AlphabetSoupCharity_Optimization.ipynb

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    "<a href=\"https://colab.research.google.com/github/BellaBeautiful4092023/Deep-Learning-Challenge/blob/main/AlphabetSoupCharity_Optimization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "source": [
    " Optimize the Model"
   ]
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   "outputs": [
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       "      <th></th>\n",
       "      <th>EIN</th>\n",
       "      <th>NAME</th>\n",
       "      <th>APPLICATION_TYPE</th>\n",
       "      <th>AFFILIATION</th>\n",
       "      <th>CLASSIFICATION</th>\n",
       "      <th>USE_CASE</th>\n",
       "      <th>ORGANIZATION</th>\n",
       "      <th>STATUS</th>\n",
       "      <th>INCOME_AMT</th>\n",
       "      <th>SPECIAL_CONSIDERATIONS</th>\n",
       "      <th>ASK_AMT</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10520599</td>\n",
       "      <td>BLUE KNIGHTS MOTORCYCLE CLUB</td>\n",
       "      <td>T10</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C1000</td>\n",
       "      <td>ProductDev</td>\n",
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       "      <td>10531628</td>\n",
       "      <td>AMERICAN CHESAPEAKE CLUB CHARITABLE TR</td>\n",
       "      <td>T3</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C2000</td>\n",
       "      <td>Preservation</td>\n",
       "      <td>Co-operative</td>\n",
       "      <td>1</td>\n",
       "      <td>1-9999</td>\n",
       "      <td>N</td>\n",
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       "      <th>3</th>\n",
       "      <td>10553066</td>\n",
       "      <td>SOUTHSIDE ATHLETIC ASSOCIATION</td>\n",
       "      <td>T3</td>\n",
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       "      <th>4</th>\n",
       "      <td>10556103</td>\n",
       "      <td>GENETIC RESEARCH INSTITUTE OF THE DESERT</td>\n",
       "      <td>T3</td>\n",
       "      <td>Independent</td>\n",
       "      <td>C1000</td>\n",
       "      <td>Heathcare</td>\n",
       "      <td>Trust</td>\n",
       "      <td>1</td>\n",
       "      <td>100000-499999</td>\n",
       "      <td>N</td>\n",
       "      <td>142590</td>\n",
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      "text/plain": [
       "        EIN                                      NAME APPLICATION_TYPE  \\\n",
       "0  10520599              BLUE KNIGHTS MOTORCYCLE CLUB              T10   \n",
       "1  10531628    AMERICAN CHESAPEAKE CLUB CHARITABLE TR               T3   \n",
       "2  10547893        ST CLOUD PROFESSIONAL FIREFIGHTERS               T5   \n",
       "3  10553066            SOUTHSIDE ATHLETIC ASSOCIATION               T3   \n",
       "4  10556103  GENETIC RESEARCH INSTITUTE OF THE DESERT               T3   \n",
       "\n",
       "        AFFILIATION CLASSIFICATION      USE_CASE  ORGANIZATION  STATUS  \\\n",
       "0       Independent          C1000    ProductDev   Association       1   \n",
       "1       Independent          C2000  Preservation  Co-operative       1   \n",
       "2  CompanySponsored          C3000    ProductDev   Association       1   \n",
       "3  CompanySponsored          C2000  Preservation         Trust       1   \n",
       "4       Independent          C1000     Heathcare         Trust       1   \n",
       "\n",
       "      INCOME_AMT SPECIAL_CONSIDERATIONS  ASK_AMT  IS_SUCCESSFUL  \n",
       "0              0                      N     5000              1  \n",
       "1         1-9999                      N   108590              1  \n",
       "2              0                      N     5000              0  \n",
       "3    10000-24999                      N     6692              1  \n",
       "4  100000-499999                      N   142590              1  "
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   ],
   "source": [
    "# Import our dependencies\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "import pandas as pd\n",
    "import tensorflow as tf\n",
    "import h5py\n",
    "\n",
    "#  Import and read the charity_data.csv.\n",
    "import pandas as pd\n",
    "application_df = pd.read_csv(\"https://static.bc-edx.com/data/dl-1-2/m21/lms/starter/charity_data.csv\")\n",
    "application_df.head()"
   ]
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      "text/plain": [
       "             EIN  STATUS   ASK_AMT  IS_SUCCESSFUL  \\\n",
       "0       10520599       1      5000              1   \n",
       "1       10531628       1    108590              1   \n",
       "2       10547893       1      5000              0   \n",
       "3       10553066       1      6692              1   \n",
       "4       10556103       1    142590              1   \n",
       "...          ...     ...       ...            ...   \n",
       "34294  996009318       1      5000              0   \n",
       "34295  996010315       1      5000              0   \n",
       "34296  996012607       1      5000              0   \n",
       "34297  996015768       1      5000              1   \n",
       "34298  996086871       1  36500179              0   \n",
       "\n",
       "       NAME_1 DAY RANCH RESCUE AND RURAL OKLAHOMA ANIMAL RESOURCE INC  \\\n",
       "0                                                  False                \n",
       "1                                                  False                \n",
       "2                                                  False                \n",
       "3                                                  False                \n",
       "4                                                  False                \n",
       "...                                                  ...                \n",
       "34294                                              False                \n",
       "34295                                              False                \n",
       "34296                                              False                \n",
       "34297                                              False                \n",
       "34298                                              False                \n",
       "\n",
       "       NAME_100 BLACK MEN OF AMERICA  NAME_100 BLACK MEN OF MEMPHIS INC  \\\n",
       "0                              False                              False   \n",
       "1                              False                              False   \n",
       "2                              False                              False   \n",
       "3                              False                              False   \n",
       "4                              False                              False   \n",
       "...                              ...                                ...   \n",
       "34294                          False                              False   \n",
       "34295                          False                              False   \n",
       "34296                          False                              False   \n",
       "34297                          False                              False   \n",
       "34298                          False                              False   \n",
       "\n",
       "       NAME_100 BLACK MEN OF WEST GEORGIA INC  NAME_1150 WEBSTER STREET INC  \\\n",
       "0                                       False                         False   \n",
       "1                                       False                         False   \n",
       "2                                       False                         False   \n",
       "3                                       False                         False   \n",
       "4                                       False                         False   \n",
       "...                                       ...                           ...   \n",
       "34294                                   False                         False   \n",
       "34295                                   False                         False   \n",
       "34296                                   False                         False   \n",
       "34297                                   False                         False   \n",
       "34298                                   False                         False   \n",
       "\n",
       "       NAME_116TH CAVALRY REGIMENT CHAPTER OF THE US CAVALRY & ARMOR ASSOCIATION  \\\n",
       "0                                                  False                           \n",
       "1                                                  False                           \n",
       "2                                                  False                           \n",
       "3                                                  False                           \n",
       "4                                                  False                           \n",
       "...                                                  ...                           \n",
       "34294                                              False                           \n",
       "34295                                              False                           \n",
       "34296                                              False                           \n",
       "34297                                              False                           \n",
       "34298                                              False                           \n",
       "\n",
       "       ...  INCOME_AMT_1-9999  INCOME_AMT_10000-24999  \\\n",
       "0      ...              False                   False   \n",
       "1      ...               True                   False   \n",
       "2      ...              False                   False   \n",
       "3      ...              False                    True   \n",
       "4      ...              False                   False   \n",
       "...    ...                ...                     ...   \n",
       "34294  ...              False                   False   \n",
       "34295  ...              False                   False   \n",
       "34296  ...              False                   False   \n",
       "34297  ...              False                   False   \n",
       "34298  ...              False                   False   \n",
       "\n",
       "       INCOME_AMT_100000-499999  INCOME_AMT_10M-50M  INCOME_AMT_1M-5M  \\\n",
       "0                         False               False             False   \n",
       "1                         False               False             False   \n",
       "2                         False               False             False   \n",
       "3                         False               False             False   \n",
       "4                          True               False             False   \n",
       "...                         ...                 ...               ...   \n",
       "34294                     False               False             False   \n",
       "34295                     False               False             False   \n",
       "34296                     False               False             False   \n",
       "34297                     False               False             False   \n",
       "34298                     False               False              True   \n",
       "\n",
       "       INCOME_AMT_25000-99999  INCOME_AMT_50M+  INCOME_AMT_5M-10M  \\\n",
       "0                       False            False              False   \n",
       "1                       False            False              False   \n",
       "2                       False            False              False   \n",
       "3                       False            False              False   \n",
       "4                       False            False              False   \n",
       "...                       ...              ...                ...   \n",
       "34294                   False            False              False   \n",
       "34295                   False            False              False   \n",
       "34296                   False            False              False   \n",
       "34297                   False            False              False   \n",
       "34298                   False            False              False   \n",
       "\n",
       "       SPECIAL_CONSIDERATIONS_N  SPECIAL_CONSIDERATIONS_Y  \n",
       "0                          True                     False  \n",
       "1                          True                     False  \n",
       "2                          True                     False  \n",
       "3                          True                     False  \n",
       "4                          True                     False  \n",
       "...                         ...                       ...  \n",
       "34294                      True                     False  \n",
       "34295                      True                     False  \n",
       "34296                      True                     False  \n",
       "34297                      True                     False  \n",
       "34298                      True                     False  \n",
       "\n",
       "[34299 rows x 19686 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Convert categorical data to numeric with `pd.get_dummies`\n",
    "numeric_app_df = pd.get_dummies(application_df)\n",
    "numeric_app_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "id": "dZgjsgk3wPyl"
   },
   "outputs": [],
   "source": [
    "# Split our preprocessed data into our features and target arrays\n",
    "y = numeric_app_df[\"IS_SUCCESSFUL\"]\n",
    "X = numeric_app_df.drop(columns =[\"IS_SUCCESSFUL\"],axis=1)\n",
    "\n",
    "# Split the preprocessed data into a training and testing dataset\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=78)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "6pUjB82kwLA0",
    "outputId": "b0cef115-11fe-4ff9-8801-c014db142c48"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([870464720, 1, 5000, ..., False, True, False], dtype=object)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.iloc[0].values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "EVGA1XG0Hr6G"
   },
   "source": [
    "First Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "id": "GQ7UHLNkwCLK"
   },
   "outputs": [],
   "source": [
    "# Create a StandardScaler instances\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the StandardScaler\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Scale the data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 219
    },
    "executionInfo": {
     "elapsed": 38,
     "status": "error",
     "timestamp": 1712368017680,
     "user": {
      "displayName": "Bella Osh",
      "userId": "10294467186037229241"
     },
     "user_tz": 300
    },
    "id": "QugqHnN_v44s",
    "outputId": "ef897d5a-502e-4a9a-8988-2c7f29460187"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:88: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">90</span>)                  │       <span style=\"color: #00af00; text-decoration-color: #00af00\">1,771,740</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>)                  │           <span style=\"color: #00af00; text-decoration-color: #00af00\">1,638</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">342</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">342</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">19</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m90\u001b[0m)                  │       \u001b[38;5;34m1,771,740\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_1 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m)                  │           \u001b[38;5;34m1,638\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_2 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m)                  │             \u001b[38;5;34m342\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_3 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m)                  │             \u001b[38;5;34m342\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_4 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m19\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,774,081</span> (6.77 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,774,081\u001b[0m (6.77 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,774,081</span> (6.77 MB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,774,081\u001b[0m (6.77 MB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the model - deep neural net, i.e., the number of input features and hidden nodes for each layer.\n",
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 =  90\n",
    "hidden_nodes_layer2 = 18\n",
    "\n",
    "nn = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn.add(\n",
    "    tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\")\n",
    ")\n",
    "\n",
    "# Second hidden layer\n",
    "nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Third hidden layer\n",
    "nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"tanh\"))\n",
    "\n",
    "#  Fourth hidden layer\n",
    "nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "\n",
    "# Output layer\n",
    "nn.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "id": "adqoe8_Qw0cT"
   },
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "AM5O8JqWS7kY"
   },
   "outputs": [],
   "source": [
    "num_samples = X_train_scaled.shape[0]\n",
    "split_index = num_samples // 5\n",
    "X_train_half = X_train_scaled[:split_index]\n",
    "y_train_half = y_train[:split_index]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "id": "4syaeB_9w4vN",
    "outputId": "b4580a9c-3690-4815-d661-f114a4cc4028"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m32s\u001b[0m 25ms/step - accuracy: 0.7389 - loss: 0.5286\n",
      "Epoch 2/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 26ms/step - accuracy: 0.8969 - loss: 0.2522\n",
      "Epoch 3/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m19s\u001b[0m 24ms/step - accuracy: 0.9621 - loss: 0.1046\n",
      "Epoch 4/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m17s\u001b[0m 21ms/step - accuracy: 0.9622 - loss: 0.0961\n",
      "Epoch 5/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 20ms/step - accuracy: 0.9653 - loss: 0.0894\n",
      "Epoch 6/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9616 - loss: 0.0929\n",
      "Epoch 7/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 20ms/step - accuracy: 0.9607 - loss: 0.0938\n",
      "Epoch 8/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 20ms/step - accuracy: 0.9606 - loss: 0.0942\n",
      "Epoch 9/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 19ms/step - accuracy: 0.9648 - loss: 0.0873\n",
      "Epoch 10/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 20ms/step - accuracy: 0.9630 - loss: 0.0868\n",
      "Epoch 11/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 19ms/step - accuracy: 0.9630 - loss: 0.0873\n",
      "Epoch 12/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9640 - loss: 0.0871\n",
      "Epoch 13/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9652 - loss: 0.0845\n",
      "Epoch 14/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 19ms/step - accuracy: 0.9635 - loss: 0.0869\n",
      "Epoch 15/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9638 - loss: 0.0869\n",
      "Epoch 16/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m21s\u001b[0m 19ms/step - accuracy: 0.9638 - loss: 0.0858\n",
      "Epoch 17/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 19ms/step - accuracy: 0.9636 - loss: 0.0883\n",
      "Epoch 18/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9651 - loss: 0.0863\n",
      "Epoch 19/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 18ms/step - accuracy: 0.9647 - loss: 0.0854\n",
      "Epoch 20/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9636 - loss: 0.0859\n",
      "Epoch 21/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 20ms/step - accuracy: 0.9658 - loss: 0.0830\n",
      "Epoch 22/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9648 - loss: 0.0845\n",
      "Epoch 23/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 19ms/step - accuracy: 0.9642 - loss: 0.0881\n",
      "Epoch 24/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 20ms/step - accuracy: 0.9652 - loss: 0.0827\n",
      "Epoch 25/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 19ms/step - accuracy: 0.9669 - loss: 0.0810\n",
      "Epoch 26/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9666 - loss: 0.0816\n",
      "Epoch 27/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9676 - loss: 0.0791\n",
      "Epoch 28/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9651 - loss: 0.0865\n",
      "Epoch 29/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 20ms/step - accuracy: 0.9655 - loss: 0.0832\n",
      "Epoch 30/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 20ms/step - accuracy: 0.9649 - loss: 0.0864\n",
      "Epoch 31/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9660 - loss: 0.0837\n",
      "Epoch 32/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 20ms/step - accuracy: 0.9651 - loss: 0.0837\n",
      "Epoch 33/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9675 - loss: 0.0814\n",
      "Epoch 34/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 18ms/step - accuracy: 0.9665 - loss: 0.0828\n",
      "Epoch 35/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9642 - loss: 0.0847\n",
      "Epoch 36/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 18ms/step - accuracy: 0.9661 - loss: 0.0840\n",
      "Epoch 37/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 19ms/step - accuracy: 0.9686 - loss: 0.0780\n",
      "Epoch 38/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9663 - loss: 0.0814\n",
      "Epoch 39/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9652 - loss: 0.0823\n",
      "Epoch 40/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9667 - loss: 0.0821\n",
      "Epoch 41/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9643 - loss: 0.0861\n",
      "Epoch 42/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9669 - loss: 0.0798\n",
      "Epoch 43/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9676 - loss: 0.0793\n",
      "Epoch 44/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 18ms/step - accuracy: 0.9679 - loss: 0.0776\n",
      "Epoch 45/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 18ms/step - accuracy: 0.9691 - loss: 0.0753\n",
      "Epoch 46/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 18ms/step - accuracy: 0.9660 - loss: 0.0817\n",
      "Epoch 47/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 20ms/step - accuracy: 0.9663 - loss: 0.0815\n",
      "Epoch 48/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 20ms/step - accuracy: 0.9666 - loss: 0.0798\n",
      "Epoch 49/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 20ms/step - accuracy: 0.9680 - loss: 0.0776\n",
      "Epoch 50/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 18ms/step - accuracy: 0.9673 - loss: 0.0807\n",
      "Epoch 51/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9678 - loss: 0.0771\n",
      "Epoch 52/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9678 - loss: 0.0787\n",
      "Epoch 53/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9697 - loss: 0.0759\n",
      "Epoch 54/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9690 - loss: 0.0760\n",
      "Epoch 55/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9692 - loss: 0.0777\n",
      "Epoch 56/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9687 - loss: 0.0766\n",
      "Epoch 57/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9660 - loss: 0.0818\n",
      "Epoch 58/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9682 - loss: 0.0780\n",
      "Epoch 59/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9681 - loss: 0.0763\n",
      "Epoch 60/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9686 - loss: 0.0762\n",
      "Epoch 61/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9666 - loss: 0.0806\n",
      "Epoch 62/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9690 - loss: 0.0765\n",
      "Epoch 63/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9695 - loss: 0.0769\n",
      "Epoch 64/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9676 - loss: 0.0787\n",
      "Epoch 65/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9683 - loss: 0.0795\n",
      "Epoch 66/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9669 - loss: 0.0784\n",
      "Epoch 67/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9681 - loss: 0.0771\n",
      "Epoch 68/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9705 - loss: 0.0751\n",
      "Epoch 69/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9667 - loss: 0.0798\n",
      "Epoch 73/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m54s\u001b[0m 67ms/step - accuracy: 0.9685 - loss: 0.0764\n",
      "Epoch 74/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m16s\u001b[0m 19ms/step - accuracy: 0.9710 - loss: 0.0720\n",
      "Epoch 75/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9695 - loss: 0.0767\n",
      "Epoch 76/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m15s\u001b[0m 19ms/step - accuracy: 0.9668 - loss: 0.0791\n",
      "Epoch 77/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 18ms/step - accuracy: 0.9698 - loss: 0.0745\n",
      "Epoch 78/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m12s\u001b[0m 15ms/step - accuracy: 0.9683 - loss: 0.0762\n",
      "Epoch 79/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 17ms/step - accuracy: 0.9703 - loss: 0.0758\n",
      "Epoch 80/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 16ms/step - accuracy: 0.9700 - loss: 0.0774\n",
      "Epoch 81/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 17ms/step - accuracy: 0.9689 - loss: 0.0776\n",
      "Epoch 82/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 16ms/step - accuracy: 0.9681 - loss: 0.0780\n",
      "Epoch 83/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 17ms/step - accuracy: 0.9692 - loss: 0.0745\n",
      "Epoch 84/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 17ms/step - accuracy: 0.9698 - loss: 0.0768\n",
      "Epoch 85/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 17ms/step - accuracy: 0.9665 - loss: 0.0766\n",
      "Epoch 86/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 17ms/step - accuracy: 0.9692 - loss: 0.0765\n",
      "Epoch 87/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 17ms/step - accuracy: 0.9697 - loss: 0.0740\n",
      "Epoch 88/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 17ms/step - accuracy: 0.9685 - loss: 0.0791\n",
      "Epoch 89/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 17ms/step - accuracy: 0.9682 - loss: 0.0781\n",
      "Epoch 90/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 17ms/step - accuracy: 0.9700 - loss: 0.0756\n",
      "Epoch 91/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 17ms/step - accuracy: 0.9670 - loss: 0.0815\n",
      "Epoch 92/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 17ms/step - accuracy: 0.9686 - loss: 0.0785\n",
      "Epoch 93/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 16ms/step - accuracy: 0.9703 - loss: 0.0750\n",
      "Epoch 94/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 17ms/step - accuracy: 0.9698 - loss: 0.0752\n",
      "Epoch 95/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 17ms/step - accuracy: 0.9687 - loss: 0.0788\n",
      "Epoch 96/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 17ms/step - accuracy: 0.9686 - loss: 0.0790\n",
      "Epoch 97/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 17ms/step - accuracy: 0.9687 - loss: 0.0795\n",
      "Epoch 98/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 17ms/step - accuracy: 0.9712 - loss: 0.0740\n",
      "Epoch 99/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m14s\u001b[0m 17ms/step - accuracy: 0.9704 - loss: 0.0745\n",
      "Epoch 100/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m13s\u001b[0m 17ms/step - accuracy: 0.9693 - loss: 0.0757\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn.fit(X_train_scaled,y_train,epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "id": "hjg9_qC_4s_q",
    "outputId": "9063ab25-eea1-4bab-87ce-effd5817db9d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "268/268 - 1s - 3ms/step - accuracy: 0.7240 - loss: 0.5666\n",
      "Loss: 0.5665626525878906, Accuracy: 0.7239649891853333\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "id": "3ALyTkbn4s_q",
    "outputId": "1474b34c-754a-4ce0-edd6-9b2d92d4cc79"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "# Export our model to HDF5 file\n",
    "nn.save('Models/AlphabetSoupCharity_Optimization.h5')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5sAqhuIMGYFd"
   },
   "source": [
    "Second Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "id": "yCWTdKHyFUHs"
   },
   "outputs": [],
   "source": [
    "# Create a StandardScaler instances\n",
    "scaler = StandardScaler()\n",
    "\n",
    "# Fit the StandardScaler\n",
    "X_scaler = scaler.fit(X_train)\n",
    "\n",
    "# Scale the data\n",
    "X_train_scaled = X_scaler.transform(X_train)\n",
    "X_test_scaled = X_scaler.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "id": "AQrhOrnMFe-T"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\anaconda3\\Lib\\site-packages\\keras\\src\\layers\\core\\dense.py:88: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(activity_regularizer=activity_regularizer, **kwargs)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential_1\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_1\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                         </span>┃<span style=\"font-weight: bold\"> Output Shape                </span>┃<span style=\"font-weight: bold\">         Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ dense_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">9</span>)                   │         <span style=\"color: #00af00; text-decoration-color: #00af00\">177,174</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">180</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">18</span>)                  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">342</span> │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)                   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">19</span> │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                        \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape               \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m        Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━┩\n",
       "│ dense_5 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m9\u001b[0m)                   │         \u001b[38;5;34m177,174\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_6 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m)                  │             \u001b[38;5;34m180\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_7 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m18\u001b[0m)                  │             \u001b[38;5;34m342\u001b[0m │\n",
       "├──────────────────────────────────────┼─────────────────────────────┼─────────────────┤\n",
       "│ dense_8 (\u001b[38;5;33mDense\u001b[0m)                      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)                   │              \u001b[38;5;34m19\u001b[0m │\n",
       "└──────────────────────────────────────┴─────────────────────────────┴─────────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">177,715</span> (694.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m177,715\u001b[0m (694.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">177,715</span> (694.20 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m177,715\u001b[0m (694.20 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Define the model - deep neural net, i.e., the number of input features and hidden nodes for each layer.\n",
    "# Define the model - deep neural net\n",
    "number_input_features = len(X_train_scaled[0])\n",
    "hidden_nodes_layer1 =  9\n",
    "hidden_nodes_layer2 = 18\n",
    "\n",
    "nn = tf.keras.models.Sequential()\n",
    "\n",
    "# First hidden layer\n",
    "nn.add(\n",
    "    tf.keras.layers.Dense(units=hidden_nodes_layer1, input_dim=number_input_features, activation=\"relu\")\n",
    ")\n",
    "\n",
    "# Second hidden layer\n",
    "nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"relu\"))\n",
    "\n",
    "# Third hidden layer\n",
    "nn.add(tf.keras.layers.Dense(units=hidden_nodes_layer2, activation=\"tanh\"))\n",
    "\n",
    "\n",
    "# Output layer\n",
    "nn.add(tf.keras.layers.Dense(units=1, activation=\"sigmoid\"))\n",
    "\n",
    "# Check the structure of the model\n",
    "nn.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "fI_Wuka2F6te"
   },
   "outputs": [],
   "source": [
    "# Compile the model\n",
    "nn.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "cELOCpK1GDLI"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 4ms/step - accuracy: 0.7301 - loss: 0.5562\n",
      "Epoch 2/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.8684 - loss: 0.3075\n",
      "Epoch 3/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9451 - loss: 0.1509\n",
      "Epoch 4/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9546 - loss: 0.1176\n",
      "Epoch 5/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9621 - loss: 0.0996\n",
      "Epoch 6/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9602 - loss: 0.0955\n",
      "Epoch 7/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9609 - loss: 0.0975\n",
      "Epoch 8/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9638 - loss: 0.0913\n",
      "Epoch 9/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9629 - loss: 0.0908\n",
      "Epoch 10/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9636 - loss: 0.0910\n",
      "Epoch 11/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9642 - loss: 0.0904\n",
      "Epoch 12/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9652 - loss: 0.0867\n",
      "Epoch 13/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9653 - loss: 0.0890\n",
      "Epoch 14/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9636 - loss: 0.0917\n",
      "Epoch 15/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9660 - loss: 0.0858\n",
      "Epoch 16/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9639 - loss: 0.0908\n",
      "Epoch 17/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9647 - loss: 0.0849\n",
      "Epoch 18/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9657 - loss: 0.0854\n",
      "Epoch 19/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9655 - loss: 0.0853\n",
      "Epoch 20/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9639 - loss: 0.0894\n",
      "Epoch 21/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9663 - loss: 0.0837\n",
      "Epoch 22/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9659 - loss: 0.0850\n",
      "Epoch 23/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9632 - loss: 0.0883\n",
      "Epoch 24/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9648 - loss: 0.0876\n",
      "Epoch 25/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9668 - loss: 0.0825\n",
      "Epoch 26/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9662 - loss: 0.0856\n",
      "Epoch 27/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9655 - loss: 0.0848\n",
      "Epoch 28/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9658 - loss: 0.0867\n",
      "Epoch 29/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9674 - loss: 0.0817\n",
      "Epoch 30/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9646 - loss: 0.0876\n",
      "Epoch 31/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9640 - loss: 0.0882\n",
      "Epoch 32/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9667 - loss: 0.0821\n",
      "Epoch 33/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9647 - loss: 0.0874\n",
      "Epoch 34/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9675 - loss: 0.0837\n",
      "Epoch 35/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9665 - loss: 0.0847\n",
      "Epoch 36/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9666 - loss: 0.0822\n",
      "Epoch 37/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9646 - loss: 0.0869\n",
      "Epoch 38/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9662 - loss: 0.0834\n",
      "Epoch 39/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9688 - loss: 0.0782\n",
      "Epoch 40/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9673 - loss: 0.0824\n",
      "Epoch 41/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9663 - loss: 0.0830\n",
      "Epoch 42/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9671 - loss: 0.0828\n",
      "Epoch 43/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9676 - loss: 0.0828\n",
      "Epoch 44/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9686 - loss: 0.0796\n",
      "Epoch 45/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9680 - loss: 0.0796\n",
      "Epoch 46/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9675 - loss: 0.0823\n",
      "Epoch 47/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9691 - loss: 0.0794\n",
      "Epoch 48/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9703 - loss: 0.0755\n",
      "Epoch 49/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9669 - loss: 0.0822\n",
      "Epoch 50/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9681 - loss: 0.0807\n",
      "Epoch 51/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9660 - loss: 0.0824\n",
      "Epoch 52/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9667 - loss: 0.0843\n",
      "Epoch 53/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9665 - loss: 0.0813\n",
      "Epoch 54/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9678 - loss: 0.0806\n",
      "Epoch 55/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9686 - loss: 0.0778\n",
      "Epoch 56/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9694 - loss: 0.0780\n",
      "Epoch 57/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9670 - loss: 0.0833\n",
      "Epoch 58/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9667 - loss: 0.0842\n",
      "Epoch 59/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9657 - loss: 0.0840\n",
      "Epoch 60/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9674 - loss: 0.0805\n",
      "Epoch 61/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9660 - loss: 0.0853\n",
      "Epoch 62/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9679 - loss: 0.0803\n",
      "Epoch 63/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 4ms/step - accuracy: 0.9673 - loss: 0.0799\n",
      "Epoch 64/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9673 - loss: 0.0813\n",
      "Epoch 65/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9673 - loss: 0.0819\n",
      "Epoch 66/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9690 - loss: 0.0797\n",
      "Epoch 67/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9664 - loss: 0.0807\n",
      "Epoch 68/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9671 - loss: 0.0807\n",
      "Epoch 69/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9668 - loss: 0.0811\n",
      "Epoch 70/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9674 - loss: 0.0792\n",
      "Epoch 71/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9676 - loss: 0.0800\n",
      "Epoch 72/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9686 - loss: 0.0794\n",
      "Epoch 73/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9690 - loss: 0.0774\n",
      "Epoch 74/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9683 - loss: 0.0804\n",
      "Epoch 75/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9679 - loss: 0.0790\n",
      "Epoch 76/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9688 - loss: 0.0779\n",
      "Epoch 77/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9681 - loss: 0.0809\n",
      "Epoch 78/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9683 - loss: 0.0786\n",
      "Epoch 79/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9695 - loss: 0.0769\n",
      "Epoch 80/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9673 - loss: 0.0793\n",
      "Epoch 81/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9699 - loss: 0.0754\n",
      "Epoch 82/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9692 - loss: 0.0778\n",
      "Epoch 83/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9699 - loss: 0.0756\n",
      "Epoch 84/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9666 - loss: 0.0797\n",
      "Epoch 85/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9683 - loss: 0.0784\n",
      "Epoch 86/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9688 - loss: 0.0785\n",
      "Epoch 87/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9702 - loss: 0.0772\n",
      "Epoch 88/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9681 - loss: 0.0796\n",
      "Epoch 89/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9695 - loss: 0.0765\n",
      "Epoch 90/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9677 - loss: 0.0800\n",
      "Epoch 91/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9684 - loss: 0.0797\n",
      "Epoch 92/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9699 - loss: 0.0756\n",
      "Epoch 93/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9671 - loss: 0.0792\n",
      "Epoch 94/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9701 - loss: 0.0767\n",
      "Epoch 95/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9676 - loss: 0.0788\n",
      "Epoch 96/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9675 - loss: 0.0804\n",
      "Epoch 97/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9681 - loss: 0.0756\n",
      "Epoch 98/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9644 - loss: 0.0829\n",
      "Epoch 99/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9680 - loss: 0.0773\n",
      "Epoch 100/100\n",
      "\u001b[1m804/804\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 3ms/step - accuracy: 0.9665 - loss: 0.0814\n"
     ]
    }
   ],
   "source": [
    "# Train the model\n",
    "fit_model = nn.fit(X_train_scaled,y_train,epochs=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "0HiRQTjyGgvq"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "268/268 - 1s - 3ms/step - accuracy: 0.6051 - loss: 0.8311\n",
      "Loss: 0.8311269879341125, Accuracy: 0.605131208896637\n"
     ]
    }
   ],
   "source": [
    "# Evaluate the model using the test data\n",
    "model_loss, model_accuracy = nn.evaluate(X_test_scaled,y_test,verbose=2)\n",
    "print(f\"Loss: {model_loss}, Accuracy: {model_accuracy}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "xXe4ZoQOGjI9"
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:You are saving your model as an HDF5 file via `model.save()` or `keras.saving.save_model(model)`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')` or `keras.saving.save_model(model, 'my_model.keras')`. \n"
     ]
    }
   ],
   "source": [
    "# Export our model to HDF5 file\n",
    "nn.save('Models/AlphabetSoupCharity_Optimization2.h5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Open the .h5 file\n",
    "with h5py.File('your_file.h5', 'r') as file:\n",
    "    # List all groups in the file\n",
    "    print(\"Groups:\", list(file.keys()))\n",
    "    # Access specific datasets within a group\n",
    "    dataset = file['group_name/dataset_name']\n",
    "    # Read data from the dataset\n",
    "    data = dataset[()]"
   ]
  }
 ],
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  "kernelspec": {
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   "language": "python",
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.11.7"
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