Acute_Inflammations_.ipynb

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      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "colab": {
      "name": "Acute_Inflammations .ipynb",
      "provenance": []
    }
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    {
      "cell_type": "markdown",
      "metadata": {
        "scrolled": false,
        "id": "pWoR4-x7CAUK",
        "colab_type": "text"
      },
      "source": [
        "# Acute Inflammations Dataset\n",
        "[Link](https://archive.ics.uci.edu/ml/datasets/Acute+Inflammations)\n",
        "\n",
        "Diagnosis of two diseases of urinary system. It will be\n",
        "the example of diagnosing of the acute inflammations of urinary bladder and acute nephritises."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VS3L_MJtCAUL",
        "colab_type": "text"
      },
      "source": [
        "Attribute information:\n",
        "\n",
        "Inputs:\n",
        "- Temperature of patient : 35C-42C\n",
        "- Occurrence of nausea : yes/no\n",
        "- Lumbar pain : yes/no\n",
        "- Urine pushing (continuous need for urination) : yes/no\n",
        "- Micturition pains : yes/no\n",
        "- Burning of urethra, itch, swelling of urethra outlet : yes/no\n",
        "\n",
        "Outputs:\n",
        "- decision 1: Inflammation of urinary bladder : yes/no\n",
        "- decision 2: Nephritis of renal pelvis origin : yes/no"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rw1uKXsBCAUM",
        "colab_type": "code",
        "colab": {},
        "outputId": "8f774075-74cf-401f-d4b6-b65bfc288fb8"
      },
      "source": [
        "import tensorflow as tf\n",
        "print(tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2.0.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "IR7x_pyHCAUR",
        "colab_type": "code",
        "colab": {},
        "outputId": "7fdc768b-7508-4148-a56c-cc37eed8473b"
      },
      "source": [
        "!pip install ppscore"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting ppscore\n",
            "  Downloading ppscore-0.0.2.tar.gz (38 kB)\n",
            "Building wheels for collected packages: ppscore\n",
            "  Building wheel for ppscore (setup.py) ... \u001b[?25ldone\n",
            "\u001b[?25h  Created wheel for ppscore: filename=ppscore-0.0.2-py2.py3-none-any.whl size=9630 sha256=0f42d934bb98ee4fb8c3c726976c6ce22229c6e5d61bc9153ce379d865de3e3e\n",
            "  Stored in directory: /home/jovyan/.cache/pip/wheels/bb/35/a9/b66dcda859b25d96509b26b47878164067f49ac1feb2a555ac\n",
            "Successfully built ppscore\n",
            "Installing collected packages: ppscore\n",
            "Successfully installed ppscore-0.0.2\n",
            "\u001b[33mWARNING: You are using pip version 20.1; however, version 20.1.1 is available.\n",
            "You should consider upgrading via the '/opt/conda/bin/python -m pip install --upgrade pip' command.\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sD68a3pRCAUU",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import warnings\n",
        "def warn(*args, **kwargs):\n",
        "    pass\n",
        "warnings.warn = warn"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "f9uaF0chCAUX",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "%matplotlib inline"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JrhCo-wyCAUa",
        "colab_type": "text"
      },
      "source": [
        "#### Load the data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "O_X_mwg0CAUb",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Load the dataset\n",
        "\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "pd_dat = pd.read_csv('data/diagnosis.csv')\n",
        "temp_dat=pd_dat\n",
        "temp_dat.columns=['temperature', 'nausea_occurence', 'lumbar_pain', 'urine_pushing', \n",
        "               'micturition_pains', 'bis','inflammation_pred', 'nephritis_pred']\n",
        "dataset = pd_dat.values"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "co-Nf3xjCAUd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build train and test data splits\n",
        "\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(dataset[:,:6], dataset[:,6:], test_size=0.33)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "WSqYzv3pCAUg",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Assign training and testing inputs/outputs\n",
        "\n",
        "temp_train, nocc_train, lumbp_train, up_train, mict_train, bis_train = np.transpose(X_train)\n",
        "temp_test, nocc_test, lumbp_test, up_test, mict_test, bis_test = np.transpose(X_test)\n",
        "\n",
        "inflam_train, nephr_train = Y_train[:, 0], Y_train[:, 1]\n",
        "inflam_test, nephr_test = Y_test[:, 0], Y_test[:, 1]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cWZx9WSSCAUl",
        "colab_type": "text"
      },
      "source": [
        "# Analysis"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "CpR_UBiECAUl",
        "colab_type": "code",
        "colab": {},
        "outputId": "3d04a631-8bd0-4b6f-b135-30d724a60328"
      },
      "source": [
        "temp_dat.head()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>temperature</th>\n",
              "      <th>nausea_occurence</th>\n",
              "      <th>lumbar_pain</th>\n",
              "      <th>urine_pushing</th>\n",
              "      <th>micturition_pains</th>\n",
              "      <th>bis</th>\n",
              "      <th>inflammation_pred</th>\n",
              "      <th>nephritis_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>35.5</td>\n",
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              "      <td>35.9</td>\n",
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              "      <th>3</th>\n",
              "      <td>36.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>36.0</td>\n",
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              "      <td>1</td>\n",
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              "      <td>0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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            ],
            "text/plain": [
              "   temperature  nausea_occurence  lumbar_pain  urine_pushing  \\\n",
              "0         35.5                 0            1              0   \n",
              "1         35.9                 0            0              1   \n",
              "2         35.9                 0            1              0   \n",
              "3         36.0                 0            0              1   \n",
              "4         36.0                 0            1              0   \n",
              "\n",
              "   micturition_pains  bis  inflammation_pred  nephritis_pred  \n",
              "0                  0    0                  0               0  \n",
              "1                  1    1                  1               0  \n",
              "2                  0    0                  0               0  \n",
              "3                  1    1                  1               0  \n",
              "4                  0    0                  0               0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "nWJV7fbtCAUo",
        "colab_type": "code",
        "colab": {},
        "outputId": "c8a17934-996a-439d-897a-2779fad3748f"
      },
      "source": [
        "temp_dat.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "    .dataframe tbody tr th {\n",
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              "\n",
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              "        text-align: right;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>temperature</th>\n",
              "      <th>nausea_occurence</th>\n",
              "      <th>lumbar_pain</th>\n",
              "      <th>urine_pushing</th>\n",
              "      <th>micturition_pains</th>\n",
              "      <th>bis</th>\n",
              "      <th>inflammation_pred</th>\n",
              "      <th>nephritis_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>120.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>120.000000</td>\n",
              "      <td>120.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>38.724167</td>\n",
              "      <td>0.241667</td>\n",
              "      <td>0.583333</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.491667</td>\n",
              "      <td>0.416667</td>\n",
              "      <td>0.491667</td>\n",
              "      <td>0.416667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1.819132</td>\n",
              "      <td>0.429888</td>\n",
              "      <td>0.495074</td>\n",
              "      <td>0.473381</td>\n",
              "      <td>0.502027</td>\n",
              "      <td>0.495074</td>\n",
              "      <td>0.502027</td>\n",
              "      <td>0.495074</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>35.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>37.100000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>37.950000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>40.600000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>41.500000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       temperature  nausea_occurence  lumbar_pain  urine_pushing  \\\n",
              "count   120.000000        120.000000   120.000000     120.000000   \n",
              "mean     38.724167          0.241667     0.583333       0.666667   \n",
              "std       1.819132          0.429888     0.495074       0.473381   \n",
              "min      35.500000          0.000000     0.000000       0.000000   \n",
              "25%      37.100000          0.000000     0.000000       0.000000   \n",
              "50%      37.950000          0.000000     1.000000       1.000000   \n",
              "75%      40.600000          0.000000     1.000000       1.000000   \n",
              "max      41.500000          1.000000     1.000000       1.000000   \n",
              "\n",
              "       micturition_pains         bis  inflammation_pred  nephritis_pred  \n",
              "count         120.000000  120.000000         120.000000      120.000000  \n",
              "mean            0.491667    0.416667           0.491667        0.416667  \n",
              "std             0.502027    0.495074           0.502027        0.495074  \n",
              "min             0.000000    0.000000           0.000000        0.000000  \n",
              "25%             0.000000    0.000000           0.000000        0.000000  \n",
              "50%             0.000000    0.000000           0.000000        0.000000  \n",
              "75%             1.000000    1.000000           1.000000        1.000000  \n",
              "max             1.000000    1.000000           1.000000        1.000000  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "teQgVMoTCAUq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Correlation and Predictive-Power Score"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "Wgs6CHlBCAUt",
        "colab_type": "code",
        "colab": {},
        "outputId": "c3db309c-4b1d-45ac-8673-7664c4ee96be"
      },
      "source": [
        "fig, axs = plt.subplots(1, 2,figsize=(15,7))\n",
        "sns.heatmap(temp_dat.corr(),linewidths=0.5,annot=True,ax=axs[0])\n",
        "import ppscore as pps\n",
        "pred_matrix=pps.matrix(temp_dat)\n",
        "sns.heatmap(pred_matrix, vmin=0, vmax=1, cmap=\"Blues\", linewidths=0.5, annot=True,ax=axs[1])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r1IGA6-lCAUw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Data Distribution"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "MPXt7tcfCAUy",
        "colab_type": "code",
        "colab": {},
        "outputId": "4dd028c7-b0ca-4f6e-c165-5bd8a0595ab0"
      },
      "source": [
        "fig, axs = plt.subplots(2,3,figsize=(15,7),tight_layout=True)\n",
        "axs[0][0].hist(temp_dat['temperature'],bins=20)\n",
        "axs[0][1].hist(temp_dat['nausea_occurence'],bins=20)\n",
        "axs[0][2].hist(temp_dat['lumbar_pain'],bins=20)\n",
        "axs[1][0].hist(temp_dat['urine_pushing'],bins=20)\n",
        "axs[1][1].hist(temp_dat['micturition_pains'],bins=20)\n",
        "axs[1][2].hist(temp_dat['bis'],bins=20)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([70.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,\n",
              "         0.,  0.,  0.,  0.,  0.,  0., 50.]),\n",
              " array([0.  , 0.05, 0.1 , 0.15, 0.2 , 0.25, 0.3 , 0.35, 0.4 , 0.45, 0.5 ,\n",
              "        0.55, 0.6 , 0.65, 0.7 , 0.75, 0.8 , 0.85, 0.9 , 0.95, 1.  ]),\n",
              " <a list of 20 Patch objects>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x504 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HZhJd9B2CAU1",
        "colab_type": "text"
      },
      "source": [
        "#### Building the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "r0XmXdmeCAU1",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Build the input layers\n",
        "from tensorflow.keras import Input,layers ,Model\n",
        "input_shape=(1,)\n",
        "temperature=Input(shape=input_shape,name='temp')\n",
        "nausea_occurence=Input(shape=input_shape,name='nausea')\n",
        "lumbar_pain=Input(shape=input_shape,name='lumbar_pain')\n",
        "urine_pushing=Input(shape=input_shape,name='urine_pushing')\n",
        "micturition_pains=Input(shape=input_shape,name='mict')\n",
        "bis=Input(shape=input_shape,name='bis')\n",
        "list_inputs=[temperature, nausea_occurence, lumbar_pain, urine_pushing, \n",
        "               micturition_pains, bis]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "deNTG8tvCAU4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Merge all input features into a single large vector\n",
        "merged_inputs=layers.concatenate([temperature, nausea_occurence, lumbar_pain, urine_pushing, \n",
        "               micturition_pains, bis])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "2u13jy5mCAU9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "inflammation_pred=layers.Dense(5,activation='relu',name='inflamation_inter')(merged_inputs)\n",
        "inflammation_pred=layers.Dense(1,activation='sigmoid',name='inflamation_pred')(inflammation_pred)\n",
        "nephritis_pred=layers.Dense(5,activation='relu',name='nephritis_inter')(merged_inputs)\n",
        "nephritis_pred=layers.Dense(1,activation='sigmoid',name='nephritis_pred')(nephritis_pred)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "cazekzv4CAVA",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Create the model object\n",
        "model=Model(inputs=list_inputs,outputs=[inflammation_pred,nephritis_pred])\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "C936JJihCAVD",
        "colab_type": "text"
      },
      "source": [
        "#### Plotting the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "tA1nAWfPCAVE",
        "colab_type": "code",
        "colab": {},
        "outputId": "7fa991c1-1939-4a93-a07a-c3da53832700"
      },
      "source": [
        "# Display the multiple input/output model\n",
        "tf.keras.utils.plot_model(model,'Multi_Feature_Inflmmation_Netowrk.png',show_shapes=True)\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "XgMtUAtKCAVG",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "model.compile(optimizers=tf.keras.optimizers.Adam(),loss={'inflamation_pred':'binary_crossentropy','nephritis_pred':'binary_crossentropy'},\n",
        "              metrics={'inflamation_pred':['acc'],'nephritis_pred':['acc','mse']},loss_weights=[1.,0.2])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "QVf0UYomCAVI",
        "colab_type": "text"
      },
      "source": [
        "#### Fit the model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "p6gwPt4lCAVJ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Define training inputs and outputs\n",
        "\n",
        "inputs_train = {'temp': temp_train, 'nausea': nocc_train, 'lumbar_pain': lumbp_train,\n",
        "                'urine_pushing': up_train, 'mict': mict_train, 'bis': bis_train}\n",
        "\n",
        "outputs_train = {'inflamation_pred': inflam_train, 'nephritis_pred': nephr_train}"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "0X9V23tWCAVL",
        "colab_type": "code",
        "colab": {},
        "outputId": "ac37dd29-8798-4acc-8044-c262f015fa65"
      },
      "source": [
        "# Train the model\n",
        "\n",
        "history=model.fit(inputs_train,outputs_train,epochs=1500,batch_size=256,verbose=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train on 80 samples\n",
            "Epoch 1/1500\n",
            "80/80 [==============================] - 2s 29ms/sample - loss: 5.6941 - inflamation_pred_loss: 4.4710 - nephritis_pred_loss: 6.1156 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 2/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 5.5737 - inflamation_pred_loss: 4.3811 - nephritis_pred_loss: 5.9627 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 3/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 5.4872 - inflamation_pred_loss: 4.3167 - nephritis_pred_loss: 5.8521 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 4/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 5.4153 - inflamation_pred_loss: 4.2633 - nephritis_pred_loss: 5.7597 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 5/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 5.3518 - inflamation_pred_loss: 4.2162 - nephritis_pred_loss: 5.6778 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 6/1500\n",
            "80/80 [==============================] - 0s 105us/sample - loss: 5.2940 - inflamation_pred_loss: 4.1734 - nephritis_pred_loss: 5.6029 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 7/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 5.2401 - inflamation_pred_loss: 4.1335 - nephritis_pred_loss: 5.5330 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 8/1500\n",
            "80/80 [==============================] - 0s 97us/sample - loss: 5.1894 - inflamation_pred_loss: 4.0960 - nephritis_pred_loss: 5.4668 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 9/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 5.1410 - inflamation_pred_loss: 4.0603 - nephritis_pred_loss: 5.4035 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 10/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 5.0945 - inflamation_pred_loss: 4.0260 - nephritis_pred_loss: 5.3426 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 11/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 5.0496 - inflamation_pred_loss: 3.9929 - nephritis_pred_loss: 5.2835 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 12/1500\n",
            "80/80 [==============================] - 0s 115us/sample - loss: 5.0060 - inflamation_pred_loss: 3.9608 - nephritis_pred_loss: 5.2261 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 13/1500\n",
            "80/80 [==============================] - 0s 118us/sample - loss: 4.9635 - inflamation_pred_loss: 3.9295 - nephritis_pred_loss: 5.1700 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 14/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 4.9220 - inflamation_pred_loss: 3.8990 - nephritis_pred_loss: 5.1150 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 15/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 4.8813 - inflamation_pred_loss: 3.8691 - nephritis_pred_loss: 5.0609 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 16/1500\n",
            "80/80 [==============================] - 0s 924us/sample - loss: 4.8413 - inflamation_pred_loss: 3.8398 - nephritis_pred_loss: 5.0077 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 17/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 4.8020 - inflamation_pred_loss: 3.8110 - nephritis_pred_loss: 4.9552 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 18/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 4.7632 - inflamation_pred_loss: 3.7826 - nephritis_pred_loss: 4.9034 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 19/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 4.7250 - inflamation_pred_loss: 3.7546 - nephritis_pred_loss: 4.8521 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 20/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 4.6872 - inflamation_pred_loss: 3.7269 - nephritis_pred_loss: 4.8013 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 21/1500\n",
            "80/80 [==============================] - 0s 103us/sample - loss: 4.6498 - inflamation_pred_loss: 3.6996 - nephritis_pred_loss: 4.7510 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 22/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 4.6127 - inflamation_pred_loss: 3.6725 - nephritis_pred_loss: 4.7010 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 23/1500\n",
            "80/80 [==============================] - 0s 962us/sample - loss: 4.5760 - inflamation_pred_loss: 3.6457 - nephritis_pred_loss: 4.6514 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 24/1500\n",
            "80/80 [==============================] - 0s 75us/sample - loss: 4.5396 - inflamation_pred_loss: 3.6192 - nephritis_pred_loss: 4.6021 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 25/1500\n",
            "80/80 [==============================] - 0s 193us/sample - loss: 4.5035 - inflamation_pred_loss: 3.5929 - nephritis_pred_loss: 4.5530 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 26/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 4.4677 - inflamation_pred_loss: 3.5668 - nephritis_pred_loss: 4.5042 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 27/1500\n",
            "80/80 [==============================] - 0s 868us/sample - loss: 4.4321 - inflamation_pred_loss: 3.5409 - nephritis_pred_loss: 4.4557 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 28/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 4.3967 - inflamation_pred_loss: 3.5152 - nephritis_pred_loss: 4.4073 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 29/1500\n",
            "80/80 [==============================] - 0s 92us/sample - loss: 4.3615 - inflamation_pred_loss: 3.4897 - nephritis_pred_loss: 4.3591 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 30/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 4.3265 - inflamation_pred_loss: 3.4643 - nephritis_pred_loss: 4.3111 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 31/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 4.2917 - inflamation_pred_loss: 3.4391 - nephritis_pred_loss: 4.2633 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 32/1500\n",
            "80/80 [==============================] - 0s 928us/sample - loss: 4.2571 - inflamation_pred_loss: 3.4140 - nephritis_pred_loss: 4.2156 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 33/1500\n",
            "80/80 [==============================] - 0s 114us/sample - loss: 4.2227 - inflamation_pred_loss: 3.3891 - nephritis_pred_loss: 4.1680 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 34/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 4.1884 - inflamation_pred_loss: 3.3643 - nephritis_pred_loss: 4.1206 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 35/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 4.1543 - inflamation_pred_loss: 3.3396 - nephritis_pred_loss: 4.0733 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 36/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 4.1203 - inflamation_pred_loss: 3.3151 - nephritis_pred_loss: 4.0260 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 37/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 4.0864 - inflamation_pred_loss: 3.2906 - nephritis_pred_loss: 3.9789 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 38/1500\n",
            "80/80 [==============================] - 0s 84us/sample - loss: 4.0527 - inflamation_pred_loss: 3.2663 - nephritis_pred_loss: 3.9318 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 39/1500\n",
            "80/80 [==============================] - 0s 92us/sample - loss: 4.0191 - inflamation_pred_loss: 3.2422 - nephritis_pred_loss: 3.8849 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.4000\n",
            "Epoch 40/1500\n",
            "80/80 [==============================] - 0s 111us/sample - loss: 3.9857 - inflamation_pred_loss: 3.2181 - nephritis_pred_loss: 3.8380 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3999\n",
            "Epoch 41/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 3.9524 - inflamation_pred_loss: 3.1941 - nephritis_pred_loss: 3.7912 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3999\n",
            "Epoch 42/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 3.9192 - inflamation_pred_loss: 3.1703 - nephritis_pred_loss: 3.7445 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3999\n",
            "Epoch 43/1500\n",
            "80/80 [==============================] - 0s 113us/sample - loss: 3.8861 - inflamation_pred_loss: 3.1465 - nephritis_pred_loss: 3.6978 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3999\n",
            "Epoch 44/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 3.8531 - inflamation_pred_loss: 3.1228 - nephritis_pred_loss: 3.6512 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3999\n",
            "Epoch 45/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 3.8202 - inflamation_pred_loss: 3.0993 - nephritis_pred_loss: 3.6047 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3999\n",
            "Epoch 46/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 3.7875 - inflamation_pred_loss: 3.0758 - nephritis_pred_loss: 3.5582 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3999\n",
            "Epoch 47/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 3.7548 - inflamation_pred_loss: 3.0525 - nephritis_pred_loss: 3.5118 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3999\n",
            "Epoch 48/1500\n",
            "80/80 [==============================] - 0s 91us/sample - loss: 3.7223 - inflamation_pred_loss: 3.0292 - nephritis_pred_loss: 3.4654 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3999\n",
            "Epoch 49/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 3.6899 - inflamation_pred_loss: 3.0061 - nephritis_pred_loss: 3.4191 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3998\n",
            "Epoch 50/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 3.6576 - inflamation_pred_loss: 2.9830 - nephritis_pred_loss: 3.3729 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3998\n",
            "Epoch 51/1500\n",
            "80/80 [==============================] - 0s 953us/sample - loss: 3.6254 - inflamation_pred_loss: 2.9600 - nephritis_pred_loss: 3.3267 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3998\n",
            "Epoch 52/1500\n",
            "80/80 [==============================] - 0s 89us/sample - loss: 3.5933 - inflamation_pred_loss: 2.9372 - nephritis_pred_loss: 3.2805 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3998\n",
            "Epoch 53/1500\n",
            "80/80 [==============================] - 0s 93us/sample - loss: 3.5613 - inflamation_pred_loss: 2.9144 - nephritis_pred_loss: 3.2344 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3997\n",
            "Epoch 54/1500\n",
            "80/80 [==============================] - 0s 993us/sample - loss: 3.5294 - inflamation_pred_loss: 2.8917 - nephritis_pred_loss: 3.1884 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3997\n",
            "Epoch 55/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 3.4976 - inflamation_pred_loss: 2.8691 - nephritis_pred_loss: 3.1424 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3997\n",
            "Epoch 56/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 3.4659 - inflamation_pred_loss: 2.8466 - nephritis_pred_loss: 3.0964 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3996\n",
            "Epoch 57/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 3.4343 - inflamation_pred_loss: 2.8242 - nephritis_pred_loss: 3.0505 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3996\n",
            "Epoch 58/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 3.4028 - inflamation_pred_loss: 2.8019 - nephritis_pred_loss: 3.0047 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3996\n",
            "Epoch 59/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 3.3715 - inflamation_pred_loss: 2.7797 - nephritis_pred_loss: 2.9589 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3995\n",
            "Epoch 60/1500\n",
            "80/80 [==============================] - 0s 82us/sample - loss: 3.3402 - inflamation_pred_loss: 2.7576 - nephritis_pred_loss: 2.9131 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3994\n",
            "Epoch 61/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 3.3090 - inflamation_pred_loss: 2.7355 - nephritis_pred_loss: 2.8675 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3994\n",
            "Epoch 62/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 3.2780 - inflamation_pred_loss: 2.7136 - nephritis_pred_loss: 2.8218 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3993\n",
            "Epoch 63/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 3.2470 - inflamation_pred_loss: 2.6917 - nephritis_pred_loss: 2.7762 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3992\n",
            "Epoch 64/1500\n",
            "80/80 [==============================] - 0s 906us/sample - loss: 3.2161 - inflamation_pred_loss: 2.6700 - nephritis_pred_loss: 2.7307 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3991\n",
            "Epoch 65/1500\n",
            "80/80 [==============================] - 0s 101us/sample - loss: 3.1854 - inflamation_pred_loss: 2.6483 - nephritis_pred_loss: 2.6852 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3990\n",
            "Epoch 66/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 3.1547 - inflamation_pred_loss: 2.6268 - nephritis_pred_loss: 2.6398 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3989\n",
            "Epoch 67/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 3.1242 - inflamation_pred_loss: 2.6053 - nephritis_pred_loss: 2.5945 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3988\n",
            "Epoch 68/1500\n",
            "80/80 [==============================] - 0s 905us/sample - loss: 3.0938 - inflamation_pred_loss: 2.5839 - nephritis_pred_loss: 2.5492 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3986\n",
            "Epoch 69/1500\n",
            "80/80 [==============================] - 0s 75us/sample - loss: 3.0634 - inflamation_pred_loss: 2.5626 - nephritis_pred_loss: 2.5040 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3985\n",
            "Epoch 70/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 3.0332 - inflamation_pred_loss: 2.5414 - nephritis_pred_loss: 2.4589 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3983\n",
            "Epoch 71/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 3.0031 - inflamation_pred_loss: 2.5203 - nephritis_pred_loss: 2.4138 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3981\n",
            "Epoch 72/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 2.9731 - inflamation_pred_loss: 2.4993 - nephritis_pred_loss: 2.3688 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3978\n",
            "Epoch 73/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 2.9432 - inflamation_pred_loss: 2.4784 - nephritis_pred_loss: 2.3240 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3976\n",
            "Epoch 74/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 2.9134 - inflamation_pred_loss: 2.4576 - nephritis_pred_loss: 2.2792 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3973\n",
            "Epoch 75/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 2.8838 - inflamation_pred_loss: 2.4369 - nephritis_pred_loss: 2.2345 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3970\n",
            "Epoch 76/1500\n",
            "80/80 [==============================] - 0s 920us/sample - loss: 2.8543 - inflamation_pred_loss: 2.4163 - nephritis_pred_loss: 2.1899 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3966\n",
            "Epoch 77/1500\n",
            "80/80 [==============================] - 0s 86us/sample - loss: 2.8248 - inflamation_pred_loss: 2.3958 - nephritis_pred_loss: 2.1454 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3962\n",
            "Epoch 78/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 2.7955 - inflamation_pred_loss: 2.3753 - nephritis_pred_loss: 2.1010 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3958\n",
            "Epoch 79/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 2.7664 - inflamation_pred_loss: 2.3550 - nephritis_pred_loss: 2.0568 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3953\n",
            "Epoch 80/1500\n",
            "80/80 [==============================] - 0s 132us/sample - loss: 2.7373 - inflamation_pred_loss: 2.3348 - nephritis_pred_loss: 2.0127 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3947\n",
            "Epoch 81/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 2.7084 - inflamation_pred_loss: 2.3147 - nephritis_pred_loss: 1.9688 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3941\n",
            "Epoch 82/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 2.6796 - inflamation_pred_loss: 2.2946 - nephritis_pred_loss: 1.9250 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3934\n",
            "Epoch 83/1500\n",
            "80/80 [==============================] - 0s 908us/sample - loss: 2.6510 - inflamation_pred_loss: 2.2747 - nephritis_pred_loss: 1.8814 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3927\n",
            "Epoch 84/1500\n",
            "80/80 [==============================] - 0s 80us/sample - loss: 2.6225 - inflamation_pred_loss: 2.2549 - nephritis_pred_loss: 1.8380 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3918\n",
            "Epoch 85/1500\n",
            "80/80 [==============================] - 0s 95us/sample - loss: 2.5941 - inflamation_pred_loss: 2.2351 - nephritis_pred_loss: 1.7948 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3909\n",
            "Epoch 86/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 2.5659 - inflamation_pred_loss: 2.2155 - nephritis_pred_loss: 1.7518 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3899\n",
            "Epoch 87/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 2.5378 - inflamation_pred_loss: 2.1960 - nephritis_pred_loss: 1.7091 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3887\n",
            "Epoch 88/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 2.5099 - inflamation_pred_loss: 2.1766 - nephritis_pred_loss: 1.6667 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3875\n",
            "Epoch 89/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 2.4822 - inflamation_pred_loss: 2.1573 - nephritis_pred_loss: 1.6245 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3861\n",
            "Epoch 90/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 2.4546 - inflamation_pred_loss: 2.1380 - nephritis_pred_loss: 1.5827 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3845\n",
            "Epoch 91/1500\n",
            "80/80 [==============================] - 0s 993us/sample - loss: 2.4272 - inflamation_pred_loss: 2.1189 - nephritis_pred_loss: 1.5412 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3829\n",
            "Epoch 92/1500\n",
            "80/80 [==============================] - 0s 84us/sample - loss: 2.4000 - inflamation_pred_loss: 2.0999 - nephritis_pred_loss: 1.5001 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3810\n",
            "Epoch 93/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 2.3729 - inflamation_pred_loss: 2.0810 - nephritis_pred_loss: 1.4594 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3789\n",
            "Epoch 94/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 2.3461 - inflamation_pred_loss: 2.0623 - nephritis_pred_loss: 1.4191 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3767\n",
            "Epoch 95/1500\n",
            "80/80 [==============================] - 0s 954us/sample - loss: 2.3194 - inflamation_pred_loss: 2.0436 - nephritis_pred_loss: 1.3794 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3743\n",
            "Epoch 96/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 2.2930 - inflamation_pred_loss: 2.0250 - nephritis_pred_loss: 1.3402 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3716\n",
            "Epoch 97/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 2.2668 - inflamation_pred_loss: 2.0065 - nephritis_pred_loss: 1.3015 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3687\n",
            "Epoch 98/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 2.2409 - inflamation_pred_loss: 1.9882 - nephritis_pred_loss: 1.2635 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3655\n",
            "Epoch 99/1500\n",
            "80/80 [==============================] - 0s 969us/sample - loss: 2.2152 - inflamation_pred_loss: 1.9699 - nephritis_pred_loss: 1.2261 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3621\n",
            "Epoch 100/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 2.1897 - inflamation_pred_loss: 1.9518 - nephritis_pred_loss: 1.1895 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3584\n",
            "Epoch 101/1500\n",
            "80/80 [==============================] - 0s 88us/sample - loss: 2.1645 - inflamation_pred_loss: 1.9338 - nephritis_pred_loss: 1.1536 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3545\n",
            "Epoch 102/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 2.1396 - inflamation_pred_loss: 1.9159 - nephritis_pred_loss: 1.1186 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3503\n",
            "Epoch 103/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 2.1150 - inflamation_pred_loss: 1.8981 - nephritis_pred_loss: 1.0844 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3457\n",
            "Epoch 104/1500\n",
            "80/80 [==============================] - 0s 108us/sample - loss: 2.0906 - inflamation_pred_loss: 1.8804 - nephritis_pred_loss: 1.0512 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3410\n",
            "Epoch 105/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 2.0666 - inflamation_pred_loss: 1.8628 - nephritis_pred_loss: 1.0190 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3359\n",
            "Epoch 106/1500\n",
            "80/80 [==============================] - 0s 989us/sample - loss: 2.0430 - inflamation_pred_loss: 1.8454 - nephritis_pred_loss: 0.9879 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3306\n",
            "Epoch 107/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 2.0196 - inflamation_pred_loss: 1.8280 - nephritis_pred_loss: 0.9579 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3251\n",
            "Epoch 108/1500\n",
            "80/80 [==============================] - 0s 90us/sample - loss: 1.9966 - inflamation_pred_loss: 1.8108 - nephritis_pred_loss: 0.9291 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3193\n",
            "Epoch 109/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 1.9740 - inflamation_pred_loss: 1.7937 - nephritis_pred_loss: 0.9015 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3133\n",
            "Epoch 110/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 1.9518 - inflamation_pred_loss: 1.7767 - nephritis_pred_loss: 0.8753 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3072\n",
            "Epoch 111/1500\n",
            "80/80 [==============================] - 0s 999us/sample - loss: 1.9299 - inflamation_pred_loss: 1.7599 - nephritis_pred_loss: 0.8503 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.3010\n",
            "Epoch 112/1500\n",
            "80/80 [==============================] - 0s 131us/sample - loss: 1.9085 - inflamation_pred_loss: 1.7431 - nephritis_pred_loss: 0.8267 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2948\n",
            "Epoch 113/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 1.8874 - inflamation_pred_loss: 1.7265 - nephritis_pred_loss: 0.8045 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2885\n",
            "Epoch 114/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 1.8668 - inflamation_pred_loss: 1.7100 - nephritis_pred_loss: 0.7837 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2822\n",
            "Epoch 115/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 1.8465 - inflamation_pred_loss: 1.6937 - nephritis_pred_loss: 0.7644 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2760\n",
            "Epoch 116/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 1.8267 - inflamation_pred_loss: 1.6774 - nephritis_pred_loss: 0.7465 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2700\n",
            "Epoch 117/1500\n",
            "80/80 [==============================] - 0s 85us/sample - loss: 1.8073 - inflamation_pred_loss: 1.6613 - nephritis_pred_loss: 0.7301 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2642\n",
            "Epoch 118/1500\n",
            "80/80 [==============================] - 0s 883us/sample - loss: 1.7883 - inflamation_pred_loss: 1.6453 - nephritis_pred_loss: 0.7150 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2586\n",
            "Epoch 119/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 1.7697 - inflamation_pred_loss: 1.6294 - nephritis_pred_loss: 0.7014 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2533\n",
            "Epoch 120/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 1.7515 - inflamation_pred_loss: 1.6137 - nephritis_pred_loss: 0.6891 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2483\n",
            "Epoch 121/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 1.7337 - inflamation_pred_loss: 1.5981 - nephritis_pred_loss: 0.6781 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2436\n",
            "Epoch 122/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 1.7162 - inflamation_pred_loss: 1.5826 - nephritis_pred_loss: 0.6683 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2393\n",
            "Epoch 123/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 1.6992 - inflamation_pred_loss: 1.5672 - nephritis_pred_loss: 0.6597 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2354\n",
            "Epoch 124/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 1.6825 - inflamation_pred_loss: 1.5520 - nephritis_pred_loss: 0.6522 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2319\n",
            "Epoch 125/1500\n",
            "80/80 [==============================] - 0s 86us/sample - loss: 1.6661 - inflamation_pred_loss: 1.5369 - nephritis_pred_loss: 0.6458 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2288\n",
            "Epoch 126/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 1.6500 - inflamation_pred_loss: 1.5220 - nephritis_pred_loss: 0.6403 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2261\n",
            "Epoch 127/1500\n",
            "80/80 [==============================] - 0s 954us/sample - loss: 1.6343 - inflamation_pred_loss: 1.5071 - nephritis_pred_loss: 0.6356 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2237\n",
            "Epoch 128/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 1.6188 - inflamation_pred_loss: 1.4924 - nephritis_pred_loss: 0.6317 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2216\n",
            "Epoch 129/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 1.6036 - inflamation_pred_loss: 1.4779 - nephritis_pred_loss: 0.6284 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2199\n",
            "Epoch 130/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 1.5886 - inflamation_pred_loss: 1.4635 - nephritis_pred_loss: 0.6257 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2184\n",
            "Epoch 131/1500\n",
            "80/80 [==============================] - 0s 991us/sample - loss: 1.5739 - inflamation_pred_loss: 1.4492 - nephritis_pred_loss: 0.6236 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2172\n",
            "Epoch 132/1500\n",
            "80/80 [==============================] - 0s 94us/sample - loss: 1.5594 - inflamation_pred_loss: 1.4351 - nephritis_pred_loss: 0.6218 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2162\n",
            "Epoch 133/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 1.5451 - inflamation_pred_loss: 1.4210 - nephritis_pred_loss: 0.6204 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2153\n",
            "Epoch 134/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 1.5310 - inflamation_pred_loss: 1.4072 - nephritis_pred_loss: 0.6193 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2147\n",
            "Epoch 135/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 1.5171 - inflamation_pred_loss: 1.3935 - nephritis_pred_loss: 0.6184 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2141\n",
            "Epoch 136/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 1.5034 - inflamation_pred_loss: 1.3799 - nephritis_pred_loss: 0.6177 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2137\n",
            "Epoch 137/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 1.4898 - inflamation_pred_loss: 1.3664 - nephritis_pred_loss: 0.6171 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2133\n",
            "Epoch 138/1500\n",
            "80/80 [==============================] - 0s 87us/sample - loss: 1.4764 - inflamation_pred_loss: 1.3531 - nephritis_pred_loss: 0.6166 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2130\n",
            "Epoch 139/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 1.4632 - inflamation_pred_loss: 1.3400 - nephritis_pred_loss: 0.6162 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2127\n",
            "Epoch 140/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 1.4501 - inflamation_pred_loss: 1.3269 - nephritis_pred_loss: 0.6158 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2125\n",
            "Epoch 141/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 1.4372 - inflamation_pred_loss: 1.3141 - nephritis_pred_loss: 0.6154 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2123\n",
            "Epoch 142/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 1.4244 - inflamation_pred_loss: 1.3013 - nephritis_pred_loss: 0.6151 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2121\n",
            "Epoch 143/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 1.4117 - inflamation_pred_loss: 1.2888 - nephritis_pred_loss: 0.6148 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2119\n",
            "Epoch 144/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 1.3992 - inflamation_pred_loss: 1.2763 - nephritis_pred_loss: 0.6145 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2118\n",
            "Epoch 145/1500\n",
            "80/80 [==============================] - 0s 89us/sample - loss: 1.3869 - inflamation_pred_loss: 1.2640 - nephritis_pred_loss: 0.6142 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2116\n",
            "Epoch 146/1500\n",
            "80/80 [==============================] - 0s 129us/sample - loss: 1.3747 - inflamation_pred_loss: 1.2519 - nephritis_pred_loss: 0.6139 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2114\n",
            "Epoch 147/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 1.3626 - inflamation_pred_loss: 1.2399 - nephritis_pred_loss: 0.6136 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2113\n",
            "Epoch 148/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 1.3507 - inflamation_pred_loss: 1.2280 - nephritis_pred_loss: 0.6132 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2111\n",
            "Epoch 149/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 1.3389 - inflamation_pred_loss: 1.2163 - nephritis_pred_loss: 0.6129 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2110\n",
            "Epoch 150/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 1.3273 - inflamation_pred_loss: 1.2048 - nephritis_pred_loss: 0.6126 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2108\n",
            "Epoch 151/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 1.3158 - inflamation_pred_loss: 1.1934 - nephritis_pred_loss: 0.6122 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2106\n",
            "Epoch 152/1500\n",
            "80/80 [==============================] - 0s 99us/sample - loss: 1.3045 - inflamation_pred_loss: 1.1821 - nephritis_pred_loss: 0.6119 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2105\n",
            "Epoch 153/1500\n",
            "80/80 [==============================] - 0s 965us/sample - loss: 1.2933 - inflamation_pred_loss: 1.1710 - nephritis_pred_loss: 0.6116 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2103\n",
            "Epoch 154/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 1.2823 - inflamation_pred_loss: 1.1600 - nephritis_pred_loss: 0.6112 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2101\n",
            "Epoch 155/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 1.2714 - inflamation_pred_loss: 1.1492 - nephritis_pred_loss: 0.6108 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2100\n",
            "Epoch 156/1500\n",
            "80/80 [==============================] - 0s 47us/sample - loss: 1.2607 - inflamation_pred_loss: 1.1386 - nephritis_pred_loss: 0.6105 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6125 - nephritis_pred_mse: 0.2098\n",
            "Epoch 157/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 1.2501 - inflamation_pred_loss: 1.1281 - nephritis_pred_loss: 0.6101 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6125 - nephritis_pred_mse: 0.2096\n",
            "Epoch 158/1500\n",
            "80/80 [==============================] - 0s 940us/sample - loss: 1.2397 - inflamation_pred_loss: 1.1177 - nephritis_pred_loss: 0.6098 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6125 - nephritis_pred_mse: 0.2094\n",
            "Epoch 159/1500\n",
            "80/80 [==============================] - 0s 82us/sample - loss: 1.2294 - inflamation_pred_loss: 1.1075 - nephritis_pred_loss: 0.6094 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6125 - nephritis_pred_mse: 0.2092\n",
            "Epoch 160/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 1.2192 - inflamation_pred_loss: 1.0974 - nephritis_pred_loss: 0.6090 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6125 - nephritis_pred_mse: 0.2091\n",
            "Epoch 161/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 1.2092 - inflamation_pred_loss: 1.0875 - nephritis_pred_loss: 0.6086 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6125 - nephritis_pred_mse: 0.2089\n",
            "Epoch 162/1500\n",
            "80/80 [==============================] - 0s 931us/sample - loss: 1.1994 - inflamation_pred_loss: 1.0778 - nephritis_pred_loss: 0.6082 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6125 - nephritis_pred_mse: 0.2087\n",
            "Epoch 163/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 1.1897 - inflamation_pred_loss: 1.0681 - nephritis_pred_loss: 0.6078 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6125 - nephritis_pred_mse: 0.2085\n",
            "Epoch 164/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 1.1802 - inflamation_pred_loss: 1.0587 - nephritis_pred_loss: 0.6074 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6125 - nephritis_pred_mse: 0.2083\n",
            "Epoch 165/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 1.1708 - inflamation_pred_loss: 1.0494 - nephritis_pred_loss: 0.6070 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6125 - nephritis_pred_mse: 0.2081\n",
            "Epoch 166/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 1.1615 - inflamation_pred_loss: 1.0402 - nephritis_pred_loss: 0.6066 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6125 - nephritis_pred_mse: 0.2079\n",
            "Epoch 167/1500\n",
            "80/80 [==============================] - 0s 80us/sample - loss: 1.1524 - inflamation_pred_loss: 1.0312 - nephritis_pred_loss: 0.6062 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6250 - nephritis_pred_mse: 0.2077\n",
            "Epoch 168/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 1.1435 - inflamation_pred_loss: 1.0223 - nephritis_pred_loss: 0.6058 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6250 - nephritis_pred_mse: 0.2075\n",
            "Epoch 169/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 1.1347 - inflamation_pred_loss: 1.0136 - nephritis_pred_loss: 0.6054 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6250 - nephritis_pred_mse: 0.2073\n",
            "Epoch 170/1500\n",
            "80/80 [==============================] - 0s 51us/sample - loss: 1.1260 - inflamation_pred_loss: 1.0050 - nephritis_pred_loss: 0.6050 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6250 - nephritis_pred_mse: 0.2071\n",
            "Epoch 171/1500\n",
            "80/80 [==============================] - 0s 998us/sample - loss: 1.1175 - inflamation_pred_loss: 0.9966 - nephritis_pred_loss: 0.6046 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6375 - nephritis_pred_mse: 0.2069\n",
            "Epoch 172/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 1.1091 - inflamation_pred_loss: 0.9883 - nephritis_pred_loss: 0.6041 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6375 - nephritis_pred_mse: 0.2067\n",
            "Epoch 173/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 1.1009 - inflamation_pred_loss: 0.9801 - nephritis_pred_loss: 0.6037 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6375 - nephritis_pred_mse: 0.2065\n",
            "Epoch 174/1500\n",
            "80/80 [==============================] - 0s 970us/sample - loss: 1.0928 - inflamation_pred_loss: 0.9722 - nephritis_pred_loss: 0.6033 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2063\n",
            "Epoch 175/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 1.0849 - inflamation_pred_loss: 0.9643 - nephritis_pred_loss: 0.6028 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2061\n",
            "Epoch 176/1500\n",
            "80/80 [==============================] - 0s 85us/sample - loss: 1.0771 - inflamation_pred_loss: 0.9566 - nephritis_pred_loss: 0.6024 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2059\n",
            "Epoch 177/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 1.0694 - inflamation_pred_loss: 0.9490 - nephritis_pred_loss: 0.6019 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2056\n",
            "Epoch 178/1500\n",
            "80/80 [==============================] - 0s 954us/sample - loss: 1.0619 - inflamation_pred_loss: 0.9416 - nephritis_pred_loss: 0.6015 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2054\n",
            "Epoch 179/1500\n",
            "80/80 [==============================] - 0s 95us/sample - loss: 1.0545 - inflamation_pred_loss: 0.9343 - nephritis_pred_loss: 0.6010 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2052\n",
            "Epoch 180/1500\n",
            "80/80 [==============================] - 0s 127us/sample - loss: 1.0473 - inflamation_pred_loss: 0.9271 - nephritis_pred_loss: 0.6006 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2050\n",
            "Epoch 181/1500\n",
            "80/80 [==============================] - 0s 51us/sample - loss: 1.0402 - inflamation_pred_loss: 0.9201 - nephritis_pred_loss: 0.6001 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2048\n",
            "Epoch 182/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 1.0332 - inflamation_pred_loss: 0.9133 - nephritis_pred_loss: 0.5996 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2045\n",
            "Epoch 183/1500\n",
            "80/80 [==============================] - 0s 891us/sample - loss: 1.0263 - inflamation_pred_loss: 0.9065 - nephritis_pred_loss: 0.5991 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2043\n",
            "Epoch 184/1500\n",
            "80/80 [==============================] - 0s 156us/sample - loss: 1.0196 - inflamation_pred_loss: 0.8999 - nephritis_pred_loss: 0.5986 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2041\n",
            "Epoch 185/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 1.0130 - inflamation_pred_loss: 0.8934 - nephritis_pred_loss: 0.5982 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2038\n",
            "Epoch 186/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 1.0066 - inflamation_pred_loss: 0.8871 - nephritis_pred_loss: 0.5977 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2036\n",
            "Epoch 187/1500\n",
            "80/80 [==============================] - 0s 946us/sample - loss: 1.0003 - inflamation_pred_loss: 0.8808 - nephritis_pred_loss: 0.5972 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2033\n",
            "Epoch 188/1500\n",
            "80/80 [==============================] - 0s 102us/sample - loss: 0.9941 - inflamation_pred_loss: 0.8747 - nephritis_pred_loss: 0.5966 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2031\n",
            "Epoch 189/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.9880 - inflamation_pred_loss: 0.8688 - nephritis_pred_loss: 0.5961 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6750 - nephritis_pred_mse: 0.2028\n",
            "Epoch 190/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.9820 - inflamation_pred_loss: 0.8629 - nephritis_pred_loss: 0.5956 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6750 - nephritis_pred_mse: 0.2026\n",
            "Epoch 191/1500\n",
            "80/80 [==============================] - 0s 934us/sample - loss: 0.9762 - inflamation_pred_loss: 0.8572 - nephritis_pred_loss: 0.5951 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6875 - nephritis_pred_mse: 0.2023\n",
            "Epoch 192/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.9705 - inflamation_pred_loss: 0.8516 - nephritis_pred_loss: 0.5946 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6875 - nephritis_pred_mse: 0.2021\n",
            "Epoch 193/1500\n",
            "80/80 [==============================] - 0s 108us/sample - loss: 0.9649 - inflamation_pred_loss: 0.8461 - nephritis_pred_loss: 0.5940 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7000 - nephritis_pred_mse: 0.2018\n",
            "Epoch 194/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.9594 - inflamation_pred_loss: 0.8407 - nephritis_pred_loss: 0.5935 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7000 - nephritis_pred_mse: 0.2016\n",
            "Epoch 195/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.9540 - inflamation_pred_loss: 0.8354 - nephritis_pred_loss: 0.5930 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7125 - nephritis_pred_mse: 0.2013\n",
            "Epoch 196/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.9488 - inflamation_pred_loss: 0.8303 - nephritis_pred_loss: 0.5924 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7000 - nephritis_pred_mse: 0.2011\n",
            "Epoch 197/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.9436 - inflamation_pred_loss: 0.8253 - nephritis_pred_loss: 0.5919 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.2007\n",
            "Epoch 198/1500\n",
            "80/80 [==============================] - 0s 134us/sample - loss: 0.9386 - inflamation_pred_loss: 0.8203 - nephritis_pred_loss: 0.5915 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2009\n",
            "Epoch 199/1500\n",
            "80/80 [==============================] - 0s 47us/sample - loss: 0.9340 - inflamation_pred_loss: 0.8155 - nephritis_pred_loss: 0.5923 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.2005\n",
            "Epoch 200/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.9294 - inflamation_pred_loss: 0.8108 - nephritis_pred_loss: 0.5929 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6000 - nephritis_pred_mse: 0.2023\n",
            "Epoch 201/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 0.9243 - inflamation_pred_loss: 0.8062 - nephritis_pred_loss: 0.5908 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1998\n",
            "Epoch 202/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.9196 - inflamation_pred_loss: 0.8017 - nephritis_pred_loss: 0.5897 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6625 - nephritis_pred_mse: 0.2001\n",
            "Epoch 203/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 0.9151 - inflamation_pred_loss: 0.7973 - nephritis_pred_loss: 0.5890 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1993\n",
            "Epoch 204/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.9107 - inflamation_pred_loss: 0.7930 - nephritis_pred_loss: 0.5885 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7125 - nephritis_pred_mse: 0.1993\n",
            "Epoch 205/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.9065 - inflamation_pred_loss: 0.7889 - nephritis_pred_loss: 0.5880 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1989\n",
            "Epoch 206/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.9023 - inflamation_pred_loss: 0.7848 - nephritis_pred_loss: 0.5875 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1988\n",
            "Epoch 207/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.8983 - inflamation_pred_loss: 0.7809 - nephritis_pred_loss: 0.5870 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1984\n",
            "Epoch 208/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.8944 - inflamation_pred_loss: 0.7771 - nephritis_pred_loss: 0.5865 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1983\n",
            "Epoch 209/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.8906 - inflamation_pred_loss: 0.7734 - nephritis_pred_loss: 0.5861 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1979\n",
            "Epoch 210/1500\n",
            "80/80 [==============================] - 0s 914us/sample - loss: 0.8869 - inflamation_pred_loss: 0.7698 - nephritis_pred_loss: 0.5856 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1978\n",
            "Epoch 211/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.8833 - inflamation_pred_loss: 0.7663 - nephritis_pred_loss: 0.5851 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1974\n",
            "Epoch 212/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.8799 - inflamation_pred_loss: 0.7630 - nephritis_pred_loss: 0.5846 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1974\n",
            "Epoch 213/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.8766 - inflamation_pred_loss: 0.7598 - nephritis_pred_loss: 0.5841 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1969\n",
            "Epoch 214/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.8734 - inflamation_pred_loss: 0.7566 - nephritis_pred_loss: 0.5838 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7125 - nephritis_pred_mse: 0.1972\n",
            "Epoch 215/1500\n",
            "80/80 [==============================] - 0s 52us/sample - loss: 0.8704 - inflamation_pred_loss: 0.7537 - nephritis_pred_loss: 0.5835 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1964\n",
            "Epoch 216/1500\n",
            "80/80 [==============================] - 0s 105us/sample - loss: 0.8675 - inflamation_pred_loss: 0.7508 - nephritis_pred_loss: 0.5834 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6375 - nephritis_pred_mse: 0.1973\n",
            "Epoch 217/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.8646 - inflamation_pred_loss: 0.7480 - nephritis_pred_loss: 0.5829 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1960\n",
            "Epoch 218/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.8619 - inflamation_pred_loss: 0.7454 - nephritis_pred_loss: 0.5822 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.6875 - nephritis_pred_mse: 0.1967\n",
            "Epoch 219/1500\n",
            "80/80 [==============================] - 0s 999us/sample - loss: 0.8592 - inflamation_pred_loss: 0.7429 - nephritis_pred_loss: 0.5815 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1955\n",
            "Epoch 220/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.8567 - inflamation_pred_loss: 0.7405 - nephritis_pred_loss: 0.5808 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1958\n",
            "Epoch 221/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.8543 - inflamation_pred_loss: 0.7382 - nephritis_pred_loss: 0.5803 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1950\n",
            "Epoch 222/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.8520 - inflamation_pred_loss: 0.7360 - nephritis_pred_loss: 0.5797 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1952\n",
            "Epoch 223/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.8498 - inflamation_pred_loss: 0.7340 - nephritis_pred_loss: 0.5792 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1945\n",
            "Epoch 224/1500\n",
            "80/80 [==============================] - 0s 962us/sample - loss: 0.8478 - inflamation_pred_loss: 0.7320 - nephritis_pred_loss: 0.5787 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1947\n",
            "Epoch 225/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.8458 - inflamation_pred_loss: 0.7302 - nephritis_pred_loss: 0.5782 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1941\n",
            "Epoch 226/1500\n",
            "80/80 [==============================] - 0s 51us/sample - loss: 0.8440 - inflamation_pred_loss: 0.7284 - nephritis_pred_loss: 0.5778 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1942\n",
            "Epoch 227/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.8422 - inflamation_pred_loss: 0.7268 - nephritis_pred_loss: 0.5773 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1936\n",
            "Epoch 228/1500\n",
            "80/80 [==============================] - 0s 969us/sample - loss: 0.8406 - inflamation_pred_loss: 0.7252 - nephritis_pred_loss: 0.5769 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1939\n",
            "Epoch 229/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.8391 - inflamation_pred_loss: 0.7238 - nephritis_pred_loss: 0.5765 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1931\n",
            "Epoch 230/1500\n",
            "80/80 [==============================] - 0s 52us/sample - loss: 0.8377 - inflamation_pred_loss: 0.7225 - nephritis_pred_loss: 0.5761 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1937\n",
            "Epoch 231/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.8363 - inflamation_pred_loss: 0.7212 - nephritis_pred_loss: 0.5757 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1926\n",
            "Epoch 232/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.8351 - inflamation_pred_loss: 0.7201 - nephritis_pred_loss: 0.5752 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1933\n",
            "Epoch 233/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.8339 - inflamation_pred_loss: 0.7190 - nephritis_pred_loss: 0.5746 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1921\n",
            "Epoch 234/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.8328 - inflamation_pred_loss: 0.7180 - nephritis_pred_loss: 0.5740 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1926\n",
            "Epoch 235/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.8318 - inflamation_pred_loss: 0.7171 - nephritis_pred_loss: 0.5735 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1917\n",
            "Epoch 236/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.8308 - inflamation_pred_loss: 0.7162 - nephritis_pred_loss: 0.5729 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1920\n",
            "Epoch 237/1500\n",
            "80/80 [==============================] - 0s 940us/sample - loss: 0.8299 - inflamation_pred_loss: 0.7155 - nephritis_pred_loss: 0.5724 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1912\n",
            "Epoch 238/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.8291 - inflamation_pred_loss: 0.7148 - nephritis_pred_loss: 0.5719 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1915\n",
            "Epoch 239/1500\n",
            "80/80 [==============================] - 0s 87us/sample - loss: 0.8284 - inflamation_pred_loss: 0.7141 - nephritis_pred_loss: 0.5714 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1907\n",
            "Epoch 240/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.8277 - inflamation_pred_loss: 0.7135 - nephritis_pred_loss: 0.5709 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1910\n",
            "Epoch 241/1500\n",
            "80/80 [==============================] - 0s 147us/sample - loss: 0.8271 - inflamation_pred_loss: 0.7130 - nephritis_pred_loss: 0.5704 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1902\n",
            "Epoch 242/1500\n",
            "80/80 [==============================] - 0s 141us/sample - loss: 0.8265 - inflamation_pred_loss: 0.7125 - nephritis_pred_loss: 0.5699 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1906\n",
            "Epoch 243/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 0.8260 - inflamation_pred_loss: 0.7121 - nephritis_pred_loss: 0.5695 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1897\n",
            "Epoch 244/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.8255 - inflamation_pred_loss: 0.7117 - nephritis_pred_loss: 0.5690 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1903\n",
            "Epoch 245/1500\n",
            "80/80 [==============================] - 0s 880us/sample - loss: 0.8251 - inflamation_pred_loss: 0.7114 - nephritis_pred_loss: 0.5686 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1893\n",
            "Epoch 246/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.8247 - inflamation_pred_loss: 0.7111 - nephritis_pred_loss: 0.5681 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1898\n",
            "Epoch 247/1500\n",
            "80/80 [==============================] - 0s 80us/sample - loss: 0.8243 - inflamation_pred_loss: 0.7108 - nephritis_pred_loss: 0.5675 - inflamation_pred_acc: 0.4625 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1888\n",
            "Epoch 248/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.8239 - inflamation_pred_loss: 0.7105 - nephritis_pred_loss: 0.5670 - inflamation_pred_acc: 0.3375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1893\n",
            "Epoch 249/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.8236 - inflamation_pred_loss: 0.7103 - nephritis_pred_loss: 0.5665 - inflamation_pred_acc: 0.3000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1883\n",
            "Epoch 250/1500\n",
            "80/80 [==============================] - 0s 115us/sample - loss: 0.8233 - inflamation_pred_loss: 0.7101 - nephritis_pred_loss: 0.5660 - inflamation_pred_acc: 0.2375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1887\n",
            "Epoch 251/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.8230 - inflamation_pred_loss: 0.7099 - nephritis_pred_loss: 0.5654 - inflamation_pred_acc: 0.2000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1878\n",
            "Epoch 252/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.8227 - inflamation_pred_loss: 0.7097 - nephritis_pred_loss: 0.5649 - inflamation_pred_acc: 0.1375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1882\n",
            "Epoch 253/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.8224 - inflamation_pred_loss: 0.7095 - nephritis_pred_loss: 0.5644 - inflamation_pred_acc: 0.1250 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1873\n",
            "Epoch 254/1500\n",
            "80/80 [==============================] - 0s 877us/sample - loss: 0.8221 - inflamation_pred_loss: 0.7093 - nephritis_pred_loss: 0.5639 - inflamation_pred_acc: 0.1250 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1877\n",
            "Epoch 255/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.8218 - inflamation_pred_loss: 0.7092 - nephritis_pred_loss: 0.5634 - inflamation_pred_acc: 0.1000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1868\n",
            "Epoch 256/1500\n",
            "80/80 [==============================] - 0s 84us/sample - loss: 0.8216 - inflamation_pred_loss: 0.7090 - nephritis_pred_loss: 0.5629 - inflamation_pred_acc: 0.0875 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1873\n",
            "Epoch 257/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.8213 - inflamation_pred_loss: 0.7088 - nephritis_pred_loss: 0.5624 - inflamation_pred_acc: 0.0750 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1864\n",
            "Epoch 258/1500\n",
            "80/80 [==============================] - 0s 927us/sample - loss: 0.8210 - inflamation_pred_loss: 0.7087 - nephritis_pred_loss: 0.5620 - inflamation_pred_acc: 0.0500 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1869\n",
            "Epoch 259/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.8208 - inflamation_pred_loss: 0.7085 - nephritis_pred_loss: 0.5615 - inflamation_pred_acc: 0.0500 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1859\n",
            "Epoch 260/1500\n",
            "80/80 [==============================] - 0s 87us/sample - loss: 0.8205 - inflamation_pred_loss: 0.7083 - nephritis_pred_loss: 0.5610 - inflamation_pred_acc: 0.0375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1864\n",
            "Epoch 261/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.8202 - inflamation_pred_loss: 0.7081 - nephritis_pred_loss: 0.5604 - inflamation_pred_acc: 0.0250 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1854\n",
            "Epoch 262/1500\n",
            "80/80 [==============================] - 0s 978us/sample - loss: 0.8200 - inflamation_pred_loss: 0.7080 - nephritis_pred_loss: 0.5599 - inflamation_pred_acc: 0.0000e+00 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1859\n",
            "Epoch 263/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.8197 - inflamation_pred_loss: 0.7078 - nephritis_pred_loss: 0.5594 - inflamation_pred_acc: 0.0375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1849\n",
            "Epoch 264/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.8194 - inflamation_pred_loss: 0.7076 - nephritis_pred_loss: 0.5589 - inflamation_pred_acc: 0.0375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1854\n",
            "Epoch 265/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.8191 - inflamation_pred_loss: 0.7074 - nephritis_pred_loss: 0.5584 - inflamation_pred_acc: 0.0500 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1844\n",
            "Epoch 266/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.8188 - inflamation_pred_loss: 0.7072 - nephritis_pred_loss: 0.5579 - inflamation_pred_acc: 0.0500 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1849\n",
            "Epoch 267/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 0.8185 - inflamation_pred_loss: 0.7071 - nephritis_pred_loss: 0.5573 - inflamation_pred_acc: 0.0625 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1839\n",
            "Epoch 268/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.8182 - inflamation_pred_loss: 0.7069 - nephritis_pred_loss: 0.5568 - inflamation_pred_acc: 0.0625 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1844\n",
            "Epoch 269/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.8179 - inflamation_pred_loss: 0.7067 - nephritis_pred_loss: 0.5563 - inflamation_pred_acc: 0.0625 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1835\n",
            "Epoch 270/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.8176 - inflamation_pred_loss: 0.7065 - nephritis_pred_loss: 0.5558 - inflamation_pred_acc: 0.0750 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1839\n",
            "Epoch 271/1500\n",
            "80/80 [==============================] - 0s 959us/sample - loss: 0.8173 - inflamation_pred_loss: 0.7063 - nephritis_pred_loss: 0.5553 - inflamation_pred_acc: 0.0750 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1830\n",
            "Epoch 272/1500\n",
            "80/80 [==============================] - 0s 52us/sample - loss: 0.8170 - inflamation_pred_loss: 0.7060 - nephritis_pred_loss: 0.5548 - inflamation_pred_acc: 0.0750 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1835\n",
            "Epoch 273/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.8167 - inflamation_pred_loss: 0.7058 - nephritis_pred_loss: 0.5543 - inflamation_pred_acc: 0.0750 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1825\n",
            "Epoch 274/1500\n",
            "80/80 [==============================] - 0s 85us/sample - loss: 0.8164 - inflamation_pred_loss: 0.7056 - nephritis_pred_loss: 0.5538 - inflamation_pred_acc: 0.0750 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1830\n",
            "Epoch 275/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.8161 - inflamation_pred_loss: 0.7054 - nephritis_pred_loss: 0.5533 - inflamation_pred_acc: 0.0750 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1820\n",
            "Epoch 276/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.8157 - inflamation_pred_loss: 0.7052 - nephritis_pred_loss: 0.5528 - inflamation_pred_acc: 0.0750 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1825\n",
            "Epoch 277/1500\n",
            "80/80 [==============================] - 0s 896us/sample - loss: 0.8154 - inflamation_pred_loss: 0.7050 - nephritis_pred_loss: 0.5523 - inflamation_pred_acc: 0.0875 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1815\n",
            "Epoch 278/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.8151 - inflamation_pred_loss: 0.7047 - nephritis_pred_loss: 0.5517 - inflamation_pred_acc: 0.0875 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1820\n",
            "Epoch 279/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.8147 - inflamation_pred_loss: 0.7045 - nephritis_pred_loss: 0.5512 - inflamation_pred_acc: 0.0875 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1810\n",
            "Epoch 280/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.8144 - inflamation_pred_loss: 0.7043 - nephritis_pred_loss: 0.5507 - inflamation_pred_acc: 0.0875 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1815\n",
            "Epoch 281/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.8141 - inflamation_pred_loss: 0.7040 - nephritis_pred_loss: 0.5502 - inflamation_pred_acc: 0.0875 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1805\n",
            "Epoch 282/1500\n",
            "80/80 [==============================] - 0s 75us/sample - loss: 0.8137 - inflamation_pred_loss: 0.7038 - nephritis_pred_loss: 0.5497 - inflamation_pred_acc: 0.0875 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1810\n",
            "Epoch 283/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.8134 - inflamation_pred_loss: 0.7036 - nephritis_pred_loss: 0.5491 - inflamation_pred_acc: 0.0875 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1800\n",
            "Epoch 284/1500\n",
            "80/80 [==============================] - 0s 49us/sample - loss: 0.8130 - inflamation_pred_loss: 0.7033 - nephritis_pred_loss: 0.5486 - inflamation_pred_acc: 0.0875 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1806\n",
            "Epoch 285/1500\n",
            "80/80 [==============================] - 0s 951us/sample - loss: 0.8127 - inflamation_pred_loss: 0.7031 - nephritis_pred_loss: 0.5481 - inflamation_pred_acc: 0.0875 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1795\n",
            "Epoch 286/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.8123 - inflamation_pred_loss: 0.7028 - nephritis_pred_loss: 0.5476 - inflamation_pred_acc: 0.1000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1801\n",
            "Epoch 287/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.8120 - inflamation_pred_loss: 0.7026 - nephritis_pred_loss: 0.5471 - inflamation_pred_acc: 0.1250 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1790\n",
            "Epoch 288/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.8117 - inflamation_pred_loss: 0.7023 - nephritis_pred_loss: 0.5466 - inflamation_pred_acc: 0.1250 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1796\n",
            "Epoch 289/1500\n",
            "80/80 [==============================] - 0s 959us/sample - loss: 0.8113 - inflamation_pred_loss: 0.7021 - nephritis_pred_loss: 0.5461 - inflamation_pred_acc: 0.1250 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1786\n",
            "Epoch 290/1500\n",
            "80/80 [==============================] - 0s 86us/sample - loss: 0.8109 - inflamation_pred_loss: 0.7018 - nephritis_pred_loss: 0.5455 - inflamation_pred_acc: 0.1250 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1791\n",
            "Epoch 291/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.8106 - inflamation_pred_loss: 0.7016 - nephritis_pred_loss: 0.5450 - inflamation_pred_acc: 0.1250 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1781\n",
            "Epoch 292/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.8102 - inflamation_pred_loss: 0.7013 - nephritis_pred_loss: 0.5445 - inflamation_pred_acc: 0.1375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1786\n",
            "Epoch 293/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.8099 - inflamation_pred_loss: 0.7011 - nephritis_pred_loss: 0.5440 - inflamation_pred_acc: 0.1500 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1776\n",
            "Epoch 294/1500\n",
            "80/80 [==============================] - 0s 897us/sample - loss: 0.8095 - inflamation_pred_loss: 0.7009 - nephritis_pred_loss: 0.5434 - inflamation_pred_acc: 0.1625 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1781\n",
            "Epoch 295/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.8092 - inflamation_pred_loss: 0.7006 - nephritis_pred_loss: 0.5429 - inflamation_pred_acc: 0.1625 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1771\n",
            "Epoch 296/1500\n",
            "80/80 [==============================] - 0s 104us/sample - loss: 0.8088 - inflamation_pred_loss: 0.7004 - nephritis_pred_loss: 0.5424 - inflamation_pred_acc: 0.1750 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1776\n",
            "Epoch 297/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.8085 - inflamation_pred_loss: 0.7001 - nephritis_pred_loss: 0.5419 - inflamation_pred_acc: 0.2125 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1766\n",
            "Epoch 298/1500\n",
            "80/80 [==============================] - 0s 938us/sample - loss: 0.8081 - inflamation_pred_loss: 0.6999 - nephritis_pred_loss: 0.5414 - inflamation_pred_acc: 0.2375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1771\n",
            "Epoch 299/1500\n",
            "80/80 [==============================] - 0s 134us/sample - loss: 0.8078 - inflamation_pred_loss: 0.6996 - nephritis_pred_loss: 0.5408 - inflamation_pred_acc: 0.2375 - nephritis_pred_acc: 0.7500 - nephritis_pred_mse: 0.1761\n",
            "Epoch 300/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.8074 - inflamation_pred_loss: 0.6994 - nephritis_pred_loss: 0.5403 - inflamation_pred_acc: 0.2375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1767\n",
            "Epoch 301/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.8071 - inflamation_pred_loss: 0.6991 - nephritis_pred_loss: 0.5398 - inflamation_pred_acc: 0.2500 - nephritis_pred_acc: 0.7625 - nephritis_pred_mse: 0.1756\n",
            "Epoch 302/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.8067 - inflamation_pred_loss: 0.6989 - nephritis_pred_loss: 0.5393 - inflamation_pred_acc: 0.2625 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1762\n",
            "Epoch 303/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 0.8064 - inflamation_pred_loss: 0.6986 - nephritis_pred_loss: 0.5387 - inflamation_pred_acc: 0.2750 - nephritis_pred_acc: 0.7625 - nephritis_pred_mse: 0.1751\n",
            "Epoch 304/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.8060 - inflamation_pred_loss: 0.6984 - nephritis_pred_loss: 0.5382 - inflamation_pred_acc: 0.3000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1757\n",
            "Epoch 305/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.8057 - inflamation_pred_loss: 0.6981 - nephritis_pred_loss: 0.5377 - inflamation_pred_acc: 0.3250 - nephritis_pred_acc: 0.7625 - nephritis_pred_mse: 0.1746\n",
            "Epoch 306/1500\n",
            "80/80 [==============================] - 0s 49us/sample - loss: 0.8053 - inflamation_pred_loss: 0.6979 - nephritis_pred_loss: 0.5372 - inflamation_pred_acc: 0.3625 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1752\n",
            "Epoch 307/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.8050 - inflamation_pred_loss: 0.6977 - nephritis_pred_loss: 0.5366 - inflamation_pred_acc: 0.3750 - nephritis_pred_acc: 0.7750 - nephritis_pred_mse: 0.1741\n",
            "Epoch 308/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.8047 - inflamation_pred_loss: 0.6974 - nephritis_pred_loss: 0.5361 - inflamation_pred_acc: 0.3750 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1747\n",
            "Epoch 309/1500\n",
            "80/80 [==============================] - 0s 47us/sample - loss: 0.8043 - inflamation_pred_loss: 0.6972 - nephritis_pred_loss: 0.5356 - inflamation_pred_acc: 0.3875 - nephritis_pred_acc: 0.7750 - nephritis_pred_mse: 0.1736\n",
            "Epoch 310/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.8040 - inflamation_pred_loss: 0.6970 - nephritis_pred_loss: 0.5351 - inflamation_pred_acc: 0.4000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1742\n",
            "Epoch 311/1500\n",
            "80/80 [==============================] - 0s 978us/sample - loss: 0.8036 - inflamation_pred_loss: 0.6967 - nephritis_pred_loss: 0.5345 - inflamation_pred_acc: 0.4250 - nephritis_pred_acc: 0.7875 - nephritis_pred_mse: 0.1731\n",
            "Epoch 312/1500\n",
            "80/80 [==============================] - 0s 137us/sample - loss: 0.8033 - inflamation_pred_loss: 0.6965 - nephritis_pred_loss: 0.5340 - inflamation_pred_acc: 0.4250 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1737\n",
            "Epoch 313/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.8029 - inflamation_pred_loss: 0.6962 - nephritis_pred_loss: 0.5335 - inflamation_pred_acc: 0.4250 - nephritis_pred_acc: 0.7875 - nephritis_pred_mse: 0.1726\n",
            "Epoch 314/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.8026 - inflamation_pred_loss: 0.6960 - nephritis_pred_loss: 0.5329 - inflamation_pred_acc: 0.4375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1732\n",
            "Epoch 315/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.8022 - inflamation_pred_loss: 0.6958 - nephritis_pred_loss: 0.5324 - inflamation_pred_acc: 0.4375 - nephritis_pred_acc: 0.8000 - nephritis_pred_mse: 0.1721\n",
            "Epoch 316/1500\n",
            "80/80 [==============================] - 0s 919us/sample - loss: 0.8019 - inflamation_pred_loss: 0.6955 - nephritis_pred_loss: 0.5319 - inflamation_pred_acc: 0.4375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1727\n",
            "Epoch 317/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.8016 - inflamation_pred_loss: 0.6953 - nephritis_pred_loss: 0.5314 - inflamation_pred_acc: 0.4375 - nephritis_pred_acc: 0.8000 - nephritis_pred_mse: 0.1716\n",
            "Epoch 318/1500\n",
            "80/80 [==============================] - 0s 86us/sample - loss: 0.8012 - inflamation_pred_loss: 0.6951 - nephritis_pred_loss: 0.5308 - inflamation_pred_acc: 0.4375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1722\n",
            "Epoch 319/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.8009 - inflamation_pred_loss: 0.6948 - nephritis_pred_loss: 0.5303 - inflamation_pred_acc: 0.4375 - nephritis_pred_acc: 0.8000 - nephritis_pred_mse: 0.1711\n",
            "Epoch 320/1500\n",
            "80/80 [==============================] - 0s 979us/sample - loss: 0.8005 - inflamation_pred_loss: 0.6946 - nephritis_pred_loss: 0.5298 - inflamation_pred_acc: 0.4375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1717\n",
            "Epoch 321/1500\n",
            "80/80 [==============================] - 0s 90us/sample - loss: 0.8002 - inflamation_pred_loss: 0.6943 - nephritis_pred_loss: 0.5292 - inflamation_pred_acc: 0.4375 - nephritis_pred_acc: 0.8000 - nephritis_pred_mse: 0.1706\n",
            "Epoch 322/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.7998 - inflamation_pred_loss: 0.6941 - nephritis_pred_loss: 0.5287 - inflamation_pred_acc: 0.4375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1712\n",
            "Epoch 323/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.7995 - inflamation_pred_loss: 0.6939 - nephritis_pred_loss: 0.5282 - inflamation_pred_acc: 0.4375 - nephritis_pred_acc: 0.8125 - nephritis_pred_mse: 0.1701\n",
            "Epoch 324/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.7991 - inflamation_pred_loss: 0.6936 - nephritis_pred_loss: 0.5276 - inflamation_pred_acc: 0.4375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1707\n",
            "Epoch 325/1500\n",
            "80/80 [==============================] - 0s 880us/sample - loss: 0.7988 - inflamation_pred_loss: 0.6934 - nephritis_pred_loss: 0.5271 - inflamation_pred_acc: 0.4375 - nephritis_pred_acc: 0.8125 - nephritis_pred_mse: 0.1696\n",
            "Epoch 326/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.7984 - inflamation_pred_loss: 0.6931 - nephritis_pred_loss: 0.5266 - inflamation_pred_acc: 0.4375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1702\n",
            "Epoch 327/1500\n",
            "80/80 [==============================] - 0s 84us/sample - loss: 0.7981 - inflamation_pred_loss: 0.6929 - nephritis_pred_loss: 0.5260 - inflamation_pred_acc: 0.4500 - nephritis_pred_acc: 0.8125 - nephritis_pred_mse: 0.1691\n",
            "Epoch 328/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.7977 - inflamation_pred_loss: 0.6926 - nephritis_pred_loss: 0.5255 - inflamation_pred_acc: 0.4500 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1697\n",
            "Epoch 329/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.7973 - inflamation_pred_loss: 0.6923 - nephritis_pred_loss: 0.5250 - inflamation_pred_acc: 0.4500 - nephritis_pred_acc: 0.8125 - nephritis_pred_mse: 0.1686\n",
            "Epoch 330/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.7970 - inflamation_pred_loss: 0.6921 - nephritis_pred_loss: 0.5244 - inflamation_pred_acc: 0.4500 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1692\n",
            "Epoch 331/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.7966 - inflamation_pred_loss: 0.6918 - nephritis_pred_loss: 0.5239 - inflamation_pred_acc: 0.4500 - nephritis_pred_acc: 0.8250 - nephritis_pred_mse: 0.1681\n",
            "Epoch 332/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.7962 - inflamation_pred_loss: 0.6916 - nephritis_pred_loss: 0.5234 - inflamation_pred_acc: 0.4500 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1687\n",
            "Epoch 333/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.7959 - inflamation_pred_loss: 0.6913 - nephritis_pred_loss: 0.5228 - inflamation_pred_acc: 0.4625 - nephritis_pred_acc: 0.8250 - nephritis_pred_mse: 0.1676\n",
            "Epoch 334/1500\n",
            "80/80 [==============================] - 0s 111us/sample - loss: 0.7955 - inflamation_pred_loss: 0.6910 - nephritis_pred_loss: 0.5223 - inflamation_pred_acc: 0.4625 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1682\n",
            "Epoch 335/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.7951 - inflamation_pred_loss: 0.6908 - nephritis_pred_loss: 0.5217 - inflamation_pred_acc: 0.5000 - nephritis_pred_acc: 0.8250 - nephritis_pred_mse: 0.1671\n",
            "Epoch 336/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.7947 - inflamation_pred_loss: 0.6905 - nephritis_pred_loss: 0.5212 - inflamation_pred_acc: 0.5125 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1677\n",
            "Epoch 337/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.7943 - inflamation_pred_loss: 0.6902 - nephritis_pred_loss: 0.5207 - inflamation_pred_acc: 0.5125 - nephritis_pred_acc: 0.8250 - nephritis_pred_mse: 0.1666\n",
            "Epoch 338/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.7939 - inflamation_pred_loss: 0.6899 - nephritis_pred_loss: 0.5201 - inflamation_pred_acc: 0.5375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1672\n",
            "Epoch 339/1500\n",
            "80/80 [==============================] - 0s 871us/sample - loss: 0.7935 - inflamation_pred_loss: 0.6896 - nephritis_pred_loss: 0.5196 - inflamation_pred_acc: 0.5625 - nephritis_pred_acc: 0.8375 - nephritis_pred_mse: 0.1661\n",
            "Epoch 340/1500\n",
            "80/80 [==============================] - 0s 107us/sample - loss: 0.7932 - inflamation_pred_loss: 0.6893 - nephritis_pred_loss: 0.5191 - inflamation_pred_acc: 0.6000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1667\n",
            "Epoch 341/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.7927 - inflamation_pred_loss: 0.6890 - nephritis_pred_loss: 0.5185 - inflamation_pred_acc: 0.6000 - nephritis_pred_acc: 0.8375 - nephritis_pred_mse: 0.1656\n",
            "Epoch 342/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.7923 - inflamation_pred_loss: 0.6887 - nephritis_pred_loss: 0.5180 - inflamation_pred_acc: 0.6750 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1662\n",
            "Epoch 343/1500\n",
            "80/80 [==============================] - 0s 975us/sample - loss: 0.7919 - inflamation_pred_loss: 0.6884 - nephritis_pred_loss: 0.5174 - inflamation_pred_acc: 0.7125 - nephritis_pred_acc: 0.8375 - nephritis_pred_mse: 0.1651\n",
            "Epoch 344/1500\n",
            "80/80 [==============================] - 0s 51us/sample - loss: 0.7915 - inflamation_pred_loss: 0.6881 - nephritis_pred_loss: 0.5169 - inflamation_pred_acc: 0.7375 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1657\n",
            "Epoch 345/1500\n",
            "80/80 [==============================] - 0s 139us/sample - loss: 0.7911 - inflamation_pred_loss: 0.6878 - nephritis_pred_loss: 0.5164 - inflamation_pred_acc: 0.7500 - nephritis_pred_acc: 0.8500 - nephritis_pred_mse: 0.1646\n",
            "Epoch 346/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.7907 - inflamation_pred_loss: 0.6875 - nephritis_pred_loss: 0.5158 - inflamation_pred_acc: 0.8125 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1652\n",
            "Epoch 347/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.7903 - inflamation_pred_loss: 0.6872 - nephritis_pred_loss: 0.5153 - inflamation_pred_acc: 0.8250 - nephritis_pred_acc: 0.8500 - nephritis_pred_mse: 0.1641\n",
            "Epoch 348/1500\n",
            "80/80 [==============================] - 0s 841us/sample - loss: 0.7898 - inflamation_pred_loss: 0.6869 - nephritis_pred_loss: 0.5147 - inflamation_pred_acc: 0.8625 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1647\n",
            "Epoch 349/1500\n",
            "80/80 [==============================] - 0s 118us/sample - loss: 0.7894 - inflamation_pred_loss: 0.6866 - nephritis_pred_loss: 0.5142 - inflamation_pred_acc: 0.8625 - nephritis_pred_acc: 0.8500 - nephritis_pred_mse: 0.1636\n",
            "Epoch 350/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.7890 - inflamation_pred_loss: 0.6862 - nephritis_pred_loss: 0.5137 - inflamation_pred_acc: 0.8625 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1642\n",
            "Epoch 351/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.7885 - inflamation_pred_loss: 0.6859 - nephritis_pred_loss: 0.5131 - inflamation_pred_acc: 0.8625 - nephritis_pred_acc: 0.8625 - nephritis_pred_mse: 0.1631\n",
            "Epoch 352/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.7881 - inflamation_pred_loss: 0.6856 - nephritis_pred_loss: 0.5126 - inflamation_pred_acc: 0.8750 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1637\n",
            "Epoch 353/1500\n",
            "80/80 [==============================] - 0s 897us/sample - loss: 0.7876 - inflamation_pred_loss: 0.6852 - nephritis_pred_loss: 0.5120 - inflamation_pred_acc: 0.8750 - nephritis_pred_acc: 0.8625 - nephritis_pred_mse: 0.1625\n",
            "Epoch 354/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.7872 - inflamation_pred_loss: 0.6849 - nephritis_pred_loss: 0.5115 - inflamation_pred_acc: 0.8750 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1632\n",
            "Epoch 355/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.7867 - inflamation_pred_loss: 0.6846 - nephritis_pred_loss: 0.5109 - inflamation_pred_acc: 0.8875 - nephritis_pred_acc: 0.8625 - nephritis_pred_mse: 0.1620\n",
            "Epoch 356/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.7863 - inflamation_pred_loss: 0.6842 - nephritis_pred_loss: 0.5104 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1627\n",
            "Epoch 357/1500\n",
            "80/80 [==============================] - 0s 996us/sample - loss: 0.7858 - inflamation_pred_loss: 0.6839 - nephritis_pred_loss: 0.5098 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.8625 - nephritis_pred_mse: 0.1615\n",
            "Epoch 358/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.7854 - inflamation_pred_loss: 0.6835 - nephritis_pred_loss: 0.5093 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1622\n",
            "Epoch 359/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.7849 - inflamation_pred_loss: 0.6832 - nephritis_pred_loss: 0.5088 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.8625 - nephritis_pred_mse: 0.1610\n",
            "Epoch 360/1500\n",
            "80/80 [==============================] - 0s 47us/sample - loss: 0.7845 - inflamation_pred_loss: 0.6828 - nephritis_pred_loss: 0.5082 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1617\n",
            "Epoch 361/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.7840 - inflamation_pred_loss: 0.6825 - nephritis_pred_loss: 0.5077 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.8625 - nephritis_pred_mse: 0.1605\n",
            "Epoch 362/1500\n",
            "80/80 [==============================] - 0s 899us/sample - loss: 0.7835 - inflamation_pred_loss: 0.6821 - nephritis_pred_loss: 0.5071 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1612\n",
            "Epoch 363/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.7831 - inflamation_pred_loss: 0.6818 - nephritis_pred_loss: 0.5066 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.8625 - nephritis_pred_mse: 0.1600\n",
            "Epoch 364/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.7826 - inflamation_pred_loss: 0.6814 - nephritis_pred_loss: 0.5060 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.7250 - nephritis_pred_mse: 0.1607\n",
            "Epoch 365/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.7822 - inflamation_pred_loss: 0.6811 - nephritis_pred_loss: 0.5055 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.8750 - nephritis_pred_mse: 0.1595\n",
            "Epoch 366/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.7817 - inflamation_pred_loss: 0.6807 - nephritis_pred_loss: 0.5049 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.7500 - nephritis_pred_mse: 0.1602\n",
            "Epoch 367/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.7812 - inflamation_pred_loss: 0.6803 - nephritis_pred_loss: 0.5044 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.8875 - nephritis_pred_mse: 0.1590\n",
            "Epoch 368/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.7807 - inflamation_pred_loss: 0.6800 - nephritis_pred_loss: 0.5038 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.7500 - nephritis_pred_mse: 0.1597\n",
            "Epoch 369/1500\n",
            "80/80 [==============================] - 0s 91us/sample - loss: 0.7803 - inflamation_pred_loss: 0.6796 - nephritis_pred_loss: 0.5033 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.9000 - nephritis_pred_mse: 0.1585\n",
            "Epoch 370/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.7798 - inflamation_pred_loss: 0.6792 - nephritis_pred_loss: 0.5027 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.7625 - nephritis_pred_mse: 0.1592\n",
            "Epoch 371/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 0.7793 - inflamation_pred_loss: 0.6789 - nephritis_pred_loss: 0.5022 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.9000 - nephritis_pred_mse: 0.1580\n",
            "Epoch 372/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.7788 - inflamation_pred_loss: 0.6785 - nephritis_pred_loss: 0.5016 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.7625 - nephritis_pred_mse: 0.1587\n",
            "Epoch 373/1500\n",
            "80/80 [==============================] - 0s 95us/sample - loss: 0.7783 - inflamation_pred_loss: 0.6781 - nephritis_pred_loss: 0.5011 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.9000 - nephritis_pred_mse: 0.1575\n",
            "Epoch 374/1500\n",
            "80/80 [==============================] - 0s 49us/sample - loss: 0.7779 - inflamation_pred_loss: 0.6778 - nephritis_pred_loss: 0.5005 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.7750 - nephritis_pred_mse: 0.1582\n",
            "Epoch 375/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.7774 - inflamation_pred_loss: 0.6774 - nephritis_pred_loss: 0.5000 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.9000 - nephritis_pred_mse: 0.1570\n",
            "Epoch 376/1500\n",
            "80/80 [==============================] - 0s 87us/sample - loss: 0.7769 - inflamation_pred_loss: 0.6770 - nephritis_pred_loss: 0.4994 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.7750 - nephritis_pred_mse: 0.1577\n",
            "Epoch 377/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.7764 - inflamation_pred_loss: 0.6766 - nephritis_pred_loss: 0.4989 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1565\n",
            "Epoch 378/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.7759 - inflamation_pred_loss: 0.6762 - nephritis_pred_loss: 0.4983 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.7875 - nephritis_pred_mse: 0.1572\n",
            "Epoch 379/1500\n",
            "80/80 [==============================] - 0s 936us/sample - loss: 0.7754 - inflamation_pred_loss: 0.6758 - nephritis_pred_loss: 0.4978 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1559\n",
            "Epoch 380/1500\n",
            "80/80 [==============================] - 0s 122us/sample - loss: 0.7749 - inflamation_pred_loss: 0.6755 - nephritis_pred_loss: 0.4972 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.7875 - nephritis_pred_mse: 0.1567\n",
            "Epoch 381/1500\n",
            "80/80 [==============================] - 0s 93us/sample - loss: 0.7744 - inflamation_pred_loss: 0.6751 - nephritis_pred_loss: 0.4967 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1554\n",
            "Epoch 382/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.7739 - inflamation_pred_loss: 0.6747 - nephritis_pred_loss: 0.4961 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.8000 - nephritis_pred_mse: 0.1561\n",
            "Epoch 383/1500\n",
            "80/80 [==============================] - 0s 921us/sample - loss: 0.7734 - inflamation_pred_loss: 0.6743 - nephritis_pred_loss: 0.4956 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1549\n",
            "Epoch 384/1500\n",
            "80/80 [==============================] - 0s 99us/sample - loss: 0.7729 - inflamation_pred_loss: 0.6739 - nephritis_pred_loss: 0.4950 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.8000 - nephritis_pred_mse: 0.1556\n",
            "Epoch 385/1500\n",
            "80/80 [==============================] - 0s 94us/sample - loss: 0.7724 - inflamation_pred_loss: 0.6735 - nephritis_pred_loss: 0.4945 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1544\n",
            "Epoch 386/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.7719 - inflamation_pred_loss: 0.6731 - nephritis_pred_loss: 0.4939 - inflamation_pred_acc: 0.9750 - nephritis_pred_acc: 0.8000 - nephritis_pred_mse: 0.1551\n",
            "Epoch 387/1500\n",
            "80/80 [==============================] - 0s 889us/sample - loss: 0.7714 - inflamation_pred_loss: 0.6727 - nephritis_pred_loss: 0.4934 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1539\n",
            "Epoch 388/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.7709 - inflamation_pred_loss: 0.6723 - nephritis_pred_loss: 0.4928 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8000 - nephritis_pred_mse: 0.1546\n",
            "Epoch 389/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.7703 - inflamation_pred_loss: 0.6719 - nephritis_pred_loss: 0.4923 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1534\n",
            "Epoch 390/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.7698 - inflamation_pred_loss: 0.6715 - nephritis_pred_loss: 0.4917 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8125 - nephritis_pred_mse: 0.1541\n",
            "Epoch 391/1500\n",
            "80/80 [==============================] - 0s 939us/sample - loss: 0.7693 - inflamation_pred_loss: 0.6711 - nephritis_pred_loss: 0.4912 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1529\n",
            "Epoch 392/1500\n",
            "80/80 [==============================] - 0s 90us/sample - loss: 0.7688 - inflamation_pred_loss: 0.6706 - nephritis_pred_loss: 0.4906 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8125 - nephritis_pred_mse: 0.1536\n",
            "Epoch 393/1500\n",
            "80/80 [==============================] - 0s 107us/sample - loss: 0.7682 - inflamation_pred_loss: 0.6702 - nephritis_pred_loss: 0.4900 - inflamation_pred_acc: 0.9750 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1524\n",
            "Epoch 394/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.7677 - inflamation_pred_loss: 0.6698 - nephritis_pred_loss: 0.4895 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8125 - nephritis_pred_mse: 0.1531\n",
            "Epoch 395/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.7672 - inflamation_pred_loss: 0.6694 - nephritis_pred_loss: 0.4889 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1519\n",
            "Epoch 396/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.7667 - inflamation_pred_loss: 0.6690 - nephritis_pred_loss: 0.4884 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8125 - nephritis_pred_mse: 0.1526\n",
            "Epoch 397/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.7661 - inflamation_pred_loss: 0.6686 - nephritis_pred_loss: 0.4878 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1514\n",
            "Epoch 398/1500\n",
            "80/80 [==============================] - 0s 75us/sample - loss: 0.7656 - inflamation_pred_loss: 0.6681 - nephritis_pred_loss: 0.4873 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8250 - nephritis_pred_mse: 0.1521\n",
            "Epoch 399/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.7651 - inflamation_pred_loss: 0.6677 - nephritis_pred_loss: 0.4867 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1509\n",
            "Epoch 400/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.7645 - inflamation_pred_loss: 0.6673 - nephritis_pred_loss: 0.4862 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8250 - nephritis_pred_mse: 0.1516\n",
            "Epoch 401/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 0.7640 - inflamation_pred_loss: 0.6669 - nephritis_pred_loss: 0.4856 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1503\n",
            "Epoch 402/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.7634 - inflamation_pred_loss: 0.6664 - nephritis_pred_loss: 0.4851 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8250 - nephritis_pred_mse: 0.1511\n",
            "Epoch 403/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.7629 - inflamation_pred_loss: 0.6660 - nephritis_pred_loss: 0.4845 - inflamation_pred_acc: 0.9000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1498\n",
            "Epoch 404/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.7623 - inflamation_pred_loss: 0.6656 - nephritis_pred_loss: 0.4839 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8250 - nephritis_pred_mse: 0.1506\n",
            "Epoch 405/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.7618 - inflamation_pred_loss: 0.6651 - nephritis_pred_loss: 0.4834 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1493\n",
            "Epoch 406/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.7612 - inflamation_pred_loss: 0.6647 - nephritis_pred_loss: 0.4828 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8375 - nephritis_pred_mse: 0.1501\n",
            "Epoch 407/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.7607 - inflamation_pred_loss: 0.6642 - nephritis_pred_loss: 0.4823 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1488\n",
            "Epoch 408/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.7601 - inflamation_pred_loss: 0.6638 - nephritis_pred_loss: 0.4817 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8375 - nephritis_pred_mse: 0.1496\n",
            "Epoch 409/1500\n",
            "80/80 [==============================] - 0s 899us/sample - loss: 0.7596 - inflamation_pred_loss: 0.6634 - nephritis_pred_loss: 0.4811 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1483\n",
            "Epoch 410/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.7590 - inflamation_pred_loss: 0.6629 - nephritis_pred_loss: 0.4806 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8375 - nephritis_pred_mse: 0.1491\n",
            "Epoch 411/1500\n",
            "80/80 [==============================] - 0s 88us/sample - loss: 0.7585 - inflamation_pred_loss: 0.6625 - nephritis_pred_loss: 0.4800 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1478\n",
            "Epoch 412/1500\n",
            "80/80 [==============================] - 0s 52us/sample - loss: 0.7579 - inflamation_pred_loss: 0.6620 - nephritis_pred_loss: 0.4795 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8500 - nephritis_pred_mse: 0.1486\n",
            "Epoch 413/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.7573 - inflamation_pred_loss: 0.6616 - nephritis_pred_loss: 0.4789 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1473\n",
            "Epoch 414/1500\n",
            "80/80 [==============================] - 0s 913us/sample - loss: 0.7568 - inflamation_pred_loss: 0.6611 - nephritis_pred_loss: 0.4784 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8500 - nephritis_pred_mse: 0.1480\n",
            "Epoch 415/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.7562 - inflamation_pred_loss: 0.6606 - nephritis_pred_loss: 0.4778 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1468\n",
            "Epoch 416/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.7556 - inflamation_pred_loss: 0.6602 - nephritis_pred_loss: 0.4772 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8500 - nephritis_pred_mse: 0.1475\n",
            "Epoch 417/1500\n",
            "80/80 [==============================] - 0s 52us/sample - loss: 0.7551 - inflamation_pred_loss: 0.6597 - nephritis_pred_loss: 0.4767 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1463\n",
            "Epoch 418/1500\n",
            "80/80 [==============================] - 0s 972us/sample - loss: 0.7545 - inflamation_pred_loss: 0.6593 - nephritis_pred_loss: 0.4761 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8625 - nephritis_pred_mse: 0.1470\n",
            "Epoch 419/1500\n",
            "80/80 [==============================] - 0s 80us/sample - loss: 0.7539 - inflamation_pred_loss: 0.6588 - nephritis_pred_loss: 0.4756 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1458\n",
            "Epoch 420/1500\n",
            "80/80 [==============================] - 0s 85us/sample - loss: 0.7533 - inflamation_pred_loss: 0.6583 - nephritis_pred_loss: 0.4750 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8625 - nephritis_pred_mse: 0.1465\n",
            "Epoch 421/1500\n",
            "80/80 [==============================] - 0s 47us/sample - loss: 0.7528 - inflamation_pred_loss: 0.6579 - nephritis_pred_loss: 0.4744 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1453\n",
            "Epoch 422/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.7522 - inflamation_pred_loss: 0.6574 - nephritis_pred_loss: 0.4739 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8625 - nephritis_pred_mse: 0.1460\n",
            "Epoch 423/1500\n",
            "80/80 [==============================] - 0s 946us/sample - loss: 0.7516 - inflamation_pred_loss: 0.6569 - nephritis_pred_loss: 0.4733 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1448\n",
            "Epoch 424/1500\n",
            "80/80 [==============================] - 0s 106us/sample - loss: 0.7510 - inflamation_pred_loss: 0.6565 - nephritis_pred_loss: 0.4728 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8625 - nephritis_pred_mse: 0.1455\n",
            "Epoch 425/1500\n",
            "80/80 [==============================] - 0s 88us/sample - loss: 0.7504 - inflamation_pred_loss: 0.6560 - nephritis_pred_loss: 0.4722 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1442\n",
            "Epoch 426/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.7498 - inflamation_pred_loss: 0.6555 - nephritis_pred_loss: 0.4716 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8625 - nephritis_pred_mse: 0.1450\n",
            "Epoch 427/1500\n",
            "80/80 [==============================] - 0s 891us/sample - loss: 0.7492 - inflamation_pred_loss: 0.6550 - nephritis_pred_loss: 0.4711 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1437\n",
            "Epoch 428/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.7487 - inflamation_pred_loss: 0.6546 - nephritis_pred_loss: 0.4705 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8625 - nephritis_pred_mse: 0.1445\n",
            "Epoch 429/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.7481 - inflamation_pred_loss: 0.6541 - nephritis_pred_loss: 0.4699 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1432\n",
            "Epoch 430/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.7475 - inflamation_pred_loss: 0.6536 - nephritis_pred_loss: 0.4694 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8625 - nephritis_pred_mse: 0.1440\n",
            "Epoch 431/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.7469 - inflamation_pred_loss: 0.6531 - nephritis_pred_loss: 0.4688 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1427\n",
            "Epoch 432/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.7463 - inflamation_pred_loss: 0.6526 - nephritis_pred_loss: 0.4683 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8750 - nephritis_pred_mse: 0.1435\n",
            "Epoch 433/1500\n",
            "80/80 [==============================] - 0s 900us/sample - loss: 0.7457 - inflamation_pred_loss: 0.6521 - nephritis_pred_loss: 0.4677 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1422\n",
            "Epoch 434/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.7451 - inflamation_pred_loss: 0.6516 - nephritis_pred_loss: 0.4671 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.8875 - nephritis_pred_mse: 0.1430\n",
            "Epoch 435/1500\n",
            "80/80 [==============================] - 0s 83us/sample - loss: 0.7445 - inflamation_pred_loss: 0.6511 - nephritis_pred_loss: 0.4666 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1417\n",
            "Epoch 436/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 0.7439 - inflamation_pred_loss: 0.6507 - nephritis_pred_loss: 0.4660 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9000 - nephritis_pred_mse: 0.1425\n",
            "Epoch 437/1500\n",
            "80/80 [==============================] - 0s 965us/sample - loss: 0.7432 - inflamation_pred_loss: 0.6502 - nephritis_pred_loss: 0.4655 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1412\n",
            "Epoch 438/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.7426 - inflamation_pred_loss: 0.6497 - nephritis_pred_loss: 0.4649 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9000 - nephritis_pred_mse: 0.1420\n",
            "Epoch 439/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 0.7420 - inflamation_pred_loss: 0.6492 - nephritis_pred_loss: 0.4643 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1407\n",
            "Epoch 440/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.7414 - inflamation_pred_loss: 0.6487 - nephritis_pred_loss: 0.4638 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9000 - nephritis_pred_mse: 0.1415\n",
            "Epoch 441/1500\n",
            "80/80 [==============================] - 0s 983us/sample - loss: 0.7408 - inflamation_pred_loss: 0.6482 - nephritis_pred_loss: 0.4632 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1402\n",
            "Epoch 442/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.7402 - inflamation_pred_loss: 0.6477 - nephritis_pred_loss: 0.4626 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9000 - nephritis_pred_mse: 0.1410\n",
            "Epoch 443/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.7396 - inflamation_pred_loss: 0.6472 - nephritis_pred_loss: 0.4621 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1397\n",
            "Epoch 444/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.7389 - inflamation_pred_loss: 0.6466 - nephritis_pred_loss: 0.4615 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9000 - nephritis_pred_mse: 0.1405\n",
            "Epoch 445/1500\n",
            "80/80 [==============================] - 0s 986us/sample - loss: 0.7383 - inflamation_pred_loss: 0.6461 - nephritis_pred_loss: 0.4610 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1392\n",
            "Epoch 446/1500\n",
            "80/80 [==============================] - 0s 101us/sample - loss: 0.7377 - inflamation_pred_loss: 0.6456 - nephritis_pred_loss: 0.4604 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1400\n",
            "Epoch 447/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.7371 - inflamation_pred_loss: 0.6451 - nephritis_pred_loss: 0.4598 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1387\n",
            "Epoch 448/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.7365 - inflamation_pred_loss: 0.6446 - nephritis_pred_loss: 0.4593 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1395\n",
            "Epoch 449/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.7358 - inflamation_pred_loss: 0.6441 - nephritis_pred_loss: 0.4587 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1382\n",
            "Epoch 450/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.7352 - inflamation_pred_loss: 0.6436 - nephritis_pred_loss: 0.4581 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1390\n",
            "Epoch 451/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.7346 - inflamation_pred_loss: 0.6430 - nephritis_pred_loss: 0.4576 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1377\n",
            "Epoch 452/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.7339 - inflamation_pred_loss: 0.6425 - nephritis_pred_loss: 0.4570 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1385\n",
            "Epoch 453/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.7333 - inflamation_pred_loss: 0.6420 - nephritis_pred_loss: 0.4564 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1372\n",
            "Epoch 454/1500\n",
            "80/80 [==============================] - 0s 935us/sample - loss: 0.7327 - inflamation_pred_loss: 0.6415 - nephritis_pred_loss: 0.4559 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1380\n",
            "Epoch 455/1500\n",
            "80/80 [==============================] - 0s 80us/sample - loss: 0.7320 - inflamation_pred_loss: 0.6410 - nephritis_pred_loss: 0.4553 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1367\n",
            "Epoch 456/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.7314 - inflamation_pred_loss: 0.6404 - nephritis_pred_loss: 0.4548 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1375\n",
            "Epoch 457/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.7307 - inflamation_pred_loss: 0.6399 - nephritis_pred_loss: 0.4542 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1362\n",
            "Epoch 458/1500\n",
            "80/80 [==============================] - 0s 968us/sample - loss: 0.7301 - inflamation_pred_loss: 0.6394 - nephritis_pred_loss: 0.4536 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1370\n",
            "Epoch 459/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.7295 - inflamation_pred_loss: 0.6388 - nephritis_pred_loss: 0.4531 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1357\n",
            "Epoch 460/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.7288 - inflamation_pred_loss: 0.6383 - nephritis_pred_loss: 0.4525 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1365\n",
            "Epoch 461/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.7282 - inflamation_pred_loss: 0.6378 - nephritis_pred_loss: 0.4519 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.1352\n",
            "Epoch 462/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 0.7275 - inflamation_pred_loss: 0.6372 - nephritis_pred_loss: 0.4514 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1360\n",
            "Epoch 463/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.7269 - inflamation_pred_loss: 0.6367 - nephritis_pred_loss: 0.4508 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9500 - nephritis_pred_mse: 0.1346\n",
            "Epoch 464/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.7262 - inflamation_pred_loss: 0.6362 - nephritis_pred_loss: 0.4502 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1355\n",
            "Epoch 465/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.7256 - inflamation_pred_loss: 0.6356 - nephritis_pred_loss: 0.4497 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9500 - nephritis_pred_mse: 0.1341\n",
            "Epoch 466/1500\n",
            "80/80 [==============================] - 0s 979us/sample - loss: 0.7249 - inflamation_pred_loss: 0.6351 - nephritis_pred_loss: 0.4491 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1350\n",
            "Epoch 467/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 0.7242 - inflamation_pred_loss: 0.6345 - nephritis_pred_loss: 0.4485 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9500 - nephritis_pred_mse: 0.1336\n",
            "Epoch 468/1500\n",
            "80/80 [==============================] - 0s 96us/sample - loss: 0.7236 - inflamation_pred_loss: 0.6340 - nephritis_pred_loss: 0.4480 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1345\n",
            "Epoch 469/1500\n",
            "80/80 [==============================] - 0s 51us/sample - loss: 0.7229 - inflamation_pred_loss: 0.6334 - nephritis_pred_loss: 0.4474 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9500 - nephritis_pred_mse: 0.1331\n",
            "Epoch 470/1500\n",
            "80/80 [==============================] - 0s 205us/sample - loss: 0.7223 - inflamation_pred_loss: 0.6329 - nephritis_pred_loss: 0.4469 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1340\n",
            "Epoch 471/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.7216 - inflamation_pred_loss: 0.6323 - nephritis_pred_loss: 0.4463 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9500 - nephritis_pred_mse: 0.1326\n",
            "Epoch 472/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.7209 - inflamation_pred_loss: 0.6318 - nephritis_pred_loss: 0.4457 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1335\n",
            "Epoch 473/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.7203 - inflamation_pred_loss: 0.6312 - nephritis_pred_loss: 0.4451 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9500 - nephritis_pred_mse: 0.1321\n",
            "Epoch 474/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.7196 - inflamation_pred_loss: 0.6307 - nephritis_pred_loss: 0.4446 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1330\n",
            "Epoch 475/1500\n",
            "80/80 [==============================] - 0s 34us/sample - loss: 0.7189 - inflamation_pred_loss: 0.6301 - nephritis_pred_loss: 0.4440 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9500 - nephritis_pred_mse: 0.1316\n",
            "Epoch 476/1500\n",
            "80/80 [==============================] - 0s 873us/sample - loss: 0.7183 - inflamation_pred_loss: 0.6296 - nephritis_pred_loss: 0.4435 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1325\n",
            "Epoch 477/1500\n",
            "80/80 [==============================] - 0s 102us/sample - loss: 0.7176 - inflamation_pred_loss: 0.6290 - nephritis_pred_loss: 0.4429 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9500 - nephritis_pred_mse: 0.1311\n",
            "Epoch 478/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.7169 - inflamation_pred_loss: 0.6284 - nephritis_pred_loss: 0.4423 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1320\n",
            "Epoch 479/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 0.7162 - inflamation_pred_loss: 0.6279 - nephritis_pred_loss: 0.4418 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9625 - nephritis_pred_mse: 0.1307\n",
            "Epoch 480/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.7156 - inflamation_pred_loss: 0.6273 - nephritis_pred_loss: 0.4412 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1315\n",
            "Epoch 481/1500\n",
            "80/80 [==============================] - 0s 84us/sample - loss: 0.7149 - inflamation_pred_loss: 0.6268 - nephritis_pred_loss: 0.4406 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9625 - nephritis_pred_mse: 0.1302\n",
            "Epoch 482/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.7142 - inflamation_pred_loss: 0.6262 - nephritis_pred_loss: 0.4401 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1310\n",
            "Epoch 483/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.7135 - inflamation_pred_loss: 0.6256 - nephritis_pred_loss: 0.4395 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9625 - nephritis_pred_mse: 0.1297\n",
            "Epoch 484/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.7128 - inflamation_pred_loss: 0.6251 - nephritis_pred_loss: 0.4389 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1305\n",
            "Epoch 485/1500\n",
            "80/80 [==============================] - 0s 995us/sample - loss: 0.7122 - inflamation_pred_loss: 0.6245 - nephritis_pred_loss: 0.4384 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9625 - nephritis_pred_mse: 0.1292\n",
            "Epoch 486/1500\n",
            "80/80 [==============================] - 0s 82us/sample - loss: 0.7115 - inflamation_pred_loss: 0.6239 - nephritis_pred_loss: 0.4378 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1300\n",
            "Epoch 487/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 0.7108 - inflamation_pred_loss: 0.6233 - nephritis_pred_loss: 0.4372 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9625 - nephritis_pred_mse: 0.1287\n",
            "Epoch 488/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.7101 - inflamation_pred_loss: 0.6228 - nephritis_pred_loss: 0.4367 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1295\n",
            "Epoch 489/1500\n",
            "80/80 [==============================] - 0s 921us/sample - loss: 0.7094 - inflamation_pred_loss: 0.6222 - nephritis_pred_loss: 0.4361 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1282\n",
            "Epoch 490/1500\n",
            "80/80 [==============================] - 0s 52us/sample - loss: 0.7087 - inflamation_pred_loss: 0.6216 - nephritis_pred_loss: 0.4356 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1290\n",
            "Epoch 491/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.7080 - inflamation_pred_loss: 0.6210 - nephritis_pred_loss: 0.4350 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1277\n",
            "Epoch 492/1500\n",
            "80/80 [==============================] - 0s 89us/sample - loss: 0.7073 - inflamation_pred_loss: 0.6204 - nephritis_pred_loss: 0.4344 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1285\n",
            "Epoch 493/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.7066 - inflamation_pred_loss: 0.6199 - nephritis_pred_loss: 0.4338 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1272\n",
            "Epoch 494/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.7059 - inflamation_pred_loss: 0.6193 - nephritis_pred_loss: 0.4333 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1280\n",
            "Epoch 495/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.7052 - inflamation_pred_loss: 0.6187 - nephritis_pred_loss: 0.4327 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1267\n",
            "Epoch 496/1500\n",
            "80/80 [==============================] - 0s 88us/sample - loss: 0.7045 - inflamation_pred_loss: 0.6181 - nephritis_pred_loss: 0.4322 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1275\n",
            "Epoch 497/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.7038 - inflamation_pred_loss: 0.6175 - nephritis_pred_loss: 0.4316 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1262\n",
            "Epoch 498/1500\n",
            "80/80 [==============================] - 0s 860us/sample - loss: 0.7031 - inflamation_pred_loss: 0.6169 - nephritis_pred_loss: 0.4310 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1270\n",
            "Epoch 499/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.7024 - inflamation_pred_loss: 0.6163 - nephritis_pred_loss: 0.4305 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1257\n",
            "Epoch 500/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.7017 - inflamation_pred_loss: 0.6158 - nephritis_pred_loss: 0.4299 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1265\n",
            "Epoch 501/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.7010 - inflamation_pred_loss: 0.6152 - nephritis_pred_loss: 0.4293 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1252\n",
            "Epoch 502/1500\n",
            "80/80 [==============================] - 0s 49us/sample - loss: 0.7003 - inflamation_pred_loss: 0.6146 - nephritis_pred_loss: 0.4288 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1261\n",
            "Epoch 503/1500\n",
            "80/80 [==============================] - 0s 981us/sample - loss: 0.6996 - inflamation_pred_loss: 0.6140 - nephritis_pred_loss: 0.4282 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1247\n",
            "Epoch 504/1500\n",
            "80/80 [==============================] - 0s 83us/sample - loss: 0.6989 - inflamation_pred_loss: 0.6134 - nephritis_pred_loss: 0.4276 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1256\n",
            "Epoch 505/1500\n",
            "80/80 [==============================] - 0s 137us/sample - loss: 0.6982 - inflamation_pred_loss: 0.6128 - nephritis_pred_loss: 0.4271 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1242\n",
            "Epoch 506/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.6975 - inflamation_pred_loss: 0.6122 - nephritis_pred_loss: 0.4265 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1251\n",
            "Epoch 507/1500\n",
            "80/80 [==============================] - 0s 972us/sample - loss: 0.6968 - inflamation_pred_loss: 0.6116 - nephritis_pred_loss: 0.4259 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1237\n",
            "Epoch 508/1500\n",
            "80/80 [==============================] - 0s 97us/sample - loss: 0.6961 - inflamation_pred_loss: 0.6110 - nephritis_pred_loss: 0.4254 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1246\n",
            "Epoch 509/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.6953 - inflamation_pred_loss: 0.6104 - nephritis_pred_loss: 0.4248 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1233\n",
            "Epoch 510/1500\n",
            "80/80 [==============================] - 0s 52us/sample - loss: 0.6946 - inflamation_pred_loss: 0.6098 - nephritis_pred_loss: 0.4243 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1241\n",
            "Epoch 511/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.6939 - inflamation_pred_loss: 0.6092 - nephritis_pred_loss: 0.4237 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1228\n",
            "Epoch 512/1500\n",
            "80/80 [==============================] - 0s 125us/sample - loss: 0.6932 - inflamation_pred_loss: 0.6086 - nephritis_pred_loss: 0.4231 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1236\n",
            "Epoch 513/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.6925 - inflamation_pred_loss: 0.6080 - nephritis_pred_loss: 0.4226 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1223\n",
            "Epoch 514/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 0.6917 - inflamation_pred_loss: 0.6073 - nephritis_pred_loss: 0.4220 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1231\n",
            "Epoch 515/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.6910 - inflamation_pred_loss: 0.6067 - nephritis_pred_loss: 0.4214 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1218\n",
            "Epoch 516/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.6903 - inflamation_pred_loss: 0.6061 - nephritis_pred_loss: 0.4209 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1226\n",
            "Epoch 517/1500\n",
            "80/80 [==============================] - 0s 85us/sample - loss: 0.6896 - inflamation_pred_loss: 0.6055 - nephritis_pred_loss: 0.4203 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1213\n",
            "Epoch 518/1500\n",
            "80/80 [==============================] - 0s 87us/sample - loss: 0.6889 - inflamation_pred_loss: 0.6049 - nephritis_pred_loss: 0.4198 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1222\n",
            "Epoch 519/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.6881 - inflamation_pred_loss: 0.6043 - nephritis_pred_loss: 0.4192 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1208\n",
            "Epoch 520/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.6874 - inflamation_pred_loss: 0.6037 - nephritis_pred_loss: 0.4186 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1217\n",
            "Epoch 521/1500\n",
            "80/80 [==============================] - 0s 953us/sample - loss: 0.6867 - inflamation_pred_loss: 0.6031 - nephritis_pred_loss: 0.4181 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1203\n",
            "Epoch 522/1500\n",
            "80/80 [==============================] - 0s 109us/sample - loss: 0.6859 - inflamation_pred_loss: 0.6024 - nephritis_pred_loss: 0.4175 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1212\n",
            "Epoch 523/1500\n",
            "80/80 [==============================] - 0s 120us/sample - loss: 0.6852 - inflamation_pred_loss: 0.6018 - nephritis_pred_loss: 0.4169 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.1199\n",
            "Epoch 524/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.6845 - inflamation_pred_loss: 0.6012 - nephritis_pred_loss: 0.4164 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1207\n",
            "Epoch 525/1500\n",
            "80/80 [==============================] - 0s 987us/sample - loss: 0.6837 - inflamation_pred_loss: 0.6006 - nephritis_pred_loss: 0.4158 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9875 - nephritis_pred_mse: 0.1194\n",
            "Epoch 526/1500\n",
            "80/80 [==============================] - 0s 93us/sample - loss: 0.6830 - inflamation_pred_loss: 0.5999 - nephritis_pred_loss: 0.4153 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1202\n",
            "Epoch 527/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.6823 - inflamation_pred_loss: 0.5993 - nephritis_pred_loss: 0.4147 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9875 - nephritis_pred_mse: 0.1189\n",
            "Epoch 528/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.6815 - inflamation_pred_loss: 0.5987 - nephritis_pred_loss: 0.4141 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1198\n",
            "Epoch 529/1500\n",
            "80/80 [==============================] - 0s 894us/sample - loss: 0.6808 - inflamation_pred_loss: 0.5981 - nephritis_pred_loss: 0.4136 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9875 - nephritis_pred_mse: 0.1184\n",
            "Epoch 530/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.6800 - inflamation_pred_loss: 0.5974 - nephritis_pred_loss: 0.4130 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1193\n",
            "Epoch 531/1500\n",
            "80/80 [==============================] - 0s 90us/sample - loss: 0.6793 - inflamation_pred_loss: 0.5968 - nephritis_pred_loss: 0.4124 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9875 - nephritis_pred_mse: 0.1179\n",
            "Epoch 532/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.6786 - inflamation_pred_loss: 0.5962 - nephritis_pred_loss: 0.4119 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1188\n",
            "Epoch 533/1500\n",
            "80/80 [==============================] - 0s 1000us/sample - loss: 0.6778 - inflamation_pred_loss: 0.5956 - nephritis_pred_loss: 0.4113 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1175\n",
            "Epoch 534/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.6771 - inflamation_pred_loss: 0.5949 - nephritis_pred_loss: 0.4108 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1183\n",
            "Epoch 535/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.6763 - inflamation_pred_loss: 0.5943 - nephritis_pred_loss: 0.4102 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1170\n",
            "Epoch 536/1500\n",
            "80/80 [==============================] - 0s 51us/sample - loss: 0.6756 - inflamation_pred_loss: 0.5937 - nephritis_pred_loss: 0.4096 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1178\n",
            "Epoch 537/1500\n",
            "80/80 [==============================] - 0s 914us/sample - loss: 0.6748 - inflamation_pred_loss: 0.5930 - nephritis_pred_loss: 0.4091 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1165\n",
            "Epoch 538/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.6741 - inflamation_pred_loss: 0.5924 - nephritis_pred_loss: 0.4085 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1174\n",
            "Epoch 539/1500\n",
            "80/80 [==============================] - 0s 89us/sample - loss: 0.6733 - inflamation_pred_loss: 0.5917 - nephritis_pred_loss: 0.4079 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1160\n",
            "Epoch 540/1500\n",
            "80/80 [==============================] - 0s 96us/sample - loss: 0.6726 - inflamation_pred_loss: 0.5911 - nephritis_pred_loss: 0.4074 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1169\n",
            "Epoch 541/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.6718 - inflamation_pred_loss: 0.5905 - nephritis_pred_loss: 0.4068 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1155\n",
            "Epoch 542/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.6711 - inflamation_pred_loss: 0.5898 - nephritis_pred_loss: 0.4063 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1164\n",
            "Epoch 543/1500\n",
            "80/80 [==============================] - 0s 85us/sample - loss: 0.6703 - inflamation_pred_loss: 0.5892 - nephritis_pred_loss: 0.4057 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1151\n",
            "Epoch 544/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.6696 - inflamation_pred_loss: 0.5885 - nephritis_pred_loss: 0.4052 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1159\n",
            "Epoch 545/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.6688 - inflamation_pred_loss: 0.5879 - nephritis_pred_loss: 0.4046 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1146\n",
            "Epoch 546/1500\n",
            "80/80 [==============================] - 0s 34us/sample - loss: 0.6681 - inflamation_pred_loss: 0.5873 - nephritis_pred_loss: 0.4040 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1155\n",
            "Epoch 547/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.6673 - inflamation_pred_loss: 0.5866 - nephritis_pred_loss: 0.4035 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1141\n",
            "Epoch 548/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.6666 - inflamation_pred_loss: 0.5860 - nephritis_pred_loss: 0.4029 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1150\n",
            "Epoch 549/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.6658 - inflamation_pred_loss: 0.5853 - nephritis_pred_loss: 0.4023 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1137\n",
            "Epoch 550/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.6650 - inflamation_pred_loss: 0.5847 - nephritis_pred_loss: 0.4018 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1145\n",
            "Epoch 551/1500\n",
            "80/80 [==============================] - 0s 34us/sample - loss: 0.6643 - inflamation_pred_loss: 0.5840 - nephritis_pred_loss: 0.4012 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1132\n",
            "Epoch 552/1500\n",
            "80/80 [==============================] - 0s 824us/sample - loss: 0.6635 - inflamation_pred_loss: 0.5834 - nephritis_pred_loss: 0.4007 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1141\n",
            "Epoch 553/1500\n",
            "80/80 [==============================] - 0s 86us/sample - loss: 0.6627 - inflamation_pred_loss: 0.5827 - nephritis_pred_loss: 0.4001 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1127\n",
            "Epoch 554/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.6620 - inflamation_pred_loss: 0.5821 - nephritis_pred_loss: 0.3996 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1136\n",
            "Epoch 555/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.6612 - inflamation_pred_loss: 0.5814 - nephritis_pred_loss: 0.3990 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1122\n",
            "Epoch 556/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.6605 - inflamation_pred_loss: 0.5808 - nephritis_pred_loss: 0.3984 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1131\n",
            "Epoch 557/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.6597 - inflamation_pred_loss: 0.5801 - nephritis_pred_loss: 0.3979 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1118\n",
            "Epoch 558/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.6589 - inflamation_pred_loss: 0.5795 - nephritis_pred_loss: 0.3973 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1127\n",
            "Epoch 559/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.6581 - inflamation_pred_loss: 0.5788 - nephritis_pred_loss: 0.3968 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1113\n",
            "Epoch 560/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.6574 - inflamation_pred_loss: 0.5781 - nephritis_pred_loss: 0.3962 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1122\n",
            "Epoch 561/1500\n",
            "80/80 [==============================] - 0s 33us/sample - loss: 0.6566 - inflamation_pred_loss: 0.5775 - nephritis_pred_loss: 0.3957 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1108\n",
            "Epoch 562/1500\n",
            "80/80 [==============================] - 0s 854us/sample - loss: 0.6558 - inflamation_pred_loss: 0.5768 - nephritis_pred_loss: 0.3951 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1117\n",
            "Epoch 563/1500\n",
            "80/80 [==============================] - 0s 100us/sample - loss: 0.6551 - inflamation_pred_loss: 0.5762 - nephritis_pred_loss: 0.3945 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1104\n",
            "Epoch 564/1500\n",
            "80/80 [==============================] - 0s 90us/sample - loss: 0.6543 - inflamation_pred_loss: 0.5755 - nephritis_pred_loss: 0.3940 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1113\n",
            "Epoch 565/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.6535 - inflamation_pred_loss: 0.5748 - nephritis_pred_loss: 0.3934 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1099\n",
            "Epoch 566/1500\n",
            "80/80 [==============================] - 0s 933us/sample - loss: 0.6527 - inflamation_pred_loss: 0.5742 - nephritis_pred_loss: 0.3929 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1108\n",
            "Epoch 567/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.6520 - inflamation_pred_loss: 0.5735 - nephritis_pred_loss: 0.3923 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1095\n",
            "Epoch 568/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.6512 - inflamation_pred_loss: 0.5728 - nephritis_pred_loss: 0.3918 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1103\n",
            "Epoch 569/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 0.6504 - inflamation_pred_loss: 0.5722 - nephritis_pred_loss: 0.3912 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1090\n",
            "Epoch 570/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.6496 - inflamation_pred_loss: 0.5715 - nephritis_pred_loss: 0.3907 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1099\n",
            "Epoch 571/1500\n",
            "80/80 [==============================] - 0s 92us/sample - loss: 0.6489 - inflamation_pred_loss: 0.5708 - nephritis_pred_loss: 0.3901 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1085\n",
            "Epoch 572/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.6481 - inflamation_pred_loss: 0.5702 - nephritis_pred_loss: 0.3896 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1094\n",
            "Epoch 573/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.6473 - inflamation_pred_loss: 0.5695 - nephritis_pred_loss: 0.3890 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1081\n",
            "Epoch 574/1500\n",
            "80/80 [==============================] - 0s 995us/sample - loss: 0.6465 - inflamation_pred_loss: 0.5688 - nephritis_pred_loss: 0.3884 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1090\n",
            "Epoch 575/1500\n",
            "80/80 [==============================] - 0s 80us/sample - loss: 0.6457 - inflamation_pred_loss: 0.5682 - nephritis_pred_loss: 0.3879 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1076\n",
            "Epoch 576/1500\n",
            "80/80 [==============================] - 0s 103us/sample - loss: 0.6450 - inflamation_pred_loss: 0.5675 - nephritis_pred_loss: 0.3873 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1085\n",
            "Epoch 577/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.6442 - inflamation_pred_loss: 0.5668 - nephritis_pred_loss: 0.3868 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1072\n",
            "Epoch 578/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 0.6434 - inflamation_pred_loss: 0.5662 - nephritis_pred_loss: 0.3862 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1080\n",
            "Epoch 579/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 0.6426 - inflamation_pred_loss: 0.5655 - nephritis_pred_loss: 0.3857 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1067\n",
            "Epoch 580/1500\n",
            "80/80 [==============================] - 0s 109us/sample - loss: 0.6418 - inflamation_pred_loss: 0.5648 - nephritis_pred_loss: 0.3851 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1076\n",
            "Epoch 581/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.6410 - inflamation_pred_loss: 0.5641 - nephritis_pred_loss: 0.3846 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1062\n",
            "Epoch 582/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.6403 - inflamation_pred_loss: 0.5635 - nephritis_pred_loss: 0.3840 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1071\n",
            "Epoch 583/1500\n",
            "80/80 [==============================] - 0s 878us/sample - loss: 0.6395 - inflamation_pred_loss: 0.5628 - nephritis_pred_loss: 0.3835 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1058\n",
            "Epoch 584/1500\n",
            "80/80 [==============================] - 0s 114us/sample - loss: 0.6387 - inflamation_pred_loss: 0.5621 - nephritis_pred_loss: 0.3829 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1067\n",
            "Epoch 585/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.6379 - inflamation_pred_loss: 0.5614 - nephritis_pred_loss: 0.3824 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1053\n",
            "Epoch 586/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.6371 - inflamation_pred_loss: 0.5607 - nephritis_pred_loss: 0.3818 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1062\n",
            "Epoch 587/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.6363 - inflamation_pred_loss: 0.5601 - nephritis_pred_loss: 0.3813 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1049\n",
            "Epoch 588/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.6355 - inflamation_pred_loss: 0.5594 - nephritis_pred_loss: 0.3807 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1058\n",
            "Epoch 589/1500\n",
            "80/80 [==============================] - 0s 874us/sample - loss: 0.6347 - inflamation_pred_loss: 0.5587 - nephritis_pred_loss: 0.3802 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1044\n",
            "Epoch 590/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.6340 - inflamation_pred_loss: 0.5580 - nephritis_pred_loss: 0.3796 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1053\n",
            "Epoch 591/1500\n",
            "80/80 [==============================] - 0s 99us/sample - loss: 0.6332 - inflamation_pred_loss: 0.5573 - nephritis_pred_loss: 0.3791 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1040\n",
            "Epoch 592/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.6324 - inflamation_pred_loss: 0.5567 - nephritis_pred_loss: 0.3785 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1049\n",
            "Epoch 593/1500\n",
            "80/80 [==============================] - 0s 977us/sample - loss: 0.6316 - inflamation_pred_loss: 0.5560 - nephritis_pred_loss: 0.3780 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1035\n",
            "Epoch 594/1500\n",
            "80/80 [==============================] - 0s 118us/sample - loss: 0.6308 - inflamation_pred_loss: 0.5553 - nephritis_pred_loss: 0.3774 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1044\n",
            "Epoch 595/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.6300 - inflamation_pred_loss: 0.5546 - nephritis_pred_loss: 0.3769 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1031\n",
            "Epoch 596/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.6292 - inflamation_pred_loss: 0.5539 - nephritis_pred_loss: 0.3763 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1040\n",
            "Epoch 597/1500\n",
            "80/80 [==============================] - 0s 927us/sample - loss: 0.6284 - inflamation_pred_loss: 0.5532 - nephritis_pred_loss: 0.3758 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1026\n",
            "Epoch 598/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.6276 - inflamation_pred_loss: 0.5526 - nephritis_pred_loss: 0.3752 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1035\n",
            "Epoch 599/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.6268 - inflamation_pred_loss: 0.5519 - nephritis_pred_loss: 0.3747 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1022\n",
            "Epoch 600/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.6260 - inflamation_pred_loss: 0.5512 - nephritis_pred_loss: 0.3741 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1031\n",
            "Epoch 601/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.6252 - inflamation_pred_loss: 0.5505 - nephritis_pred_loss: 0.3736 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1017\n",
            "Epoch 602/1500\n",
            "80/80 [==============================] - 0s 87us/sample - loss: 0.6244 - inflamation_pred_loss: 0.5498 - nephritis_pred_loss: 0.3730 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1026\n",
            "Epoch 603/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 0.6236 - inflamation_pred_loss: 0.5491 - nephritis_pred_loss: 0.3725 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1013\n",
            "Epoch 604/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.6228 - inflamation_pred_loss: 0.5484 - nephritis_pred_loss: 0.3720 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1022\n",
            "Epoch 605/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.6220 - inflamation_pred_loss: 0.5477 - nephritis_pred_loss: 0.3714 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1009\n",
            "Epoch 606/1500\n",
            "80/80 [==============================] - 0s 75us/sample - loss: 0.6212 - inflamation_pred_loss: 0.5470 - nephritis_pred_loss: 0.3709 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1017\n",
            "Epoch 607/1500\n",
            "80/80 [==============================] - 0s 171us/sample - loss: 0.6204 - inflamation_pred_loss: 0.5464 - nephritis_pred_loss: 0.3703 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1004\n",
            "Epoch 608/1500\n",
            "80/80 [==============================] - 0s 51us/sample - loss: 0.6196 - inflamation_pred_loss: 0.5457 - nephritis_pred_loss: 0.3698 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1013\n",
            "Epoch 609/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.6188 - inflamation_pred_loss: 0.5450 - nephritis_pred_loss: 0.3692 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.1000\n",
            "Epoch 610/1500\n",
            "80/80 [==============================] - 0s 33us/sample - loss: 0.6180 - inflamation_pred_loss: 0.5443 - nephritis_pred_loss: 0.3687 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1009\n",
            "Epoch 611/1500\n",
            "80/80 [==============================] - 0s 853us/sample - loss: 0.6172 - inflamation_pred_loss: 0.5436 - nephritis_pred_loss: 0.3681 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0995\n",
            "Epoch 612/1500\n",
            "80/80 [==============================] - 0s 106us/sample - loss: 0.6164 - inflamation_pred_loss: 0.5429 - nephritis_pred_loss: 0.3676 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1004\n",
            "Epoch 613/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.6156 - inflamation_pred_loss: 0.5422 - nephritis_pred_loss: 0.3670 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0991\n",
            "Epoch 614/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.6148 - inflamation_pred_loss: 0.5415 - nephritis_pred_loss: 0.3665 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.1000\n",
            "Epoch 615/1500\n",
            "80/80 [==============================] - 0s 82us/sample - loss: 0.6140 - inflamation_pred_loss: 0.5408 - nephritis_pred_loss: 0.3660 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0987\n",
            "Epoch 616/1500\n",
            "80/80 [==============================] - 0s 93us/sample - loss: 0.6132 - inflamation_pred_loss: 0.5401 - nephritis_pred_loss: 0.3654 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.0996\n",
            "Epoch 617/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.6124 - inflamation_pred_loss: 0.5394 - nephritis_pred_loss: 0.3649 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0982\n",
            "Epoch 618/1500\n",
            "80/80 [==============================] - 0s 33us/sample - loss: 0.6116 - inflamation_pred_loss: 0.5387 - nephritis_pred_loss: 0.3643 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.0991\n",
            "Epoch 619/1500\n",
            "80/80 [==============================] - 0s 962us/sample - loss: 0.6108 - inflamation_pred_loss: 0.5380 - nephritis_pred_loss: 0.3638 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0978\n",
            "Epoch 620/1500\n",
            "80/80 [==============================] - 0s 89us/sample - loss: 0.6100 - inflamation_pred_loss: 0.5373 - nephritis_pred_loss: 0.3633 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.0987\n",
            "Epoch 621/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.6092 - inflamation_pred_loss: 0.5366 - nephritis_pred_loss: 0.3627 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0974\n",
            "Epoch 622/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.6084 - inflamation_pred_loss: 0.5359 - nephritis_pred_loss: 0.3622 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.0983\n",
            "Epoch 623/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.6076 - inflamation_pred_loss: 0.5352 - nephritis_pred_loss: 0.3616 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0969\n",
            "Epoch 624/1500\n",
            "80/80 [==============================] - 0s 900us/sample - loss: 0.6067 - inflamation_pred_loss: 0.5345 - nephritis_pred_loss: 0.3611 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.0978\n",
            "Epoch 625/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.6059 - inflamation_pred_loss: 0.5338 - nephritis_pred_loss: 0.3606 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0965\n",
            "Epoch 626/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.6051 - inflamation_pred_loss: 0.5331 - nephritis_pred_loss: 0.3600 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.0974\n",
            "Epoch 627/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.6043 - inflamation_pred_loss: 0.5324 - nephritis_pred_loss: 0.3595 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0961\n",
            "Epoch 628/1500\n",
            "80/80 [==============================] - 0s 987us/sample - loss: 0.6035 - inflamation_pred_loss: 0.5317 - nephritis_pred_loss: 0.3589 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.0970\n",
            "Epoch 629/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.6027 - inflamation_pred_loss: 0.5310 - nephritis_pred_loss: 0.3584 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0956\n",
            "Epoch 630/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.6019 - inflamation_pred_loss: 0.5303 - nephritis_pred_loss: 0.3579 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.0965\n",
            "Epoch 631/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.6011 - inflamation_pred_loss: 0.5296 - nephritis_pred_loss: 0.3573 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0952\n",
            "Epoch 632/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.6003 - inflamation_pred_loss: 0.5289 - nephritis_pred_loss: 0.3568 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.0961\n",
            "Epoch 633/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.5995 - inflamation_pred_loss: 0.5282 - nephritis_pred_loss: 0.3563 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0948\n",
            "Epoch 634/1500\n",
            "80/80 [==============================] - 0s 75us/sample - loss: 0.5987 - inflamation_pred_loss: 0.5275 - nephritis_pred_loss: 0.3557 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.0957\n",
            "Epoch 635/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.5978 - inflamation_pred_loss: 0.5268 - nephritis_pred_loss: 0.3552 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0944\n",
            "Epoch 636/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.5970 - inflamation_pred_loss: 0.5261 - nephritis_pred_loss: 0.3547 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9125 - nephritis_pred_mse: 0.0953\n",
            "Epoch 637/1500\n",
            "80/80 [==============================] - 0s 950us/sample - loss: 0.5962 - inflamation_pred_loss: 0.5254 - nephritis_pred_loss: 0.3541 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0939\n",
            "Epoch 638/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.5954 - inflamation_pred_loss: 0.5247 - nephritis_pred_loss: 0.3536 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.0948\n",
            "Epoch 639/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.5946 - inflamation_pred_loss: 0.5240 - nephritis_pred_loss: 0.3530 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0935\n",
            "Epoch 640/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.5938 - inflamation_pred_loss: 0.5233 - nephritis_pred_loss: 0.3525 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.0944\n",
            "Epoch 641/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.5930 - inflamation_pred_loss: 0.5226 - nephritis_pred_loss: 0.3520 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0931\n",
            "Epoch 642/1500\n",
            "80/80 [==============================] - 0s 950us/sample - loss: 0.5922 - inflamation_pred_loss: 0.5219 - nephritis_pred_loss: 0.3514 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.0940\n",
            "Epoch 643/1500\n",
            "80/80 [==============================] - 0s 80us/sample - loss: 0.5913 - inflamation_pred_loss: 0.5212 - nephritis_pred_loss: 0.3509 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0927\n",
            "Epoch 644/1500\n",
            "80/80 [==============================] - 0s 104us/sample - loss: 0.5905 - inflamation_pred_loss: 0.5205 - nephritis_pred_loss: 0.3504 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.0936\n",
            "Epoch 645/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.5897 - inflamation_pred_loss: 0.5197 - nephritis_pred_loss: 0.3498 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0922\n",
            "Epoch 646/1500\n",
            "80/80 [==============================] - 0s 968us/sample - loss: 0.5889 - inflamation_pred_loss: 0.5190 - nephritis_pred_loss: 0.3493 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.0931\n",
            "Epoch 647/1500\n",
            "80/80 [==============================] - 0s 93us/sample - loss: 0.5881 - inflamation_pred_loss: 0.5183 - nephritis_pred_loss: 0.3488 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0918\n",
            "Epoch 648/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.5873 - inflamation_pred_loss: 0.5176 - nephritis_pred_loss: 0.3483 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.0927\n",
            "Epoch 649/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.5865 - inflamation_pred_loss: 0.5169 - nephritis_pred_loss: 0.3477 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0914\n",
            "Epoch 650/1500\n",
            "80/80 [==============================] - 0s 951us/sample - loss: 0.5856 - inflamation_pred_loss: 0.5162 - nephritis_pred_loss: 0.3472 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.0923\n",
            "Epoch 651/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 0.5848 - inflamation_pred_loss: 0.5155 - nephritis_pred_loss: 0.3467 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0910\n",
            "Epoch 652/1500\n",
            "80/80 [==============================] - 0s 92us/sample - loss: 0.5840 - inflamation_pred_loss: 0.5148 - nephritis_pred_loss: 0.3461 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.0919\n",
            "Epoch 653/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.5832 - inflamation_pred_loss: 0.5141 - nephritis_pred_loss: 0.3456 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0906\n",
            "Epoch 654/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.5824 - inflamation_pred_loss: 0.5134 - nephritis_pred_loss: 0.3451 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.0915\n",
            "Epoch 655/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.5816 - inflamation_pred_loss: 0.5127 - nephritis_pred_loss: 0.3445 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0902\n",
            "Epoch 656/1500\n",
            "80/80 [==============================] - 0s 866us/sample - loss: 0.5808 - inflamation_pred_loss: 0.5120 - nephritis_pred_loss: 0.3440 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.0911\n",
            "Epoch 657/1500\n",
            "80/80 [==============================] - 0s 85us/sample - loss: 0.5799 - inflamation_pred_loss: 0.5112 - nephritis_pred_loss: 0.3435 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0898\n",
            "Epoch 658/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.5791 - inflamation_pred_loss: 0.5105 - nephritis_pred_loss: 0.3430 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.0907\n",
            "Epoch 659/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.5783 - inflamation_pred_loss: 0.5098 - nephritis_pred_loss: 0.3424 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0893\n",
            "Epoch 660/1500\n",
            "80/80 [==============================] - 0s 52us/sample - loss: 0.5775 - inflamation_pred_loss: 0.5091 - nephritis_pred_loss: 0.3419 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.0902\n",
            "Epoch 661/1500\n",
            "80/80 [==============================] - 0s 897us/sample - loss: 0.5767 - inflamation_pred_loss: 0.5084 - nephritis_pred_loss: 0.3414 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0889\n",
            "Epoch 662/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.5759 - inflamation_pred_loss: 0.5077 - nephritis_pred_loss: 0.3409 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9375 - nephritis_pred_mse: 0.0898\n",
            "Epoch 663/1500\n",
            "80/80 [==============================] - 0s 75us/sample - loss: 0.5751 - inflamation_pred_loss: 0.5070 - nephritis_pred_loss: 0.3403 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0885\n",
            "Epoch 664/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.5742 - inflamation_pred_loss: 0.5063 - nephritis_pred_loss: 0.3398 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9500 - nephritis_pred_mse: 0.0894\n",
            "Epoch 665/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.5734 - inflamation_pred_loss: 0.5056 - nephritis_pred_loss: 0.3393 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0881\n",
            "Epoch 666/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.5726 - inflamation_pred_loss: 0.5049 - nephritis_pred_loss: 0.3388 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9500 - nephritis_pred_mse: 0.0890\n",
            "Epoch 667/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.5718 - inflamation_pred_loss: 0.5041 - nephritis_pred_loss: 0.3382 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0877\n",
            "Epoch 668/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.5710 - inflamation_pred_loss: 0.5034 - nephritis_pred_loss: 0.3377 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9500 - nephritis_pred_mse: 0.0886\n",
            "Epoch 669/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.5702 - inflamation_pred_loss: 0.5027 - nephritis_pred_loss: 0.3372 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0873\n",
            "Epoch 670/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.5693 - inflamation_pred_loss: 0.5020 - nephritis_pred_loss: 0.3367 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9500 - nephritis_pred_mse: 0.0882\n",
            "Epoch 671/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.5685 - inflamation_pred_loss: 0.5013 - nephritis_pred_loss: 0.3361 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0869\n",
            "Epoch 672/1500\n",
            "80/80 [==============================] - 0s 88us/sample - loss: 0.5677 - inflamation_pred_loss: 0.5006 - nephritis_pred_loss: 0.3356 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9500 - nephritis_pred_mse: 0.0878\n",
            "Epoch 673/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.5669 - inflamation_pred_loss: 0.4999 - nephritis_pred_loss: 0.3351 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0865\n",
            "Epoch 674/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.5661 - inflamation_pred_loss: 0.4992 - nephritis_pred_loss: 0.3346 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9500 - nephritis_pred_mse: 0.0874\n",
            "Epoch 675/1500\n",
            "80/80 [==============================] - 0s 918us/sample - loss: 0.5653 - inflamation_pred_loss: 0.4985 - nephritis_pred_loss: 0.3341 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0861\n",
            "Epoch 676/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.5644 - inflamation_pred_loss: 0.4977 - nephritis_pred_loss: 0.3335 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9625 - nephritis_pred_mse: 0.0870\n",
            "Epoch 677/1500\n",
            "80/80 [==============================] - 0s 130us/sample - loss: 0.5636 - inflamation_pred_loss: 0.4970 - nephritis_pred_loss: 0.3330 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0857\n",
            "Epoch 678/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.5628 - inflamation_pred_loss: 0.4963 - nephritis_pred_loss: 0.3325 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9625 - nephritis_pred_mse: 0.0866\n",
            "Epoch 679/1500\n",
            "80/80 [==============================] - 0s 969us/sample - loss: 0.5620 - inflamation_pred_loss: 0.4956 - nephritis_pred_loss: 0.3320 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0853\n",
            "Epoch 680/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.5612 - inflamation_pred_loss: 0.4949 - nephritis_pred_loss: 0.3315 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9625 - nephritis_pred_mse: 0.0862\n",
            "Epoch 681/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.5604 - inflamation_pred_loss: 0.4942 - nephritis_pred_loss: 0.3309 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0849\n",
            "Epoch 682/1500\n",
            "80/80 [==============================] - 0s 51us/sample - loss: 0.5596 - inflamation_pred_loss: 0.4935 - nephritis_pred_loss: 0.3304 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.0858\n",
            "Epoch 683/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.5587 - inflamation_pred_loss: 0.4928 - nephritis_pred_loss: 0.3299 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0845\n",
            "Epoch 684/1500\n",
            "80/80 [==============================] - 0s 931us/sample - loss: 0.5579 - inflamation_pred_loss: 0.4920 - nephritis_pred_loss: 0.3294 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.0854\n",
            "Epoch 685/1500\n",
            "80/80 [==============================] - 0s 120us/sample - loss: 0.5571 - inflamation_pred_loss: 0.4913 - nephritis_pred_loss: 0.3289 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0841\n",
            "Epoch 686/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.5563 - inflamation_pred_loss: 0.4906 - nephritis_pred_loss: 0.3284 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.0850\n",
            "Epoch 687/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.5555 - inflamation_pred_loss: 0.4899 - nephritis_pred_loss: 0.3278 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0837\n",
            "Epoch 688/1500\n",
            "80/80 [==============================] - 0s 940us/sample - loss: 0.5547 - inflamation_pred_loss: 0.4892 - nephritis_pred_loss: 0.3273 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.0846\n",
            "Epoch 689/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.5538 - inflamation_pred_loss: 0.4885 - nephritis_pred_loss: 0.3268 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0833\n",
            "Epoch 690/1500\n",
            "80/80 [==============================] - 0s 97us/sample - loss: 0.5530 - inflamation_pred_loss: 0.4878 - nephritis_pred_loss: 0.3263 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.0842\n",
            "Epoch 691/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.5522 - inflamation_pred_loss: 0.4871 - nephritis_pred_loss: 0.3258 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0829\n",
            "Epoch 692/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.5514 - inflamation_pred_loss: 0.4864 - nephritis_pred_loss: 0.3253 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.0838\n",
            "Epoch 693/1500\n",
            "80/80 [==============================] - 0s 881us/sample - loss: 0.5506 - inflamation_pred_loss: 0.4856 - nephritis_pred_loss: 0.3248 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0826\n",
            "Epoch 694/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.5498 - inflamation_pred_loss: 0.4849 - nephritis_pred_loss: 0.3242 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.0835\n",
            "Epoch 695/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.5490 - inflamation_pred_loss: 0.4842 - nephritis_pred_loss: 0.3237 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0822\n",
            "Epoch 696/1500\n",
            "80/80 [==============================] - 0s 52us/sample - loss: 0.5481 - inflamation_pred_loss: 0.4835 - nephritis_pred_loss: 0.3232 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.0831\n",
            "Epoch 697/1500\n",
            "80/80 [==============================] - 0s 982us/sample - loss: 0.5473 - inflamation_pred_loss: 0.4828 - nephritis_pred_loss: 0.3227 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0818\n",
            "Epoch 698/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.5465 - inflamation_pred_loss: 0.4821 - nephritis_pred_loss: 0.3222 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.0827\n",
            "Epoch 699/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.5457 - inflamation_pred_loss: 0.4814 - nephritis_pred_loss: 0.3217 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0814\n",
            "Epoch 700/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.5449 - inflamation_pred_loss: 0.4807 - nephritis_pred_loss: 0.3212 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.0823\n",
            "Epoch 701/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.5441 - inflamation_pred_loss: 0.4799 - nephritis_pred_loss: 0.3207 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0810\n",
            "Epoch 702/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.5433 - inflamation_pred_loss: 0.4792 - nephritis_pred_loss: 0.3202 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.0819\n",
            "Epoch 703/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.5425 - inflamation_pred_loss: 0.4785 - nephritis_pred_loss: 0.3196 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0806\n",
            "Epoch 704/1500\n",
            "80/80 [==============================] - 0s 87us/sample - loss: 0.5416 - inflamation_pred_loss: 0.4778 - nephritis_pred_loss: 0.3191 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9750 - nephritis_pred_mse: 0.0815\n",
            "Epoch 705/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 0.5408 - inflamation_pred_loss: 0.4771 - nephritis_pred_loss: 0.3186 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0802\n",
            "Epoch 706/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.5400 - inflamation_pred_loss: 0.4764 - nephritis_pred_loss: 0.3181 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9875 - nephritis_pred_mse: 0.0811\n",
            "Epoch 707/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.5392 - inflamation_pred_loss: 0.4757 - nephritis_pred_loss: 0.3176 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0799\n",
            "Epoch 708/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 0.5384 - inflamation_pred_loss: 0.4750 - nephritis_pred_loss: 0.3171 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9875 - nephritis_pred_mse: 0.0808\n",
            "Epoch 709/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.5376 - inflamation_pred_loss: 0.4743 - nephritis_pred_loss: 0.3166 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0795\n",
            "Epoch 710/1500\n",
            "80/80 [==============================] - 0s 976us/sample - loss: 0.5368 - inflamation_pred_loss: 0.4735 - nephritis_pred_loss: 0.3161 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 0.9875 - nephritis_pred_mse: 0.0804\n",
            "Epoch 711/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.5360 - inflamation_pred_loss: 0.4728 - nephritis_pred_loss: 0.3156 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0791\n",
            "Epoch 712/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.5351 - inflamation_pred_loss: 0.4721 - nephritis_pred_loss: 0.3151 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0800\n",
            "Epoch 713/1500\n",
            "80/80 [==============================] - 0s 91us/sample - loss: 0.5343 - inflamation_pred_loss: 0.4714 - nephritis_pred_loss: 0.3146 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0787\n",
            "Epoch 714/1500\n",
            "80/80 [==============================] - 0s 959us/sample - loss: 0.5335 - inflamation_pred_loss: 0.4707 - nephritis_pred_loss: 0.3141 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0796\n",
            "Epoch 715/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 0.5327 - inflamation_pred_loss: 0.4700 - nephritis_pred_loss: 0.3136 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0784\n",
            "Epoch 716/1500\n",
            "80/80 [==============================] - 0s 117us/sample - loss: 0.5319 - inflamation_pred_loss: 0.4693 - nephritis_pred_loss: 0.3131 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0793\n",
            "Epoch 717/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.5311 - inflamation_pred_loss: 0.4686 - nephritis_pred_loss: 0.3126 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0780\n",
            "Epoch 718/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.5303 - inflamation_pred_loss: 0.4679 - nephritis_pred_loss: 0.3121 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0789\n",
            "Epoch 719/1500\n",
            "80/80 [==============================] - 0s 33us/sample - loss: 0.5295 - inflamation_pred_loss: 0.4672 - nephritis_pred_loss: 0.3116 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0776\n",
            "Epoch 720/1500\n",
            "80/80 [==============================] - 0s 857us/sample - loss: 0.5287 - inflamation_pred_loss: 0.4664 - nephritis_pred_loss: 0.3111 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0785\n",
            "Epoch 721/1500\n",
            "80/80 [==============================] - 0s 89us/sample - loss: 0.5279 - inflamation_pred_loss: 0.4657 - nephritis_pred_loss: 0.3106 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0772\n",
            "Epoch 722/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.5270 - inflamation_pred_loss: 0.4650 - nephritis_pred_loss: 0.3101 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0781\n",
            "Epoch 723/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.5262 - inflamation_pred_loss: 0.4643 - nephritis_pred_loss: 0.3096 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0769\n",
            "Epoch 724/1500\n",
            "80/80 [==============================] - 0s 977us/sample - loss: 0.5254 - inflamation_pred_loss: 0.4636 - nephritis_pred_loss: 0.3091 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0778\n",
            "Epoch 725/1500\n",
            "80/80 [==============================] - 0s 101us/sample - loss: 0.5246 - inflamation_pred_loss: 0.4629 - nephritis_pred_loss: 0.3086 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0765\n",
            "Epoch 726/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.5238 - inflamation_pred_loss: 0.4622 - nephritis_pred_loss: 0.3081 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0774\n",
            "Epoch 727/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.5230 - inflamation_pred_loss: 0.4615 - nephritis_pred_loss: 0.3076 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0761\n",
            "Epoch 728/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.5222 - inflamation_pred_loss: 0.4608 - nephritis_pred_loss: 0.3071 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0770\n",
            "Epoch 729/1500\n",
            "80/80 [==============================] - 0s 886us/sample - loss: 0.5214 - inflamation_pred_loss: 0.4601 - nephritis_pred_loss: 0.3066 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0758\n",
            "Epoch 730/1500\n",
            "80/80 [==============================] - 0s 158us/sample - loss: 0.5206 - inflamation_pred_loss: 0.4594 - nephritis_pred_loss: 0.3061 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0767\n",
            "Epoch 731/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.5198 - inflamation_pred_loss: 0.4586 - nephritis_pred_loss: 0.3056 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0754\n",
            "Epoch 732/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.5190 - inflamation_pred_loss: 0.4579 - nephritis_pred_loss: 0.3051 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0763\n",
            "Epoch 733/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.5181 - inflamation_pred_loss: 0.4572 - nephritis_pred_loss: 0.3046 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0750\n",
            "Epoch 734/1500\n",
            "80/80 [==============================] - 0s 944us/sample - loss: 0.5173 - inflamation_pred_loss: 0.4565 - nephritis_pred_loss: 0.3041 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0759\n",
            "Epoch 735/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.5165 - inflamation_pred_loss: 0.4558 - nephritis_pred_loss: 0.3036 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0747\n",
            "Epoch 736/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.5157 - inflamation_pred_loss: 0.4551 - nephritis_pred_loss: 0.3031 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0756\n",
            "Epoch 737/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.5149 - inflamation_pred_loss: 0.4544 - nephritis_pred_loss: 0.3026 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0743\n",
            "Epoch 738/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.5141 - inflamation_pred_loss: 0.4537 - nephritis_pred_loss: 0.3021 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0752\n",
            "Epoch 739/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.5133 - inflamation_pred_loss: 0.4530 - nephritis_pred_loss: 0.3016 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0739\n",
            "Epoch 740/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.5125 - inflamation_pred_loss: 0.4523 - nephritis_pred_loss: 0.3011 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0748\n",
            "Epoch 741/1500\n",
            "80/80 [==============================] - 0s 82us/sample - loss: 0.5117 - inflamation_pred_loss: 0.4516 - nephritis_pred_loss: 0.3006 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0736\n",
            "Epoch 742/1500\n",
            "80/80 [==============================] - 0s 926us/sample - loss: 0.5109 - inflamation_pred_loss: 0.4508 - nephritis_pred_loss: 0.3002 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0745\n",
            "Epoch 743/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.5101 - inflamation_pred_loss: 0.4501 - nephritis_pred_loss: 0.2997 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0732\n",
            "Epoch 744/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.5093 - inflamation_pred_loss: 0.4494 - nephritis_pred_loss: 0.2992 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0741\n",
            "Epoch 745/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 0.5085 - inflamation_pred_loss: 0.4487 - nephritis_pred_loss: 0.2987 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0729\n",
            "Epoch 746/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.5076 - inflamation_pred_loss: 0.4480 - nephritis_pred_loss: 0.2982 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0738\n",
            "Epoch 747/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.5068 - inflamation_pred_loss: 0.4473 - nephritis_pred_loss: 0.2977 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0725\n",
            "Epoch 748/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.5060 - inflamation_pred_loss: 0.4466 - nephritis_pred_loss: 0.2972 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0734\n",
            "Epoch 749/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.5052 - inflamation_pred_loss: 0.4459 - nephritis_pred_loss: 0.2967 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0722\n",
            "Epoch 750/1500\n",
            "80/80 [==============================] - 0s 945us/sample - loss: 0.5044 - inflamation_pred_loss: 0.4452 - nephritis_pred_loss: 0.2963 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0731\n",
            "Epoch 751/1500\n",
            "80/80 [==============================] - 0s 95us/sample - loss: 0.5036 - inflamation_pred_loss: 0.4445 - nephritis_pred_loss: 0.2958 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0718\n",
            "Epoch 752/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.5028 - inflamation_pred_loss: 0.4438 - nephritis_pred_loss: 0.2953 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0727\n",
            "Epoch 753/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.5020 - inflamation_pred_loss: 0.4430 - nephritis_pred_loss: 0.2948 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0715\n",
            "Epoch 754/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.5012 - inflamation_pred_loss: 0.4423 - nephritis_pred_loss: 0.2943 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0723\n",
            "Epoch 755/1500\n",
            "80/80 [==============================] - 0s 917us/sample - loss: 0.5004 - inflamation_pred_loss: 0.4416 - nephritis_pred_loss: 0.2938 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0711\n",
            "Epoch 756/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.4996 - inflamation_pred_loss: 0.4409 - nephritis_pred_loss: 0.2933 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0720\n",
            "Epoch 757/1500\n",
            "80/80 [==============================] - 0s 88us/sample - loss: 0.4988 - inflamation_pred_loss: 0.4402 - nephritis_pred_loss: 0.2928 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0708\n",
            "Epoch 758/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.4980 - inflamation_pred_loss: 0.4395 - nephritis_pred_loss: 0.2924 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0716\n",
            "Epoch 759/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.4972 - inflamation_pred_loss: 0.4388 - nephritis_pred_loss: 0.2919 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0704\n",
            "Epoch 760/1500\n",
            "80/80 [==============================] - 0s 947us/sample - loss: 0.4964 - inflamation_pred_loss: 0.4381 - nephritis_pred_loss: 0.2914 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0713\n",
            "Epoch 761/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.4956 - inflamation_pred_loss: 0.4374 - nephritis_pred_loss: 0.2909 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0701\n",
            "Epoch 762/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.4948 - inflamation_pred_loss: 0.4367 - nephritis_pred_loss: 0.2904 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0709\n",
            "Epoch 763/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.4939 - inflamation_pred_loss: 0.4360 - nephritis_pred_loss: 0.2900 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0697\n",
            "Epoch 764/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.4931 - inflamation_pred_loss: 0.4352 - nephritis_pred_loss: 0.2895 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0706\n",
            "Epoch 765/1500\n",
            "80/80 [==============================] - 0s 90us/sample - loss: 0.4923 - inflamation_pred_loss: 0.4345 - nephritis_pred_loss: 0.2890 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0694\n",
            "Epoch 766/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 0.4915 - inflamation_pred_loss: 0.4338 - nephritis_pred_loss: 0.2885 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0703\n",
            "Epoch 767/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.4907 - inflamation_pred_loss: 0.4331 - nephritis_pred_loss: 0.2880 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0690\n",
            "Epoch 768/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.4899 - inflamation_pred_loss: 0.4324 - nephritis_pred_loss: 0.2876 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0699\n",
            "Epoch 769/1500\n",
            "80/80 [==============================] - 0s 910us/sample - loss: 0.4891 - inflamation_pred_loss: 0.4317 - nephritis_pred_loss: 0.2871 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0687\n",
            "Epoch 770/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.4883 - inflamation_pred_loss: 0.4310 - nephritis_pred_loss: 0.2866 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0696\n",
            "Epoch 771/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.4875 - inflamation_pred_loss: 0.4303 - nephritis_pred_loss: 0.2861 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0683\n",
            "Epoch 772/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.4867 - inflamation_pred_loss: 0.4296 - nephritis_pred_loss: 0.2857 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0692\n",
            "Epoch 773/1500\n",
            "80/80 [==============================] - 0s 895us/sample - loss: 0.4859 - inflamation_pred_loss: 0.4289 - nephritis_pred_loss: 0.2852 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0680\n",
            "Epoch 774/1500\n",
            "80/80 [==============================] - 0s 97us/sample - loss: 0.4851 - inflamation_pred_loss: 0.4282 - nephritis_pred_loss: 0.2847 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0689\n",
            "Epoch 775/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.4843 - inflamation_pred_loss: 0.4275 - nephritis_pred_loss: 0.2842 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0677\n",
            "Epoch 776/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.4835 - inflamation_pred_loss: 0.4268 - nephritis_pred_loss: 0.2838 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0685\n",
            "Epoch 777/1500\n",
            "80/80 [==============================] - 0s 951us/sample - loss: 0.4827 - inflamation_pred_loss: 0.4260 - nephritis_pred_loss: 0.2833 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0673\n",
            "Epoch 778/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.4819 - inflamation_pred_loss: 0.4253 - nephritis_pred_loss: 0.2828 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0682\n",
            "Epoch 779/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.4811 - inflamation_pred_loss: 0.4246 - nephritis_pred_loss: 0.2823 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0670\n",
            "Epoch 780/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.4803 - inflamation_pred_loss: 0.4239 - nephritis_pred_loss: 0.2819 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0679\n",
            "Epoch 781/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.4795 - inflamation_pred_loss: 0.4232 - nephritis_pred_loss: 0.2814 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0667\n",
            "Epoch 782/1500\n",
            "80/80 [==============================] - 0s 109us/sample - loss: 0.4787 - inflamation_pred_loss: 0.4225 - nephritis_pred_loss: 0.2809 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0675\n",
            "Epoch 783/1500\n",
            "80/80 [==============================] - 0s 49us/sample - loss: 0.4779 - inflamation_pred_loss: 0.4218 - nephritis_pred_loss: 0.2804 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0663\n",
            "Epoch 784/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.4771 - inflamation_pred_loss: 0.4211 - nephritis_pred_loss: 0.2800 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0672\n",
            "Epoch 785/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.4763 - inflamation_pred_loss: 0.4204 - nephritis_pred_loss: 0.2795 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0660\n",
            "Epoch 786/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.4755 - inflamation_pred_loss: 0.4197 - nephritis_pred_loss: 0.2790 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0669\n",
            "Epoch 787/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 0.4747 - inflamation_pred_loss: 0.4190 - nephritis_pred_loss: 0.2786 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0657\n",
            "Epoch 788/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.4739 - inflamation_pred_loss: 0.4183 - nephritis_pred_loss: 0.2781 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0665\n",
            "Epoch 789/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.4731 - inflamation_pred_loss: 0.4176 - nephritis_pred_loss: 0.2776 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0653\n",
            "Epoch 790/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.4723 - inflamation_pred_loss: 0.4169 - nephritis_pred_loss: 0.2772 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0662\n",
            "Epoch 791/1500\n",
            "80/80 [==============================] - 0s 956us/sample - loss: 0.4715 - inflamation_pred_loss: 0.4162 - nephritis_pred_loss: 0.2767 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0650\n",
            "Epoch 792/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.4707 - inflamation_pred_loss: 0.4155 - nephritis_pred_loss: 0.2762 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0659\n",
            "Epoch 793/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 0.4699 - inflamation_pred_loss: 0.4148 - nephritis_pred_loss: 0.2758 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0647\n",
            "Epoch 794/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.4691 - inflamation_pred_loss: 0.4141 - nephritis_pred_loss: 0.2753 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0655\n",
            "Epoch 795/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.4683 - inflamation_pred_loss: 0.4134 - nephritis_pred_loss: 0.2748 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0643\n",
            "Epoch 796/1500\n",
            "80/80 [==============================] - 0s 101us/sample - loss: 0.4675 - inflamation_pred_loss: 0.4127 - nephritis_pred_loss: 0.2744 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0652\n",
            "Epoch 797/1500\n",
            "80/80 [==============================] - 0s 116us/sample - loss: 0.4667 - inflamation_pred_loss: 0.4120 - nephritis_pred_loss: 0.2739 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0640\n",
            "Epoch 798/1500\n",
            "80/80 [==============================] - 0s 49us/sample - loss: 0.4659 - inflamation_pred_loss: 0.4113 - nephritis_pred_loss: 0.2734 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0649\n",
            "Epoch 799/1500\n",
            "80/80 [==============================] - 0s 49us/sample - loss: 0.4651 - inflamation_pred_loss: 0.4106 - nephritis_pred_loss: 0.2730 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0637\n",
            "Epoch 800/1500\n",
            "80/80 [==============================] - 0s 940us/sample - loss: 0.4644 - inflamation_pred_loss: 0.4098 - nephritis_pred_loss: 0.2725 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0646\n",
            "Epoch 801/1500\n",
            "80/80 [==============================] - 0s 138us/sample - loss: 0.4636 - inflamation_pred_loss: 0.4091 - nephritis_pred_loss: 0.2720 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0634\n",
            "Epoch 802/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.4628 - inflamation_pred_loss: 0.4084 - nephritis_pred_loss: 0.2716 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0642\n",
            "Epoch 803/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.4620 - inflamation_pred_loss: 0.4077 - nephritis_pred_loss: 0.2711 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0630\n",
            "Epoch 804/1500\n",
            "80/80 [==============================] - 0s 34us/sample - loss: 0.4612 - inflamation_pred_loss: 0.4070 - nephritis_pred_loss: 0.2707 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0639\n",
            "Epoch 805/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 0.4604 - inflamation_pred_loss: 0.4063 - nephritis_pred_loss: 0.2702 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0627\n",
            "Epoch 806/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.4596 - inflamation_pred_loss: 0.4056 - nephritis_pred_loss: 0.2697 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0636\n",
            "Epoch 807/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.4588 - inflamation_pred_loss: 0.4049 - nephritis_pred_loss: 0.2693 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0624\n",
            "Epoch 808/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.4580 - inflamation_pred_loss: 0.4042 - nephritis_pred_loss: 0.2688 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0633\n",
            "Epoch 809/1500\n",
            "80/80 [==============================] - 0s 924us/sample - loss: 0.4572 - inflamation_pred_loss: 0.4035 - nephritis_pred_loss: 0.2684 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0621\n",
            "Epoch 810/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.4564 - inflamation_pred_loss: 0.4028 - nephritis_pred_loss: 0.2679 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0629\n",
            "Epoch 811/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.4556 - inflamation_pred_loss: 0.4021 - nephritis_pred_loss: 0.2674 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0618\n",
            "Epoch 812/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 0.4548 - inflamation_pred_loss: 0.4014 - nephritis_pred_loss: 0.2670 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0626\n",
            "Epoch 813/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.4541 - inflamation_pred_loss: 0.4007 - nephritis_pred_loss: 0.2665 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0615\n",
            "Epoch 814/1500\n",
            "80/80 [==============================] - 0s 973us/sample - loss: 0.4533 - inflamation_pred_loss: 0.4001 - nephritis_pred_loss: 0.2661 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0623\n",
            "Epoch 815/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.4525 - inflamation_pred_loss: 0.3994 - nephritis_pred_loss: 0.2656 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0611\n",
            "Epoch 816/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.4517 - inflamation_pred_loss: 0.3987 - nephritis_pred_loss: 0.2652 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0620\n",
            "Epoch 817/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.4509 - inflamation_pred_loss: 0.3980 - nephritis_pred_loss: 0.2647 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0608\n",
            "Epoch 818/1500\n",
            "80/80 [==============================] - 0s 999us/sample - loss: 0.4501 - inflamation_pred_loss: 0.3973 - nephritis_pred_loss: 0.2643 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0617\n",
            "Epoch 819/1500\n",
            "80/80 [==============================] - 0s 108us/sample - loss: 0.4493 - inflamation_pred_loss: 0.3966 - nephritis_pred_loss: 0.2638 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0605\n",
            "Epoch 820/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.4485 - inflamation_pred_loss: 0.3959 - nephritis_pred_loss: 0.2634 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0614\n",
            "Epoch 821/1500\n",
            "80/80 [==============================] - 0s 970us/sample - loss: 0.4478 - inflamation_pred_loss: 0.3952 - nephritis_pred_loss: 0.2629 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0602\n",
            "Epoch 822/1500\n",
            "80/80 [==============================] - 0s 114us/sample - loss: 0.4470 - inflamation_pred_loss: 0.3945 - nephritis_pred_loss: 0.2625 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0611\n",
            "Epoch 823/1500\n",
            "80/80 [==============================] - 0s 75us/sample - loss: 0.4462 - inflamation_pred_loss: 0.3938 - nephritis_pred_loss: 0.2620 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0599\n",
            "Epoch 824/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.4454 - inflamation_pred_loss: 0.3931 - nephritis_pred_loss: 0.2616 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0607\n",
            "Epoch 825/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 0.4446 - inflamation_pred_loss: 0.3924 - nephritis_pred_loss: 0.2611 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0596\n",
            "Epoch 826/1500\n",
            "80/80 [==============================] - 0s 889us/sample - loss: 0.4438 - inflamation_pred_loss: 0.3917 - nephritis_pred_loss: 0.2607 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0604\n",
            "Epoch 827/1500\n",
            "80/80 [==============================] - 0s 85us/sample - loss: 0.4431 - inflamation_pred_loss: 0.3910 - nephritis_pred_loss: 0.2602 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0593\n",
            "Epoch 828/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.4423 - inflamation_pred_loss: 0.3903 - nephritis_pred_loss: 0.2598 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0601\n",
            "Epoch 829/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.4415 - inflamation_pred_loss: 0.3896 - nephritis_pred_loss: 0.2593 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0590\n",
            "Epoch 830/1500\n",
            "80/80 [==============================] - 0s 958us/sample - loss: 0.4407 - inflamation_pred_loss: 0.3889 - nephritis_pred_loss: 0.2589 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0598\n",
            "Epoch 831/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.4399 - inflamation_pred_loss: 0.3882 - nephritis_pred_loss: 0.2584 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0587\n",
            "Epoch 832/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.4391 - inflamation_pred_loss: 0.3876 - nephritis_pred_loss: 0.2580 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0595\n",
            "Epoch 833/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.4384 - inflamation_pred_loss: 0.3869 - nephritis_pred_loss: 0.2575 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0583\n",
            "Epoch 834/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.4376 - inflamation_pred_loss: 0.3862 - nephritis_pred_loss: 0.2571 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0592\n",
            "Epoch 835/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 0.4368 - inflamation_pred_loss: 0.3855 - nephritis_pred_loss: 0.2566 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0580\n",
            "Epoch 836/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.4360 - inflamation_pred_loss: 0.3848 - nephritis_pred_loss: 0.2562 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0589\n",
            "Epoch 837/1500\n",
            "80/80 [==============================] - 0s 98us/sample - loss: 0.4352 - inflamation_pred_loss: 0.3841 - nephritis_pred_loss: 0.2557 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0577\n",
            "Epoch 838/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.4345 - inflamation_pred_loss: 0.3834 - nephritis_pred_loss: 0.2553 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0586\n",
            "Epoch 839/1500\n",
            "80/80 [==============================] - 0s 92us/sample - loss: 0.4337 - inflamation_pred_loss: 0.3827 - nephritis_pred_loss: 0.2549 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0574\n",
            "Epoch 840/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.4329 - inflamation_pred_loss: 0.3820 - nephritis_pred_loss: 0.2544 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0583\n",
            "Epoch 841/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.4321 - inflamation_pred_loss: 0.3813 - nephritis_pred_loss: 0.2540 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0571\n",
            "Epoch 842/1500\n",
            "80/80 [==============================] - 0s 927us/sample - loss: 0.4314 - inflamation_pred_loss: 0.3807 - nephritis_pred_loss: 0.2535 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0580\n",
            "Epoch 843/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 0.4306 - inflamation_pred_loss: 0.3800 - nephritis_pred_loss: 0.2531 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0568\n",
            "Epoch 844/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.4298 - inflamation_pred_loss: 0.3793 - nephritis_pred_loss: 0.2527 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0577\n",
            "Epoch 845/1500\n",
            "80/80 [==============================] - 0s 75us/sample - loss: 0.4290 - inflamation_pred_loss: 0.3786 - nephritis_pred_loss: 0.2522 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0565\n",
            "Epoch 846/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.4283 - inflamation_pred_loss: 0.3779 - nephritis_pred_loss: 0.2518 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0574\n",
            "Epoch 847/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.4275 - inflamation_pred_loss: 0.3772 - nephritis_pred_loss: 0.2513 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0562\n",
            "Epoch 848/1500\n",
            "80/80 [==============================] - 0s 93us/sample - loss: 0.4267 - inflamation_pred_loss: 0.3765 - nephritis_pred_loss: 0.2509 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0571\n",
            "Epoch 849/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.4260 - inflamation_pred_loss: 0.3759 - nephritis_pred_loss: 0.2505 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0559\n",
            "Epoch 850/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.4252 - inflamation_pred_loss: 0.3752 - nephritis_pred_loss: 0.2500 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0568\n",
            "Epoch 851/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.4244 - inflamation_pred_loss: 0.3745 - nephritis_pred_loss: 0.2496 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0557\n",
            "Epoch 852/1500\n",
            "80/80 [==============================] - 0s 901us/sample - loss: 0.4236 - inflamation_pred_loss: 0.3738 - nephritis_pred_loss: 0.2491 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0565\n",
            "Epoch 853/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.4229 - inflamation_pred_loss: 0.3731 - nephritis_pred_loss: 0.2487 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0554\n",
            "Epoch 854/1500\n",
            "80/80 [==============================] - 0s 94us/sample - loss: 0.4221 - inflamation_pred_loss: 0.3724 - nephritis_pred_loss: 0.2483 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0562\n",
            "Epoch 855/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.4213 - inflamation_pred_loss: 0.3718 - nephritis_pred_loss: 0.2478 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0551\n",
            "Epoch 856/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.4206 - inflamation_pred_loss: 0.3711 - nephritis_pred_loss: 0.2474 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0559\n",
            "Epoch 857/1500\n",
            "80/80 [==============================] - 0s 969us/sample - loss: 0.4198 - inflamation_pred_loss: 0.3704 - nephritis_pred_loss: 0.2470 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0548\n",
            "Epoch 858/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.4190 - inflamation_pred_loss: 0.3697 - nephritis_pred_loss: 0.2465 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0556\n",
            "Epoch 859/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 0.4183 - inflamation_pred_loss: 0.3690 - nephritis_pred_loss: 0.2461 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0545\n",
            "Epoch 860/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.4175 - inflamation_pred_loss: 0.3684 - nephritis_pred_loss: 0.2457 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0553\n",
            "Epoch 861/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.4167 - inflamation_pred_loss: 0.3677 - nephritis_pred_loss: 0.2452 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0542\n",
            "Epoch 862/1500\n",
            "80/80 [==============================] - 0s 892us/sample - loss: 0.4160 - inflamation_pred_loss: 0.3670 - nephritis_pred_loss: 0.2448 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0550\n",
            "Epoch 863/1500\n",
            "80/80 [==============================] - 0s 93us/sample - loss: 0.4152 - inflamation_pred_loss: 0.3663 - nephritis_pred_loss: 0.2444 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0539\n",
            "Epoch 864/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.4144 - inflamation_pred_loss: 0.3657 - nephritis_pred_loss: 0.2440 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0547\n",
            "Epoch 865/1500\n",
            "80/80 [==============================] - 0s 80us/sample - loss: 0.4137 - inflamation_pred_loss: 0.3650 - nephritis_pred_loss: 0.2435 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0536\n",
            "Epoch 866/1500\n",
            "80/80 [==============================] - 0s 890us/sample - loss: 0.4129 - inflamation_pred_loss: 0.3643 - nephritis_pred_loss: 0.2431 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0544\n",
            "Epoch 867/1500\n",
            "80/80 [==============================] - 0s 106us/sample - loss: 0.4122 - inflamation_pred_loss: 0.3636 - nephritis_pred_loss: 0.2427 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0533\n",
            "Epoch 868/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.4114 - inflamation_pred_loss: 0.3630 - nephritis_pred_loss: 0.2422 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0542\n",
            "Epoch 869/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.4106 - inflamation_pred_loss: 0.3623 - nephritis_pred_loss: 0.2418 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0530\n",
            "Epoch 870/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.4099 - inflamation_pred_loss: 0.3616 - nephritis_pred_loss: 0.2414 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0539\n",
            "Epoch 871/1500\n",
            "80/80 [==============================] - 0s 860us/sample - loss: 0.4091 - inflamation_pred_loss: 0.3609 - nephritis_pred_loss: 0.2409 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0528\n",
            "Epoch 872/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.4084 - inflamation_pred_loss: 0.3603 - nephritis_pred_loss: 0.2405 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0536\n",
            "Epoch 873/1500\n",
            "80/80 [==============================] - 0s 112us/sample - loss: 0.4076 - inflamation_pred_loss: 0.3596 - nephritis_pred_loss: 0.2401 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0525\n",
            "Epoch 874/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.4068 - inflamation_pred_loss: 0.3589 - nephritis_pred_loss: 0.2397 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0533\n",
            "Epoch 875/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.4061 - inflamation_pred_loss: 0.3582 - nephritis_pred_loss: 0.2392 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0522\n",
            "Epoch 876/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.4053 - inflamation_pred_loss: 0.3576 - nephritis_pred_loss: 0.2388 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0530\n",
            "Epoch 877/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.4046 - inflamation_pred_loss: 0.3569 - nephritis_pred_loss: 0.2384 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0519\n",
            "Epoch 878/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.4038 - inflamation_pred_loss: 0.3562 - nephritis_pred_loss: 0.2380 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0527\n",
            "Epoch 879/1500\n",
            "80/80 [==============================] - 0s 114us/sample - loss: 0.4031 - inflamation_pred_loss: 0.3556 - nephritis_pred_loss: 0.2376 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0516\n",
            "Epoch 880/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.4023 - inflamation_pred_loss: 0.3549 - nephritis_pred_loss: 0.2371 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0525\n",
            "Epoch 881/1500\n",
            "80/80 [==============================] - 0s 51us/sample - loss: 0.4016 - inflamation_pred_loss: 0.3542 - nephritis_pred_loss: 0.2367 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0514\n",
            "Epoch 882/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.4008 - inflamation_pred_loss: 0.3536 - nephritis_pred_loss: 0.2363 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0522\n",
            "Epoch 883/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.4001 - inflamation_pred_loss: 0.3529 - nephritis_pred_loss: 0.2359 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0511\n",
            "Epoch 884/1500\n",
            "80/80 [==============================] - 0s 849us/sample - loss: 0.3993 - inflamation_pred_loss: 0.3522 - nephritis_pred_loss: 0.2355 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0519\n",
            "Epoch 885/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.3986 - inflamation_pred_loss: 0.3516 - nephritis_pred_loss: 0.2350 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0508\n",
            "Epoch 886/1500\n",
            "80/80 [==============================] - 0s 96us/sample - loss: 0.3978 - inflamation_pred_loss: 0.3509 - nephritis_pred_loss: 0.2346 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0516\n",
            "Epoch 887/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.3971 - inflamation_pred_loss: 0.3502 - nephritis_pred_loss: 0.2342 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0505\n",
            "Epoch 888/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.3963 - inflamation_pred_loss: 0.3496 - nephritis_pred_loss: 0.2338 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0513\n",
            "Epoch 889/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.3956 - inflamation_pred_loss: 0.3489 - nephritis_pred_loss: 0.2334 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0503\n",
            "Epoch 890/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 0.3948 - inflamation_pred_loss: 0.3482 - nephritis_pred_loss: 0.2329 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0511\n",
            "Epoch 891/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.3941 - inflamation_pred_loss: 0.3476 - nephritis_pred_loss: 0.2325 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0500\n",
            "Epoch 892/1500\n",
            "80/80 [==============================] - 0s 919us/sample - loss: 0.3933 - inflamation_pred_loss: 0.3469 - nephritis_pred_loss: 0.2321 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0508\n",
            "Epoch 893/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.3926 - inflamation_pred_loss: 0.3463 - nephritis_pred_loss: 0.2317 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0497\n",
            "Epoch 894/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.3919 - inflamation_pred_loss: 0.3456 - nephritis_pred_loss: 0.2313 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0505\n",
            "Epoch 895/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.3911 - inflamation_pred_loss: 0.3449 - nephritis_pred_loss: 0.2309 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0494\n",
            "Epoch 896/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.3904 - inflamation_pred_loss: 0.3443 - nephritis_pred_loss: 0.2305 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0502\n",
            "Epoch 897/1500\n",
            "80/80 [==============================] - 0s 94us/sample - loss: 0.3896 - inflamation_pred_loss: 0.3436 - nephritis_pred_loss: 0.2300 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0492\n",
            "Epoch 898/1500\n",
            "80/80 [==============================] - 0s 109us/sample - loss: 0.3889 - inflamation_pred_loss: 0.3430 - nephritis_pred_loss: 0.2296 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0500\n",
            "Epoch 899/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.3881 - inflamation_pred_loss: 0.3423 - nephritis_pred_loss: 0.2292 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0489\n",
            "Epoch 900/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.3874 - inflamation_pred_loss: 0.3416 - nephritis_pred_loss: 0.2288 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0497\n",
            "Epoch 901/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.3867 - inflamation_pred_loss: 0.3410 - nephritis_pred_loss: 0.2284 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0486\n",
            "Epoch 902/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.3859 - inflamation_pred_loss: 0.3403 - nephritis_pred_loss: 0.2280 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0494\n",
            "Epoch 903/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.3852 - inflamation_pred_loss: 0.3397 - nephritis_pred_loss: 0.2276 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0484\n",
            "Epoch 904/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.3845 - inflamation_pred_loss: 0.3390 - nephritis_pred_loss: 0.2272 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0492\n",
            "Epoch 905/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.3837 - inflamation_pred_loss: 0.3384 - nephritis_pred_loss: 0.2267 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0481\n",
            "Epoch 906/1500\n",
            "80/80 [==============================] - 0s 97us/sample - loss: 0.3830 - inflamation_pred_loss: 0.3377 - nephritis_pred_loss: 0.2263 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0489\n",
            "Epoch 907/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.3823 - inflamation_pred_loss: 0.3371 - nephritis_pred_loss: 0.2259 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0478\n",
            "Epoch 908/1500\n",
            "80/80 [==============================] - 0s 49us/sample - loss: 0.3815 - inflamation_pred_loss: 0.3364 - nephritis_pred_loss: 0.2255 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0486\n",
            "Epoch 909/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.3808 - inflamation_pred_loss: 0.3358 - nephritis_pred_loss: 0.2251 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0476\n",
            "Epoch 910/1500\n",
            "80/80 [==============================] - 0s 875us/sample - loss: 0.3801 - inflamation_pred_loss: 0.3351 - nephritis_pred_loss: 0.2247 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0484\n",
            "Epoch 911/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.3793 - inflamation_pred_loss: 0.3345 - nephritis_pred_loss: 0.2243 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0473\n",
            "Epoch 912/1500\n",
            "80/80 [==============================] - 0s 103us/sample - loss: 0.3786 - inflamation_pred_loss: 0.3338 - nephritis_pred_loss: 0.2239 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0481\n",
            "Epoch 913/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.3779 - inflamation_pred_loss: 0.3332 - nephritis_pred_loss: 0.2235 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0470\n",
            "Epoch 914/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.3771 - inflamation_pred_loss: 0.3325 - nephritis_pred_loss: 0.2231 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0478\n",
            "Epoch 915/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.3764 - inflamation_pred_loss: 0.3319 - nephritis_pred_loss: 0.2227 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0468\n",
            "Epoch 916/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.3757 - inflamation_pred_loss: 0.3312 - nephritis_pred_loss: 0.2223 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0476\n",
            "Epoch 917/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.3750 - inflamation_pred_loss: 0.3306 - nephritis_pred_loss: 0.2219 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0465\n",
            "Epoch 918/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.3742 - inflamation_pred_loss: 0.3299 - nephritis_pred_loss: 0.2215 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0473\n",
            "Epoch 919/1500\n",
            "80/80 [==============================] - 0s 102us/sample - loss: 0.3735 - inflamation_pred_loss: 0.3293 - nephritis_pred_loss: 0.2211 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0463\n",
            "Epoch 920/1500\n",
            "80/80 [==============================] - 0s 83us/sample - loss: 0.3728 - inflamation_pred_loss: 0.3286 - nephritis_pred_loss: 0.2207 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0470\n",
            "Epoch 921/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.3721 - inflamation_pred_loss: 0.3280 - nephritis_pred_loss: 0.2203 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0460\n",
            "Epoch 922/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 0.3713 - inflamation_pred_loss: 0.3274 - nephritis_pred_loss: 0.2199 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0468\n",
            "Epoch 923/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.3706 - inflamation_pred_loss: 0.3267 - nephritis_pred_loss: 0.2195 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0457\n",
            "Epoch 924/1500\n",
            "80/80 [==============================] - 0s 109us/sample - loss: 0.3699 - inflamation_pred_loss: 0.3261 - nephritis_pred_loss: 0.2191 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0465\n",
            "Epoch 925/1500\n",
            "80/80 [==============================] - 0s 98us/sample - loss: 0.3692 - inflamation_pred_loss: 0.3254 - nephritis_pred_loss: 0.2187 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0455\n",
            "Epoch 926/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.3684 - inflamation_pred_loss: 0.3248 - nephritis_pred_loss: 0.2183 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0463\n",
            "Epoch 927/1500\n",
            "80/80 [==============================] - 0s 866us/sample - loss: 0.3677 - inflamation_pred_loss: 0.3242 - nephritis_pred_loss: 0.2179 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0452\n",
            "Epoch 928/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 0.3670 - inflamation_pred_loss: 0.3235 - nephritis_pred_loss: 0.2175 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0460\n",
            "Epoch 929/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.3663 - inflamation_pred_loss: 0.3229 - nephritis_pred_loss: 0.2171 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0450\n",
            "Epoch 930/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.3656 - inflamation_pred_loss: 0.3222 - nephritis_pred_loss: 0.2167 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0458\n",
            "Epoch 931/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.3649 - inflamation_pred_loss: 0.3216 - nephritis_pred_loss: 0.2163 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0447\n",
            "Epoch 932/1500\n",
            "80/80 [==============================] - 0s 935us/sample - loss: 0.3641 - inflamation_pred_loss: 0.3210 - nephritis_pred_loss: 0.2159 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0455\n",
            "Epoch 933/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.3634 - inflamation_pred_loss: 0.3203 - nephritis_pred_loss: 0.2155 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0445\n",
            "Epoch 934/1500\n",
            "80/80 [==============================] - 0s 115us/sample - loss: 0.3627 - inflamation_pred_loss: 0.3197 - nephritis_pred_loss: 0.2151 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0452\n",
            "Epoch 935/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.3620 - inflamation_pred_loss: 0.3191 - nephritis_pred_loss: 0.2147 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0442\n",
            "Epoch 936/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.3613 - inflamation_pred_loss: 0.3184 - nephritis_pred_loss: 0.2143 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0450\n",
            "Epoch 937/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.3606 - inflamation_pred_loss: 0.3178 - nephritis_pred_loss: 0.2139 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0440\n",
            "Epoch 938/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.3599 - inflamation_pred_loss: 0.3172 - nephritis_pred_loss: 0.2135 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0447\n",
            "Epoch 939/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.3591 - inflamation_pred_loss: 0.3165 - nephritis_pred_loss: 0.2131 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0437\n",
            "Epoch 940/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.3584 - inflamation_pred_loss: 0.3159 - nephritis_pred_loss: 0.2128 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0445\n",
            "Epoch 941/1500\n",
            "80/80 [==============================] - 0s 900us/sample - loss: 0.3577 - inflamation_pred_loss: 0.3153 - nephritis_pred_loss: 0.2124 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0435\n",
            "Epoch 942/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.3570 - inflamation_pred_loss: 0.3146 - nephritis_pred_loss: 0.2120 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0442\n",
            "Epoch 943/1500\n",
            "80/80 [==============================] - 0s 113us/sample - loss: 0.3563 - inflamation_pred_loss: 0.3140 - nephritis_pred_loss: 0.2116 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0432\n",
            "Epoch 944/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.3556 - inflamation_pred_loss: 0.3134 - nephritis_pred_loss: 0.2112 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0440\n",
            "Epoch 945/1500\n",
            "80/80 [==============================] - 0s 975us/sample - loss: 0.3549 - inflamation_pred_loss: 0.3127 - nephritis_pred_loss: 0.2108 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0430\n",
            "Epoch 946/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 0.3542 - inflamation_pred_loss: 0.3121 - nephritis_pred_loss: 0.2104 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0437\n",
            "Epoch 947/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.3535 - inflamation_pred_loss: 0.3115 - nephritis_pred_loss: 0.2100 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0427\n",
            "Epoch 948/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.3528 - inflamation_pred_loss: 0.3109 - nephritis_pred_loss: 0.2096 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0435\n",
            "Epoch 949/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.3521 - inflamation_pred_loss: 0.3102 - nephritis_pred_loss: 0.2092 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0425\n",
            "Epoch 950/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.3514 - inflamation_pred_loss: 0.3096 - nephritis_pred_loss: 0.2089 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0433\n",
            "Epoch 951/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.3507 - inflamation_pred_loss: 0.3090 - nephritis_pred_loss: 0.2085 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0422\n",
            "Epoch 952/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.3500 - inflamation_pred_loss: 0.3084 - nephritis_pred_loss: 0.2081 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0430\n",
            "Epoch 953/1500\n",
            "80/80 [==============================] - 0s 944us/sample - loss: 0.3493 - inflamation_pred_loss: 0.3077 - nephritis_pred_loss: 0.2077 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0420\n",
            "Epoch 954/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.3486 - inflamation_pred_loss: 0.3071 - nephritis_pred_loss: 0.2073 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0428\n",
            "Epoch 955/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.3479 - inflamation_pred_loss: 0.3065 - nephritis_pred_loss: 0.2069 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0418\n",
            "Epoch 956/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.3472 - inflamation_pred_loss: 0.3059 - nephritis_pred_loss: 0.2066 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0425\n",
            "Epoch 957/1500\n",
            "80/80 [==============================] - 0s 989us/sample - loss: 0.3465 - inflamation_pred_loss: 0.3052 - nephritis_pred_loss: 0.2062 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0415\n",
            "Epoch 958/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.3458 - inflamation_pred_loss: 0.3046 - nephritis_pred_loss: 0.2058 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0423\n",
            "Epoch 959/1500\n",
            "80/80 [==============================] - 0s 96us/sample - loss: 0.3451 - inflamation_pred_loss: 0.3040 - nephritis_pred_loss: 0.2054 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0413\n",
            "Epoch 960/1500\n",
            "80/80 [==============================] - 0s 121us/sample - loss: 0.3444 - inflamation_pred_loss: 0.3034 - nephritis_pred_loss: 0.2050 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0420\n",
            "Epoch 961/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.3437 - inflamation_pred_loss: 0.3028 - nephritis_pred_loss: 0.2046 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0410\n",
            "Epoch 962/1500\n",
            "80/80 [==============================] - 0s 34us/sample - loss: 0.3430 - inflamation_pred_loss: 0.3021 - nephritis_pred_loss: 0.2043 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0418\n",
            "Epoch 963/1500\n",
            "80/80 [==============================] - 0s 868us/sample - loss: 0.3423 - inflamation_pred_loss: 0.3015 - nephritis_pred_loss: 0.2039 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0408\n",
            "Epoch 964/1500\n",
            "80/80 [==============================] - 0s 82us/sample - loss: 0.3416 - inflamation_pred_loss: 0.3009 - nephritis_pred_loss: 0.2035 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0416\n",
            "Epoch 965/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.3409 - inflamation_pred_loss: 0.3003 - nephritis_pred_loss: 0.2031 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0406\n",
            "Epoch 966/1500\n",
            "80/80 [==============================] - 0s 994us/sample - loss: 0.3402 - inflamation_pred_loss: 0.2997 - nephritis_pred_loss: 0.2028 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0413\n",
            "Epoch 967/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.3395 - inflamation_pred_loss: 0.2991 - nephritis_pred_loss: 0.2024 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0403\n",
            "Epoch 968/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.3389 - inflamation_pred_loss: 0.2985 - nephritis_pred_loss: 0.2020 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0411\n",
            "Epoch 969/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.3382 - inflamation_pred_loss: 0.2978 - nephritis_pred_loss: 0.2016 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0401\n",
            "Epoch 970/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.3375 - inflamation_pred_loss: 0.2972 - nephritis_pred_loss: 0.2012 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0408\n",
            "Epoch 971/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.3368 - inflamation_pred_loss: 0.2966 - nephritis_pred_loss: 0.2009 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0399\n",
            "Epoch 972/1500\n",
            "80/80 [==============================] - 0s 88us/sample - loss: 0.3361 - inflamation_pred_loss: 0.2960 - nephritis_pred_loss: 0.2005 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0406\n",
            "Epoch 973/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.3354 - inflamation_pred_loss: 0.2954 - nephritis_pred_loss: 0.2001 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0396\n",
            "Epoch 974/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.3347 - inflamation_pred_loss: 0.2948 - nephritis_pred_loss: 0.1997 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0404\n",
            "Epoch 975/1500\n",
            "80/80 [==============================] - 0s 951us/sample - loss: 0.3341 - inflamation_pred_loss: 0.2942 - nephritis_pred_loss: 0.1994 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0394\n",
            "Epoch 976/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.3334 - inflamation_pred_loss: 0.2936 - nephritis_pred_loss: 0.1990 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0401\n",
            "Epoch 977/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.3327 - inflamation_pred_loss: 0.2930 - nephritis_pred_loss: 0.1986 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0392\n",
            "Epoch 978/1500\n",
            "80/80 [==============================] - 0s 90us/sample - loss: 0.3320 - inflamation_pred_loss: 0.2924 - nephritis_pred_loss: 0.1983 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0399\n",
            "Epoch 979/1500\n",
            "80/80 [==============================] - 0s 934us/sample - loss: 0.3313 - inflamation_pred_loss: 0.2918 - nephritis_pred_loss: 0.1979 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0389\n",
            "Epoch 980/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.3307 - inflamation_pred_loss: 0.2911 - nephritis_pred_loss: 0.1975 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0397\n",
            "Epoch 981/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.3300 - inflamation_pred_loss: 0.2905 - nephritis_pred_loss: 0.1971 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0387\n",
            "Epoch 982/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.3293 - inflamation_pred_loss: 0.2899 - nephritis_pred_loss: 0.1968 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0395\n",
            "Epoch 983/1500\n",
            "80/80 [==============================] - 0s 34us/sample - loss: 0.3286 - inflamation_pred_loss: 0.2893 - nephritis_pred_loss: 0.1964 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0385\n",
            "Epoch 984/1500\n",
            "80/80 [==============================] - 0s 956us/sample - loss: 0.3279 - inflamation_pred_loss: 0.2887 - nephritis_pred_loss: 0.1960 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0392\n",
            "Epoch 985/1500\n",
            "80/80 [==============================] - 0s 80us/sample - loss: 0.3273 - inflamation_pred_loss: 0.2881 - nephritis_pred_loss: 0.1957 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0383\n",
            "Epoch 986/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.3266 - inflamation_pred_loss: 0.2875 - nephritis_pred_loss: 0.1953 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0390\n",
            "Epoch 987/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.3259 - inflamation_pred_loss: 0.2869 - nephritis_pred_loss: 0.1949 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0380\n",
            "Epoch 988/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.3252 - inflamation_pred_loss: 0.2863 - nephritis_pred_loss: 0.1946 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0388\n",
            "Epoch 989/1500\n",
            "80/80 [==============================] - 0s 895us/sample - loss: 0.3246 - inflamation_pred_loss: 0.2857 - nephritis_pred_loss: 0.1942 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0378\n",
            "Epoch 990/1500\n",
            "80/80 [==============================] - 0s 91us/sample - loss: 0.3239 - inflamation_pred_loss: 0.2851 - nephritis_pred_loss: 0.1938 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0385\n",
            "Epoch 991/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 0.3232 - inflamation_pred_loss: 0.2845 - nephritis_pred_loss: 0.1935 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0376\n",
            "Epoch 992/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.3226 - inflamation_pred_loss: 0.2839 - nephritis_pred_loss: 0.1931 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0383\n",
            "Epoch 993/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.3219 - inflamation_pred_loss: 0.2833 - nephritis_pred_loss: 0.1927 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0374\n",
            "Epoch 994/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.3212 - inflamation_pred_loss: 0.2827 - nephritis_pred_loss: 0.1924 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0381\n",
            "Epoch 995/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.3206 - inflamation_pred_loss: 0.2822 - nephritis_pred_loss: 0.1920 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0371\n",
            "Epoch 996/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.3199 - inflamation_pred_loss: 0.2816 - nephritis_pred_loss: 0.1916 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0379\n",
            "Epoch 997/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.3192 - inflamation_pred_loss: 0.2810 - nephritis_pred_loss: 0.1913 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0369\n",
            "Epoch 998/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.3186 - inflamation_pred_loss: 0.2804 - nephritis_pred_loss: 0.1909 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0376\n",
            "Epoch 999/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.3179 - inflamation_pred_loss: 0.2798 - nephritis_pred_loss: 0.1905 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0367\n",
            "Epoch 1000/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.3172 - inflamation_pred_loss: 0.2792 - nephritis_pred_loss: 0.1902 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0374\n",
            "Epoch 1001/1500\n",
            "80/80 [==============================] - 0s 47us/sample - loss: 0.3166 - inflamation_pred_loss: 0.2786 - nephritis_pred_loss: 0.1898 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0365\n",
            "Epoch 1002/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.3159 - inflamation_pred_loss: 0.2780 - nephritis_pred_loss: 0.1895 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0372\n",
            "Epoch 1003/1500\n",
            "80/80 [==============================] - 0s 838us/sample - loss: 0.3152 - inflamation_pred_loss: 0.2774 - nephritis_pred_loss: 0.1891 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0363\n",
            "Epoch 1004/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.3146 - inflamation_pred_loss: 0.2768 - nephritis_pred_loss: 0.1887 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0370\n",
            "Epoch 1005/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.3139 - inflamation_pred_loss: 0.2763 - nephritis_pred_loss: 0.1884 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0360\n",
            "Epoch 1006/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.3133 - inflamation_pred_loss: 0.2757 - nephritis_pred_loss: 0.1880 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0368\n",
            "Epoch 1007/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.3126 - inflamation_pred_loss: 0.2751 - nephritis_pred_loss: 0.1877 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0358\n",
            "Epoch 1008/1500\n",
            "80/80 [==============================] - 0s 98us/sample - loss: 0.3120 - inflamation_pred_loss: 0.2745 - nephritis_pred_loss: 0.1873 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0365\n",
            "Epoch 1009/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.3113 - inflamation_pred_loss: 0.2739 - nephritis_pred_loss: 0.1869 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0356\n",
            "Epoch 1010/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.3106 - inflamation_pred_loss: 0.2733 - nephritis_pred_loss: 0.1866 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0363\n",
            "Epoch 1011/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.3100 - inflamation_pred_loss: 0.2727 - nephritis_pred_loss: 0.1862 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0354\n",
            "Epoch 1012/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.3093 - inflamation_pred_loss: 0.2722 - nephritis_pred_loss: 0.1859 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0361\n",
            "Epoch 1013/1500\n",
            "80/80 [==============================] - 0s 112us/sample - loss: 0.3087 - inflamation_pred_loss: 0.2716 - nephritis_pred_loss: 0.1855 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0352\n",
            "Epoch 1014/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 0.3080 - inflamation_pred_loss: 0.2710 - nephritis_pred_loss: 0.1852 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0359\n",
            "Epoch 1015/1500\n",
            "80/80 [==============================] - 0s 990us/sample - loss: 0.3074 - inflamation_pred_loss: 0.2704 - nephritis_pred_loss: 0.1848 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0350\n",
            "Epoch 1016/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.3067 - inflamation_pred_loss: 0.2698 - nephritis_pred_loss: 0.1845 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0357\n",
            "Epoch 1017/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.3061 - inflamation_pred_loss: 0.2693 - nephritis_pred_loss: 0.1841 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0348\n",
            "Epoch 1018/1500\n",
            "80/80 [==============================] - 0s 49us/sample - loss: 0.3054 - inflamation_pred_loss: 0.2687 - nephritis_pred_loss: 0.1838 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0355\n",
            "Epoch 1019/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.3048 - inflamation_pred_loss: 0.2681 - nephritis_pred_loss: 0.1834 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0346\n",
            "Epoch 1020/1500\n",
            "80/80 [==============================] - 0s 887us/sample - loss: 0.3041 - inflamation_pred_loss: 0.2675 - nephritis_pred_loss: 0.1831 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0353\n",
            "Epoch 1021/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.3035 - inflamation_pred_loss: 0.2670 - nephritis_pred_loss: 0.1827 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0343\n",
            "Epoch 1022/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.3029 - inflamation_pred_loss: 0.2664 - nephritis_pred_loss: 0.1824 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0350\n",
            "Epoch 1023/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.3022 - inflamation_pred_loss: 0.2658 - nephritis_pred_loss: 0.1820 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0341\n",
            "Epoch 1024/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.3016 - inflamation_pred_loss: 0.2652 - nephritis_pred_loss: 0.1817 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0348\n",
            "Epoch 1025/1500\n",
            "80/80 [==============================] - 0s 932us/sample - loss: 0.3009 - inflamation_pred_loss: 0.2647 - nephritis_pred_loss: 0.1813 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0339\n",
            "Epoch 1026/1500\n",
            "80/80 [==============================] - 0s 80us/sample - loss: 0.3003 - inflamation_pred_loss: 0.2641 - nephritis_pred_loss: 0.1810 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0346\n",
            "Epoch 1027/1500\n",
            "80/80 [==============================] - 0s 107us/sample - loss: 0.2996 - inflamation_pred_loss: 0.2635 - nephritis_pred_loss: 0.1806 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0337\n",
            "Epoch 1028/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.2990 - inflamation_pred_loss: 0.2630 - nephritis_pred_loss: 0.1803 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0344\n",
            "Epoch 1029/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.2984 - inflamation_pred_loss: 0.2624 - nephritis_pred_loss: 0.1799 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0335\n",
            "Epoch 1030/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.2977 - inflamation_pred_loss: 0.2618 - nephritis_pred_loss: 0.1796 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0342\n",
            "Epoch 1031/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.2971 - inflamation_pred_loss: 0.2613 - nephritis_pred_loss: 0.1792 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0333\n",
            "Epoch 1032/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.2965 - inflamation_pred_loss: 0.2607 - nephritis_pred_loss: 0.1789 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0340\n",
            "Epoch 1033/1500\n",
            "80/80 [==============================] - 0s 163us/sample - loss: 0.2958 - inflamation_pred_loss: 0.2601 - nephritis_pred_loss: 0.1785 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0331\n",
            "Epoch 1034/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.2952 - inflamation_pred_loss: 0.2596 - nephritis_pred_loss: 0.1782 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0338\n",
            "Epoch 1035/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.2946 - inflamation_pred_loss: 0.2590 - nephritis_pred_loss: 0.1778 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0329\n",
            "Epoch 1036/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.2939 - inflamation_pred_loss: 0.2584 - nephritis_pred_loss: 0.1775 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0336\n",
            "Epoch 1037/1500\n",
            "80/80 [==============================] - 0s 963us/sample - loss: 0.2933 - inflamation_pred_loss: 0.2579 - nephritis_pred_loss: 0.1771 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0327\n",
            "Epoch 1038/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.2927 - inflamation_pred_loss: 0.2573 - nephritis_pred_loss: 0.1768 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0334\n",
            "Epoch 1039/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.2920 - inflamation_pred_loss: 0.2567 - nephritis_pred_loss: 0.1765 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0325\n",
            "Epoch 1040/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.2914 - inflamation_pred_loss: 0.2562 - nephritis_pred_loss: 0.1761 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0332\n",
            "Epoch 1041/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.2908 - inflamation_pred_loss: 0.2556 - nephritis_pred_loss: 0.1758 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0323\n",
            "Epoch 1042/1500\n",
            "80/80 [==============================] - 0s 960us/sample - loss: 0.2902 - inflamation_pred_loss: 0.2551 - nephritis_pred_loss: 0.1754 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0330\n",
            "Epoch 1043/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.2895 - inflamation_pred_loss: 0.2545 - nephritis_pred_loss: 0.1751 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0321\n",
            "Epoch 1044/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.2889 - inflamation_pred_loss: 0.2540 - nephritis_pred_loss: 0.1748 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0328\n",
            "Epoch 1045/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.2883 - inflamation_pred_loss: 0.2534 - nephritis_pred_loss: 0.1744 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0319\n",
            "Epoch 1046/1500\n",
            "80/80 [==============================] - 0s 191us/sample - loss: 0.2877 - inflamation_pred_loss: 0.2528 - nephritis_pred_loss: 0.1741 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0326\n",
            "Epoch 1047/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 0.2870 - inflamation_pred_loss: 0.2523 - nephritis_pred_loss: 0.1737 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0317\n",
            "Epoch 1048/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.2864 - inflamation_pred_loss: 0.2517 - nephritis_pred_loss: 0.1734 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0324\n",
            "Epoch 1049/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.2858 - inflamation_pred_loss: 0.2512 - nephritis_pred_loss: 0.1731 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0315\n",
            "Epoch 1050/1500\n",
            "80/80 [==============================] - 0s 795us/sample - loss: 0.2852 - inflamation_pred_loss: 0.2506 - nephritis_pred_loss: 0.1727 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0322\n",
            "Epoch 1051/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.2846 - inflamation_pred_loss: 0.2501 - nephritis_pred_loss: 0.1724 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0313\n",
            "Epoch 1052/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.2839 - inflamation_pred_loss: 0.2495 - nephritis_pred_loss: 0.1720 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0320\n",
            "Epoch 1053/1500\n",
            "80/80 [==============================] - 0s 82us/sample - loss: 0.2833 - inflamation_pred_loss: 0.2490 - nephritis_pred_loss: 0.1717 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0311\n",
            "Epoch 1054/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.2827 - inflamation_pred_loss: 0.2484 - nephritis_pred_loss: 0.1714 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0318\n",
            "Epoch 1055/1500\n",
            "80/80 [==============================] - 0s 950us/sample - loss: 0.2821 - inflamation_pred_loss: 0.2479 - nephritis_pred_loss: 0.1710 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0309\n",
            "Epoch 1056/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 0.2815 - inflamation_pred_loss: 0.2473 - nephritis_pred_loss: 0.1707 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0316\n",
            "Epoch 1057/1500\n",
            "80/80 [==============================] - 0s 75us/sample - loss: 0.2809 - inflamation_pred_loss: 0.2468 - nephritis_pred_loss: 0.1704 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0307\n",
            "Epoch 1058/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 0.2802 - inflamation_pred_loss: 0.2462 - nephritis_pred_loss: 0.1700 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0314\n",
            "Epoch 1059/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.2796 - inflamation_pred_loss: 0.2457 - nephritis_pred_loss: 0.1697 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0305\n",
            "Epoch 1060/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.2790 - inflamation_pred_loss: 0.2452 - nephritis_pred_loss: 0.1694 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0312\n",
            "Epoch 1061/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.2784 - inflamation_pred_loss: 0.2446 - nephritis_pred_loss: 0.1690 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0303\n",
            "Epoch 1062/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.2778 - inflamation_pred_loss: 0.2441 - nephritis_pred_loss: 0.1687 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0310\n",
            "Epoch 1063/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.2772 - inflamation_pred_loss: 0.2435 - nephritis_pred_loss: 0.1684 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0301\n",
            "Epoch 1064/1500\n",
            "80/80 [==============================] - 0s 901us/sample - loss: 0.2766 - inflamation_pred_loss: 0.2430 - nephritis_pred_loss: 0.1680 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0308\n",
            "Epoch 1065/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.2760 - inflamation_pred_loss: 0.2424 - nephritis_pred_loss: 0.1677 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0300\n",
            "Epoch 1066/1500\n",
            "80/80 [==============================] - 0s 80us/sample - loss: 0.2754 - inflamation_pred_loss: 0.2419 - nephritis_pred_loss: 0.1674 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0306\n",
            "Epoch 1067/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 0.2748 - inflamation_pred_loss: 0.2414 - nephritis_pred_loss: 0.1671 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0298\n",
            "Epoch 1068/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.2742 - inflamation_pred_loss: 0.2408 - nephritis_pred_loss: 0.1667 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0304\n",
            "Epoch 1069/1500\n",
            "80/80 [==============================] - 0s 846us/sample - loss: 0.2736 - inflamation_pred_loss: 0.2403 - nephritis_pred_loss: 0.1664 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0296\n",
            "Epoch 1070/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.2730 - inflamation_pred_loss: 0.2398 - nephritis_pred_loss: 0.1661 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0302\n",
            "Epoch 1071/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.2724 - inflamation_pred_loss: 0.2392 - nephritis_pred_loss: 0.1657 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0294\n",
            "Epoch 1072/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.2718 - inflamation_pred_loss: 0.2387 - nephritis_pred_loss: 0.1654 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0300\n",
            "Epoch 1073/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.2712 - inflamation_pred_loss: 0.2382 - nephritis_pred_loss: 0.1651 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0292\n",
            "Epoch 1074/1500\n",
            "80/80 [==============================] - 0s 989us/sample - loss: 0.2706 - inflamation_pred_loss: 0.2376 - nephritis_pred_loss: 0.1648 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0298\n",
            "Epoch 1075/1500\n",
            "80/80 [==============================] - 0s 110us/sample - loss: 0.2700 - inflamation_pred_loss: 0.2371 - nephritis_pred_loss: 0.1644 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0290\n",
            "Epoch 1076/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.2694 - inflamation_pred_loss: 0.2366 - nephritis_pred_loss: 0.1641 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0297\n",
            "Epoch 1077/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.2688 - inflamation_pred_loss: 0.2360 - nephritis_pred_loss: 0.1638 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0288\n",
            "Epoch 1078/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.2682 - inflamation_pred_loss: 0.2355 - nephritis_pred_loss: 0.1635 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0295\n",
            "Epoch 1079/1500\n",
            "80/80 [==============================] - 0s 136us/sample - loss: 0.2676 - inflamation_pred_loss: 0.2350 - nephritis_pred_loss: 0.1631 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0286\n",
            "Epoch 1080/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.2670 - inflamation_pred_loss: 0.2344 - nephritis_pred_loss: 0.1628 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0293\n",
            "Epoch 1081/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.2664 - inflamation_pred_loss: 0.2339 - nephritis_pred_loss: 0.1625 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0285\n",
            "Epoch 1082/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.2658 - inflamation_pred_loss: 0.2334 - nephritis_pred_loss: 0.1622 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0291\n",
            "Epoch 1083/1500\n",
            "80/80 [==============================] - 0s 929us/sample - loss: 0.2652 - inflamation_pred_loss: 0.2329 - nephritis_pred_loss: 0.1618 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0283\n",
            "Epoch 1084/1500\n",
            "80/80 [==============================] - 0s 101us/sample - loss: 0.2647 - inflamation_pred_loss: 0.2323 - nephritis_pred_loss: 0.1615 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0289\n",
            "Epoch 1085/1500\n",
            "80/80 [==============================] - 0s 97us/sample - loss: 0.2641 - inflamation_pred_loss: 0.2318 - nephritis_pred_loss: 0.1612 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0281\n",
            "Epoch 1086/1500\n",
            "80/80 [==============================] - 0s 52us/sample - loss: 0.2635 - inflamation_pred_loss: 0.2313 - nephritis_pred_loss: 0.1609 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0287\n",
            "Epoch 1087/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.2629 - inflamation_pred_loss: 0.2308 - nephritis_pred_loss: 0.1606 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0279\n",
            "Epoch 1088/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.2623 - inflamation_pred_loss: 0.2303 - nephritis_pred_loss: 0.1602 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0285\n",
            "Epoch 1089/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.2617 - inflamation_pred_loss: 0.2297 - nephritis_pred_loss: 0.1599 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0277\n",
            "Epoch 1090/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.2611 - inflamation_pred_loss: 0.2292 - nephritis_pred_loss: 0.1596 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0284\n",
            "Epoch 1091/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.2606 - inflamation_pred_loss: 0.2287 - nephritis_pred_loss: 0.1593 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0276\n",
            "Epoch 1092/1500\n",
            "80/80 [==============================] - 0s 935us/sample - loss: 0.2600 - inflamation_pred_loss: 0.2282 - nephritis_pred_loss: 0.1590 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0282\n",
            "Epoch 1093/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.2594 - inflamation_pred_loss: 0.2277 - nephritis_pred_loss: 0.1587 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0274\n",
            "Epoch 1094/1500\n",
            "80/80 [==============================] - 0s 103us/sample - loss: 0.2588 - inflamation_pred_loss: 0.2272 - nephritis_pred_loss: 0.1583 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0280\n",
            "Epoch 1095/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.2582 - inflamation_pred_loss: 0.2266 - nephritis_pred_loss: 0.1580 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0272\n",
            "Epoch 1096/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.2577 - inflamation_pred_loss: 0.2261 - nephritis_pred_loss: 0.1577 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0278\n",
            "Epoch 1097/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.2571 - inflamation_pred_loss: 0.2256 - nephritis_pred_loss: 0.1574 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0270\n",
            "Epoch 1098/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.2565 - inflamation_pred_loss: 0.2251 - nephritis_pred_loss: 0.1571 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0276\n",
            "Epoch 1099/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.2559 - inflamation_pred_loss: 0.2246 - nephritis_pred_loss: 0.1568 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0268\n",
            "Epoch 1100/1500\n",
            "80/80 [==============================] - 0s 937us/sample - loss: 0.2554 - inflamation_pred_loss: 0.2241 - nephritis_pred_loss: 0.1565 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0275\n",
            "Epoch 1101/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.2548 - inflamation_pred_loss: 0.2236 - nephritis_pred_loss: 0.1561 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0267\n",
            "Epoch 1102/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.2542 - inflamation_pred_loss: 0.2231 - nephritis_pred_loss: 0.1558 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0273\n",
            "Epoch 1103/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.2536 - inflamation_pred_loss: 0.2225 - nephritis_pred_loss: 0.1555 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0265\n",
            "Epoch 1104/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.2531 - inflamation_pred_loss: 0.2220 - nephritis_pred_loss: 0.1552 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0271\n",
            "Epoch 1105/1500\n",
            "80/80 [==============================] - 0s 960us/sample - loss: 0.2525 - inflamation_pred_loss: 0.2215 - nephritis_pred_loss: 0.1549 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0263\n",
            "Epoch 1106/1500\n",
            "80/80 [==============================] - 0s 106us/sample - loss: 0.2519 - inflamation_pred_loss: 0.2210 - nephritis_pred_loss: 0.1546 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0269\n",
            "Epoch 1107/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.2514 - inflamation_pred_loss: 0.2205 - nephritis_pred_loss: 0.1543 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0262\n",
            "Epoch 1108/1500\n",
            "80/80 [==============================] - 0s 52us/sample - loss: 0.2508 - inflamation_pred_loss: 0.2200 - nephritis_pred_loss: 0.1540 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0268\n",
            "Epoch 1109/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.2502 - inflamation_pred_loss: 0.2195 - nephritis_pred_loss: 0.1536 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0260\n",
            "Epoch 1110/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.2497 - inflamation_pred_loss: 0.2190 - nephritis_pred_loss: 0.1533 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0266\n",
            "Epoch 1111/1500\n",
            "80/80 [==============================] - 0s 47us/sample - loss: 0.2491 - inflamation_pred_loss: 0.2185 - nephritis_pred_loss: 0.1530 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0258\n",
            "Epoch 1112/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.2486 - inflamation_pred_loss: 0.2180 - nephritis_pred_loss: 0.1527 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0264\n",
            "Epoch 1113/1500\n",
            "80/80 [==============================] - 0s 936us/sample - loss: 0.2480 - inflamation_pred_loss: 0.2175 - nephritis_pred_loss: 0.1524 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0256\n",
            "Epoch 1114/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.2474 - inflamation_pred_loss: 0.2170 - nephritis_pred_loss: 0.1521 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0262\n",
            "Epoch 1115/1500\n",
            "80/80 [==============================] - 0s 85us/sample - loss: 0.2469 - inflamation_pred_loss: 0.2165 - nephritis_pred_loss: 0.1518 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0255\n",
            "Epoch 1116/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.2463 - inflamation_pred_loss: 0.2160 - nephritis_pred_loss: 0.1515 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0261\n",
            "Epoch 1117/1500\n",
            "80/80 [==============================] - 0s 948us/sample - loss: 0.2458 - inflamation_pred_loss: 0.2155 - nephritis_pred_loss: 0.1512 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0253\n",
            "Epoch 1118/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.2452 - inflamation_pred_loss: 0.2150 - nephritis_pred_loss: 0.1509 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0259\n",
            "Epoch 1119/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.2446 - inflamation_pred_loss: 0.2145 - nephritis_pred_loss: 0.1506 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0251\n",
            "Epoch 1120/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.2441 - inflamation_pred_loss: 0.2140 - nephritis_pred_loss: 0.1503 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0257\n",
            "Epoch 1121/1500\n",
            "80/80 [==============================] - 0s 997us/sample - loss: 0.2435 - inflamation_pred_loss: 0.2135 - nephritis_pred_loss: 0.1500 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0250\n",
            "Epoch 1122/1500\n",
            "80/80 [==============================] - 0s 126us/sample - loss: 0.2430 - inflamation_pred_loss: 0.2131 - nephritis_pred_loss: 0.1497 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0256\n",
            "Epoch 1123/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.2424 - inflamation_pred_loss: 0.2126 - nephritis_pred_loss: 0.1493 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0248\n",
            "Epoch 1124/1500\n",
            "80/80 [==============================] - 0s 51us/sample - loss: 0.2419 - inflamation_pred_loss: 0.2121 - nephritis_pred_loss: 0.1490 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0254\n",
            "Epoch 1125/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.2413 - inflamation_pred_loss: 0.2116 - nephritis_pred_loss: 0.1487 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0246\n",
            "Epoch 1126/1500\n",
            "80/80 [==============================] - 0s 33us/sample - loss: 0.2408 - inflamation_pred_loss: 0.2111 - nephritis_pred_loss: 0.1484 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0252\n",
            "Epoch 1127/1500\n",
            "80/80 [==============================] - 0s 875us/sample - loss: 0.2402 - inflamation_pred_loss: 0.2106 - nephritis_pred_loss: 0.1481 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0245\n",
            "Epoch 1128/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 0.2397 - inflamation_pred_loss: 0.2101 - nephritis_pred_loss: 0.1478 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0251\n",
            "Epoch 1129/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.2391 - inflamation_pred_loss: 0.2096 - nephritis_pred_loss: 0.1475 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0243\n",
            "Epoch 1130/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 0.2386 - inflamation_pred_loss: 0.2092 - nephritis_pred_loss: 0.1472 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0249\n",
            "Epoch 1131/1500\n",
            "80/80 [==============================] - 0s 907us/sample - loss: 0.2381 - inflamation_pred_loss: 0.2087 - nephritis_pred_loss: 0.1469 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0242\n",
            "Epoch 1132/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.2375 - inflamation_pred_loss: 0.2082 - nephritis_pred_loss: 0.1466 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0247\n",
            "Epoch 1133/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.2370 - inflamation_pred_loss: 0.2077 - nephritis_pred_loss: 0.1463 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0240\n",
            "Epoch 1134/1500\n",
            "80/80 [==============================] - 0s 90us/sample - loss: 0.2364 - inflamation_pred_loss: 0.2072 - nephritis_pred_loss: 0.1460 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0246\n",
            "Epoch 1135/1500\n",
            "80/80 [==============================] - 0s 989us/sample - loss: 0.2359 - inflamation_pred_loss: 0.2067 - nephritis_pred_loss: 0.1457 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0238\n",
            "Epoch 1136/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.2354 - inflamation_pred_loss: 0.2063 - nephritis_pred_loss: 0.1454 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0244\n",
            "Epoch 1137/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.2348 - inflamation_pred_loss: 0.2058 - nephritis_pred_loss: 0.1451 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0237\n",
            "Epoch 1138/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.2343 - inflamation_pred_loss: 0.2053 - nephritis_pred_loss: 0.1449 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0242\n",
            "Epoch 1139/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.2337 - inflamation_pred_loss: 0.2048 - nephritis_pred_loss: 0.1446 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0235\n",
            "Epoch 1140/1500\n",
            "80/80 [==============================] - 0s 915us/sample - loss: 0.2332 - inflamation_pred_loss: 0.2043 - nephritis_pred_loss: 0.1443 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0241\n",
            "Epoch 1141/1500\n",
            "80/80 [==============================] - 0s 113us/sample - loss: 0.2327 - inflamation_pred_loss: 0.2039 - nephritis_pred_loss: 0.1440 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0234\n",
            "Epoch 1142/1500\n",
            "80/80 [==============================] - 0s 100us/sample - loss: 0.2321 - inflamation_pred_loss: 0.2034 - nephritis_pred_loss: 0.1437 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0239\n",
            "Epoch 1143/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.2316 - inflamation_pred_loss: 0.2029 - nephritis_pred_loss: 0.1434 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0232\n",
            "Epoch 1144/1500\n",
            "80/80 [==============================] - 0s 971us/sample - loss: 0.2311 - inflamation_pred_loss: 0.2025 - nephritis_pred_loss: 0.1431 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0238\n",
            "Epoch 1145/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.2305 - inflamation_pred_loss: 0.2020 - nephritis_pred_loss: 0.1428 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0230\n",
            "Epoch 1146/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.2300 - inflamation_pred_loss: 0.2015 - nephritis_pred_loss: 0.1425 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0236\n",
            "Epoch 1147/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.2295 - inflamation_pred_loss: 0.2010 - nephritis_pred_loss: 0.1422 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0229\n",
            "Epoch 1148/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.2290 - inflamation_pred_loss: 0.2006 - nephritis_pred_loss: 0.1419 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0234\n",
            "Epoch 1149/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.2284 - inflamation_pred_loss: 0.2001 - nephritis_pred_loss: 0.1416 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0227\n",
            "Epoch 1150/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.2279 - inflamation_pred_loss: 0.1996 - nephritis_pred_loss: 0.1413 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0233\n",
            "Epoch 1151/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.2274 - inflamation_pred_loss: 0.1992 - nephritis_pred_loss: 0.1410 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0226\n",
            "Epoch 1152/1500\n",
            "80/80 [==============================] - 0s 949us/sample - loss: 0.2268 - inflamation_pred_loss: 0.1987 - nephritis_pred_loss: 0.1407 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0231\n",
            "Epoch 1153/1500\n",
            "80/80 [==============================] - 0s 111us/sample - loss: 0.2263 - inflamation_pred_loss: 0.1982 - nephritis_pred_loss: 0.1405 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0224\n",
            "Epoch 1154/1500\n",
            "80/80 [==============================] - 0s 80us/sample - loss: 0.2258 - inflamation_pred_loss: 0.1978 - nephritis_pred_loss: 0.1402 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0230\n",
            "Epoch 1155/1500\n",
            "80/80 [==============================] - 0s 47us/sample - loss: 0.2253 - inflamation_pred_loss: 0.1973 - nephritis_pred_loss: 0.1399 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0223\n",
            "Epoch 1156/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.2248 - inflamation_pred_loss: 0.1968 - nephritis_pred_loss: 0.1396 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0228\n",
            "Epoch 1157/1500\n",
            "80/80 [==============================] - 0s 871us/sample - loss: 0.2242 - inflamation_pred_loss: 0.1964 - nephritis_pred_loss: 0.1393 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0221\n",
            "Epoch 1158/1500\n",
            "80/80 [==============================] - 0s 49us/sample - loss: 0.2237 - inflamation_pred_loss: 0.1959 - nephritis_pred_loss: 0.1390 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0227\n",
            "Epoch 1159/1500\n",
            "80/80 [==============================] - 0s 86us/sample - loss: 0.2232 - inflamation_pred_loss: 0.1955 - nephritis_pred_loss: 0.1387 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0220\n",
            "Epoch 1160/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 0.2227 - inflamation_pred_loss: 0.1950 - nephritis_pred_loss: 0.1384 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0225\n",
            "Epoch 1161/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.2222 - inflamation_pred_loss: 0.1945 - nephritis_pred_loss: 0.1381 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0218\n",
            "Epoch 1162/1500\n",
            "80/80 [==============================] - 0s 88us/sample - loss: 0.2217 - inflamation_pred_loss: 0.1941 - nephritis_pred_loss: 0.1379 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0224\n",
            "Epoch 1163/1500\n",
            "80/80 [==============================] - 0s 101us/sample - loss: 0.2211 - inflamation_pred_loss: 0.1936 - nephritis_pred_loss: 0.1376 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0217\n",
            "Epoch 1164/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.2206 - inflamation_pred_loss: 0.1932 - nephritis_pred_loss: 0.1373 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0222\n",
            "Epoch 1165/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.2201 - inflamation_pred_loss: 0.1927 - nephritis_pred_loss: 0.1370 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0215\n",
            "Epoch 1166/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.2196 - inflamation_pred_loss: 0.1923 - nephritis_pred_loss: 0.1367 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0221\n",
            "Epoch 1167/1500\n",
            "80/80 [==============================] - 0s 93us/sample - loss: 0.2191 - inflamation_pred_loss: 0.1918 - nephritis_pred_loss: 0.1364 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0214\n",
            "Epoch 1168/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.2186 - inflamation_pred_loss: 0.1914 - nephritis_pred_loss: 0.1362 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0219\n",
            "Epoch 1169/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.2181 - inflamation_pred_loss: 0.1909 - nephritis_pred_loss: 0.1359 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0212\n",
            "Epoch 1170/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.2176 - inflamation_pred_loss: 0.1905 - nephritis_pred_loss: 0.1356 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0218\n",
            "Epoch 1171/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 0.2171 - inflamation_pred_loss: 0.1900 - nephritis_pred_loss: 0.1353 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0211\n",
            "Epoch 1172/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.2166 - inflamation_pred_loss: 0.1896 - nephritis_pred_loss: 0.1350 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0216\n",
            "Epoch 1173/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 0.2161 - inflamation_pred_loss: 0.1891 - nephritis_pred_loss: 0.1347 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0209\n",
            "Epoch 1174/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.2156 - inflamation_pred_loss: 0.1887 - nephritis_pred_loss: 0.1345 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0215\n",
            "Epoch 1175/1500\n",
            "80/80 [==============================] - 0s 921us/sample - loss: 0.2150 - inflamation_pred_loss: 0.1882 - nephritis_pred_loss: 0.1342 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0208\n",
            "Epoch 1176/1500\n",
            "80/80 [==============================] - 0s 95us/sample - loss: 0.2145 - inflamation_pred_loss: 0.1878 - nephritis_pred_loss: 0.1339 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0213\n",
            "Epoch 1177/1500\n",
            "80/80 [==============================] - 0s 93us/sample - loss: 0.2140 - inflamation_pred_loss: 0.1873 - nephritis_pred_loss: 0.1336 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0206\n",
            "Epoch 1178/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.2135 - inflamation_pred_loss: 0.1869 - nephritis_pred_loss: 0.1333 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0212\n",
            "Epoch 1179/1500\n",
            "80/80 [==============================] - 0s 93us/sample - loss: 0.2130 - inflamation_pred_loss: 0.1864 - nephritis_pred_loss: 0.1331 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0205\n",
            "Epoch 1180/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.2126 - inflamation_pred_loss: 0.1860 - nephritis_pred_loss: 0.1328 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0210\n",
            "Epoch 1181/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.2121 - inflamation_pred_loss: 0.1856 - nephritis_pred_loss: 0.1325 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0203\n",
            "Epoch 1182/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.2116 - inflamation_pred_loss: 0.1851 - nephritis_pred_loss: 0.1322 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0209\n",
            "Epoch 1183/1500\n",
            "80/80 [==============================] - 0s 962us/sample - loss: 0.2111 - inflamation_pred_loss: 0.1847 - nephritis_pred_loss: 0.1320 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0202\n",
            "Epoch 1184/1500\n",
            "80/80 [==============================] - 0s 131us/sample - loss: 0.2106 - inflamation_pred_loss: 0.1842 - nephritis_pred_loss: 0.1317 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0207\n",
            "Epoch 1185/1500\n",
            "80/80 [==============================] - 0s 103us/sample - loss: 0.2101 - inflamation_pred_loss: 0.1838 - nephritis_pred_loss: 0.1314 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0201\n",
            "Epoch 1186/1500\n",
            "80/80 [==============================] - 0s 51us/sample - loss: 0.2096 - inflamation_pred_loss: 0.1834 - nephritis_pred_loss: 0.1311 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0206\n",
            "Epoch 1187/1500\n",
            "80/80 [==============================] - 0s 877us/sample - loss: 0.2091 - inflamation_pred_loss: 0.1829 - nephritis_pred_loss: 0.1308 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0199\n",
            "Epoch 1188/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.2086 - inflamation_pred_loss: 0.1825 - nephritis_pred_loss: 0.1306 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0204\n",
            "Epoch 1189/1500\n",
            "80/80 [==============================] - 0s 128us/sample - loss: 0.2081 - inflamation_pred_loss: 0.1821 - nephritis_pred_loss: 0.1303 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0198\n",
            "Epoch 1190/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.2076 - inflamation_pred_loss: 0.1816 - nephritis_pred_loss: 0.1300 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0203\n",
            "Epoch 1191/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.2071 - inflamation_pred_loss: 0.1812 - nephritis_pred_loss: 0.1298 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0196\n",
            "Epoch 1192/1500\n",
            "80/80 [==============================] - 0s 875us/sample - loss: 0.2067 - inflamation_pred_loss: 0.1808 - nephritis_pred_loss: 0.1295 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0202\n",
            "Epoch 1193/1500\n",
            "80/80 [==============================] - 0s 138us/sample - loss: 0.2062 - inflamation_pred_loss: 0.1803 - nephritis_pred_loss: 0.1292 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0195\n",
            "Epoch 1194/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.2057 - inflamation_pred_loss: 0.1799 - nephritis_pred_loss: 0.1289 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0200\n",
            "Epoch 1195/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.2052 - inflamation_pred_loss: 0.1795 - nephritis_pred_loss: 0.1287 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0194\n",
            "Epoch 1196/1500\n",
            "80/80 [==============================] - 0s 931us/sample - loss: 0.2047 - inflamation_pred_loss: 0.1790 - nephritis_pred_loss: 0.1284 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0199\n",
            "Epoch 1197/1500\n",
            "80/80 [==============================] - 0s 83us/sample - loss: 0.2042 - inflamation_pred_loss: 0.1786 - nephritis_pred_loss: 0.1281 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0192\n",
            "Epoch 1198/1500\n",
            "80/80 [==============================] - 0s 79us/sample - loss: 0.2038 - inflamation_pred_loss: 0.1782 - nephritis_pred_loss: 0.1279 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0197\n",
            "Epoch 1199/1500\n",
            "80/80 [==============================] - 0s 129us/sample - loss: 0.2033 - inflamation_pred_loss: 0.1778 - nephritis_pred_loss: 0.1276 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0191\n",
            "Epoch 1200/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.2028 - inflamation_pred_loss: 0.1773 - nephritis_pred_loss: 0.1273 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0196\n",
            "Epoch 1201/1500\n",
            "80/80 [==============================] - 0s 865us/sample - loss: 0.2023 - inflamation_pred_loss: 0.1769 - nephritis_pred_loss: 0.1270 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0190\n",
            "Epoch 1202/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.2018 - inflamation_pred_loss: 0.1765 - nephritis_pred_loss: 0.1268 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0195\n",
            "Epoch 1203/1500\n",
            "80/80 [==============================] - 0s 80us/sample - loss: 0.2014 - inflamation_pred_loss: 0.1761 - nephritis_pred_loss: 0.1265 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0188\n",
            "Epoch 1204/1500\n",
            "80/80 [==============================] - 0s 101us/sample - loss: 0.2009 - inflamation_pred_loss: 0.1756 - nephritis_pred_loss: 0.1262 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0193\n",
            "Epoch 1205/1500\n",
            "80/80 [==============================] - 0s 991us/sample - loss: 0.2004 - inflamation_pred_loss: 0.1752 - nephritis_pred_loss: 0.1260 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0187\n",
            "Epoch 1206/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.1999 - inflamation_pred_loss: 0.1748 - nephritis_pred_loss: 0.1257 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0192\n",
            "Epoch 1207/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.1995 - inflamation_pred_loss: 0.1744 - nephritis_pred_loss: 0.1254 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0186\n",
            "Epoch 1208/1500\n",
            "80/80 [==============================] - 0s 49us/sample - loss: 0.1990 - inflamation_pred_loss: 0.1740 - nephritis_pred_loss: 0.1252 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0191\n",
            "Epoch 1209/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.1985 - inflamation_pred_loss: 0.1735 - nephritis_pred_loss: 0.1249 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0184\n",
            "Epoch 1210/1500\n",
            "80/80 [==============================] - 0s 943us/sample - loss: 0.1981 - inflamation_pred_loss: 0.1731 - nephritis_pred_loss: 0.1246 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0189\n",
            "Epoch 1211/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.1976 - inflamation_pred_loss: 0.1727 - nephritis_pred_loss: 0.1244 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0183\n",
            "Epoch 1212/1500\n",
            "80/80 [==============================] - 0s 47us/sample - loss: 0.1971 - inflamation_pred_loss: 0.1723 - nephritis_pred_loss: 0.1241 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0188\n",
            "Epoch 1213/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.1966 - inflamation_pred_loss: 0.1719 - nephritis_pred_loss: 0.1238 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0182\n",
            "Epoch 1214/1500\n",
            "80/80 [==============================] - 0s 960us/sample - loss: 0.1962 - inflamation_pred_loss: 0.1715 - nephritis_pred_loss: 0.1236 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0187\n",
            "Epoch 1215/1500\n",
            "80/80 [==============================] - 0s 95us/sample - loss: 0.1957 - inflamation_pred_loss: 0.1711 - nephritis_pred_loss: 0.1233 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0180\n",
            "Epoch 1216/1500\n",
            "80/80 [==============================] - 0s 110us/sample - loss: 0.1953 - inflamation_pred_loss: 0.1706 - nephritis_pred_loss: 0.1231 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0185\n",
            "Epoch 1217/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.1948 - inflamation_pred_loss: 0.1702 - nephritis_pred_loss: 0.1228 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0179\n",
            "Epoch 1218/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.1943 - inflamation_pred_loss: 0.1698 - nephritis_pred_loss: 0.1225 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0184\n",
            "Epoch 1219/1500\n",
            "80/80 [==============================] - 0s 838us/sample - loss: 0.1939 - inflamation_pred_loss: 0.1694 - nephritis_pred_loss: 0.1223 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0178\n",
            "Epoch 1220/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.1934 - inflamation_pred_loss: 0.1690 - nephritis_pred_loss: 0.1220 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0183\n",
            "Epoch 1221/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.1929 - inflamation_pred_loss: 0.1686 - nephritis_pred_loss: 0.1217 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0176\n",
            "Epoch 1222/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.1925 - inflamation_pred_loss: 0.1682 - nephritis_pred_loss: 0.1215 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0181\n",
            "Epoch 1223/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.1920 - inflamation_pred_loss: 0.1678 - nephritis_pred_loss: 0.1212 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0175\n",
            "Epoch 1224/1500\n",
            "80/80 [==============================] - 0s 51us/sample - loss: 0.1916 - inflamation_pred_loss: 0.1674 - nephritis_pred_loss: 0.1210 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0180\n",
            "Epoch 1225/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.1911 - inflamation_pred_loss: 0.1670 - nephritis_pred_loss: 0.1207 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0174\n",
            "Epoch 1226/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.1907 - inflamation_pred_loss: 0.1666 - nephritis_pred_loss: 0.1204 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0179\n",
            "Epoch 1227/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.1902 - inflamation_pred_loss: 0.1662 - nephritis_pred_loss: 0.1202 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0173\n",
            "Epoch 1228/1500\n",
            "80/80 [==============================] - 0s 34us/sample - loss: 0.1898 - inflamation_pred_loss: 0.1658 - nephritis_pred_loss: 0.1199 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0177\n",
            "Epoch 1229/1500\n",
            "80/80 [==============================] - 0s 969us/sample - loss: 0.1893 - inflamation_pred_loss: 0.1654 - nephritis_pred_loss: 0.1197 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0171\n",
            "Epoch 1230/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.1889 - inflamation_pred_loss: 0.1650 - nephritis_pred_loss: 0.1194 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0176\n",
            "Epoch 1231/1500\n",
            "80/80 [==============================] - 0s 52us/sample - loss: 0.1884 - inflamation_pred_loss: 0.1646 - nephritis_pred_loss: 0.1192 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0170\n",
            "Epoch 1232/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.1880 - inflamation_pred_loss: 0.1642 - nephritis_pred_loss: 0.1189 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0175\n",
            "Epoch 1233/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.1875 - inflamation_pred_loss: 0.1638 - nephritis_pred_loss: 0.1186 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0169\n",
            "Epoch 1234/1500\n",
            "80/80 [==============================] - 0s 90us/sample - loss: 0.1871 - inflamation_pred_loss: 0.1634 - nephritis_pred_loss: 0.1184 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0174\n",
            "Epoch 1235/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 0.1866 - inflamation_pred_loss: 0.1630 - nephritis_pred_loss: 0.1181 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0168\n",
            "Epoch 1236/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.1862 - inflamation_pred_loss: 0.1626 - nephritis_pred_loss: 0.1179 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0172\n",
            "Epoch 1237/1500\n",
            "80/80 [==============================] - 0s 927us/sample - loss: 0.1857 - inflamation_pred_loss: 0.1622 - nephritis_pred_loss: 0.1176 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0166\n",
            "Epoch 1238/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.1853 - inflamation_pred_loss: 0.1618 - nephritis_pred_loss: 0.1174 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0171\n",
            "Epoch 1239/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.1848 - inflamation_pred_loss: 0.1614 - nephritis_pred_loss: 0.1171 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0165\n",
            "Epoch 1240/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.1844 - inflamation_pred_loss: 0.1610 - nephritis_pred_loss: 0.1169 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0170\n",
            "Epoch 1241/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.1839 - inflamation_pred_loss: 0.1606 - nephritis_pred_loss: 0.1166 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0164\n",
            "Epoch 1242/1500\n",
            "80/80 [==============================] - 0s 84us/sample - loss: 0.1835 - inflamation_pred_loss: 0.1602 - nephritis_pred_loss: 0.1164 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0169\n",
            "Epoch 1243/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.1831 - inflamation_pred_loss: 0.1598 - nephritis_pred_loss: 0.1161 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0163\n",
            "Epoch 1244/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 0.1826 - inflamation_pred_loss: 0.1595 - nephritis_pred_loss: 0.1159 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0167\n",
            "Epoch 1245/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.1822 - inflamation_pred_loss: 0.1591 - nephritis_pred_loss: 0.1156 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0162\n",
            "Epoch 1246/1500\n",
            "80/80 [==============================] - 0s 897us/sample - loss: 0.1818 - inflamation_pred_loss: 0.1587 - nephritis_pred_loss: 0.1154 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0166\n",
            "Epoch 1247/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.1813 - inflamation_pred_loss: 0.1583 - nephritis_pred_loss: 0.1151 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0160\n",
            "Epoch 1248/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.1809 - inflamation_pred_loss: 0.1579 - nephritis_pred_loss: 0.1149 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0165\n",
            "Epoch 1249/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.1804 - inflamation_pred_loss: 0.1575 - nephritis_pred_loss: 0.1146 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0159\n",
            "Epoch 1250/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.1800 - inflamation_pred_loss: 0.1571 - nephritis_pred_loss: 0.1144 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0164\n",
            "Epoch 1251/1500\n",
            "80/80 [==============================] - 0s 841us/sample - loss: 0.1796 - inflamation_pred_loss: 0.1568 - nephritis_pred_loss: 0.1141 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0158\n",
            "Epoch 1252/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.1792 - inflamation_pred_loss: 0.1564 - nephritis_pred_loss: 0.1139 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0163\n",
            "Epoch 1253/1500\n",
            "80/80 [==============================] - 0s 84us/sample - loss: 0.1787 - inflamation_pred_loss: 0.1560 - nephritis_pred_loss: 0.1136 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0157\n",
            "Epoch 1254/1500\n",
            "80/80 [==============================] - 0s 101us/sample - loss: 0.1783 - inflamation_pred_loss: 0.1556 - nephritis_pred_loss: 0.1134 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0161\n",
            "Epoch 1255/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.1779 - inflamation_pred_loss: 0.1552 - nephritis_pred_loss: 0.1131 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0156\n",
            "Epoch 1256/1500\n",
            "80/80 [==============================] - 0s 886us/sample - loss: 0.1774 - inflamation_pred_loss: 0.1549 - nephritis_pred_loss: 0.1129 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0160\n",
            "Epoch 1257/1500\n",
            "80/80 [==============================] - 0s 106us/sample - loss: 0.1770 - inflamation_pred_loss: 0.1545 - nephritis_pred_loss: 0.1126 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0155\n",
            "Epoch 1258/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.1766 - inflamation_pred_loss: 0.1541 - nephritis_pred_loss: 0.1124 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0159\n",
            "Epoch 1259/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.1762 - inflamation_pred_loss: 0.1537 - nephritis_pred_loss: 0.1121 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0153\n",
            "Epoch 1260/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.1757 - inflamation_pred_loss: 0.1534 - nephritis_pred_loss: 0.1119 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0158\n",
            "Epoch 1261/1500\n",
            "80/80 [==============================] - 0s 953us/sample - loss: 0.1753 - inflamation_pred_loss: 0.1530 - nephritis_pred_loss: 0.1116 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0152\n",
            "Epoch 1262/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.1749 - inflamation_pred_loss: 0.1526 - nephritis_pred_loss: 0.1114 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0157\n",
            "Epoch 1263/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.1745 - inflamation_pred_loss: 0.1522 - nephritis_pred_loss: 0.1112 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0151\n",
            "Epoch 1264/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.1740 - inflamation_pred_loss: 0.1519 - nephritis_pred_loss: 0.1109 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0156\n",
            "Epoch 1265/1500\n",
            "80/80 [==============================] - 0s 987us/sample - loss: 0.1736 - inflamation_pred_loss: 0.1515 - nephritis_pred_loss: 0.1107 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0150\n",
            "Epoch 1266/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.1732 - inflamation_pred_loss: 0.1511 - nephritis_pred_loss: 0.1104 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0154\n",
            "Epoch 1267/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 0.1728 - inflamation_pred_loss: 0.1507 - nephritis_pred_loss: 0.1102 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0149\n",
            "Epoch 1268/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.1724 - inflamation_pred_loss: 0.1504 - nephritis_pred_loss: 0.1100 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0153\n",
            "Epoch 1269/1500\n",
            "80/80 [==============================] - 0s 902us/sample - loss: 0.1720 - inflamation_pred_loss: 0.1500 - nephritis_pred_loss: 0.1097 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0148\n",
            "Epoch 1270/1500\n",
            "80/80 [==============================] - 0s 98us/sample - loss: 0.1715 - inflamation_pred_loss: 0.1496 - nephritis_pred_loss: 0.1095 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0152\n",
            "Epoch 1271/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.1711 - inflamation_pred_loss: 0.1493 - nephritis_pred_loss: 0.1092 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0147\n",
            "Epoch 1272/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.1707 - inflamation_pred_loss: 0.1489 - nephritis_pred_loss: 0.1090 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0151\n",
            "Epoch 1273/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.1703 - inflamation_pred_loss: 0.1485 - nephritis_pred_loss: 0.1087 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0146\n",
            "Epoch 1274/1500\n",
            "80/80 [==============================] - 0s 81us/sample - loss: 0.1699 - inflamation_pred_loss: 0.1482 - nephritis_pred_loss: 0.1085 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0150\n",
            "Epoch 1275/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.1695 - inflamation_pred_loss: 0.1478 - nephritis_pred_loss: 0.1083 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0144\n",
            "Epoch 1276/1500\n",
            "80/80 [==============================] - 0s 87us/sample - loss: 0.1691 - inflamation_pred_loss: 0.1475 - nephritis_pred_loss: 0.1080 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0149\n",
            "Epoch 1277/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.1687 - inflamation_pred_loss: 0.1471 - nephritis_pred_loss: 0.1078 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0143\n",
            "Epoch 1278/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.1682 - inflamation_pred_loss: 0.1467 - nephritis_pred_loss: 0.1076 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0148\n",
            "Epoch 1279/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.1678 - inflamation_pred_loss: 0.1464 - nephritis_pred_loss: 0.1073 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0142\n",
            "Epoch 1280/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.1674 - inflamation_pred_loss: 0.1460 - nephritis_pred_loss: 0.1071 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0147\n",
            "Epoch 1281/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.1670 - inflamation_pred_loss: 0.1457 - nephritis_pred_loss: 0.1068 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0141\n",
            "Epoch 1282/1500\n",
            "80/80 [==============================] - 0s 916us/sample - loss: 0.1666 - inflamation_pred_loss: 0.1453 - nephritis_pred_loss: 0.1066 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0145\n",
            "Epoch 1283/1500\n",
            "80/80 [==============================] - 0s 110us/sample - loss: 0.1662 - inflamation_pred_loss: 0.1449 - nephritis_pred_loss: 0.1064 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0140\n",
            "Epoch 1284/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.1658 - inflamation_pred_loss: 0.1446 - nephritis_pred_loss: 0.1061 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0144\n",
            "Epoch 1285/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.1654 - inflamation_pred_loss: 0.1442 - nephritis_pred_loss: 0.1059 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0139\n",
            "Epoch 1286/1500\n",
            "80/80 [==============================] - 0s 952us/sample - loss: 0.1650 - inflamation_pred_loss: 0.1439 - nephritis_pred_loss: 0.1057 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0143\n",
            "Epoch 1287/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.1646 - inflamation_pred_loss: 0.1435 - nephritis_pred_loss: 0.1054 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0138\n",
            "Epoch 1288/1500\n",
            "80/80 [==============================] - 0s 142us/sample - loss: 0.1642 - inflamation_pred_loss: 0.1432 - nephritis_pred_loss: 0.1052 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0142\n",
            "Epoch 1289/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.1638 - inflamation_pred_loss: 0.1428 - nephritis_pred_loss: 0.1050 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0137\n",
            "Epoch 1290/1500\n",
            "80/80 [==============================] - 0s 924us/sample - loss: 0.1634 - inflamation_pred_loss: 0.1425 - nephritis_pred_loss: 0.1047 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0141\n",
            "Epoch 1291/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.1630 - inflamation_pred_loss: 0.1421 - nephritis_pred_loss: 0.1045 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0136\n",
            "Epoch 1292/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.1626 - inflamation_pred_loss: 0.1418 - nephritis_pred_loss: 0.1043 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0140\n",
            "Epoch 1293/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.1622 - inflamation_pred_loss: 0.1414 - nephritis_pred_loss: 0.1040 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0135\n",
            "Epoch 1294/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.1618 - inflamation_pred_loss: 0.1411 - nephritis_pred_loss: 0.1038 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0139\n",
            "Epoch 1295/1500\n",
            "80/80 [==============================] - 0s 116us/sample - loss: 0.1614 - inflamation_pred_loss: 0.1407 - nephritis_pred_loss: 0.1036 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0134\n",
            "Epoch 1296/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.1610 - inflamation_pred_loss: 0.1404 - nephritis_pred_loss: 0.1034 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0138\n",
            "Epoch 1297/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.1607 - inflamation_pred_loss: 0.1400 - nephritis_pred_loss: 0.1031 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0133\n",
            "Epoch 1298/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.1603 - inflamation_pred_loss: 0.1397 - nephritis_pred_loss: 0.1029 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0137\n",
            "Epoch 1299/1500\n",
            "80/80 [==============================] - 0s 34us/sample - loss: 0.1599 - inflamation_pred_loss: 0.1393 - nephritis_pred_loss: 0.1027 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0132\n",
            "Epoch 1300/1500\n",
            "80/80 [==============================] - 0s 880us/sample - loss: 0.1595 - inflamation_pred_loss: 0.1390 - nephritis_pred_loss: 0.1024 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0136\n",
            "Epoch 1301/1500\n",
            "80/80 [==============================] - 0s 89us/sample - loss: 0.1591 - inflamation_pred_loss: 0.1387 - nephritis_pred_loss: 0.1022 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0131\n",
            "Epoch 1302/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.1587 - inflamation_pred_loss: 0.1383 - nephritis_pred_loss: 0.1020 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0135\n",
            "Epoch 1303/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.1583 - inflamation_pred_loss: 0.1380 - nephritis_pred_loss: 0.1017 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0130\n",
            "Epoch 1304/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.1579 - inflamation_pred_loss: 0.1376 - nephritis_pred_loss: 0.1015 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0134\n",
            "Epoch 1305/1500\n",
            "80/80 [==============================] - 0s 921us/sample - loss: 0.1576 - inflamation_pred_loss: 0.1373 - nephritis_pred_loss: 0.1013 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0129\n",
            "Epoch 1306/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.1572 - inflamation_pred_loss: 0.1370 - nephritis_pred_loss: 0.1011 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0133\n",
            "Epoch 1307/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.1568 - inflamation_pred_loss: 0.1366 - nephritis_pred_loss: 0.1008 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0128\n",
            "Epoch 1308/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.1564 - inflamation_pred_loss: 0.1363 - nephritis_pred_loss: 0.1006 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0132\n",
            "Epoch 1309/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.1560 - inflamation_pred_loss: 0.1359 - nephritis_pred_loss: 0.1004 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0127\n",
            "Epoch 1310/1500\n",
            "80/80 [==============================] - 0s 104us/sample - loss: 0.1556 - inflamation_pred_loss: 0.1356 - nephritis_pred_loss: 0.1002 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0131\n",
            "Epoch 1311/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 0.1553 - inflamation_pred_loss: 0.1353 - nephritis_pred_loss: 0.0999 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0126\n",
            "Epoch 1312/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.1549 - inflamation_pred_loss: 0.1349 - nephritis_pred_loss: 0.0997 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0130\n",
            "Epoch 1313/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.1545 - inflamation_pred_loss: 0.1346 - nephritis_pred_loss: 0.0995 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0125\n",
            "Epoch 1314/1500\n",
            "80/80 [==============================] - 0s 934us/sample - loss: 0.1541 - inflamation_pred_loss: 0.1343 - nephritis_pred_loss: 0.0993 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0129\n",
            "Epoch 1315/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.1537 - inflamation_pred_loss: 0.1339 - nephritis_pred_loss: 0.0990 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0124\n",
            "Epoch 1316/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.1534 - inflamation_pred_loss: 0.1336 - nephritis_pred_loss: 0.0988 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0128\n",
            "Epoch 1317/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.1530 - inflamation_pred_loss: 0.1333 - nephritis_pred_loss: 0.0986 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0123\n",
            "Epoch 1318/1500\n",
            "80/80 [==============================] - 0s 983us/sample - loss: 0.1526 - inflamation_pred_loss: 0.1329 - nephritis_pred_loss: 0.0984 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0127\n",
            "Epoch 1319/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.1522 - inflamation_pred_loss: 0.1326 - nephritis_pred_loss: 0.0982 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0122\n",
            "Epoch 1320/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.1519 - inflamation_pred_loss: 0.1323 - nephritis_pred_loss: 0.0979 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0126\n",
            "Epoch 1321/1500\n",
            "80/80 [==============================] - 0s 84us/sample - loss: 0.1515 - inflamation_pred_loss: 0.1320 - nephritis_pred_loss: 0.0977 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0121\n",
            "Epoch 1322/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.1511 - inflamation_pred_loss: 0.1316 - nephritis_pred_loss: 0.0975 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0125\n",
            "Epoch 1323/1500\n",
            "80/80 [==============================] - 0s 898us/sample - loss: 0.1508 - inflamation_pred_loss: 0.1313 - nephritis_pred_loss: 0.0973 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0120\n",
            "Epoch 1324/1500\n",
            "80/80 [==============================] - 0s 91us/sample - loss: 0.1504 - inflamation_pred_loss: 0.1310 - nephritis_pred_loss: 0.0971 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0124\n",
            "Epoch 1325/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.1500 - inflamation_pred_loss: 0.1307 - nephritis_pred_loss: 0.0968 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0119\n",
            "Epoch 1326/1500\n",
            "80/80 [==============================] - 0s 49us/sample - loss: 0.1497 - inflamation_pred_loss: 0.1303 - nephritis_pred_loss: 0.0966 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0123\n",
            "Epoch 1327/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 0.1493 - inflamation_pred_loss: 0.1300 - nephritis_pred_loss: 0.0964 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0118\n",
            "Epoch 1328/1500\n",
            "80/80 [==============================] - 0s 931us/sample - loss: 0.1489 - inflamation_pred_loss: 0.1297 - nephritis_pred_loss: 0.0962 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0122\n",
            "Epoch 1329/1500\n",
            "80/80 [==============================] - 0s 128us/sample - loss: 0.1486 - inflamation_pred_loss: 0.1294 - nephritis_pred_loss: 0.0960 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0117\n",
            "Epoch 1330/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.1482 - inflamation_pred_loss: 0.1290 - nephritis_pred_loss: 0.0957 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0121\n",
            "Epoch 1331/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 0.1478 - inflamation_pred_loss: 0.1287 - nephritis_pred_loss: 0.0955 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0116\n",
            "Epoch 1332/1500\n",
            "80/80 [==============================] - 0s 925us/sample - loss: 0.1475 - inflamation_pred_loss: 0.1284 - nephritis_pred_loss: 0.0953 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0120\n",
            "Epoch 1333/1500\n",
            "80/80 [==============================] - 0s 144us/sample - loss: 0.1471 - inflamation_pred_loss: 0.1281 - nephritis_pred_loss: 0.0951 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0115\n",
            "Epoch 1334/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.1467 - inflamation_pred_loss: 0.1278 - nephritis_pred_loss: 0.0949 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0119\n",
            "Epoch 1335/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.1464 - inflamation_pred_loss: 0.1275 - nephritis_pred_loss: 0.0947 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0114\n",
            "Epoch 1336/1500\n",
            "80/80 [==============================] - 0s 47us/sample - loss: 0.1460 - inflamation_pred_loss: 0.1271 - nephritis_pred_loss: 0.0945 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0118\n",
            "Epoch 1337/1500\n",
            "80/80 [==============================] - 0s 112us/sample - loss: 0.1457 - inflamation_pred_loss: 0.1268 - nephritis_pred_loss: 0.0942 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0113\n",
            "Epoch 1338/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.1453 - inflamation_pred_loss: 0.1265 - nephritis_pred_loss: 0.0940 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0117\n",
            "Epoch 1339/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.1450 - inflamation_pred_loss: 0.1262 - nephritis_pred_loss: 0.0938 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0113\n",
            "Epoch 1340/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 0.1446 - inflamation_pred_loss: 0.1259 - nephritis_pred_loss: 0.0936 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0116\n",
            "Epoch 1341/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.1442 - inflamation_pred_loss: 0.1256 - nephritis_pred_loss: 0.0934 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0112\n",
            "Epoch 1342/1500\n",
            "80/80 [==============================] - 0s 56us/sample - loss: 0.1439 - inflamation_pred_loss: 0.1253 - nephritis_pred_loss: 0.0932 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0115\n",
            "Epoch 1343/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.1435 - inflamation_pred_loss: 0.1249 - nephritis_pred_loss: 0.0930 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0111\n",
            "Epoch 1344/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.1432 - inflamation_pred_loss: 0.1246 - nephritis_pred_loss: 0.0927 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0114\n",
            "Epoch 1345/1500\n",
            "80/80 [==============================] - 0s 999us/sample - loss: 0.1428 - inflamation_pred_loss: 0.1243 - nephritis_pred_loss: 0.0925 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0110\n",
            "Epoch 1346/1500\n",
            "80/80 [==============================] - 0s 114us/sample - loss: 0.1425 - inflamation_pred_loss: 0.1240 - nephritis_pred_loss: 0.0923 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0114\n",
            "Epoch 1347/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.1421 - inflamation_pred_loss: 0.1237 - nephritis_pred_loss: 0.0921 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0109\n",
            "Epoch 1348/1500\n",
            "80/80 [==============================] - 0s 43us/sample - loss: 0.1418 - inflamation_pred_loss: 0.1234 - nephritis_pred_loss: 0.0919 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0113\n",
            "Epoch 1349/1500\n",
            "80/80 [==============================] - 0s 905us/sample - loss: 0.1414 - inflamation_pred_loss: 0.1231 - nephritis_pred_loss: 0.0917 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0108\n",
            "Epoch 1350/1500\n",
            "80/80 [==============================] - 0s 103us/sample - loss: 0.1411 - inflamation_pred_loss: 0.1228 - nephritis_pred_loss: 0.0915 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0112\n",
            "Epoch 1351/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.1407 - inflamation_pred_loss: 0.1225 - nephritis_pred_loss: 0.0913 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0107\n",
            "Epoch 1352/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 0.1404 - inflamation_pred_loss: 0.1222 - nephritis_pred_loss: 0.0911 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0111\n",
            "Epoch 1353/1500\n",
            "80/80 [==============================] - 0s 49us/sample - loss: 0.1400 - inflamation_pred_loss: 0.1219 - nephritis_pred_loss: 0.0909 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0106\n",
            "Epoch 1354/1500\n",
            "80/80 [==============================] - 0s 954us/sample - loss: 0.1397 - inflamation_pred_loss: 0.1216 - nephritis_pred_loss: 0.0906 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0110\n",
            "Epoch 1355/1500\n",
            "80/80 [==============================] - 0s 110us/sample - loss: 0.1394 - inflamation_pred_loss: 0.1213 - nephritis_pred_loss: 0.0904 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0106\n",
            "Epoch 1356/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.1390 - inflamation_pred_loss: 0.1210 - nephritis_pred_loss: 0.0902 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0109\n",
            "Epoch 1357/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.1387 - inflamation_pred_loss: 0.1207 - nephritis_pred_loss: 0.0900 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0105\n",
            "Epoch 1358/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.1383 - inflamation_pred_loss: 0.1204 - nephritis_pred_loss: 0.0898 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0108\n",
            "Epoch 1359/1500\n",
            "80/80 [==============================] - 0s 874us/sample - loss: 0.1380 - inflamation_pred_loss: 0.1201 - nephritis_pred_loss: 0.0896 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0104\n",
            "Epoch 1360/1500\n",
            "80/80 [==============================] - 0s 100us/sample - loss: 0.1376 - inflamation_pred_loss: 0.1198 - nephritis_pred_loss: 0.0894 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0107\n",
            "Epoch 1361/1500\n",
            "80/80 [==============================] - 0s 47us/sample - loss: 0.1373 - inflamation_pred_loss: 0.1195 - nephritis_pred_loss: 0.0892 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0103\n",
            "Epoch 1362/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.1370 - inflamation_pred_loss: 0.1192 - nephritis_pred_loss: 0.0890 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0106\n",
            "Epoch 1363/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.1366 - inflamation_pred_loss: 0.1189 - nephritis_pred_loss: 0.0888 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0102\n",
            "Epoch 1364/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.1363 - inflamation_pred_loss: 0.1186 - nephritis_pred_loss: 0.0886 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0106\n",
            "Epoch 1365/1500\n",
            "80/80 [==============================] - 0s 118us/sample - loss: 0.1360 - inflamation_pred_loss: 0.1183 - nephritis_pred_loss: 0.0884 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0101\n",
            "Epoch 1366/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.1356 - inflamation_pred_loss: 0.1180 - nephritis_pred_loss: 0.0882 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0105\n",
            "Epoch 1367/1500\n",
            "80/80 [==============================] - 0s 62us/sample - loss: 0.1353 - inflamation_pred_loss: 0.1177 - nephritis_pred_loss: 0.0880 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0100\n",
            "Epoch 1368/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.1350 - inflamation_pred_loss: 0.1174 - nephritis_pred_loss: 0.0878 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0104\n",
            "Epoch 1369/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 0.1346 - inflamation_pred_loss: 0.1171 - nephritis_pred_loss: 0.0876 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0100\n",
            "Epoch 1370/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.1343 - inflamation_pred_loss: 0.1168 - nephritis_pred_loss: 0.0874 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0103\n",
            "Epoch 1371/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.1340 - inflamation_pred_loss: 0.1165 - nephritis_pred_loss: 0.0872 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0099\n",
            "Epoch 1372/1500\n",
            "80/80 [==============================] - 0s 34us/sample - loss: 0.1336 - inflamation_pred_loss: 0.1162 - nephritis_pred_loss: 0.0870 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0102\n",
            "Epoch 1373/1500\n",
            "80/80 [==============================] - 0s 33us/sample - loss: 0.1333 - inflamation_pred_loss: 0.1159 - nephritis_pred_loss: 0.0868 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0098\n",
            "Epoch 1374/1500\n",
            "80/80 [==============================] - 0s 857us/sample - loss: 0.1330 - inflamation_pred_loss: 0.1157 - nephritis_pred_loss: 0.0866 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0101\n",
            "Epoch 1375/1500\n",
            "80/80 [==============================] - 0s 60us/sample - loss: 0.1326 - inflamation_pred_loss: 0.1154 - nephritis_pred_loss: 0.0864 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0097\n",
            "Epoch 1376/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.1323 - inflamation_pred_loss: 0.1151 - nephritis_pred_loss: 0.0862 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0101\n",
            "Epoch 1377/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.1320 - inflamation_pred_loss: 0.1148 - nephritis_pred_loss: 0.0860 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0096\n",
            "Epoch 1378/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.1317 - inflamation_pred_loss: 0.1145 - nephritis_pred_loss: 0.0858 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0100\n",
            "Epoch 1379/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.1313 - inflamation_pred_loss: 0.1142 - nephritis_pred_loss: 0.0856 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0096\n",
            "Epoch 1380/1500\n",
            "80/80 [==============================] - 0s 76us/sample - loss: 0.1310 - inflamation_pred_loss: 0.1139 - nephritis_pred_loss: 0.0854 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0099\n",
            "Epoch 1381/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.1307 - inflamation_pred_loss: 0.1136 - nephritis_pred_loss: 0.0852 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0095\n",
            "Epoch 1382/1500\n",
            "80/80 [==============================] - 0s 63us/sample - loss: 0.1304 - inflamation_pred_loss: 0.1134 - nephritis_pred_loss: 0.0850 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0098\n",
            "Epoch 1383/1500\n",
            "80/80 [==============================] - 0s 89us/sample - loss: 0.1300 - inflamation_pred_loss: 0.1131 - nephritis_pred_loss: 0.0848 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0094\n",
            "Epoch 1384/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.1297 - inflamation_pred_loss: 0.1128 - nephritis_pred_loss: 0.0846 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0097\n",
            "Epoch 1385/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.1294 - inflamation_pred_loss: 0.1125 - nephritis_pred_loss: 0.0844 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0093\n",
            "Epoch 1386/1500\n",
            "80/80 [==============================] - 0s 52us/sample - loss: 0.1291 - inflamation_pred_loss: 0.1122 - nephritis_pred_loss: 0.0842 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0097\n",
            "Epoch 1387/1500\n",
            "80/80 [==============================] - 0s 929us/sample - loss: 0.1287 - inflamation_pred_loss: 0.1120 - nephritis_pred_loss: 0.0840 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0093\n",
            "Epoch 1388/1500\n",
            "80/80 [==============================] - 0s 74us/sample - loss: 0.1284 - inflamation_pred_loss: 0.1117 - nephritis_pred_loss: 0.0838 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0096\n",
            "Epoch 1389/1500\n",
            "80/80 [==============================] - 0s 75us/sample - loss: 0.1281 - inflamation_pred_loss: 0.1114 - nephritis_pred_loss: 0.0836 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0092\n",
            "Epoch 1390/1500\n",
            "80/80 [==============================] - 0s 59us/sample - loss: 0.1278 - inflamation_pred_loss: 0.1111 - nephritis_pred_loss: 0.0834 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0095\n",
            "Epoch 1391/1500\n",
            "80/80 [==============================] - 0s 47us/sample - loss: 0.1275 - inflamation_pred_loss: 0.1108 - nephritis_pred_loss: 0.0832 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0091\n",
            "Epoch 1392/1500\n",
            "80/80 [==============================] - 0s 940us/sample - loss: 0.1272 - inflamation_pred_loss: 0.1106 - nephritis_pred_loss: 0.0830 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0094\n",
            "Epoch 1393/1500\n",
            "80/80 [==============================] - 0s 88us/sample - loss: 0.1268 - inflamation_pred_loss: 0.1103 - nephritis_pred_loss: 0.0828 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0090\n",
            "Epoch 1394/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.1265 - inflamation_pred_loss: 0.1100 - nephritis_pred_loss: 0.0826 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0093\n",
            "Epoch 1395/1500\n",
            "80/80 [==============================] - 0s 44us/sample - loss: 0.1262 - inflamation_pred_loss: 0.1097 - nephritis_pred_loss: 0.0824 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0090\n",
            "Epoch 1396/1500\n",
            "80/80 [==============================] - 0s 966us/sample - loss: 0.1259 - inflamation_pred_loss: 0.1095 - nephritis_pred_loss: 0.0822 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0093\n",
            "Epoch 1397/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.1256 - inflamation_pred_loss: 0.1092 - nephritis_pred_loss: 0.0820 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0089\n",
            "Epoch 1398/1500\n",
            "80/80 [==============================] - 0s 117us/sample - loss: 0.1253 - inflamation_pred_loss: 0.1089 - nephritis_pred_loss: 0.0818 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0092\n",
            "Epoch 1399/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.1250 - inflamation_pred_loss: 0.1086 - nephritis_pred_loss: 0.0816 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0088\n",
            "Epoch 1400/1500\n",
            "80/80 [==============================] - 0s 924us/sample - loss: 0.1247 - inflamation_pred_loss: 0.1084 - nephritis_pred_loss: 0.0815 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0091\n",
            "Epoch 1401/1500\n",
            "80/80 [==============================] - 0s 143us/sample - loss: 0.1243 - inflamation_pred_loss: 0.1081 - nephritis_pred_loss: 0.0813 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0087\n",
            "Epoch 1402/1500\n",
            "80/80 [==============================] - 0s 84us/sample - loss: 0.1240 - inflamation_pred_loss: 0.1078 - nephritis_pred_loss: 0.0811 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0090\n",
            "Epoch 1403/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.1237 - inflamation_pred_loss: 0.1075 - nephritis_pred_loss: 0.0809 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0087\n",
            "Epoch 1404/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.1234 - inflamation_pred_loss: 0.1073 - nephritis_pred_loss: 0.0807 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0090\n",
            "Epoch 1405/1500\n",
            "80/80 [==============================] - 0s 66us/sample - loss: 0.1231 - inflamation_pred_loss: 0.1070 - nephritis_pred_loss: 0.0805 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0086\n",
            "Epoch 1406/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.1228 - inflamation_pred_loss: 0.1067 - nephritis_pred_loss: 0.0803 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0089\n",
            "Epoch 1407/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.1225 - inflamation_pred_loss: 0.1065 - nephritis_pred_loss: 0.0801 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0085\n",
            "Epoch 1408/1500\n",
            "80/80 [==============================] - 0s 50us/sample - loss: 0.1222 - inflamation_pred_loss: 0.1062 - nephritis_pred_loss: 0.0799 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0088\n",
            "Epoch 1409/1500\n",
            "80/80 [==============================] - 0s 35us/sample - loss: 0.1219 - inflamation_pred_loss: 0.1059 - nephritis_pred_loss: 0.0797 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0084\n",
            "Epoch 1410/1500\n",
            "80/80 [==============================] - 0s 131us/sample - loss: 0.1216 - inflamation_pred_loss: 0.1057 - nephritis_pred_loss: 0.0796 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0087\n",
            "Epoch 1411/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.1213 - inflamation_pred_loss: 0.1054 - nephritis_pred_loss: 0.0794 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0084\n",
            "Epoch 1412/1500\n",
            "80/80 [==============================] - 0s 46us/sample - loss: 0.1210 - inflamation_pred_loss: 0.1051 - nephritis_pred_loss: 0.0792 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0087\n",
            "Epoch 1413/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.1207 - inflamation_pred_loss: 0.1049 - nephritis_pred_loss: 0.0790 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0083\n",
            "Epoch 1414/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.1204 - inflamation_pred_loss: 0.1046 - nephritis_pred_loss: 0.0788 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0086\n",
            "Epoch 1415/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.1201 - inflamation_pred_loss: 0.1044 - nephritis_pred_loss: 0.0786 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0082\n",
            "Epoch 1416/1500\n",
            "80/80 [==============================] - 0s 85us/sample - loss: 0.1198 - inflamation_pred_loss: 0.1041 - nephritis_pred_loss: 0.0784 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0085\n",
            "Epoch 1417/1500\n",
            "80/80 [==============================] - 0s 163us/sample - loss: 0.1195 - inflamation_pred_loss: 0.1038 - nephritis_pred_loss: 0.0782 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0082\n",
            "Epoch 1418/1500\n",
            "80/80 [==============================] - 0s 87us/sample - loss: 0.1192 - inflamation_pred_loss: 0.1036 - nephritis_pred_loss: 0.0781 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0085\n",
            "Epoch 1419/1500\n",
            "80/80 [==============================] - 0s 68us/sample - loss: 0.1189 - inflamation_pred_loss: 0.1033 - nephritis_pred_loss: 0.0779 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0081\n",
            "Epoch 1420/1500\n",
            "80/80 [==============================] - 0s 34us/sample - loss: 0.1186 - inflamation_pred_loss: 0.1031 - nephritis_pred_loss: 0.0777 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0084\n",
            "Epoch 1421/1500\n",
            "80/80 [==============================] - 0s 857us/sample - loss: 0.1183 - inflamation_pred_loss: 0.1028 - nephritis_pred_loss: 0.0775 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0080\n",
            "Epoch 1422/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.1180 - inflamation_pred_loss: 0.1025 - nephritis_pred_loss: 0.0773 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0083\n",
            "Epoch 1423/1500\n",
            "80/80 [==============================] - 0s 85us/sample - loss: 0.1177 - inflamation_pred_loss: 0.1023 - nephritis_pred_loss: 0.0771 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0080\n",
            "Epoch 1424/1500\n",
            "80/80 [==============================] - 0s 73us/sample - loss: 0.1174 - inflamation_pred_loss: 0.1020 - nephritis_pred_loss: 0.0770 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0082\n",
            "Epoch 1425/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.1171 - inflamation_pred_loss: 0.1018 - nephritis_pred_loss: 0.0768 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0079\n",
            "Epoch 1426/1500\n",
            "80/80 [==============================] - 0s 979us/sample - loss: 0.1168 - inflamation_pred_loss: 0.1015 - nephritis_pred_loss: 0.0766 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0082\n",
            "Epoch 1427/1500\n",
            "80/80 [==============================] - 0s 132us/sample - loss: 0.1165 - inflamation_pred_loss: 0.1013 - nephritis_pred_loss: 0.0764 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0078\n",
            "Epoch 1428/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.1162 - inflamation_pred_loss: 0.1010 - nephritis_pred_loss: 0.0762 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0081\n",
            "Epoch 1429/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.1160 - inflamation_pred_loss: 0.1007 - nephritis_pred_loss: 0.0760 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0077\n",
            "Epoch 1430/1500\n",
            "80/80 [==============================] - 0s 33us/sample - loss: 0.1157 - inflamation_pred_loss: 0.1005 - nephritis_pred_loss: 0.0759 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0080\n",
            "Epoch 1431/1500\n",
            "80/80 [==============================] - 0s 904us/sample - loss: 0.1154 - inflamation_pred_loss: 0.1002 - nephritis_pred_loss: 0.0757 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0077\n",
            "Epoch 1432/1500\n",
            "80/80 [==============================] - 0s 122us/sample - loss: 0.1151 - inflamation_pred_loss: 0.1000 - nephritis_pred_loss: 0.0755 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0080\n",
            "Epoch 1433/1500\n",
            "80/80 [==============================] - 0s 64us/sample - loss: 0.1148 - inflamation_pred_loss: 0.0997 - nephritis_pred_loss: 0.0753 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0076\n",
            "Epoch 1434/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 0.1145 - inflamation_pred_loss: 0.0995 - nephritis_pred_loss: 0.0751 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0079\n",
            "Epoch 1435/1500\n",
            "80/80 [==============================] - 0s 39us/sample - loss: 0.1142 - inflamation_pred_loss: 0.0992 - nephritis_pred_loss: 0.0750 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0076\n",
            "Epoch 1436/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.1139 - inflamation_pred_loss: 0.0990 - nephritis_pred_loss: 0.0748 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0078\n",
            "Epoch 1437/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.1137 - inflamation_pred_loss: 0.0987 - nephritis_pred_loss: 0.0746 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0075\n",
            "Epoch 1438/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.1134 - inflamation_pred_loss: 0.0985 - nephritis_pred_loss: 0.0744 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0078\n",
            "Epoch 1439/1500\n",
            "80/80 [==============================] - 0s 40us/sample - loss: 0.1131 - inflamation_pred_loss: 0.0982 - nephritis_pred_loss: 0.0742 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0074\n",
            "Epoch 1440/1500\n",
            "80/80 [==============================] - 0s 953us/sample - loss: 0.1128 - inflamation_pred_loss: 0.0980 - nephritis_pred_loss: 0.0741 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0077\n",
            "Epoch 1441/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 0.1125 - inflamation_pred_loss: 0.0977 - nephritis_pred_loss: 0.0739 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0074\n",
            "Epoch 1442/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 0.1122 - inflamation_pred_loss: 0.0975 - nephritis_pred_loss: 0.0737 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0076\n",
            "Epoch 1443/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.1120 - inflamation_pred_loss: 0.0972 - nephritis_pred_loss: 0.0735 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0073\n",
            "Epoch 1444/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.1117 - inflamation_pred_loss: 0.0970 - nephritis_pred_loss: 0.0734 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0076\n",
            "Epoch 1445/1500\n",
            "80/80 [==============================] - 0s 923us/sample - loss: 0.1114 - inflamation_pred_loss: 0.0968 - nephritis_pred_loss: 0.0732 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0072\n",
            "Epoch 1446/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.1111 - inflamation_pred_loss: 0.0965 - nephritis_pred_loss: 0.0730 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0075\n",
            "Epoch 1447/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.1108 - inflamation_pred_loss: 0.0963 - nephritis_pred_loss: 0.0728 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0072\n",
            "Epoch 1448/1500\n",
            "80/80 [==============================] - 0s 47us/sample - loss: 0.1106 - inflamation_pred_loss: 0.0960 - nephritis_pred_loss: 0.0727 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0074\n",
            "Epoch 1449/1500\n",
            "80/80 [==============================] - 0s 49us/sample - loss: 0.1103 - inflamation_pred_loss: 0.0958 - nephritis_pred_loss: 0.0725 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0071\n",
            "Epoch 1450/1500\n",
            "80/80 [==============================] - 0s 988us/sample - loss: 0.1100 - inflamation_pred_loss: 0.0955 - nephritis_pred_loss: 0.0723 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0074\n",
            "Epoch 1451/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.1097 - inflamation_pred_loss: 0.0953 - nephritis_pred_loss: 0.0721 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0070\n",
            "Epoch 1452/1500\n",
            "80/80 [==============================] - 0s 95us/sample - loss: 0.1095 - inflamation_pred_loss: 0.0951 - nephritis_pred_loss: 0.0720 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0073\n",
            "Epoch 1453/1500\n",
            "80/80 [==============================] - 0s 92us/sample - loss: 0.1092 - inflamation_pred_loss: 0.0948 - nephritis_pred_loss: 0.0718 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0070\n",
            "Epoch 1454/1500\n",
            "80/80 [==============================] - 0s 936us/sample - loss: 0.1089 - inflamation_pred_loss: 0.0946 - nephritis_pred_loss: 0.0716 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0072\n",
            "Epoch 1455/1500\n",
            "80/80 [==============================] - 0s 67us/sample - loss: 0.1086 - inflamation_pred_loss: 0.0943 - nephritis_pred_loss: 0.0714 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0069\n",
            "Epoch 1456/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.1084 - inflamation_pred_loss: 0.0941 - nephritis_pred_loss: 0.0713 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0072\n",
            "Epoch 1457/1500\n",
            "80/80 [==============================] - 0s 37us/sample - loss: 0.1081 - inflamation_pred_loss: 0.0939 - nephritis_pred_loss: 0.0711 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0069\n",
            "Epoch 1458/1500\n",
            "80/80 [==============================] - 0s 32us/sample - loss: 0.1078 - inflamation_pred_loss: 0.0936 - nephritis_pred_loss: 0.0709 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0071\n",
            "Epoch 1459/1500\n",
            "80/80 [==============================] - 0s 932us/sample - loss: 0.1075 - inflamation_pred_loss: 0.0934 - nephritis_pred_loss: 0.0707 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0068\n",
            "Epoch 1460/1500\n",
            "80/80 [==============================] - 0s 48us/sample - loss: 0.1073 - inflamation_pred_loss: 0.0932 - nephritis_pred_loss: 0.0706 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0071\n",
            "Epoch 1461/1500\n",
            "80/80 [==============================] - 0s 100us/sample - loss: 0.1070 - inflamation_pred_loss: 0.0929 - nephritis_pred_loss: 0.0704 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0067\n",
            "Epoch 1462/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.1067 - inflamation_pred_loss: 0.0927 - nephritis_pred_loss: 0.0702 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0070\n",
            "Epoch 1463/1500\n",
            "80/80 [==============================] - 0s 42us/sample - loss: 0.1065 - inflamation_pred_loss: 0.0924 - nephritis_pred_loss: 0.0701 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0067\n",
            "Epoch 1464/1500\n",
            "80/80 [==============================] - 0s 71us/sample - loss: 0.1062 - inflamation_pred_loss: 0.0922 - nephritis_pred_loss: 0.0699 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0069\n",
            "Epoch 1465/1500\n",
            "80/80 [==============================] - 0s 69us/sample - loss: 0.1059 - inflamation_pred_loss: 0.0920 - nephritis_pred_loss: 0.0697 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0066\n",
            "Epoch 1466/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.1057 - inflamation_pred_loss: 0.0917 - nephritis_pred_loss: 0.0696 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0069\n",
            "Epoch 1467/1500\n",
            "80/80 [==============================] - 0s 41us/sample - loss: 0.1054 - inflamation_pred_loss: 0.0915 - nephritis_pred_loss: 0.0694 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0066\n",
            "Epoch 1468/1500\n",
            "80/80 [==============================] - 0s 867us/sample - loss: 0.1051 - inflamation_pred_loss: 0.0913 - nephritis_pred_loss: 0.0692 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0068\n",
            "Epoch 1469/1500\n",
            "80/80 [==============================] - 0s 90us/sample - loss: 0.1049 - inflamation_pred_loss: 0.0911 - nephritis_pred_loss: 0.0690 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0065\n",
            "Epoch 1470/1500\n",
            "80/80 [==============================] - 0s 78us/sample - loss: 0.1046 - inflamation_pred_loss: 0.0908 - nephritis_pred_loss: 0.0689 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0068\n",
            "Epoch 1471/1500\n",
            "80/80 [==============================] - 0s 57us/sample - loss: 0.1043 - inflamation_pred_loss: 0.0906 - nephritis_pred_loss: 0.0687 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0065\n",
            "Epoch 1472/1500\n",
            "80/80 [==============================] - 0s 915us/sample - loss: 0.1041 - inflamation_pred_loss: 0.0904 - nephritis_pred_loss: 0.0685 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0067\n",
            "Epoch 1473/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 0.1038 - inflamation_pred_loss: 0.0901 - nephritis_pred_loss: 0.0684 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0064\n",
            "Epoch 1474/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.1035 - inflamation_pred_loss: 0.0899 - nephritis_pred_loss: 0.0682 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0066\n",
            "Epoch 1475/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.1033 - inflamation_pred_loss: 0.0897 - nephritis_pred_loss: 0.0680 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0063\n",
            "Epoch 1476/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.1030 - inflamation_pred_loss: 0.0894 - nephritis_pred_loss: 0.0679 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0066\n",
            "Epoch 1477/1500\n",
            "80/80 [==============================] - 0s 921us/sample - loss: 0.1028 - inflamation_pred_loss: 0.0892 - nephritis_pred_loss: 0.0677 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0063\n",
            "Epoch 1478/1500\n",
            "80/80 [==============================] - 0s 94us/sample - loss: 0.1025 - inflamation_pred_loss: 0.0890 - nephritis_pred_loss: 0.0675 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0065\n",
            "Epoch 1479/1500\n",
            "80/80 [==============================] - 0s 91us/sample - loss: 0.1022 - inflamation_pred_loss: 0.0888 - nephritis_pred_loss: 0.0674 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0062\n",
            "Epoch 1480/1500\n",
            "80/80 [==============================] - 0s 45us/sample - loss: 0.1020 - inflamation_pred_loss: 0.0885 - nephritis_pred_loss: 0.0672 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0065\n",
            "Epoch 1481/1500\n",
            "80/80 [==============================] - 0s 968us/sample - loss: 0.1017 - inflamation_pred_loss: 0.0883 - nephritis_pred_loss: 0.0670 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0062\n",
            "Epoch 1482/1500\n",
            "80/80 [==============================] - 0s 61us/sample - loss: 0.1015 - inflamation_pred_loss: 0.0881 - nephritis_pred_loss: 0.0669 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0064\n",
            "Epoch 1483/1500\n",
            "80/80 [==============================] - 0s 72us/sample - loss: 0.1012 - inflamation_pred_loss: 0.0879 - nephritis_pred_loss: 0.0667 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0061\n",
            "Epoch 1484/1500\n",
            "80/80 [==============================] - 0s 55us/sample - loss: 0.1010 - inflamation_pred_loss: 0.0876 - nephritis_pred_loss: 0.0666 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0064\n",
            "Epoch 1485/1500\n",
            "80/80 [==============================] - 0s 1ms/sample - loss: 0.1007 - inflamation_pred_loss: 0.0874 - nephritis_pred_loss: 0.0664 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0061\n",
            "Epoch 1486/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.1004 - inflamation_pred_loss: 0.0872 - nephritis_pred_loss: 0.0662 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0063\n",
            "Epoch 1487/1500\n",
            "80/80 [==============================] - 0s 65us/sample - loss: 0.1002 - inflamation_pred_loss: 0.0870 - nephritis_pred_loss: 0.0661 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0060\n",
            "Epoch 1488/1500\n",
            "80/80 [==============================] - 0s 54us/sample - loss: 0.0999 - inflamation_pred_loss: 0.0868 - nephritis_pred_loss: 0.0659 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0062\n",
            "Epoch 1489/1500\n",
            "80/80 [==============================] - 0s 899us/sample - loss: 0.0997 - inflamation_pred_loss: 0.0865 - nephritis_pred_loss: 0.0657 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0060\n",
            "Epoch 1490/1500\n",
            "80/80 [==============================] - 0s 77us/sample - loss: 0.0994 - inflamation_pred_loss: 0.0863 - nephritis_pred_loss: 0.0656 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0062\n",
            "Epoch 1491/1500\n",
            "80/80 [==============================] - 0s 95us/sample - loss: 0.0992 - inflamation_pred_loss: 0.0861 - nephritis_pred_loss: 0.0654 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0059\n",
            "Epoch 1492/1500\n",
            "80/80 [==============================] - 0s 53us/sample - loss: 0.0989 - inflamation_pred_loss: 0.0859 - nephritis_pred_loss: 0.0653 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0061\n",
            "Epoch 1493/1500\n",
            "80/80 [==============================] - 0s 38us/sample - loss: 0.0987 - inflamation_pred_loss: 0.0857 - nephritis_pred_loss: 0.0651 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0058\n",
            "Epoch 1494/1500\n",
            "80/80 [==============================] - 0s 897us/sample - loss: 0.0984 - inflamation_pred_loss: 0.0854 - nephritis_pred_loss: 0.0649 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0061\n",
            "Epoch 1495/1500\n",
            "80/80 [==============================] - 0s 153us/sample - loss: 0.0982 - inflamation_pred_loss: 0.0852 - nephritis_pred_loss: 0.0648 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0058\n",
            "Epoch 1496/1500\n",
            "80/80 [==============================] - 0s 70us/sample - loss: 0.0979 - inflamation_pred_loss: 0.0850 - nephritis_pred_loss: 0.0646 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0060\n",
            "Epoch 1497/1500\n",
            "80/80 [==============================] - 0s 58us/sample - loss: 0.0977 - inflamation_pred_loss: 0.0848 - nephritis_pred_loss: 0.0645 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0057\n",
            "Epoch 1498/1500\n",
            "80/80 [==============================] - 0s 36us/sample - loss: 0.0974 - inflamation_pred_loss: 0.0846 - nephritis_pred_loss: 0.0643 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0060\n",
            "Epoch 1499/1500\n",
            "80/80 [==============================] - 0s 877us/sample - loss: 0.0972 - inflamation_pred_loss: 0.0844 - nephritis_pred_loss: 0.0641 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0057\n",
            "Epoch 1500/1500\n",
            "80/80 [==============================] - 0s 106us/sample - loss: 0.0969 - inflamation_pred_loss: 0.0841 - nephritis_pred_loss: 0.0640 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0059\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": true,
        "id": "DzcTCCJCCAVN",
        "colab_type": "code",
        "colab": {},
        "outputId": "4e2ee275-1631-4ae6-c6a2-3b24bd931c94"
      },
      "source": [
        "history.history.keys()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['loss', 'inflamation_pred_loss', 'nephritis_pred_loss', 'inflamation_pred_acc', 'nephritis_pred_acc', 'nephritis_pred_mse'])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vAwH2pskCAVP",
        "colab_type": "text"
      },
      "source": [
        "#### Plot the learning curves"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "dtss-1GWCAVQ",
        "colab_type": "code",
        "colab": {},
        "outputId": "fbf1b8a5-7734-4de1-c074-8d721d3cc196"
      },
      "source": [
        "# Plot the training accuracy\n",
        "\n",
        "acc_keys = [k for k in history.history.keys() if k in ('inflamation_pred_acc', 'nephritis_pred_acc')] \n",
        "loss_keys = [k for k in history.history.keys() if not k in acc_keys]\n",
        "\n",
        "for k, v in history.history.items():\n",
        "    if k in acc_keys:\n",
        "        plt.figure(1)\n",
        "        plt.plot(v)\n",
        "    else:\n",
        "        plt.figure(2)\n",
        "        plt.plot(v)\n",
        "\n",
        "plt.figure(1)\n",
        "plt.title('Accuracy vs. epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(acc_keys, loc='upper right')\n",
        "\n",
        "plt.figure(2)\n",
        "plt.title('Loss vs. epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.xlabel('Epoch')\n",
        "plt.legend(loss_keys, loc='upper right')\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "scrolled": false,
        "id": "XtTzHg6CCAVS",
        "colab_type": "code",
        "colab": {},
        "outputId": "bab20499-8df3-4c84-cb7f-1f7caaf7290d"
      },
      "source": [
        "# Evaluate the model\n",
        "model.evaluate([temp_test, nocc_test, lumbp_test, up_test, mict_test, bis_test],[inflam_test,nephr_test])\n"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "40/1 [================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================================] - 1s 15ms/sample - loss: 0.1120 - inflamation_pred_loss: 0.1036 - nephritis_pred_loss: 0.0344 - inflamation_pred_acc: 1.0000 - nephritis_pred_acc: 1.0000 - nephritis_pred_mse: 0.0043\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.10587291866540909, 0.103606775, 0.034425057, 1.0, 1.0, 0.004309124]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 31
        }
      ]
    }
  ]
}