EPICS-Softmax-MultiClass.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "PgUn3W2z3AMr"
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "XJ7WrbsW3AMr"
   },
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   "source": [
    "df=pd.read_csv(\"Symptoms_DS.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
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       "      (vertigo) Paroymsal  Positional Vertigo   AIDS   Acne  \\\n",
       "0                                       False  False  False   \n",
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     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "y=pd.get_dummies(df[\"prognosis\"])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Aakash\\AppData\\Local\\Temp\\ipykernel_18108\\3845594315.py:1: FutureWarning: Downcasting behavior in `replace` is deprecated and will be removed in a future version. To retain the old behavior, explicitly call `result.infer_objects(copy=False)`. To opt-in to the future behavior, set `pd.set_option('future.no_silent_downcasting', True)`\n",
      "  y=y.replace({True: 1, False: 0})\n"
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       "      (vertigo) Paroymsal  Positional Vertigo  AIDS  Acne  \\\n",
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     "execution_count": 60,
     "metadata": {},
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   "source": [
    "y=y.replace({True: 1, False: 0})\n",
    "y"
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  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.drop([\"prognosis\"],axis=1)"
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  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
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  {
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   "execution_count": 63,
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
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       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 41 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   (vertigo) Paroymsal  Positional Vertigo  AIDS  Acne  Alcoholic hepatitis  \\\n",
       "0                                        0     0     0                    0   \n",
       "1                                        0     0     0                    0   \n",
       "2                                        0     0     0                    0   \n",
       "3                                        0     0     0                    0   \n",
       "4                                        0     0     0                    0   \n",
       "\n",
       "   Allergy  Arthritis  Bronchial Asthma  Cervical spondylosis  Chicken pox  \\\n",
       "0        0          0                 0                     0            0   \n",
       "1        0          0                 0                     0            0   \n",
       "2        0          0                 0                     0            0   \n",
       "3        0          0                 0                     0            0   \n",
       "4        0          0                 0                     0            0   \n",
       "\n",
       "   Chronic cholestasis  ...  Osteoarthristis  Paralysis (brain hemorrhage)  \\\n",
       "0                    0  ...                0                             0   \n",
       "1                    0  ...                0                             0   \n",
       "2                    0  ...                0                             0   \n",
       "3                    0  ...                0                             0   \n",
       "4                    0  ...                0                             0   \n",
       "\n",
       "   Peptic ulcer diseae  Pneumonia  Psoriasis  Tuberculosis  Typhoid  \\\n",
       "0                    0          0          0             0        0   \n",
       "1                    0          0          0             0        0   \n",
       "2                    0          0          0             0        0   \n",
       "3                    0          0          0             0        0   \n",
       "4                    0          0          0             0        0   \n",
       "\n",
       "   Urinary tract infection  Varicose veins  hepatitis A  \n",
       "0                        0               0            0  \n",
       "1                        0               0            0  \n",
       "2                        0               0            0  \n",
       "3                        0               0            0  \n",
       "4                        0               0            0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4962, 132)\n",
      "(4962, 41)\n"
     ]
    }
   ],
   "source": [
    "print(x.shape)\n",
    "print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "x_train, x_val_test, y_train, y_val_test = train_test_split(x, y, test_size=0.3, random_state=42)\n",
    "x_val, x_test, y_val, y_test = train_test_split(x_val_test, y_val_test, test_size=0.5, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "b1GEMuOV3AM5",
    "outputId": "8b61b707-1d02-4b8c-830a-2043ba778031"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "((3473, 132), (745, 132), (744, 132), (3473, 41), (745, 41), (744, 41))"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape,x_test.shape,x_val.shape,y_train.shape,y_test.shape,y_val.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "    }\n",
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       "      <th></th>\n",
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       "      <th>skin_rash</th>\n",
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       "      <th>shivering</th>\n",
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       "      <th>joint_pain</th>\n",
       "      <th>stomach_pain</th>\n",
       "      <th>acidity</th>\n",
       "      <th>ulcers_on_tongue</th>\n",
       "      <th>...</th>\n",
       "      <th>pus_filled_pimples</th>\n",
       "      <th>blackheads</th>\n",
       "      <th>scurring</th>\n",
       "      <th>skin_peeling</th>\n",
       "      <th>silver_like_dusting</th>\n",
       "      <th>small_dents_in_nails</th>\n",
       "      <th>inflammatory_nails</th>\n",
       "      <th>blister</th>\n",
       "      <th>red_sore_around_nose</th>\n",
       "      <th>yellow_crust_ooze</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "<p>5 rows × 132 columns</p>\n",
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       "      itching  skin_rash  nodal_skin_eruptions  continuous_sneezing  \\\n",
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       "1650        0          0                     0                    1   \n",
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       "3734  ...                   0           0         0             0   \n",
       "112   ...                   0           0         0             0   \n",
       "\n",
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       "112                     0                     0                   0        0   \n",
       "\n",
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       "\n",
       "[5 rows x 132 columns]"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>(vertigo) Paroymsal  Positional Vertigo</th>\n",
       "      <th>AIDS</th>\n",
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       "      <th>Alcoholic hepatitis</th>\n",
       "      <th>Allergy</th>\n",
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      ],
      "text/plain": [
       "      (vertigo) Paroymsal  Positional Vertigo  AIDS  Acne  \\\n",
       "4686                                        0     0     0   \n",
       "3176                                        0     0     0   \n",
       "1650                                        0     0     0   \n",
       "3734                                        0     0     0   \n",
       "112                                         0     0     0   \n",
       "\n",
       "      Alcoholic hepatitis  Allergy  Arthritis  Bronchial Asthma  \\\n",
       "4686                    0        0          0                 0   \n",
       "3176                    0        0          0                 0   \n",
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       "\n",
       "      Cervical spondylosis  Chicken pox  Chronic cholestasis  ...  \\\n",
       "4686                     1            0                    0  ...   \n",
       "3176                     0            0                    0  ...   \n",
       "1650                     0            0                    0  ...   \n",
       "3734                     0            0                    1  ...   \n",
       "112                      0            0                    0  ...   \n",
       "\n",
       "      Osteoarthristis  Paralysis (brain hemorrhage)  Peptic ulcer diseae  \\\n",
       "4686                0                             0                    0   \n",
       "3176                0                             0                    0   \n",
       "1650                0                             0                    0   \n",
       "3734                0                             0                    0   \n",
       "112                 0                             0                    0   \n",
       "\n",
       "      Pneumonia  Psoriasis  Tuberculosis  Typhoid  Urinary tract infection  \\\n",
       "4686          0          0             0        0                        0   \n",
       "3176          0          0             0        0                        0   \n",
       "1650          0          0             0        0                        0   \n",
       "3734          0          0             0        0                        0   \n",
       "112           0          0             0        0                        0   \n",
       "\n",
       "      Varicose veins  hepatitis A  \n",
       "4686               0            0  \n",
       "3176               0            1  \n",
       "1650               0            0  \n",
       "3734               0            0  \n",
       "112                0            0  \n",
       "\n",
       "[5 rows x 41 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "_tdNXk6mALqM"
   },
   "source": [
    "**Deep Neural Network**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import Dense , Dropout\n",
    "from tensorflow.keras.optimizers import Adam"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.16.1'"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Dense(units=132,activation=\"relu\",kernel_initializer=\"random_uniform\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(units=66,activation=\"relu\",kernel_initializer=\"random_uniform\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(units=33,activation=\"relu\",kernel_initializer=\"random_uniform\"))\n",
    "model.add(Dropout(0.5))\n",
    "model.add(Dense(units=41,activation=\"softmax\",kernel_initializer=\"random_uniform\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "109/109 - 3s - 27ms/step - accuracy: 0.0429 - loss: 3.5780 - val_accuracy: 0.0430 - val_loss: 3.1152\n",
      "Epoch 2/50\n",
      "109/109 - 0s - 4ms/step - accuracy: 0.1336 - loss: 2.8211 - val_accuracy: 0.5013 - val_loss: 2.0115\n",
      "Epoch 3/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.2787 - loss: 2.1595 - val_accuracy: 0.7392 - val_loss: 1.2634\n",
      "Epoch 4/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.3709 - loss: 1.7928 - val_accuracy: 0.9274 - val_loss: 0.8980\n",
      "Epoch 5/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.4460 - loss: 1.5496 - val_accuracy: 0.8898 - val_loss: 0.6919\n",
      "Epoch 6/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.5004 - loss: 1.3842 - val_accuracy: 0.9825 - val_loss: 0.5249\n",
      "Epoch 7/50\n",
      "109/109 - 0s - 4ms/step - accuracy: 0.5445 - loss: 1.2835 - val_accuracy: 0.9651 - val_loss: 0.4309\n",
      "Epoch 8/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.5805 - loss: 1.1995 - val_accuracy: 0.9987 - val_loss: 0.3451\n",
      "Epoch 9/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.6047 - loss: 1.1055 - val_accuracy: 0.9987 - val_loss: 0.2831\n",
      "Epoch 10/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.6381 - loss: 1.0236 - val_accuracy: 0.9987 - val_loss: 0.2307\n",
      "Epoch 11/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.6464 - loss: 0.9933 - val_accuracy: 0.9960 - val_loss: 0.2078\n",
      "Epoch 12/50\n",
      "109/109 - 1s - 5ms/step - accuracy: 0.6631 - loss: 0.9636 - val_accuracy: 0.9987 - val_loss: 0.1636\n",
      "Epoch 13/50\n",
      "109/109 - 1s - 5ms/step - accuracy: 0.6890 - loss: 0.8889 - val_accuracy: 0.9987 - val_loss: 0.1414\n",
      "Epoch 14/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.6971 - loss: 0.8580 - val_accuracy: 0.9987 - val_loss: 0.1306\n",
      "Epoch 15/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.7276 - loss: 0.7965 - val_accuracy: 0.9987 - val_loss: 0.1041\n",
      "Epoch 16/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.7316 - loss: 0.7792 - val_accuracy: 0.9987 - val_loss: 0.0855\n",
      "Epoch 17/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.7426 - loss: 0.7495 - val_accuracy: 0.9987 - val_loss: 0.0729\n",
      "Epoch 18/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.7440 - loss: 0.7434 - val_accuracy: 0.9987 - val_loss: 0.0710\n",
      "Epoch 19/50\n",
      "109/109 - 0s - 4ms/step - accuracy: 0.7587 - loss: 0.7088 - val_accuracy: 0.9987 - val_loss: 0.0647\n",
      "Epoch 20/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.7725 - loss: 0.6815 - val_accuracy: 0.9987 - val_loss: 0.0570\n",
      "Epoch 21/50\n",
      "109/109 - 0s - 4ms/step - accuracy: 0.7815 - loss: 0.6215 - val_accuracy: 0.9987 - val_loss: 0.0415\n",
      "Epoch 22/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.7763 - loss: 0.6462 - val_accuracy: 0.9987 - val_loss: 0.0455\n",
      "Epoch 23/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.7806 - loss: 0.6240 - val_accuracy: 0.9987 - val_loss: 0.0338\n",
      "Epoch 24/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.7861 - loss: 0.6098 - val_accuracy: 0.9987 - val_loss: 0.0353\n",
      "Epoch 25/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.7907 - loss: 0.6146 - val_accuracy: 0.9987 - val_loss: 0.0405\n",
      "Epoch 26/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.7921 - loss: 0.6112 - val_accuracy: 0.9987 - val_loss: 0.0295\n",
      "Epoch 27/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.7999 - loss: 0.5714 - val_accuracy: 0.9987 - val_loss: 0.0253\n",
      "Epoch 28/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.7976 - loss: 0.5722 - val_accuracy: 0.9987 - val_loss: 0.0295\n",
      "Epoch 29/50\n",
      "109/109 - 1s - 6ms/step - accuracy: 0.7976 - loss: 0.5715 - val_accuracy: 0.9987 - val_loss: 0.0282\n",
      "Epoch 30/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8166 - loss: 0.5312 - val_accuracy: 0.9987 - val_loss: 0.0228\n",
      "Epoch 31/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8189 - loss: 0.5240 - val_accuracy: 0.9987 - val_loss: 0.0192\n",
      "Epoch 32/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8056 - loss: 0.5544 - val_accuracy: 0.9987 - val_loss: 0.0212\n",
      "Epoch 33/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8074 - loss: 0.5450 - val_accuracy: 0.9987 - val_loss: 0.0272\n",
      "Epoch 34/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8229 - loss: 0.5216 - val_accuracy: 0.9987 - val_loss: 0.0187\n",
      "Epoch 35/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8123 - loss: 0.5398 - val_accuracy: 0.9987 - val_loss: 0.0197\n",
      "Epoch 36/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8111 - loss: 0.5493 - val_accuracy: 0.9987 - val_loss: 0.0229\n",
      "Epoch 37/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8327 - loss: 0.4913 - val_accuracy: 0.9987 - val_loss: 0.0187\n",
      "Epoch 38/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8163 - loss: 0.5155 - val_accuracy: 0.9987 - val_loss: 0.0170\n",
      "Epoch 39/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8330 - loss: 0.4829 - val_accuracy: 0.9987 - val_loss: 0.0162\n",
      "Epoch 40/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8434 - loss: 0.4681 - val_accuracy: 0.9987 - val_loss: 0.0156\n",
      "Epoch 41/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8313 - loss: 0.4826 - val_accuracy: 0.9987 - val_loss: 0.0161\n",
      "Epoch 42/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8517 - loss: 0.4373 - val_accuracy: 0.9987 - val_loss: 0.0139\n",
      "Epoch 43/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8474 - loss: 0.4794 - val_accuracy: 0.9987 - val_loss: 0.0155\n",
      "Epoch 44/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8431 - loss: 0.4420 - val_accuracy: 0.9987 - val_loss: 0.0139\n",
      "Epoch 45/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8514 - loss: 0.4277 - val_accuracy: 0.9987 - val_loss: 0.0127\n",
      "Epoch 46/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8408 - loss: 0.4582 - val_accuracy: 0.9987 - val_loss: 0.0161\n",
      "Epoch 47/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8655 - loss: 0.4172 - val_accuracy: 0.9987 - val_loss: 0.0134\n",
      "Epoch 48/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8454 - loss: 0.4454 - val_accuracy: 0.9987 - val_loss: 0.0130\n",
      "Epoch 49/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8529 - loss: 0.4400 - val_accuracy: 0.9987 - val_loss: 0.0098\n",
      "Epoch 50/50\n",
      "109/109 - 0s - 3ms/step - accuracy: 0.8618 - loss: 0.4073 - val_accuracy: 0.9987 - val_loss: 0.0098\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=\"categorical_crossentropy\",optimizer=Adam() ,metrics=[\"accuracy\"])\n",
    "history = model.fit(x_train, y_train, batch_size=32, epochs=50, verbose=2, validation_data=(x_val,y_val)) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "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_2\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential_2\"\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_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\">132</span>)            │        <span style=\"color: #00af00; text-decoration-color: #00af00\">17,556</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">132</span>)            │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                 │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">8,778</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">66</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">2,211</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">33</span>)             │             <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">41</span>)             │         <span style=\"color: #00af00; text-decoration-color: #00af00\">1,394</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_8 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m132\u001b[0m)            │        \u001b[38;5;34m17,556\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m132\u001b[0m)            │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_9 (\u001b[38;5;33mDense\u001b[0m)                 │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m)             │         \u001b[38;5;34m8,778\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m66\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_10 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m)             │         \u001b[38;5;34m2,211\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m)             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m33\u001b[0m)             │             \u001b[38;5;34m0\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense_11 (\u001b[38;5;33mDense\u001b[0m)                │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m41\u001b[0m)             │         \u001b[38;5;34m1,394\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\">89,819</span> (350.86 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m89,819\u001b[0m (350.86 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\">29,939</span> (116.95 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m29,939\u001b[0m (116.95 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"
    },
    {
     "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\"> Optimizer params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">59,880</span> (233.91 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Optimizer params: \u001b[0m\u001b[38;5;34m59,880\u001b[0m (233.91 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "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": [
    "model.save(\"epics_model_vsc.h5\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "A total of 1 objects could not be loaded. Example error message for object <keras.src.optimizers.adam.Adam object at 0x0000013018B8B710>:\n\n'Unable to synchronously open object (bad object header version number)'\n\nList of objects that could not be loaded:\n[<keras.src.optimizers.adam.Adam object at 0x0000013018B8B710>]",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[40], line 2\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mtensorflow\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mkeras\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmodels\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m load_model\n\u001b[1;32m----> 2\u001b[0m lm \u001b[38;5;241m=\u001b[39m \u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mepics_model_vsc.keras\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32mc:\\Users\\Aakash\\anaconda3\\envs\\virenv\\Lib\\site-packages\\keras\\src\\saving\\saving_api.py:176\u001b[0m, in \u001b[0;36mload_model\u001b[1;34m(filepath, custom_objects, compile, safe_mode)\u001b[0m\n\u001b[0;32m    173\u001b[0m         is_keras_zip \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m    175\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m is_keras_zip:\n\u001b[1;32m--> 176\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43msaving_lib\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_model\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    177\u001b[0m \u001b[43m        \u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    178\u001b[0m \u001b[43m        \u001b[49m\u001b[43mcustom_objects\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcustom_objects\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    179\u001b[0m \u001b[43m        \u001b[49m\u001b[38;5;28;43mcompile\u001b[39;49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mcompile\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    180\u001b[0m \u001b[43m        \u001b[49m\u001b[43msafe_mode\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msafe_mode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    181\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    182\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mstr\u001b[39m(filepath)\u001b[38;5;241m.\u001b[39mendswith((\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.h5\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m.hdf5\u001b[39m\u001b[38;5;124m\"\u001b[39m)):\n\u001b[0;32m    183\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m legacy_h5_format\u001b[38;5;241m.\u001b[39mload_model_from_hdf5(filepath)\n",
      "File \u001b[1;32mc:\\Users\\Aakash\\anaconda3\\envs\\virenv\\Lib\\site-packages\\keras\\src\\saving\\saving_lib.py:192\u001b[0m, in \u001b[0;36mload_model\u001b[1;34m(filepath, custom_objects, compile, safe_mode)\u001b[0m\n\u001b[0;32m    189\u001b[0m         asset_store\u001b[38;5;241m.\u001b[39mclose()\n\u001b[0;32m    191\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m failed_trackables:\n\u001b[1;32m--> 192\u001b[0m         \u001b[43m_raise_loading_failure\u001b[49m\u001b[43m(\u001b[49m\u001b[43merror_msgs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    193\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m model\n",
      "File \u001b[1;32mc:\\Users\\Aakash\\anaconda3\\envs\\virenv\\Lib\\site-packages\\keras\\src\\saving\\saving_lib.py:273\u001b[0m, in \u001b[0;36m_raise_loading_failure\u001b[1;34m(error_msgs, warn_only)\u001b[0m\n\u001b[0;32m    271\u001b[0m     warnings\u001b[38;5;241m.\u001b[39mwarn(msg)\n\u001b[0;32m    272\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m--> 273\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(msg)\n",
      "\u001b[1;31mValueError\u001b[0m: A total of 1 objects could not be loaded. Example error message for object <keras.src.optimizers.adam.Adam object at 0x0000013018B8B710>:\n\n'Unable to synchronously open object (bad object header version number)'\n\nList of objects that could not be loaded:\n[<keras.src.optimizers.adam.Adam object at 0x0000013018B8B710>]"
     ]
    }
   ],
   "source": [
    "# from tensorflow import keras\n",
    "# from keras.layers import Dense\n",
    "# from keras.models import Sequential, load_model\n",
    "# lm = load_model(\"epics_model_vsc.keras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "id": "ljPuiSWRBh9y"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1/1 [==============================] - 0s 30ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[7.7641041e-16, 1.4470887e-29, 6.3495676e-35, 4.3500628e-12,\n",
       "        4.5721160e-34, 8.3629945e-18, 2.8034449e-06, 5.3598615e-27,\n",
       "        1.0157845e-09, 9.8831151e-03, 9.7139673e-21, 3.9578145e-04,\n",
       "        2.2368095e-30, 3.1130319e-24, 9.7179626e-30, 2.5261622e-30,\n",
       "        3.4382428e-20, 3.5465069e-19, 3.5676603e-12, 9.5118936e-19,\n",
       "        3.5256209e-04, 9.8894238e-01, 9.9727768e-05, 5.0940462e-28,\n",
       "        2.1791610e-13, 3.4510169e-19, 1.3451713e-14, 1.7124203e-14,\n",
       "        3.9055223e-05, 1.5025769e-11, 4.5895057e-18, 5.2721618e-14,\n",
       "        1.2951501e-28, 5.7056399e-16, 1.9862502e-13, 7.3285663e-15,\n",
       "        9.2394377e-11, 4.8065200e-08, 0.0000000e+00, 1.2122962e-24,\n",
       "        2.8444253e-04]], dtype=float32)"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred=lm.predict(x_test.iloc[[0]])\n",
    "pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'0.00'"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float(format(pred[0][0] * 100, '.2f'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "21"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred.argmax()"
   ]
  }
 ],
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