tensorflow-checkpoint.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "02d96630-dec9-4a8c-94dc-b76ad2d3dd93",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "e6d612ee-0c70-42f2-a43c-105d026eef76",
   "metadata": {},
   "outputs": [],
   "source": [
    "dataFrame=pd.read_excel(\"bisiklet_fiyatlari.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "5f41f28e-f53a-4f56-96e2-cb5374306043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fiyat</th>\n",
       "      <th>BisikletOzellik1</th>\n",
       "      <th>BisikletOzellik2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>807.673876</td>\n",
       "      <td>1749.628226</td>\n",
       "      <td>1749.590668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>959.227520</td>\n",
       "      <td>1748.007826</td>\n",
       "      <td>1751.824206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>718.020033</td>\n",
       "      <td>1750.122967</td>\n",
       "      <td>1747.977026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>945.668885</td>\n",
       "      <td>1749.916440</td>\n",
       "      <td>1750.771646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>955.542968</td>\n",
       "      <td>1750.780519</td>\n",
       "      <td>1750.592430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fiyat  BisikletOzellik1  BisikletOzellik2\n",
       "0  807.673876       1749.628226       1749.590668\n",
       "1  959.227520       1748.007826       1751.824206\n",
       "2  718.020033       1750.122967       1747.977026\n",
       "3  945.668885       1749.916440       1750.771646\n",
       "4  955.542968       1750.780519       1750.592430"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "acbe313f-324d-431d-a8b9-77a4932eaa9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import seaborn as sbn\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "441d2f9a-442c-40e4-9275-e47ef05cad96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x1dba8067710>"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 750x750 with 12 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.pairplot(dataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "6b4fd594-1a0c-4fe4-98b3-9b70e8506236",
   "metadata": {},
   "outputs": [],
   "source": [
    "##veriyi test train olrak 2 ye bçlmek 1. adımı"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "id": "5a7eef58-d3b5-42e7-96f7-c391288cf26b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "id": "6caba155-12e6-45b2-9a52-ecb317c3f4a1",
   "metadata": {},
   "outputs": [],
   "source": [
    "##train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "155c1b8f-62d7-486b-8bca-99b264e37bf5",
   "metadata": {},
   "outputs": [],
   "source": [
    "#y=wx+b\n",
    "#y-> label\n",
    "#x->feature(özellik)\n",
    "y=dataFrame[\"Fiyat\"].values\n",
    "\n",
    "x=dataFrame[[\"BisikletOzellik1\",\"BisikletOzellik2\"]].values\n",
    "\n",
    "x_train,x_test,y_train,y_test=train_test_split(x,y,test_size=0.33,random_state=15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "id": "fa2901af-9743-45fa-9f03-bb0fe71c2560",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(670, 2)"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "85dedf0f-c74d-45bb-af31-19a2a09931eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(330, 2)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "c8e1728e-ff8a-4174-b1c9-a9dc17b66b3a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#scaling verilerin kolay işlenebilmesi için verileri 0 ile 1 ararsındaki bir rakama transform ediyoruz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "14ee8082-4282-4a1e-ba72-3d6ad0b44be8",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "62f66458-be99-4a54-bbfe-537382c564d1",
   "metadata": {},
   "outputs": [],
   "source": [
    "scaler =MinMaxScaler()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "f41472ad-edc3-46cf-8158-2a8bf77bb62c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MinMaxScaler<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.preprocessing.MinMaxScaler.html\">?<span>Documentation for MinMaxScaler</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MinMaxScaler()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MinMaxScaler()"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaler.fit(x_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "033c43c7-f000-427d-9ce3-7fc68161b9e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x_train=scaler.transform(x_train)\n",
    "x_test=scaler.transform(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "63220726-747a-4da3-a192-ed93281c334c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.3177906 , 0.64341466],\n",
       "       [0.61991638, 0.89583174],\n",
       "       [0.53950097, 0.0980286 ],\n",
       "       ...,\n",
       "       [0.2352117 , 0.52644765],\n",
       "       [0.7576794 , 0.19157421],\n",
       "       [0.4292982 , 0.16530301]])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "d30c5199-d99e-42ec-8f71-a20b9c7a9b7b",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "e63a29da-f111-4c52-abc3-1e032ed9f990",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import Dense"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "ef2bc899-0447-476a-bcd0-4ebf49a9743a",
   "metadata": {},
   "outputs": [],
   "source": [
    "model=Sequential()\n",
    "model.add(Dense(5,activation=\"relu\"))\n",
    "model.add(Dense(5,activation=\"relu\"))\n",
    "model.add(Dense(5,activation=\"relu\"))\n",
    "\n",
    "model.add(Dense(1))\n",
    "\n",
    "model.compile(optimizer=\"rmsprop\",loss=\"mse\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "cddd36be-ab89-4413-a1bf-f9cb7a636317",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 6ms/step - loss: 790618.1875\n",
      "Epoch 2/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 774337.3750\n",
      "Epoch 3/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 793480.3750\n",
      "Epoch 4/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 797880.8750\n",
      "Epoch 5/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 799630.3125\n",
      "Epoch 6/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 798425.1250\n",
      "Epoch 7/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 797844.4375\n",
      "Epoch 8/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 807084.2500\n",
      "Epoch 9/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 789236.0000\n",
      "Epoch 10/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 791252.8750\n",
      "Epoch 11/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 798599.8750\n",
      "Epoch 12/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 793767.5000\n",
      "Epoch 13/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 779363.9375\n",
      "Epoch 14/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 785321.3125\n",
      "Epoch 15/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 801488.5625\n",
      "Epoch 16/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 781851.0625\n",
      "Epoch 17/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 781477.5625\n",
      "Epoch 18/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 785098.1875\n",
      "Epoch 19/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 770286.9375\n",
      "Epoch 20/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 781698.5625 \n",
      "Epoch 21/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 777938.8750\n",
      "Epoch 22/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 781788.5000\n",
      "Epoch 23/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 766835.0000\n",
      "Epoch 24/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 747358.6250\n",
      "Epoch 25/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 736155.6250\n",
      "Epoch 26/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 740400.6875 \n",
      "Epoch 27/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 748290.0000\n",
      "Epoch 28/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 736508.4375\n",
      "Epoch 29/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 722865.3750\n",
      "Epoch 30/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 736155.1875\n",
      "Epoch 31/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 714542.6875\n",
      "Epoch 32/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 723530.3125\n",
      "Epoch 33/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 694159.5000\n",
      "Epoch 34/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 684498.3125\n",
      "Epoch 35/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 673848.1250\n",
      "Epoch 36/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 653077.5000\n",
      "Epoch 37/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 660577.3750\n",
      "Epoch 38/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 629690.8125\n",
      "Epoch 39/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 626129.1875\n",
      "Epoch 40/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 608082.5000\n",
      "Epoch 41/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 597426.7500\n",
      "Epoch 42/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 578479.5625\n",
      "Epoch 43/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 580569.6875\n",
      "Epoch 44/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 553523.2500\n",
      "Epoch 45/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 526454.8750\n",
      "Epoch 46/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 512207.3125\n",
      "Epoch 47/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 489689.5938\n",
      "Epoch 48/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 463864.4688\n",
      "Epoch 49/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 449157.5312\n",
      "Epoch 50/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 432268.2812\n",
      "Epoch 51/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 407996.6250\n",
      "Epoch 52/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 384212.6562\n",
      "Epoch 53/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 362987.0312\n",
      "Epoch 54/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 345512.1562\n",
      "Epoch 55/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 310329.3438\n",
      "Epoch 56/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 283481.5625\n",
      "Epoch 57/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 268085.5938\n",
      "Epoch 58/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 240240.0000\n",
      "Epoch 59/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 214741.7812\n",
      "Epoch 60/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 189775.0781\n",
      "Epoch 61/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 163894.0625\n",
      "Epoch 62/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 144766.8906\n",
      "Epoch 63/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 126885.5234\n",
      "Epoch 64/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 98124.7734 \n",
      "Epoch 65/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 83841.6641 \n",
      "Epoch 66/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 66501.6953\n",
      "Epoch 67/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 49359.4180 \n",
      "Epoch 68/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 36648.7617\n",
      "Epoch 69/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 23929.5137 \n",
      "Epoch 70/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 16498.8594 \n",
      "Epoch 71/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 8817.2148\n",
      "Epoch 72/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 6219.6841\n",
      "Epoch 73/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5993.7529\n",
      "Epoch 74/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5326.1753\n",
      "Epoch 75/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4792.4404\n",
      "Epoch 76/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 5128.0220\n",
      "Epoch 77/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4791.0527 \n",
      "Epoch 78/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 4378.2744\n",
      "Epoch 79/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4200.4849\n",
      "Epoch 80/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4424.5586\n",
      "Epoch 81/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4211.7852\n",
      "Epoch 82/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 4051.8616\n",
      "Epoch 83/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4037.5005 \n",
      "Epoch 84/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4213.3232 \n",
      "Epoch 85/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3740.4514\n",
      "Epoch 86/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 4001.5273 \n",
      "Epoch 87/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3733.5107\n",
      "Epoch 88/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3793.4990\n",
      "Epoch 89/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3702.4187\n",
      "Epoch 90/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3675.2383\n",
      "Epoch 91/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3315.0229\n",
      "Epoch 92/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3181.5227\n",
      "Epoch 93/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 3113.5981\n",
      "Epoch 94/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 3127.2283\n",
      "Epoch 95/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 3024.4470\n",
      "Epoch 96/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2709.8701\n",
      "Epoch 97/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2679.3630\n",
      "Epoch 98/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2530.1196\n",
      "Epoch 99/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2497.6960\n",
      "Epoch 100/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2400.7700\n",
      "Epoch 101/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2247.1377\n",
      "Epoch 102/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2319.7725\n",
      "Epoch 103/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 2378.2571\n",
      "Epoch 104/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 2375.0825\n",
      "Epoch 105/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1861.9197\n",
      "Epoch 106/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1850.8328\n",
      "Epoch 107/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1904.0962\n",
      "Epoch 108/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1719.4535\n",
      "Epoch 109/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1680.8070\n",
      "Epoch 110/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1574.4314\n",
      "Epoch 111/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1416.7990\n",
      "Epoch 112/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 1430.7478 \n",
      "Epoch 113/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1411.9993 \n",
      "Epoch 114/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1433.1143\n",
      "Epoch 115/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1194.4142\n",
      "Epoch 116/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1266.0499\n",
      "Epoch 117/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1146.5570\n",
      "Epoch 118/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1065.4552\n",
      "Epoch 119/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 992.8970 \n",
      "Epoch 120/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 1006.3381\n",
      "Epoch 121/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 938.4081 \n",
      "Epoch 122/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 893.4358 \n",
      "Epoch 123/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 847.3604 \n",
      "Epoch 124/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 777.2760 \n",
      "Epoch 125/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 716.3242 \n",
      "Epoch 126/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 665.9174 \n",
      "Epoch 127/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 614.7125 \n",
      "Epoch 128/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 573.2427 \n",
      "Epoch 129/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 510.2946 \n",
      "Epoch 130/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 496.4896 \n",
      "Epoch 131/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 468.8851 \n",
      "Epoch 132/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 398.7655 \n",
      "Epoch 133/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 384.8900 \n",
      "Epoch 134/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 370.7692\n",
      "Epoch 135/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 301.5602 \n",
      "Epoch 136/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 282.7255 \n",
      "Epoch 137/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 287.0565\n",
      "Epoch 138/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 245.3242\n",
      "Epoch 139/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 238.3310 \n",
      "Epoch 140/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 229.6433\n",
      "Epoch 141/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 193.6361 \n",
      "Epoch 142/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 168.1156 \n",
      "Epoch 143/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 176.3137\n",
      "Epoch 144/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 156.2859\n",
      "Epoch 145/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 138.4304\n",
      "Epoch 146/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 133.4000\n",
      "Epoch 147/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 113.0319\n",
      "Epoch 148/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 119.7611 \n",
      "Epoch 149/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 99.9842\n",
      "Epoch 150/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 97.6165\n",
      "Epoch 151/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 91.6962\n",
      "Epoch 152/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 95.4116\n",
      "Epoch 153/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 85.7464\n",
      "Epoch 154/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 86.4604\n",
      "Epoch 155/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 85.9040\n",
      "Epoch 156/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 86.9867\n",
      "Epoch 157/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 83.2969\n",
      "Epoch 158/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 82.0067\n",
      "Epoch 159/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 76.0661\n",
      "Epoch 160/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 80.5255\n",
      "Epoch 161/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 74.3629\n",
      "Epoch 162/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 71.8396\n",
      "Epoch 163/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 80.5655\n",
      "Epoch 164/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 75.3742\n",
      "Epoch 165/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 79.1944\n",
      "Epoch 166/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 76.7207\n",
      "Epoch 167/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 74.1850\n",
      "Epoch 168/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 74.5847\n",
      "Epoch 169/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 72.2001\n",
      "Epoch 170/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 77.8476\n",
      "Epoch 171/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 88.6907\n",
      "Epoch 172/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 84.2324\n",
      "Epoch 173/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 72.9841\n",
      "Epoch 174/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 74.7066\n",
      "Epoch 175/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 70.8696\n",
      "Epoch 176/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 80.8402\n",
      "Epoch 177/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 72.8382\n",
      "Epoch 178/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 83.7379 \n",
      "Epoch 179/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 81.1893\n",
      "Epoch 180/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 80.2209\n",
      "Epoch 181/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 76.7443\n",
      "Epoch 182/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 77.1890\n",
      "Epoch 183/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 75.0005\n",
      "Epoch 184/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 77.5319\n",
      "Epoch 185/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 71.0807\n",
      "Epoch 186/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 75.5406\n",
      "Epoch 187/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 79.2053\n",
      "Epoch 188/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 77.7136\n",
      "Epoch 189/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 75.7461\n",
      "Epoch 190/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 73.3353\n",
      "Epoch 191/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 80.4748\n",
      "Epoch 192/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 74.9541\n",
      "Epoch 193/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 74.6393\n",
      "Epoch 194/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 70.2870\n",
      "Epoch 195/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 71.6877\n",
      "Epoch 196/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 82.5673 \n",
      "Epoch 197/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 72.8702\n",
      "Epoch 198/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 71.1499 \n",
      "Epoch 199/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 75.5884\n",
      "Epoch 200/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 75.5639\n",
      "Epoch 201/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 77.2666\n",
      "Epoch 202/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 87.6022  \n",
      "Epoch 203/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 72.1231 \n",
      "Epoch 204/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 71.3323 \n",
      "Epoch 205/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 79.4636\n",
      "Epoch 206/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 75.6447 \n",
      "Epoch 207/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 77.6001 \n",
      "Epoch 208/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 74.6140\n",
      "Epoch 209/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 79.5358 \n",
      "Epoch 210/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 77.9112\n",
      "Epoch 211/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 73.2109\n",
      "Epoch 212/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 71.1545\n",
      "Epoch 213/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 80.2450\n",
      "Epoch 214/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 80.3384\n",
      "Epoch 215/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 73.4462\n",
      "Epoch 216/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 72.5396\n",
      "Epoch 217/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 78.9179\n",
      "Epoch 218/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 80.7855\n",
      "Epoch 219/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 74.0433\n",
      "Epoch 220/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 76.6521\n",
      "Epoch 221/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 82.3530\n",
      "Epoch 222/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 73.3411\n",
      "Epoch 223/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 81.4883\n",
      "Epoch 224/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 76.8709\n",
      "Epoch 225/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 74.0210\n",
      "Epoch 226/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 71.3500\n",
      "Epoch 227/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 73.6725\n",
      "Epoch 228/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 75.9933\n",
      "Epoch 229/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 73.2436\n",
      "Epoch 230/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 67.2970\n",
      "Epoch 231/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 75.1170\n",
      "Epoch 232/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 73.3928\n",
      "Epoch 233/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 75.1805\n",
      "Epoch 234/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 73.3393\n",
      "Epoch 235/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 75.2176 \n",
      "Epoch 236/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 81.1542  \n",
      "Epoch 237/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 77.4267 \n",
      "Epoch 238/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 75.1255 \n",
      "Epoch 239/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 72.8834\n",
      "Epoch 240/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 79.2405 \n",
      "Epoch 241/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 71.6142\n",
      "Epoch 242/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 4ms/step - loss: 77.9697 \n",
      "Epoch 243/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 70.8068\n",
      "Epoch 244/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 81.7562 \n",
      "Epoch 245/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 75.4160\n",
      "Epoch 246/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 80.3236 \n",
      "Epoch 247/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 71.7304\n",
      "Epoch 248/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 70.3658 \n",
      "Epoch 249/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 5ms/step - loss: 72.7387\n",
      "Epoch 250/250\n",
      "\u001b[1m21/21\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 68.5898\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x1dba7f8a510>"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(x_train,y_train,epochs=250)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "ef2347d3-9167-45ac-8e58-963956d16cd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "loss=model.history.history[\"loss\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "d6384739-e0eb-448e-9ab6-7c19dad59cf9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: >"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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1tbU4d+7cLd+Xy+WC0+n0eZD/Xp73Feh1QPGfr+BkLceQiIiGp34Hod///vdobGzE888/DwCw2WwAgLi4OJ+6uLg4bZ/NZoPBYEBUVFSvNbGxsT1eLzY21qem++tERUXBYDD0WtP1e1fNzWRnZ2trk1RVRWJi4q0HgW4pIWoslkyLBwD8urgqwN0QERHdXL+D0K9//WssWbLEZ1YGAHQ6nc/vItJjW3fda25WPxA10rlQurd+Nm7cCIfDoT0uXLjQa+90ay8+fg8A4N3PanDJ2RrgboiIiHrqVxA6f/48ioqK8Pd///faNpPJBKDnbEt9fb02E2MymeB2u2G323utuXTpUo/XvHz5sk9N99ex2+1oa2vrtaa+vh5Az1mrGymKgsjISJ8H9c8DiePx8KQotHkEvzl0LtDtEBER9dCvIPTGG28gNjYWS5cu1bYlJyfDZDJpZ5IBHeuIDhw4gLlz5wIAUlJSEBYW5lNTV1eHyspKrWbOnDlwOBw4cuSIVnP48GE4HA6fmsrKStTV1Wk1BQUFUBQFKSkpWs3Bgwd9TqkvKCiA2WzGpEmT+vO2qR/+7rGOWaG3D1fjurs9wN0QERF14+8qbI/HIxMnTpQNGzb02Ld161ZRVVX27t0rVqtVMjIyJD4+XpxOp1azatUqSUhIkKKiIjl69KjMnz9fZsyYIe3t7VrN4sWLZfr06VJSUiIlJSUybdo0SU9P1/a3t7eLxWKRBQsWyNGjR6WoqEgSEhIkKytLq2lsbJS4uDjJyMgQq9Uqe/fulcjISMnJyfHr/fKssTvT7vHKE9s+kqQN78lvDlUFuh0iIgoSff3+9jsIffDBBwJATp8+3WOf1+uVTZs2iclkEkVR5IknnhCr1epT09LSIllZWRIdHS1Go1HS09Olurrap6ahoUFWrlwpEREREhERIStXrhS73e5Tc/78eVm6dKkYjUaJjo6WrKwsn1PlRUSOHz8ujz/+uCiKIiaTSTZv3uzXqfMiDEID4TeHqiRpw3uSuu0jaff4N/5ERET90dfvb50IL7XcG6fTCVVV4XA4uF6on6672zEn+yM4Wtrwy7+dibQpt16jRURENBD6+v3Ne43RoBtrCMUzD3dchuCt0vMB7oaIiOhLDEI0JFbMmggAOHjmMqobrge4GyIiog4MQjQkku4KxxNfuxsiwO+OVAe6HSIiIgAMQjSEVnbOCv3npxfgavfcppqIiGjwMQjRkFkwORZxkQquNrvx8an6QLdDRETEIERDJzREj//nwQQAwH+V1wS4GyIiIgYhGmJ/89AEAMAnp+vRcM0V4G6IiCjYMQjRkLo3LgLTE1S0ewXvflYb6HaIiCjIMQjRkHv6wY5Zob1HeXiMiIgCi0GIhtzyByYgVK+DtcaBLy41BbodIiIKYgxCNOSiww34q8mxAIA9Ry8GuBsiIgpmDEIUEH/zUMfZY78/VgOPl7e7IyKiwGAQooD4q8l3Y/zYMFxyuvDff74S6HaIiChIMQhRQCihIVg+wwyAh8eIiChwGIQoYJ7qPHus6OQltLbxlhtERDT0GIQoYB5IGI94dQya3R4Un+HhMSIiGnoMQhQwer0Oi6aaAAD7K+sC3A0REQUjBiEKqCWWjiBUdPIS3O3eAHdDRETBhkGIAmrmpGjEjFPgbG1HydmGQLdDRERBhkGIAipEr8OiqXEAgD9aeXiMiIiGFoMQBdwSSzwAoODkJbR7eHiMiIiGDoMQBdyse6IRNTYMV5vdOFJ1NdDtEBFREGEQooALC9EjbUrn4bFKW4C7ISKiYMIgRMPCkmkdh8fyT9jg5b3HiIhoiDAI0bDw6FdiEDEmFJebXCivtge6HSIiChIMQjQsGEL1WDA5FgBQ9PmlAHdDRETBgkGIho2/6gxCn5y6HOBOiIgoWDAI0bCR+rW7odcBpy81oaaxJdDtEBFREGAQomFj/FgDHpoYBQD46FR9gLshIqJgwCBEw8qXh8cYhIiIaPAxCNGwMr8zCP33X66gtc0T4G6IiGi08zsI1dTU4Nlnn8Vdd92FsWPH4oEHHkB5ebm2X0SwefNmmM1mGI1GzJs3DydOnPB5DpfLhdWrVyMmJgbh4eFYvnw5Ll686FNjt9uRmZkJVVWhqioyMzPR2NjoU1NdXY1ly5YhPDwcMTExWLNmDdxut0+N1WpFamoqjEYjJkyYgC1btkCE16kZriabIhCvjkFrmxelvAkrERENMr+CkN1ux6OPPoqwsDD88Y9/xMmTJ/GTn/wE48eP12q2bduG3Nxc7NixA2VlZTCZTEhLS0NTU5NWs3btWuzbtw95eXkoLi7GtWvXkJ6eDo/nyxmAFStWoKKiAvn5+cjPz0dFRQUyMzO1/R6PB0uXLkVzczOKi4uRl5eHPXv2YP369VqN0+lEWloazGYzysrKsH37duTk5CA3N7c/Y0VDQKfTYd59HbNCH/PwGBERDTbxw4YNG+Sxxx675X6v1ysmk0m2bt2qbWttbRVVVWXXrl0iItLY2ChhYWGSl5en1dTU1Iher5f8/HwRETl58qQAkNLSUq2mpKREAMipU6dERGT//v2i1+ulpqZGq9m9e7coiiIOh0NERHbu3Cmqqkpra6tWk52dLWazWbxeb5/es8PhEADac9LgKzhhk6QN78ljP/qwz/+eiIiIbtTX72+/ZoTeffddzJw5E9/4xjcQGxuLBx98EL/85S+1/VVVVbDZbFi4cKG2TVEUpKam4tChQwCA8vJytLW1+dSYzWZYLBatpqSkBKqqYtasWVrN7NmzoaqqT43FYoHZbNZqFi1aBJfLpR2qKykpQWpqKhRF8ampra3FuXPnbvoeXS4XnE6nz4OG1tyv3AVDiB4XrrbgL5ebA90OERGNYn4FobNnz+LnP/857r33XnzwwQdYtWoV1qxZg9/+9rcAAJut44aZcXFxPn8XFxen7bPZbDAYDIiKiuq1JjY2tsfrx8bG+tR0f52oqCgYDIZea7p+76rpLjs7W1uXpKoqEhMTbzMqNNDClVDMuicaAA+PERHR4PIrCHm9Xjz00EN47bXX8OCDD+If//Ef8eKLL+LnP/+5T51Op/P5XUR6bOuue83N6geiRjoXSt+qn40bN8LhcGiPCxcu9No3DY6/6londJpBiIiIBo9fQSg+Ph5Tpkzx2Xb//fejuroaAGAymQD0nG2pr6/XZmJMJhPcbjfsdnuvNZcu9bzf1OXLl31qur+O3W5HW1tbrzX19R1frN1nirooioLIyEifBw29rtPoj1RdRbOrPcDdEBHRaOVXEHr00Udx+vRpn21ffPEFkpKSAADJyckwmUwoLCzU9rvdbhw4cABz584FAKSkpCAsLMynpq6uDpWVlVrNnDlz4HA4cOTIEa3m8OHDcDgcPjWVlZWoq6vTagoKCqAoClJSUrSagwcP+pxSX1BQALPZjEmTJvnz1mmITYoJR2K0Ee1ewZGqq4Fuh4iIRit/VmAfOXJEQkND5Yc//KGcOXNG3n77bRk7dqy89dZbWs3WrVtFVVXZu3evWK1WycjIkPj4eHE6nVrNqlWrJCEhQYqKiuTo0aMyf/58mTFjhrS3t2s1ixcvlunTp0tJSYmUlJTItGnTJD09Xdvf3t4uFotFFixYIEePHpWioiJJSEiQrKwsraaxsVHi4uIkIyNDrFar7N27VyIjIyUnJ6fP75lnjQXOhv/6TJI2vCf/6w8nAt0KERGNMH39/vYrCImI/OEPfxCLxSKKosjkyZPl9ddf99nv9Xpl06ZNYjKZRFEUeeKJJ8RqtfrUtLS0SFZWlkRHR4vRaJT09HSprq72qWloaJCVK1dKRESEREREyMqVK8Vut/vUnD9/XpYuXSpGo1Gio6MlKyvL51R5EZHjx4/L448/LoqiiMlkks2bN/t1SjaDUOC8W1EjSRvek0U/PRDoVoiIaITp6/e3ToSXWe6N0+mEqqpwOBxcLzTEGq65kPK/iwAAn/7Lk4gZp9zmL4iIiDr09fub9xqjYeuucQommyIAAIf+wtttEBHRwGMQomHtsa/GAAAO/flKgDshIqLRiEGIhrVHO4NQMYMQERENAgYhGtYeSY5GqF6Hi/YWVDdcD3Q7REQ0yjAI0bAWroTiwYnjAXBWiIiIBh6DEA17XYfH/vsvDEJERDSwGIRo2Hv0hgXTXi+v9kBERAOHQYiGvQcSxyPcEAL79TZ8bnMGuh0iIhpFGIRo2AsL0eOR5GgAwH9znRAREQ0gBiEaEboOj5We5Q1YiYho4DAI0YjQNSNUdu4qPFwnREREA4RBiEaEKfGRCDeEoKm1HadtTYFuh4iIRgkGIRoRQkP0eCgpCkDHrBAREdFAYBCiEePhSR2Hx44wCBER0QBhEKIRQ1snVHUVIlwnREREd45BiEaMBxLHIyxEh/omF87zvmNERDQAGIRoxBgTFoLpCeMB8PAYERENDAYhGlG61gmVVTEIERHRnWMQohFl1g3XEyIiIrpTDEI0ojyUFAWdDjjXcB31ztZAt0NERCMcgxCNKKoxDJNNkQC4ToiIiO4cgxCNOI9M6rywItcJERHRHWIQohHnkeS7AABHztkD3AkREY10DEI04jyc3DEjdMrmhKOlLcDdEBHRSMYgRCNObMQYTLprLESAo9WcFSIiov5jEKIR6aGJHbNCx6obA9sIERGNaAxCNCI9mNQVhDgjRERE/ccgRCPSQxPHAwAqqhvh9fIGrERE1D8MQjQi3RcXgbGGEDS52vHny9cC3Q4REY1QDEI0IoWG6DE9QQUAHD3Pw2NERNQ/DEI0Yj3IBdNERHSH/ApCmzdvhk6n83mYTCZtv4hg8+bNMJvNMBqNmDdvHk6cOOHzHC6XC6tXr0ZMTAzCw8OxfPlyXLx40afGbrcjMzMTqqpCVVVkZmaisbHRp6a6uhrLli1DeHg4YmJisGbNGrjdbp8aq9WK1NRUGI1GTJgwAVu2bIEI15OMFl1njvEUeiIi6i+/Z4SmTp2Kuro67WG1WrV927ZtQ25uLnbs2IGysjKYTCakpaWhqalJq1m7di327duHvLw8FBcX49q1a0hPT4fH49FqVqxYgYqKCuTn5yM/Px8VFRXIzMzU9ns8HixduhTNzc0oLi5GXl4e9uzZg/Xr12s1TqcTaWlpMJvNKCsrw/bt25GTk4Pc3Fy/B4mGpwc7F0yfqb/GCysSEVH/iB82bdokM2bMuOk+r9crJpNJtm7dqm1rbW0VVVVl165dIiLS2NgoYWFhkpeXp9XU1NSIXq+X/Px8ERE5efKkAJDS0lKtpqSkRADIqVOnRERk//79otfrpaamRqvZvXu3KIoiDodDRER27twpqqpKa2urVpOdnS1ms1m8Xm+f37PD4RAA2vPS8PL4jz6SpA3vyYHT9YFuhYiIhpG+fn/7PSN05swZmM1mJCcn45lnnsHZs2cBAFVVVbDZbFi4cKFWqygKUlNTcejQIQBAeXk52trafGrMZjMsFotWU1JSAlVVMWvWLK1m9uzZUFXVp8ZiscBsNms1ixYtgsvlQnl5uVaTmpoKRVF8ampra3Hu3Llbvj+XywWn0+nzoOGr6zR6rhMiIqL+8CsIzZo1C7/97W/xwQcf4Je//CVsNhvmzp2LhoYG2Gw2AEBcXJzP38TFxWn7bDYbDAYDoqKieq2JjY3t8dqxsbE+Nd1fJyoqCgaDodeart+7am4mOztbW5ukqioSExN7HxQKqAe5ToiIiO6AX0FoyZIl+Ju/+RtMmzYNTz75JN5//30AwG9+8xutRqfT+fyNiPTY1l33mpvVD0SNdC6U7q2fjRs3wuFwaI8LFy702jsFVteC6YoLvLAiERH5745Onw8PD8e0adNw5swZ7eyx7rMt9fX12kyMyWSC2+2G3W7vtebSpUs9Xuvy5cs+Nd1fx263o62trdea+vp6AD1nrW6kKAoiIyN9HjR8TY6PwJgwPRwtbTh7pTnQ7RAR0QhzR0HI5XLh888/R3x8PJKTk2EymVBYWKjtd7vdOHDgAObOnQsASElJQVhYmE9NXV0dKisrtZo5c+bA4XDgyJEjWs3hw4fhcDh8aiorK1FXV6fVFBQUQFEUpKSkaDUHDx70OaW+oKAAZrMZkyZNupO3TcNIWIge0yeMB8D7jhERUT/4swJ7/fr18sknn8jZs2eltLRU0tPTJSIiQs6dOyciIlu3bhVVVWXv3r1itVolIyND4uPjxel0as+xatUqSUhIkKKiIjl69KjMnz9fZsyYIe3t7VrN4sWLZfr06VJSUiIlJSUybdo0SU9P1/a3t7eLxWKRBQsWyNGjR6WoqEgSEhIkKytLq2lsbJS4uDjJyMgQq9Uqe/fulcjISMnJyfHnLfOssRHgtf0nJWnDe/K9PccD3QoREQ0Tff3+DvUnNF28eBEZGRm4cuUK7r77bsyePRulpaVISkoCAHz3u99FS0sLXn75ZdjtdsyaNQsFBQWIiIjQnuOnP/0pQkND8c1vfhMtLS1YsGAB3nzzTYSEhGg1b7/9NtasWaOdXbZ8+XLs2LFD2x8SEoL3338fL7/8Mh599FEYjUasWLECOTk5Wo2qqigsLMQrr7yCmTNnIioqCuvWrcO6dev6kxdpGHswkXeiJyKi/tGJ8FLLvXE6nVBVFQ6Hg+uFhqlLzlbMeu1D6HVA5Q8WYazBr3xPRESjUF+/v3mvMRrx4iLHIC5SgVeAk7W87hMREfUdgxCNCtM6F0x/dtER2EaIiGhEYRCiUWFGggoAOH6xMbCNEBHRiMIgRKPC9MTxAAArZ4SIiMgPDEI0Kkyb0DEjdPZKM+9ET0REfcYgRKNCdLgBidFGAMCJGs4KERFR3zAI0agxnQumiYjITwxCNGpM54JpIiLyE4MQjRrTE8YDAI5zRoiIiPqIQYhGDcuESOh0QE1jCxquuQLdDhERjQAMQjRqRIwJwz0x4QA4K0RERH3DIESjygweHiMiIj8wCNGoMo0LpomIyA8MQjSqdC2Y/uyiAyIS2GaIiGjYYxCiUWVKfCRC9DpcueaCzdka6HaIiGiYYxCiUcVoCMHX4iIAAJ9d4DohIiLqHYMQjTpdd6K31jQGthEiIhr2GIRo1JnaeQPWE7XOAHdCRETDHYMQjToWcyQAoLKGC6aJiKh3DEI06kw2RUKvA65cc6O+iVeYJiKiW2MQolHHaAjBV2PHAeiYFSIiIroVBiEalSxmrhMiIqLbYxCiUalrwTRnhIiIqDcMQjQqTe1cMM0ZISIi6g2DEI1KUzqDUE1jC+zN7gB3Q0REwxWDEI1KkWPCMOmusQA4K0RERLfGIESjlrZOqJbrhIiI6OYYhGjUmnrDhRWJiIhuhkGIRi2eQk9ERLfDIESjVteMUNWVZjS1tgW4GyIiGo4YhGjUumucArM6BgDweV1TgLshIqLh6I6CUHZ2NnQ6HdauXattExFs3rwZZrMZRqMR8+bNw4kTJ3z+zuVyYfXq1YiJiUF4eDiWL1+Oixcv+tTY7XZkZmZCVVWoqorMzEw0Njb61FRXV2PZsmUIDw9HTEwM1qxZA7fb91Rpq9WK1NRUGI1GTJgwAVu2bOGNOIPIFDMvrEhERLfW7yBUVlaG119/HdOnT/fZvm3bNuTm5mLHjh0oKyuDyWRCWloampq+/C/ytWvXYt++fcjLy0NxcTGuXbuG9PR0eDwerWbFihWoqKhAfn4+8vPzUVFRgczMTG2/x+PB0qVL0dzcjOLiYuTl5WHPnj1Yv369VuN0OpGWlgaz2YyysjJs374dOTk5yM3N7e/bphHGMqFzwTTPHCMiopuRfmhqapJ7771XCgsLJTU1Vb797W+LiIjX6xWTySRbt27ValtbW0VVVdm1a5eIiDQ2NkpYWJjk5eVpNTU1NaLX6yU/P19ERE6ePCkApLS0VKspKSkRAHLq1CkREdm/f7/o9XqpqanRanbv3i2KoojD4RARkZ07d4qqqtLa2qrVZGdni9lsFq/X26f36nA4BID2nDSyFJ6wSdKG92TRTw8EuhUiIhpCff3+7teM0CuvvIKlS5fiySef9NleVVUFm82GhQsXatsURUFqaioOHToEACgvL0dbW5tPjdlshsVi0WpKSkqgqipmzZql1cyePRuqqvrUWCwWmM1mrWbRokVwuVwoLy/XalJTU6Eoik9NbW0tzp0715+3TiOMpfNaQmfqr6G1zXObaiIiCjah/v5BXl4ejh49irKysh77bDYbACAuLs5ne1xcHM6fP6/VGAwGREVF9ajp+nubzYbY2Ngezx8bG+tT0/11oqKiYDAYfGomTZrU43W69iUnJ/d4DZfLBZfLpf3udPLU65EsLlJBdLgBV5vd+OJSE6YnjA90S0RENIz4NSN04cIFfPvb38Zbb72FMWPG3LJOp9P5/C4iPbZ1173mZvUDUSOdC6Vv1U92dra2QFtVVSQmJvbaNw1vOp0O98dHAAA+r2OoJSIiX34FofLyctTX1yMlJQWhoaEIDQ3FgQMH8B//8R8IDQ31mW25UX19vbbPZDLB7XbDbrf3WnPp0qUer3/58mWfmu6vY7fb0dbW1mtNfX09gJ6zVl02btwIh8OhPS5cuHD7gaFh7X5Tx4JpnkJPRETd+RWEFixYAKvVioqKCu0xc+ZMrFy5EhUVFbjnnntgMplQWFio/Y3b7caBAwcwd+5cAEBKSgrCwsJ8aurq6lBZWanVzJkzBw6HA0eOHNFqDh8+DIfD4VNTWVmJuro6raagoACKoiAlJUWrOXjwoM8p9QUFBTCbzT0OmXVRFAWRkZE+DxrZ7o/v+Hd4kjNCRETU3Z2uyr7xrDERka1bt4qqqrJ3716xWq2SkZEh8fHx4nQ6tZpVq1ZJQkKCFBUVydGjR2X+/PkyY8YMaW9v12oWL14s06dPl5KSEikpKZFp06ZJenq6tr+9vV0sFossWLBAjh49KkVFRZKQkCBZWVlaTWNjo8TFxUlGRoZYrVbZu3evREZGSk5OTp/fH88aG/lO1DgkacN7YtmU3+ezBYmIaGTr6/e334ulb+e73/0uWlpa8PLLL8Nut2PWrFkoKChARESEVvPTn/4UoaGh+OY3v4mWlhYsWLAAb775JkJCQrSat99+G2vWrNHOLlu+fDl27Nih7Q8JCcH777+Pl19+GY8++iiMRiNWrFiBnJwcrUZVVRQWFuKVV17BzJkzERUVhXXr1mHdunUD/bZpGPtq7DiEhejQ1NqOmsYWJESNDXRLREQ0TOhEeJnl3jidTqiqCofDwcNkI9jifzuIU7Ym/PJvZyJtys3XhxER0ejR1+9v3muMgsKU+K4F01wnREREX2IQoqBwP4MQERHdBIMQBQUGISIiuhkGIQoKXRdVPH/1Oppd7QHuhoiIhgsGIQoKd41TEBuhQAQ4ZeOFFYmIqAODEAUNHh4jIqLuGIQoaDAIERFRdwxCFDR481UiIuqOQYiCRte1hE7ZmuD18jqiRETEIERBJDkmHIZQPa67Pai+ej3Q7RAR0TDAIERBIzREj/vieHiMiIi+xCBEQYXrhIiI6EYMQhRUus4cO1nHawkRERGDEAUZnkJPREQ3YhCioHK/qSMI1TS2wNHSFuBuiIgo0BiEKKioY8NgVscAAE5xVoiIKOgxCFHQ4eExIiLqwiBEQefLIMQF00REwY5BiIKOFoRsnBEiIgp2DEIUdLquJXTa1oR2jzfA3RARUSAxCFHQSborHMawELjavTjX0BzodoiIKIAYhCjohOh1uM/UMSvECysSEQU3BiEKSjxzjIiIAAYhClJTblgnREREwYtBiILSfZ1XmOZFFYmIghuDEAWlrjVCtY5W3mqDiCiIMQhRUFKNYZgw3giAh8eIiIIZgxAFrcmds0KneGFFIqKgxSBEQavr8BhvtUFEFLwYhChoTe48hf40Z4SIiIIWgxAFrftNX55C7/VKgLshIqJAYBCioJUcEw5DiB7Nbg8u2lsC3Q4REQWAX0Ho5z//OaZPn47IyEhERkZizpw5+OMf/6jtFxFs3rwZZrMZRqMR8+bNw4kTJ3yew+VyYfXq1YiJiUF4eDiWL1+Oixcv+tTY7XZkZmZCVVWoqorMzEw0Njb61FRXV2PZsmUIDw9HTEwM1qxZA7fb7VNjtVqRmpoKo9GICRMmYMuWLRDhf/lTh9AQPb4aOw4AF0wTEQUrv4JQQkICtm7dik8//RSffvop5s+fj69//eta2Nm2bRtyc3OxY8cOlJWVwWQyIS0tDU1NXy5GXbt2Lfbt24e8vDwUFxfj2rVrSE9Ph8fj0WpWrFiBiooK5OfnIz8/HxUVFcjMzNT2ezweLF26FM3NzSguLkZeXh727NmD9evXazVOpxNpaWkwm80oKyvD9u3bkZOTg9zc3H4PFo0+k+O7zhzjgmkioqAkdygqKkp+9atfidfrFZPJJFu3btX2tba2iqqqsmvXLhERaWxslLCwMMnLy9NqampqRK/XS35+voiInDx5UgBIaWmpVlNSUiIA5NSpUyIisn//ftHr9VJTU6PV7N69WxRFEYfDISIiO3fuFFVVpbW1VavJzs4Ws9ksXq+3z+/P4XAIAO15aXR5/cBfJGnDe/LSW58GuhUiIhpAff3+7vcaIY/Hg7y8PDQ3N2POnDmoqqqCzWbDwoULtRpFUZCamopDhw4BAMrLy9HW1uZTYzabYbFYtJqSkhKoqopZs2ZpNbNnz4aqqj41FosFZrNZq1m0aBFcLhfKy8u1mtTUVCiK4lNTW1uLc+fO3fJ9uVwuOJ1OnweNXl2n0J/iKfREREHJ7yBktVoxbtw4KIqCVatWYd++fZgyZQpsNhsAIC4uzqc+Li5O22ez2WAwGBAVFdVrTWxsbI/XjY2N9anp/jpRUVEwGAy91nT93lVzM9nZ2draJFVVkZiY2PuA0IjWdWjsXEMzWtye21QTEdFo43cQuu+++1BRUYHS0lK89NJLeO6553Dy5Eltv06n86kXkR7buutec7P6gaiRzoXSvfWzceNGOBwO7XHhwoVee6eR7e5xCu4KN8ArwJl6zgoREQUbv4OQwWDAV7/6VcycORPZ2dmYMWMG/v3f/x0mkwlAz9mW+vp6bSbGZDLB7XbDbrf3WnPp0qUer3v58mWfmu6vY7fb0dbW1mtNfX09gJ6zVjdSFEU7K67rQaOXTqf7csE0D48REQWdO76OkIjA5XIhOTkZJpMJhYWF2j63240DBw5g7ty5AICUlBSEhYX51NTV1aGyslKrmTNnDhwOB44cOaLVHD58GA6Hw6emsrISdXV1Wk1BQQEURUFKSopWc/DgQZ9T6gsKCmA2mzFp0qQ7fds0itwX1xF2eeYYEVEQ8mcF9saNG+XgwYNSVVUlx48fl+9///ui1+uloKBARES2bt0qqqrK3r17xWq1SkZGhsTHx4vT6dSeY9WqVZKQkCBFRUVy9OhRmT9/vsyYMUPa29u1msWLF8v06dOlpKRESkpKZNq0aZKenq7tb29vF4vFIgsWLJCjR49KUVGRJCQkSFZWllbT2NgocXFxkpGRIVarVfbu3SuRkZGSk5Pjz1vmWWNB4J2yakna8J5kvF4S6FaIiGiA9PX7268g9MILL0hSUpIYDAa5++67ZcGCBVoIEhHxer2yadMmMZlMoiiKPPHEE2K1Wn2eo6WlRbKysiQ6OlqMRqOkp6dLdXW1T01DQ4OsXLlSIiIiJCIiQlauXCl2u92n5vz587J06VIxGo0SHR0tWVlZPqfKi4gcP35cHn/8cVEURUwmk2zevNmvU+dFGISCwfELjZK04T154Acf+P35ICKi4amv3986EV5quTdOpxOqqsLhcHC90CjV2ubBlFfz4RXgyPcXIDZyTKBbIiKiO9TX72/ea4yC3piwEEyKCQfAdUJERMGGQYgIwP2mrgXTvIAmEVEwYRAiAjCZV5gmIgpKDEJEuOFWGzw0RkQUVBiEiADcH99xaOzP9dfQ5vEGuBsiIhoqDEJEACaMN2KcEgq3x4uqK82BboeIiIYIgxARAL1epx0e+7yOC6aJiIIFgxBRp64gdJrrhIiIggaDEFGn+7lgmogo6DAIEXWa3Llg+hQPjRERBQ0GIaJOX4vrmBGqdbTC0dIW4G6IiGgoMAgRdVKNYZgw3giA64SIiIIFgxDRDbQrTPNWG0REQYFBiOgGk+O7TqHnjBARUTBgECK6wX2dN189zRkhIqKgwCBEdIP7b7iWkNcrAe6GiIgGG4MQ0Q2SY8JhCNGj2e3BRXtLoNshIqJBxiBEdIPQED2+GjsOABdMExEFAwYhom66FkzzCtNERKMfgxBRN/d3LpjmjBAR0ejHIETUjTYjxFPoiYhGPQYhom667kJ/rqEZLW5PgLshIqLBxCBE1M3d4xTcFW6AV4Az9ZwVIiIazRiEiLrR6XQ8PEZEFCQYhIhuYnLngunPuWCaiGhUYxAiuon7brjCNBERjV4MQkQ30XUK/ed1TojwVhtERKMVgxDRTdwbNw56HWC/3obLTa5At0NERIOEQYjoJsaEhWBSTDgAXmGaiGg0YxAiugVeYZqIaPRjECK6hckmnkJPRDTa+RWEsrOz8fDDDyMiIgKxsbF46qmncPr0aZ8aEcHmzZthNpthNBoxb948nDhxwqfG5XJh9erViImJQXh4OJYvX46LFy/61NjtdmRmZkJVVaiqiszMTDQ2NvrUVFdXY9myZQgPD0dMTAzWrFkDt9vtU2O1WpGamgqj0YgJEyZgy5YtXPxKfTI5vusUegYhIqLRyq8gdODAAbzyyisoLS1FYWEh2tvbsXDhQjQ3N2s127ZtQ25uLnbs2IGysjKYTCakpaWhqenLL5O1a9di3759yMvLQ3FxMa5du4b09HR4PF/ezmDFihWoqKhAfn4+8vPzUVFRgczMTG2/x+PB0qVL0dzcjOLiYuTl5WHPnj1Yv369VuN0OpGWlgaz2YyysjJs374dOTk5yM3N7ddgUXDpmhH6S/01tHm8Ae6GiIgGhdyB+vp6ASAHDhwQERGv1ysmk0m2bt2q1bS2toqqqrJr1y4REWlsbJSwsDDJy8vTampqakSv10t+fr6IiJw8eVIASGlpqVZTUlIiAOTUqVMiIrJ//37R6/VSU1Oj1ezevVsURRGHwyEiIjt37hRVVaW1tVWryc7OFrPZLF6vt0/v0eFwCADtOSl4eDxemfpqviRteE9O25yBboeIiPzQ1+/vO1oj5HA4AADR0dEAgKqqKthsNixcuFCrURQFqampOHToEACgvLwcbW1tPjVmsxkWi0WrKSkpgaqqmDVrllYze/ZsqKrqU2OxWGA2m7WaRYsWweVyoby8XKtJTU2Foig+NbW1tTh37txN35PL5YLT6fR5UHDS63XahRU/r+PngIhoNOp3EBIRrFu3Do899hgsFgsAwGazAQDi4uJ8auPi4rR9NpsNBoMBUVFRvdbExsb2eM3Y2Fifmu6vExUVBYPB0GtN1+9dNd1lZ2dr65JUVUViYuJtRoJGs8m8wjQR0ajW7yCUlZWF48ePY/fu3T326XQ6n99FpMe27rrX3Kx+IGqkc6H0rfrZuHEjHA6H9rhw4UKvfdPopp05xiBERDQq9SsIrV69Gu+++y4+/vhjJCQkaNtNJhOAnrMt9fX12kyMyWSC2+2G3W7vtebSpUs9Xvfy5cs+Nd1fx263o62trdea+vp6AD1nrbooioLIyEifBwWvrjPHTvHQGBHRqORXEBIRZGVlYe/evfjoo4+QnJzssz85ORkmkwmFhYXaNrfbjQMHDmDu3LkAgJSUFISFhfnU1NXVobKyUquZM2cOHA4Hjhw5otUcPnwYDofDp6ayshJ1dXVaTUFBARRFQUpKilZz8OBBn1PqCwoKYDabMWnSJH/eOgWprjVCtY5WOK63BbgbIiIacP6swH7ppZdEVVX55JNPpK6uTntcv35dq9m6dauoqip79+4Vq9UqGRkZEh8fL07nl2fdrFq1ShISEqSoqEiOHj0q8+fPlxkzZkh7e7tWs3jxYpk+fbqUlJRISUmJTJs2TdLT07X97e3tYrFYZMGCBXL06FEpKiqShIQEycrK0moaGxslLi5OMjIyxGq1yt69eyUyMlJycnL6/J551hjNzf5Qkja8J4fPNgS6FSIi6qO+fn/7FYQA3PTxxhtvaDVer1c2bdokJpNJFEWRJ554QqxWq8/ztLS0SFZWlkRHR4vRaJT09HSprq72qWloaJCVK1dKRESEREREyMqVK8Vut/vUnD9/XpYuXSpGo1Gio6MlKyvL51R5EZHjx4/L448/LoqiiMlkks2bN/f51HkRBiESeeGNI5K04T35zaGqQLdCRER91Nfvb50IL7PcG6fTCVVV4XA4uF4oSP34g1P42cd/QcYjE5H99LRAt0NERH3Q1+9v3muM6DYmd9589TRvvkpENOowCBHdxo3XEvJ6OYFKRDSaMAgR3UZyTDgMIXo0uz24aG8JdDtERDSAGISIbiM0RI9748YBAD7n4TEiolGFQYioD+7jrTaIiEYlBiGiPri/c8H0Kc4IERGNKgxCRH0wOb7znmN1nBEiIhpNGISI+qDrFPpzDc1ocXsC3A0REQ0UBiGiPrg7QsFd4QZ4BfjiEmeFiIhGCwYhoj6aYu6YFTpRy3VCRESjBYMQUR9ZJqgAAGuNI8CdEBHRQGEQIuoji7kjCJ2oZRAiIhotGISI+sgyofMU+romtHm8Ae6GiIgGAoMQUR9NjB6LiDGhcHu8OHPpWqDbISKiAcAgRNRHOp1OOzxWycNjRESjAoMQkR+6Do+d4IJpIqJRgUGIyA9dZ45V8hR6IqJRgUGIyA9TOw+Nnax1wuOVAHdDRER3ikGIyA/JMeEYawhBS5sHZy9zwTQR0UjHIETkhxC9DlPiO9YJccE0EdHIxyBE5CdtnVAN1wkREY10DEJEfpraec+xSp45RkQ04jEIEfmpa0boZK0TXi6YJiIa0RiEiPz01dhxMITq0eRqR/XV64Fuh4iI7gCDEJGfwkL0uJ8LpomIRgUGIaJ+sGjrhLhgmohoJGMQIuqHL88c44wQEdFIxiBE1A9dN1+11jggwgXTREQjFYMQUT/cZ4qAIVQPR0sbzjdwwTQR0UjFIETUD4ZQvXaF6c8uNga2GSIi6jcGIaJ+eiBxPADgswtcJ0RENFL5HYQOHjyIZcuWwWw2Q6fT4fe//73PfhHB5s2bYTabYTQaMW/ePJw4ccKnxuVyYfXq1YiJiUF4eDiWL1+Oixcv+tTY7XZkZmZCVVWoqorMzEw0Njb61FRXV2PZsmUIDw9HTEwM1qxZA7fb7VNjtVqRmpoKo9GICRMmYMuWLVzTQQNiekLHOiHOCBERjVx+B6Hm5mbMmDEDO3bsuOn+bdu2ITc3Fzt27EBZWRlMJhPS0tLQ1NSk1axduxb79u1DXl4eiouLce3aNaSnp8Pj8Wg1K1asQEVFBfLz85Gfn4+KigpkZmZq+z0eD5YuXYrm5mYUFxcjLy8Pe/bswfr167Uap9OJtLQ0mM1mlJWVYfv27cjJyUFubq6/b5uohxmdM0Inah1o83gD2wwREfWP3AEAsm/fPu13r9crJpNJtm7dqm1rbW0VVVVl165dIiLS2NgoYWFhkpeXp9XU1NSIXq+X/Px8ERE5efKkAJDS0lKtpqSkRADIqVOnRERk//79otfrpaamRqvZvXu3KIoiDodDRER27twpqqpKa2urVpOdnS1ms1m8Xm+f3qPD4RAA2nMSdfF4vGLZlC9JG96TyprGQLdDREQ36Ov394CuEaqqqoLNZsPChQu1bYqiIDU1FYcOHQIAlJeXo62tzafGbDbDYrFoNSUlJVBVFbNmzdJqZs+eDVVVfWosFgvMZrNWs2jRIrhcLpSXl2s1qampUBTFp6a2thbnzp276XtwuVxwOp0+D6Kb0et12uGx4xe5ToiIaCQa0CBks9kAAHFxcT7b4+LitH02mw0GgwFRUVG91sTGxvZ4/tjYWJ+a7q8TFRUFg8HQa03X71013WVnZ2vrklRVRWJi4u3fOAWtGQnjAQAV1Y0B7YOIiPpnUM4a0+l0Pr+LSI9t3XWvuVn9QNRI50LpW/WzceNGOBwO7XHhwoVe+6bg9uDEjkB/7II9wJ0QEVF/DGgQMplMAHrOttTX12szMSaTCW63G3a7vdeaS5cu9Xj+y5cv+9R0fx273Y62trZea+rr6wH0nLXqoigKIiMjfR5Et/LQxPEAgC8uXYPjeltgmyEiIr8NaBBKTk6GyWRCYWGhts3tduPAgQOYO3cuACAlJQVhYWE+NXV1daisrNRq5syZA4fDgSNHjmg1hw8fhsPh8KmprKxEXV2dVlNQUABFUZCSkqLVHDx40OeU+oKCApjNZkyaNGkg3zoFqbvGKUiOCQcAHOWsEBHRiON3ELp27RoqKipQUVEBoGOBdEVFBaqrq6HT6bB27Vq89tpr2LdvHyorK/H8889j7NixWLFiBQBAVVX83d/9HdavX48PP/wQx44dw7PPPotp06bhySefBADcf//9WLx4MV588UWUlpaitLQUL774ItLT03HfffcBABYuXIgpU6YgMzMTx44dw4cffojvfOc7ePHFF7VZnBUrVkBRFDz//POorKzEvn378Nprr2HdunW3PVRH1FcPdR4eO3qeQYiIaMTx93S0jz/+WAD0eDz33HMi0nEK/aZNm8RkMomiKPLEE0+I1Wr1eY6WlhbJysqS6OhoMRqNkp6eLtXV1T41DQ0NsnLlSomIiJCIiAhZuXKl2O12n5rz58/L0qVLxWg0SnR0tGRlZfmcKi8icvz4cXn88cdFURQxmUyyefPmPp86L8LT5+n23i49L0kb3pOM10sC3QoREXXq6/e3ToSXWe6N0+mEqqpwOBxcL0Q3ddrWhEX/dhBjDSE4vmkhQkN45xoiokDr6/c3/x+b6A7dGzsOEWNCcd3twSlb0+3/gIiIhg0GIaI7pNfrtHVC5VwnREQ0ojAIEQ2Ahyd1BKEjVVcD3AkREfmDQYhoAMy+5y4AwOGqBnDZHRHRyMEgRDQApieMx5gwPa5cc+Mvl68Fuh0iIuojBiGiAWAI1SMlqePwWMlZHh4jIhopGISIBsis5I7DY6VnGwLcCRER9RWDENEAmZUcDQA4fPYq1wkREY0QDEJEA2RG4ngooXpcuebiOiEiohGCQYhogIwJC9HWCRWfuRLgboiIqC8YhIgG0OP33g0A+BODEBHRiMAgRDSAnvhaDACg5GwD3O3eAHdDRES3wyBENIDuN0UiZpyC624PPj3P0+iJiIY7BiGiAaTX6/DEvR2zQge/4OExIqLhjkGIaIA9/rWuIHQ5wJ0QEdHtMAgRDbCuBdMn65y45GwNcDdERNQbBiGiARYzTsGDE8cDAD44YQtsM0RE1CsGIaJBsMRiAgD80cogREQ0nDEIEQ2CJZZ4AMDhqgY0XHMFuBsiIroVBiGiQZAYPRZTzZHwClB48lKg2yEioltgECIaJNrhsUoeHiMiGq4YhIgGyV9P6zg8VvznK6jn2WNERMMSgxDRILnn7nGYmRQFj1fwf8ovBrodIiK6CQYhokH0zCMTAQDvlF2A1ysB7oaIiLpjECIaRH89zYQIJRTVV6+j5GxDoNshIqJuGISIBtFYQyiWP2AGAPy25FxgmyEioh4YhIgG2XNzJ0GnAz44cQmVNY5At0NERDdgECIaZF+Li8Cy6R2zQrmFXwS4GyIiuhGDENEQWPvkvQjR6/DRqXp8eu5qoNshIqJODEJEQ+Ceu8fh/30oAQCw7j8/g6OlLcAdERERwCBENGQ2/vVkJEQZUX31Or7zfz7j6fRERMNAUAShnTt3Ijk5GWPGjEFKSgr+9Kc/BbolCkLjxxqwc+VDMIToUXjyEp55vRR/rm+CCAMREVGg6GSU/7/wO++8g8zMTOzcuROPPvoofvGLX+BXv/oVTp48iYkTJ972751OJ1RVhcPhQGRk5BB0TKPde8drseG/jqPZ7QEAxIwzIF41whCqhxKqv+GfId1+10Pp3Hbj9rCQLx+GUN0Nv+t894XoEXbDfkNnTYheB51OF+BRISIaWH39/h71QWjWrFl46KGH8POf/1zbdv/99+Opp55Cdnb2bf+eQYgGw4Wr1/Hq/63EgS8uI9BHyHQ6+ASjmwapUD0M3YNVaPegpUPoDT93/V3Xc4Xq9QjV6xDaGb7CQvSd/9QhRK9HmL5je2jIl3VajV6PkBDdlzV6PfR6IESvg16nQ6iegY6IfPX1+zt0CHsacm63G+Xl5fje977ns33hwoU4dOhQgLoiAhKjx+KN//EIWtwenL7UBPt1N1xtXrjaPXC3e+Fq93b7Z/ftHrg9XrjavGjzCtravWjzdDzcHtF+bmvv9rvHizaPb/ISAdydzzvS6XRAiE4Hvb4zHHX+3D0w6fVf1oXoOraF3FAX0rldp+t4Th06ftZ3bgO+/FkHQKfTQa8DoNV9+TcdD51PXdfP2j+1bTe+Rmdt53MN1ngNyvMOxnMy5I5qT94fh8fujQnIa4/qIHTlyhV4PB7ExcX5bI+Li4PNZrvp37hcLrhcLu13p9M5qD1ScDMaQvBA4vghfU0RQbu3Mxy1C9w+IckLd7vcEKo6glNbuxft3s5QdbPQ1X6TENb5XO3eLwOYp/N1PV5Bu6djX0cvAo/X27lN0O7p2N5+k/reZtBEgHYRwCtwD92QEtEdio1UGIQGU/f/khCRW/7XRXZ2Nn7wgx8MRVtEAaHT6bTDXjAEuhv/eTsDklc6gpVHBF5v58+dv3u8Aq8X2s9dj9v+jQg8Xmg/d2QqgaDj/zdEAEHX9s5tAOBT9+XPEPGp6/oZ3eq6fkbn63TUdbzWoB06HaRVEYPxrIO1gEMGpVvqj4cmRgXstUd1EIqJiUFISEiP2Z/6+voes0RdNm7ciHXr1mm/O51OJCYmDmqfRNR3er0OBj0PkxDRwBjVp88bDAakpKSgsLDQZ3thYSHmzp17079RFAWRkZE+DyIiIhqdRvWMEACsW7cOmZmZmDlzJubMmYPXX38d1dXVWLVqVaBbIyIiogAb9UHoW9/6FhoaGrBlyxbU1dXBYrFg//79SEpKCnRrREREFGCj/jpCd4rXESIiIhp5+vr9ParXCBERERH1hkGIiIiIghaDEBEREQUtBiEiIiIKWgxCREREFLQYhIiIiChoMQgRERFR0GIQIiIioqDFIERERERBa9TfYuNOdV142+l0BrgTIiIi6quu7+3b3UCDQeg2mpqaAACJiYkB7oSIiIj81dTUBFVVb7mf9xq7Da/Xi9raWkRERECn0w3oczudTiQmJuLChQu8j9kg4jgPHY710OA4Dx2O9dAYjHEWETQ1NcFsNkOvv/VKIM4I3YZer0dCQsKgvkZkZCT/BzYEOM5Dh2M9NDjOQ4djPTQGepx7mwnqwsXSREREFLQYhIiIiChoMQgFkKIo2LRpExRFCXQroxrHeehwrIcGx3nocKyHRiDHmYuliYiIKGhxRoiIiIiCFoMQERERBS0GISIiIgpaDEJEREQUtBiEAmTnzp1ITk7GmDFjkJKSgj/96U+BbmlE27x5M3Q6nc/DZDJp+0UEmzdvhtlshtFoxLx583DixIkAdjxyHDx4EMuWLYPZbIZOp8Pvf/97n/19GVuXy4XVq1cjJiYG4eHhWL58OS5evDiE72L4u904P//88z0+47Nnz/ap4TjfXnZ2Nh5++GFEREQgNjYWTz31FE6fPu1Tw8/0wOjLWA+HzzWDUAC88847WLt2Lf75n/8Zx44dw+OPP44lS5aguro60K2NaFOnTkVdXZ32sFqt2r5t27YhNzcXO3bsQFlZGUwmE9LS0rR7ydGtNTc3Y8aMGdixY8dN9/dlbNeuXYt9+/YhLy8PxcXFuHbtGtLT0+HxeIbqbQx7txtnAFi8eLHPZ3z//v0++znOt3fgwAG88sorKC0tRWFhIdrb27Fw4UI0NzdrNfxMD4y+jDUwDD7XQkPukUcekVWrVvlsmzx5snzve98LUEcj36ZNm2TGjBk33ef1esVkMsnWrVu1ba2traKqquzatWuIOhwdAMi+ffu03/syto2NjRIWFiZ5eXlaTU1Njej1esnPzx+y3keS7uMsIvLcc8/J17/+9Vv+Dce5f+rr6wWAHDhwQET4mR5M3cdaZHh8rjkjNMTcbjfKy8uxcOFCn+0LFy7EoUOHAtTV6HDmzBmYzWYkJyfjmWeewdmzZwEAVVVVsNlsPmOuKApSU1M55neoL2NbXl6OtrY2nxqz2QyLxcLx99Mnn3yC2NhYfO1rX8OLL76I+vp6bR/HuX8cDgcAIDo6GgA/04Op+1h3CfTnmkFoiF25cgUejwdxcXE+2+Pi4mCz2QLU1cg3a9Ys/Pa3v8UHH3yAX/7yl7DZbJg7dy4aGhq0ceWYD7y+jK3NZoPBYEBUVNQta+j2lixZgrfffhsfffQRfvKTn6CsrAzz58+Hy+UCwHHuDxHBunXr8Nhjj8FisQDgZ3qw3GysgeHxuebd5wNEp9P5/C4iPbZR3y1ZskT7edq0aZgzZw6+8pWv4De/+Y228I5jPnj6M7Ycf/9861vf0n62WCyYOXMmkpKS8P777+Ppp5++5d9xnG8tKysLx48fR3FxcY99/EwPrFuN9XD4XHNGaIjFxMQgJCSkR5Ktr6/v8V8g1H/h4eGYNm0azpw5o509xjEfeH0ZW5PJBLfbDbvdfssa8l98fDySkpJw5swZABxnf61evRrvvvsuPv74YyQkJGjb+ZkeeLca65sJxOeaQWiIGQwGpKSkoLCw0Gd7YWEh5s6dG6CuRh+Xy4XPP/8c8fHxSE5Ohslk8hlzt9uNAwcOcMzvUF/GNiUlBWFhYT41dXV1qKys5PjfgYaGBly4cAHx8fEAOM59JSLIysrC3r178dFHHyE5OdlnPz/TA+d2Y30zAflcD8iSa/JLXl6ehIWFya9//Ws5efKkrF27VsLDw+XcuXOBbm3EWr9+vXzyySdy9uxZKS0tlfT0dImIiNDGdOvWraKqquzdu1esVqtkZGRIfHy8OJ3OAHc+/DU1NcmxY8fk2LFjAkByc3Pl2LFjcv78eRHp29iuWrVKEhISpKioSI4ePSrz58+XGTNmSHt7e6De1rDT2zg3NTXJ+vXr5dChQ1JVVSUff/yxzJkzRyZMmMBx9tNLL70kqqrKJ598InV1ddrj+vXrWg0/0wPjdmM9XD7XDEIB8rOf/UySkpLEYDDIQw895HM6IfnvW9/6lsTHx0tYWJiYzWZ5+umn5cSJE9p+r9crmzZtEpPJJIqiyBNPPCFWqzWAHY8cH3/8sQDo8XjuuedEpG9j29LSIllZWRIdHS1Go1HS09Oluro6AO9m+OptnK9fvy4LFy6Uu+++W8LCwmTixIny3HPP9RhDjvPt3WyMAcgbb7yh1fAzPTBuN9bD5XOt62yWiIiIKOhwjRAREREFLQYhIiIiCloMQkRERBS0GISIiIgoaDEIERERUdBiECIiIqKgxSBEREREQYtBiIiIiIIWgxAREREFLQYhIiIiCloMQkRERBS0GISIiIgoaP3/oNXdSpvUjYMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.lineplot(x=range(len(loss)),y=loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "6ffa94a3-b749-4894-835c-4ec4e19ed304",
   "metadata": {},
   "outputs": [],
   "source": [
    "trainLoss=model.evaluate(x_train,y_train,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "ec8ce086-1131-456c-99f9-421112c11ee8",
   "metadata": {},
   "outputs": [],
   "source": [
    "testLoss=model.evaluate(x_test,y_test,verbose=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "702ee43d-40cc-46cc-ba33-28353d0cf024",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "73.07734680175781"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "trainLoss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "c54d59d6-563e-4b70-8aa5-6d8f28eb2feb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76.19615936279297"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "testLoss"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "19913f88-6025-4fec-9eac-9768a84ed6b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m11/11\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 13ms/step\n"
     ]
    }
   ],
   "source": [
    "testTahminleri=model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "c1551f8e-2540-435a-9ef6-d5e6942e7ab7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sbn.scatterplot(x=\"gerçek y\",y=\"tahmin y\",data=tahminDF)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "129e5d7c-0202-47c6-b628-04c5fd4b010b",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import mean_absolute_error,mean_squared_error"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "064e8b1a-f13d-4e49-8cda-ab6d90825295",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.992065958290718"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean_absolute_error(tahminDF[\"gerçek y\"],tahminDF[\"tahmin y\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "id": "6ed813d7-04bc-4da2-ab2a-ccefa446bf72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fiyat</th>\n",
       "      <th>BisikletOzellik1</th>\n",
       "      <th>BisikletOzellik2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "      <td>1000.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>872.677801</td>\n",
       "      <td>1750.024800</td>\n",
       "      <td>1749.964733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>164.124504</td>\n",
       "      <td>1.704531</td>\n",
       "      <td>1.659578</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>390.856887</td>\n",
       "      <td>1744.852108</td>\n",
       "      <td>1744.742389</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>757.795031</td>\n",
       "      <td>1748.831119</td>\n",
       "      <td>1748.803186</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>879.168705</td>\n",
       "      <td>1750.017350</td>\n",
       "      <td>1750.003926</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>988.612778</td>\n",
       "      <td>1751.115765</td>\n",
       "      <td>1751.129414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1355.213745</td>\n",
       "      <td>1755.613884</td>\n",
       "      <td>1754.666038</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             Fiyat  BisikletOzellik1  BisikletOzellik2\n",
       "count  1000.000000       1000.000000       1000.000000\n",
       "mean    872.677801       1750.024800       1749.964733\n",
       "std     164.124504          1.704531          1.659578\n",
       "min     390.856887       1744.852108       1744.742389\n",
       "25%     757.795031       1748.831119       1748.803186\n",
       "50%     879.168705       1750.017350       1750.003926\n",
       "75%     988.612778       1751.115765       1751.129414\n",
       "max    1355.213745       1755.613884       1754.666038"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "1c823485-b5b8-47db-8292-2364f3620eeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "yeniBisikletOzellikleri=[[1750,1750]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 255,
   "id": "095a3125-76ee-4e5c-af01-16da59d1f903",
   "metadata": {},
   "outputs": [],
   "source": [
    "yeniBisikletOzellikleri=scaler.transform(yeniBisikletOzellikleri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 257,
   "id": "d416f753-f56f-424f-8cce-d35a3140a2e2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 78ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[874.3308]], dtype=float32)"
      ]
     },
     "execution_count": 257,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.predict(yeniBisikletOzellikleri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "f9a46e88-f184-42cf-9076-dbf09dba40b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Fiyat</th>\n",
       "      <th>BisikletOzellik1</th>\n",
       "      <th>BisikletOzellik2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>807.673876</td>\n",
       "      <td>1749.628226</td>\n",
       "      <td>1749.590668</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>959.227520</td>\n",
       "      <td>1748.007826</td>\n",
       "      <td>1751.824206</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>718.020033</td>\n",
       "      <td>1750.122967</td>\n",
       "      <td>1747.977026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>945.668885</td>\n",
       "      <td>1749.916440</td>\n",
       "      <td>1750.771646</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>955.542968</td>\n",
       "      <td>1750.780519</td>\n",
       "      <td>1750.592430</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        Fiyat  BisikletOzellik1  BisikletOzellik2\n",
       "0  807.673876       1749.628226       1749.590668\n",
       "1  959.227520       1748.007826       1751.824206\n",
       "2  718.020033       1750.122967       1747.977026\n",
       "3  945.668885       1749.916440       1750.771646\n",
       "4  955.542968       1750.780519       1750.592430"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dataFrame.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "515c488f-7e4d-4479-bc31-800046791655",
   "metadata": {},
   "outputs": [],
   "source": [
    "from tensorflow.keras.models import load_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 265,
   "id": "922edb0d-98fb-4439-975e-2f860787baac",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.save(\"bisiklet_modeli.keras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "baba413f-573c-424d-9cb0-2ce7e33addc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "sonradanCagirilanModel=load_model(\"bisiklet_modeli.keras\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "a1db1b8a-c600-414c-9683-7310450694ae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 189ms/step\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[874.3308]], dtype=float32)"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sonradanCagirilanModel.predict(yeniBisikletOzellikleri)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "819da98f-75b6-43a8-a58c-20973bb637fb",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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