13.성능검증(K교차).ipynb

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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 아래 풀어놓은 데이터 하나로 모아둠\n",
    "# from tensorflow.keras.models import Sequential\n",
    "# from tensorflow.keras.layers import Dense\n",
    "# from sklearn.model_selection import KFold\n",
    "# from sklearn.metrics import accuracy_score\n",
    "# import pandas as pd\n",
    "\n",
    "# df = pd.read_csv('./data/sonar3.csv', header=None)\n",
    "\n",
    "# X=df.iloc[:,0:60]\n",
    "# y=df.iloc[:,60]\n",
    "\n",
    "# model = Sequential()\n",
    "# model.add(Dense(24, input_dim=60, activation='relu'))\n",
    "# model.add(Dense(10, activation = 'relu'))\n",
    "# model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "# model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "# history = model.fit(x_train,y_train,epochs=200, batch_size=10, verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "      <th>5</th>\n",
       "      <th>6</th>\n",
       "      <th>7</th>\n",
       "      <th>8</th>\n",
       "      <th>9</th>\n",
       "      <th>...</th>\n",
       "      <th>51</th>\n",
       "      <th>52</th>\n",
       "      <th>53</th>\n",
       "      <th>54</th>\n",
       "      <th>55</th>\n",
       "      <th>56</th>\n",
       "      <th>57</th>\n",
       "      <th>58</th>\n",
       "      <th>59</th>\n",
       "      <th>60</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0200</td>\n",
       "      <td>0.0371</td>\n",
       "      <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",
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       "      <td>0.2111</td>\n",
       "      <td>...</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",
       "      <td>0</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>0.0084</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",
       "      <td>0</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>0.0232</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",
       "      <td>0</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>0.0121</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",
       "      <td>0</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>0.0031</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",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 61 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   ...      51      52      53      54      55      56      57  \\\n",
       "0  0.2111  ...  0.0027  0.0065  0.0159  0.0072  0.0167  0.0180  0.0084   \n",
       "1  0.2872  ...  0.0084  0.0089  0.0048  0.0094  0.0191  0.0140  0.0049   \n",
       "2  0.6194  ...  0.0232  0.0166  0.0095  0.0180  0.0244  0.0316  0.0164   \n",
       "3  0.1264  ...  0.0121  0.0036  0.0150  0.0085  0.0073  0.0050  0.0044   \n",
       "4  0.4459  ...  0.0031  0.0054  0.0105  0.0110  0.0015  0.0072  0.0048   \n",
       "\n",
       "       58      59  60  \n",
       "0  0.0090  0.0032   0  \n",
       "1  0.0052  0.0044   0  \n",
       "2  0.0095  0.0078   0  \n",
       "3  0.0040  0.0117   0  \n",
       "4  0.0107  0.0094   0  \n",
       "\n",
       "[5 rows x 61 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('./data/sonar3.csv', header=None)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(208, 61)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n",
       "       36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53,\n",
       "       54, 55, 56, 57, 58, 59, 60],\n",
       "      dtype='int64')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "attachments": {
    "image.png": {
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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "60\n",
       "1    111\n",
       "0     97\n",
       "Name: count, dtype: int64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[60].value_counts()"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df.iloc[:,0:60]\n",
    "y = df.iloc[:,60]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "21/21 [==============================] - 1s 2ms/step - loss: 0.6882 - accuracy: 0.5337\n",
      "Epoch 2/200\n",
      "21/21 [==============================] - 0s 961us/step - loss: 0.6785 - accuracy: 0.5577\n",
      "Epoch 3/200\n",
      "21/21 [==============================] - 0s 450us/step - loss: 0.6687 - accuracy: 0.5625\n",
      "Epoch 4/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.6604 - accuracy: 0.5673\n",
      "Epoch 5/200\n",
      "21/21 [==============================] - 0s 626us/step - loss: 0.6516 - accuracy: 0.6010\n",
      "Epoch 6/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.6395 - accuracy: 0.6346\n",
      "Epoch 7/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.6271 - accuracy: 0.6635\n",
      "Epoch 8/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.6154 - accuracy: 0.6346\n",
      "Epoch 9/200\n",
      "21/21 [==============================] - 0s 801us/step - loss: 0.5982 - accuracy: 0.7067\n",
      "Epoch 10/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.5809 - accuracy: 0.7163\n",
      "Epoch 11/200\n",
      "21/21 [==============================] - 0s 969us/step - loss: 0.5659 - accuracy: 0.6923\n",
      "Epoch 12/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.5460 - accuracy: 0.7404\n",
      "Epoch 13/200\n",
      "21/21 [==============================] - 0s 950us/step - loss: 0.5288 - accuracy: 0.7452\n",
      "Epoch 14/200\n",
      "21/21 [==============================] - 0s 674us/step - loss: 0.5112 - accuracy: 0.7837\n",
      "Epoch 15/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.5000 - accuracy: 0.7452\n",
      "Epoch 16/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.4910 - accuracy: 0.7933\n",
      "Epoch 17/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.4725 - accuracy: 0.8077\n",
      "Epoch 18/200\n",
      "21/21 [==============================] - 0s 971us/step - loss: 0.4616 - accuracy: 0.8125\n",
      "Epoch 19/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.4516 - accuracy: 0.7788\n",
      "Epoch 20/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.4456 - accuracy: 0.8221\n",
      "Epoch 21/200\n",
      "21/21 [==============================] - 0s 598us/step - loss: 0.4312 - accuracy: 0.8413\n",
      "Epoch 22/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.4257 - accuracy: 0.7933\n",
      "Epoch 23/200\n",
      "21/21 [==============================] - 0s 984us/step - loss: 0.4253 - accuracy: 0.8221\n",
      "Epoch 24/200\n",
      "21/21 [==============================] - 0s 951us/step - loss: 0.4130 - accuracy: 0.8125\n",
      "Epoch 25/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.4054 - accuracy: 0.8558\n",
      "Epoch 26/200\n",
      "21/21 [==============================] - 0s 963us/step - loss: 0.4031 - accuracy: 0.8269\n",
      "Epoch 27/200\n",
      "21/21 [==============================] - 0s 533us/step - loss: 0.3955 - accuracy: 0.8510\n",
      "Epoch 28/200\n",
      "21/21 [==============================] - 0s 923us/step - loss: 0.3917 - accuracy: 0.8413\n",
      "Epoch 29/200\n",
      "21/21 [==============================] - 0s 945us/step - loss: 0.3828 - accuracy: 0.8365\n",
      "Epoch 30/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.3834 - accuracy: 0.8413\n",
      "Epoch 31/200\n",
      "21/21 [==============================] - 0s 907us/step - loss: 0.3779 - accuracy: 0.8413\n",
      "Epoch 32/200\n",
      "21/21 [==============================] - 0s 967us/step - loss: 0.3736 - accuracy: 0.8269\n",
      "Epoch 33/200\n",
      "21/21 [==============================] - 0s 775us/step - loss: 0.3767 - accuracy: 0.8365\n",
      "Epoch 34/200\n",
      "21/21 [==============================] - 0s 934us/step - loss: 0.3671 - accuracy: 0.8558\n",
      "Epoch 35/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.3587 - accuracy: 0.8558\n",
      "Epoch 36/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.3525 - accuracy: 0.8558\n",
      "Epoch 37/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.3529 - accuracy: 0.8269\n",
      "Epoch 38/200\n",
      "21/21 [==============================] - 0s 917us/step - loss: 0.3431 - accuracy: 0.8654\n",
      "Epoch 39/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.3466 - accuracy: 0.8606\n",
      "Epoch 40/200\n",
      "21/21 [==============================] - 0s 824us/step - loss: 0.3392 - accuracy: 0.8702\n",
      "Epoch 41/200\n",
      "21/21 [==============================] - 0s 933us/step - loss: 0.3299 - accuracy: 0.8606\n",
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      "21/21 [==============================] - 0s 939us/step - loss: 0.3329 - accuracy: 0.8846\n",
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      "21/21 [==============================] - 0s 924us/step - loss: 0.3299 - accuracy: 0.8558\n",
      "Epoch 44/200\n",
      "21/21 [==============================] - 0s 963us/step - loss: 0.3244 - accuracy: 0.8798\n",
      "Epoch 45/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.3257 - accuracy: 0.8558\n",
      "Epoch 46/200\n",
      "21/21 [==============================] - 0s 925us/step - loss: 0.3152 - accuracy: 0.8750\n",
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      "Epoch 48/200\n",
      "21/21 [==============================] - 0s 914us/step - loss: 0.3058 - accuracy: 0.8798\n",
      "Epoch 49/200\n",
      "21/21 [==============================] - 0s 798us/step - loss: 0.2996 - accuracy: 0.8990\n",
      "Epoch 50/200\n",
      "21/21 [==============================] - 0s 937us/step - loss: 0.2952 - accuracy: 0.8558\n",
      "Epoch 51/200\n",
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      "Epoch 52/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.2887 - accuracy: 0.8942\n",
      "Epoch 53/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.2864 - accuracy: 0.8942\n",
      "Epoch 54/200\n",
      "21/21 [==============================] - 0s 925us/step - loss: 0.2841 - accuracy: 0.8894\n",
      "Epoch 55/200\n",
      "21/21 [==============================] - 0s 725us/step - loss: 0.2862 - accuracy: 0.8846\n",
      "Epoch 56/200\n",
      "21/21 [==============================] - 0s 988us/step - loss: 0.2857 - accuracy: 0.8990\n",
      "Epoch 57/200\n",
      "21/21 [==============================] - 0s 887us/step - loss: 0.2746 - accuracy: 0.8798\n",
      "Epoch 58/200\n",
      "21/21 [==============================] - 0s 727us/step - loss: 0.2733 - accuracy: 0.8942\n",
      "Epoch 59/200\n",
      "21/21 [==============================] - 0s 996us/step - loss: 0.2717 - accuracy: 0.9038\n",
      "Epoch 60/200\n",
      "21/21 [==============================] - 0s 985us/step - loss: 0.2719 - accuracy: 0.8798\n",
      "Epoch 61/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.2556 - accuracy: 0.9038\n",
      "Epoch 62/200\n",
      "21/21 [==============================] - 0s 617us/step - loss: 0.2545 - accuracy: 0.9183\n",
      "Epoch 63/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.2542 - accuracy: 0.9038\n",
      "Epoch 64/200\n",
      "21/21 [==============================] - 0s 911us/step - loss: 0.2486 - accuracy: 0.9135\n",
      "Epoch 65/200\n",
      "21/21 [==============================] - 0s 966us/step - loss: 0.2516 - accuracy: 0.9038\n",
      "Epoch 66/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.2510 - accuracy: 0.9087\n",
      "Epoch 67/200\n",
      "21/21 [==============================] - 0s 915us/step - loss: 0.2448 - accuracy: 0.9038\n",
      "Epoch 68/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.2404 - accuracy: 0.9135\n",
      "Epoch 69/200\n",
      "21/21 [==============================] - 0s 893us/step - loss: 0.2315 - accuracy: 0.9231\n",
      "Epoch 70/200\n",
      "21/21 [==============================] - 0s 972us/step - loss: 0.2345 - accuracy: 0.9183\n",
      "Epoch 71/200\n",
      "21/21 [==============================] - 0s 841us/step - loss: 0.2315 - accuracy: 0.9231\n",
      "Epoch 72/200\n",
      "21/21 [==============================] - 0s 819us/step - loss: 0.2336 - accuracy: 0.9038\n",
      "Epoch 73/200\n",
      "21/21 [==============================] - 0s 991us/step - loss: 0.2196 - accuracy: 0.9279\n",
      "Epoch 74/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.2286 - accuracy: 0.9038\n",
      "Epoch 75/200\n",
      "21/21 [==============================] - 0s 878us/step - loss: 0.2326 - accuracy: 0.9279\n",
      "Epoch 76/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.2211 - accuracy: 0.9231\n",
      "Epoch 77/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.2101 - accuracy: 0.9327\n",
      "Epoch 78/200\n",
      "21/21 [==============================] - 0s 996us/step - loss: 0.2250 - accuracy: 0.9183\n",
      "Epoch 79/200\n",
      "21/21 [==============================] - 0s 731us/step - loss: 0.2141 - accuracy: 0.9231\n",
      "Epoch 80/200\n",
      "21/21 [==============================] - 0s 997us/step - loss: 0.2043 - accuracy: 0.9423\n",
      "Epoch 81/200\n",
      "21/21 [==============================] - 0s 856us/step - loss: 0.1991 - accuracy: 0.9375\n",
      "Epoch 82/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.2109 - accuracy: 0.9231\n",
      "Epoch 83/200\n",
      "21/21 [==============================] - 0s 442us/step - loss: 0.1988 - accuracy: 0.9519\n",
      "Epoch 84/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.1992 - accuracy: 0.9423\n",
      "Epoch 85/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1883 - accuracy: 0.9375\n",
      "Epoch 86/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1842 - accuracy: 0.9519\n",
      "Epoch 87/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1824 - accuracy: 0.9471\n",
      "Epoch 88/200\n",
      "21/21 [==============================] - 0s 950us/step - loss: 0.1840 - accuracy: 0.9519\n",
      "Epoch 89/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1813 - accuracy: 0.9375\n",
      "Epoch 90/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1807 - accuracy: 0.9375\n",
      "Epoch 91/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1724 - accuracy: 0.9519\n",
      "Epoch 92/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1668 - accuracy: 0.9423\n",
      "Epoch 93/200\n",
      "21/21 [==============================] - 0s 865us/step - loss: 0.1641 - accuracy: 0.9567\n",
      "Epoch 94/200\n",
      "21/21 [==============================] - 0s 983us/step - loss: 0.1646 - accuracy: 0.9615\n",
      "Epoch 95/200\n",
      "21/21 [==============================] - 0s 676us/step - loss: 0.1601 - accuracy: 0.9519\n",
      "Epoch 96/200\n",
      "21/21 [==============================] - 0s 781us/step - loss: 0.1607 - accuracy: 0.9519\n",
      "Epoch 97/200\n",
      "21/21 [==============================] - 0s 979us/step - loss: 0.1508 - accuracy: 0.9567\n",
      "Epoch 98/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.1548 - accuracy: 0.9519\n",
      "Epoch 99/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1560 - accuracy: 0.9663\n",
      "Epoch 100/200\n",
      "21/21 [==============================] - 0s 849us/step - loss: 0.1490 - accuracy: 0.9567\n",
      "Epoch 101/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1441 - accuracy: 0.9760\n",
      "Epoch 102/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1451 - accuracy: 0.9615\n",
      "Epoch 103/200\n",
      "21/21 [==============================] - 0s 391us/step - loss: 0.1405 - accuracy: 0.9519\n",
      "Epoch 104/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1365 - accuracy: 0.9760\n",
      "Epoch 105/200\n",
      "21/21 [==============================] - 0s 910us/step - loss: 0.1400 - accuracy: 0.9615\n",
      "Epoch 106/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1309 - accuracy: 0.9712\n",
      "Epoch 107/200\n",
      "21/21 [==============================] - 0s 989us/step - loss: 0.1368 - accuracy: 0.9567\n",
      "Epoch 108/200\n",
      "21/21 [==============================] - 0s 297us/step - loss: 0.1240 - accuracy: 0.9712\n",
      "Epoch 109/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1257 - accuracy: 0.9760\n",
      "Epoch 110/200\n",
      "21/21 [==============================] - 0s 899us/step - loss: 0.1253 - accuracy: 0.9663\n",
      "Epoch 111/200\n",
      "21/21 [==============================] - 0s 459us/step - loss: 0.1446 - accuracy: 0.9471\n",
      "Epoch 112/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1199 - accuracy: 0.9663\n",
      "Epoch 113/200\n",
      "21/21 [==============================] - 0s 781us/step - loss: 0.1138 - accuracy: 0.9760\n",
      "Epoch 114/200\n",
      "21/21 [==============================] - 0s 956us/step - loss: 0.1130 - accuracy: 0.9712\n",
      "Epoch 115/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.1151 - accuracy: 0.9760\n",
      "Epoch 116/200\n",
      "21/21 [==============================] - 0s 872us/step - loss: 0.1105 - accuracy: 0.9760\n",
      "Epoch 117/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.1058 - accuracy: 0.9808\n",
      "Epoch 118/200\n",
      "21/21 [==============================] - 0s 890us/step - loss: 0.1058 - accuracy: 0.9808\n",
      "Epoch 119/200\n",
      "21/21 [==============================] - 0s 941us/step - loss: 0.1004 - accuracy: 0.9760\n",
      "Epoch 120/200\n",
      "21/21 [==============================] - 0s 418us/step - loss: 0.1089 - accuracy: 0.9808\n",
      "Epoch 121/200\n",
      "21/21 [==============================] - 0s 726us/step - loss: 0.1002 - accuracy: 0.9856\n",
      "Epoch 122/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0960 - accuracy: 0.9808\n",
      "Epoch 123/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0974 - accuracy: 0.9760\n",
      "Epoch 124/200\n",
      "21/21 [==============================] - 0s 967us/step - loss: 0.0902 - accuracy: 0.9808\n",
      "Epoch 125/200\n",
      "21/21 [==============================] - 0s 999us/step - loss: 0.0886 - accuracy: 0.9808\n",
      "Epoch 126/200\n",
      "21/21 [==============================] - 0s 956us/step - loss: 0.0864 - accuracy: 0.9808\n",
      "Epoch 127/200\n",
      "21/21 [==============================] - 0s 503us/step - loss: 0.0869 - accuracy: 0.9808\n",
      "Epoch 128/200\n",
      "21/21 [==============================] - 0s 781us/step - loss: 0.0868 - accuracy: 0.9856\n",
      "Epoch 129/200\n",
      "21/21 [==============================] - 0s 729us/step - loss: 0.0829 - accuracy: 0.9856\n",
      "Epoch 130/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0804 - accuracy: 0.9856\n",
      "Epoch 131/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0782 - accuracy: 0.9856\n",
      "Epoch 132/200\n",
      "21/21 [==============================] - 0s 963us/step - loss: 0.0775 - accuracy: 0.9856\n",
      "Epoch 133/200\n",
      "21/21 [==============================] - 0s 979us/step - loss: 0.0737 - accuracy: 0.9856\n",
      "Epoch 134/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0733 - accuracy: 0.9856\n",
      "Epoch 135/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.0734 - accuracy: 0.9856\n",
      "Epoch 136/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.0692 - accuracy: 0.9856\n",
      "Epoch 137/200\n",
      "21/21 [==============================] - 0s 996us/step - loss: 0.0736 - accuracy: 0.9856\n",
      "Epoch 138/200\n",
      "21/21 [==============================] - 0s 329us/step - loss: 0.0671 - accuracy: 0.9904\n",
      "Epoch 139/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.0751 - accuracy: 0.9952\n",
      "Epoch 140/200\n",
      "21/21 [==============================] - 0s 900us/step - loss: 0.0642 - accuracy: 0.9856\n",
      "Epoch 141/200\n",
      "21/21 [==============================] - 0s 982us/step - loss: 0.0661 - accuracy: 0.9904\n",
      "Epoch 142/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0667 - accuracy: 0.9856\n",
      "Epoch 143/200\n",
      "21/21 [==============================] - 0s 912us/step - loss: 0.0614 - accuracy: 0.9904\n",
      "Epoch 144/200\n",
      "21/21 [==============================] - 0s 699us/step - loss: 0.0629 - accuracy: 0.9856\n",
      "Epoch 145/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0583 - accuracy: 0.9904\n",
      "Epoch 146/200\n",
      "21/21 [==============================] - 0s 996us/step - loss: 0.0576 - accuracy: 0.9904\n",
      "Epoch 147/200\n",
      "21/21 [==============================] - 0s 919us/step - loss: 0.0575 - accuracy: 0.9952\n",
      "Epoch 148/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0678 - accuracy: 0.9808\n",
      "Epoch 149/200\n",
      "21/21 [==============================] - 0s 960us/step - loss: 0.0536 - accuracy: 0.9904\n",
      "Epoch 150/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0581 - accuracy: 0.9904\n",
      "Epoch 151/200\n",
      "21/21 [==============================] - 0s 926us/step - loss: 0.0514 - accuracy: 0.9952\n",
      "Epoch 152/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0507 - accuracy: 0.9952\n",
      "Epoch 153/200\n",
      "21/21 [==============================] - 0s 781us/step - loss: 0.0592 - accuracy: 0.9904\n",
      "Epoch 154/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.0504 - accuracy: 0.9904\n",
      "Epoch 155/200\n",
      "21/21 [==============================] - 0s 781us/step - loss: 0.0507 - accuracy: 0.9904\n",
      "Epoch 156/200\n",
      "21/21 [==============================] - 0s 783us/step - loss: 0.0465 - accuracy: 0.9952\n",
      "Epoch 157/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.0472 - accuracy: 0.9952\n",
      "Epoch 158/200\n",
      "21/21 [==============================] - 0s 969us/step - loss: 0.0531 - accuracy: 0.9904\n",
      "Epoch 159/200\n",
      "21/21 [==============================] - 0s 833us/step - loss: 0.0451 - accuracy: 0.9952\n",
      "Epoch 160/200\n",
      "21/21 [==============================] - 0s 783us/step - loss: 0.0404 - accuracy: 0.9952\n",
      "Epoch 161/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.0413 - accuracy: 0.9904\n",
      "Epoch 162/200\n",
      "21/21 [==============================] - 0s 847us/step - loss: 0.0410 - accuracy: 0.9952\n",
      "Epoch 163/200\n",
      "21/21 [==============================] - 0s 783us/step - loss: 0.0398 - accuracy: 0.9904\n",
      "Epoch 164/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.0394 - accuracy: 0.9952\n",
      "Epoch 165/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0415 - accuracy: 0.9904\n",
      "Epoch 166/200\n",
      "21/21 [==============================] - 0s 283us/step - loss: 0.0400 - accuracy: 0.9952\n",
      "Epoch 167/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0362 - accuracy: 1.0000\n",
      "Epoch 168/200\n",
      "21/21 [==============================] - 0s 953us/step - loss: 0.0350 - accuracy: 1.0000\n",
      "Epoch 169/200\n",
      "21/21 [==============================] - 0s 752us/step - loss: 0.0360 - accuracy: 0.9952\n",
      "Epoch 170/200\n",
      "21/21 [==============================] - 0s 781us/step - loss: 0.0327 - accuracy: 1.0000\n",
      "Epoch 171/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0362 - accuracy: 0.9952\n",
      "Epoch 172/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.0352 - accuracy: 1.0000\n",
      "Epoch 173/200\n",
      "21/21 [==============================] - 0s 201us/step - loss: 0.0317 - accuracy: 1.0000\n",
      "Epoch 174/200\n",
      "21/21 [==============================] - 0s 547us/step - loss: 0.0376 - accuracy: 0.9952\n",
      "Epoch 175/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.0390 - accuracy: 0.9952\n",
      "Epoch 176/200\n",
      "21/21 [==============================] - 0s 910us/step - loss: 0.0327 - accuracy: 1.0000\n",
      "Epoch 177/200\n",
      "21/21 [==============================] - 0s 781us/step - loss: 0.0298 - accuracy: 1.0000\n",
      "Epoch 178/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.0311 - accuracy: 0.9904\n",
      "Epoch 179/200\n",
      "21/21 [==============================] - 0s 781us/step - loss: 0.0282 - accuracy: 1.0000\n",
      "Epoch 180/200\n",
      "21/21 [==============================] - 0s 782us/step - loss: 0.0274 - accuracy: 1.0000\n",
      "Epoch 181/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0295 - accuracy: 0.9952\n",
      "Epoch 182/200\n",
      "21/21 [==============================] - 0s 950us/step - loss: 0.0280 - accuracy: 1.0000\n",
      "Epoch 183/200\n",
      "21/21 [==============================] - 0s 924us/step - loss: 0.0302 - accuracy: 1.0000\n",
      "Epoch 184/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0285 - accuracy: 0.9952\n",
      "Epoch 185/200\n",
      "21/21 [==============================] - 0s 977us/step - loss: 0.0275 - accuracy: 1.0000\n",
      "Epoch 186/200\n",
      "21/21 [==============================] - 0s 946us/step - loss: 0.0247 - accuracy: 1.0000\n",
      "Epoch 187/200\n",
      "21/21 [==============================] - 0s 899us/step - loss: 0.0239 - accuracy: 1.0000\n",
      "Epoch 188/200\n",
      "21/21 [==============================] - 0s 950us/step - loss: 0.0228 - accuracy: 1.0000\n",
      "Epoch 189/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0244 - accuracy: 1.0000\n",
      "Epoch 190/200\n",
      "21/21 [==============================] - 0s 945us/step - loss: 0.0245 - accuracy: 1.0000\n",
      "Epoch 191/200\n",
      "21/21 [==============================] - 0s 991us/step - loss: 0.0230 - accuracy: 1.0000\n",
      "Epoch 192/200\n",
      "21/21 [==============================] - 0s 938us/step - loss: 0.0215 - accuracy: 1.0000\n",
      "Epoch 193/200\n",
      "21/21 [==============================] - 0s 900us/step - loss: 0.0206 - accuracy: 1.0000\n",
      "Epoch 194/200\n",
      "21/21 [==============================] - 0s 600us/step - loss: 0.0214 - accuracy: 1.0000\n",
      "Epoch 195/200\n",
      "21/21 [==============================] - 0s 2ms/step - loss: 0.0213 - accuracy: 1.0000\n",
      "Epoch 196/200\n",
      "21/21 [==============================] - 0s 945us/step - loss: 0.0200 - accuracy: 1.0000\n",
      "Epoch 197/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0198 - accuracy: 1.0000\n",
      "Epoch 198/200\n",
      "21/21 [==============================] - 0s 958us/step - loss: 0.0207 - accuracy: 1.0000\n",
      "Epoch 199/200\n",
      "21/21 [==============================] - 0s 935us/step - loss: 0.0203 - accuracy: 1.0000\n",
      "Epoch 200/200\n",
      "21/21 [==============================] - 0s 1ms/step - loss: 0.0212 - accuracy: 1.0000\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(24, input_dim=60, activation = 'relu'))\n",
    "model.add(Dense(10, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "history=model.fit(X,y,epochs=200, batch_size=10)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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jUhnhuZ45VC471pJfq9eqN6dnrhaSH0rNZekDADgQl7euB8j66dE2P6nRuWp1tNbZ2n+XRuvKje/JjcekMsJzPXMIHVebrzU/1b81Q/Tm9MxlZa1pzRoAAHPh97RuSfiplPwZf0KEeclz1ft8rfW8h7kjvHIAAFjbwf54QOqbsfWckPMjwszSHHo+91jlMkRr/xTpPyKsfcRITst6RXyPUvcsPJe6XpLrr+fj6y35tWyrkRzpY9VSk0jNbz0HADhQlz9vPVDynw7DlrL2f17syU+NKeW09u/hneetp754TO0+tsxh6WuZP6XUz6vGlpxQ77gUyQpbiud8AIC5HfSPB8gnMGFbi3zak/vUyWteyUnNwSdNV8x2Dyz19NTs9Xzvw+tG6gtbyuxrAAD4Ofp/XKBGN4q1b449m4BcdilLx6hSv9Z6YvFcypKbG5tSy7Nmta43vEc6R3wswnOtc6Sk1mPJtcy/zT4pveNSJMvCaz4AwNyO6lde5Vi+gVvs+pt8aDS3NN6zZq+snhwZo2RsfCzCXI9acxm1bOvc28rJ6R2X47UeAMD+O/pPWi3f9Lz6xGSMWOOb7sg387XWm2Kdy2Lt+zi65tr41HVdu3Xe3BxeOTW943Jqed7zAQDmxa+82hH9ZitNHktDmm5K9H7l2iHyWptXjr5Wc63WBwCAXnzSevqNtPbN3PrN1rIp0KxU39K1VpZ1lZTGj2aHrFmec/byqCGX4bW+2XJGSR01M9QJAFgfm1bDN2ePPvrN1/IN1jJfjVdGymhuyFpnrpaYZ20xj3sqUmvxqtuzxjXvpdUsdQAAdo/fHgAAAIDp8TOtAAAAmB6bVgAAAEyPTSsAAACmx6YVAAAA02PTCgAAgOmxaQUAAMD02LQCAABgemxaAQAAMD02rQAAAJgem1YAAABMj00rAAAApnfm5NTmMTrde/fdl/889/TTl//05pU/W87aRurc5ho955rpOV77Hu5yrTpGrbXGbdqH+xDPp7zmne0e9NSDK9a8d17Zs+XkzPQ6PNhNq95kC48nQuZb8wn1yt9FTu65GK0jzk3ljazX615ZjNYpdHxvlldOyCOjJJeva8mJx6xdp4fcmjzr3of70GrfXwuH8pzUnodQ73p1Dh0/eu/ivJDX8zJbTk5Lvt63mEd9B/1Jq8eTmLr5qczWuXJPqoqzcvneOdY15HJipX7WjBG9c4zWJuNL4myP+XT8SFZrjvQRuX69tWiu8srP9bfkxDXl9Ky3plRf7Zqw1jTrfVhjztxat3kP4pzeelI0u2dsyLpWVZqvdy0twjk85stllM6XxGNqNabyWurJ8a5Tlfq11pgkm9ZDdc9dd5mah56cljGt+bn+pRzverz6hKS/panwsVUqp0fL+FLfsJ6whcLj+FooN15Zc0KlftaMUGpMLqc1X/vLn3Eb5ZGRYskt9Wmpa+b7kNM7n46TP+M2ypKR6mM9Z+WxFjFTjmRoSwnPj85XGt97LTZao+rJyY1Jnbfke/UpmeZ/xPrW8/cvF546c03zILv6WsP+SD1/qdZD3gXqO0Ftem6Xwnq09UitbTZaY6y1Xumbaiq8D6n5Wmj2aM6+2/Z9sM6n/eKm9DWgbYRmW2pK9ZFzYW24lt43bdyr4zPFplU2qG+88tTm6Pvk/Gtf++jmaLvki6HWUFb7S0WuSZ9e4XNRmyfXJ7ymfxGG9Fxu/C6FtddIn9TaRteUyp2B1iR/xs2D3nddvzQ99qbZOVrDLuiatYY174MozSctRe+N9g+bh3B+zS3Vc+zC+5VrKXI+fs70Xq9B5yvlyzVtI1I54blc27ba3Jb7FT+HrXb+M62v/ulPL29++7nNUdoPvf+J5W23PLw5sivdvNDoTRQ9T4ZlTLiGlvxcdmnOteoJx4Ra1hNL1Wo9ty0tc7fWGfcPj1NZufzWnFhqfKyWESrNmbrW2l+11hn3t/Qt9emRqlnU5indBxVmz3YfrLWJ3JxyPjc2XpOwrEtZ+rbWE14r9a0ZGRvyyhmRq6F0v3rrLmWqUrZlXumjemqMWeaM5cakzrfkh2sLeaxzpz/TeunCEycvPblU28Vn7tiM2A35GQxtOT0/p5Ebk5rP0jduues5uWupsaWcbUjNbz23LZa5pU+qlaSuh+dq10NyPm4qfJwTj4lZMmKpMbkcr7l76twXpXunTR3qfWhZ17buQWoe6zkrr7Vs656U5GqQ83FT4WOr3Jj4fCm7lKFNlXJUalzMkhPLjUmd78lfw04/aZX/9P/63/zO5ijvhrfesrz75y5sjrYrfnfR8s6kpuXdSCm/Ze5aTkprPduQmt96TuTWamVZu2VuvZ7rm5LqG56rXQ/F52s5IUvfWkaOjAvlMkr5cUZKrf5DkLsPqfXOdB8sz19JuI5ZXwtxXd7PScs9LM0xktMyNkXzcvchPh8e58bk1Ppbs+VaSqp/y5wi17+Wk5KrU8RZPflrOLhNa+lJsOh9caz9hJbyW+b2qjOXI+dHtNQWz5Wrx2O9PXL3YqTOsF9u/amsXH58Ps5PjVGlsaqWMYtcnXJ+RO/adznvTPehJFfrqNnugfc617pvays9L+H58Lhlra33pbV/Tikndy113queVjLviOaaZdO6K7P/eMAsH51Ldim/ZW6vOtdcryePOiVj7fVa83P9wvOWPiLVz5IjRuawkrG1Gkbysd+O5bn3Xuc+37e49tRawnNrrtUru5STu5Y6v8ZaJTNsMzjo/xFr1Lbe5eg7ldL4Un7L3K11am2h3nXOyHLvRet9a2XJL/UJr9X6qVQfS04pX1gyrFrmypE+FiN17oN9vg9ete/LPYjr9KxHsnexPuu9F6X6wpxUv3B9HmtN1d2T2ZOTqz91vnetWlfP2F2Y4l/EKv1O1re/99Hlnbd/YXPUJvUiSSk9WfELIffC6H3BKEutufzS3Jqr11vqzPW1ZMTzrkHnqLHWWtO7Fsu9qN3Tluu1viW1nNbskVqEjK8ZyVctdZZq8qglpHN55+bMeh8sWmovWfselMaocKzXupR3nrfR+sLxnlmh1tyRnLiPV02h1BwluflFbw1msmmdwTefu++6HwvYBstH3paPx0c/Oq+Nr82da7HUuZRaP0uOda41edU5upbe8TKutb6RWr1y1GhGbbxHjcKaM1KPXOupt2dML+tctX6l63KtdU06ptY8WHNq/bZdj5V3npXMa20jwvEjWbWx1myPHOmjLceSkzMyNuSVUzLNv4j1rrNPLu+57+SaNgt556BtRmF9ccN+43kEvv/pjX495BqufOJVarU+KnWtpcX0+Ymfs1TDtXZ1b2rP6U5sNq9Ha5Z3GDJe2i9++MMnn/i1X0see5A8q9ycUo+FV80jLDVIH0sbMTq+hddcHjke903ag/fdd/L4pz+dPPYgeRa1+Uavp3it0eI//fZvbx6VHfJ9sM6zxhpTtrXubdj2Wkbny41vzfXKKRnJisfKcSqvNsdIDVZT/EzrLsm7B493L005588vy223XXn86qvL8tJLy6e+9KXlM5/97NXj5ezZpuvW4/PPPLPcdtNNTf3f8vLLy++9+OJy8eLF5ZOPPGIe/0sPP3y5fzw+dfzjp32vjr/xxmW5+eYrx4Msz4tXnxIZb7H112KBR05TRvh1cenSsjz//PKfX3hh+Q+/8RtXj5ef/dmm69bj//Xss8tPvvmmqf9D99+//Ppjjy1v+4u/WM595zvL//zzP796fOuv/Mp1/fX43gcfrN+L8B6IP/qj5ctnziz/5YtfXB755V9e3vf1ry9ff9/7sscfkr/O77lnM/jU6fiW4/99+nfMv/nEJzZHpwr9v/zHf5yt50M///OX+1w3/nR99378412vq219DbW8Zks1jdahPL4OZ2F9DoXHmj3uXarmnkyvnJyRtca1aU7ufE5qjTm9tR79pnUnHnhgWU6/yS233rosDz20LDfcsCyPP86xHp8757ZpxR752MeW5QMfWJY777zyNSI+8pHDOf7856+8xkv+8A+X5StfWZbPfW5ZfvVXr7wB/LEfO5zjn/qpZTndvANAF9m0Ystef/3k5M47T05+5mdOTv7qrziOj3Gcvvvdk5MHHzw5OXv25OTZZw/v2Opznzs5uf32K38e4jEAdGLTuiuyUfvrv94cnOL42mMcJ9novfji5uDUoR1bffWrmwcbh3YMAB348QAAAABMb5pfeQUAAADksGkFAADA9Ni0AgAAYHpHvWmV3ynW8nvF1jZbPTn7UicAADgcR/M/YukmK/6FtnK+9Zfc5rI89NQjWjaRHnX31gkAANDj4DatpQ1laqM1sklcY9M2kutRk2SEcnlrrR8AACDl4H48YMaNlGzwcm0mUo/cv7DNViMAADhOB/njAbr5iuU2YKm+JZqvea3jVarOXO0WufXFUvmleb3rBAAAaHWwm9ac0c2X5wbOM0usNda7TgAAgFYH+9sDZEMVtxGySctt1OScXAMAAMA6+D2tFeFmtbTxbd245jbAo7TeWovl6pdza9QJAADQgp9pPbWLTZm1xm3XZp0/Vz8AAMAa+JnWHWy+SnOyGQQAALjeQW5aW+Q2iaWNr0XvprR107pGnZpZWoNoqRMAAGAEm9bGTaLqGWcZ01tPzlrr864TAACg5KD/RyzZWEmbwT5u8vT+pRoAAMA2HfSm1bJJXHsjqZu8ffxUUmrONQAAgG3iV16tjE0eAADAODatyAp/HCBuAAAA23Tw/yOWdYPV+mmo5K7xCap3bm9ebdxa6wcAAEg5+t8eAAAAgPnx4wEAAACYHptWAAAATI9NKwAAAKbHphUAAADTY9MKAACA6bFpBQAAwPTYtAIAAGB6bFoBAAAwPTatAAAAmB6bVgAAAEyPTSsAAACmd+bk1Obx0bn37rsv/3nu6acv/9lrFznaN2e0lhKv9QqPLM969sHa6/XKny1HrHnvDvm+pezL87IvZruf+/T6wfE46k2rkC8cr78ktpXj1WeEtQalffVcONaj1lpGal5P4VprPGrwuGclXvmWnPjepfqP1KP5Ot5rbSle2bPl5Izky1ih43uy4ow16Bw1HjX03s/UfejNCo1kxPctleNRI47LwW1a4y+UWPwFkvui8c6xfmHmckJefWJaa0qc1ZOv4rGlrFRNqb6WekZqtvDIj9dbui+tc8XZsTCvlF/Kicf01JkymhOO96opZZb1qt4cGRfKZYzWGY4fqXWkhlHW+aVfKDWmllXKiMda6yrxyChZOx+H5yA/aW35Qmj9osn1L+V41+PVxyKXUzpfImPisR61WjI85ikZzU+Nz2V6ryXOa81vrVPOW+jYWj1hXq2OWlZOXLNk6DlLdjw+Fo7bRk5Oakwup6fOXH2lrJLecR6sc6f6Wc9ZxWNLWXItVOrXUk+cm6OZrfnAIpvWGXzzuftOXnpyuab1uueuuzaP6lr6ilz/Us4a9Ui/UvOSyxqZIx7rUa8lw2OeEs97Ekpd815LnNean+vvVWcpx1J7eK6lJumbGpvLCx+3iMftKqfUP3VtND88bs1SMs7SvFkzS/3iayN1WrNS51v6elo7H4dnit8ecOGpM8sbrzy1Ofo+Of/a1z66OUJI3p2WmofRd8EyXlusdE2E13OtRyon1XqlsuJ2aGRN+jrpWat1TKqPPI5fo3Jcymnh9bVUklpDD6+cVjKvtl2QNVuaF12rZG5r3TpPrllJ39S90LV4CusrZVv6AFdtNq87c/GZO677hDVuly48seltY3n3Jn20tcj1L+VY5tBaLH23ZWRN8fXwuHStl0fGGqx1lfqlro2uNx5fO87Rfrn+tZzUdes5YT0fHufG5KTG5vJas0VqzK5ySv3XyA+PW7O2TerL1Vi7lhNfK/WtsWS11CLkXNisclmxlkxA7PST1r//xh8sb377uc1R3ut/+7ubR2PCd3S1d99h37Cp3PkW4fjeeqytlYxZ4913j5F1HLJd3xeZV1+zs7xWQp73J5XjkS3jS1/3Vl45uefRI19z5U9tvcKMnmYVjpH15+6BXktl585rZiutJ0Wv5a730LX11Aq422xed+LbX300+clq3F7+b7dsRtjou8K4peTOi9K1WC0n1VJy57cpriFVU63OUoYlX1n7yvmwbUs8b2sLxccidU7kzluF43Pzhi2WOifi87l+ypptnU/F58Pj3Jic1NhcnjwOW0mpT21syCsnpJmlbNGSn+obnmvJ2keyvrCl1O5BeD3uWzsWpfzW/iXWrN58HK+D3LS2KH3RtHxBeX3x7fqLODd/fN5Sp/TRFrJmtZwv9R1p22adf7S2cHwqK5cv51vmtvTVzFL26PnwODcmJzU2l2fNro3Zds7acnXU6j82pXuQula6f7X+sdb+NTI2bCkj+ThOO/2VV/LjAX/3wi9sjvJ+4AfvWH74g3+2OfKj/wkl95895Lr1P4m09C2p5bT8Z5+Wemr3Iua1XpHLajnvWc8+GF1vabxcE/tyP+O1pNYWniutPSU1NpdnyU71ic9tM6eVZIYs+bk6wvM9tca1lLRkt+SmtK5Dle5B6lp4Lr6ey6rlhOR8KNUH2Kad/57WV//0p6s/1/pD739iedstD2+O7PQLrvSFlvtiFaVrsZa+Iv7LQHj+hdBaT6ue/NY15+ZInV97vS1S6xSe9Y2uNx4f1iznLfm5dcasObl+tesirj8Wrid8bJEam8urZZeu7yInR8aJnrGxtWu18M72zJOskOVeqfBcfL1U48icVvEcOb35OE5T/OMC8qutct7+3keXd97+hc1RO8sXTs8XrObq9ZYv7lzfloyakax4bSmt+b1rjq+P5IhSHw+lOmo1Kkut1iylmTlxVmt+TsuaPdcbC8e3Zkn/kIxNnRO5bO1vnXftHIt4rM6dk6u3NH94faTWGu9sa570qwlzarnh9bhv7bjHaEZtvEeNOC7T/ItY33r+/ut+V+t77hsvbeSLRq7lxGOsX3wj9YhSTTFLPTnW9Vh4rjnXz1Kv55pSPGtYu9Yar/lb1lszUk9Yx5r3dtv3rWYkZ3SsqI0P5+iZT+ex6F1Lysi9KbHk6prjfvFYjxpHM2rjPWrEcZniHxcQ7zr75OVNath2Tb6Ycm1XdO64nlQ7FIe4JlwvfJ7jhnnIRiNsKdt63nQOna/UDsWhrQdoMc2mdU3yF+svPfzw8smPf/zqX7R67OXmd7xj86hM/rLJ/UUv9RziX0alNcv5Q1lzaZ3ikNa6Brk/uTaK+z5Onwu5l2EbeX54XvzMei/1dZNqQKtpfjzAxfnzy3LbbVcev/rqsrz00vKpL31p+cxnP3v1eDl7tum69fj8M88st910U1P/t7z88vJ7L764XLx4cfnkI4+Yx1/ecJ/2j8enjn/8tO/V8TfeeLq7vvnKcYX1L5SWvyhTmV5/0Up2LavlL8mRunLzjN6rFK/7F7PcTwtrTq2fVz3CMyu27ftWM5IjY0Oakzs/qrfWuJ6SXdda45nrkTWasdZ9wvE6rE3rAw8sy2OPLcutty7LQw8tyw03LMvjj3Osx+fOmTetAAAAMzmsTeulS1c2rvLnF7+4LD/6oxyHx/opNAAAwJ45rE2rkA2a/Kdw+XSR4+uPAQAA9tDhbVoBAABwcI7itwcAAABgv7FpBQAAwPTYtAIAAGB6bFoBAAAwPTatAAAAmB6bVgAAAEyPTSsAAACmx6YVAAAA02PTCgAAgOmxaQUAAMD02LQCAABgemxaAQAAMD02rQAAAJgem1YAAABMj00rAAAApsemFQAAANNj0woAAIDJLcs/Azvp3S4GDnBpAAAAAElFTkSuQmCC"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "|![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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bOwAAAIATweYOAAAA4ESwuQMAAAA4EWzuAAAAAE4EmzsAAACAE8HmDgAAAOBEsLkDAAAAOJGH2NzRnwTK/ixQZibHivrajpxdR/VwC2sHAAC4VQ+xuRv9e45yEzHzNyF3/D3JLOfsBojjZQ59Xu1BH7N23E8AAICzOMWfH5MbBu8LP83RY9FGg+fKOCuHxPmsOVmsVOmLVPqp1qzSOb0aUe1sLMJx1RzeHAAAgLO6+3fu+Is8H9nmQJJx8nzUTKzEfejjGuj+ecc16PXq8wz1KWOu1TcAAMCtOHRz96sff/fpvWefeu7o4C/kEr6gP4/uhXdYeFPEn/NH/ryK5ls16ejmqrJycx8AAACP4rDN3c++/8bTz9/9xuXst2iD94u//JPL2XWs2nBce+OSofnR4ZG16KPeHNE5HxFZSx4VsgcAAACoO2RzR+/YffT+s8vZiz78wbcvn42TG5BsEyKt2lSM1r8H3kZNr7l6VNC87HFZ8bgBAADcu0N+oYLetYs2d+RzX3nz6aWXv3U5s3lf8K3r2Vz9kdG5ZOUgXjzzrldEOSWuP0Lnl3nkmO7F622GldOqK3k97OgPAADgptHm7tr+6j//wW9+9PZTeHzw3795me177dVXL589z7peucbn8rr3ueTlkbzYTBQ3mnNGZa2zKjUqdXf0BgAAcOsO+bHs3/77v3v5zFeZQ+/I6Hdwqu/UWO8E8bmV12PV68RHrNxHq/RDfWeHh8ZWrHlVHgAAgHtzyI9l6f+5s36ZQvrS6/W25GbB+4IefbGvjul5URyJYjOV+dU5lmovHC/nW9c6vL6t6525UjYOAABwVof9I8bR/3f3hVfeevrMF79+OVtjxSZhZsNQjaV5pDq32rvWWYs1N8udqdRe1T8AAMAjOeyfQvmd33/7402cRu/Yrd7YRW5tg0D93FJPvImSGzbrmsT981qso6I6z+L1BgAAcHaH/iPGtImjzZw84HbwJo7QRzq3rgEAAMDtOMXflq2Qm5JRMzlW1NcqOb3NVxTHMXqOV8+67tVlWd8ZrxcAAIBH9zCbOwAAAIBHcOiPZQEAAABgLWzuAAAAAE4EmzsAAACAE8HmDgAAAOBEsLkDAAAAOBFs7gAAAABOBJs7AAAAgBPB5g4AAADgRLC5AwAAADgRbO4AAAAATuQh/vyY97dSr2W2/ki8/tuuOvboe+IZ7WvFeijHLd2PW3iMVveQ5eNxJufdwv2YEa1t1Mz9JDvu6a0+TqN9rVgP5ZiNJ7dyT3f0c2trvHcP87dlo/+4+EkVmX3CRfUrZuMtnZx8j1b3QHTu0bXO3qMonnuM7Lo3WV6vt04cz+VrMrbSA/H6ICP5LDOxmah/tqu25Rr3k3TiV/bk5ZpZi8Y1OGdnrdJoHBuJn+mdYyMz6yHdNcmevLgoJ8fP9r3KNe7xjLvf3EU3WN7Y6EmTqTzhPBzn5Vjdf7UfUs3JuvOZ15PXi1dH5vHG9XWvNvN66BqNHVmTFI3P9CTjRvMQK9bKX0ExUS+VPKPrIFHtSNZXJ6fVg3ctIuePrkvSObKc0XinH5pr8Xrxcss83ri+7tVmXg8a58nqRjks3fkS90Sivqv59VwvNsvZqVlB+TLX7Gcp2tyd0Wuvvnr57BP6vKMaG9Xs1o9yrdLJyXPp40gvWYwct+bqa5U5zLuuRfNoLDu6dIyVI8sbjY/0RCp9VXhx1Z6teVkv2XiEYrNjpW4+b/5sXyvWpXNkOaPxbj+dWtZcfa0yh3nXtUp8Vrdai3XnMx3n5anm78RnOas1O6KcR/SzyqG/UPGrH3/36b1nn3ruWOGmd9MFK/qnHPLwZHN4jPuhj3R483eQ9dm1e9Drt46OVWviGOvwerLm8rGKzFWtQWOy55H7MYNr00fvWIHvQyefvA8cz0ekM3cU591dZwfqVT8OdH6NNejaO+pSPu+w0PWj7keVXIM+QLhs8q7up++8/psfvf1kHh/+8M3LrDHWbnp0h12Ny2p26me5MtX4KCeNZTWzcamTS8/1YkfnebJ5NB4dHd58fb2S15rT7Uca6UGS8zu9VebSuTw061qHzq+PGTpHNaeO0bwc1bl0TR4d3vxKHllTHl1ZjBzXc73Y0Xkea17lmjyv1prh1aj0GtFzO3V2i2pm/dB4dBzpkHfu6B27j95/djl70Yc/+Pblsz7avevvPDx6128d1rwKmlftQxqNW416yPqY7bN7T2fIWrKedc3D98Q77k1n7VWUS94L+ryan+fKw7qvdI2P1WRu6+jSa5E5+JzGPPoeZPNHcS+yVkb31iVrymMF6o2P3WQtWc+6VjEat5vsq9sbPa4ydtXjfDT5vLWOIx3yCxU/+/4b4eaOfO4rbz699PK3Lmc1/ISzburOJ5SVW1/j3pjXI1nRf7Wevq7jqrLesv7luJ7rxY7O81jz6NqIrF611109RXWs/LoHjWO8eTp/JZ81J4ut5NYoZkS3Tgf35NWQ6/TWbF2vXovQfBLFdHPOyGrJcT3Xix2d57HmVa7J80otmjMiq2Fdr/TTtSNnJqp5RD+rHLK5+8n3vvb06w/euZzZPvvlbz59/qvfuZzl5INgPSA7HySde6S+HB+JH7Ejp2dm/V7s6DxPdd4Ks2uaFdXZ3YOVvyPqJcu1ch2R7prYSH/R46P7sOZF8RLnqs7V83QvVVm9rH85rud6saPzPNa8yjV5Xq01w6tR6XUFr86Iam9Z/mv3swxt7q4t+v/t+Oj8f3fWz7b1tV0//67UJlH9yvwo/tqol24/2Xw5bs3V1ypzmHddi+bRWHZ06Rgrx0jeiqh2pa8qipXHrG6OznzZp3ccbUc/O9a1I6cnqyXHrbn6WmUO865rlfisbrUWobnZ4dFj3twoxz07y7oOeeeO/p+7n7/7jcuZ7Uuv19qKvnuQY9m8jBVr5fTqdK8TORbN81BMJqpNKr1VeL1Y6/NyyxzeeOe6Vp1nGY0dXZMkc0hRnM4rz6OxDituNNeolfWu3bu2637O5qB4ctS94foa9yPX561V5vDGO9e1aB7XzvJXa1VkubgnEvU90g/nlusiq9Y2a3Rdt+awf8Q4+v/uvvDKW0+f+eLXL2e+zoOQzY3Gq3W6Oap5SWduVZazu54ZMt9obi+OrkcqdbMcZKTnTHYvsp6zWCbn6bgsj2W0L9mTp9NLp/fVtVmWt5Izu2dRDq4/0ntV1oN0jX4k2VunT8mL47V4Vtbt5Mj6IiP9SKNrIjq2kovXNNt3prqua/UzjDZ3R/nle2+98OPYXbK3WqPxFW/TzubY8VZx5Z5Ex0oy32ju2Z6y+NVrrpjpabRfHTeSZ0dfpBu7e/6sar1oXiXHznVx7k6Nnf1ostZo3dl+V9Tt5pjtOTO7Ju9jZLRmR6fGNfoZdeg/YkzvztGPX+UBt4W+K7EOgAg9R/g7W6n6XXEH5fSOaPyeXPN+VvF95Prco9UnALGeL/r8Gvh5ah3R+D25+78tW0UPDD+JLNkDF8VWZPUzs/GWo++JZ3Stu+/xEfejsiavr1X9zNxX3dtsTzO9VHj3kq2u3V3P6P1cuS7OFcVkc1b200F1R3KPxrHZeNLNsfsed/rhXuR8Lz7Ku3tNXbfWj/QwmzsAAACAR3Doj2UBAAAAYC1s7gAAAABOBJs7AAAAgBPB5g4AAADgRLC5AwAAADiRh/qnUMiRv5p8a27xntzz4zTSu/5Vehm7+15EtcmO+jM5d98PAICzOM3mTr/wW18I6Jr1hYHnZrIvKl6eW/li1Lkn16T7uoWeqnb3XsnHPWgr+uisx+uDyBwz92gmFgDgUZzqnTv9wp+da9Z49YtJNK+aQ6KYCqtfC8/TvXi9VepX15T1RGQfXk8S55ztQYpyRfVGe89QnixfNO6NZfVlTFa/wqqnc0Y9re4HAOD0aHN3BtbfeNPXKn8HTs7p/N24aG4nT4eXt9PLaG/duGy+HK/mXt2DJ+stG490clui8W4vlh05OjlnYgEAHtXpf6GCvtPnw6PnWJ/zuYfeTZBz5YF3Gu6Xfvz4cV6hmpuu8aFxjHXI3BY9f4dKHwAAsNYpfizLX0D0F5Ls/N5564nWWb0ndD3TuZdRT0SOZ3MJz+E+K73wXI+Vw+pFX5Pn1vyI7imrZbHmZHHVGLomZb1InE/nINU8uierRwAAeN7db+6iF//sCwOdj5D5R8geZuj1MO86se5Bde4MyiVRXusayepa45VeK3M0r5bGc0ZqEC+uks/rMYqrxlTqW6K4zlh2DgAAL7rrzZ33Qs/Xz/6FIVu/hca0aO6q+5XlkuPeXO59tN9s3GLF6GvyvFKD5nRY9Ud4PbPqtQj3FsVEOeWY14/U6Q0A4GHQ5u5RnO1/xvbWE62z8z+or7xfWS45bvVY7SWaV80hWTH6mjwfqXEU6lUelup6ohwdMoeVb0UNAICzO9U/hTJKvxtgyd6JyOx4h8F6Z4Nk73jIMS8HWbmuqA6R49ncUdl6ovsQ9TbTe9YT6eRbrbseMrMmr57MeeT9AAC4B6f7d+4yI18YRr7ASbPxliindR94ro7r9DazjixWjs/U2YV6IlZfO3uv5OPetBV9rF4P0Tm9/iWev6MfAICzOd07d9GLvzfW+eJimY0fMfpFTsd18ozWJN494nwy90ydI8z07t0XKcoX1av0wvU7PWdm1xSprAkA4OHR5u5Mov8nZ3Ss4l7+XyDdZ6fvnWuUue/lXrLZ3mfWG8VW887U9+zISXblBQA4E7xzdzH7jsCqdxQoz4hqbd2ndT5idu2yD/35iFXxFV7vVSMxLIqt5p2p79mRk+zKCwBwJqfc3EWiL4SR7AvKbPxRbvGL5T1/AR/pfddzr9rHbH3Ljpxk5P4CADwa/LYsAAAAwImc/m/LAgAAADwSbO4AAAAATgSbOwAAAIATweYOAAAA4ESwuQMAAAA4EWzuAAAAAE4EmzsAAACAE8HmDgAAAOBEsLkDAAAAOBFs7gAAAABO5CH+/Bj/ncuz/U3K7rr03/vUcUffpx31Z3POxB99Py2VnngOk3PvdU2RKD66F+QW78cOO+9xxbXu8xkfz5k1rbgflOPWH/czepi/LRs9wfgJFOk+uWZzynieZz3Ro3WN6OSz+pk1Ut9SuUcynsetNXV60qJYq9YqUe6Z9ZBKPNfXdqyVRD3JXqI5o73NxBLur5qjO79i9z0iVrysq1lzV66ZcH2Zd0cdyVvzypp6XTNrmr0f3fiVvT+y02zu+AlBrCfCzBNkNHbHk1LnrNSgOZFuPmlkPrPiuvksOsfsmqx4uhbh+Vntbm8dXu6R9TCKm1lTFitVeuJcXl59vTMvUsnZ0c2xoiaz1m7ljq57RvNWr3Vxr1FfXh2OjWT9RWuIxjSvF28dWV1mzbFivfrM60PiHFlNLx4StLm7d6+9+urls0/oc2JdYzSWHSOsPPIYoeNG83g6+XgufazE6TlWTCVPpJKTzr2DxyV9zrzrUjankmNElLfTkzW3E69lsaM6fXbmVsyuiePpYzXXbE3m5bGud2vq+bO1uvU9WV+jdSpx0Zxu3Wy+HPfm6uvWvGqsJ8s5Mg65Q3+h4lc//u7Te88+9dzRZe3q6Zy/K6jgeProHaOsXHxYqG/vmFXNl83hMV4Dr8ebT+R8lsV0WTUs3C9/zh9lLOXiYzdZSx8zonivBp3L+0CfW3m8eMIx1iFzV1CMxHl24Nwyv7wmr2uVORrP53tCH+W9y/A86zga9dB9rOG6rMeIn3876brXqPmQLpu8q/vpO6//5kdvP5nHhz988zIr5+3q9fVs90/j0THCyiOPETqumseaV73GaCyr541Xr2f5PRxnxVeuyfNoTPKuS5U5q0VrIVlPNC4PLYtfTdfr9FSJJdWc1WsemstHppN3hq7j1e30Y83t5PWuyWOUjs3OGdeNjgorjo6uLEaOW3O9eH29Os9TyRfNqdaB5x3yzh29Y/fR+88uZy/68Affvnx2PfTdQ3SMsHLocw99J8PHraj0nY3vIL8TpI/XvGfycZLHUai2fAxG7gfFyPtZIdfeOXbj9fNRXc8ufG8rfVyrV6qz8h7tvM+Ul48RM71xnOxBHxVWHB0ryMfxGmQ9WdO6lhmJAd8hv1Dxs++/EW7uyOe+8ubTSy9/63LmoyeC9R+Gvm7NG30Sjf6H6PWqRb1bPVf70bFWnNWjVbPCW4Okr3vzPJW80RwW9eDFS1GNLHYFXotXS/Zh9cTxVddYE9P9Vvrv9mflJJW8XuzZVNbJ96tzn/Q9Znruivts5bDqe3VW9LBK1osc99Ztxevr1XmeSr5oTrUOPO+Qzd1Pvve1p19/8M7lzPbZL3/z6fNf/c7lzOc98NGTZSeqMyLqVZ7vXtfqfMzLO7oemkeqc2fWVIn35ljXufeumTVI17gfK+l63j299TVRjRHc12z8rOweyXFrrhef5WXVeR6O13myc2mkB4oZkdXJepHj1lwvXl+vzvNU8kVzqnXgeXf/zh0ZefJINJa55pMrWk80diS+h1Evld53rKeTk9fBOnHW3B3r6RhdD+vG6/mWkR44Rt9PWa+bt2rHmjS9rntj9W89VjNrXBnvfU6iOjSW6fQY1cpksXLcm6uvW/OqsZ4s58g45A7Z3NH/c/fzd79xObN96fVeW/QEYNYTYeYJMhtrifLpevI8Gqvw+pG8fBwbjWe9yPrW3O56Kqo5rXmjsXTOVq+namY9ZDbe0q2f4VxZ3k6urtl7QlbkWIHvk7yvJLu33rgcm13jTLwVy9f0WKfOTE9kJp5iLZxP5o7qyDzWHC82yilF8STLXa0DzzvsHzGO3r37witvPX3mi1+/nK0RPUH4SRYZeXJlNaOcsic5T8dleboqfXnjK3rp5qD5ZGddPcY1Izw/y0tme7d01mMZjec1Ra693oqj19Tpn/vZcR+J7iW7N52+ozxkx5qyHvX4qjVp1ho78V0y90wdL5bX45mpvar3h0abu6P88r23XvhnUHbJfp16x69bRzlH6+m41X1n+Wg8OmaN5NhddyZ/Fruid8vsembid60pMlvz6DV18+/qh/N6H2dkOUZrzPam4zv5urVne+2QtWbqzvY8Er+q90d26D9iTO/O0Y9f5QG3j76Lso57Rv1b34ne63eNs+s52/2AnHxs+fHX54+I1u0d0TjAkQ77sey10X9s0Rel7D/GKDbi5R3Np2Xr6jrqPrGR9WQ9kWpOnWv3eq5xP6XZe1uJ370mS3afM1n87jV1+1/ZD+eSMV4/3T6lLDZbE9HxM/14duRk2Rp31h3NPXs/jo5/VA+zuQMAAAB4BIf+WBYAAAAA1sLmDgAAAOBEsLkDAAAAOBFs7gAAAABOBJs7AAAAgBN5iM0d/So1HUe6hR601T09who7dtQ+cj0Ea9rv1vqJ3FOvAI/k7v8plOiFRf7bODQv+7dyvFydOJ7L1zo9jNbv0H1lPXWtzhfx7heRPVyzJ220Nq/Nio1yRnGr7LiflZy8Nm1FL179HfdTrsPLW7kfkVV9r+hV5pBmewOAAG3uzsb6cyXZnzCJxkf//ImOG63RqU9zvYN5n2esXFo2Fo2vYOX3anI/0VERza/msHixWc6ZmoTi+bB08kd5pGxONJ6NeYekz6VorKtad0XN2Rwreh0dA4A5p/ux7Ox3vB1UyzuOwmunj9bno2hNnIeOkTXO9pDhHqt4Lq/JOjIr7oulu5ZVVq/niDVIci3yOIL1mNL5queMxLXo40j+a/YKAOud6i9U8AuP9yKkX6w074WrEsc15dzsXButr8k6Vs1sXPPmZLktlXojKC+h3Py55NWc6ceLlddH8/MaZB4pyqnnal6s16u+7s3zVOZX51g6vRCrVlTfq8uq9b0aXj9Spwbx8q3o1RLlHYkBgEkfv393AvItfuvt/sqPAEbi9Lg8p8/1cQ2yjlUzG9e8OVluS6Vel8zZ7WmmHy826yeTxY/krPDy6uvd+pX5O3J6rNiZfB26jle30w/N5SPTzSt5sZ2cAHAdp/ixrP4ukz73vlu8NuqFD416HDluydG9Uc2jHnurlu6n68j1PILZx2cWP558rOiFcvCR6dSjuat7BYDruOsfy9ILDvFedOQLkvXixPFdUR45pmtaPewg61g1Zb8k68nr28sd5dO1LVk/hPN4c2UfUU9ZvxVyTTpXNT/nqPRp5ZQ9dMg8Xq/6ujfPU5mv+6f5+lqVV4vzWePdNe12a/1EdK+rHzcA6DvV/3MXOeLF0nrRu0YPso5VMxvXvDlZ7lsR9URjkdm17Lgfu+6xl1df79avzN+1JibzW7X0NTofsXMNlnvpEwCu62E2d7fA+qJCKi/QnRdjWUd/zqzxiJ7nxVXz7SLXSLq9rO5/Nt/serp0v1b/3TVF8+X6KnM8UT+VNUQ97jC7porZNXGP0X2y5gDA8U6zuVvxYunlqL5weS+GZObFj3J04rN1yHyd3DKvF9PtdSWrdrefynx5HypG78fMerweu7HW/GoPzOuFcJ5uTimKrY7N1N9hRT87clg5vTp0PTPbHwDY8GPZi2g8i5WsuZXamWr9CtlPZ20Vq/NVRXU7Pd3K/ZhZz0xsxYocWtZzxort9OnN5do71puZrdlZv8fqU+bMakTjK/oDANtD/G3Za+EXK/mCaF3T+AWOPnoHwKPi57/+b0IeFu96x4ocFs7L/VvHrch6il7bAOAY2Nwtwps4Qh/p3Lr2iPhedI8O7/7SNX4MmKyhj2j8mjrrATgCPxe95yoAHAc/lhW8F6gojmP0HK9edD2S9T7K62fU6nxd+j4e2QuZvR+j69FxbMX92PEYZzm99bDZfqL6u2ofuaaqqEed26q3e40AYMNvywIAAACcCH4sCwAAAHAi2NwBAAAAnAg2dwAAAAAngs0dAAAAwIlgcwcAAABwItjcAQAAAJwINncAAAAAJ4LNHQAAAMCJYHMHAAAAcCIPsbmjP4GT/RmcyGy85eieZuMtO3Ie6RbXM9vT0fGembwretqxpiOtuCer3WJPs0bXtOJe3OK9rK6L51XnV6zMtcLR/eBvywr8QFjzKvFdXs6oD2m2p5F47o3p+Cynjtdm+7FYOaM4OT9bD/FydddSVekpcnQ84/vGuWby7loT9xhZcS9m6XtJZu/JrCN68h6vlTX1ukbXNHsvonjvPkir7smq+1Gh16XreLV1j7N0H5KssfNeZO56c1e9waR6k715nXhPp6dKveoc1qk/ajTnTC9RbCevNTeLX1Wb0HyPzOPlnY2XZC49txJPvH68XqK8UT/EivXqM6+PjpG4rC/irdHCc3UvXm+j9TOcN+phZ09ebhKNaV4v3jq83DKPN66ve7WZ10PXSCz3lq0lys05PKPrYVnt2fwZXeMaNV20ubtnr7366uWz36pes3jzqvGWkZyVetkcPZ6dR2hudDD5eYWOHxHFV3N787L4FbUzOk83bzc+m9+p36nlza3Ur8Z6ovjsGLEjTo9duzeS9bCzp2hOt242X45bc/W1yhzmXdeieTSWHR1yvhWbjWdGYixRnlU1IrrGNWp6Dv1/7n714+8+vffsU88dRzp0l72YtRY6p+sjKNY6eKyL+uAe6eDzURyvjwo5byT+TGjN+vHkx+cIR/fDtemjdwBIt/DfENfn56h1VOn10Od0TR6jOD7qR9aJjgorjo9ZnGNlzhmHbe5+9v03nn7+7jcuZ79FG7xf/OWfXM6u7+gHpOIWnjjcQ/dFQsbxiwTR5x0cy4e+FtG9yDg6MhxnHZX43W6lj3unH1t5jLJy8bGTVU8eR7D6kEfFrf+3eATrXvAxi+4pH13cA8dHPck60ZGxYuQxQ65lVc5pl3fwruqX7731mx+9/RQeVdbbnnTNOiJy3JqbxTOupQ/mXdf0PHkw+blFj2fnFRQj46zzju78impOa56+tqO/TNZDpyeaax0RPZ6dR6y5dE0ezJtr0dejefpg3vVrGK0XxemxztwVrJxH9zTKW4s8mJ6rz1lnnj6Yd30nq46+Js+9vug6H55ojMgc1twsfoeo5hH9sEN+oYLetfvo/WeXM9vnvvLm00svf+ty5uMds1S9xirzo3itOreT01KJpzlMz+30ybz5nMvKKeM7ot5mcnJsthb9+bVk9bl/VlmHVFmTrGHVz+JZNleOW3O9eH29Os9j5RtRqUWqfWlRnNVzNHekfsTKeXRPo7Je5Lie68WOzvNY8+jaiKieV0dek+fW/BWqfUg7+mBcK6qx615UHLK5+8n3vvb06w/euZzZPvvlbz59/qvfuZz5Kg848a4R7+bLGCveU53byWk5Mt6Lne3p1ljroWsjqvdF1vTqZ7miOZX4SCc+myvHrblevL5eneepzltltF4Up8c6c2dxvqyH1T1RzIisTtaLHNdzvdjReZ7qvFlWHbqm8Rw935pbYdXMru2qLXHOaA6zer4WvHNX1Imvzu3mlGbWwmbuiRc7kxM+Ed1bFt3j7DGYfYw68Z1evLn6ujWvGuuJ4jOV/FqW18tp9SlzyTFvTWT1umQt73NyVE9dWawct+bqa5U5zLuuRfNoLFOpwWQtq242vkJWl+yqPerIfg7Z3NFvyVq/TCF96fVaW3TzLNmToKsTX507M8+Lpesd1TVpur6sm+X0epzpJRLlrdyv0b46On3QXK+naIxZcyr1peo98fJaa4l6l3msOV5slFOqztNG47ROnxrH6Ryd3mbWYcXytaN6IjPxFGvhfDK3V0fm8MY717XqPMtILK8n63mmr4iVV1/bVbuCapOj6muH/SPG0bt3X3jlrafPfPHrl7NY9cHM5q18YDiXh2tUeo/mVOIj1fhsPaS6pp3rsczmzPolq3vOWD11etl5T7pkrpm8Xixdj2S1s3gy2rM0s3amc3RyjtbP4q7VE80lo7W6ZO7ROl4cr8VTqZvlICM9e2QvM31lPel4Pd+rzXFZ/lnR2q+ONndHsX5rdpfKb61c+zdbZnua7XfHerOcO9dj2X2PdvSc2b2mzMo1y1wzeXeuaeV6PStq6BydnLvWeM2edq3BImuN1p3tN4t/tPtBohy77wfn312n6tB/xJjenaMfv8oDnkffBVjf7dzUdwgAkKL/Zr0jGl/Fys1HNL6TVY+PaBzgVvBzkr8e89fso5+nh/1Y9trkzfdkD8bqzVSlJ6Z7W9FLp37VzH1e3QuZXWMW762F3eOaMrPxnpm8O9dEY5Ed92KFXY/TjJ09HfU4ja5p9l5k8Y92P0iUY/X94HxRXGXOLg+zuQMAAAB4BIf+WBYAAAAA1sLmDgAAAOBEsLkDAAAAOBFs7gAAAABOBJs7AAAAgBPB5g4AAADgRLC5AwAAADgRbO4AAAAATgSbOwAAAIATweYOAAAA4ETw58cWO/Jvye0yuqYV94JyzMaTVY/H7seX8zNdZ3f9EbfW0y3eo1mza9pxT852n2/xHt+Ce17XWR+TitNs7vhB9Ox6cK0nD13b+WTy1rqypl7X6Jpm78VI/KrePZV83IO2oo/V67Fk/fO4PI96WnE/vBxE58n6maHXPqu6rtk1jcTr3nR8ljNaG5ntx2LljOLk/Gw9mdn4W8H3i9dyq+vSfZJu7zxfu8X1dpz+nbvVT0rvyRSdM+9JJGW9RuuJxjSvF28dXm6ZxxvX173azOtB4zxZ3SiHp5rbEo17Y1zPI2Oy+lqWm3g9RXXkeDR3dMxiza9eYzSWyXrq9j1C18hqynVZ83b0PJpzppcotpPXmpvF0ziz5mW9Zaq1PVG85OWS8XIt0bokztuZG6nm0fOqvY+O3QXa3J3Ra6+++vGxg86bnVdV4qI53brZfDluzdXXKnOYd12rxGd1q7W0Sm1LND7aizSSY6SnrI4cH8lPshqaNb96TerW1WbjK3SNqGZlbqdnmhsdTH5eoeNHRPHV3N68Tm5rbla/k7+rG9/ptZN711yLF1/tfXTsHhz6CxW/+vF3n9579qnnjlm02+YdNx18DmtZ39Xw/d5N175W3Sr5vNOHvmeanu+pzNlhpO7M/bhl1nr4mMU5KjlpTN9HvuejKN46eKyL18B5+HwUx+ujQs6rxtOYXjevA25D5XHU5HNRH/rxvjuXTd7V/fSd13/zo7efzOPDH755mVVDO2w+PCt34TpXds7oenZUWHF0dGUxclzP9WJH53mseZVr8rxaS4piKvmsOVlcNaZSX4tivLGsjhwf6WmEVce7Jg/NunYrvN5mr8+smWKt+Cgnx8g5en4UX9XJEfVCvFzV6948Fo1XYrOjI5svx6u5eR59rMTwPO+I8Lg1T17L8pzVIe/c0Tt2H73/7HL2og9/8O3LZzW0w+bDs2oXPrOj5zju1ToqrDg6VpDfuewma8l61rWK0ThYTz4WnWOl1f9tXAPdg1vqlx8Xvo+dx4ljovVEY6vpe0ufV9dyC7h3+ugdXfx48n3Q5x0Uwz1wP5U8PNc6PLpWp1+aO3Lcm0N+oeJn338j3NyRz33lzaeXXv7W5cw2esOjJ02Gaup4qw+vhhV/lKwXOa7nerGj8zzWvMo1eV6txXi+F0fXJZ47Que3cmteX5Eoxhvz1sRzZdxITyOynljWz7X6reJ1jfRcvV5dM/dCvPmcy8op4zui3mZycmy2Fv25VL3uzWPReBZLKnOqOr1mfZNofGTM48XI697nj+SQzd1Pvve1p19/8M7lzPbZL3/z6fNf/c7l7Dbwk0Q/WbJzaeSJRjEjsjpZL3Jcz/ViR+d5rHmVa/K8Wotlsd18t4B6jtzLeqr3Pps3cj+yGE+lj5k1Va978yq6te9Vd536enY/aDyS3cssf0elVx635vJaKv14tbIeVrhGjVt01+/cXZt8knifk+jJRGOZzhMxqpXJYuW4NVdfq8xh3nWtEp/VrdYiWS7SyTeC8mdm63fXMNrTNdZi2f0YHSFakx6z5s7cEy92Jue92X2PKyh/plo/61WO71pXtp4VNb3es9pkx5qv5ZDNHf0/dz9/9xuXM9uXXu+1tftJYj1B+Joes+Z6OnMtM/EUa+F8MrdXR+bwxjvXtWge187yr6rVyUdzLJU+MtX1RFbkkEbzre6D7cp7LdQ/6ayBY4gVN3NPdGxWS5JzpZleIlHeLJZU4615ND66rhEz9bJYOX7tdY2gHsmqPu9hzZHD/hHj6N27L7zy1tNnvvj1y9m82Qcpi9fjnXrduWS0VpfMPVrHi+O1eFbWreTo1MnmRuPVXjLVXj2d9ZLRnnashXPO3oNR16rffYwy1XydxyzLGY2vXh+ZzXl0fNdMPYq1cD6Z+9rrGtXp01u/dA9rdtHm7ii/fO+tF/4ZlB2iX4Ve8WvSOkcnZ7f+in6rZK3RurP9rqg724OW5YvGq72s7lkbyT/a04617L4/mWs9PivrHPE4RONH9JM5Or5rZz2Z+9rrGsE90sdqv/ewrlGHvXN3TdFufsV3JDqHdT7C6mtFv1Wy1mjd2X5X1J3tQcvyRePVXlb0TDlGzPaurViLNpJz5f3YsSbCPcrc1rURO3rOckbjR/ST2RFP10Zwntn4UXItel0zPa1eD+fT4951Sa/rTB56c7frgd35hOEnrGdn3ZHcs/dixb1c/XhU8tEcS7UPL56tXE/VaE871pLlJDvv0eo1cb4orjInQvGr70klp3evVvdCZtd4dPytusV18fMq6yvqnXN47vmxfIjNHQAAAMCjOPRvywIAAADAWtjcAQAAAJwINncAAAAAJ4LNHQAAAMCJYHMHAAAAcCIP80+hkKN+rXlH/UpO/Wvecm4WH8WSo9bk2dHPrBU94Z781s713OK9AgAYddebO35BtugXaZrrvXBHedjsi35UX6uuq5PTcq34aD2M88z0FMV2eshwLjnfukaqPfEcL3clBxmp74136V6k6noyM7GStfYst17fTB86l7ZijQDwuO7+nTvrBbl6TcrGI/KF2ssxk59U1yR7iVDcSLycb8XP6va0qp9urNcnXdP9dnvSubu9SVnsTO6qznpojFlzOv3KXBLHd/vSY51eqro5ab60uh8AuFO0ubtn1t+Gq16TRv/GnI7z8ozmJ6M55bg1d6Yn0onXc+mcr/Hn8lqFnhvFyvze0WHN71zzDh6X9HlHFjuTu6q6nsq8br9Rzko9EtWMxjooTzeXNX9VPwBw3/DO3YX+DlizYr2cI/U93BfFWj16OSs9RD3pWta8KN5Sqd/JWckndXJnKJelsx4e03Os3Nm4xcqtZbmi2Aovv87r9amvZ+vRovhq7qhmNFZB8UT2RLKcO3sCgBP4eIt3x6rfvdI1eazg5bGuj9SUMd2clfl0Lg+m5xHvmjwyeg6fyxx6jseal8XKGtbRYc3nazKnNY/o6/I8GuuaiZ3l1bauV+d21xPFd3JbY9H8CMXx4clyz8QCwPk91D+FQt/N8rEbffcsjy793Td93snD8+VhrZuu8TFiNF72M5qjS9axjlV25PTox/hWyMf3llXvHa1FzqVjdH383IjiR3MDAJBT/FjWol8crRdjLzbDebwXeK+WNVfjnry5Mk8npzWvc716zULzMpynktObo+vQnEptS6UHi46L1iNzyDk6xsthXbdiJSvPSlwvqlPpm1nr6awhip/N3UX5R3g9Wf3uXgMA3IdT/j93lh0vel7Oa73oenU6vJ50nuo6Z2U5o/Ed/XiqtVb05OWwrutr3pwR0To4ZzSHVfpm+ro3zxPFZ2MjOr1Jupcq3edofQA4l7vf3FVFL56VF/IoVo55daL6FbrHmVxsRU/XXFNWLxrXtSwzayHd9Vg6Oaz16mvWnKN5PVV6764nylmpZ+n2UDGS04rZ0RsA3CHa3D2Cmf/JuPI/N/PhWV1/Jt8q11xTpdbqfjq667F0c1Tmd3tYjep3euD5XkwnF5H5+GDyc6LPPdV5Ge5HH1XW3E48AJzXKd65o+9Wyeh3rBwfmf1uePQ76iguyzm7Lo6fXbvWWVOnhyxvZnSdnfV4RnPodel5ldhKfzOiHrq6ubL1y7Fq7hXr6fTlseat6A0A7t8pflt29sWM4+mjd9wjax3yyFTm7FbtNcM5OJ913KOZNVxjzbzZoI9HuMYaAQBuzUP9Uyj3yPvCeM/foZ9tTSvWc7Z7Qn3L3nl91hqhhu+fvI/VawDwWE7zCxWVF7Hoi2QWP/sFlvLP5ND9zfZDKj3tvC+r15StZ+dayIr13Ps94XyVmqP3Z/aeeKq5V/XA90HbtT4AeBwP89uyAAAAAI8AP5YFAAAAOBFs7gAAAABOBJs7AAAAgBPB5g4AAADgRLC5AwAAADgRbO4AAAAATgSbOwAAAIATweYOAAAA4ESwuQMAAAA4EWzuAAAAAE4EmzsAAACAE8HmDgAAAOBEsLkDAAAAOBFs7gAAAABO4+np/wFlkuq7WIk9ygAAAABJRU5ErkJggg=="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 학습셋과 테스트셋을 구분 \n",
    "x_train, x_test, y_train, y_test = train_test_split(X,y,test_size=0.3, shuffle=True)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 3ms/step - loss: 0.0126 - accuracy: 1.0000\n",
      "[0.012582190334796906, 1.0]\n",
      "정확도 accuracy: 1.0\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test)\n",
    "print(score)\n",
    "print('정확도 accuracy:', score[1])"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.7609 - accuracy: 0.4207\n",
      "Epoch 2/200\n",
      "15/15 [==============================] - 0s 993us/step - loss: 0.6903 - accuracy: 0.5379\n",
      "Epoch 3/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.6701 - accuracy: 0.5655\n",
      "Epoch 4/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.6572 - accuracy: 0.5862\n",
      "Epoch 5/200\n",
      "15/15 [==============================] - 0s 569us/step - loss: 0.6440 - accuracy: 0.6966\n",
      "Epoch 6/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.6306 - accuracy: 0.7172\n",
      "Epoch 7/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.6173 - accuracy: 0.6828\n",
      "Epoch 8/200\n",
      "15/15 [==============================] - 0s 929us/step - loss: 0.6022 - accuracy: 0.7379\n",
      "Epoch 9/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.5881 - accuracy: 0.7310\n",
      "Epoch 10/200\n",
      "15/15 [==============================] - 0s 647us/step - loss: 0.5713 - accuracy: 0.7724\n",
      "Epoch 11/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.5637 - accuracy: 0.7448\n",
      "Epoch 12/200\n",
      "15/15 [==============================] - 0s 960us/step - loss: 0.5569 - accuracy: 0.7724\n",
      "Epoch 13/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.5336 - accuracy: 0.7586\n",
      "Epoch 14/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.5224 - accuracy: 0.7793\n",
      "Epoch 15/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.5064 - accuracy: 0.7793\n",
      "Epoch 16/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.4970 - accuracy: 0.8138\n",
      "Epoch 17/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.4809 - accuracy: 0.7931\n",
      "Epoch 18/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.4721 - accuracy: 0.8207\n",
      "Epoch 19/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.4569 - accuracy: 0.8345\n",
      "Epoch 20/200\n",
      "15/15 [==============================] - 0s 982us/step - loss: 0.4466 - accuracy: 0.8138\n",
      "Epoch 21/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.4360 - accuracy: 0.8276\n",
      "Epoch 22/200\n",
      "15/15 [==============================] - 0s 987us/step - loss: 0.4310 - accuracy: 0.8345\n",
      "Epoch 23/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.4352 - accuracy: 0.8000\n",
      "Epoch 24/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.4120 - accuracy: 0.8414\n",
      "Epoch 25/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.4035 - accuracy: 0.8414\n",
      "Epoch 26/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.3944 - accuracy: 0.8483\n",
      "Epoch 27/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.3937 - accuracy: 0.8414\n",
      "Epoch 28/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.3916 - accuracy: 0.8414\n",
      "Epoch 29/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.3769 - accuracy: 0.8483\n",
      "Epoch 30/200\n",
      "15/15 [==============================] - 0s 979us/step - loss: 0.3678 - accuracy: 0.8552\n",
      "Epoch 31/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.3611 - accuracy: 0.8483\n",
      "Epoch 32/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.3589 - accuracy: 0.8552\n",
      "Epoch 33/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.3508 - accuracy: 0.8483\n",
      "Epoch 34/200\n",
      "15/15 [==============================] - 0s 901us/step - loss: 0.3465 - accuracy: 0.8621\n",
      "Epoch 35/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.3480 - accuracy: 0.8483\n",
      "Epoch 36/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.3326 - accuracy: 0.8621\n",
      "Epoch 37/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.3359 - accuracy: 0.8621\n",
      "Epoch 38/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.3253 - accuracy: 0.8690\n",
      "Epoch 39/200\n",
      "15/15 [==============================] - 0s 995us/step - loss: 0.3238 - accuracy: 0.8759\n",
      "Epoch 40/200\n",
      "15/15 [==============================] - 0s 927us/step - loss: 0.3350 - accuracy: 0.8414\n",
      "Epoch 41/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.3133 - accuracy: 0.8828\n",
      "Epoch 42/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.3062 - accuracy: 0.8897\n",
      "Epoch 43/200\n",
      "15/15 [==============================] - 0s 929us/step - loss: 0.3004 - accuracy: 0.8828\n",
      "Epoch 44/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.3009 - accuracy: 0.8897\n",
      "Epoch 45/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.3018 - accuracy: 0.8897\n",
      "Epoch 46/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2920 - accuracy: 0.8759\n",
      "Epoch 47/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2888 - accuracy: 0.8966\n",
      "Epoch 48/200\n",
      "15/15 [==============================] - 0s 317us/step - loss: 0.2844 - accuracy: 0.8897\n",
      "Epoch 49/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2819 - accuracy: 0.8828\n",
      "Epoch 50/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2778 - accuracy: 0.8897\n",
      "Epoch 51/200\n",
      "15/15 [==============================] - 0s 932us/step - loss: 0.2750 - accuracy: 0.8897\n",
      "Epoch 52/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2671 - accuracy: 0.8897\n",
      "Epoch 53/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2634 - accuracy: 0.8897\n",
      "Epoch 54/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2616 - accuracy: 0.8897\n",
      "Epoch 55/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2682 - accuracy: 0.8828\n",
      "Epoch 56/200\n",
      "15/15 [==============================] - 0s 514us/step - loss: 0.2707 - accuracy: 0.8897\n",
      "Epoch 57/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2487 - accuracy: 0.9034\n",
      "Epoch 58/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2510 - accuracy: 0.9172\n",
      "Epoch 59/200\n",
      "15/15 [==============================] - 0s 955us/step - loss: 0.2433 - accuracy: 0.9103\n",
      "Epoch 60/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2419 - accuracy: 0.9034\n",
      "Epoch 61/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2379 - accuracy: 0.9103\n",
      "Epoch 62/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2350 - accuracy: 0.9172\n",
      "Epoch 63/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2340 - accuracy: 0.9103\n",
      "Epoch 64/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2303 - accuracy: 0.9310\n",
      "Epoch 65/200\n",
      "15/15 [==============================] - 0s 819us/step - loss: 0.2335 - accuracy: 0.9103\n",
      "Epoch 66/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.2272 - accuracy: 0.9172\n",
      "Epoch 67/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2225 - accuracy: 0.9310\n",
      "Epoch 68/200\n",
      "15/15 [==============================] - 0s 506us/step - loss: 0.2187 - accuracy: 0.9241\n",
      "Epoch 69/200\n",
      "15/15 [==============================] - 0s 944us/step - loss: 0.2143 - accuracy: 0.9241\n",
      "Epoch 70/200\n",
      "15/15 [==============================] - 0s 995us/step - loss: 0.2153 - accuracy: 0.9310\n",
      "Epoch 71/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2120 - accuracy: 0.9241\n",
      "Epoch 72/200\n",
      "15/15 [==============================] - 0s 956us/step - loss: 0.2171 - accuracy: 0.9034\n",
      "Epoch 73/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2107 - accuracy: 0.9310\n",
      "Epoch 74/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2028 - accuracy: 0.9448\n",
      "Epoch 75/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1995 - accuracy: 0.9310\n",
      "Epoch 76/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2045 - accuracy: 0.9241\n",
      "Epoch 77/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2005 - accuracy: 0.9310\n",
      "Epoch 78/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1948 - accuracy: 0.9448\n",
      "Epoch 79/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.2033 - accuracy: 0.9241\n",
      "Epoch 80/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1988 - accuracy: 0.9379\n",
      "Epoch 81/200\n",
      "15/15 [==============================] - 0s 747us/step - loss: 0.1842 - accuracy: 0.9448\n",
      "Epoch 82/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.1896 - accuracy: 0.9379\n",
      "Epoch 83/200\n",
      "15/15 [==============================] - 0s 946us/step - loss: 0.1788 - accuracy: 0.9379\n",
      "Epoch 84/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1815 - accuracy: 0.9310\n",
      "Epoch 85/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1859 - accuracy: 0.9379\n",
      "Epoch 86/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1788 - accuracy: 0.9448\n",
      "Epoch 87/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1718 - accuracy: 0.9586\n",
      "Epoch 88/200\n",
      "15/15 [==============================] - 0s 704us/step - loss: 0.1683 - accuracy: 0.9517\n",
      "Epoch 89/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1704 - accuracy: 0.9448\n",
      "Epoch 90/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1664 - accuracy: 0.9448\n",
      "Epoch 91/200\n",
      "15/15 [==============================] - 0s 975us/step - loss: 0.1670 - accuracy: 0.9586\n",
      "Epoch 92/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1596 - accuracy: 0.9586\n",
      "Epoch 93/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1623 - accuracy: 0.9448\n",
      "Epoch 94/200\n",
      "15/15 [==============================] - 0s 874us/step - loss: 0.1599 - accuracy: 0.9586\n",
      "Epoch 95/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1603 - accuracy: 0.9655\n",
      "Epoch 96/200\n",
      "15/15 [==============================] - 0s 929us/step - loss: 0.1507 - accuracy: 0.9586\n",
      "Epoch 97/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1524 - accuracy: 0.9586\n",
      "Epoch 98/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1513 - accuracy: 0.9517\n",
      "Epoch 99/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1453 - accuracy: 0.9586\n",
      "Epoch 100/200\n",
      "15/15 [==============================] - 0s 788us/step - loss: 0.1444 - accuracy: 0.9586\n",
      "Epoch 101/200\n",
      "15/15 [==============================] - 0s 915us/step - loss: 0.1428 - accuracy: 0.9655\n",
      "Epoch 102/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1472 - accuracy: 0.9724\n",
      "Epoch 103/200\n",
      "15/15 [==============================] - 0s 233us/step - loss: 0.1397 - accuracy: 0.9655\n",
      "Epoch 104/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1418 - accuracy: 0.9724\n",
      "Epoch 105/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1382 - accuracy: 0.9517\n",
      "Epoch 106/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1402 - accuracy: 0.9724\n",
      "Epoch 107/200\n",
      "15/15 [==============================] - 0s 967us/step - loss: 0.1364 - accuracy: 0.9724\n",
      "Epoch 108/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1301 - accuracy: 0.9655\n",
      "Epoch 109/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1271 - accuracy: 0.9655\n",
      "Epoch 110/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.1286 - accuracy: 0.9793\n",
      "Epoch 111/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1264 - accuracy: 0.9586\n",
      "Epoch 112/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1211 - accuracy: 0.9724\n",
      "Epoch 113/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1239 - accuracy: 0.9724\n",
      "Epoch 114/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1218 - accuracy: 0.9655\n",
      "Epoch 115/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1212 - accuracy: 0.9724\n",
      "Epoch 116/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.1158 - accuracy: 0.9724\n",
      "Epoch 117/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.1184 - accuracy: 0.9724\n",
      "Epoch 118/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.1153 - accuracy: 0.9724\n",
      "Epoch 119/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.1126 - accuracy: 0.9793\n",
      "Epoch 120/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1106 - accuracy: 0.9724\n",
      "Epoch 121/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1139 - accuracy: 0.9724\n",
      "Epoch 122/200\n",
      "15/15 [==============================] - 0s 906us/step - loss: 0.1091 - accuracy: 0.9793\n",
      "Epoch 123/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.1073 - accuracy: 0.9724\n",
      "Epoch 124/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1076 - accuracy: 0.9724\n",
      "Epoch 125/200\n",
      "15/15 [==============================] - 0s 928us/step - loss: 0.1009 - accuracy: 0.9793\n",
      "Epoch 126/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.1002 - accuracy: 0.9793\n",
      "Epoch 127/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1015 - accuracy: 0.9724\n",
      "Epoch 128/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0985 - accuracy: 0.9793\n",
      "Epoch 129/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0975 - accuracy: 0.9724\n",
      "Epoch 130/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.1000 - accuracy: 0.9793\n",
      "Epoch 131/200\n",
      "15/15 [==============================] - 0s 981us/step - loss: 0.0963 - accuracy: 0.9793\n",
      "Epoch 132/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0942 - accuracy: 0.9793\n",
      "Epoch 133/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.0924 - accuracy: 0.9793\n",
      "Epoch 134/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0976 - accuracy: 0.9862\n",
      "Epoch 135/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0945 - accuracy: 0.9724\n",
      "Epoch 136/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0921 - accuracy: 0.9862\n",
      "Epoch 137/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0949 - accuracy: 0.9862\n",
      "Epoch 138/200\n",
      "15/15 [==============================] - 0s 964us/step - loss: 0.0857 - accuracy: 0.9793\n",
      "Epoch 139/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0854 - accuracy: 0.9862\n",
      "Epoch 140/200\n",
      "15/15 [==============================] - 0s 988us/step - loss: 0.0839 - accuracy: 0.9862\n",
      "Epoch 141/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0830 - accuracy: 0.9793\n",
      "Epoch 142/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0812 - accuracy: 0.9862\n",
      "Epoch 143/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0789 - accuracy: 0.9862\n",
      "Epoch 144/200\n",
      "15/15 [==============================] - 0s 928us/step - loss: 0.0802 - accuracy: 0.9862\n",
      "Epoch 145/200\n",
      "15/15 [==============================] - 0s 576us/step - loss: 0.0788 - accuracy: 0.9793\n",
      "Epoch 146/200\n",
      "15/15 [==============================] - 0s 955us/step - loss: 0.0800 - accuracy: 0.9862\n",
      "Epoch 147/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0815 - accuracy: 0.9862\n",
      "Epoch 148/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0724 - accuracy: 0.9862\n",
      "Epoch 149/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0785 - accuracy: 0.9862\n",
      "Epoch 150/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0800 - accuracy: 0.9862\n",
      "Epoch 151/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0728 - accuracy: 0.9793\n",
      "Epoch 152/200\n",
      "15/15 [==============================] - 0s 685us/step - loss: 0.0749 - accuracy: 0.9862\n",
      "Epoch 153/200\n",
      "15/15 [==============================] - 0s 969us/step - loss: 0.0732 - accuracy: 0.9793\n",
      "Epoch 154/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0698 - accuracy: 0.9862\n",
      "Epoch 155/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0695 - accuracy: 0.9862\n",
      "Epoch 156/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0656 - accuracy: 0.9862\n",
      "Epoch 157/200\n",
      "15/15 [==============================] - 0s 931us/step - loss: 0.0658 - accuracy: 0.9931\n",
      "Epoch 158/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0714 - accuracy: 0.9724\n",
      "Epoch 159/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0717 - accuracy: 0.9862\n",
      "Epoch 160/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0640 - accuracy: 0.9862\n",
      "Epoch 161/200\n",
      "15/15 [==============================] - 0s 441us/step - loss: 0.0632 - accuracy: 0.9862\n",
      "Epoch 162/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0632 - accuracy: 0.9931\n",
      "Epoch 163/200\n",
      "15/15 [==============================] - 0s 947us/step - loss: 0.0598 - accuracy: 0.9931\n",
      "Epoch 164/200\n",
      "15/15 [==============================] - 0s 974us/step - loss: 0.0604 - accuracy: 0.9931\n",
      "Epoch 165/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0597 - accuracy: 0.9931\n",
      "Epoch 166/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0667 - accuracy: 0.9862\n",
      "Epoch 167/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0560 - accuracy: 0.9931\n",
      "Epoch 168/200\n",
      "15/15 [==============================] - 0s 961us/step - loss: 0.0568 - accuracy: 0.9931\n",
      "Epoch 169/200\n",
      "15/15 [==============================] - 0s 954us/step - loss: 0.0555 - accuracy: 0.9931\n",
      "Epoch 170/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0560 - accuracy: 0.9931\n",
      "Epoch 171/200\n",
      "15/15 [==============================] - 0s 515us/step - loss: 0.0535 - accuracy: 0.9931\n",
      "Epoch 172/200\n",
      "15/15 [==============================] - 0s 938us/step - loss: 0.0571 - accuracy: 0.9931\n",
      "Epoch 173/200\n",
      "15/15 [==============================] - 0s 998us/step - loss: 0.0526 - accuracy: 0.9931\n",
      "Epoch 174/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0515 - accuracy: 0.9931\n",
      "Epoch 175/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0512 - accuracy: 0.9931\n",
      "Epoch 176/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0497 - accuracy: 0.9931\n",
      "Epoch 177/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0504 - accuracy: 0.9931\n",
      "Epoch 178/200\n",
      "15/15 [==============================] - 0s 849us/step - loss: 0.0513 - accuracy: 0.9931\n",
      "Epoch 179/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0472 - accuracy: 0.9931\n",
      "Epoch 180/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.0461 - accuracy: 0.9931\n",
      "Epoch 181/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0492 - accuracy: 0.9931\n",
      "Epoch 182/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0452 - accuracy: 0.9931\n",
      "Epoch 183/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0444 - accuracy: 0.9931\n",
      "Epoch 184/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0454 - accuracy: 0.9931\n",
      "Epoch 185/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0434 - accuracy: 0.9931\n",
      "Epoch 186/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0424 - accuracy: 0.9931\n",
      "Epoch 187/200\n",
      "15/15 [==============================] - 0s 990us/step - loss: 0.0410 - accuracy: 0.9931\n",
      "Epoch 188/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0413 - accuracy: 0.9931\n",
      "Epoch 189/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0431 - accuracy: 0.9931\n",
      "Epoch 190/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0417 - accuracy: 0.9931\n",
      "Epoch 191/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0430 - accuracy: 0.9931\n",
      "Epoch 192/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0422 - accuracy: 0.9931\n",
      "Epoch 193/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.0383 - accuracy: 0.9931\n",
      "Epoch 194/200\n",
      "15/15 [==============================] - 0s 960us/step - loss: 0.0380 - accuracy: 0.9931\n",
      "Epoch 195/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0378 - accuracy: 0.9931\n",
      "Epoch 196/200\n",
      "15/15 [==============================] - 0s 560us/step - loss: 0.0368 - accuracy: 0.9931\n",
      "Epoch 197/200\n",
      "15/15 [==============================] - 0s 2ms/step - loss: 0.0404 - accuracy: 0.9931\n",
      "Epoch 198/200\n",
      "15/15 [==============================] - 0s 929us/step - loss: 0.0449 - accuracy: 0.9931\n",
      "Epoch 199/200\n",
      "15/15 [==============================] - 0s 948us/step - loss: 0.0384 - accuracy: 0.9931\n",
      "Epoch 200/200\n",
      "15/15 [==============================] - 0s 1ms/step - loss: 0.0371 - accuracy: 0.9931\n",
      "2/2 [==============================] - 0s 3ms/step - loss: 0.9106 - accuracy: 0.8254\n",
      "Test accuracy: 0.8253968358039856\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(Dense(24, input_dim=60, activation = 'relu'))\n",
    "model.add(Dense(10, activation='relu'))\n",
    "model.add(Dense(1, activation='sigmoid'))\n",
    "\n",
    "model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "history=model.fit(x_train,y_train,epochs=200, batch_size=10)\n",
    "\n",
    "score = model.evaluate(x_test, y_test)\n",
    "print('Test accuracy:',score[1])"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\ProgramData\\anaconda3\\envs\\ai2\\lib\\site-packages\\keras\\src\\engine\\training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
      "  saving_api.save_model(\n"
     ]
    }
   ],
   "source": [
    "model.save('./data/model/my_model.hdf5')"
   ]
  },
  {
   "attachments": {
    "image-2.png": {
     "image/png": 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G/JrBo4YnRkJx/PrBryXe3KORXkfhdWojqwa4He7/1zf/fH9Y58c//M5Gm9hV0G7+ctwa/CLYGtfGta/vCOj5wv271edPNtxHTz/LWCveE+OFNTRN7VwNGduK98YRMtYTH4H05OsG3/saMsaCfWpxlr4Ga1ljVWS/tF4A4OEe/dh+f2wS+f1TeuLUbkptzoo/koiPzNje9VPeCLKmpUVx2nzG+ltrb/kq8ejV8jQsLQvLt4T1o76YMqemU6NVN6IlKXW9viw/kfOENuc952W0RuR8eY6OCS3fgvOYlqbmhdHmavHlea0WM6Lrwavv0aYYwuOhx+tMWn6ifrX41dYMLgP35vTnf/G3tre96x/2IxvrZuy5eWl+BO8TI/IkKj211mtpR+pGiOpG4nu0NTwamb5q89r5SN0WLX2rVsQHxUosTaKlG6ktKfO8OqV/xlqHpVvqabFebxplrqUVqePVjWiWaLnec4RVu5yLaER0CSu+hlenR7sHqjNCj0dem7XG0peMO8MzuCHkP3yyxr9/4YX911TbRH8xepVflo748MbKuFrOrPVHdSPxI54zfdFcOSy0eB4l2rleWvpWrYiPVizNR/SIrHiPzmxvGiMaZa6l1apD83JIarm18x60XO85wqpdzkU0IrqEFW9BeXJo9GpfAnJtt7h+sDb33/TUs/s21ebn3vKb+6M2/G6sxHqHdinQGrzvGMv11voyCnsqhzZ3bVBP5WhRxssxC+r7TP0IPWvt8V7edzSADveHr01Pv68Ned/IkYnsd63n1rXQ/EXGmVB9uTZ6fLYnACT3Hn/txSePv/rh/VCHNrDPvPvb+5Gf8mavPdHLJ0qNyJPHo1dS8yHr8nzLszVfzrW0shjxXDLiOZp7lK+SDC1LQ85542pQDDPqeZSaX+86Iv6j8YzsF9Hbs7K+5Uebq8V7da16Lbx+emqXcxENS1cjGs9QnocebaLHl9cTMcNXORddQ81/r1dww9DHpzP+zmmE7B8d9OpRnjY0aucJa04jGt9Lq05rTXJ+xHM0d4avMk/Dq1Ujom/F1uboPA/G0jmCVv3R+RItns61hqQ8jhDR0uZq8V5dq14Lr5+e2uVcRCOiS1jxLVq5M7VH6NWO5kXirdiZvQDXydT/IcqL991Z7V2Zxux3atF3lBajWll9sXTKvBHP0dxZvkbWYMF+W9qyvuWF9Uq0+Flr8tKqPzpfkrHeEQ0t13uOqMUSLY1anJdabc85JhJfnqdjwhNLeHUjtHJnamtQjpdeXxG8a/DEjfQS3B4X93/rr3KDZ/rI0Dq6L1Y9mhthZB2RPhzds5L3v+992599/ON3j7O8WDpU79GjR/vRtj399NOh45Y/ed21WGtezjFWvWi8hdWzGr/z27+9/eQnP7l7/Mu/8ivb9/7jP7aHf/3Xd8dEOf/nn/zk3WON33/ve+/yOZ68cL7WJ1mPj6V+qcfxtWOuwT2VerKe5ofQ/Nfw+GekH46XlPM1fxak0fIb1WR6c0dqZuP14olbaV3gAqDN6dlk/ejgSDJ9ZGgd3ZdVrkNJxFf6Gl59dX+w88or+4Md4/hDf/RHofg7lOO/+au/2g9eI0HvDZTHjx7tDy6b8H3w3//95Mn//u9+8Brf/CaOVzoO3Jd07Vujl97ckZrZRLxYsSutCVwGF/nJqZeZ79Iy3wVmaB3dl8z1ZxLx5e2Ze53/+I/b9pd/uW1/8ifb9vnPb9sf/uG2vetdruNXPvnJ7df+9E/d8bXjr/7SL22/8fd/350fOv6Lv9i2/ZPfSyZ8L//bv23bBz+4bZ/61Lb98z9v2+/93rb96q/ieJXjD33otSftZ/aLZTPzdaxX2/u6RMzyzkTXUPM+2ye4PpbYnAJwNXz0o9v2xS9u2+PH2/bpT2/bJz5x3cdvfvO+8Bvj4cPX34hQH1566fXNOo7XOX7b2/YLBQC4SGhzCgBI5KWX3vgj/ms/vlX+9m/f+CNkHK91DAC4WPDJKQAAAAAAWIb7+1cAAAAAAABOB5tTAAAAAACwDNicAgAAAACAZcDmFAAAAAAALAM2pwAAAAAAYBmwOQUAAAAAAMuAzSkAAAAAAFgGbE4BAAAAAMAyYHMKAAAAAACWAZtTAAAAAACwDNicAgAAAACAZTh9c/qe5567G1lk64HL4VqvvWddmWvP1LpULrEHl+Y52+/s9V+a32yO8HtpPQHzuPfkNfbHp0E342defnk/GidbD6xB+aKlXePea8/a0dzSk8XIPelZV+/aNXq0ar3I8jQb9s9+R/pZ64XGaA2ZP+L5DEb9lj0Y0WMtRtNZ2a/FiGfJ6Po9HFEDrM/UzWn55KndcNGbkXWz9CSa5xG9a2S0HzP7OXrtV/RFUL6HWo1WfpkX9WvFH7V2omf9Mkd6HfXtIaM3Nf9eKIfIXCtrMjXt0fUTUiNDz2I1v7PXWzJSj3IlNZ2eGqU249Wp5Wu0NDO1bpmfbk5/9P0vbY8+9467k8xTz7ywvfWdD/ejGNoNVrvpauctrJwePaLmmejRu2Rm9Zfx5sscC9br9Ub05Hr9Eb2+CI+3Xv9aTkRrljdJRo2oxqhnD1YNmqtR89jrOXOtmlZN31OXYpiWhkdPIrUtvPqWnqYR9Vsyms+UvmuavfW0vJpWtIYVH9HyxEa9WWRqXSW0Of3elz/w5FsPt+qI8vyDB/ujn0Wbs+JrRGu0aOX0aF4yM9e7ojbn0deoxsz1MJ4aPT5qORGtWd4kGTWiGiOeKdc7IpTxrWMPPTk1LC1trlW7nG9ptPRGiejXfGX6Hc0nNI2abk89KydSW8MT69XL1PKQqXWN3P/B1z+yvfqNj+1bVZ3vfvbt+6N50LsIHi3wjuM2kfeIdZ94YiQUx/cTfaXhzT0SuS5tnEWrXzS38vN1Rg/5PmqNVchcexbafUPHs7zyPcCjhifmSKSf2qhBc9p9OLPP147suzVAg3/5u2fVT0vL8fhrL+772TbWOwJtLvoOooyn43JE6ckhInXLuFp8z3k5avCcFV/O8ZCUx0QZr8Uw1lwNLcd7rgbFWvFeLdZpjRWp+erxK9cqRwYenVZMVCPLe40e/TKnddxCxtPjaH6Jla/NReOJ8rw8tvQstDzvOY0yTh7XHp+BVV+b6/E7s4Yn1qt3tBawuUcbz32fahL9/VN6Z1C+I9POEbXzNfhdRy0nqse0dEu0OrXa5flarZ7zrXOcy/RoEFKn1C9jieh5i5ofz7kSiiE8Hnq8ZsE+e5G+LS2K0+ZH1p3dN49eKyaq0bMGyhlB1qtptWK8nmvrY83o2omaJqHNlf7lfMufhOOs+hY1b55zGjKOHpfIOY/eLKz6mm+ix69Wp1Y72pPWGrxanlivXnQN4Gdxb05//hd/a3vbu/5hP/JBF0iSfQPV8kZvDOm7x3M5F/XpPV+LI7S5SL2otkakXguvxx7tHqjOCBkeo2uNxI/2Mfs6ePtt1bQ0OE/6zl5DDy0P5bzHM8UQnrie9Wt5NS2rhjdHHlt6Flqe91xJGWMde/RKKGcEy5tEm+vxy5S+I3VblNpMRKemUeLRHOkT2JE/urfGv3/hhf3D1nwiH4GXsVpu5kfqpBWtUc7VYkfP1+IIbS5SL6rN0JwcGla+xUztSyS61kj8aB+PuA6zaxyxhhYtD+W8FU9z0TVF4xmu1arZM1eel8eWXgvKlUPDoy9jtPjW/JFY9VveZ3F2TzKgNXgH0LlHv3P64x9+Z9+q1nnLb7y4/cKv//F+dA61dyPl+RnvWrQaFh4/o+cjHohIvVosUYsn5PlIvSNgj72c4ZmI+LY8Rvreil2hl5H1XCqe6yDnL60nGeuT585e/6X5JVoeJSv4vVTQuxj36B86Pf7qh/dDnTc99ez2zLu/vR/FoAvixbpwrQsr53tugog+EalRix09H/FAROpZ2l4f0fMtKM9DjzbR48vriej15aXlP7K+nl4czYhH73WbrU/0XDOpL+dHenIGHr+1tTJSY2T9so5FTb+sXfNytF+iVaPUqsWP+JVwPamlnbsmsnp3M9DHp9l/5zSb6EffPR+Vt3LKeSveGzt6vhZHaHORehHtiC5habdo5c7UHmGmNjPSG5qT80f4HWW2xyN60KpB89pg5GOiPD6T0qtGhl+pMaqX5deKy/TrIbPGbK0j+nEW17y2GUz7H6JKtHcNs94p9b5Dqfmp6dXW5Imt1SJquoSnnnaOiJyX58r51jFB54jyPKHFe2nlztTW4HV6GPHlpdWbGmWetxes2bu2EbweNbw9na1PjPSu7MFIT2ZwhB9ZY7ReK9+jH9EY8Uu5XnprlIz4lbB3qZWlvSrR9Wk9uiWm/t/6JdrFmXFD9mpyHt8UjKXVG8t1avGaLp8rc1oerHnPHJ8rY4naHJ2P5HshDSunNW/RmztS08sRNSSRerO8ke4ILU+ze3rENStr1GrO7mWNo3swWq+Vn7GeI/1mM2v95VcLihlB6q+qRbBeqx/Xyqmb0/I4i1m62VyKz5XgJ6xFb097r8cR1/HoeyVS72hvWcz2fUZfVrsWR/sZrUf5LTLXk+H30vpb5pfnjl7TytxyL6ZvTqm5XrIuwqVcUDwJ48zsWa/2Eff4ETUkkV4c7S0Lj+8Rv2f0JXLdjsDbg1XWf3T/Mvx6yVhXr1/2KXO1c8xoX66BW+/BoZ+cgtfhJ6Xk1p+IAAAAAAAENqcAAAAAAGAZ7u9fAQAAAAAAOB1sTgEAAAAAwDJgcwoAAAAAAJYBm1MAAAAAALAM2JwCAAAAAIBlwOYUAAAAAAAsA/6UFFCx/kByD9l6YC20v90rubTrfmn3/2p+PfmZnnu0OKckq4cAgH6wOQVV6MV75IW6/IYxqgfWxHNdM68931cl2ffWiGf2KPMze6CRrT+q58nP9BzRsmIzPQEA+pi6OaUnuYSe8Hjiv5HRfkTzy2uiwXoZ10pq9Opl+Bjh7PqjZF/HEo/+JfbQuy6JjC/zZ/cgqs/eazmjflm/hVXfoszz+vXEja4dADDGtM2p9uTmF5tbe9JbL3S9L4LcSyKa763piWv5kBreukxL+wjYw1n1Rxn177kGnuvqiTkCuZ6S0t+o5zI/qmd5JTL8Wjk9ehJPfqZnr9YsXwCAPKb8g6jaE5vO0aB58Dq9L4DcyzPh68wj+7qusMaz648y6t9zDWierr01Mvqo6WrDgtdTjhVhX6VXHqAP6p11n9Ac+gvAueBf64MutBfw1os+uF7o2lsjA02Xh5w/ArrPrXEJkM/Z/Sr7Uo6zoHVrfmgcdQ8BAOpM+bF+7xOc8iSWhozlF5pafG1OO+/1wLlWfDnHlDFaDS23Fqedt6j5YljP0rZ8y/Py2NKzsGpJatplHGH5kPEU1+ub4NxSk9DOacg4ouWl1GUPGl5tS+NM2P+oN219o2su83v0IjlR/VZ8j98jqPka8bvqWgG4Vab+zinhfcJrLw7eFyGrVm1OO6/Vq9ViWvFE7RzjiSe82llY2pbHEo7r9arljZwjvOfpmNBiLTiPaWl6/RC1WKKc02oxEe2abi9ajQjluqSfqLZ3zTVKL4z0xGR504jEEq34qB6hrTmCrGdpUZw2H/XL9KwVADAR2pzO5PkHD346akTmarE9+vJ8NN+jyUS1a0S1R+nxXZ6Xx71eLc0Sbw0trpbr1dTw1on4ISLxM7WPgurLYeGJkdRiWcfSk+fLmNaxh0gOxcphwfNWXEujh6hmJL61lpEBADiW6b9zSu9GedC709F31rcG9+zIvl3rdTqjlyAH+TpCw8ITw9C9YMWylhZT5tLjs+8ty6/GCp6PQPalZwAAjuXQfxDFT3TtxZDOaeNItPo0zoBrZ75AyjVZw1OP5ilWwrmrUa5rRY9nwH0pRwQtvzZWgzxdw70Q7W+5bnqcfX3YUzm0OQAA+Bn2T1BTaf0YpJyP/NikFmtpeHIiHoiID0s7Eh/V9hD1VkIxPDTkeY+eRpln6XhjtfORWC/eOhE/RCQ+qq0RjS/pzae8kVHDmmNaMdq8PFfOe2qW1HLoPA/Go2/FlHMevVFaNSIePLEU4x0AgPNY/k9JrfDOGu/u6/AnkdfwCRR4nZXud3l/aaMVo0Hrq81FIA3ZqyzdEtItB68vUo/zaszwPhPuRQReI/euNgAA5zJlc0pP7toLh/YCyfEl3lgtt0WZE/EwQo9XojfvGhm9VrVeZt1bWUTXGfEf1a5B8Z6xAuwlsr4W3MdsXYY0tdFDb14P3JPW8KDl0RjpBQBgbab/96X0VWK9mPTGcp2oNp+TeS0P1rxnjs+VsURLS2q08r2QTi3PmvMiNaJ6co1PP/309ujRozfkl/N/9vGP70dvhOI4nyCN97/vfT+jx8h4mudjqc/5jNQnymOuw56lnuZP0pov8fhnpB+Ol5TzhBbT8jSLmbVHtcv82X3K1s9e/2y89TxxR3sHABTc/XD/CsDvCPVh9S27pyG9R4/2BzuvvLI/2MHx/mDn6ONXX90fnPvcm1n71Pu/g9X8zl5vibcexXkGAOA88N+X3jjLfjrwla9s2wc/+Prjz39+2373d3G80vGLL77+eIc+afIMcFto94A2joZe91oDAHAe036sfxTaCxteWK6Ej3502774xW17/HjbPv3pbfvEJ3C80vGb37xfKAAAACCRu89PAViVl156w4+QcbzYMQAAAJDMxX9yCgAAAAAArgf8zikAAAAAAFgGbE4BAAAAAMAyYHMKAAAAAACWAZtTAAAAAACwDNicAgAAAACAZcDmFAAAAAAALAM2pwAAAAAAYBG27f8AN/J18ptCqMwAAAAASUVORK5CYII="
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image-2.png](attachment:image-2.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential, load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "del model # 테스트를 위해 조금전 만든 모델을 삭제 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 모델이 저장된 위치와 이름까지 적어준다. \n",
    "model = load_model('./data/model/my_model.hdf5')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 4ms/step - loss: 0.9106 - accuracy: 0.8254\n",
      "Test accuracy [0.9106044173240662, 0.8253968358039856]\n"
     ]
    }
   ],
   "source": [
    "score = model.evaluate(x_test, y_test)\n",
    "print('Test accuracy', score)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "k = 5 # 몇개의 파일로 나눌것인가\n",
    "kf = KFold(n_splits=k, shuffle=True) # 사이킷런을 KFold()함수로 불러옴. 샘플이 치우치지 않게 셔플옵션 트루.\n",
    "\n",
    "acc_score = [] # 정확도가 채워질 acc_score라는 빈리스트를 준비 \n",
    "for train_index, test_index in kf. split(X):\n",
    "    x_train, x_test = X.iloc[train_index,:], X.iloc[test_index,:]\n",
    "    y_train, y_test = y.iloc[test_index], y.iloc[test_index]\n",
    "    #split()에 의해 k개의 학습셋, 테스트셋으로 분리되며 for문에 의해 k번 반복\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 0s/step - loss: 0.1642 - accuracy: 0.9756\n"
     ]
    }
   ],
   "source": [
    "accuracy = model.evaluate(x_test, y_test)[1]  # 정확도를 구한다.\n",
    "acc_score.append(accuracy)  # acc_score 리스트에 저장\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "## 위코드 전체포함 이해 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 마지막 광물 \n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense\n",
    "from sklearn.model_selection import KFold\n",
    "from sklearn.metrics import accuracy_score\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2/2 [==============================] - 0s 2ms/step - loss: 0.5518 - accuracy: 0.8095\n",
      "WARNING:tensorflow:5 out of the last 11 calls to <function Model.make_test_function.<locals>.test_function at 0x0000016F49C17CA0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "2/2 [==============================] - 0s 0s/step - loss: 0.5171 - accuracy: 0.7619\n",
      "WARNING:tensorflow:5 out of the last 11 calls to <function Model.make_test_function.<locals>.test_function at 0x0000016F4AE7E8B0> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has reduce_retracing=True option that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/guide/function#controlling_retracing and https://www.tensorflow.org/api_docs/python/tf/function for  more details.\n",
      "2/2 [==============================] - 0s 0s/step - loss: 0.5229 - accuracy: 0.8095\n",
      "2/2 [==============================] - 0s 16ms/step - loss: 0.2219 - accuracy: 0.8780\n",
      "2/2 [==============================] - 0s 0s/step - loss: 0.6125 - accuracy: 0.8049\n",
      "정확도: [0.8095238208770752, 0.761904776096344, 0.8095238208770752, 0.8780487775802612, 0.8048780560493469]\n",
      "정확도 평균: 0.8127758502960205\n"
     ]
    }
   ],
   "source": [
    "df = pd.read_csv('./data/sonar3.csv', header=None) # 데이터 부르기 \n",
    "\n",
    "# 음파관련 속성을 x로, 광물의 종류를 y로 저장 \n",
    "X=df.iloc[:,0:60]\n",
    "y=df.iloc[:,60]\n",
    "\n",
    "# 몇겹으로 나눌것인지 정한다. \n",
    "k = 5\n",
    "\n",
    "# KFold 함수를 불러온다. 분할하기 전에 샘플이 치우치지 않도록 섞어 준다. \n",
    "kf = KFold(n_splits=k, shuffle=True)\n",
    "\n",
    "# 정확도가 채워질 빈 리스트를 준비 \n",
    "acc_score = []\n",
    "\n",
    "def model_fn():\n",
    "    model = Sequential() # 딥러닝 모델의 구조를 시작\n",
    "    model.add(Dense(24, input_dim=60, activation='relu'))\n",
    "    model.add(Dense(10, activation = 'relu'))\n",
    "    model.add(Dense(1, activation='sigmoid'))\n",
    "    return model\n",
    "\n",
    "# k겹 교차 검증을 이용해 k번의 학습을 실행 \n",
    "# for 문에 의해 k번 반복 \n",
    "for train_index, test_index in kf.split(X):\n",
    "    x_train, x_test = X.iloc[train_index,:], X.iloc[test_index,:]\n",
    "    y_train, y_test = y.iloc[train_index], y.iloc[test_index]\n",
    "    #split()에 의해 k개의 학습셋, 테스트셋으로 분리되며 for문에 의해 k번 반복\n",
    "\n",
    "    model = model_fn()\n",
    "    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])\n",
    "    history = model.fit(x_train,y_train,epochs=200, batch_size=10, verbose=0)\n",
    "\n",
    "    accuracy = model.evaluate(x_test,y_test)[1] # 정확도를 구한다. \n",
    "    acc_score.append(accuracy)    # 어펜드해서 정확도를 acc_score 빈리스트에 저장 \n",
    "\n",
    "# k번 실시된 정확도의 평균을 구한다. \n",
    "acc_score_avg = sum(acc_score) / k \n",
    "\n",
    "#결과를 출력 \n",
    "print('정확도:', acc_score)\n",
    "print('정확도 평균:', acc_score_avg)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "attachments": {
    "image.png": {
     "image/png": 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"
    }
   },
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "ai2",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
   "mimetype": "text/x-python",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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 },
 "nbformat": 4,
 "nbformat_minor": 2
}