Untitled12.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9985cbaa-312e-4485-9504-0dae58b6c377",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "90a01c01-06d5-41cc-af54-104d4d9d5ba0",
   "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>Polygon Name</th>\n",
       "      <th>Polygon Index</th>\n",
       "      <th>Date</th>\n",
       "      <th>Mean NDVI (Buffered)</th>\n",
       "      <th>Mean NDVI (Original Polygon)</th>\n",
       "      <th>Mean NDWI (Buffered)</th>\n",
       "      <th>Mean NDWI (Original Polygon)</th>\n",
       "      <th>Mean LAI (Buffered)</th>\n",
       "      <th>Mean LAI (Original Polygon)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mısır_1</td>\n",
       "      <td>1</td>\n",
       "      <td>2023-03-30</td>\n",
       "      <td>0.0305</td>\n",
       "      <td>0.0257</td>\n",
       "      <td>-0.0274</td>\n",
       "      <td>-0.0209</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.0190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mısır_1</td>\n",
       "      <td>1</td>\n",
       "      <td>2023-03-27</td>\n",
       "      <td>0.0320</td>\n",
       "      <td>0.0232</td>\n",
       "      <td>-0.0418</td>\n",
       "      <td>-0.0310</td>\n",
       "      <td>0.0210</td>\n",
       "      <td>0.0101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>mısır_1</td>\n",
       "      <td>1</td>\n",
       "      <td>2023-04-24</td>\n",
       "      <td>0.0676</td>\n",
       "      <td>0.0495</td>\n",
       "      <td>-0.0729</td>\n",
       "      <td>-0.0539</td>\n",
       "      <td>0.1498</td>\n",
       "      <td>0.1050</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>mısır_1</td>\n",
       "      <td>1</td>\n",
       "      <td>2023-03-17</td>\n",
       "      <td>0.0036</td>\n",
       "      <td>0.0001</td>\n",
       "      <td>-0.0063</td>\n",
       "      <td>-0.0041</td>\n",
       "      <td>-0.0816</td>\n",
       "      <td>-0.0738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>mısır_1</td>\n",
       "      <td>1</td>\n",
       "      <td>2023-11-20</td>\n",
       "      <td>0.0148</td>\n",
       "      <td>0.0112</td>\n",
       "      <td>-0.0165</td>\n",
       "      <td>-0.0128</td>\n",
       "      <td>-0.0412</td>\n",
       "      <td>-0.0336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>982</th>\n",
       "      <td>mısır5_hsyn</td>\n",
       "      <td>7</td>\n",
       "      <td>2023-12-20</td>\n",
       "      <td>0.0965</td>\n",
       "      <td>0.0496</td>\n",
       "      <td>-0.1361</td>\n",
       "      <td>-0.0838</td>\n",
       "      <td>0.2641</td>\n",
       "      <td>0.1293</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>983</th>\n",
       "      <td>mısır5_hsyn</td>\n",
       "      <td>7</td>\n",
       "      <td>2023-11-05</td>\n",
       "      <td>0.1175</td>\n",
       "      <td>0.0550</td>\n",
       "      <td>-0.1601</td>\n",
       "      <td>-0.0806</td>\n",
       "      <td>0.3398</td>\n",
       "      <td>0.1488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>984</th>\n",
       "      <td>mısır5_hsyn</td>\n",
       "      <td>7</td>\n",
       "      <td>2023-01-04</td>\n",
       "      <td>0.0693</td>\n",
       "      <td>0.0255</td>\n",
       "      <td>-0.0983</td>\n",
       "      <td>-0.0433</td>\n",
       "      <td>0.1655</td>\n",
       "      <td>0.0422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>985</th>\n",
       "      <td>mısır5_hsyn</td>\n",
       "      <td>7</td>\n",
       "      <td>2023-12-30</td>\n",
       "      <td>0.0923</td>\n",
       "      <td>0.0516</td>\n",
       "      <td>-0.1399</td>\n",
       "      <td>-0.0908</td>\n",
       "      <td>0.2486</td>\n",
       "      <td>0.1367</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>986</th>\n",
       "      <td>mısır5_hsyn</td>\n",
       "      <td>7</td>\n",
       "      <td>2023-01-19</td>\n",
       "      <td>0.0563</td>\n",
       "      <td>0.0195</td>\n",
       "      <td>-0.0826</td>\n",
       "      <td>-0.0341</td>\n",
       "      <td>0.1185</td>\n",
       "      <td>0.0204</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>987 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Polygon Name  Polygon Index        Date  Mean NDVI (Buffered)  \\\n",
       "0        mısır_1              1  2023-03-30                0.0305   \n",
       "1        mısır_1              1  2023-03-27                0.0320   \n",
       "2        mısır_1              1  2023-04-24                0.0676   \n",
       "3        mısır_1              1  2023-03-17                0.0036   \n",
       "4        mısır_1              1  2023-11-20                0.0148   \n",
       "..           ...            ...         ...                   ...   \n",
       "982  mısır5_hsyn              7  2023-12-20                0.0965   \n",
       "983  mısır5_hsyn              7  2023-11-05                0.1175   \n",
       "984  mısır5_hsyn              7  2023-01-04                0.0693   \n",
       "985  mısır5_hsyn              7  2023-12-30                0.0923   \n",
       "986  mısır5_hsyn              7  2023-01-19                0.0563   \n",
       "\n",
       "     Mean NDVI (Original Polygon)  Mean NDWI (Buffered)  \\\n",
       "0                          0.0257               -0.0274   \n",
       "1                          0.0232               -0.0418   \n",
       "2                          0.0495               -0.0729   \n",
       "3                          0.0001               -0.0063   \n",
       "4                          0.0112               -0.0165   \n",
       "..                            ...                   ...   \n",
       "982                        0.0496               -0.1361   \n",
       "983                        0.0550               -0.1601   \n",
       "984                        0.0255               -0.0983   \n",
       "985                        0.0516               -0.1399   \n",
       "986                        0.0195               -0.0826   \n",
       "\n",
       "     Mean NDWI (Original Polygon)  Mean LAI (Buffered)  \\\n",
       "0                         -0.0209               0.0155   \n",
       "1                         -0.0310               0.0210   \n",
       "2                         -0.0539               0.1498   \n",
       "3                         -0.0041              -0.0816   \n",
       "4                         -0.0128              -0.0412   \n",
       "..                            ...                  ...   \n",
       "982                       -0.0838               0.2641   \n",
       "983                       -0.0806               0.3398   \n",
       "984                       -0.0433               0.1655   \n",
       "985                       -0.0908               0.2486   \n",
       "986                       -0.0341               0.1185   \n",
       "\n",
       "     Mean LAI (Original Polygon)  \n",
       "0                         0.0190  \n",
       "1                         0.0101  \n",
       "2                         0.1050  \n",
       "3                        -0.0738  \n",
       "4                        -0.0336  \n",
       "..                           ...  \n",
       "982                       0.1293  \n",
       "983                       0.1488  \n",
       "984                       0.0422  \n",
       "985                       0.1367  \n",
       "986                       0.0204  \n",
       "\n",
       "[987 rows x 9 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Excel dosyasını okuma\n",
    "file_path = 'polygons_data_with_features_fixed.xlsx'  # Buraya kendi dosya yolunuzu girin\n",
    "df = pd.read_excel(file_path)\n",
    "\n",
    "# Veriyi görüntülemek için\n",
    "df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "dd7a573a-543e-4285-9331-49097a6c9b8c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Polygon Name\n",
      "mısır04_hsyn    141\n",
      "mısır2          141\n",
      "mısır2_hsyn     141\n",
      "mısır3_hsyn     141\n",
      "mısır5_hsyn     141\n",
      "mısır_1         141\n",
      "mısır_hsyn      141\n",
      "dtype: int64\n"
     ]
    }
   ],
   "source": [
    "# Her tarlanın veri sayısını bulalım\n",
    "tarla_veri_sayisi = df.groupby('Polygon Name').size()\n",
    "\n",
    "# Sonuçları yazdır\n",
    "print(tarla_veri_sayisi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cdc38bfd-a2ea-4241-975f-e2eb24d71ecd",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mehdiarat/miniconda3/envs/yolov10/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      "/Users/mehdiarat/miniconda3/envs/yolov10/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:473: ValueWarning: A date index has been provided, but it has no associated frequency information and so will be ignored when e.g. forecasting.\n",
      "  self._init_dates(dates, freq)\n",
      " This problem is unconstrained.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "RUNNING THE L-BFGS-B CODE\n",
      "\n",
      "           * * *\n",
      "\n",
      "Machine precision = 2.220D-16\n",
      " N =            5     M =           10\n",
      "\n",
      "At X0         0 variables are exactly at the bounds\n",
      "\n",
      "At iterate    0    f= -8.71185D-01    |proj g|=  2.08314D+00\n",
      "\n",
      "At iterate    5    f= -8.86623D-01    |proj g|=  1.33481D-01\n",
      "\n",
      "At iterate   10    f= -9.04664D-01    |proj g|=  1.26791D+00\n",
      "\n",
      "At iterate   15    f= -9.10971D-01    |proj g|=  7.93624D-01\n",
      "\n",
      "At iterate   20    f= -9.48673D-01    |proj g|=  1.84265D+00\n",
      "\n",
      "At iterate   25    f= -9.68738D-01    |proj g|=  4.12404D-01\n",
      "\n",
      "At iterate   30    f= -9.85811D-01    |proj g|=  6.80668D-02\n",
      "\n",
      "At iterate   35    f= -9.86626D-01    |proj g|=  5.59937D-02\n",
      "\n",
      "At iterate   40    f= -9.89361D-01    |proj g|=  7.22262D-03\n",
      "\n",
      "           * * *\n",
      "\n",
      "Tit   = total number of iterations\n",
      "Tnf   = total number of function evaluations\n",
      "Tnint = total number of segments explored during Cauchy searches\n",
      "Skip  = number of BFGS updates skipped\n",
      "Nact  = number of active bounds at final generalized Cauchy point\n",
      "Projg = norm of the final projected gradient\n",
      "F     = final function value\n",
      "\n",
      "           * * *\n",
      "\n",
      "   N    Tit     Tnf  Tnint  Skip  Nact     Projg        F\n",
      "    5     42     57      1     0     0   3.381D-02  -9.894D-01\n",
      "  F = -0.98938396179273669     \n",
      "\n",
      "CONVERGENCE: REL_REDUCTION_OF_F_<=_FACTR*EPSMCH             \n",
      "                                     SARIMAX Results                                      \n",
      "==========================================================================================\n",
      "Dep. Variable:       Mean NDVI (Original Polygon)   No. Observations:                  141\n",
      "Model:             SARIMAX(1, 1, 1)x(1, 1, 1, 12)   Log Likelihood                 139.503\n",
      "Date:                            Thu, 12 Sep 2024   AIC                           -269.006\n",
      "Time:                                    10:51:48   BIC                           -254.746\n",
      "Sample:                                         0   HQIC                          -263.212\n",
      "                                            - 141                                         \n",
      "Covariance Type:                              opg                                         \n",
      "==============================================================================\n",
      "                 coef    std err          z      P>|z|      [0.025      0.975]\n",
      "------------------------------------------------------------------------------\n",
      "ar.L1         -0.2406      0.090     -2.660      0.008      -0.418      -0.063\n",
      "ma.L1         -0.5387      0.085     -6.312      0.000      -0.706      -0.371\n",
      "ar.S.L12       0.1138      0.166      0.684      0.494      -0.212       0.440\n",
      "ma.S.L12      -0.9889      3.373     -0.293      0.769      -7.599       5.621\n",
      "sigma2         0.0054      0.018      0.304      0.761      -0.029       0.040\n",
      "===================================================================================\n",
      "Ljung-Box (L1) (Q):                   0.06   Jarque-Bera (JB):                78.17\n",
      "Prob(Q):                              0.80   Prob(JB):                         0.00\n",
      "Heteroskedasticity (H):               2.40   Skew:                            -0.26\n",
      "Prob(H) (two-sided):                  0.00   Kurtosis:                         6.79\n",
      "===================================================================================\n",
      "\n",
      "Warnings:\n",
      "[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mehdiarat/miniconda3/envs/yolov10/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:836: ValueWarning: No supported index is available. Prediction results will be given with an integer index beginning at `start`.\n",
      "  return get_prediction_index(\n",
      "/Users/mehdiarat/miniconda3/envs/yolov10/lib/python3.9/site-packages/statsmodels/tsa/base/tsa_model.py:836: FutureWarning: No supported index is available. In the next version, calling this method in a model without a supported index will result in an exception.\n",
      "  return get_prediction_index(\n",
      "/var/folders/gd/zf2q87154y9bm0v01rrxs_s40000gn/T/ipykernel_14479/350823212.py:30: FutureWarning: 'M' is deprecated and will be removed in a future version, please use 'ME' instead.\n",
      "  forecast_index = pd.date_range(start=ndvi_series.index[-1], periods=12, freq='M')\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Gerçek Hasat: 26905\n",
      "Tahmin Edilen Hasat: 142.9\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from statsmodels.tsa.statespace.sarimax import SARIMAX\n",
    "from sklearn.metrics import mean_squared_error\n",
    "\n",
    "# Veriyi tarihe göre sıralayalım\n",
    "df['Date'] = pd.to_datetime(df['Date'])\n",
    "df = df.sort_values('Date')\n",
    "\n",
    "# Tarlaları ayrı ayrı zaman serisi modelleriyle işleyebiliriz. Öncelikle bir tarlayı ele alalım:\n",
    "tarla_1 = df[df['Polygon Name'] == 'mısır_1']\n",
    "\n",
    "# NDVI, NDWI ve LAI verilerinin zaman serisini oluşturalım\n",
    "# NDVI, NDWI ve LAI verilerinin zaman serisini oluşturalım\n",
    "ndvi_series = tarla_1.set_index('Date')['Mean NDVI (Original Polygon)']\n",
    "ndwi_series = tarla_1.set_index('Date')['Mean NDWI (Original Polygon)']\n",
    "lai_series = tarla_1.set_index('Date')['Mean LAI (Original Polygon)']\n",
    "\n",
    "# Zaman serisi modelini eğitmek için NDVI verisini kullanacağız (SARIMA ile):\n",
    "# Burada SARIMA(p,d,q)(P,D,Q,s) modelinin parametrelerini optimize edebilirsiniz.\n",
    "sarima_model = SARIMAX(ndvi_series, order=(1, 1, 1), seasonal_order=(1, 1, 1, 12))\n",
    "sarima_result = sarima_model.fit()\n",
    "\n",
    "# Modelin sonuçlarını inceleyelim\n",
    "print(sarima_result.summary())\n",
    "\n",
    "# Modelden tahmin yapalım (geçmişe ve geleceğe yönelik):\n",
    "ndvi_forecast = sarima_result.get_forecast(steps=12)\n",
    "forecast_index = pd.date_range(start=ndvi_series.index[-1], periods=12, freq='M')\n",
    "forecast_mean = ndvi_forecast.predicted_mean\n",
    "forecast_ci = ndvi_forecast.conf_int()\n",
    "\n",
    "# Tahmin sonuçlarını görselleştirelim\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(ndvi_series.index, ndvi_series, label='Gerçek NDVI')\n",
    "plt.plot(forecast_index, forecast_mean, label='Tahmin Edilen NDVI')\n",
    "plt.fill_between(forecast_index, forecast_ci.iloc[:, 0], forecast_ci.iloc[:, 1], color='r', alpha=0.3)\n",
    "plt.title('NDVI Tahminleri')\n",
    "plt.legend()\n",
    "plt.show()\n",
    "\n",
    "# Hasat ile ilişkilendirme (örneğin en son 3 ayın NDVI ortalamasını kullanabiliriz)\n",
    "average_ndvi_last_3_months = ndvi_series[-3:].mean()\n",
    "\n",
    "# Burada basit bir ilişkilendirme yapabiliriz. Gerçek hasat verisiyle tahmini karşılaştırabiliriz.\n",
    "# Örnek hasat verisi (mısır_1 tarlası için 500 birim hasat)\n",
    "actual_harvest = 26905\n",
    "\n",
    "# Önerilen tahmin (örneğin: NDVI ortalamasına dayalı basit bir model)\n",
    "predicted_harvest = average_ndvi_last_3_months * 1000  # Bu katsayı sizin modelinize göre ayarlanabilir\n",
    "\n",
    "print(f\"Gerçek Hasat: {actual_harvest}\")\n",
    "print(f\"Tahmin Edilen Hasat: {predicted_harvest}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "c6f96e1a-41b0-42d0-8f01-b6987b9c45ab",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mehdiarat/miniconda3/envs/yolov10/lib/python3.9/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 11ms/step - loss: 0.3145 \n",
      "Epoch 2/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.1748 \n",
      "Epoch 3/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0799 \n",
      "Epoch 4/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0717 \n",
      "Epoch 5/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0904 \n",
      "Epoch 6/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0684 \n",
      "Epoch 7/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0563 \n",
      "Epoch 8/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0568 \n",
      "Epoch 9/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0563 \n",
      "Epoch 10/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0600 \n",
      "Epoch 11/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0594 \n",
      "Epoch 12/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0546 \n",
      "Epoch 13/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0513 \n",
      "Epoch 14/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0473 \n",
      "Epoch 15/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0486 \n",
      "Epoch 16/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0493 \n",
      "Epoch 17/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0495 \n",
      "Epoch 18/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0460 \n",
      "Epoch 19/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0457 \n",
      "Epoch 20/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0441 \n",
      "Epoch 21/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0437 \n",
      "Epoch 22/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0469 \n",
      "Epoch 23/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0455 \n",
      "Epoch 24/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0440 \n",
      "Epoch 25/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0410 \n",
      "Epoch 26/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0428 \n",
      "Epoch 27/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0427 \n",
      "Epoch 28/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0397 \n",
      "Epoch 29/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0393 \n",
      "Epoch 30/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0422 \n",
      "Epoch 31/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0400 \n",
      "Epoch 32/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0405 \n",
      "Epoch 33/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0409 \n",
      "Epoch 34/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0385 \n",
      "Epoch 35/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0369 \n",
      "Epoch 36/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0351 \n",
      "Epoch 37/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0404 \n",
      "Epoch 38/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0393 \n",
      "Epoch 39/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0386 \n",
      "Epoch 40/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0405 \n",
      "Epoch 41/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0396 \n",
      "Epoch 42/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0367 \n",
      "Epoch 43/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0362 \n",
      "Epoch 44/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0362 \n",
      "Epoch 45/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0404 \n",
      "Epoch 46/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0387 \n",
      "Epoch 47/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0351 \n",
      "Epoch 48/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0374 \n",
      "Epoch 49/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0386 \n",
      "Epoch 50/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0384 \n",
      "Epoch 51/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0364 \n",
      "Epoch 52/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0369 \n",
      "Epoch 53/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0375 \n",
      "Epoch 54/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0372 \n",
      "Epoch 55/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0375 \n",
      "Epoch 56/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0361 \n",
      "Epoch 57/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0375 \n",
      "Epoch 58/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 6ms/step - loss: 0.0362 \n",
      "Epoch 59/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0353 \n",
      "Epoch 60/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0372 \n",
      "Epoch 61/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0364 \n",
      "Epoch 62/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0364 \n",
      "Epoch 63/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0361 \n",
      "Epoch 64/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0375 \n",
      "Epoch 65/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0384 \n",
      "Epoch 66/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0371 \n",
      "Epoch 67/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0377 \n",
      "Epoch 68/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0367 \n",
      "Epoch 69/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0373 \n",
      "Epoch 70/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0375 \n",
      "Epoch 71/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0383 \n",
      "Epoch 72/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0383 \n",
      "Epoch 73/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0376 \n",
      "Epoch 74/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0383 \n",
      "Epoch 75/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0361 \n",
      "Epoch 76/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0368 \n",
      "Epoch 77/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0365 \n",
      "Epoch 78/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0358 \n",
      "Epoch 79/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0392 \n",
      "Epoch 80/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0378 \n",
      "Epoch 81/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0370 \n",
      "Epoch 82/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 10ms/step - loss: 0.0383\n",
      "Epoch 83/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0365 \n",
      "Epoch 84/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step - loss: 0.0363 \n",
      "Epoch 85/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0371 \n",
      "Epoch 86/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0371 \n",
      "Epoch 87/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0358 \n",
      "Epoch 88/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0381 \n",
      "Epoch 89/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0325 \n",
      "Epoch 90/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 9ms/step - loss: 0.0376 \n",
      "Epoch 91/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0375 \n",
      "Epoch 92/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0356 \n",
      "Epoch 93/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0352 \n",
      "Epoch 94/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0356 \n",
      "Epoch 95/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0380 \n",
      "Epoch 96/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0360 \n",
      "Epoch 97/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0352 \n",
      "Epoch 98/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0344 \n",
      "Epoch 99/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0364 \n",
      "Epoch 100/100\n",
      "\u001b[1m2/2\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 7ms/step - loss: 0.0356 \n",
      "\u001b[1m3/3\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 36ms/step\n",
      "\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 8ms/step\n"
     ]
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from tensorflow.keras.models import Sequential\n",
    "from tensorflow.keras.layers import LSTM, Dense\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Zaman serisini hazırlayalım\n",
    "ndvi_series = df[df['Polygon Name'] == 'mısır_1'].set_index('Date')['Mean NDVI (Original Polygon)']\n",
    "\n",
    "# Veriyi ölçeklendirelim (LSTM modelleri genellikle normalleştirilmiş verilerle daha iyi çalışır)\n",
    "scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "ndvi_scaled = scaler.fit_transform(ndvi_series.values.reshape(-1, 1))\n",
    "\n",
    "# Zaman serisi için veri pencereleri oluşturma (örn. son 30 günün verisiyle tahmin yapalım)\n",
    "def create_dataset(dataset, time_step=30):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(dataset)-time_step-1):\n",
    "        a = dataset[i:(i+time_step), 0]\n",
    "        dataX.append(a)\n",
    "        dataY.append(dataset[i + time_step, 0])\n",
    "    return np.array(dataX), np.array(dataY)\n",
    "\n",
    "time_step = 30  # Son 30 günün verisi ile tahmin yapılacak\n",
    "X, y = create_dataset(ndvi_scaled, time_step)\n",
    "\n",
    "# Veriyi yeniden şekillendirelim [samples, time steps, features]\n",
    "X = X.reshape(X.shape[0], X.shape[1], 1)\n",
    "\n",
    "# Eğitim ve test setlerine ayıralım (80% eğitim, 20% test)\n",
    "train_size = int(len(X) * 0.8)\n",
    "test_size = len(X) - train_size\n",
    "X_train, X_test = X[0:train_size], X[train_size:len(X)]\n",
    "y_train, y_test = y[0:train_size], y[train_size:len(y)]\n",
    "\n",
    "# LSTM modelini oluşturalım\n",
    "model = Sequential()\n",
    "model.add(LSTM(50, return_sequences=True, input_shape=(time_step, 1)))\n",
    "model.add(LSTM(50, return_sequences=False))\n",
    "model.add(Dense(25))\n",
    "model.add(Dense(1))\n",
    "\n",
    "# Modeli derleyelim\n",
    "model.compile(optimizer='adam', loss='mean_squared_error')\n",
    "\n",
    "# Modeli eğitelim\n",
    "model.fit(X_train, y_train, batch_size=64, epochs=100)\n",
    "\n",
    "# Modelin tahminini yapalım\n",
    "train_predict = model.predict(X_train)\n",
    "test_predict = model.predict(X_test)\n",
    "\n",
    "# Veriyi orijinal ölçeğe geri çevirelim\n",
    "train_predict = scaler.inverse_transform(train_predict)\n",
    "test_predict = scaler.inverse_transform(test_predict)\n",
    "y_train_orig = scaler.inverse_transform([y_train])\n",
    "y_test_orig = scaler.inverse_transform([y_test])\n",
    "\n",
    "# Tahmin sonuçlarını görselleştirelim\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(ndvi_series.index[-len(y_test):], y_test_orig[0], label='Gerçek NDVI')\n",
    "plt.plot(ndvi_series.index[-len(test_predict):], test_predict, label='Tahmin Edilen NDVI')\n",
    "plt.title('Gerçek ve Tahmin Edilen NDVI (LSTM)')\n",
    "plt.xlabel('Tarih')\n",
    "plt.ylabel('NDVI')\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "180a1d4f-b5c0-43dc-ac29-9f2afdc7b3b2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Örnek hasat verisi (Her tarla için bir hasat miktarı)\n",
    "# Bu array'in gerçek verilerinizle değiştirilmesi gerekiyor\n",
    "harvest_data = np.array([500, 450, 480, 520, 510, 495, 470, 530])  # 8 tarla için örnek değerler\n",
    "\n",
    "# Bu veriyi mevcut DataFrame'e ekleyelim\n",
    "df['Harvest'] = np.repeat(harvest_data, len(df) // len(harvest_data))\n",
    "\n",
    "# Eğitimi yapacağımız kolonları seçelim\n",
    "X = df[['NDVI (BuffEl) (Original Polygon)', 'NDWI (BuffWI) (Original Polygon)', 'LAI (BuffEl) (Original Polygon)']]\n",
    "y = df['Harvest']\n",
    "\n",
    "# Eğitim ve test setlerini ayıralım\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
    "\n",
    "# Model oluşturma ve eğitme (Lineer Regresyon örneği)\n",
    "model = LinearRegression()\n",
    "model.fit(X_train, y_train)\n",
    "\n",
    "# Tahmin yapma\n",
    "y_pred = model.predict(X_test)\n",
    "\n",
    "# Sonuçları değerlendirme\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "\n",
    "print(f\"MSE: {mse}\")\n",
    "print(f\"R2 Skoru: {r2}\")\n"
   ]
  }
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