11_dropout_layer.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f5932eb3-7c95-4991-b875-6cf419fe319a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "2458c7f1-34b1-4638-bf55-6d913ec93f67",
   "metadata": {},
   "outputs": [],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "1a06ba53-9b14-48c3-90b6-caacfb9f6300",
   "metadata": {},
   "outputs": [
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       "      <td>0.0019</td>\n",
       "      <td>R</td>\n",
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       "      <th>198</th>\n",
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       "      <td>0.0788</td>\n",
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       "      <th>82</th>\n",
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       "      <td>0.0421</td>\n",
       "      <td>0.0573</td>\n",
       "      <td>0.0130</td>\n",
       "      <td>0.0183</td>\n",
       "      <td>0.1019</td>\n",
       "      <td>0.1054</td>\n",
       "      <td>0.1070</td>\n",
       "      <td>0.2302</td>\n",
       "      <td>0.2259</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0028</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0120</td>\n",
       "      <td>0.0087</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0061</td>\n",
       "      <td>0.0030</td>\n",
       "      <td>0.0078</td>\n",
       "      <td>R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>0.0228</td>\n",
       "      <td>0.0853</td>\n",
       "      <td>0.1000</td>\n",
       "      <td>0.0428</td>\n",
       "      <td>0.1117</td>\n",
       "      <td>0.1651</td>\n",
       "      <td>0.1597</td>\n",
       "      <td>0.2116</td>\n",
       "      <td>0.3295</td>\n",
       "      <td>0.3517</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0172</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>0.0260</td>\n",
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       "      <td>0.0125</td>\n",
       "      <td>0.0116</td>\n",
       "      <td>0.0093</td>\n",
       "      <td>0.0012</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>M</td>\n",
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       "      <th>111</th>\n",
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       "      <td>0.0885</td>\n",
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       "      <td>0.2287</td>\n",
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       "      <td>0.0740</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0257</td>\n",
       "      <td>0.0082</td>\n",
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       "      <td>0.0171</td>\n",
       "      <td>0.0146</td>\n",
       "      <td>0.0134</td>\n",
       "      <td>0.0056</td>\n",
       "      <td>M</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "<p>5 rows × 61 columns</p>\n",
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       "         0       1       2       3       4       5       6       7       8   \\\n",
       "81   0.0100  0.0194  0.0155  0.0489  0.0839  0.1009  0.1627  0.2071  0.2696   \n",
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       "111  0.0530  0.0885  0.1997  0.2604  0.3225  0.2247  0.0617  0.2287  0.0950   \n",
       "\n",
       "         9   ...      51      52      53      54      55      56      57  \\\n",
       "81   0.2990  ...  0.0130  0.0073  0.0077  0.0075  0.0060  0.0080  0.0019   \n",
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       "125  0.3517  ...  0.0172  0.0191  0.0260  0.0140  0.0125  0.0116  0.0093   \n",
       "111  0.0740  ...  0.0244  0.0199  0.0257  0.0082  0.0151  0.0171  0.0146   \n",
       "\n",
       "         58      59  60  \n",
       "81   0.0053  0.0019   R  \n",
       "198  0.0035  0.0060   M  \n",
       "82   0.0030  0.0078   R  \n",
       "125  0.0012  0.0036   M  \n",
       "111  0.0134  0.0056   M  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"./sonar_dataset.csv\", header=None)\n",
    "df.sample(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6445f2ee-7dc2-4e26-aeb3-4d09f1998d94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(208, 61)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "52bd511f-025a-4c57-9fdc-f52737f57669",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     0\n",
       "1     0\n",
       "2     0\n",
       "3     0\n",
       "4     0\n",
       "     ..\n",
       "56    0\n",
       "57    0\n",
       "58    0\n",
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       "60    0\n",
       "Length: 61, dtype: int64"
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     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "# check for nan values\n",
    "df.isna().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f55feea4-3bde-4d31-bdc8-c0415cf29799",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "            17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "            34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50,\n",
       "            51, 52, 53, 54, 55, 56, 57, 58, 59, 60],\n",
       "           dtype='int64')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "e5c05604-14fd-4625-9dc0-141ff6524dc1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    111\n",
       "R     97\n",
       "Name: 60, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[60].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0a0e6762-5543-4884-925e-e898192f0fcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    R\n",
       "1    R\n",
       "2    R\n",
       "3    R\n",
       "4    R\n",
       "Name: 60, dtype: object"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = df.drop(60, axis=1)\n",
    "y = df[60]\n",
    "y.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "45299091-800b-49a0-9383-1de7a4f21d99",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "     R\n",
       "36   1\n",
       "23   1\n",
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       "92   1"
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     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "y = pd.get_dummies(y, drop_first=True)\n",
    "y.sample(5) # R --> 1 and M --> 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "4e8211a3-43c2-42d6-8bc5-ff4311fa4cab",
   "metadata": {},
   "outputs": [
    {
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       "R\n",
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       "1     97\n",
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     "metadata": {},
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   "source": [
    "y.value_counts()"
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   "cell_type": "code",
   "execution_count": 11,
   "id": "be118aec-591f-4fb3-bc57-1b02ebeeb866",
   "metadata": {},
   "outputs": [
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       "      <td>0.0428</td>\n",
       "      <td>0.0207</td>\n",
       "      <td>0.0954</td>\n",
       "      <td>0.0986</td>\n",
       "      <td>0.1539</td>\n",
       "      <td>0.1601</td>\n",
       "      <td>0.3109</td>\n",
       "      <td>0.2111</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>0.0065</td>\n",
       "      <td>0.0159</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0167</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0032</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0453</td>\n",
       "      <td>0.0523</td>\n",
       "      <td>0.0843</td>\n",
       "      <td>0.0689</td>\n",
       "      <td>0.1183</td>\n",
       "      <td>0.2583</td>\n",
       "      <td>0.2156</td>\n",
       "      <td>0.3481</td>\n",
       "      <td>0.3337</td>\n",
       "      <td>0.2872</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0089</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0094</td>\n",
       "      <td>0.0191</td>\n",
       "      <td>0.0140</td>\n",
       "      <td>0.0049</td>\n",
       "      <td>0.0052</td>\n",
       "      <td>0.0044</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0262</td>\n",
       "      <td>0.0582</td>\n",
       "      <td>0.1099</td>\n",
       "      <td>0.1083</td>\n",
       "      <td>0.0974</td>\n",
       "      <td>0.2280</td>\n",
       "      <td>0.2431</td>\n",
       "      <td>0.3771</td>\n",
       "      <td>0.5598</td>\n",
       "      <td>0.6194</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0033</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>0.0166</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0180</td>\n",
       "      <td>0.0244</td>\n",
       "      <td>0.0316</td>\n",
       "      <td>0.0164</td>\n",
       "      <td>0.0095</td>\n",
       "      <td>0.0078</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0100</td>\n",
       "      <td>0.0171</td>\n",
       "      <td>0.0623</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0205</td>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.1098</td>\n",
       "      <td>0.1276</td>\n",
       "      <td>0.0598</td>\n",
       "      <td>0.1264</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0241</td>\n",
       "      <td>0.0121</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0073</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0044</td>\n",
       "      <td>0.0040</td>\n",
       "      <td>0.0117</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0762</td>\n",
       "      <td>0.0666</td>\n",
       "      <td>0.0481</td>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0590</td>\n",
       "      <td>0.0649</td>\n",
       "      <td>0.1209</td>\n",
       "      <td>0.2467</td>\n",
       "      <td>0.3564</td>\n",
       "      <td>0.4459</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0156</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0054</td>\n",
       "      <td>0.0105</td>\n",
       "      <td>0.0110</td>\n",
       "      <td>0.0015</td>\n",
       "      <td>0.0072</td>\n",
       "      <td>0.0048</td>\n",
       "      <td>0.0107</td>\n",
       "      <td>0.0094</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 60 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       0       1       2       3       4       5       6       7       8   \\\n",
       "0  0.0200  0.0371  0.0428  0.0207  0.0954  0.0986  0.1539  0.1601  0.3109   \n",
       "1  0.0453  0.0523  0.0843  0.0689  0.1183  0.2583  0.2156  0.3481  0.3337   \n",
       "2  0.0262  0.0582  0.1099  0.1083  0.0974  0.2280  0.2431  0.3771  0.5598   \n",
       "3  0.0100  0.0171  0.0623  0.0205  0.0205  0.0368  0.1098  0.1276  0.0598   \n",
       "4  0.0762  0.0666  0.0481  0.0394  0.0590  0.0649  0.1209  0.2467  0.3564   \n",
       "\n",
       "       9   ...      50      51      52      53      54      55      56  \\\n",
       "0  0.2111  ...  0.0232  0.0027  0.0065  0.0159  0.0072  0.0167  0.0180   \n",
       "1  0.2872  ...  0.0125  0.0084  0.0089  0.0048  0.0094  0.0191  0.0140   \n",
       "2  0.6194  ...  0.0033  0.0232  0.0166  0.0095  0.0180  0.0244  0.0316   \n",
       "3  0.1264  ...  0.0241  0.0121  0.0036  0.0150  0.0085  0.0073  0.0050   \n",
       "4  0.4459  ...  0.0156  0.0031  0.0054  0.0105  0.0110  0.0015  0.0072   \n",
       "\n",
       "       57      58      59  \n",
       "0  0.0084  0.0090  0.0032  \n",
       "1  0.0049  0.0052  0.0044  \n",
       "2  0.0164  0.0095  0.0078  \n",
       "3  0.0044  0.0040  0.0117  \n",
       "4  0.0048  0.0107  0.0094  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "08aa1aeb-26db-4344-a141-514f39bc73b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(X,y, test_size=0.25, random_state=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f5356211-3b18-4df5-88ab-d004ae53a768",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>2</th>\n",
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       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
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       "      <th>58</th>\n",
       "      <th>59</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>67</th>\n",
       "      <td>0.0368</td>\n",
       "      <td>0.0403</td>\n",
       "      <td>0.0317</td>\n",
       "      <td>0.0293</td>\n",
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       "      <td>0.0078</td>\n",
       "      <td>0.0068</td>\n",
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       "      <th>14</th>\n",
       "      <td>0.0124</td>\n",
       "      <td>0.0433</td>\n",
       "      <td>0.0604</td>\n",
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       "      <td>0.0597</td>\n",
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       "      <td>0.0531</td>\n",
       "      <td>0.0343</td>\n",
       "      <td>0.1052</td>\n",
       "      <td>0.2120</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0752</td>\n",
       "      <td>0.1444</td>\n",
       "      <td>0.1487</td>\n",
       "      <td>0.1484</td>\n",
       "      <td>0.2442</td>\n",
       "      <td>0.2822</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0027</td>\n",
       "      <td>0.0077</td>\n",
       "      <td>0.0026</td>\n",
       "      <td>0.0031</td>\n",
       "      <td>0.0083</td>\n",
       "      <td>0.0020</td>\n",
       "      <td>0.0084</td>\n",
       "      <td>0.0108</td>\n",
       "      <td>0.0083</td>\n",
       "      <td>0.0033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>179</th>\n",
       "      <td>0.0394</td>\n",
       "      <td>0.0420</td>\n",
       "      <td>0.0446</td>\n",
       "      <td>0.0551</td>\n",
       "      <td>0.0597</td>\n",
       "      <td>0.1416</td>\n",
       "      <td>0.0956</td>\n",
       "      <td>0.0802</td>\n",
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       "      <td>0.2558</td>\n",
       "      <td>...</td>\n",
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       "      <td>0.0068</td>\n",
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       "      <td>0.0087</td>\n",
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       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.0126</td>\n",
       "      <td>0.0149</td>\n",
       "      <td>0.0641</td>\n",
       "      <td>0.1732</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.2559</td>\n",
       "      <td>0.2947</td>\n",
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       "      <td>0.4983</td>\n",
       "      <td>0.5920</td>\n",
       "      <td>...</td>\n",
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       "         0       1       2       3       4       5       6       7       8   \\\n",
       "67   0.0368  0.0403  0.0317  0.0293  0.0820  0.1342  0.1161  0.0663  0.0155   \n",
       "14   0.0124  0.0433  0.0604  0.0449  0.0597  0.0355  0.0531  0.0343  0.1052   \n",
       "164  0.0163  0.0198  0.0202  0.0386  0.0752  0.1444  0.1487  0.1484  0.2442   \n",
       "179  0.0394  0.0420  0.0446  0.0551  0.0597  0.1416  0.0956  0.0802  0.1618   \n",
       "19   0.0126  0.0149  0.0641  0.1732  0.2565  0.2559  0.2947  0.4110  0.4983   \n",
       "\n",
       "         9   ...      50      51      52      53      54      55      56  \\\n",
       "67   0.0506  ...  0.0058  0.0091  0.0160  0.0160  0.0081  0.0070  0.0135   \n",
       "14   0.2120  ...  0.0078  0.0083  0.0057  0.0174  0.0188  0.0054  0.0114   \n",
       "164  0.2822  ...  0.0027  0.0077  0.0026  0.0031  0.0083  0.0020  0.0084   \n",
       "179  0.2558  ...  0.0118  0.0146  0.0040  0.0114  0.0032  0.0062  0.0101   \n",
       "19   0.5920  ...  0.0153  0.0092  0.0035  0.0098  0.0121  0.0006  0.0181   \n",
       "\n",
       "         57      58      59  \n",
       "67   0.0067  0.0078  0.0068  \n",
       "14   0.0196  0.0147  0.0062  \n",
       "164  0.0108  0.0083  0.0033  \n",
       "179  0.0068  0.0053  0.0087  \n",
       "19   0.0094  0.0116  0.0063  \n",
       "\n",
       "[5 rows x 60 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "82bfa9c8-f1e7-4ad3-a478-e6a5bd7e86a9",
   "metadata": {},
   "source": [
    "# Using Deep Learning Model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd5ff979-63bb-4eb4-b378-2ac46ddf77cf",
   "metadata": {},
   "source": [
    "## Model without Dropout Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "6eb24b95-22c3-4038-b19d-0c31145e6dac",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "from tensorflow import keras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "d36d874f-9467-43c0-8a50-7600948095a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Metal device set to: Apple M1\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-18 09:00:26.829226: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:305] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
      "2023-01-18 09:00:26.829427: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:271] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-18 09:00:27.339556: W tensorflow/core/platform/profile_utils/cpu_utils.cc:128] Failed to get CPU frequency: 0 Hz\n",
      "2023-01-18 09:00:27.513670: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 2s 21ms/step - loss: 0.6859 - accuracy: 0.5256\n",
      "Epoch 2/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.6653 - accuracy: 0.5641\n",
      "Epoch 3/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.6494 - accuracy: 0.6795\n",
      "Epoch 4/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.6526 - accuracy: 0.5769\n",
      "Epoch 5/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.6087 - accuracy: 0.7756\n",
      "Epoch 6/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.5787 - accuracy: 0.7692\n",
      "Epoch 7/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.5586 - accuracy: 0.7436\n",
      "Epoch 8/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.5293 - accuracy: 0.7949\n",
      "Epoch 9/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.4959 - accuracy: 0.8205\n",
      "Epoch 10/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.4665 - accuracy: 0.8205\n",
      "Epoch 11/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.4388 - accuracy: 0.8333\n",
      "Epoch 12/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.4094 - accuracy: 0.8269\n",
      "Epoch 13/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.3824 - accuracy: 0.8654\n",
      "Epoch 14/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.3798 - accuracy: 0.8462\n",
      "Epoch 15/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.3607 - accuracy: 0.8590\n",
      "Epoch 16/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.3491 - accuracy: 0.8462\n",
      "Epoch 17/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.3203 - accuracy: 0.8654\n",
      "Epoch 18/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.3197 - accuracy: 0.8910\n",
      "Epoch 19/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.2963 - accuracy: 0.8910\n",
      "Epoch 20/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.2692 - accuracy: 0.9038\n",
      "Epoch 21/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.2629 - accuracy: 0.9038\n",
      "Epoch 22/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.2518 - accuracy: 0.9295\n",
      "Epoch 23/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.2679 - accuracy: 0.8846\n",
      "Epoch 24/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.2822 - accuracy: 0.8654\n",
      "Epoch 25/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.2133 - accuracy: 0.9295\n",
      "Epoch 26/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.2082 - accuracy: 0.9231\n",
      "Epoch 27/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.1829 - accuracy: 0.9487\n",
      "Epoch 28/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.1797 - accuracy: 0.9615\n",
      "Epoch 29/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.1940 - accuracy: 0.9359\n",
      "Epoch 30/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.1628 - accuracy: 0.9487\n",
      "Epoch 31/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.1599 - accuracy: 0.9359\n",
      "Epoch 32/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.1407 - accuracy: 0.9744\n",
      "Epoch 33/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.1301 - accuracy: 0.9679\n",
      "Epoch 34/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.1214 - accuracy: 0.9744\n",
      "Epoch 35/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.1172 - accuracy: 0.9679\n",
      "Epoch 36/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.1142 - accuracy: 0.9679\n",
      "Epoch 37/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0920 - accuracy: 0.9872\n",
      "Epoch 38/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.1537 - accuracy: 0.9359\n",
      "Epoch 39/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.1966 - accuracy: 0.9231\n",
      "Epoch 40/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.1007 - accuracy: 0.9744\n",
      "Epoch 41/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0917 - accuracy: 0.9679\n",
      "Epoch 42/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0774 - accuracy: 0.9872\n",
      "Epoch 43/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0799 - accuracy: 0.9872\n",
      "Epoch 44/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0668 - accuracy: 0.9936\n",
      "Epoch 45/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0660 - accuracy: 0.9936\n",
      "Epoch 46/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0538 - accuracy: 0.9936\n",
      "Epoch 47/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0576 - accuracy: 0.9936\n",
      "Epoch 48/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0486 - accuracy: 0.9936\n",
      "Epoch 49/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0408 - accuracy: 0.9936\n",
      "Epoch 50/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0453 - accuracy: 0.9936\n",
      "Epoch 51/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0432 - accuracy: 1.0000\n",
      "Epoch 52/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0347 - accuracy: 1.0000\n",
      "Epoch 53/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0325 - accuracy: 1.0000\n",
      "Epoch 54/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0353 - accuracy: 1.0000\n",
      "Epoch 55/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0300 - accuracy: 1.0000\n",
      "Epoch 56/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0265 - accuracy: 1.0000\n",
      "Epoch 57/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0224 - accuracy: 1.0000\n",
      "Epoch 58/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0231 - accuracy: 1.0000\n",
      "Epoch 59/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0261 - accuracy: 1.0000\n",
      "Epoch 60/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0247 - accuracy: 1.0000\n",
      "Epoch 61/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0326 - accuracy: 0.9872\n",
      "Epoch 62/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0243 - accuracy: 1.0000\n",
      "Epoch 63/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0190 - accuracy: 1.0000\n",
      "Epoch 64/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0213 - accuracy: 1.0000\n",
      "Epoch 65/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0217 - accuracy: 1.0000\n",
      "Epoch 66/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0125 - accuracy: 1.0000\n",
      "Epoch 67/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0117 - accuracy: 1.0000\n",
      "Epoch 68/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0122 - accuracy: 1.0000\n",
      "Epoch 69/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0109 - accuracy: 1.0000\n",
      "Epoch 70/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0106 - accuracy: 1.0000\n",
      "Epoch 71/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0090 - accuracy: 1.0000\n",
      "Epoch 72/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0084 - accuracy: 1.0000\n",
      "Epoch 73/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0081 - accuracy: 1.0000\n",
      "Epoch 74/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0074 - accuracy: 1.0000\n",
      "Epoch 75/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0072 - accuracy: 1.0000\n",
      "Epoch 76/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0067 - accuracy: 1.0000\n",
      "Epoch 77/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0071 - accuracy: 1.0000\n",
      "Epoch 78/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0066 - accuracy: 1.0000\n",
      "Epoch 79/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0060 - accuracy: 1.0000\n",
      "Epoch 80/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0065 - accuracy: 1.0000\n",
      "Epoch 81/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0055 - accuracy: 1.0000\n",
      "Epoch 82/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0051 - accuracy: 1.0000\n",
      "Epoch 83/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0054 - accuracy: 1.0000\n",
      "Epoch 84/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.0045 - accuracy: 1.0000\n",
      "Epoch 85/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0043 - accuracy: 1.0000\n",
      "Epoch 86/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0041 - accuracy: 1.0000\n",
      "Epoch 87/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0040 - accuracy: 1.0000\n",
      "Epoch 88/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0039 - accuracy: 1.0000\n",
      "Epoch 89/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0037 - accuracy: 1.0000\n",
      "Epoch 90/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0035 - accuracy: 1.0000\n",
      "Epoch 91/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0034 - accuracy: 1.0000\n",
      "Epoch 92/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0035 - accuracy: 1.0000\n",
      "Epoch 93/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0032 - accuracy: 1.0000\n",
      "Epoch 94/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0032 - accuracy: 1.0000\n",
      "Epoch 95/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0037 - accuracy: 1.0000\n",
      "Epoch 96/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0048 - accuracy: 1.0000\n",
      "Epoch 97/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0032 - accuracy: 1.0000\n",
      "Epoch 98/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0028 - accuracy: 1.0000\n",
      "Epoch 99/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0025 - accuracy: 1.0000\n",
      "Epoch 100/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.0023 - accuracy: 1.0000\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x2913c61f0>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = keras.Sequential([\n",
    "    keras.layers.Dense(60, input_dim=60, activation=\"relu\"),\n",
    "    keras.layers.Dense(30, activation=\"relu\"),\n",
    "    keras.layers.Dense(15, activation=\"relu\"),\n",
    "    keras.layers.Dense(1, activation=\"sigmoid\")\n",
    "])\n",
    "\n",
    "model.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
    "\n",
    "model.fit(X_train,y_train, epochs=100, batch_size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b15ff81e-fe87-4577-93fd-4766134696b5",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-18 09:01:02.159716: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 33ms/step - loss: 1.0739 - accuracy: 0.7308\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[1.073946475982666, 0.7307692766189575]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce65dc6a-9225-44a2-a57e-e3f802a0fc83",
   "metadata": {},
   "source": [
    "### Training Accuracy >>> Test Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "0d3acc4e-7d7f-4b4f-b6c1-37c6ce0663f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 12ms/step\n",
      "[3.7646018e-08 9.9302471e-01 5.5878121e-01 3.0829982e-05 9.9999654e-01\n",
      " 9.9718153e-01 6.0633998e-02 9.9999893e-01 2.0276913e-05 9.9999976e-01]\n",
      "[0. 1. 1. 0. 1. 1. 0. 1. 0. 1.]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-18 09:03:04.676634: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "y_pred = model.predict(X_test).reshape(-1)\n",
    "print(y_pred[:10])\n",
    "\n",
    "# round the values to nearest integer ie 0 or 1\n",
    "y_pred = np.round(y_pred)\n",
    "print(y_pred[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ed45806-5d41-4da7-8ecb-0824c6e6a3ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>R</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>186</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>155</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>165</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>200</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>58</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>151</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>202</th>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     R\n",
       "186  0\n",
       "155  0\n",
       "165  0\n",
       "200  0\n",
       "58   1\n",
       "34   1\n",
       "151  0\n",
       "18   1\n",
       "202  0\n",
       "62   1"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_test[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "ed6abe8f-9457-4c5c-bf02-6ec6950a5157",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.70      0.85      0.77        27\n",
      "           1       0.79      0.60      0.68        25\n",
      "\n",
      "    accuracy                           0.73        52\n",
      "   macro avg       0.74      0.73      0.72        52\n",
      "weighted avg       0.74      0.73      0.73        52\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "\n",
    "print(classification_report(y_test,y_pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "10609f56-e051-4df6-aeb8-f173a13c5f24",
   "metadata": {},
   "source": [
    "## Model with Dropout Layer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6392bd13-3d67-4c0f-8f0e-8a1f3ee40024",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-18 09:08:08.627585: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20/20 [==============================] - 1s 11ms/step - loss: 0.8204 - accuracy: 0.4167\n",
      "Epoch 2/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.7139 - accuracy: 0.5256\n",
      "Epoch 3/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.6949 - accuracy: 0.5000\n",
      "Epoch 4/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.6859 - accuracy: 0.5128\n",
      "Epoch 5/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.6880 - accuracy: 0.5256\n",
      "Epoch 6/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.6658 - accuracy: 0.5833\n",
      "Epoch 7/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.6832 - accuracy: 0.5385\n",
      "Epoch 8/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.6455 - accuracy: 0.6218\n",
      "Epoch 9/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.6463 - accuracy: 0.6282\n",
      "Epoch 10/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.6509 - accuracy: 0.6282\n",
      "Epoch 11/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.6320 - accuracy: 0.6346\n",
      "Epoch 12/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.6452 - accuracy: 0.6154\n",
      "Epoch 13/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.6232 - accuracy: 0.6603\n",
      "Epoch 14/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.5990 - accuracy: 0.6795\n",
      "Epoch 15/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.6027 - accuracy: 0.6474\n",
      "Epoch 16/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.6191 - accuracy: 0.6282\n",
      "Epoch 17/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.5934 - accuracy: 0.6923\n",
      "Epoch 18/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.5968 - accuracy: 0.6795\n",
      "Epoch 19/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.5492 - accuracy: 0.7372\n",
      "Epoch 20/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.5017 - accuracy: 0.7564\n",
      "Epoch 21/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.5188 - accuracy: 0.7308\n",
      "Epoch 22/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.5081 - accuracy: 0.7372\n",
      "Epoch 23/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.5185 - accuracy: 0.7500\n",
      "Epoch 24/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.4940 - accuracy: 0.7244\n",
      "Epoch 25/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.4836 - accuracy: 0.7564\n",
      "Epoch 26/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.4841 - accuracy: 0.7564\n",
      "Epoch 27/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.5013 - accuracy: 0.7244\n",
      "Epoch 28/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.4509 - accuracy: 0.7692\n",
      "Epoch 29/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.4838 - accuracy: 0.7500\n",
      "Epoch 30/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.4330 - accuracy: 0.7821\n",
      "Epoch 31/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.4649 - accuracy: 0.8077\n",
      "Epoch 32/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.4111 - accuracy: 0.8141\n",
      "Epoch 33/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.4468 - accuracy: 0.8269\n",
      "Epoch 34/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.4631 - accuracy: 0.7821\n",
      "Epoch 35/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3983 - accuracy: 0.8590\n",
      "Epoch 36/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3914 - accuracy: 0.8397\n",
      "Epoch 37/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.4463 - accuracy: 0.8077\n",
      "Epoch 38/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3852 - accuracy: 0.8205\n",
      "Epoch 39/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3670 - accuracy: 0.8590\n",
      "Epoch 40/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3992 - accuracy: 0.8269\n",
      "Epoch 41/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3893 - accuracy: 0.8526\n",
      "Epoch 42/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3702 - accuracy: 0.8590\n",
      "Epoch 43/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3582 - accuracy: 0.9038\n",
      "Epoch 44/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3627 - accuracy: 0.8718\n",
      "Epoch 45/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3543 - accuracy: 0.8462\n",
      "Epoch 46/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3260 - accuracy: 0.8782\n",
      "Epoch 47/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3149 - accuracy: 0.8782\n",
      "Epoch 48/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3564 - accuracy: 0.8590\n",
      "Epoch 49/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3242 - accuracy: 0.8718\n",
      "Epoch 50/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3837 - accuracy: 0.8397\n",
      "Epoch 51/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3171 - accuracy: 0.8718\n",
      "Epoch 52/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3006 - accuracy: 0.8718\n",
      "Epoch 53/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3172 - accuracy: 0.8654\n",
      "Epoch 54/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3252 - accuracy: 0.9038\n",
      "Epoch 55/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3230 - accuracy: 0.8782\n",
      "Epoch 56/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3287 - accuracy: 0.8782\n",
      "Epoch 57/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2981 - accuracy: 0.8846\n",
      "Epoch 58/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2696 - accuracy: 0.9103\n",
      "Epoch 59/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3197 - accuracy: 0.8782\n",
      "Epoch 60/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2894 - accuracy: 0.8718\n",
      "Epoch 61/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2977 - accuracy: 0.8910\n",
      "Epoch 62/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2791 - accuracy: 0.8910\n",
      "Epoch 63/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2761 - accuracy: 0.8718\n",
      "Epoch 64/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3074 - accuracy: 0.8846\n",
      "Epoch 65/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.3309 - accuracy: 0.8654\n",
      "Epoch 66/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2940 - accuracy: 0.9167\n",
      "Epoch 67/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2488 - accuracy: 0.8974\n",
      "Epoch 68/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2871 - accuracy: 0.8654\n",
      "Epoch 69/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2410 - accuracy: 0.9231\n",
      "Epoch 70/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2601 - accuracy: 0.9038\n",
      "Epoch 71/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2704 - accuracy: 0.9038\n",
      "Epoch 72/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2859 - accuracy: 0.9038\n",
      "Epoch 73/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2568 - accuracy: 0.9359\n",
      "Epoch 74/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2415 - accuracy: 0.9295\n",
      "Epoch 75/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2264 - accuracy: 0.9231\n",
      "Epoch 76/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2124 - accuracy: 0.9487\n",
      "Epoch 77/100\n",
      "20/20 [==============================] - 0s 6ms/step - loss: 0.2745 - accuracy: 0.8910\n",
      "Epoch 78/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2256 - accuracy: 0.9167\n",
      "Epoch 79/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2042 - accuracy: 0.9167\n",
      "Epoch 80/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2536 - accuracy: 0.9231\n",
      "Epoch 81/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2413 - accuracy: 0.9295\n",
      "Epoch 82/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2811 - accuracy: 0.8654\n",
      "Epoch 83/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2008 - accuracy: 0.9423\n",
      "Epoch 84/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2078 - accuracy: 0.9487\n",
      "Epoch 85/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2352 - accuracy: 0.9295\n",
      "Epoch 86/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.1829 - accuracy: 0.9487\n",
      "Epoch 87/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2383 - accuracy: 0.9167\n",
      "Epoch 88/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2239 - accuracy: 0.9231\n",
      "Epoch 89/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2082 - accuracy: 0.9231\n",
      "Epoch 90/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2269 - accuracy: 0.9167\n",
      "Epoch 91/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2445 - accuracy: 0.8974\n",
      "Epoch 92/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2200 - accuracy: 0.9231\n",
      "Epoch 93/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2579 - accuracy: 0.9103\n",
      "Epoch 94/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2081 - accuracy: 0.9231\n",
      "Epoch 95/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2217 - accuracy: 0.9295\n",
      "Epoch 96/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2075 - accuracy: 0.9551\n",
      "Epoch 97/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2033 - accuracy: 0.9359\n",
      "Epoch 98/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2367 - accuracy: 0.9295\n",
      "Epoch 99/100\n",
      "20/20 [==============================] - 0s 8ms/step - loss: 0.2110 - accuracy: 0.9167\n",
      "Epoch 100/100\n",
      "20/20 [==============================] - 0s 7ms/step - loss: 0.2015 - accuracy: 0.9295\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.callbacks.History at 0x290b2ad00>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeld = keras.Sequential([\n",
    "    keras.layers.Dense(60, input_dim=60, activation=\"relu\"),\n",
    "    keras.layers.Dropout(0.5),\n",
    "    keras.layers.Dense(30, activation=\"relu\"),\n",
    "    keras.layers.Dropout(0.5),\n",
    "    keras.layers.Dense(15, activation=\"relu\"),\n",
    "    keras.layers.Dropout(0.5),\n",
    "    keras.layers.Dense(1, activation=\"sigmoid\")\n",
    "])\n",
    "\n",
    "modeld.compile(loss=\"binary_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"])\n",
    "\n",
    "modeld.fit(X_train, y_train, epochs=100, batch_size=8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "0c774329-18d7-474b-8e4e-25c6347d9099",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 30ms/step - loss: 0.5716 - accuracy: 0.7692\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-18 09:08:43.138467: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[0.5715765953063965, 0.7692307829856873]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "modeld.evaluate(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4cadc8b5-864a-4fc7-8fb0-89dc48684374",
   "metadata": {},
   "source": [
    "#### Training Accuracy is still good but Test Accuracy Improved"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "1f369f31-fde9-455f-bdaf-55405a5c5b67",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 14ms/step\n",
      "[0.04708212 0.9368822  0.9924541  0.17500322 0.99971163 0.9903931\n",
      " 0.62597126 0.99970603 0.16729002 0.99983174]\n",
      "[0. 1. 1. 0. 1. 1. 1. 1. 0. 1.]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2023-01-18 09:11:28.165655: I tensorflow/core/grappler/optimizers/custom_graph_optimizer_registry.cc:113] Plugin optimizer for device_type GPU is enabled.\n"
     ]
    }
   ],
   "source": [
    "y_pred = modeld.predict(X_test).reshape(-1)\n",
    "print(y_pred[:10])\n",
    "\n",
    "# round the values to nearest integer ie 0 to 1\n",
    "y_pred = np.round(y_pred)\n",
    "print(y_pred[:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "3d8a103a-a7a3-423e-acbd-f197546454a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.89      0.63      0.74        27\n",
      "           1       0.70      0.92      0.79        25\n",
      "\n",
      "    accuracy                           0.77        52\n",
      "   macro avg       0.80      0.77      0.77        52\n",
      "weighted avg       0.80      0.77      0.77        52\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "68ba4886-333b-43e6-bbcc-5863dc897b68",
   "metadata": {},
   "outputs": [],
   "source": [
    "# You can see that by using dropout layer, test accuracy increased from 0.73 to 0.77"
   ]
  }
 ],
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