feedforward_network__diabetes_prediction

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            "Requirement already satisfied: imbalanced-learn in /usr/local/lib/python3.10/dist-packages (0.10.1)\n",
            "Collecting imbalanced-learn\n",
            "  Downloading imbalanced_learn-0.12.0-py3-none-any.whl (257 kB)\n",
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            "\u001b[?25hRequirement already satisfied: numpy>=1.17.3 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (1.25.2)\n",
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            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from imbalanced-learn) (3.2.0)\n",
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            "    Uninstalling imbalanced-learn-0.10.1:\n",
            "      Successfully uninstalled imbalanced-learn-0.10.1\n",
            "Successfully installed imbalanced-learn-0.12.0\n"
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        "id": "INR3CT8rS9Rv"
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      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import StandardScaler\n",
        "import tensorflow as tf\n",
        "from imblearn.over_sampling import RandomOverSampler"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv(\"diabetes.csv\")"
      ],
      "metadata": {
        "id": "m699oQ22TBDo"
      },
      "execution_count": 4,
      "outputs": []
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        "df.head()"
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      "execution_count": 5,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   Pregnancies  Glucose  BloodPressure  SkinThickness  Insulin   BMI  \\\n",
              "0            6      148             72             35        0  33.6   \n",
              "1            1       85             66             29        0  26.6   \n",
              "2            8      183             64              0        0  23.3   \n",
              "3            1       89             66             23       94  28.1   \n",
              "4            0      137             40             35      168  43.1   \n",
              "\n",
              "   DiabetesPedigreeFunction  Age  Outcome  \n",
              "0                     0.627   50        1  \n",
              "1                     0.351   31        0  \n",
              "2                     0.672   32        1  \n",
              "3                     0.167   21        0  \n",
              "4                     2.288   33        1  "
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              "type": "dataframe",
              "variable_name": "df",
              "summary": "{\n  \"name\": \"df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 17,\n        \"samples\": [\n          6,\n          1,\n          3\n        ],\n        \"num_unique_values\": 17,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31,\n        \"min\": 0,\n        \"max\": 199,\n        \"samples\": [\n          151,\n          101,\n          112\n        ],\n        \"num_unique_values\": 136,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 19,\n        \"min\": 0,\n        \"max\": 122,\n        \"samples\": [\n          86,\n          46,\n          85\n        ],\n        \"num_unique_values\": 47,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 0,\n        \"max\": 99,\n        \"samples\": [\n          7,\n          12,\n          48\n        ],\n        \"num_unique_values\": 51,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 115,\n        \"min\": 0,\n        \"max\": 846,\n        \"samples\": [\n          52,\n          41,\n          183\n        ],\n        \"num_unique_values\": 186,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.884160320375446,\n        \"min\": 0.0,\n        \"max\": 67.1,\n        \"samples\": [\n          19.9,\n          31.0,\n          38.1\n        ],\n        \"num_unique_values\": 248,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.3313285950127749,\n        \"min\": 0.078,\n        \"max\": 2.42,\n        \"samples\": [\n          1.731,\n          0.426,\n          0.138\n        ],\n        \"num_unique_values\": 517,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 11,\n        \"min\": 21,\n        \"max\": 81,\n        \"samples\": [\n          60,\n          47,\n          72\n        ],\n        \"num_unique_values\": 52,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 0,\n        \"max\": 1,\n        \"samples\": [\n          0,\n          1\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
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        "df.columns"
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          "data": {
            "text/plain": [
              "Index(['Pregnancies', 'Glucose', 'BloodPressure', 'SkinThickness', 'Insulin',\n",
              "       'BMI', 'DiabetesPedigreeFunction', 'Age', 'Outcome'],\n",
              "      dtype='object')"
            ]
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          "metadata": {},
          "execution_count": 6
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    {
      "cell_type": "code",
      "source": [
        "for i in range(len(df.columns[:-1])):\n",
        "  label = df.columns[i]\n",
        "  plt.hist(df[df['Outcome']==1][label], color='blue', label=\"Diabetes\", alpha=0.7, density=True, bins=15)\n",
        "  plt.hist(df[df['Outcome']==0][label], color='red', label=\"No diabetes\", alpha=0.7, density=True, bins=15)\n",
        "  plt.title(label)\n",
        "  plt.ylabel(\"Probability\")\n",
        "  plt.xlabel(label)\n",
        "  plt.legend()\n",
        "  plt.show()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "BULFaNw3TeJV",
        "outputId": "072ce8c5-7c27-496f-855a-5663d68f62e4"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X = df[df.columns[:-1]].values\n",
        "y = df[df.columns[-1]].values\n",
        "y"
      ],
      "metadata": {
        "id": "wJVA9Lc8UiMx",
        "outputId": "8122d31f-cdbb-416f-d426-c42979608619",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0,\n",
              "       1, 1, 1, 1, 1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1,\n",
              "       0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 1, 0, 0, 1, 0,\n",
              "       1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0,\n",
              "       1, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1,\n",
              "       1, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1, 1, 0, 0, 1, 1, 1, 1,\n",
              "       1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0,\n",
              "       1, 1, 0, 0, 0, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1,\n",
              "       0, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 1, 1, 0, 0, 1, 1, 0, 1, 0, 1,\n",
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          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X.shape, y.shape"
      ],
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        },
        "id": "ROrVPYHkadbe",
        "outputId": "2779fc29-b4f2-4b19-c835-f8e0c5ee6db3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((768, 8), (768,))"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "scaler = StandardScaler()\n",
        "X = scaler.fit_transform(X)\n",
        "data = np.hstack((X, np.reshape(y, (-1, 1))))\n",
        "transformed_df = pd.DataFrame(data, columns=df.columns)\n",
        "transformed_df"
      ],
      "metadata": {
        "id": "JftXjjF8aFma",
        "outputId": "555bd540-9f9b-42ba-f4d4-44e525cd3cc8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        }
      },
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Pregnancies   Glucose  BloodPressure  SkinThickness   Insulin       BMI  \\\n",
              "0       0.639947  0.848324       0.149641       0.907270 -0.692891  0.204013   \n",
              "1      -0.844885 -1.123396      -0.160546       0.530902 -0.692891 -0.684422   \n",
              "2       1.233880  1.943724      -0.263941      -1.288212 -0.692891 -1.103255   \n",
              "3      -0.844885 -0.998208      -0.160546       0.154533  0.123302 -0.494043   \n",
              "4      -1.141852  0.504055      -1.504687       0.907270  0.765836  1.409746   \n",
              "..           ...       ...            ...            ...       ...       ...   \n",
              "763     1.827813 -0.622642       0.356432       1.722735  0.870031  0.115169   \n",
              "764    -0.547919  0.034598       0.046245       0.405445 -0.692891  0.610154   \n",
              "765     0.342981  0.003301       0.149641       0.154533  0.279594 -0.735190   \n",
              "766    -0.844885  0.159787      -0.470732      -1.288212 -0.692891 -0.240205   \n",
              "767    -0.844885 -0.873019       0.046245       0.656358 -0.692891 -0.202129   \n",
              "\n",
              "     DiabetesPedigreeFunction       Age  Outcome  \n",
              "0                    0.468492  1.425995      1.0  \n",
              "1                   -0.365061 -0.190672      0.0  \n",
              "2                    0.604397 -0.105584      1.0  \n",
              "3                   -0.920763 -1.041549      0.0  \n",
              "4                    5.484909 -0.020496      1.0  \n",
              "..                        ...       ...      ...  \n",
              "763                 -0.908682  2.532136      0.0  \n",
              "764                 -0.398282 -0.531023      0.0  \n",
              "765                 -0.685193 -0.275760      0.0  \n",
              "766                 -0.371101  1.170732      1.0  \n",
              "767                 -0.473785 -0.871374      0.0  \n",
              "\n",
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              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('transformed_df');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "transformed_df",
              "summary": "{\n  \"name\": \"transformed_df\",\n  \"rows\": 768,\n  \"fields\": [\n    {\n      \"column\": \"Pregnancies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -1.1418515161634994,\n        \"max\": 3.906578350084603,\n        \"samples\": [\n          0.6399472601593605,\n          -0.8448850534430227,\n          -0.25095212800206945\n        ],\n        \"num_unique_values\": 17,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Glucose\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -3.78365371377963,\n        \"max\": 2.4444782063079162,\n        \"samples\": [\n          0.9422152306084076,\n          -0.6226420357452472,\n          -0.2783734371474431\n        ],\n        \"num_unique_values\": 136,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BloodPressure\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -3.572597239872642,\n        \"max\": 2.734528247420465,\n        \"samples\": [\n          0.87340925117004,\n          -1.1945007446637657,\n          0.8217115012741948\n        ],\n        \"num_unique_values\": 47,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"SkinThickness\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -1.288212212945236,\n        \"max\": 4.921865835155969,\n        \"samples\": [\n          -0.8491157853017168,\n          -0.5354754798420599,\n          1.7227347194674694\n        ],\n        \"num_unique_values\": 51,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Insulin\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -0.6928905722954676,\n        \"max\": 6.652839378368462,\n        \"samples\": [\n          -0.2413799843113963,\n          -0.3368918394618729,\n          0.8960793815715525\n        ],\n        \"num_unique_values\": 186,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"BMI\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -4.060473872668307,\n        \"max\": 4.455807490825071,\n        \"samples\": [\n          -1.5347808900525368,\n          -0.1259772665834382,\n          0.7751493754553548\n        ],\n        \"num_unique_values\": 248,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DiabetesPedigreeFunction\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781402,\n        \"min\": -1.1895531764897846,\n        \"max\": 5.883564765877941,\n        \"samples\": [\n          3.802702997179665,\n          -0.13855187676990055,\n          -1.0083460558484254\n        ],\n        \"num_unique_values\": 517,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.0006516781401997,\n        \"min\": -1.0415494364835025,\n        \"max\": 4.063715751598595,\n        \"samples\": [\n          2.276872935769861,\n          1.1707321450187396,\n          3.2979259733862807\n        ],\n        \"num_unique_values\": 52,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Outcome\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.47695137724279896,\n        \"min\": 0.0,\n        \"max\": 1.0,\n        \"samples\": [\n          0.0,\n          1.0\n        ],\n        \"num_unique_values\": 2,\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "over = RandomOverSampler()\n",
        "X, y = over.fit_resample(X, y)\n",
        "data = np.hstack((X, np.reshape(y, (-1, 1))))\n",
        "transformed_df = pd.DataFrame(data, columns=df.columns)"
      ],
      "metadata": {
        "id": "A5oHtGZjbejl"
      },
      "execution_count": 11,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "len(transformed_df[transformed_df[\"Outcome\"]==1]), len(transformed_df[transformed_df[\"Outcome\"]==0])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sOqdGfTza-Gl",
        "outputId": "df0ccfa2-9b6e-468a-b6de-49b9d0450f18"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(500, 500)"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_temp, y_train, y_temp = train_test_split(X, y, test_size=0.4, random_state=0)\n",
        "X_valid, X_test, y_valid, y_test = train_test_split(X_temp, y_temp, test_size=0.5, random_state=0)"
      ],
      "metadata": {
        "id": "mo2-uhrBVZPC"
      },
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.Sequential([\n",
        "                             tf.keras.layers.Dense(16, activation='relu'), # if x <= 0 --> 0, x > 0 --> x\n",
        "                             tf.keras.layers.Dense(16, activation='relu'),\n",
        "                             tf.keras.layers.Dense(1, activation=\"sigmoid\")\n",
        "])"
      ],
      "metadata": {
        "id": "uWNbwA7mWFj3"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),\n",
        "              loss=tf.keras.losses.BinaryCrossentropy(),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "ve21a--WXTDg"
      },
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_train, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gRTstfSMX05S",
        "outputId": "cce5adfb-acf3-4363-9c13-6cc7c750881b"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "19/19 [==============================] - 0s 2ms/step - loss: 0.7594 - accuracy: 0.5067\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.7593581080436707, 0.5066666603088379]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_valid, y_valid)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lr6QZEL_YBMb",
        "outputId": "7b1a905d-2de6-4a69-8f0c-bf8e04c14a0e"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 3ms/step - loss: 0.7783 - accuracy: 0.4500\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.778327465057373, 0.44999998807907104]"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(X_train, y_train, batch_size=13, epochs=200, validation_data=(X_valid, y_valid))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OlxgtY6pYF1E",
        "outputId": "e5d6cc91-3d22-4482-b0fd-c64bc27929a6"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "47/47 [==============================] - 1s 11ms/step - loss: 0.0370 - accuracy: 0.9867 - val_loss: 1.3370 - val_accuracy: 0.8200\n",
            "Epoch 2/200\n",
            "47/47 [==============================] - 0s 6ms/step - loss: 0.0371 - accuracy: 0.9867 - val_loss: 1.3452 - val_accuracy: 0.8250\n",
            "Epoch 3/200\n",
            "47/47 [==============================] - 0s 6ms/step - loss: 0.0361 - accuracy: 0.9850 - val_loss: 1.3786 - val_accuracy: 0.8100\n",
            "Epoch 4/200\n",
            "47/47 [==============================] - 0s 9ms/step - loss: 0.0367 - accuracy: 0.9850 - val_loss: 1.3479 - val_accuracy: 0.8250\n",
            "Epoch 5/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0369 - accuracy: 0.9883 - val_loss: 1.3751 - val_accuracy: 0.8150\n",
            "Epoch 6/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0366 - accuracy: 0.9900 - val_loss: 1.3595 - val_accuracy: 0.8200\n",
            "Epoch 7/200\n",
            "47/47 [==============================] - 0s 6ms/step - loss: 0.0366 - accuracy: 0.9883 - val_loss: 1.3534 - val_accuracy: 0.8250\n",
            "Epoch 8/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0357 - accuracy: 0.9900 - val_loss: 1.3544 - val_accuracy: 0.8200\n",
            "Epoch 9/200\n",
            "47/47 [==============================] - 0s 8ms/step - loss: 0.0376 - accuracy: 0.9883 - val_loss: 1.3234 - val_accuracy: 0.8250\n",
            "Epoch 10/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0388 - accuracy: 0.9850 - val_loss: 1.3588 - val_accuracy: 0.8250\n",
            "Epoch 11/200\n",
            "47/47 [==============================] - 0s 10ms/step - loss: 0.0358 - accuracy: 0.9850 - val_loss: 1.3464 - val_accuracy: 0.8250\n",
            "Epoch 12/200\n",
            "47/47 [==============================] - 0s 7ms/step - loss: 0.0362 - accuracy: 0.9933 - val_loss: 1.3348 - val_accuracy: 0.8250\n",
            "Epoch 13/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0401 - accuracy: 0.9867 - val_loss: 1.3815 - val_accuracy: 0.8250\n",
            "Epoch 14/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0387 - accuracy: 0.9900 - val_loss: 1.3796 - val_accuracy: 0.8250\n",
            "Epoch 15/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0356 - accuracy: 0.9900 - val_loss: 1.3557 - val_accuracy: 0.8250\n",
            "Epoch 16/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0355 - accuracy: 0.9883 - val_loss: 1.3316 - val_accuracy: 0.8250\n",
            "Epoch 17/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0356 - accuracy: 0.9883 - val_loss: 1.4055 - val_accuracy: 0.8200\n",
            "Epoch 18/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0369 - accuracy: 0.9883 - val_loss: 1.3900 - val_accuracy: 0.8200\n",
            "Epoch 19/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0348 - accuracy: 0.9900 - val_loss: 1.3838 - val_accuracy: 0.8200\n",
            "Epoch 20/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0349 - accuracy: 0.9867 - val_loss: 1.3767 - val_accuracy: 0.8200\n",
            "Epoch 21/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0353 - accuracy: 0.9883 - val_loss: 1.3992 - val_accuracy: 0.8200\n",
            "Epoch 22/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0346 - accuracy: 0.9917 - val_loss: 1.3674 - val_accuracy: 0.8200\n",
            "Epoch 23/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0361 - accuracy: 0.9900 - val_loss: 1.3940 - val_accuracy: 0.8250\n",
            "Epoch 24/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0353 - accuracy: 0.9917 - val_loss: 1.3656 - val_accuracy: 0.8250\n",
            "Epoch 25/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0364 - accuracy: 0.9900 - val_loss: 1.3840 - val_accuracy: 0.8150\n",
            "Epoch 26/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0362 - accuracy: 0.9917 - val_loss: 1.4141 - val_accuracy: 0.8200\n",
            "Epoch 27/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0354 - accuracy: 0.9900 - val_loss: 1.4211 - val_accuracy: 0.8100\n",
            "Epoch 28/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0355 - accuracy: 0.9933 - val_loss: 1.3745 - val_accuracy: 0.8250\n",
            "Epoch 29/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0346 - accuracy: 0.9900 - val_loss: 1.3980 - val_accuracy: 0.8200\n",
            "Epoch 30/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0340 - accuracy: 0.9933 - val_loss: 1.3907 - val_accuracy: 0.8200\n",
            "Epoch 31/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0348 - accuracy: 0.9900 - val_loss: 1.3880 - val_accuracy: 0.8200\n",
            "Epoch 32/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0332 - accuracy: 0.9933 - val_loss: 1.4143 - val_accuracy: 0.8150\n",
            "Epoch 33/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0338 - accuracy: 0.9917 - val_loss: 1.4125 - val_accuracy: 0.8150\n",
            "Epoch 34/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0348 - accuracy: 0.9883 - val_loss: 1.4026 - val_accuracy: 0.8250\n",
            "Epoch 35/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0340 - accuracy: 0.9933 - val_loss: 1.3942 - val_accuracy: 0.8250\n",
            "Epoch 36/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0375 - accuracy: 0.9933 - val_loss: 1.4719 - val_accuracy: 0.8350\n",
            "Epoch 37/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0410 - accuracy: 0.9917 - val_loss: 1.4424 - val_accuracy: 0.8200\n",
            "Epoch 38/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0355 - accuracy: 0.9900 - val_loss: 1.4481 - val_accuracy: 0.8250\n",
            "Epoch 39/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0353 - accuracy: 0.9917 - val_loss: 1.4688 - val_accuracy: 0.8200\n",
            "Epoch 40/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0355 - accuracy: 0.9917 - val_loss: 1.3908 - val_accuracy: 0.8250\n",
            "Epoch 41/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0337 - accuracy: 0.9933 - val_loss: 1.4123 - val_accuracy: 0.8200\n",
            "Epoch 42/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0340 - accuracy: 0.9900 - val_loss: 1.4102 - val_accuracy: 0.8250\n",
            "Epoch 43/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0342 - accuracy: 0.9917 - val_loss: 1.4214 - val_accuracy: 0.8150\n",
            "Epoch 44/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0326 - accuracy: 0.9933 - val_loss: 1.4175 - val_accuracy: 0.8250\n",
            "Epoch 45/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0346 - accuracy: 0.9917 - val_loss: 1.4346 - val_accuracy: 0.8200\n",
            "Epoch 46/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0335 - accuracy: 0.9933 - val_loss: 1.4273 - val_accuracy: 0.8150\n",
            "Epoch 47/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0341 - accuracy: 0.9933 - val_loss: 1.4223 - val_accuracy: 0.8150\n",
            "Epoch 48/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0327 - accuracy: 0.9917 - val_loss: 1.4200 - val_accuracy: 0.8250\n",
            "Epoch 49/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0324 - accuracy: 0.9933 - val_loss: 1.4097 - val_accuracy: 0.8250\n",
            "Epoch 50/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0338 - accuracy: 0.9917 - val_loss: 1.4048 - val_accuracy: 0.8250\n",
            "Epoch 51/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0347 - accuracy: 0.9917 - val_loss: 1.4437 - val_accuracy: 0.8200\n",
            "Epoch 52/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0326 - accuracy: 0.9933 - val_loss: 1.4184 - val_accuracy: 0.8200\n",
            "Epoch 53/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0316 - accuracy: 0.9933 - val_loss: 1.4529 - val_accuracy: 0.8200\n",
            "Epoch 54/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0328 - accuracy: 0.9917 - val_loss: 1.4414 - val_accuracy: 0.8150\n",
            "Epoch 55/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0327 - accuracy: 0.9917 - val_loss: 1.4456 - val_accuracy: 0.8150\n",
            "Epoch 56/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0319 - accuracy: 0.9950 - val_loss: 1.4126 - val_accuracy: 0.8250\n",
            "Epoch 57/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0333 - accuracy: 0.9917 - val_loss: 1.4277 - val_accuracy: 0.8150\n",
            "Epoch 58/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0318 - accuracy: 0.9900 - val_loss: 1.4506 - val_accuracy: 0.8200\n",
            "Epoch 59/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0311 - accuracy: 0.9933 - val_loss: 1.4534 - val_accuracy: 0.8200\n",
            "Epoch 60/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0335 - accuracy: 0.9900 - val_loss: 1.4606 - val_accuracy: 0.8200\n",
            "Epoch 61/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0317 - accuracy: 0.9933 - val_loss: 1.4319 - val_accuracy: 0.8100\n",
            "Epoch 62/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0320 - accuracy: 0.9917 - val_loss: 1.4809 - val_accuracy: 0.8100\n",
            "Epoch 63/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0318 - accuracy: 0.9933 - val_loss: 1.4364 - val_accuracy: 0.8250\n",
            "Epoch 64/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0306 - accuracy: 0.9933 - val_loss: 1.4828 - val_accuracy: 0.8150\n",
            "Epoch 65/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0313 - accuracy: 0.9917 - val_loss: 1.4620 - val_accuracy: 0.8200\n",
            "Epoch 66/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0360 - accuracy: 0.9900 - val_loss: 1.5337 - val_accuracy: 0.8150\n",
            "Epoch 67/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0319 - accuracy: 0.9950 - val_loss: 1.4869 - val_accuracy: 0.8150\n",
            "Epoch 68/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0324 - accuracy: 0.9933 - val_loss: 1.4233 - val_accuracy: 0.8250\n",
            "Epoch 69/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0315 - accuracy: 0.9950 - val_loss: 1.4642 - val_accuracy: 0.8100\n",
            "Epoch 70/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0310 - accuracy: 0.9933 - val_loss: 1.5017 - val_accuracy: 0.8100\n",
            "Epoch 71/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0313 - accuracy: 0.9933 - val_loss: 1.4977 - val_accuracy: 0.8100\n",
            "Epoch 72/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0311 - accuracy: 0.9933 - val_loss: 1.4416 - val_accuracy: 0.8250\n",
            "Epoch 73/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0312 - accuracy: 0.9917 - val_loss: 1.4334 - val_accuracy: 0.8250\n",
            "Epoch 74/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0465 - accuracy: 0.9900 - val_loss: 1.4688 - val_accuracy: 0.8150\n",
            "Epoch 75/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0361 - accuracy: 0.9933 - val_loss: 1.4684 - val_accuracy: 0.8050\n",
            "Epoch 76/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0308 - accuracy: 0.9933 - val_loss: 1.4752 - val_accuracy: 0.8200\n",
            "Epoch 77/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0298 - accuracy: 0.9983 - val_loss: 1.4757 - val_accuracy: 0.8200\n",
            "Epoch 78/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0299 - accuracy: 0.9933 - val_loss: 1.4623 - val_accuracy: 0.8200\n",
            "Epoch 79/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0323 - accuracy: 0.9950 - val_loss: 1.5204 - val_accuracy: 0.8000\n",
            "Epoch 80/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0304 - accuracy: 0.9967 - val_loss: 1.5273 - val_accuracy: 0.8150\n",
            "Epoch 81/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0343 - accuracy: 0.9917 - val_loss: 1.4831 - val_accuracy: 0.8250\n",
            "Epoch 82/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0299 - accuracy: 0.9967 - val_loss: 1.4627 - val_accuracy: 0.8200\n",
            "Epoch 83/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0325 - accuracy: 0.9933 - val_loss: 1.4749 - val_accuracy: 0.8200\n",
            "Epoch 84/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0287 - accuracy: 0.9950 - val_loss: 1.5153 - val_accuracy: 0.8050\n",
            "Epoch 85/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0298 - accuracy: 0.9933 - val_loss: 1.4867 - val_accuracy: 0.8200\n",
            "Epoch 86/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0289 - accuracy: 0.9950 - val_loss: 1.4877 - val_accuracy: 0.8150\n",
            "Epoch 87/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0295 - accuracy: 0.9933 - val_loss: 1.4982 - val_accuracy: 0.8100\n",
            "Epoch 88/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0300 - accuracy: 0.9933 - val_loss: 1.5242 - val_accuracy: 0.8100\n",
            "Epoch 89/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0288 - accuracy: 0.9950 - val_loss: 1.5143 - val_accuracy: 0.8150\n",
            "Epoch 90/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0286 - accuracy: 0.9967 - val_loss: 1.5206 - val_accuracy: 0.8100\n",
            "Epoch 91/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0299 - accuracy: 0.9933 - val_loss: 1.4963 - val_accuracy: 0.8200\n",
            "Epoch 92/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0286 - accuracy: 0.9967 - val_loss: 1.5183 - val_accuracy: 0.8100\n",
            "Epoch 93/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0290 - accuracy: 0.9933 - val_loss: 1.5062 - val_accuracy: 0.8200\n",
            "Epoch 94/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0293 - accuracy: 0.9967 - val_loss: 1.5154 - val_accuracy: 0.8150\n",
            "Epoch 95/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0290 - accuracy: 0.9967 - val_loss: 1.4908 - val_accuracy: 0.8200\n",
            "Epoch 96/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0287 - accuracy: 0.9950 - val_loss: 1.4707 - val_accuracy: 0.8250\n",
            "Epoch 97/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0278 - accuracy: 0.9967 - val_loss: 1.5136 - val_accuracy: 0.8100\n",
            "Epoch 98/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0302 - accuracy: 0.9933 - val_loss: 1.5615 - val_accuracy: 0.8100\n",
            "Epoch 99/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0292 - accuracy: 0.9933 - val_loss: 1.4411 - val_accuracy: 0.8300\n",
            "Epoch 100/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0301 - accuracy: 0.9917 - val_loss: 1.5055 - val_accuracy: 0.8150\n",
            "Epoch 101/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0290 - accuracy: 0.9933 - val_loss: 1.5338 - val_accuracy: 0.8150\n",
            "Epoch 102/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0284 - accuracy: 0.9950 - val_loss: 1.5125 - val_accuracy: 0.8200\n",
            "Epoch 103/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0286 - accuracy: 0.9950 - val_loss: 1.5577 - val_accuracy: 0.8050\n",
            "Epoch 104/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0342 - accuracy: 0.9933 - val_loss: 1.5223 - val_accuracy: 0.8200\n",
            "Epoch 105/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0283 - accuracy: 0.9950 - val_loss: 1.5311 - val_accuracy: 0.8050\n",
            "Epoch 106/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0291 - accuracy: 0.9950 - val_loss: 1.4642 - val_accuracy: 0.8200\n",
            "Epoch 107/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0293 - accuracy: 0.9933 - val_loss: 1.5323 - val_accuracy: 0.8150\n",
            "Epoch 108/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0270 - accuracy: 0.9983 - val_loss: 1.5395 - val_accuracy: 0.8150\n",
            "Epoch 109/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0294 - accuracy: 0.9950 - val_loss: 1.5749 - val_accuracy: 0.7950\n",
            "Epoch 110/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0297 - accuracy: 0.9950 - val_loss: 1.5500 - val_accuracy: 0.8250\n",
            "Epoch 111/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0272 - accuracy: 0.9950 - val_loss: 1.5762 - val_accuracy: 0.8050\n",
            "Epoch 112/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0277 - accuracy: 0.9933 - val_loss: 1.5423 - val_accuracy: 0.8150\n",
            "Epoch 113/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0274 - accuracy: 0.9933 - val_loss: 1.5373 - val_accuracy: 0.8200\n",
            "Epoch 114/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0309 - accuracy: 0.9917 - val_loss: 1.5073 - val_accuracy: 0.8150\n",
            "Epoch 115/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0286 - accuracy: 0.9967 - val_loss: 1.5646 - val_accuracy: 0.8150\n",
            "Epoch 116/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0279 - accuracy: 0.9950 - val_loss: 1.5496 - val_accuracy: 0.8150\n",
            "Epoch 117/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0358 - accuracy: 0.9900 - val_loss: 1.5796 - val_accuracy: 0.8150\n",
            "Epoch 118/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0379 - accuracy: 0.9933 - val_loss: 1.5682 - val_accuracy: 0.8050\n",
            "Epoch 119/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0301 - accuracy: 0.9950 - val_loss: 1.5436 - val_accuracy: 0.8300\n",
            "Epoch 120/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0275 - accuracy: 0.9950 - val_loss: 1.5982 - val_accuracy: 0.8100\n",
            "Epoch 121/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0266 - accuracy: 0.9950 - val_loss: 1.5831 - val_accuracy: 0.8000\n",
            "Epoch 122/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0262 - accuracy: 0.9967 - val_loss: 1.5626 - val_accuracy: 0.8100\n",
            "Epoch 123/200\n",
            "47/47 [==============================] - 0s 6ms/step - loss: 0.0265 - accuracy: 0.9967 - val_loss: 1.5774 - val_accuracy: 0.8000\n",
            "Epoch 124/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0267 - accuracy: 0.9950 - val_loss: 1.5425 - val_accuracy: 0.8150\n",
            "Epoch 125/200\n",
            "47/47 [==============================] - 0s 6ms/step - loss: 0.0273 - accuracy: 0.9950 - val_loss: 1.5605 - val_accuracy: 0.8100\n",
            "Epoch 126/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0266 - accuracy: 0.9983 - val_loss: 1.5774 - val_accuracy: 0.8050\n",
            "Epoch 127/200\n",
            "47/47 [==============================] - 0s 6ms/step - loss: 0.0263 - accuracy: 0.9950 - val_loss: 1.5518 - val_accuracy: 0.8200\n",
            "Epoch 128/200\n",
            "47/47 [==============================] - 0s 6ms/step - loss: 0.0261 - accuracy: 0.9967 - val_loss: 1.5497 - val_accuracy: 0.8150\n",
            "Epoch 129/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0261 - accuracy: 0.9983 - val_loss: 1.5676 - val_accuracy: 0.8100\n",
            "Epoch 130/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0257 - accuracy: 0.9933 - val_loss: 1.5709 - val_accuracy: 0.8100\n",
            "Epoch 131/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0269 - accuracy: 0.9950 - val_loss: 1.5748 - val_accuracy: 0.8250\n",
            "Epoch 132/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0278 - accuracy: 0.9950 - val_loss: 1.5376 - val_accuracy: 0.8250\n",
            "Epoch 133/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0399 - accuracy: 0.9900 - val_loss: 1.6416 - val_accuracy: 0.8100\n",
            "Epoch 134/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0293 - accuracy: 0.9950 - val_loss: 1.6381 - val_accuracy: 0.7950\n",
            "Epoch 135/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0266 - accuracy: 0.9967 - val_loss: 1.5734 - val_accuracy: 0.8250\n",
            "Epoch 136/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0262 - accuracy: 0.9950 - val_loss: 1.5925 - val_accuracy: 0.8050\n",
            "Epoch 137/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0259 - accuracy: 0.9950 - val_loss: 1.5793 - val_accuracy: 0.8200\n",
            "Epoch 138/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0259 - accuracy: 0.9967 - val_loss: 1.6391 - val_accuracy: 0.8100\n",
            "Epoch 139/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0254 - accuracy: 0.9950 - val_loss: 1.6017 - val_accuracy: 0.8050\n",
            "Epoch 140/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0252 - accuracy: 0.9950 - val_loss: 1.5758 - val_accuracy: 0.8200\n",
            "Epoch 141/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0244 - accuracy: 0.9983 - val_loss: 1.6275 - val_accuracy: 0.7950\n",
            "Epoch 142/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0253 - accuracy: 0.9967 - val_loss: 1.6304 - val_accuracy: 0.7950\n",
            "Epoch 143/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0250 - accuracy: 0.9950 - val_loss: 1.5899 - val_accuracy: 0.8250\n",
            "Epoch 144/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0274 - accuracy: 0.9950 - val_loss: 1.6093 - val_accuracy: 0.8200\n",
            "Epoch 145/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0261 - accuracy: 0.9950 - val_loss: 1.6423 - val_accuracy: 0.7950\n",
            "Epoch 146/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0258 - accuracy: 0.9950 - val_loss: 1.6250 - val_accuracy: 0.8050\n",
            "Epoch 147/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0251 - accuracy: 0.9967 - val_loss: 1.5727 - val_accuracy: 0.8200\n",
            "Epoch 148/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0246 - accuracy: 0.9967 - val_loss: 1.6245 - val_accuracy: 0.8100\n",
            "Epoch 149/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0240 - accuracy: 0.9967 - val_loss: 1.6144 - val_accuracy: 0.8150\n",
            "Epoch 150/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0241 - accuracy: 0.9967 - val_loss: 1.6159 - val_accuracy: 0.8000\n",
            "Epoch 151/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0248 - accuracy: 0.9967 - val_loss: 1.6622 - val_accuracy: 0.8000\n",
            "Epoch 152/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0256 - accuracy: 0.9950 - val_loss: 1.5783 - val_accuracy: 0.8200\n",
            "Epoch 153/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0245 - accuracy: 0.9967 - val_loss: 1.5714 - val_accuracy: 0.8150\n",
            "Epoch 154/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0244 - accuracy: 0.9950 - val_loss: 1.6789 - val_accuracy: 0.7950\n",
            "Epoch 155/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0249 - accuracy: 0.9983 - val_loss: 1.6783 - val_accuracy: 0.7950\n",
            "Epoch 156/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0249 - accuracy: 0.9967 - val_loss: 1.5987 - val_accuracy: 0.8150\n",
            "Epoch 157/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0244 - accuracy: 0.9933 - val_loss: 1.6019 - val_accuracy: 0.8100\n",
            "Epoch 158/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0240 - accuracy: 0.9983 - val_loss: 1.6394 - val_accuracy: 0.8150\n",
            "Epoch 159/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0239 - accuracy: 0.9950 - val_loss: 1.6630 - val_accuracy: 0.8000\n",
            "Epoch 160/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0240 - accuracy: 0.9950 - val_loss: 1.6533 - val_accuracy: 0.8050\n",
            "Epoch 161/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0236 - accuracy: 0.9967 - val_loss: 1.6328 - val_accuracy: 0.8050\n",
            "Epoch 162/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0305 - accuracy: 0.9933 - val_loss: 1.6506 - val_accuracy: 0.8100\n",
            "Epoch 163/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0258 - accuracy: 0.9967 - val_loss: 1.6151 - val_accuracy: 0.8200\n",
            "Epoch 164/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0236 - accuracy: 0.9983 - val_loss: 1.6551 - val_accuracy: 0.8100\n",
            "Epoch 165/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0235 - accuracy: 0.9967 - val_loss: 1.6331 - val_accuracy: 0.8200\n",
            "Epoch 166/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0231 - accuracy: 0.9950 - val_loss: 1.6305 - val_accuracy: 0.8250\n",
            "Epoch 167/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0242 - accuracy: 0.9967 - val_loss: 1.6603 - val_accuracy: 0.8150\n",
            "Epoch 168/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0237 - accuracy: 0.9983 - val_loss: 1.6353 - val_accuracy: 0.8150\n",
            "Epoch 169/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0239 - accuracy: 0.9950 - val_loss: 1.6181 - val_accuracy: 0.8150\n",
            "Epoch 170/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0232 - accuracy: 0.9967 - val_loss: 1.6442 - val_accuracy: 0.8200\n",
            "Epoch 171/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0235 - accuracy: 0.9983 - val_loss: 1.6336 - val_accuracy: 0.8100\n",
            "Epoch 172/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0234 - accuracy: 0.9967 - val_loss: 1.6785 - val_accuracy: 0.8050\n",
            "Epoch 173/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0243 - accuracy: 0.9967 - val_loss: 1.6776 - val_accuracy: 0.7950\n",
            "Epoch 174/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0228 - accuracy: 0.9983 - val_loss: 1.6450 - val_accuracy: 0.8100\n",
            "Epoch 175/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0247 - accuracy: 0.9967 - val_loss: 1.7088 - val_accuracy: 0.8000\n",
            "Epoch 176/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0235 - accuracy: 0.9950 - val_loss: 1.6265 - val_accuracy: 0.8050\n",
            "Epoch 177/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0240 - accuracy: 0.9967 - val_loss: 1.6640 - val_accuracy: 0.8100\n",
            "Epoch 178/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0229 - accuracy: 0.9950 - val_loss: 1.6634 - val_accuracy: 0.8050\n",
            "Epoch 179/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0221 - accuracy: 0.9967 - val_loss: 1.6151 - val_accuracy: 0.8200\n",
            "Epoch 180/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0223 - accuracy: 0.9983 - val_loss: 1.6792 - val_accuracy: 0.8000\n",
            "Epoch 181/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0223 - accuracy: 0.9967 - val_loss: 1.6330 - val_accuracy: 0.8200\n",
            "Epoch 182/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0220 - accuracy: 0.9967 - val_loss: 1.6749 - val_accuracy: 0.8150\n",
            "Epoch 183/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0215 - accuracy: 0.9983 - val_loss: 1.7128 - val_accuracy: 0.8000\n",
            "Epoch 184/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0256 - accuracy: 0.9983 - val_loss: 1.6876 - val_accuracy: 0.8000\n",
            "Epoch 185/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0230 - accuracy: 0.9950 - val_loss: 1.6217 - val_accuracy: 0.8200\n",
            "Epoch 186/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0229 - accuracy: 0.9983 - val_loss: 1.6525 - val_accuracy: 0.8150\n",
            "Epoch 187/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0236 - accuracy: 0.9983 - val_loss: 1.6565 - val_accuracy: 0.8250\n",
            "Epoch 188/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0210 - accuracy: 0.9950 - val_loss: 1.6534 - val_accuracy: 0.8100\n",
            "Epoch 189/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0221 - accuracy: 0.9967 - val_loss: 1.6801 - val_accuracy: 0.8150\n",
            "Epoch 190/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0222 - accuracy: 0.9967 - val_loss: 1.6506 - val_accuracy: 0.8050\n",
            "Epoch 191/200\n",
            "47/47 [==============================] - 0s 3ms/step - loss: 0.0227 - accuracy: 0.9967 - val_loss: 1.6873 - val_accuracy: 0.8050\n",
            "Epoch 192/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0254 - accuracy: 0.9933 - val_loss: 1.6831 - val_accuracy: 0.8150\n",
            "Epoch 193/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0221 - accuracy: 0.9983 - val_loss: 1.6975 - val_accuracy: 0.8050\n",
            "Epoch 194/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0208 - accuracy: 0.9950 - val_loss: 1.7461 - val_accuracy: 0.7950\n",
            "Epoch 195/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0224 - accuracy: 0.9933 - val_loss: 1.6453 - val_accuracy: 0.8050\n",
            "Epoch 196/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0220 - accuracy: 0.9983 - val_loss: 1.6955 - val_accuracy: 0.8150\n",
            "Epoch 197/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0218 - accuracy: 0.9967 - val_loss: 1.6705 - val_accuracy: 0.8150\n",
            "Epoch 198/200\n",
            "47/47 [==============================] - 0s 4ms/step - loss: 0.0210 - accuracy: 0.9983 - val_loss: 1.7232 - val_accuracy: 0.8000\n",
            "Epoch 199/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0217 - accuracy: 0.9967 - val_loss: 1.6788 - val_accuracy: 0.8150\n",
            "Epoch 200/200\n",
            "47/47 [==============================] - 0s 5ms/step - loss: 0.0215 - accuracy: 0.9967 - val_loss: 1.6832 - val_accuracy: 0.8100\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.src.callbacks.History at 0x7d578cd13520>"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model.evaluate(X_test, y_test)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EHs9emUWZmxc",
        "outputId": "a295f000-05c4-4b90-a3cf-69747f769488"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7/7 [==============================] - 0s 3ms/step - loss: 2.9903 - accuracy: 0.7550\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2.990323543548584, 0.7549999952316284]"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "QfKmTaHDcLhv"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}