkBAtXbpUn376qX70ox9p/fr1+vvf/66PPvpIjzzyiG677bagfQZfffWVbrrpJr300kv66KOP9Pnnn2v16tV6/PHHgzbOsWPHlJGRoYEDB+rOO+80feaB1JStMmPGDL344ot66KGHtGvXLpWVlWnlypWaPn36Rfe9aNEinTlzRklJSXr11Ve1b98+lZWVacGCBXK5XGrevLlSUlI0e/ZslZWV6d133w3KuGc1adJEZWVl2r1792UNuCXf71pZWZlyc3N18uRJ/eQnP/FpF8j3Q5JuuukmLVy4UDt27NC2bdt0zz33+GRD2rZtq+bNm6uwsFDl5eU6fvy491htba2ys7O1e/durV+/Xvn5+crJyfEGhDfddJP+8Ic/6P3339fHH3+sUaNGmT6/zp07q7i4WG63+7Itf0cDWbE0wt8Snc8//9wICwu7bMscLwV/4x48eNDo3r27kZKSYhw/fvyixxgwYIBx7733Gvfcc48RERFhXH311ca0adMu2TJH+XkNaY8ePbxttm7datx+++1GTEyM4XA4jG7duhnjxo0z9u3bd1Fj79+/3xg1apQRGxtrNGvWzIiLizNyc3ONY8eO+bTbu3evMXLkSKN9+/ZGWFiY0alTJ2PEiBHG9u3bL2r8c1VXVxtTpkwxbrjhBiMyMtK46qqrjB49ehjTp083LTVrrGXLlvn9rHXOMrGzyxzPXcK5Y8cOQ5Lx+eefX/Q11Pc9KiwsNPr37280b97ciIiIMJKSkoxnn332osc0DMP48ssvjfHjxxudOnUywsLCjA4dOhg//elPvcsYd+/ebbhcLqN58+ZGfHy88ec//zkoyxzP59vLHL/9/Tr3eLB8+7vWqlUr48YbbzReeeUVv+0D+X4cOnTIuPnmm40WLVoY3bt3N9avX++zzNEwDOP3v/+9ERcXZ4SGhpqWOc6YMcNo3bq10bJlS2Ps2LFGdXW197zjx48bw4YNMyIiIoy4uDhj2bJlPsscDcMw3njjDaNbt25G06ZNWeZoE7zu2WYGDhyo+Ph4zZ8//3JfCoArwOjRo1VZWclTEK9AvIsBAACYECAAAAATSgwAAMCEDAIAADAhQAAAACYECAAAwIQAAQAAmBAgAAAAEwIEAABgQoAAAABMCBAAAIDJ/we2zficVt31gwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Data.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HbrREAGhuzSA",
        "outputId": "00e129be-2faf-4c15-e3b3-d058be34351b"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "517zKqg5u1ar",
        "outputId": "4f7be6f2-8b7e-4553-c420-2848e9d9fb7f"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               N           P           K  ...          Mn           B      Output\n",
              "count  880.00000  880.000000  880.000000  ...  880.000000  880.000000  880.000000\n",
              "mean   246.73750   14.562159  499.978409  ...    8.666500    0.590159    0.588636\n",
              "std     77.38886   21.967755  124.222838  ...    4.298828    0.570800    0.575462\n",
              "min      6.00000    2.900000   11.000000  ...    0.110000    0.060000    0.000000\n",
              "25%    201.00000    6.800000  412.000000  ...    6.225000    0.270000    0.000000\n",
              "50%    257.00000    8.100000  475.000000  ...    8.345000    0.405000    1.000000\n",
              "75%    307.00000   10.550000  581.000000  ...   11.472500    0.610000    1.000000\n",
              "max    383.00000  125.000000  887.000000  ...   31.000000    2.820000    2.000000\n",
              "\n",
              "[8 rows x 13 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-12e1f715-3c1b-4a7a-ac5c-00fb80205479\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>N</th>\n",
              "      <th>P</th>\n",
              "      <th>K</th>\n",
              "      <th>pH</th>\n",
              "      <th>EC</th>\n",
              "      <th>OC</th>\n",
              "      <th>S</th>\n",
              "      <th>Zn</th>\n",
              "      <th>Fe</th>\n",
              "      <th>Cu</th>\n",
              "      <th>Mn</th>\n",
              "      <th>B</th>\n",
              "      <th>Output</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>880.00000</td>\n",
              "      <td>880.000000</td>\n",
              "      <td>880.000000</td>\n",
              "      <td>880.000000</td>\n",
              "      <td>880.000000</td>\n",
              "      <td>880.000000</td>\n",
              "      <td>880.000000</td>\n",
              "      <td>880.000000</td>\n",
              "      <td>880.000000</td>\n",
              "      <td>880.000000</td>\n",
              "      <td>880.000000</td>\n",
              "      <td>880.000000</td>\n",
              "      <td>880.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>246.73750</td>\n",
              "      <td>14.562159</td>\n",
              "      <td>499.978409</td>\n",
              "      <td>7.510500</td>\n",
              "      <td>0.543659</td>\n",
              "      <td>0.617989</td>\n",
              "      <td>7.545080</td>\n",
              "      <td>0.469273</td>\n",
              "      <td>4.140523</td>\n",
              "      <td>0.952443</td>\n",
              "      <td>8.666500</td>\n",
              "      <td>0.590159</td>\n",
              "      <td>0.588636</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>77.38886</td>\n",
              "      <td>21.967755</td>\n",
              "      <td>124.222838</td>\n",
              "      <td>0.464912</td>\n",
              "      <td>0.141597</td>\n",
              "      <td>0.842986</td>\n",
              "      <td>4.424184</td>\n",
              "      <td>1.894234</td>\n",
              "      <td>3.110011</td>\n",
              "      <td>0.465900</td>\n",
              "      <td>4.298828</td>\n",
              "      <td>0.570800</td>\n",
              "      <td>0.575462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>6.00000</td>\n",
              "      <td>2.900000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.900000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.100000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.070000</td>\n",
              "      <td>0.210000</td>\n",
              "      <td>0.090000</td>\n",
              "      <td>0.110000</td>\n",
              "      <td>0.060000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>201.00000</td>\n",
              "      <td>6.800000</td>\n",
              "      <td>412.000000</td>\n",
              "      <td>7.350000</td>\n",
              "      <td>0.430000</td>\n",
              "      <td>0.380000</td>\n",
              "      <td>4.700000</td>\n",
              "      <td>0.280000</td>\n",
              "      <td>2.050000</td>\n",
              "      <td>0.630000</td>\n",
              "      <td>6.225000</td>\n",
              "      <td>0.270000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>257.00000</td>\n",
              "      <td>8.100000</td>\n",
              "      <td>475.000000</td>\n",
              "      <td>7.500000</td>\n",
              "      <td>0.545000</td>\n",
              "      <td>0.590000</td>\n",
              "      <td>6.640000</td>\n",
              "      <td>0.360000</td>\n",
              "      <td>3.565000</td>\n",
              "      <td>0.930000</td>\n",
              "      <td>8.345000</td>\n",
              "      <td>0.405000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>307.00000</td>\n",
              "      <td>10.550000</td>\n",
              "      <td>581.000000</td>\n",
              "      <td>7.630000</td>\n",
              "      <td>0.640000</td>\n",
              "      <td>0.780000</td>\n",
              "      <td>8.750000</td>\n",
              "      <td>0.470000</td>\n",
              "      <td>6.320000</td>\n",
              "      <td>1.250000</td>\n",
              "      <td>11.472500</td>\n",
              "      <td>0.610000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>383.00000</td>\n",
              "      <td>125.000000</td>\n",
              "      <td>887.000000</td>\n",
              "      <td>11.150000</td>\n",
              "      <td>0.950000</td>\n",
              "      <td>24.000000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>42.000000</td>\n",
              "      <td>44.000000</td>\n",
              "      <td>3.020000</td>\n",
              "      <td>31.000000</td>\n",
              "      <td>2.820000</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
              "  .colab-df-quickchart:hover {\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
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              "\n",
              "  @keyframes spin {\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
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              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-bb831232-116d-4343-99c8-c5b54c456558 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"Data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"N\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 265.54457206299315,\n        \"min\": 6.0,\n        \"max\": 880.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          246.7375,\n          257.0,\n          880.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"P\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 304.2252038010364,\n        \"min\": 2.9,\n        \"max\": 880.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          14.56215909090909,\n          8.1,\n          880.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"K\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 313.35112921964037,\n        \"min\": 11.0,\n        \"max\": 887.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          499.9784090909091,\n          475.0,\n          880.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"pH\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 309.0014369405242,\n        \"min\": 0.4649121658515394,\n        \"max\": 880.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.5104999999999995,\n          7.5,\n          880.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"EC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 310.9578888213911,\n        \"min\": 0.1,\n        \"max\": 880.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5436590909090908,\n          0.545,\n          880.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"OC\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 309.8563152761273,\n        \"min\": 0.1,\n        \"max\": 880.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.6179886363636365,\n          0.59,\n          880.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"S\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 308.049072136366,\n        \"min\": 0.64,\n        \"max\": 880.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7.545079545454546,\n          6.64,\n          880.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Zn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 309.1669438675823,\n        \"min\": 0.07,\n        \"max\": 880.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.46927272727272723,\n          0.36,\n          880.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Fe\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 308.2601690405681,\n        \"min\": 0.21,\n        \"max\": 880.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          4.140522727272727,\n          3.565,\n          880.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Cu\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 310.7575789547352,\n        \"min\": 0.09,\n        \"max\": 880.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.9524431818181819,\n          0.93,\n          880.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Mn\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 307.72300213074936,\n        \"min\": 0.11,\n        \"max\": 880.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          8.666500000000001,\n          8.344999999999999,\n          880.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"B\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 310.859173970845,\n        \"min\": 0.06,\n        \"max\": 880.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          0.5901590909090909,\n          0.405,\n          880.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Output\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 310.8668163530475,\n        \"min\": 0.0,\n        \"max\": 880.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          880.0,\n          0.5886363636363636,\n          2.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.utils import resample\n",
        "\n",
        "class_0 = Data[Data['Output'] == 0]\n",
        "class_1 = Data[Data['Output'] == 1]\n",
        "class_2 = Data[Data['Output'] == 2]\n",
        "\n",
        "class_2_oversampled = resample(class_2,\n",
        "                               replace=True,\n",
        "                               n_samples=200,\n",
        "                               random_state=42)\n",
        "\n",
        "oversampled_data = pd.concat([class_0, class_1, class_2_oversampled])\n",
        "Data = oversampled_data"
      ],
      "metadata": {
        "id": "357DBZ9Ru3p7"
      },
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Data['Output'].hist(figsize=(10, 4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "j_3oRacKvUEr",
        "outputId": "f25a4573-58a1-431d-c1fd-36c51d430171"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(4, 3, figsize=(15, 15))\n",
        "axes = axes.flatten()\n",
        "for i, column in enumerate(Data.columns):\n",
        "  if column == 'Output':\n",
        "    continue\n",
        "  sns.boxplot(data=Data[column], ax=axes[i])\n",
        "  axes[i].set_title(f'Boxplot of {column}')\n",
        "  axes[i].set_xlabel('')\n",
        "  axes[i].set_ylabel('Values')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "0QRlrJ7SvXxY",
        "outputId": "fe6e51b3-991d-42ae-f265-438f35bbab92"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 12 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Q1 = Data.quantile(0.25)\n",
        "Q3 = Data.quantile(0.75)\n",
        "IQR = Q3 - Q1\n",
        "\n",
        "lower_bound = Q1 - 1.5 * IQR\n",
        "upper_bound = Q3 + 1.5 * IQR\n",
        "\n",
        "filtered_features = Data[~((Data < lower_bound) | (Data > upper_bound)).any(axis=1)]\n",
        "\n",
        "print(\"Original shape:\", Data.shape)\n",
        "print(\"Shape after removing outliers:\", filtered_features.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f8rVsMbsvyCl",
        "outputId": "7931f296-3958-43ba-f8ba-448f09239c4f"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original shape: (1041, 13)\n",
            "Shape after removing outliers: (716, 13)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fig, axes = plt.subplots(4, 3, figsize=(15, 15))\n",
        "axes = axes.flatten()\n",
        "for i, column in enumerate(filtered_features.columns):\n",
        "  if column == 'Output':\n",
        "    continue\n",
        "  sns.boxplot(data=filtered_features[column], ax=axes[i])\n",
        "  axes[i].set_title(f'Boxplot of {column}')\n",
        "  axes[i].set_xlabel('')\n",
        "  axes[i].set_ylabel('Values')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 646
        },
        "id": "P2OPysU4wTGy",
        "outputId": "d6d7911c-d742-4aa4-f872-a22a69883248"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x1500 with 12 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "labels = filtered_features[['Output']]\n",
        "features = filtered_features.drop('Output', axis=1)"
      ],
      "metadata": {
        "id": "UJp53AB6wwj4"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Features Shape: {features.shape}\")\n",
        "print(f\"Labels Shape: {labels.shape}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "frJNQREuxPNH",
        "outputId": "d9a47312-fd3a-446d-ec93-0de15aba98d2"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Features Shape: (716, 12)\n",
            "Labels Shape: (716, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.figure(figsize=(15, 8))\n",
        "sns.heatmap(features.join(labels).corr(), annot=True, cmap='coolwarm')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "PFrWNCd4xXPd",
        "outputId": "0bd1fd2c-c50c-4ed9-fd20-d76993e6c5d9"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 23
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "features.var()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
        },
        "id": "MYk_QQjjxtws",
        "outputId": "8295276f-dc0d-42a4-ca80-43e798b09379"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "N      5146.512207\n",
              "P        12.153910\n",
              "K     12126.570135\n",
              "pH        0.063922\n",
              "EC        0.019929\n",
              "OC        0.096161\n",
              "S         5.223278\n",
              "Zn        0.013422\n",
              "Fe        5.837588\n",
              "Cu        0.185546\n",
              "Mn       13.503260\n",
              "B         0.035824\n",
              "dtype: float64"
            ],
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>0</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>N</th>\n",
              "      <td>5146.512207</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>P</th>\n",
              "      <td>12.153910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>K</th>\n",
              "      <td>12126.570135</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>pH</th>\n",
              "      <td>0.063922</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EC</th>\n",
              "      <td>0.019929</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OC</th>\n",
              "      <td>0.096161</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S</th>\n",
              "      <td>5.223278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zn</th>\n",
              "      <td>0.013422</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fe</th>\n",
              "      <td>5.837588</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cu</th>\n",
              "      <td>0.185546</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mn</th>\n",
              "      <td>13.503260</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.035824</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "scaled_features = scaler.fit_transform(features)\n",
        "\n",
        "scaled_features_df = pd.DataFrame(scaled_features, columns=features.columns)\n",
        "scaled_features_df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 226
        },
        "id": "W3TNTUsQxvuu",
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      },
      "execution_count": 25,
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              "          N         P         K        pH  ...        Fe        Cu        Mn         B\n",
              "0  0.269113  0.313187  0.536379  0.438596  ...  0.009671  0.338308  0.439224  0.056818\n",
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              "3  0.174312  0.296703  0.624365  0.412281  ...  0.071567  0.398010  0.104699  0.772727\n",
              "4  0.461774  0.131868  0.446701  0.561404  ...  0.881044  0.298507  0.455567  0.715909\n",
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              "      <td>0.543860</td>\n",
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              "      <td>0.561404</td>\n",
              "      <td>0.440000</td>\n",
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              "      <td>0.881044</td>\n",
              "      <td>0.298507</td>\n",
              "      <td>0.455567</td>\n",
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          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "scaled_features_df.var()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 460
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        "id": "qUi8jSNDyA1W",
        "outputId": "9e0c484e-340d-4e0b-dbae-42ee2da466f3"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "N     0.048130\n",
              "P     0.036692\n",
              "K     0.034719\n",
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              "OC    0.070247\n",
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              "Zn    0.036070\n",
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              "Cu    0.045926\n",
              "Mn    0.035222\n",
              "B     0.046260\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>pH</th>\n",
              "      <td>0.049186</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>EC</th>\n",
              "      <td>0.035429</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OC</th>\n",
              "      <td>0.070247</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>S</th>\n",
              "      <td>0.036273</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Zn</th>\n",
              "      <td>0.036070</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Fe</th>\n",
              "      <td>0.054600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Cu</th>\n",
              "      <td>0.045926</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Mn</th>\n",
              "      <td>0.035222</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>B</th>\n",
              "      <td>0.046260</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div><br><label><b>dtype:</b> float64</label>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "Input, testInput, Target, testTarget = train_test_split(scaled_features_df, labels, test_size = 0.2, shuffle=True, random_state= 42)\n",
        "trainInput, validationInput, trainTarget, validationTarget = train_test_split(Input, Target, test_size = 0.2, shuffle=True, random_state=42)"
      ],
      "metadata": {
        "id": "PDverqFSyDaL"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Training input shape:\", trainInput.shape)\n",
        "print(\"Validation input shape:\", validationInput.shape)\n",
        "print(\"Test input shape:\", testInput.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WCgESfH8yk6a",
        "outputId": "026a86fe-08f8-4c9d-d2a8-ac8e5db77e39"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training input shape: (457, 12)\n",
            "Validation input shape: (115, 12)\n",
            "Test input shape: (144, 12)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, Input\n",
        "from tensorflow.keras import initializers\n",
        "from tensorflow.keras.optimizers import SGD\n",
        "\n",
        "weight_initialization = initializers.HeNormal()\n",
        "model = Sequential([Input(shape=(12,)),\n",
        "                    Dense(64, activation='relu', kernel_initializer=weight_initialization),\n",
        "                    Dense(32, activation='relu', kernel_initializer=weight_initialization),\n",
        "                    Dense(16, activation='relu', kernel_initializer=weight_initialization),\n",
        "                    Dense(3, activation='softmax')])\n",
        "\n",
        "model.compile(\n",
        "    optimizer=SGD(learning_rate=0.01, momentum=0.9),\n",
        "    loss = 'sparse_categorical_crossentropy',\n",
        "    metrics=['accuracy']\n",
        ")"
      ],
      "metadata": {
        "id": "X58--pyjyx39"
      },
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "checkpoint_callback = ModelCheckpoint(\n",
        "    'best_weights.keras',\n",
        "    monitor='val_loss',\n",
        "    save_best_only=True,\n",
        "    mode='min',\n",
        "    verbose=1\n",
        ")\n",
        "\n",
        "history = model.fit(\n",
        "    trainInput, trainTarget,\n",
        "    validation_data=(validationInput, validationTarget),\n",
        "    epochs = 100,\n",
        "    batch_size= 32,\n",
        "    callbacks=[checkpoint_callback],\n",
        "    verbose=1\n",
        ")\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "6I4zgEsz0JLW",
        "outputId": "5b2ddf6f-1f07-4ce2-d7a0-d94f3d21a017"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m11s\u001b[0m 805ms/step - accuracy: 0.0938 - loss: 1.5935\n",
            "Epoch 1: val_loss improved from inf to 1.14866, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 19ms/step - accuracy: 0.2190 - loss: 1.3515 - val_accuracy: 0.4174 - val_loss: 1.1487\n",
            "Epoch 2/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.3438 - loss: 1.1005\n",
            "Epoch 2: val_loss improved from 1.14866 to 1.04625, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.4716 - loss: 1.0217 - val_accuracy: 0.5826 - val_loss: 1.0462\n",
            "Epoch 3/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.7812 - loss: 0.7959\n",
            "Epoch 3: val_loss improved from 1.04625 to 0.95615, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.6928 - loss: 0.8903 - val_accuracy: 0.7130 - val_loss: 0.9562\n",
            "Epoch 4/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 62ms/step - accuracy: 0.8125 - loss: 0.7972\n",
            "Epoch 4: val_loss improved from 0.95615 to 0.88711, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.6922 - loss: 0.8606 - val_accuracy: 0.6870 - val_loss: 0.8871\n",
            "Epoch 5/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.8438 - loss: 0.6379\n",
            "Epoch 5: val_loss improved from 0.88711 to 0.76429, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.7654 - loss: 0.7546 - val_accuracy: 0.7217 - val_loss: 0.7643\n",
            "Epoch 6/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - accuracy: 0.7188 - loss: 0.7402\n",
            "Epoch 6: val_loss improved from 0.76429 to 0.67704, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7473 - loss: 0.6832 - val_accuracy: 0.7217 - val_loss: 0.6770\n",
            "Epoch 7/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.7188 - loss: 0.6779\n",
            "Epoch 7: val_loss improved from 0.67704 to 0.67488, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.7893 - loss: 0.6046 - val_accuracy: 0.7043 - val_loss: 0.6749\n",
            "Epoch 8/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.7188 - loss: 0.5870\n",
            "Epoch 8: val_loss improved from 0.67488 to 0.56562, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8008 - loss: 0.5059 - val_accuracy: 0.7217 - val_loss: 0.5656\n",
            "Epoch 9/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.8125 - loss: 0.5177\n",
            "Epoch 9: val_loss improved from 0.56562 to 0.47992, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8167 - loss: 0.4617 - val_accuracy: 0.7391 - val_loss: 0.4799\n",
            "Epoch 10/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 32ms/step - accuracy: 0.7812 - loss: 0.5089\n",
            "Epoch 10: val_loss improved from 0.47992 to 0.42612, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8339 - loss: 0.4103 - val_accuracy: 0.7478 - val_loss: 0.4261\n",
            "Epoch 11/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.7188 - loss: 0.5165\n",
            "Epoch 11: val_loss improved from 0.42612 to 0.42533, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8392 - loss: 0.3645 - val_accuracy: 0.7652 - val_loss: 0.4253\n",
            "Epoch 12/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.9062 - loss: 0.2583\n",
            "Epoch 12: val_loss did not improve from 0.42533\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8735 - loss: 0.3250 - val_accuracy: 0.7652 - val_loss: 0.4324\n",
            "Epoch 13/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 0.8125 - loss: 0.3178\n",
            "Epoch 13: val_loss improved from 0.42533 to 0.36292, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.8660 - loss: 0.2960 - val_accuracy: 0.7913 - val_loss: 0.3629\n",
            "Epoch 14/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.8750 - loss: 0.2455\n",
            "Epoch 14: val_loss improved from 0.36292 to 0.32145, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8768 - loss: 0.2624 - val_accuracy: 0.8261 - val_loss: 0.3214\n",
            "Epoch 15/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.8438 - loss: 0.3387\n",
            "Epoch 15: val_loss improved from 0.32145 to 0.30249, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.8665 - loss: 0.2970 - val_accuracy: 0.8348 - val_loss: 0.3025\n",
            "Epoch 16/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.8125 - loss: 0.2780\n",
            "Epoch 16: val_loss did not improve from 0.30249\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8608 - loss: 0.2688 - val_accuracy: 0.8348 - val_loss: 0.4058\n",
            "Epoch 17/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.9062 - loss: 0.1892\n",
            "Epoch 17: val_loss improved from 0.30249 to 0.29274, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.8919 - loss: 0.2473 - val_accuracy: 0.8261 - val_loss: 0.2927\n",
            "Epoch 18/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 0.9375 - loss: 0.1333\n",
            "Epoch 18: val_loss did not improve from 0.29274\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9220 - loss: 0.2022 - val_accuracy: 0.8783 - val_loss: 0.3099\n",
            "Epoch 19/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.9062 - loss: 0.2285\n",
            "Epoch 19: val_loss improved from 0.29274 to 0.28112, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9042 - loss: 0.2585 - val_accuracy: 0.8609 - val_loss: 0.2811\n",
            "Epoch 20/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.8750 - loss: 0.2672\n",
            "Epoch 20: val_loss did not improve from 0.28112\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8846 - loss: 0.2764 - val_accuracy: 0.7826 - val_loss: 0.3788\n",
            "Epoch 21/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 31ms/step - accuracy: 0.9062 - loss: 0.1898\n",
            "Epoch 21: val_loss did not improve from 0.28112\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9163 - loss: 0.2046 - val_accuracy: 0.8348 - val_loss: 0.3267\n",
            "Epoch 22/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9062 - loss: 0.1895\n",
            "Epoch 22: val_loss did not improve from 0.28112\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9034 - loss: 0.2150 - val_accuracy: 0.8000 - val_loss: 0.3390\n",
            "Epoch 23/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 23ms/step - accuracy: 0.9375 - loss: 0.1880\n",
            "Epoch 23: val_loss improved from 0.28112 to 0.25087, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9068 - loss: 0.2297 - val_accuracy: 0.8783 - val_loss: 0.2509\n",
            "Epoch 24/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 49ms/step - accuracy: 0.9375 - loss: 0.2815\n",
            "Epoch 24: val_loss improved from 0.25087 to 0.24267, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9383 - loss: 0.2304 - val_accuracy: 0.9391 - val_loss: 0.2427\n",
            "Epoch 25/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 67ms/step - accuracy: 0.9688 - loss: 0.1306\n",
            "Epoch 25: val_loss improved from 0.24267 to 0.23402, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9214 - loss: 0.1786 - val_accuracy: 0.9043 - val_loss: 0.2340\n",
            "Epoch 26/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.9688 - loss: 0.1110\n",
            "Epoch 26: val_loss improved from 0.23402 to 0.22865, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9561 - loss: 0.1598 - val_accuracy: 0.8957 - val_loss: 0.2286\n",
            "Epoch 27/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.9375 - loss: 0.2635\n",
            "Epoch 27: val_loss did not improve from 0.22865\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9306 - loss: 0.1989 - val_accuracy: 0.8348 - val_loss: 0.3010\n",
            "Epoch 28/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.9688 - loss: 0.0769\n",
            "Epoch 28: val_loss did not improve from 0.22865\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9403 - loss: 0.1495 - val_accuracy: 0.8000 - val_loss: 0.2730\n",
            "Epoch 29/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 35ms/step - accuracy: 0.8750 - loss: 0.1696\n",
            "Epoch 29: val_loss improved from 0.22865 to 0.21340, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9066 - loss: 0.1838 - val_accuracy: 0.9217 - val_loss: 0.2134\n",
            "Epoch 30/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 0.9375 - loss: 0.1661\n",
            "Epoch 30: val_loss did not improve from 0.21340\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9162 - loss: 0.2057 - val_accuracy: 0.8522 - val_loss: 0.3241\n",
            "Epoch 31/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.9375 - loss: 0.1732\n",
            "Epoch 31: val_loss did not improve from 0.21340\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9240 - loss: 0.2088 - val_accuracy: 0.9304 - val_loss: 0.2708\n",
            "Epoch 32/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 0.9062 - loss: 0.1921\n",
            "Epoch 32: val_loss did not improve from 0.21340\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9096 - loss: 0.2115 - val_accuracy: 0.8348 - val_loss: 0.2862\n",
            "Epoch 33/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 69ms/step - accuracy: 0.9062 - loss: 0.1658\n",
            "Epoch 33: val_loss improved from 0.21340 to 0.21030, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9184 - loss: 0.1870 - val_accuracy: 0.9391 - val_loss: 0.2103\n",
            "Epoch 34/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 1.0000 - loss: 0.1079\n",
            "Epoch 34: val_loss improved from 0.21030 to 0.19879, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9571 - loss: 0.1443 - val_accuracy: 0.9130 - val_loss: 0.1988\n",
            "Epoch 35/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 180ms/step - accuracy: 0.9375 - loss: 0.1475\n",
            "Epoch 35: val_loss did not improve from 0.19879\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - accuracy: 0.9508 - loss: 0.1577 - val_accuracy: 0.8957 - val_loss: 0.2167\n",
            "Epoch 36/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 37ms/step - accuracy: 1.0000 - loss: 0.0727\n",
            "Epoch 36: val_loss did not improve from 0.19879\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9652 - loss: 0.1313 - val_accuracy: 0.9391 - val_loss: 0.2029\n",
            "Epoch 37/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.9375 - loss: 0.1212\n",
            "Epoch 37: val_loss did not improve from 0.19879\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9483 - loss: 0.1485 - val_accuracy: 0.8783 - val_loss: 0.2446\n",
            "Epoch 38/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.9688 - loss: 0.0998\n",
            "Epoch 38: val_loss did not improve from 0.19879\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9233 - loss: 0.1974 - val_accuracy: 0.8783 - val_loss: 0.2875\n",
            "Epoch 39/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 51ms/step - accuracy: 0.9062 - loss: 0.1880\n",
            "Epoch 39: val_loss did not improve from 0.19879\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9450 - loss: 0.1399 - val_accuracy: 0.9304 - val_loss: 0.1995\n",
            "Epoch 40/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 53ms/step - accuracy: 1.0000 - loss: 0.0455\n",
            "Epoch 40: val_loss did not improve from 0.19879\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9668 - loss: 0.1088 - val_accuracy: 0.8957 - val_loss: 0.2206\n",
            "Epoch 41/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 73ms/step - accuracy: 0.9688 - loss: 0.1340\n",
            "Epoch 41: val_loss improved from 0.19879 to 0.19577, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.9601 - loss: 0.1395 - val_accuracy: 0.9304 - val_loss: 0.1958\n",
            "Epoch 42/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 145ms/step - accuracy: 0.9375 - loss: 0.1709\n",
            "Epoch 42: val_loss did not improve from 0.19577\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9539 - loss: 0.1199 - val_accuracy: 0.9043 - val_loss: 0.2315\n",
            "Epoch 43/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 58ms/step - accuracy: 1.0000 - loss: 0.1025\n",
            "Epoch 43: val_loss did not improve from 0.19577\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9733 - loss: 0.1108 - val_accuracy: 0.8348 - val_loss: 0.3262\n",
            "Epoch 44/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 60ms/step - accuracy: 0.9375 - loss: 0.0935\n",
            "Epoch 44: val_loss did not improve from 0.19577\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9560 - loss: 0.1080 - val_accuracy: 0.9217 - val_loss: 0.1980\n",
            "Epoch 45/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 45ms/step - accuracy: 0.9688 - loss: 0.0789\n",
            "Epoch 45: val_loss did not improve from 0.19577\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9560 - loss: 0.1243 - val_accuracy: 0.9130 - val_loss: 0.2059\n",
            "Epoch 46/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 54ms/step - accuracy: 0.9688 - loss: 0.1099\n",
            "Epoch 46: val_loss did not improve from 0.19577\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9504 - loss: 0.1375 - val_accuracy: 0.9304 - val_loss: 0.2561\n",
            "Epoch 47/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 48ms/step - accuracy: 0.9062 - loss: 0.1673\n",
            "Epoch 47: val_loss did not improve from 0.19577\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9415 - loss: 0.1759 - val_accuracy: 0.9130 - val_loss: 0.2000\n",
            "Epoch 48/100\n",
            "\u001b[1m14/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m━━\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9763 - loss: 0.0975 \n",
            "Epoch 48: val_loss did not improve from 0.19577\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - accuracy: 0.9754 - loss: 0.0990 - val_accuracy: 0.9043 - val_loss: 0.2118\n",
            "Epoch 49/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 146ms/step - accuracy: 1.0000 - loss: 0.0662\n",
            "Epoch 49: val_loss improved from 0.19577 to 0.19387, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - accuracy: 0.9720 - loss: 0.1158 - val_accuracy: 0.9304 - val_loss: 0.1939\n",
            "Epoch 50/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m2s\u001b[0m 178ms/step - accuracy: 1.0000 - loss: 0.0574\n",
            "Epoch 50: val_loss did not improve from 0.19387\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9736 - loss: 0.1167 - val_accuracy: 0.9130 - val_loss: 0.2162\n",
            "Epoch 51/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 65ms/step - accuracy: 0.9688 - loss: 0.1938\n",
            "Epoch 51: val_loss improved from 0.19387 to 0.19044, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9643 - loss: 0.1210 - val_accuracy: 0.9130 - val_loss: 0.1904\n",
            "Epoch 52/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.9688 - loss: 0.1132\n",
            "Epoch 52: val_loss did not improve from 0.19044\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9546 - loss: 0.1408 - val_accuracy: 0.8696 - val_loss: 0.2523\n",
            "Epoch 53/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 83ms/step - accuracy: 0.9062 - loss: 0.1474\n",
            "Epoch 53: val_loss did not improve from 0.19044\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9569 - loss: 0.1164 - val_accuracy: 0.9043 - val_loss: 0.2063\n",
            "Epoch 54/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 71ms/step - accuracy: 0.9688 - loss: 0.0795\n",
            "Epoch 54: val_loss did not improve from 0.19044\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9827 - loss: 0.0813 - val_accuracy: 0.8696 - val_loss: 0.2256\n",
            "Epoch 55/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - accuracy: 0.9375 - loss: 0.1088\n",
            "Epoch 55: val_loss improved from 0.19044 to 0.18763, saving model to best_weights.keras\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9728 - loss: 0.0916 - val_accuracy: 0.9478 - val_loss: 0.1876\n",
            "Epoch 56/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.9688 - loss: 0.0868\n",
            "Epoch 56: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9754 - loss: 0.0926 - val_accuracy: 0.9304 - val_loss: 0.1999\n",
            "Epoch 57/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 1.0000 - loss: 0.0590\n",
            "Epoch 57: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9718 - loss: 0.1086 - val_accuracy: 0.9130 - val_loss: 0.2409\n",
            "Epoch 58/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 83ms/step - accuracy: 0.9062 - loss: 0.1925\n",
            "Epoch 58: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9478 - loss: 0.1208 - val_accuracy: 0.9217 - val_loss: 0.2097\n",
            "Epoch 59/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 85ms/step - accuracy: 1.0000 - loss: 0.0362\n",
            "Epoch 59: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9342 - loss: 0.1862 - val_accuracy: 0.9391 - val_loss: 0.2388\n",
            "Epoch 60/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 70ms/step - accuracy: 1.0000 - loss: 0.0787\n",
            "Epoch 60: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9301 - loss: 0.1610 - val_accuracy: 0.9043 - val_loss: 0.2482\n",
            "Epoch 61/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 56ms/step - accuracy: 0.9688 - loss: 0.0440\n",
            "Epoch 61: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9625 - loss: 0.0911 - val_accuracy: 0.9130 - val_loss: 0.2261\n",
            "Epoch 62/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 86ms/step - accuracy: 1.0000 - loss: 0.0205\n",
            "Epoch 62: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9783 - loss: 0.0721 - val_accuracy: 0.9130 - val_loss: 0.2451\n",
            "Epoch 63/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 81ms/step - accuracy: 0.9688 - loss: 0.0563\n",
            "Epoch 63: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9712 - loss: 0.0881 - val_accuracy: 0.9391 - val_loss: 0.1967\n",
            "Epoch 64/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.9688 - loss: 0.0542\n",
            "Epoch 64: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9415 - loss: 0.1153 - val_accuracy: 0.9304 - val_loss: 0.2451\n",
            "Epoch 65/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 83ms/step - accuracy: 0.9688 - loss: 0.0661\n",
            "Epoch 65: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9494 - loss: 0.1230 - val_accuracy: 0.9130 - val_loss: 0.2356\n",
            "Epoch 66/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 79ms/step - accuracy: 1.0000 - loss: 0.0515\n",
            "Epoch 66: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9567 - loss: 0.1118 - val_accuracy: 0.9391 - val_loss: 0.2268\n",
            "Epoch 67/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.9688 - loss: 0.0738\n",
            "Epoch 67: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9446 - loss: 0.1586 - val_accuracy: 0.8696 - val_loss: 0.4411\n",
            "Epoch 68/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 64ms/step - accuracy: 0.8438 - loss: 0.3392\n",
            "Epoch 68: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9186 - loss: 0.1886 - val_accuracy: 0.9130 - val_loss: 0.2261\n",
            "Epoch 69/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.9688 - loss: 0.0973\n",
            "Epoch 69: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9573 - loss: 0.1117 - val_accuracy: 0.9391 - val_loss: 0.2280\n",
            "Epoch 70/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 68ms/step - accuracy: 0.9688 - loss: 0.2715\n",
            "Epoch 70: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9695 - loss: 0.1231 - val_accuracy: 0.9391 - val_loss: 0.2640\n",
            "Epoch 71/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 24ms/step - accuracy: 1.0000 - loss: 0.0650\n",
            "Epoch 71: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9516 - loss: 0.1194 - val_accuracy: 0.9565 - val_loss: 0.2397\n",
            "Epoch 72/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 77ms/step - accuracy: 0.9688 - loss: 0.0667\n",
            "Epoch 72: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9724 - loss: 0.0828 - val_accuracy: 0.9130 - val_loss: 0.2669\n",
            "Epoch 73/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 84ms/step - accuracy: 0.9375 - loss: 0.1273\n",
            "Epoch 73: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9718 - loss: 0.0918 - val_accuracy: 0.9391 - val_loss: 0.2136\n",
            "Epoch 74/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 85ms/step - accuracy: 0.9688 - loss: 0.1311\n",
            "Epoch 74: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9632 - loss: 0.1035 - val_accuracy: 0.9391 - val_loss: 0.1920\n",
            "Epoch 75/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0274\n",
            "Epoch 75: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9874 - loss: 0.0560 - val_accuracy: 0.9478 - val_loss: 0.1924\n",
            "Epoch 76/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 81ms/step - accuracy: 1.0000 - loss: 0.0390\n",
            "Epoch 76: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9830 - loss: 0.0664 - val_accuracy: 0.9391 - val_loss: 0.1904\n",
            "Epoch 77/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0751\n",
            "Epoch 77: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9938 - loss: 0.0588 - val_accuracy: 0.9130 - val_loss: 0.2423\n",
            "Epoch 78/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 30ms/step - accuracy: 1.0000 - loss: 0.0297\n",
            "Epoch 78: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9880 - loss: 0.0578 - val_accuracy: 0.9130 - val_loss: 0.2144\n",
            "Epoch 79/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 0.0347\n",
            "Epoch 79: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9886 - loss: 0.0560 - val_accuracy: 0.9391 - val_loss: 0.2002\n",
            "Epoch 80/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0243\n",
            "Epoch 80: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9818 - loss: 0.0605 - val_accuracy: 0.9478 - val_loss: 0.2708\n",
            "Epoch 81/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.9375 - loss: 0.0967\n",
            "Epoch 81: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9379 - loss: 0.1376 - val_accuracy: 0.8783 - val_loss: 0.2840\n",
            "Epoch 82/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.9688 - loss: 0.1091\n",
            "Epoch 82: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9660 - loss: 0.0980 - val_accuracy: 0.8609 - val_loss: 0.3527\n",
            "Epoch 83/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 80ms/step - accuracy: 0.9688 - loss: 0.1613\n",
            "Epoch 83: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9713 - loss: 0.0785 - val_accuracy: 0.9043 - val_loss: 0.3986\n",
            "Epoch 84/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.9688 - loss: 0.0615\n",
            "Epoch 84: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9376 - loss: 0.1664 - val_accuracy: 0.9043 - val_loss: 0.2224\n",
            "Epoch 85/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 1.0000 - loss: 0.0555\n",
            "Epoch 85: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - accuracy: 0.9558 - loss: 0.1196 - val_accuracy: 0.9130 - val_loss: 0.2059\n",
            "Epoch 86/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 40ms/step - accuracy: 1.0000 - loss: 0.0221\n",
            "Epoch 86: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9771 - loss: 0.0768 - val_accuracy: 0.9478 - val_loss: 0.2059\n",
            "Epoch 87/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 27ms/step - accuracy: 0.9688 - loss: 0.1281\n",
            "Epoch 87: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9821 - loss: 0.0700 - val_accuracy: 0.9304 - val_loss: 0.2236\n",
            "Epoch 88/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 1.0000 - loss: 0.0244\n",
            "Epoch 88: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9819 - loss: 0.0555 - val_accuracy: 0.9304 - val_loss: 0.1926\n",
            "Epoch 89/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.9688 - loss: 0.0705\n",
            "Epoch 89: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9858 - loss: 0.0574 - val_accuracy: 0.9043 - val_loss: 0.2395\n",
            "Epoch 90/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 82ms/step - accuracy: 0.9688 - loss: 0.0549\n",
            "Epoch 90: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9772 - loss: 0.0529 - val_accuracy: 0.9217 - val_loss: 0.1990\n",
            "Epoch 91/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 29ms/step - accuracy: 0.9688 - loss: 0.0439\n",
            "Epoch 91: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9761 - loss: 0.0600 - val_accuracy: 0.9304 - val_loss: 0.1907\n",
            "Epoch 92/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 89ms/step - accuracy: 1.0000 - loss: 0.0289\n",
            "Epoch 92: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9907 - loss: 0.0406 - val_accuracy: 0.9130 - val_loss: 0.2492\n",
            "Epoch 93/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 0.9688 - loss: 0.0553\n",
            "Epoch 93: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9828 - loss: 0.0510 - val_accuracy: 0.9391 - val_loss: 0.2100\n",
            "Epoch 94/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 75ms/step - accuracy: 1.0000 - loss: 0.0227\n",
            "Epoch 94: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9804 - loss: 0.0563 - val_accuracy: 0.9478 - val_loss: 0.2122\n",
            "Epoch 95/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 36ms/step - accuracy: 0.9688 - loss: 0.0886\n",
            "Epoch 95: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9855 - loss: 0.0496 - val_accuracy: 0.9217 - val_loss: 0.2722\n",
            "Epoch 96/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 26ms/step - accuracy: 1.0000 - loss: 0.0439\n",
            "Epoch 96: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9841 - loss: 0.0515 - val_accuracy: 0.9217 - val_loss: 0.2307\n",
            "Epoch 97/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 84ms/step - accuracy: 1.0000 - loss: 0.0166\n",
            "Epoch 97: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9862 - loss: 0.0398 - val_accuracy: 0.9478 - val_loss: 0.2037\n",
            "Epoch 98/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 28ms/step - accuracy: 1.0000 - loss: 0.0306\n",
            "Epoch 98: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9933 - loss: 0.0352 - val_accuracy: 0.9304 - val_loss: 0.1955\n",
            "Epoch 99/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 25ms/step - accuracy: 0.9375 - loss: 0.0748\n",
            "Epoch 99: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.9787 - loss: 0.0473 - val_accuracy: 0.9304 - val_loss: 0.2190\n",
            "Epoch 100/100\n",
            "\u001b[1m 1/15\u001b[0m \u001b[32m━\u001b[0m\u001b[37m━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[1m1s\u001b[0m 85ms/step - accuracy: 1.0000 - loss: 0.0126\n",
            "Epoch 100: val_loss did not improve from 0.18763\n",
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - accuracy: 0.9892 - loss: 0.0340 - val_accuracy: 0.9391 - val_loss: 0.1911\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m)                  │             \u001b[38;5;34m832\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m)                  │           \u001b[38;5;34m2,080\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m)                  │             \u001b[38;5;34m528\u001b[0m │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m3\u001b[0m)                   │              \u001b[38;5;34m51\u001b[0m │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
              "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">832</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">2,080</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">528</span> │\n",
              "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
              "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">3</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
              "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m6,984\u001b[0m (27.29 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">6,984</span> (27.29 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m3,491\u001b[0m (13.64 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,491</span> (13.64 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m3,493\u001b[0m (13.65 KB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">3,493</span> (13.65 KB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.load_weights('best_weights.keras')\n",
        "loss, accuracy = model.evaluate(testInput, testTarget, verbose=0)\n",
        "print(f\"test Accuracy: {accuracy:.2f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tHRIutyT0zHn",
        "outputId": "d3a9238f-a1ea-4fb7-e388-35ddbbbba16b"
      },
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "test Accuracy: 0.88\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "plt.figure(figsize=(12, 4))\n",
        "plt.subplot(1, 2, 1)\n",
        "plt.plot(history.history['accuracy'], label='Train Accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='Validation Accuracy')\n",
        "plt.title('Accuracy Over Epochs')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "\n",
        "plt.subplot(1, 2, 2)\n",
        "plt.plot(history.history['loss'], label='Train Loss')\n",
        "plt.plot(history.history['val_loss'], label='Validation Loss')\n",
        "plt.title('Loss Over Epochs')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 223
        },
        "id": "4Uhcd8nk0-89",
        "outputId": "7a7002c2-ba7d-443b-fb7f-2b5a057a0fab"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x400 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.utils import plot_model\n",
        "from keras.models import load_model\n",
        "plot_model(model, to_file='model_architecture.png',show_shapes=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 839
        },
        "id": "PAF64zMW1oYo",
        "outputId": "3b333866-f411-404f-985e-1336449238d0"
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.save('model.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-cgLMo3R10so",
        "outputId": "f7082c5d-46e1-4e9e-8cf1-7954f43242eb"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pickle\n",
        "with open('history.pkl', 'wb') as file:\n",
        "  pickle.dump(history.history, file)"
      ],
      "metadata": {
        "id": "iAPAtmhQ13FW"
      },
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in model.layers:\n",
        "  weights, biases = layer.get_weights()\n",
        "  plt.figure(figsize=(12, 5))\n",
        "  plt.subplot(1, 2, 1)\n",
        "  plt.hist(weights.flatten(), bins=30, color='blue')\n",
        "  plt.title(f\"Weight Distribution for {layer.name}\")\n",
        "  plt.xlabel(\"Weight value\")\n",
        "  plt.ylabel(\"Frequency\")\n",
        "\n",
        "  plt.subplot(1, 2, 2)\n",
        "  plt.hist(biases.flatten(), bins=30, color='orange')\n",
        "  plt.title(f\"Bias Distribution for {layer.name}\")\n",
        "  plt.xlabel(\"Bias value\")\n",
        "  plt.ylabel(\"Frequency\")\n",
        "\n",
        "  plt.tight_layout()\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "yrvCrYvh1-di",
        "outputId": "d0e7ea59-7cd0-42b5-c4fb-616521a51ace"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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ZM/PV/8CBA7rtttsUFRWlnTt3avTo0Ro6dKiWL19exJECObnyWbthwwZ17dpVt956qzZv3qwtW7Zo+PDhKleubKW6ruYpkjR9+nTZbLYijrB4Kuh9O3r0qI4eParnn39eP/zwg+Lj47Vs2TINGTLEwqg9g1zMNQW9b6tWrVK/fv20cuVKbdiwQeHh4br11lt15MgRiyP3PFfyTEk6ePCgxo0bpw4dOlgUafFT0Ht34cIF3XLLLTp48KA++ugj7d69W7Nnz9bVV19dtIEaFGtz5swxgYGBV+yXlZVlQkNDzX//+19H2+nTp43dbjfvv/9+EUZYdvz0009GktmyZYujbenSpcZms5kjR47kuV+nTp3MqFGjLIiw9GvdurWJjY11rGdmZpqwsDATFxeXa/9//vOf5rbbbnNqa9OmjXnggQeKNM6ypqCvS37/XYN7SDKLFy++bJ9///vfpmnTpk5td999t4mOji7CyICcXP2sbdOmjXnsscesCLHYcvXeGWPMjh07zNVXX22OHTuWr38zSpPC3Le/+/DDD423t7fJyMgoijCLDXIx1xT0vl3q4sWLxt/f38ydO7eoQiy2XLl3Fy9eNG3btjVvvvmmiYmJMb169bIg0uKnoPfutddeM3Xq1DEXLlywKkRjjDFl6+ujUuzAgQNKTExUly5dHG2BgYFq06aNNmzY4MHISo8NGzYoKChI119/vaOtS5cuKleunDZt2nTZfd977z1dddVVatasmSZMmKCzZ88WdbilzoULF7Rt2zan93i5cuXUpUuXPN/jGzZscOovSdHR0fxNuJErr4skpaamqlatWgoPD1evXr30448/WhEu8sDfCooLVz5rjx8/rk2bNqlatWpq27atQkJC1KlTJ61du9aqsIsFV/OUs2fPqn///po5c6ZCQ0OtCLVYKUx+93fJyckKCAiQl5dXUYRZLJCLucbVXOnvzp49q4yMDAUHBxdVmMWSq/fuySefVLVq1crE6MW8uHLvPv30U0VGRio2NlYhISFq1qyZnnnmGWVmZhZprBSlSonExERJUkhIiFN7SEiIYxsKJzExUdWqVXNq8/LyUnBw8GXvcf/+/fXuu+9q5cqVmjBhgt555x3dc889RR1uqfP7778rMzOzQO/xxMRE/iaKmCuvS8OGDfX222/rk08+0bvvvqusrCy1bdtWv/32mxUhIxd5/a2kpKSUuTl54FmufNb++uuvkqRJkybp/vvv17Jly3Tdddfp5ptvLlNza7qap4wZM0Zt27ZVr169ijrEYsnV+/Z3v//+u5566ql8/1SypCIXc40r9+1Sjz76qMLCwnIU+Eo7V+7d2rVr9dZbb2n27NlWhFhsuXLvfv31V3300UfKzMzU//73Pz3++ON64YUX9PTTTxdprBSlLDR+/Pgck/teuvzyyy+eDrPMKerXZdiwYYqOjlbz5s01YMAAzZs3T4sXL9b+/fvdeBVAyREZGal7771XLVu2VKdOnbRo0SJVrVpVr7/+uqdDA1BEivKzNisrS5L0wAMPaPDgwbr22mv14osvOgrgJV1R3rtPP/1U33zzjaZPn+7eoIsBq/LulJQU3XbbbWrSpIkmTZpU+MCBS0ydOlULFizQ4sWLeTDMFZw5c0YDBw7U7NmzddVVV3k6nBInKytL1apV0xtvvKFWrVrp7rvv1v/93/9p1qxZRXre0ju+tBh6+OGHNWjQoMv2qVOnjkvHzh5unZSUpOrVqzvak5KS1LJlS5eOWVbk93UJDQ3NMSncxYsX9ccffxRouHubNm0kSfv27VPdunULHG9ZddVVV6l8+fJKSkpyak9KSsrz/oeGhhaoPwrOldflUhUqVNC1116rffv2FUWIyIe8/lYCAgLk6+vroahQmhTlZ2123tOkSROn9saNG+vw4cOuB11MFOW9++abb7R///4cT3ru06ePOnTooFWrVhUics+yIr87c+aMunbtKn9/fy1evFgVKlQobNjFGrmYawqTKz3//POaOnWqvv76a11zzTVFGWaxVNB7t3//fh08eFA9e/Z0tGV/ceHl5aXdu3eXmf//cuV9V716dVWoUEHly5d3tDVu3FiJiYm6cOGCvL29iyRWilIWqlq1qqpWrVokx46IiFBoaKhWrFjhKEKlpKRo06ZNBXqCX1mU39clMjJSp0+f1rZt29SqVStJfyZzWVlZjkJTfuzcuVOSnIqHuDJvb2+1atVKK1ascDzWNSsrSytWrNDw4cNz3ScyMlIrVqzQ6NGjHW1fffWVIiMjLYi4bHDldblUZmamvv/+e3Xv3r0II8XlREZG5njcL38rcKei/KytXbu2wsLCtHv3bqf2PXv2qFu3boUP3sOK8t6NHz9eQ4cOdWpr3ry5XnzxRaf/qSuJijq/S0lJUXR0tOx2uz799NMyMYKFXMw1ruZKzz33nKZMmaLly5c7zXlWlhT03jVq1Ejff/+9U9tjjz2mM2fOaMaMGQoPD7ci7GLBlfddu3btNH/+fGVlZTmeXrtnzx5Vr169yApSknj6XnF16NAhs2PHDjN58mRTqVIls2PHDrNjxw5z5swZR5+GDRuaRYsWOdanTp1qgoKCzCeffGK+++4706tXLxMREWHOnTvniUsolbp27WquvfZas2nTJrN27VpTv359069fP8f23377zTRs2NBs2rTJGGPMvn37zJNPPmm2bt1qDhw4YD755BNTp04d07FjR09dQom2YMECY7fbTXx8vPnpp5/MsGHDTFBQkElMTDTGGDNw4EAzfvx4R/9169YZLy8v8/zzz5uff/7ZTJw40VSoUMF8//33nrqEUqmgr8vkyZPN8uXLzf79+822bdtM3759jY+Pj/nxxx89dQmlzpkzZxyfG5LMtGnTzI4dO8yhQ4eMMcaMHz/eDBw40NH/119/NRUrVjSPPPKI+fnnn83MmTNN+fLlzbJlyzx1CSjDCvpZa4wxL774ogkICDALFy40e/fuNY899pjx8fEx+/bt88QleIwr9+5SKmNP3zOm4PctOTnZtGnTxjRv3tzs27fPHDt2zLFcvHjRU5dhCXIx1xT0vk2dOtV4e3ubjz76yOn99ff/FywrCnrvLlWWn75X0Ht3+PBh4+/vb4YPH252795tPv/8c1OtWjXz9NNPF2mcFKWKqZiYGCMpx7Jy5UpHH0lmzpw5jvWsrCzz+OOPm5CQEGO3283NN99sdu/ebX3wpdjJkydNv379TKVKlUxAQIAZPHiw04fDgQMHnF6nw4cPm44dO5rg4GBjt9tNvXr1zCOPPGKSk5M9dAUl38svv2xq1qxpvL29TevWrc3GjRsd2zp16mRiYmKc+n/44YemQYMGxtvb2zRt2tR88cUXFkdcNhTkdRk9erSjb0hIiOnevbvZvn27B6IuvVauXJnrZ0j26xATE2M6deqUY5+WLVsab29vU6dOHafPF8BKBf2szRYXF2dq1KhhKlasaCIjI823335rceSe5+q9+7uyWJQq6H3L699YSebAgQOeuQgLkYu5piD3rVatWrm+vyZOnGh94MVAQd9zf1eWi1LGFPzerV+/3rRp08bY7XZTp04dM2XKlCIvttuMMaboxmEBAAAAAAAAOfH0PQAAAAAAAFiOohQAAAAAAAAsR1EKAAAAAAAAlqMoBQAAAAAAAMtRlAIAAAAAAIDlKEoBAAAAAADAchSlAAAAAAAAYDmKUgAAAAAAALAcRSkAJcKqVatks9l0+vTpfO8zadIktWzZsshiKiibzaYlS5Z4OgwAAFDKHTx4UDabTTt37vR0KHlyJbcDUPpQlALgVrNmzZK/v78uXrzoaEtNTVWFChXUuXNnp77Zycj+/fuveNy2bdvq2LFjCgwMdGu8nTt31ujRo916TAAAgKIyaNAg2Ww2x1KlShV17dpV3333naNPeHi4jh07pmbNmnkwUgC4MopSANwqKipKqamp2rp1q6Pt22+/VWhoqDZt2qTz58872leuXKmaNWuqbt26Vzyut7e3QkNDZbPZiiRuAACAkqJr1646duyYjh07phUrVsjLy0s9evRwbC9fvrxCQ0Pl5eXlwSgB4MooSgFwq4YNG6p69epatWqVo23VqlXq1auXIiIitHHjRqf2qKgoSVJWVpbi4uIUEREhX19ftWjRQh999JFT30uHeM+ePVvh4eGqWLGi7rjjDk2bNk1BQUE5YnrnnXdUu3ZtBQYGqm/fvjpz5oykP79pXL16tWbMmOH4tvHgwYM59v/Pf/6jNm3a5Ghv0aKFnnzySUnSli1bdMstt+iqq65SYGCgOnXqpO3bt+d5n3K7np07d+aIYe3aterQoYN8fX0VHh6ukSNHKi0tLc/jAgCA0s9utys0NFShoaFq2bKlxo8fr4SEBJ04cUJSzp/vZWZmasiQIY48q2HDhpoxY4bTMVetWqXWrVvLz89PQUFBateunQ4dOpTr+du2batHH33Uqe3EiROqUKGC1qxZI+nP/Ov666+Xv7+/QkND1b9/fx0/fjzPa8pt2oXp06erdu3aTm1vvvmmGjduLB8fHzVq1EivvvrqlW4XgGKMohQAt4uKitLKlSsd6ytXrlTnzp3VqVMnR/u5c+e0adMmR1EqLi5O8+bN06xZs/Tjjz9qzJgxuueee7R69epcz7Fu3To9+OCDGjVqlHbu3KlbbrlFU6ZMydFv//79WrJkiT7//HN9/vnnWr16taZOnSpJmjFjhiIjI3X//fc7vm0MDw/PcYwBAwZo8+bNTj8z/PHHH/Xdd9+pf//+kqQzZ84oJiZGa9eu1caNG1W/fn11797dUQBzxf79+9W1a1f16dNH3333nT744AOtXbtWw4cPd/mYAACgdElNTdW7776revXqqUqVKrn2ycrKUo0aNbRw4UL99NNPeuKJJ/Sf//xHH374oSTp4sWL6t27tzp16qTvvvtOGzZs0LBhw/IcoT5gwAAtWLBAxhhH2wcffKCwsDB16NBBkpSRkaGnnnpKu3bt0pIlS3Tw4EENGjSoUNf63nvv6YknntCUKVP0888/65lnntHjjz+uuXPnFuq4ADzIAICbzZ492/j5+ZmMjAyTkpJivLy8zPHjx838+fNNx44djTHGrFixwkgyhw4dMufPnzcVK1Y069evdzrOkCFDTL9+/YwxxqxcudJIMqdOnTLGGHP33Xeb2267zan/gAEDTGBgoGN94sSJpmLFiiYlJcXR9sgjj5g2bdo41jt16mRGjRp1xWtq0aKFefLJJx3rEyZMcDrOpTIzM42/v7/57LPPHG2SzOLFi3O9HmOM2bFjh5FkDhw44Lj+YcOGOR3322+/NeXKlTPnzp27YswAAKD0iYmJMeXLlzd+fn7Gz8/PSDLVq1c327Ztc/Q5cOCAkWR27NiR53FiY2NNnz59jDHGnDx50kgyq1atylcMx48fN15eXmbNmjWOtsjISPPoo4/muc+WLVuMJHPmzBljTM5caOLEiaZFixZO+7z44oumVq1ajvW6deua+fPnO/V56qmnTGRkZL7iBlD8MFIKgNt17txZaWlp2rJli7799ls1aNBAVatWVadOnRzzSq1atUp16tRRzZo1tW/fPp09e1a33HKLKlWq5FjmzZuX5yTou3fvVuvWrZ3aLl2XpNq1a8vf39+xXr169csOHc/LgAEDNH/+fEmSMUbvv/++BgwY4NielJSk+++/X/Xr11dgYKACAgKUmpqqw4cPF/hc2Xbt2qX4+HinexIdHa2srCwdOHDA5eMCAICSLSoqSjt37tTOnTu1efNmRUdHq1u3bnn+3E6SZs6cqVatWqlq1aqqVKmS3njjDUeeEhwcrEGDBik6Olo9e/bUjBkzdOzYsTyPVbVqVd1666167733JEkHDhzQhg0bnHKjbdu2qWfPnqpZs6b8/f3VqVMnSXI5N0pLS9P+/fs1ZMgQp9zo6aefztdDcwAUT8x8B8Dt6tWrpxo1amjlypU6deqUIwkJCwtTeHi41q9fr5UrV+qmm26S9Oewc0n64osvdPXVVzsdy263FyqWChUqOK3bbDZlZWUV+Dj9+vXTo48+qu3bt+vcuXNKSEjQ3Xff7dgeExOjkydPasaMGapVq5bsdrsiIyN14cKFXI9Xrtyf3wmYvw17z8jIcOqTmpqqBx54QCNHjsyxf82aNQt8DQAAoHTw8/NTvXr1HOtvvvmmAgMDNXv2bD399NM5+i9YsEDjxo3TCy+8oMjISPn7++u///2vNm3a5OgzZ84cjRw5UsuWLdMHH3ygxx57TF999ZVuvPHGXGMYMGCARo4cqZdfflnz589X8+bN1bx5c0l/FpCio6MVHR2t9957T1WrVtXhw4cVHR192dzo73mR5JwbZeeLs2fPzjHXZ/ny5S93uwAUYxSlABSJqKgorVq1SqdOndIjjzziaO/YsaOWLl2qzZs366GHHpIkNWnSRHa7XYcPH3YUsK6kYcOG2rJli1Pbpev54e3trczMzCv2q1Gjhjp16qT33ntP586d0y233KJq1ao5tq9bt06vvvqqunfvLklKSEjQ77//nufxqlatKkk6duyYKleuLEmOyUizXXfddfrpp5+ckk4AAIBL2Ww2lStXTufOnct1+7p169S2bVv961//crTlNrro2muv1bXXXqsJEyYoMjJS8+fPz7Mo1atXLw0bNkzLli3T/Pnzde+99zq2/fLLLzp58qSmTp3qmK/z709mzk3VqlWVmJgoY4xjLqu/50YhISEKCwvTr7/+6jQiC0DJRlEKQJGIiopSbGysMjIynApNnTp10vDhw3XhwgXHJOf+/v4aN26cxowZo6ysLLVv317Jyclat26dAgICFBMTk+P4I0aMUMeOHTVt2jT17NlT33zzjZYuXZrnhJx5qV27tjZt2qSDBw+qUqVKCg4OdoxiutSAAQM0ceJEXbhwQS+++KLTtvr16zueMpOSkqJHHnlEvr6+eZ63Xr16Cg8P16RJkzRlyhTt2bNHL7zwglOfRx99VDfeeKOGDx+uoUOHys/PTz/99JO++uorvfLKKwW6TgAAUHqkp6crMTFRknTq1Cm98sorSk1NVc+ePXPtX79+fc2bN0/Lly9XRESE3nnnHW3ZskURERGS/vz53RtvvKHbb79dYWFh2r17t/bu3etUaLqUn5+fevfurccff1w///yz+vXr59hWs2ZNeXt76+WXX9aDDz6oH374QU899dRlr6lz5846ceKEnnvuOd11111atmyZli5dqoCAAEefyZMna+TIkQoMDFTXrl2Vnp6urVu36tSpUxo7dmy+7x+A4oM5pQAUiaioKJ07d0716tVTSEiIo71Tp046c+aMGjZsqOrVqzvan3rqKT3++OOKi4tT48aN1bVrV33xxReOZOlS7dq106xZszRt2jS1aNFCy5Yt05gxY+Tj41OgOMeNG6fy5curSZMmjqHlebnrrrt08uRJnT17Vr1793ba9tZbb+nUqVO67rrrNHDgQI0cOdJpJNWlKlSooPfff1+//PKLrrnmGj377LM5httfc801Wr16tfbs2aMOHTro2muv1RNPPKGwsLACXSMAAChdli1bpurVq6t69epq06aNtmzZooULF6pz58659n/ggQd055136u6771abNm108uRJp1FTFStW1C+//KI+ffqoQYMGGjZsmGJjY/XAAw9cNo4BAwZo165d6tChg9PUAlWrVlV8fLwWLlyoJk2aaOrUqXr++ecve6zGjRvr1Vdf1cyZM9WiRQtt3rxZ48aNc+ozdOhQvfnmm5ozZ46aN2+uTp06KT4+Ps98EUDxZzOX/nAXAEqo+++/X7/88ou+/fZbT4cCAAAAALgCfr4HoMR6/vnndcstt8jPz09Lly7V3Llz9eqrr3o6LAAAAABAPjBSCkCJ9c9//lOrVq3SmTNnVKdOHY0YMUIPPvigp8MCAAAAAOQDRSkAAAAAAABYjonOAQAAAAAAYDmKUgAAAAAAALAcRSkAAAAAAABYjqIUAAAAAAAALEdRCgAAAAAAAJajKAUAAAAAAADLUZQCAAAAAACA5ShKAQAAAAAAwHIUpQAAAAAAAGC5/wfbIySaTJBvdAAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x500 with 2 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for layer in model.layers:\n",
        "  _, biases = layer.get_weights()\n",
        "  plt.figure(figsize=(8, 4))\n",
        "  plt.bar(range(len(biases)), biases, color='purple')\n",
        "  plt.title(f\"Bias Visualization for {layer.name}\")\n",
        "  plt.xlabel(\"Neuron Index\")\n",
        "  plt.ylabel(\"Bias Value\")\n",
        "  plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BqKDUgTK2r23",
        "outputId": "a8ae001d-5376-4a70-bab3-82193633382f"
      },
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x400 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import shap\n",
        "\n",
        "explainer = shap.KernelExplainer(model.predict, trainInput)\n",
        "shap_values = explainer.shap_values(testInput[:15], nsamples=100)\n",
        "shap.summary_plot(shap_values, testInput[:15])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000,
          "referenced_widgets": [
            "1ac8b6867a03405bbe92c8a56f83f912",
            "5628c745156844a6ad31eb4d0d4669fa",
            "4494669a6be543a3827209a9d55c9db7",
            "cb449887166648bd973efb00936c45e1",
            "84046ed9d8014017a89c0ad1f28434f5",
            "6eff48683d4542fe97fac020bdf6006d",
            "c298e88cc80c45429afc9b14c89c7ac0",
            "c57166a607a94d9e951585e5c36796ad",
            "78390cf43aa34c43b8dce2f1aa9f1c66",
            "c4d658864ba64f7cac15febd68e56c09",
            "301d8236713443d98ee401300ebf9274"
          ]
        },
        "id": "MXYGnreY2-XJ",
        "outputId": "80a506c8-8ed7-4949-8c70-2ab3f3f07d43"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m15/15\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:shap:Using 457 background data samples could cause slower run times. Consider using shap.sample(data, K) or shap.kmeans(data, K) to summarize the background as K samples.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/15 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "1ac8b6867a03405bbe92c8a56f83f912"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 18ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 22ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 98ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 3ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 28ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 27ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 63ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 31ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 20ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 2ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 34ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 30ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 1ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 19ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step\n",
            "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 49ms/step\n",
            "\u001b[1m1429/1429\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1150x660 with 3 Axes>"
            ],
            "image/png": 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ye4eivae3bdsm27alTi0itudVwq//3d0hLUV+QrIi3/fk0O5PXEUZ8ZXbqMX92L59e9h0rT5XNdyGJ96h5PTdV+IcdvgbTVw118fLXJFPcud0s/0wdbg+p9421Zz+79x8v9sIHZESsYjVKlVKjY9s36vP2x1bSHGVYbpCiZIkjyqDxRE7qgkEDiuiqX1aKGw/Mtt6IuaxLUuZLXZfAolW/9vfdF1sw1R93d9ob8OyLLVLCWp9k6bam09uyelXuq+o2mO1u3PaYbMfsdrGgVi2bR/SR7SsrCxdccUV+tOf/qSJEydKkm6//Xa98847euGFF9S1a1cNHDhQxx9/vP7+978fUjH1zncbpBP/LO2sHEDpl1urdbT8ilfz4clq9dWSqkGY3qQ2CpbuPjha156ismeWyy6pPDPhUECp2i6HQtLAjrI+vFVWgjtym1Hk9/s1e/ZsSdLUqVPlcrlqdXs19fV7+Zr/+CbZISloWfLFuxT6dTyUp5lbv4TcCvyaxUrinPq4bVP5452SVfm8H9dGevc8pzxxkW9SjY2d9Yvsk++Xin+9/NAhQ9Znf5bVMn3fy/iDKhw5W4Flv1Q2OCwlzz1Xzp1F8v9xQdV8ztN7yb3xJ1nfb6xs8Lik127Wxo8C2nLPt3KrQn30uVzyaY2OUnZihiZddY42ZlZu2+2QfMV+qWL3QX1Aa4c+ujxBSe7dr11+jk+PXP2z/BWVncCW1OOEprrk+iNq/Pyg4Vn0U1C/nVesd596TF3zK8+KhWRpg9rq9aNb6Q/f/U/lvhbapk5Vy7jGdVWvhafGquR6w3iw5Z5+//vf691339Xjjz+uxx57LBqrrB/6tpdWPSEt/FxyxynQtYsyv8pXfK+mSh55hJRztrToCyklQe6zByv08S8K/ZQj58nd5OjXRp6bTpF30Q+yktxyH9VC1ns/SK3SpbEDZLmj8tI0KEef3FRHdEvUqm+L1bS5W226JmpFVok88Q4dOThFJeW2ln5Rpq9zpZ/k0lkeSyd1suS3LLVLtXRmZ0tx1XzSbYysgR2lNQ9JC7OkJI90zkBZSZGf8MOWcTmVtnSqfG/+rNCGArlP7SZntwxJkvPELgq++7McPVrIMaqHLH9QevULKbdIGjtIapepdqOlJme2V8lnOaroNkbO7HXKWfqBVjvdujuwSau6xKtV+0SN62opv8yh//wQ1PaikIZ3dGhcrzi59wqATVu4ddusXlr2aq62Z/s0YHi6+g5koCWqN66HU0den6aFw69X1swP1f/9r7UtJUlfXTZKLTs20+ZPk9SkmUvFTTsrtLpI3Ue3VPMx1Vx2Q4SonJGQpOnTp+ull17SU089pSuuuKJxnJGo5+rbGQk0LPQ/xAp9L7qidi/cpZdeqqSkpMZ1RgIAgEYuakEiPT1dEydO1MqVK6O1SgAAcJiL6rfzTJgwQRkZGdFcJQAAOIwd8oi+gQMHKisrq9rH4uPjtXjx4hoXBQAA6ge+LxgAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAgEalojykvI2ttXVVR636vizW5dR7BAkAQKPh94X02J2btXVVZ+VtbKsZ923V0td3xLqseo0gAQBoNJZ/UazsTb6wtrcX5SkYsGNUUf1HkAAANBrFhYGItoqykAIECWMECQBAo9F3YIqczvC2nkcmyRPP26EpnjkAQKOR2dKtKde0kiepVJYjqCMHJ2niH1rHuqx6LS7WBQAAUJf6HJWk7sd8I0maMnWqXC7eCmuCMxIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABiLi3UBAADUpXK/rU/KOmtHIFk9N9s6oWOsK6rfOCMBAGg0vAFbJ88Lam7BsXqzpJ+GPRfUE18EYl1WvUaQAAA0Got+DOrr7PC2v37gVyBox6agBoAgAQBoNLYURwaG/HKpnJMSxggSAIBGY0w3p5xWeNspHR1K8VjVL4ADIkgAABqN7hkOzRvnUIazWJZCOq2zpbnj3LEuq17jrg0AQKNybk+Hij9/VbYtXXLhVLlcnI2oCc5IAAAaJYv8EBUECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAYqrCjtOyzdLmar5xEKgtnryQ4n+2tGx1QJuK6Hs1QZAAEDPf+dvqpuILNfIVSx1mBvWXj4OxLgmNQM61H2vA7T4NeKRc7kHzdM6NG3XzB/Q9UwQJADERCEnzKo5XhSq/VTBoS3/73NaKXD4dovaUfLBZO5/8Ttav3axJWYX+vHCZHvg8qKzsUGyLq6cIEgBiYlOxVGgnRrR/mU2QQO0p/zInoq1jboGSvT59tY2+Z4IgASAm2iZLqVZZRPuglnzdIGpPwqDmEW3rM9JU4nFrIH3PCEECQEy4nNLE+E/kkV+S5LCkW4dYOrI5B3PUnuSTjlD6Fb1l/9rNChM8+tv44bphiFODWvGWaIIf7QIQM/1cm/Rg3EvqPmKiembGqV0qIQK1r+Vjw/Re29Wy8hxqeekEzT8iQe3T6HumCBIAYireCujkduIXGFGnvJkOKVM6qbuLvldDnMcBAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjHH7JwCgUVn4XUC3rztNrrJ4zZ6WL5clnTEkQdeclyIPt4IeMs5IAAAajVW5QV38klfBikQdEQjJDkg+v7To43L9682SWJdXLxEkAACNxhsr/QqGpCbByF/6XLbcG4OK6j+CBACg0WiVUvm257MiL2FkpPGWaIJnDQDQaJzTz6UBbRzKcTrl26PdFSddcnpyzOqqzxhsCQBoNDxxlt6/PF5/fHKZtlek6rhuPdQ61akRR8erfQveEk3wrAEAGpVEt6Xj03+RJE0df5RcLleMK6rfuLQBAACMESQAAIAxggQAADBmPEYiKytLV1xxRVhbQkKC2rdvrzPOOEPnn3++nE5njQsEAACHrxoPthw9erSOO+442bat3NxcvfHGG5o+fbrWrVun2267LRo1AgCAw1SNg0SPHj10+umnV02fe+65Ou+88/Tqq6/qiiuuULNmzWq6CQAAcJiK+u2fycnJ6tu3r9577z1t2bKFINHQzXpXmv+p1DxNmjZW6teh6qHgjznyPvShQluL5Dq7t9yXD5FVzbfJmQpsL1PuA1+rYkWeEo9vrczrj5Ijad+3cW2Zu1bZ/1kvdzOPOlzTU6kDwvtmfkFQ8xcXa8NWv/p28+icUSnyuGPwAz4/bJQefk3K3imdM1T67Ugpis9bdexQSL6nvpD/tR/kaJsmzw0nytmj+SGvJ+QLasc/lqvknY1yd0tX1nlH6V/5ybJtKc6yVeaXRne0NKWPpYe/kl4oGKv4nQ698fst2pIcrzVpSXIHQuqXFNI9Y+NVVC4t/rJCyQkOXXByonp34Da9hij4c668Dy1TaFOBXGf2kvvKobIcNRzC99nP0j/ekEoqpEnDpfOPq3pobYH07I5T9EsoQ3f+I6D+rW3ddqJLx7apXz/Y9dNar157t0RlFbZOOSZRwwYlxqSOqAcJ27a1efNmSVJ6enq0V4/DyT0LpNtf3D298HPp/x6ROrVUaEuhSo6dIbugXJIUeHuVQhsLlHDvqVHZtB0Mad3wV+T9cackqWTpJpV9vk0d3zqr2vl/efh7/XzzN1XTOYs26tisMUrqlipJ8gds3fhQrrZuD0iSlv/o1ZoNPv35DxlRqfegbcqTjr1VKiqrnH57ubQlX7rzwlrdbMUti+V9cFnVtH/RD0r54To5WqUe0nq2/PZdFcz9qXJi6SYlvrBO7974G5W7XZJtSyFpyXpb07MsZZdZkiNDaiblexIULPSpqNyWZOm9AqfGPFuuLl5/1bo/WF6hZ29sqq5tCRMNSSinWCXH/lN2fmWfD7yzWqH1+Up4eIz5Sr9eK534Z8lf+fesN7+WisulS0cov9zWsc/b2ulqXzX71q3SkpcD+uSiOA1qVT/CxJoNPt34cK4Cv+7iV99VqKw8pFOH1f23c9b4ro2KigoVFBRo586dWr16te655x6tWrVKffv2Vbt27aJRIw5XT74VPl1SIT33gSTJ98K3VSFiF++Tn0Zt0yXvba4KEVVtizfIt66w2vk3zPg5bDpYFtDmOWuqpr/5oaIqROzy2fIK5eWHt9W6uR/sDhG77P08R5lt2/LO+Dy8bWe5fC8uP6T1BAu8Kngh/HluWViqk76v/OIfWZb06zE6e69d3OFyq2CvLwXa4YqTd48zMb6A9Pqn4X0K9Z/vpeVVIWIX71NfyK7mR7UO2sx3doeIXX79O5q/ytbOYORbnz9kaeaKGmyzji3+qLQqROzyxvulMamlxkHi6aef1ogRIzRy5EhddNFFev311zVs2DA9/PDD0aivxvLz8+X17v5Ft5KSEhUXF1dN+3w+7dixI2yZ7Ozs/U5v27ZNtm3X+21s3749bPqQt1HNH3pxYdGvj9kRjyloR28/9nGQsfexjZA/cn47sLttx46dEY9LUqia3TgU0XhOQ3sdEGujb9iBYGTxezw/B7ON/B07pGqeL2fI/OC89+rKyirCpg+Hv6O62IaperG/1Rwr7GCwZtuo5u/IX+Hd10NVvHv8HRyWz9Ue09X9WQVDUTzGHgLL3nMvD8Gu2z/HjRunESNGyLIsJSQkqF27dkpLSzNZJeqY3+/X7NmzJUlTp0499K+J/fOL0t0Ldk/Hu6Xl06XubRTasFNFvR+RSnf/LI7n2uOV8MiZ0Shdtj+oVT3mybeuqKotaXgbdXp/fLXzr7l7hdbcsbxq2nI7dOxXY5TSO12S5PPb+u3t25Sbv/tAcnRvj/52TWZU6j1o67ZJfa+Vyvb4OeMbzpYenFSrmy370+vyPfbJ7oZkt1JXTpPjiPRDWs/GC95S4X9WV03vSE7QmJsuVmm8u+rShiQ1S7K0Y49MkF5WoeBOn4pdu6+2NvEH1G2PSxtOh/SvG5qqZ3subTQkoS2FKuo5XSre3efdfzhGiU+cbb7Sz36WTrgtPDU8fpl01enaXmqr89N+lYTCP0c7ZGvZhXE6vm39uLTx01qvrn8wNyxQXHFhusaeUveXNmo8RqJdu3YaMmRINGpBfXPnhVLTFGnBr4MtbxondW8jSXK0b6LkZZfLe9/7lYMtz+otz7QTorZpy+VUxw/Ga/tdX6piRZ6Sjm+t5n8evM/5O9/WV650V9Vgy47TeleFCElyuyw9eEOmXnyjqGqw5UVnHNr4gKjo1FL64C7p/kW/DrYcIl0bnfC1PwnTz5CjVYr8r6+Uo02aPLeedMghQpLazh4hd7d0lSzZKE/XdH133kAdXeaWbUtOy1K5r3Kw5W/7W7r/86Dmf1ukpAKpz7YibUmK14+JLsWFQurpCeqe0+NV7I3/dbClpd+ckkSIaIAcbdKU/OEV8t77nkKbCuU6s6c8N5xYs5Ue011663bp0Tek0gpp4onSZSMlSc2TLH14kaUpc7Zqg91UTlecemVa+vNwV70JEZLUo7NHf/tThl5dWqKyipBOGZoYk/ERUhTOSPzpT3/SxIkTo10X6kCNz0gANUD/Q6zQ96KLr8gGAADGCBIAAMAYQQIAABgzHmw5cOBAZWVlRbMWAABQz3BGAgAAGCNIAAAAY1H/rQ0AAA5nIV9QiVkOFW9P1BPK1RFDM3VW7zi5nPXneyQOJwQJAECjEfKH9M2p7ynjM7cyFFDojaV6bGR/PXlWZy39XaKcDsLEoeLSBgCg0dj++iYVfpZXNe2QNOXjlfpwTUD/+6mOf6SvgSBIAAAajfL1JRFt6eU+xfsDWr+zhr/S10gRJAAAjUbGqW2qfs5+l+/aNFNFvEundedqvwmCBACg0Ujpna6eTw+R/9ff5PuuTTPNO/dozb0gQV0yeEs0QfwCADQqrSd20tu+ZbL80m8vGalpCS45GGRpjPgFAGh8HJLtkZLcFiGihggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJADElKNQKvomX6FAKNaloBEpDCVofbCZVuWGtCI7GOty6jW+RwJAzKS/EqeU9536KvS24o9I1IBXTlLaUc1iXRYauNs+kR4uPl92YUD3/d0ryav+rR16c2qiWqfx+fpQ8YwBiIn897cp9d04WaHKe/grNpXph8s/i3FVaOg+3mzroSxLdnlI8u0+E7F8a0g3veWNYWX1F0ECQEwUfp4X0Vb0bb6CFZxmRu35bOuvP8zlj7yU9tkGfv3TBEECQEwk92sS0ZbUI03OeGcMqkFjcWTzX/8nLvLtr39r+p4JggSAmMg4rbVKB+4++xCX6lKvxwbHsCI0BiPbW5rY05YS4sLCxBHplu491RPDyuovBlsCiAnLYWnHJX4VjQpo9FEj1fzk1opLdce6LDRwlmXp2VFSx42vKjcxWaeffJJczjid1Nkpdxy/uWGCIAEgpvxtbWWe0UZxLlesS0Ej0sa5U22cOzW6q1MuF2+FNcGlDQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAYQQIAABgjSAAAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMBYXKwLANAwBL7dIu/DHyq0vUTu8X3lvnyILMuKdVkAahlBAkCNBdftUPEJT0mlPklSYOkahXKKlfDXkTGuDEBt49IGgBrzPfd1VYjYxTvj8xhVA6AuESQA1JxdTVuoukYADQ1BAkCNuScdJSW4wto8lw+JUTUA6hJjJADUmLNLhlKWXa6Kh5bJzimR69y+8vzhmFiXBaAOECQAREXcoCOU/J8JsS4DQB3j0gYAADBGkAAAAMa4tNFAbCyydd37IX28xVa/TEsXNPfpy4+KVVAc0olHJ+iycalyu/hyIACN28bP8/Teo6tUtKmjtqal6YSV+coYkKZHx7jVtRmfrU0QJBqIMxcFtSK38v+XbLD1wVqnjt0ckNOWXnm3VKGQdNWFabEtEgBiqGBTqf57zTcKBWx5JHXM3SlXIKR3g510anGaVl3pltPBB65DRfxqAH7Is6tCxC7+OId2Jrqrpt/7qryOqwKAw8vad3MUCoR/v0nrnYVK9/q1NTeoL7bw3ScmCBINQJpHqi5DxwV3/1EkJ5KyATRunhRXRFvA6VRQUsCylB5f9zU1BASJBqBtiqWJvcKDQjOfX2kV/qrpi05NqeuyAOCw0nV0S6W0Ck8Lq1tmamtyvE7vGadembwlmmCMRAMx61SHTmpnVw22POsIl977NFmFJZWDLQf08MS6RACIKU+ySxfMPUZZL/2iD9/doDVpzVQ6sKOuHZyoSwY4Y11evUWQaCCcDktT+lia0mdXi0NTz0qNZUkAcNhJaOLW0N920o9xy5Sp9Zo6tZ9cLt4Ka4LzOAAAwBgxDEB0lfsk21bAJzlTXLKcDqmkXHLFSZ7IwW4A6jeCBIDoCIakP82R/cx7ki+oImVoW8t+atWuSE2yPpPiXdLVZ0j3XixZ3EUENBRc2gAQHTPekZ58R5YvIEu2mipXTbet0oYvXfKFXFKZV7p/ofTSR7GuFEAUESQARMdb/xfRlKqdkhwqVtPdjf/7pu5qAlDrCBIAoqNjZkSTV5X37Lu1xzerdmpRVxUBqAMECQDRcf0YqVWTqsmAnMrREUp15itZBZWNHVtIV50Wm/oA1AoGWwKIjg7NpZUPSwu+kG9dgQo9LdRqYBulHpki69UvpJQEafxQKTkh1pUCiCKCBIDoSU+SLjtZbklhFzquOj1GBQGobVzaAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDHjIJGVlaWBAwdq3rx5EY99/fXXOvHEEzV69GitXr26RgUCAIDDV9TPSHz00Ue6+uqrlZaWpmeffVZdu3aN9iYAAMBhIqpBYvHixbr++uvVpk0bPfvss2rbtm00Vw8AAA4zUfuK7AULFuiBBx5Qr1699NhjjyktLS1aq8ZBWrktqAXfBxRwWDo/tEWZ/81SRTBJrl5HaGeTZPkS3CpNSlDm9h3q4C+UhneMynZ/+rlCOTl+9e4Vr4wMV9hja9eU6uc31ql7qk+dL+orJbj3u65Cr60319pKdEmDm4a04mefmjdxakAPtyzLikq9iA6/L6Tvvi1VKCj1PSpJirP04Ec+bc4PaZxVpI6JQTlCIcmWCts0UV6ppaN7eZTZxBm2nlDIoaysMslyakD/RMXH193QrYLioL783qv0FIcG9vLI4aCPxdR3G6Uv10gDO0tHto/qqgNLf1Zo/ucqt136zm6trWXt9U2bllr2b69Obu1X+2RpaL94JSfsu/95A7b+t85WRUAa09lSiof+IkUpSMyePVtPPvmkBg0apOnTpysxMTEaq8UhuO/dCt36XkCKd+nGj15TnyUvSZJyE5pqYbez5IurfAOvcLu0vk0r9d20XRdcvlCtL83U1v5JRtu0bVuPP7FdX35VJklyOKTf/TZTxx+XLEmaNSdbL3xmS2omSZrwwquaOvtkqV1Gtetbsd3WSf8OKL+icjrJH9CAzYVyh2wd3dOj+/7YVE4nf7iHg535fj38103akRuQJCWlOfVsm5YqtOIkOfSvUKomfvmDhq7fKkmqcMXpg77dVZ6SoFsvbaITj6784S6vz63/WzFIH3+aL0lKS3Pq9ltbqVUrV7XbjaZvf/bq9ifyVeGzJUm9O7v00DUZ8rgPnz62vtBWh7TDp55adcd86c5Xdk/fcpZ070VRWXXpmJmKf3Op3ArILelIV5LWdzhN8ascem9HE92WnKiW/oA6xBdq+rUZ6nxEZP/LKbV1wosBrd5ZOZ2ZKH1wYZx6ZTSS12c/ahz9FyxYoCeffFLDhw/XP/7xD0JEDOSWhPTXJRWSJ07NSot013vzqx77vPXgqhAhSfE+v5oWFml5hy5an9pMR87PM97uDysrqkKEJIVC0osv7lAgYGt7flAvfRoKm//FTsco9/6397m+2z8KVoUISSp1xWlzWuUbztc/evXx8op9LIm69s7rO6tChCT9HHD9GiIqhRwOLezfTcFfzyLF+wPqvXGrAkFpxvxChUKVb96bN3dQRcXuY0ZhYVCLXttZJ/vw9IKiqhAhST+s9WvpF2X7WaLu3ftZMNYl1I2t+dLdi8LbHnhd2pBb41X7P1iruDc/lVO7+2uKv1QD8r5Telm5umTnqrPXr9Vul4pLbc16raja9TyaFaoKEZKUWybd8UkjeX0OoMZBIi+v8o2obdu2crv3f9o6FvLz8+X1equmS0pKVFxcXDXt8/m0Y8eOsGWys7P3O71t2zbZ9u4DUKy3sXZHSH7bIVmWOhbkyhPc/QezMz5de/P4/JKk3NR0JecF5PCHjPZj61Z/xLqLikMqKQlqc05Aob0uRYQcTm1YW7zPbfyUb2tvZe7dp8E3ZFfuVyxeD1Ox7hu1tY0tm8rD5i/wRH6CK473qGSP9tSyyiCYuzOkcq+t7du3q6w88mzYrn5V2/uxMTugvf2yxXtYvR4/5keUeEjqS//b8fkPUjD8g4dCtrQqu8bbCP24XU75tLd0b6EkKbW8QvG2Le+vl7U2bgtUu40fciNDw3c54cfAw/FvtS6OfzW+tDFlyhR98803ev7552Xbtq699tqarjKqmjZtGjadnJwcNu12u9WsWbOwtlatWu13umXLlofVNo5s7VQTj62dtq0VLdopNzFFmWWVHadt8VYVxoePVylNiJdl2+q4PVt5nTwKuRxG+9G7V7z21qaNS+npceoR71CiFVCZvbuLJfoq1Pu4lvJ4PNVu4+R2Dq3eGX4waVK++w91QI/KoBqL18NUrPtGbW2jd78Urfph98GqdWnk2aJWhSVKq9h9AM9JT5EkdWvnUlKCQ+7mzZWe9r127gy/1NWrZ0Kd7MeAHm59/p037PGjeyWqWbPwfh3L1+PkdjU7bV5f+l+zUYOk1ASpaI+AmuSRhnSp8TbiTu4inxIVp/A+uiWp8vHtaSkqcjiU+utZsgHdPdVuY2RHp95YF358GtU5/MPz4fi3WhfHvxqfkYiPj9ejjz6qwYMH64UXXtD06dNrukocogSXpX9fnKim8svniNNF5/1RW1KaSJIGbflGGUUFkiRb0s7UZJUneHR21idq3j5RX01ubrzdNm3cmjSxmeLjKw92LVvE6feXZ0qSEuMdumVqupoEKw8MTcuKdKvvKyXcePo+13fvMIdGtK9cl9OSugW8alVUIY/L0iVnpahvF88+l0XdOvn0dA08NkWWJVmWNKy3W4OaBqRfPxlllpRp8hffVc2/vVmafmjXWu1axemmqelV7W1ab1RmZrZ2nbwa0D9B485OV124+jdp6t6h8oyJK066cHSyjukXGY5j6aYhjeQ7A5PjpRf/KLVMr5xunia98EcpteaXyp3dM2U9Okm+uFTZqjwOrk1pr28zemtNy0ytbJmhDe44da/w6agebl06LrXa9fy+v0OTelvaNR731I6W7jq+kbw+B2DZe54TOQRZWVm64oor9Kc//UkTJ05URUWFrrvuOn355Ze66KKLNG3atGjXigMIBG2t3RGUbTnUKTWkwIotCqWlyO2Jky/BLYfLUqnPUqozqLiScoXapGj27NmSpKlTp8rlMhvg5vWGVFgUVGZGXMSdFYGgre2rdqp5ukNxrdIPan1bi2154qRmCZa25QWUmuxQYh2O5MfBKy4KKBSS0tIrzzxtKAhqW7HUJyEgq/Jqm0IBW44UtwpKQmqVsfsMld/vr+p/48+dJKcjTunpUbuR7KDl5AeUlODY72h91BF/QNq0Q2rbTHJHty/YZT6Fvt8iu0miduSG9OKy91QY79KwsWfpqOZx8gcUcUdRdfLKbPlDUqtkBlnuErVXKj4+Xo888oimTZuml156SbZt6/rrr4/W6nEQ4pyWujff9ZI65B60+/apXSfgqvJ9RoJC/sgxDiY8HoeaZ1Z/EI5zWmrds2m1j+1L65Tdf6AtM+r+jQUHLyU1/PVpn+5U+3RJijwgJ+wnDKamOOVyxea1btGUPnbYcMVJnVrUyqqtRLecgytveW/awa+k70uVJOmEdg65XAcOELtkJBIg9hbVCL4rTAwdOlT//ve/9eCDD0Zz9QAA4DAT9XN5Ho9H06dP1zHHHKP//Oc/euCBB2R49QQAABzmjM/pDRw4UFlZWdU+5vF49PjjjxsXBQAA6gdGFwEAAGMECQAAYIwgAQAAjBEkAACAMYIEAAAwRpAAAADGCBIAAMAY3w0LICq+yrb1xLchlfqlyX0sndmZzylAY0CQAFBj3+bYOv7fQfmCldOvrLY173RpQi/CBNDQ8VcOoMae+r9QVYjY5bFvQrEpBkCdIkgAqLG9Q8S+2gA0PAQJADU2pY9De/+48iV9ObwAjQFjJADU2IlHWHp9nEOPfm2r1G9rUi+HrhxAkAAaA4IEgKgY09mhMZ1jXQWAusZHBgAAYIwgAQAAjHFpAwDQ6AQCTq3f0EU33bJNLZq7NH58urp0jo91WfUSQQIA0OisWt1beTtaSApo+/aAVq+p0MMPtlV6Om+Lh4pLGwCARqW8PKS8Hc3D2ioqbGV9XRajiuo3ggQAoFFxOi05HJHfvOrx7P1tKDgYBAkAQKPidltq3WpTWFtmZpwGDUqKUUX1GxeDAACNTscOq5WUWKL0JoPUsqVbo0amKd7DZ2sTBAkAQKNjWVKLFtmaOrWZXC5XrMup14hfAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGAsLtYF1CbbtlVcXBzrMg5bfr9f5eXlkqSioiK5XK4YV3T4SklJkWVZh7QM/W//6H8Hj/4XXfS9Q3Og/mfZtm3XYT11qqioSGlpabEuAw1AYWGhUlNTD2kZ+h+ihf6HWDpQ/2vQQaK2EnlJSYnOOOMMvfnmm0pOTo76+usS+3JwDpdPhA3p9ZIa1v7Q/+qXhrQvUmz7X4O+tGFZ1iGn+IPhcDjkdDqVmppa7zsg+1J7aqP/HW77WFMNaX8Ot32h/+1fQ9oXKbb7w2BLAABgjCABAACMESQMuN1u/fa3v5Xb7Y51KTXGvtQvDW0fG9L+NKR92ZeGtI8NaV+k2O5Pgx5sCQAAahdnJAAAgDGCBAAAMNagb/+Mpg8//FAzZszQhg0b1LJlS02ZMkVjx47d7zJbt26tdp4+ffpozpw5tVTpbuvXr9eDDz6oFStWKCkpSaeffrquvPLKA36Lm23beu655zR//nwVFBSoW7duuu6669S3b99ar3lfTPflzDPPVHZ2dkT7J598Io/HU1vlRhV9L7Z9T6L/0f849u0PQeIgLF++XDfccIPOOussTZs2TV999ZX+9re/KTExUSNGjDjg8n/4wx80cODAqunExMTaLFdS5bfaXXHFFWrXrp0eeughbd++XY8++qgqKip000037XfZ5557Tk8//bSuuuoqde3aVfPnz9dVV12lF154QW3btq312vdWk32RpFNOOUUTJkwIa6svA6zoe7HtexL9j/7Hse+AbBzQH/7wB3vq1Klhbbfeeqt97rnn7ne5LVu22EcffbS9ZMmS2iyvWrNmzbKPP/54u6CgoKrtlVdesQcPHmxv3759n8tVVFTYw4YNs5944omqNp/PZ48ZM8a+7777arXmfTHdF9u27TFjxtj3339/bZdYa+h7se17tk3/o/9x7DsQxkgcgM/nU1ZWVkT6HjVqlH755Rdt3bo1RpXt36effqrBgweHfdf+yJEjFQqF9Pnnn+9zuRUrVqi0tDRsf10ul0466SR98skntVrzvpjuS31H34t935Pof/Q/jn0HQpA4gM2bNysQCKhDhw5h7R07dpRUef3qQO6//34NHjxYI0eO1N13363CwsJaqDTc+vXrI2pOSUlRRkbGfmve9Vh1+7tt2zZVVFREt9CDYLovuyxevFjHHHOMTjjhBF199dVas2ZN7RQaZfS9SrHsexL9j/7Hse9AGCNxAEVFRZIqX7w97foO+12PV8ftduvcc8/V0KFDlZKSou+//16zZs3SypUrNXfuXMXF1d7TX1RUFFGzVLkf+6u5qKhIbrc7YjBOSkpK1Y8AxcfHR73e/THdF0kaNmyY+vTpo5YtW2rLli2aNWuWLr300phecz9Y9L3dy8Wq7+2qi/63G/2v7tSXvtcog0RJSYny8vIOOF+bNm1qtJ2MjAzdfPPNVdNHH320OnfurGuuuUbvv/++Ro4cWaP148BuuOGGqv8fMGCAhg4dqvHjx+v5558Pe23qCn2vcaH/VaL/1b267HuNMkgsXbpUd9999wHnW7BgQVX6LikpCXtsVxo81F/XO+6445SQkKAff/yxVv+YUlNTI2qWpOLi4v3WnJqaKp/PJ6/XG5bMi4uLZVlWtem4tpnuS3UyMjLUv39//fjjj9Eq75DQ9+pX39tVF/1vN/pf3akvfa9RBomzzz5bZ5999kHN6/P5FBcXp/Xr1+uYY46pat/X9bTDRYcOHSKuoe36NLK/mnc9tmHDBnXr1q2qff369WrZsmVMTi2b7svhiL7XYb/LSYdX39tVF/2P/sexb98YbHkAbrdbAwcO1LvvvhvWvmTJEnXs2FGtW7c+pPV99NFHKi8vV69evaJZZoRjjz1WX375pYqLi6vali5dKofDoaFDh+5zuX79+ikpKUlLly6tagsEAnr//fd13HHH1WrN+2K6L9XJzc3V8uXLa/35jwb6Xuz7nkT/o/9x7DuQRnlG4lBddtlluvzyy3X//fdrxIgR+vrrr7V48WLdd999YfMNGTJEZ5xxhv7yl79Ikh599FE5HA716dNHKSkp+uGHHzRnzhz16tVLw4cPr9Wax48fr5dfflnTpk3TJZdcou3bt+sf//iHzjnnHGVmZlbN9/vf/17Z2dl69dVXJUkej0dTp07VzJkz1aRJE3Xp0kXz589XYWFhxBeb1BXTfVm8eLE+/vhjHXfcccrMzNTmzZs1Z84cOZ3OmO3LoaLvxbbvSfQ/+h/HvgMhSByE/v3768EHH9SMGTP02muvqWXLlrr99tsj7q8OBoMKhUJV0x07dtSCBQu0cOFCVVRUqHnz5ho7dqwuv/zyWh21LFVeW5sxY4YeeughTZs2TUlJSTr77LN15ZVXRtQcDAbD2iZPnizbtvX8889r586d6tatmx5//PGYjTI33Zc2bdooNzdX06dPV3FxsVJSUjRo0CBdfvnlNR5MVlfoe7HtexL9j/7Hse9A+BlxAABgjDESAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSQD02Z84cWZalDz74INalHFY++OADWZalOXPmxLqUqGvI+4b6iSCBRmPdunX63e9+px49eigxMVFNmjRRz549NXnyZL3//vth83bo0EF9+vTZ57qmTJkiy7KUl5dX7eM//vijLMuSZVn66KOP9rmeXfPs+hcfH6+uXbvquuuuU35+vtmOHqI77rij6jv665Ply5frjjvuiPh1RAB1i9/aQKOQlZWlE088US6XS5MmTVLv3r1VXl6u1atX65133lFKSopOOumkqG3v2WefVUpKihISEjRr1iydcMIJ+5y3f//+mjZtmiQpPz9f//vf//Too49qyZIl+vrrr+V2u/e57MSJE3XhhRfud54DufPOOzV58uSD/nnpw8Xy5ct15513avjw4RE/qTxs2DCVl5fL5XLFpjigESFIoFG48847VVZWpuXLl+vII4+MeHzbtm1R25bf79e8efN03nnnKS0tTTNnztRjjz2mlJSUaudv06ZN2C/yXX311TrzzDP1xhtv6LXXXtN55523z205nU45nc6o1R5tu34wqK45HA7Fx8fX+XaBxohLG2gUVq9erWbNmlUbIiSpZcuWUdvWf//7X23fvl2TJ0/WlClTVFpaqpdffvmQ1jF69GhJ0po1a/Y7X3VjJHa1vffee3r44YfVuXNneTwedevWTc8991zVfOvXr5dlWZKk5557LuwSy56WLl2qUaNGKT09XfHx8erXr5+eeuqpiFo6dOig4cOH69tvv9Xo0aOVlpamfv36SaoMFLfffruGDBmijIwMeTwedenSRTfffLPKysoi1mXbtp555hkNGTJEycnJSk5OVt++fat+pvqOO+7Q1KlTJUknnXRSVd1TpkyRtO9xBKWlpbrllluqnpOWLVtq0qRJ2rBhQ9h8ey4/e/Zs9e7dWx6PR+3bt9eDDz6439dEkgoKChQfH69zzjmn2sdvueUWWZal5cuXS5K2bt2qadOmqX///mrSpIni4+PVq1cvPfDAAxG/UFmd/Y2Vqe6MjVR5lm7cuHFVr0f37t11zz33KBAIHHB7wJ44I4FGoXPnzvr555+1cOHCfR7c9xYMBvc5BsLr9e5zuWeffVYdO3bUCSecIMuyNGDAAM2aNUuXXXbZQde7evVqSVJGRsZBL7O3W2+9VeXl5br88svl8Xg0Y8YMTZkyRV26dNFxxx2nzMxMzZs3TxMnTtQJJ5yg3/3udxHrmDlzpq644goNHTpUt912m5KSkrRkyRL9/ve/19q1a/XQQw+Fzb9x40adfPLJOu+88zR+/HiVlJRIkrZs2aJ//etfGj9+vH7zm98oLi5Oy5Yt04MPPqhvv/1Wb7/9dth6Jk6cqBdeeEFDhgzRbbfdpvT0dP30009asGCB7rrrLp1zzjnKzs7WzJkzdeutt6pnz56SKl/nffH7/Ro9erQ++eQTnXvuuZo2bZpWr16tGTNm6J133lFWVlbEz0U/9dRTysnJ0aWXXqr09HQ9//zzuummm9S2bVv95je/2ee20tPTNXbsWL322mvKz89X06ZNqx4LhUJ64YUX1K9fP/Xv31+StGLFCi1cuFDjxo1T586d5ff7tXjxYt18881at26dnn766X1uy8Sbb76pc845R126dNG0adPUtGlTffbZZ/rLX/6i5cuXa/78+VHdHho4G2gEPv30U9vlctmS7K5du9pTp061//nPf9orV66sdv727dvbkg74Lzc3N2y5LVu22E6n0/7rX/9a1fb3v//dllTttiTZo0aNsnNzc+3c3Fx71apV9iOPPGK7XC47LS3NzsnJ2e9+zZ4925Zkv//++xFt/fv3t71eb1X75s2bbbfbbV944YURNUyePDli3Vu3brU9Ho990UUXRTx29dVX2w6Hw167dm3Ec/bMM89EzO/1em2fzxfRfvvtt9uS7C+++KKq7eWXX7Yl2RMmTLCDwWDY/HtOV7fvu7z//vu2JHv27NlVbTNnzrQl2TfccEPYvG+88UbV9vZevlWrVnZBQUFVe2lpqZ2RkWEPHTo0Ypt727XeJ598Mqx96dKltiR7+vTpVW1lZWV2KBSKWMeECRNsh8Nhb926db/7tr/n4sQTT7Tbt29fNV1eXm63aNHCPuGEE2y/3x827yOPPLLP9QD7wqUNNArHHHOMvv76a02ePFmFhYWaPXu2rrzySvXq1UvDhg3TunXrIpbp0KGDlixZUu2/UaNGVbudOXPmKBQKadKkSVVtF198sVwul2bNmlXtMu+8844yMzOVmZmpbt266brrrlOvXr30zjvvqHnz5sb7fOWVV4YNwmzTpo26detWdbbjQBYsWCCv16tLL71UeXl5Yf/OPPNMhUIhLV26NGyZpk2bVl1y2JPb7a4a+BgIBLRz507l5eVpxIgRkqQvvviiat4XXnhBkvTwww/L4Qg/RO09fSgWLVokh8OhW265Jaz9jDPOUP/+/fXaa68pFAqFPTZ16lSlpaVVTScmJmro0KEH9RyOHj1aLVq00Ny5c8Pa586dq7i4OF188cVVbQkJCVWXlHw+n/Lz85WXl6fRo0crFAopKyvrkPd3X5YsWaKcnBxNnTpVBQUFYa/r6aefLqmyTwIHi0sbaDT69u1bdc18w4YNWrZsmf71r3/po48+0llnnRVxh0RSUlLVG93enn/++Yg227Y1a9Ys9evXT6FQKGx8w3HHHad58+bpvvvuU1xc+J/dkCFDdPfdd0tS1XX4du3a1XR31alTp4i2Zs2aRYwH2Jcff/xRkvb5HEhSTk5O2HTnzp33Ofjzn//8p5566in98MMPEW/YO3furPr/1atXq1WrVmrRosVB1XmwfvnlF7Vu3VpNmjSJeKx3795avny58vLywsLbvp7DHTt2HHB7u8LCI488olWrVqlbt24qLS3VwoULNWrUqLD9CwQCuv/++zV37lytWbNGtm2HrWvP56emdr2ul1xyyT7n2ft1BfaHIIFGqX379po0aVLV+IBPPvlEX375pY4//njjdS5btkxr166VJHXt2rXaed54442I2ywzMjL2+2Ztal9v6Hu/Se3Lrvnmzp2rVq1aVTvP3m+0iYmJ1c73yCOPaNq0aRo1apSuvvpqtW7dWm63W1u2bNGUKVMigsXhoqZ3xEyaNEmPPPKI5s6dq7vvvlsLFy5USUmJJk+eHDbfddddp8cff1wXXHCBbrvtNjVv3lwul0vffPONbrrppgM+P3sPkN3T3oMnd72uDz30UNUYjb21bt36IPYOqESQQKNmWZaGDBmiTz75RFu2bKnRumbNmiWPx6O5c+dWewr+8ssv17PPPltvvq9hVxiKRtCZN2+eOnTooLfeeivsuVm8eHHEvN26ddNrr72mnJyc/Z6V2N+bZ3U6deqkxYsXq6CgQOnp6WGPrVy5UqmpqTUa3FqdI488UkceeaSef/55/e1vf9PcuXOrBmLuad68eRo2bJj+/e9/h7Uf6K6dXXYN5qzuS8x++eWXsO/T2PW67u+MG3AoGCOBRmHJkiXV3tZWXl5edT24V69exusvLCzUggULNGrUKJ1//vk699xzI/6NHTtWb731lrKzs423UxuSk5OrfQM6//zz5fF49Ne//lXl5eURjxcWFu737pU9OZ1OWZYVdjZk1+n8ve0aO3DjjTdGfBLfc/nk5GRJ1b95Vufss89WKBSK2OZbb72lb7/9VmPHjq3RGIx9mTx5sjZs2KAXX3xR7733ni644IKI77hwOp0RZ4pKS0v16KOPHtQ2unXrJkkRY1Zeeuklbd26Naxt9OjRat68ue6///5qn7vy8nIVFxcf1HYBiTMSaCSuvfZa7dixQ2PHjlXfvn2VmJioTZs26cUXX9SqVas0adIk9e3b13j9L730ksrLyzV+/Ph9zjN+/HjNmTNHzz33nG6++WbjbUXb0KFDtXTpUj3wwANq166dLMvShRdeqLZt22rGjBm67LLL1LNnT02cOFHt27dXbm6uvvvuO7366qtauXJltd9RsLdzzz1Xt9xyi0477TSdc845Kioq0osvvljtN0+ed955uuCCCzR37lytXr1aY8eOVZMmTbRq1Sq9/fbb+v777yVJgwYNksPh0D333KOdO3cqKSlJHTt21JAhQ6qtYcqUKXruuef0wAMPaP369Ro2bJjWrFmjf/7zn2rRooXuvffeGj2P+3LxxRfrxhtv1JVXXqlQKBRxWUOqfH6efvppXXDBBRoxYoRycnI0a9YsNWvW7KC20b17d40YMUJPP/20bNtW//79tXz5ci1atEhdunSR3++vmjcpKUlz587V2Wefre7du+uSSy5Rly5dVFBQoJ9++kkLFy7UokWLNHz48Gg9BWjoYna/CFCH3n77bfvKK6+0+/XrZzdr1sx2Op1206ZN7eHDh9vPPvtsxG2G7du3t3v37r3P9U2ePDns9s+BAwfacXFxdn5+/j6XqaiosFNSUuxu3bpVtUmyzzjjDOP92t/tnwdzK6Bt2/aqVavskSNH2ikpKVW3te7p448/ts8++2w7MzPTdrlcdqtWrezhw4fbDz/8sF1eXl41X/v27e0TTzyx2joDgYB977332p07d7bdbrfdrl07+4YbbrBXrlxpSwq7Xda2K2/zfOKJJ+wBAwbYCQkJdnJyst23b1/7jjvuCJtvzpw5ds+ePatu7d11G2t1t0jatm2XlJTYN998s92xY0fb5XLZmZmZ9oQJE+z169eHzbev5W1792t/KMaMGVN163F1SktL7euvv95u166d7fF47C5dutj33Xdf1a2ie9axr9qys7Ptc889105JSbGTkpLsU0891V65cmW1r7lt2/Z3331nX3zxxXbr1q1tl8tlN2/e3D7mmGPsu+66y96xY8ch7R8aN8u2D3LkFQAAwF4YIwEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADAGEECAAAYI0gAAABjBAkAAGCMIAEAAIwRJAAAgDGCBAAAMEaQAAAAxggSAADA2P8DvBGGN2lf4pEAAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras.models import load_model\n",
        "\n",
        "def get_model(path):\n",
        "  pretrained_model = load_model(path)\n",
        "  return pretrained_model"
      ],
      "metadata": {
        "id": "RRAi3J4B3LAV"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install keras_tuner"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_o58EFgR4E2M",
        "outputId": "d5fb345c-a833-47f0-a83f-6cd792bd5ee8"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting keras_tuner\n",
            "  Downloading keras_tuner-1.4.7-py3-none-any.whl.metadata (5.4 kB)\n",
            "Requirement already satisfied: keras in /usr/local/lib/python3.10/dist-packages (from keras_tuner) (3.5.0)\n",
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            "Collecting kt-legacy (from keras_tuner)\n",
            "  Downloading kt_legacy-1.0.5-py3-none-any.whl.metadata (221 bytes)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from keras->keras_tuner) (1.4.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from keras->keras_tuner) (1.26.4)\n",
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            "\u001b[?25hDownloading kt_legacy-1.0.5-py3-none-any.whl (9.6 kB)\n",
            "Installing collected packages: kt-legacy, keras_tuner\n",
            "Successfully installed keras_tuner-1.4.7 kt-legacy-1.0.5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from keras_tuner import HyperParameters\n",
        "import tensorflow as tf\n",
        "import os\n",
        "\n",
        "# Use the correct path where the model was saved\n",
        "# You may need to adjust this path based on where you saved your model previously\n",
        "path = 'model.h5'  # Update this if necessary\n",
        "\n",
        "# Removing the check for the model as it has already been saved in a previous cell.\n",
        "# if not os.path.exists(path):\n",
        "#   raise ValueError(f\"Model file not fount at {path}. Please make sure to save the model before attempting to load it.\")\n",
        "\n",
        "\n",
        "def model_with_hp(hp):\n",
        "  fine_tuned_model = get_model(path)\n",
        "\n",
        "  fine_tuned_model.compile(\n",
        "      optimizer=tf.keras.optimizers.Adam(learning_rate=hp.Float('learning_rate', 1e-4, 2e-2, sampling='LOG')),\n",
        "      loss='sparse_categorical_crossentropy',\n",
        "      metrics=['accuracy']\n",
        "  )\n",
        "\n",
        "  return fine_tuned_model"
      ],
      "metadata": {
        "id": "T77QPEln3ck-"
      },
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from keras_tuner import Hyperband\n",
        "\n",
        "tuner = Hyperband(\n",
        "    model_with_hp,\n",
        "    objective='val_accuracy',\n",
        "    max_epochs=10,\n",
        "    factor=3,\n",
        "    directory='tuner_result',\n",
        "    project_name='hyperparameter_tuning'\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mCNlXFkH4DQ_",
        "outputId": "ae703a4e-9fc5-4a83-de01-15e39644d6ec"
      },
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuner.search(trainInput, trainTarget, epochs=5, validation_data=(validationInput, validationTarget), batch_size=32)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H9LGiZzt5XL9",
        "outputId": "12ed2512-ede7-4b1c-9963-22a96c089bc4"
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Trial 30 Complete [00h 00m 04s]\n",
            "val_accuracy: 0.95652174949646\n",
            "\n",
            "Best val_accuracy So Far: 0.95652174949646\n",
            "Total elapsed time: 00h 01m 51s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tuner.results_summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDC_Fz0Z6O16",
        "outputId": "0c6e079b-7186-4aaa-d0d6-ec82348f9e93"
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Results summary\n",
            "Results in tuner_result/hyperparameter_tuning\n",
            "Showing 10 best trials\n",
            "Objective(name=\"val_accuracy\", direction=\"max\")\n",
            "\n",
            "Trial 0019 summary\n",
            "Hyperparameters:\n",
            "learning_rate: 0.012925360866291275\n",
            "tuner/epochs: 4\n",
            "tuner/initial_epoch: 0\n",
            "tuner/bracket: 1\n",
            "tuner/round: 0\n",
            "Score: 0.95652174949646\n",
            "\n",
            "Trial 0029 summary\n",
            "Hyperparameters:\n",
            "learning_rate: 0.002808057731484277\n",
            "tuner/epochs: 10\n",
            "tuner/initial_epoch: 0\n",
            "tuner/bracket: 0\n",
            "tuner/round: 0\n",
            "Score: 0.95652174949646\n",
            "\n",
            "Trial 0000 summary\n",
            "Hyperparameters:\n",
            "learning_rate: 0.0007158000302516951\n",
            "tuner/epochs: 2\n",
            "tuner/initial_epoch: 0\n",
            "tuner/bracket: 2\n",
            "tuner/round: 0\n",
            "Score: 0.947826087474823\n",
            "\n",
            "Trial 0003 summary\n",
            "Hyperparameters:\n",
            "learning_rate: 0.00019071538372142778\n",
            "tuner/epochs: 2\n",
            "tuner/initial_epoch: 0\n",
            "tuner/bracket: 2\n",
            "tuner/round: 0\n",
            "Score: 0.947826087474823\n",
            "\n",
            "Trial 0004 summary\n",
            "Hyperparameters:\n",
            "learning_rate: 0.00028192428464246446\n",
            "tuner/epochs: 2\n",
            "tuner/initial_epoch: 0\n",
            "tuner/bracket: 2\n",
            "tuner/round: 0\n",
            "Score: 0.947826087474823\n",
            "\n",
            "Trial 0012 summary\n",
            "Hyperparameters:\n",
            "learning_rate: 0.0007158000302516951\n",
            "tuner/epochs: 4\n",
            "tuner/initial_epoch: 2\n",
            "tuner/bracket: 2\n",
            "tuner/round: 1\n",
            "tuner/trial_id: 0000\n",
            "Score: 0.947826087474823\n",
            "\n",
            "Trial 0013 summary\n",
            "Hyperparameters:\n",
            "learning_rate: 0.00019071538372142778\n",
            "tuner/epochs: 4\n",
            "tuner/initial_epoch: 2\n",
            "tuner/bracket: 2\n",
            "tuner/round: 1\n",
            "tuner/trial_id: 0003\n",
            "Score: 0.947826087474823\n",
            "\n",
            "Trial 0014 summary\n",
            "Hyperparameters:\n",
            "learning_rate: 0.00028192428464246446\n",
            "tuner/epochs: 4\n",
            "tuner/initial_epoch: 2\n",
            "tuner/bracket: 2\n",
            "tuner/round: 1\n",
            "tuner/trial_id: 0004\n",
            "Score: 0.947826087474823\n",
            "\n",
            "Trial 0016 summary\n",
            "Hyperparameters:\n",
            "learning_rate: 0.0007158000302516951\n",
            "tuner/epochs: 10\n",
            "tuner/initial_epoch: 4\n",
            "tuner/bracket: 2\n",
            "tuner/round: 2\n",
            "tuner/trial_id: 0012\n",
            "Score: 0.947826087474823\n",
            "\n",
            "Trial 0017 summary\n",
            "Hyperparameters:\n",
            "learning_rate: 0.00019071538372142778\n",
            "tuner/epochs: 10\n",
            "tuner/initial_epoch: 4\n",
            "tuner/bracket: 2\n",
            "tuner/round: 2\n",
            "tuner/trial_id: 0013\n",
            "Score: 0.947826087474823\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "best_model = tuner.get_best_models(num_models=1)[0]\n",
        "\n",
        "test_loss, test_acc = best_model.evaluate(testInput, testTarget)\n",
        "print(f\"Test Accuracy: {test_acc}\")\n",
        "\n",
        "best_hyperparameters = tuner.get_best_hyperparameters()[0]\n",
        "print(f\"Best Hyperparameters:\", best_hyperparameters.values)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2Eajqhc6sj_",
        "outputId": "f39116f3-4c2f-45f9-858a-22b36e0daded"
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "WARNING:absl:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
            "/usr/local/lib/python3.10/dist-packages/keras/src/saving/saving_lib.py:713: UserWarning: Skipping variable loading for optimizer 'adam', because it has 2 variables whereas the saved optimizer has 18 variables. \n",
            "  saveable.load_own_variables(weights_store.get(inner_path))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m5/5\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - accuracy: 0.8927 - loss: 0.3543  \n",
            "Test Accuracy: 0.8888888955116272\n",
            "Best Hyperparameters: {'learning_rate': 0.012925360866291275, 'tuner/epochs': 4, 'tuner/initial_epoch': 0, 'tuner/bracket': 1, 'tuner/round': 0}\n"
          ]
        }
      ]
    }
  ]
}