tsMixer_electricty.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    " \n",
    "df = pd.read_csv('electricity.csv')\n",
    "\n",
    "# \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>Exogenous1</th>\n",
       "      <th>Exogenous2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BE</td>\n",
       "      <td>10/22/2016 0:00</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>49593.0</td>\n",
       "      <td>57253.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>BE</td>\n",
       "      <td>10/22/2016 1:00</td>\n",
       "      <td>37.100000</td>\n",
       "      <td>46073.0</td>\n",
       "      <td>51887.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BE</td>\n",
       "      <td>10/22/2016 2:00</td>\n",
       "      <td>37.100000</td>\n",
       "      <td>44927.0</td>\n",
       "      <td>51896.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BE</td>\n",
       "      <td>10/22/2016 3:00</td>\n",
       "      <td>44.750000</td>\n",
       "      <td>44483.0</td>\n",
       "      <td>48428.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BE</td>\n",
       "      <td>10/22/2016 4:00</td>\n",
       "      <td>37.100000</td>\n",
       "      <td>44338.0</td>\n",
       "      <td>46721.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8395</th>\n",
       "      <td>PJM</td>\n",
       "      <td>12/23/2018 19:00</td>\n",
       "      <td>32.461970</td>\n",
       "      <td>98288.0</td>\n",
       "      <td>11711.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8396</th>\n",
       "      <td>PJM</td>\n",
       "      <td>12/23/2018 20:00</td>\n",
       "      <td>32.052179</td>\n",
       "      <td>97194.0</td>\n",
       "      <td>11637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8397</th>\n",
       "      <td>PJM</td>\n",
       "      <td>12/23/2018 21:00</td>\n",
       "      <td>27.632347</td>\n",
       "      <td>94319.0</td>\n",
       "      <td>11433.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8398</th>\n",
       "      <td>PJM</td>\n",
       "      <td>12/23/2018 22:00</td>\n",
       "      <td>24.746053</td>\n",
       "      <td>90106.0</td>\n",
       "      <td>11108.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8399</th>\n",
       "      <td>PJM</td>\n",
       "      <td>12/23/2018 23:00</td>\n",
       "      <td>23.333499</td>\n",
       "      <td>85547.0</td>\n",
       "      <td>10581.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8400 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     unique_id                ds          y  Exogenous1  Exogenous2\n",
       "0           BE   10/22/2016 0:00  70.000000     49593.0     57253.0\n",
       "1           BE   10/22/2016 1:00  37.100000     46073.0     51887.0\n",
       "2           BE   10/22/2016 2:00  37.100000     44927.0     51896.0\n",
       "3           BE   10/22/2016 3:00  44.750000     44483.0     48428.0\n",
       "4           BE   10/22/2016 4:00  37.100000     44338.0     46721.0\n",
       "...        ...               ...        ...         ...         ...\n",
       "8395       PJM  12/23/2018 19:00  32.461970     98288.0     11711.0\n",
       "8396       PJM  12/23/2018 20:00  32.052179     97194.0     11637.0\n",
       "8397       PJM  12/23/2018 21:00  27.632347     94319.0     11433.0\n",
       "8398       PJM  12/23/2018 22:00  24.746053     90106.0     11108.0\n",
       "8399       PJM  12/23/2018 23:00  23.333499     85547.0     10581.0\n",
       "\n",
       "[8400 rows x 5 columns]"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "BE     1680\n",
       "DE     1680\n",
       "FR     1680\n",
       "NP     1680\n",
       "PJM    1680\n",
       "Name: unique_id, dtype: int64"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Count the number of occurrences of each unique ID in the dataframe\n",
    "df.unique_id.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
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       "    }\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>unique_id</th>\n",
       "      <th>ds</th>\n",
       "      <th>y</th>\n",
       "      <th>Exogenous1</th>\n",
       "      <th>Exogenous2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1680</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-10-22 00:00:00</td>\n",
       "      <td>19.10</td>\n",
       "      <td>587.25</td>\n",
       "      <td>16972.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1681</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-10-22 01:00:00</td>\n",
       "      <td>19.03</td>\n",
       "      <td>623.00</td>\n",
       "      <td>16254.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1682</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-10-22 02:00:00</td>\n",
       "      <td>16.90</td>\n",
       "      <td>650.00</td>\n",
       "      <td>15940.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1683</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-10-22 03:00:00</td>\n",
       "      <td>12.98</td>\n",
       "      <td>687.25</td>\n",
       "      <td>15959.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1684</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-10-22 04:00:00</td>\n",
       "      <td>9.24</td>\n",
       "      <td>717.25</td>\n",
       "      <td>16071.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3355</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-12-30 19:00:00</td>\n",
       "      <td>16.18</td>\n",
       "      <td>1519.50</td>\n",
       "      <td>19326.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3356</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-12-30 20:00:00</td>\n",
       "      <td>11.13</td>\n",
       "      <td>1526.75</td>\n",
       "      <td>18135.25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3357</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-12-30 21:00:00</td>\n",
       "      <td>4.62</td>\n",
       "      <td>1514.75</td>\n",
       "      <td>17631.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3358</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-12-30 22:00:00</td>\n",
       "      <td>4.71</td>\n",
       "      <td>1484.75</td>\n",
       "      <td>17553.75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3359</th>\n",
       "      <td>DE</td>\n",
       "      <td>2017-12-30 23:00:00</td>\n",
       "      <td>-0.27</td>\n",
       "      <td>1440.75</td>\n",
       "      <td>16754.50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1680 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     unique_id                  ds      y  Exogenous1  Exogenous2\n",
       "1680        DE 2017-10-22 00:00:00  19.10      587.25    16972.75\n",
       "1681        DE 2017-10-22 01:00:00  19.03      623.00    16254.50\n",
       "1682        DE 2017-10-22 02:00:00  16.90      650.00    15940.25\n",
       "1683        DE 2017-10-22 03:00:00  12.98      687.25    15959.50\n",
       "1684        DE 2017-10-22 04:00:00   9.24      717.25    16071.50\n",
       "...        ...                 ...    ...         ...         ...\n",
       "3355        DE 2017-12-30 19:00:00  16.18     1519.50    19326.00\n",
       "3356        DE 2017-12-30 20:00:00  11.13     1526.75    18135.25\n",
       "3357        DE 2017-12-30 21:00:00   4.62     1514.75    17631.50\n",
       "3358        DE 2017-12-30 22:00:00   4.71     1484.75    17553.75\n",
       "3359        DE 2017-12-30 23:00:00  -0.27     1440.75    16754.50\n",
       "\n",
       "[1680 rows x 5 columns]"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Filter the dataframe to include only rows where the 'unique_id' is \"DE\"\n",
    "df = df[df['unique_id'] == \"DE\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['ds'] = pd.to_datetime(df['ds'])\n",
    "data = df[['y', 'Exogenous1', 'Exogenous2']].values\n",
    "\n",
    "scaler = MinMaxScaler()\n",
    "data = scaler.fit_transform(data)\n",
    "\n",
    " \n",
    "X = data[:, 1:]   \n",
    "y = data[:, 0]   \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    " \n",
    "def create_sequences(X, y, seq_length):\n",
    "    sequences = []\n",
    "    labels = []\n",
    "    for i in range(len(X) - seq_length):\n",
    "        sequences.append(X[i:i+seq_length])\n",
    "        labels.append(y[i+seq_length])\n",
    "    return torch.FloatTensor(sequences), torch.FloatTensor(labels)\n",
    "\n",
    "seq_length = 3# 24 saatlik geçmiş veriyi kullanarak tahmin yapalım\n",
    "X_seq, y_seq = create_sequences(X, y, seq_length)\n",
    "\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_seq, y_seq, test_size=0.3, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader, TensorDataset\n",
    "train_dataset = TensorDataset(X_train, y_train)\n",
    "test_dataset = TensorDataset(X_test, y_test)\n",
    "\n",
    "batch_size = 32\n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "from torchtsmixer import TSMixer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([504, 3, 2])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_test.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([21, 3, 2])\n",
      "torch.Size([21])\n"
     ]
    }
   ],
   "source": [
    "for x,y in train_loader:\n",
    "    print(x.shape)\n",
    "    print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([32, 3, 2])\n",
      "torch.Size([32])\n",
      "torch.Size([24, 3, 2])\n",
      "torch.Size([24])\n"
     ]
    }
   ],
   "source": [
    "for x,y in test_loader:\n",
    "    print(x.shape)\n",
    "    print(y.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "sequence_length = 3\n",
    "prediction_length = 1 \n",
    "input_channels = 2 \n",
    "output_channels = 1  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = TSMixer(sequence_length, prediction_length, input_channels, output_channels)\n",
    "criterion = nn.MSELoss()  \n",
    "optimizer = optim.Adam(model.parameters(), lr=0.0001)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch [1/10000], Loss: 1.1112\n",
      "Epoch [2/10000], Loss: 1.0986\n",
      "Epoch [3/10000], Loss: 1.1139\n",
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      "Epoch [1598/10000], Loss: 0.0981\n",
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      "Epoch [1600/10000], Loss: 0.0955\n",
      "Epoch [1601/10000], Loss: 0.1001\n",
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      "Epoch [1618/10000], Loss: 0.0909\n",
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      "Epoch [1622/10000], Loss: 0.0897\n",
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      "Epoch [1624/10000], Loss: 0.0997\n",
      "Epoch [1625/10000], Loss: 0.0882\n",
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      "Epoch [1629/10000], Loss: 0.0994\n",
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      "Epoch [1661/10000], Loss: 0.0842\n",
      "Epoch [1662/10000], Loss: 0.0871\n",
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      "Epoch [1686/10000], Loss: 0.0889\n",
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      "Epoch [1688/10000], Loss: 0.0757\n",
      "Epoch [1689/10000], Loss: 0.0984\n",
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      "Epoch [1696/10000], Loss: 0.0891\n",
      "Epoch [1697/10000], Loss: 0.0906\n",
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      "Epoch [1700/10000], Loss: 0.0873\n",
      "Epoch [1701/10000], Loss: 0.0858\n",
      "Epoch [1702/10000], Loss: 0.0946\n",
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      "Epoch [1705/10000], Loss: 0.0997\n",
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      "Epoch [1710/10000], Loss: 0.0898\n",
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      "Epoch [1712/10000], Loss: 0.0879\n",
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      "Epoch [1715/10000], Loss: 0.0911\n",
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      "Epoch [1722/10000], Loss: 0.0849\n",
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      "Epoch [1727/10000], Loss: 0.0897\n",
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      "Epoch [1729/10000], Loss: 0.0863\n",
      "Epoch [1730/10000], Loss: 0.0839\n",
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      "Epoch [1734/10000], Loss: 0.1154\n",
      "Epoch [1735/10000], Loss: 0.1007\n",
      "Epoch [1736/10000], Loss: 0.0988\n",
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      "Epoch [1738/10000], Loss: 0.0841\n",
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      "Epoch [1765/10000], Loss: 0.0871\n",
      "Epoch [1766/10000], Loss: 0.0846\n",
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      "Epoch [1770/10000], Loss: 0.0994\n",
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      "Epoch [1773/10000], Loss: 0.0894\n",
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      "Epoch [1776/10000], Loss: 0.0886\n",
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      "Epoch [1778/10000], Loss: 0.0795\n",
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      "Epoch [1781/10000], Loss: 0.0735\n",
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      "Epoch [1801/10000], Loss: 0.0761\n",
      "Epoch [1802/10000], Loss: 0.0816\n",
      "Epoch [1803/10000], Loss: 0.0842\n",
      "Epoch [1804/10000], Loss: 0.0959\n",
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      "Epoch [1809/10000], Loss: 0.0956\n",
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      "Epoch [1811/10000], Loss: 0.0955\n",
      "Epoch [1812/10000], Loss: 0.0838\n",
      "Epoch [1813/10000], Loss: 0.0885\n",
      "Epoch [1814/10000], Loss: 0.0813\n",
      "Epoch [1815/10000], Loss: 0.0894\n",
      "Epoch [1816/10000], Loss: 0.0887\n",
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      "Epoch [1823/10000], Loss: 0.0931\n",
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      "Epoch [1828/10000], Loss: 0.0839\n",
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      "Epoch [1831/10000], Loss: 0.0859\n",
      "Epoch [1832/10000], Loss: 0.0787\n",
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      "Epoch [1834/10000], Loss: 0.0841\n",
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      "Epoch [1836/10000], Loss: 0.0898\n",
      "Epoch [1837/10000], Loss: 0.0861\n",
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      "Epoch [1858/10000], Loss: 0.0994\n",
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      "Epoch [1867/10000], Loss: 0.0871\n",
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      "Epoch [1903/10000], Loss: 0.0856\n",
      "Epoch [1904/10000], Loss: 0.0789\n",
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      "Epoch [1910/10000], Loss: 0.0842\n",
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      "Epoch [1913/10000], Loss: 0.0787\n",
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      "Epoch [1989/10000], Loss: 0.0868\n",
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      "Epoch [1994/10000], Loss: 0.0703\n",
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      "Epoch [1999/10000], Loss: 0.0855\n",
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      "Epoch [2010/10000], Loss: 0.0858\n",
      "Epoch [2011/10000], Loss: 0.0846\n",
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      "Epoch [2013/10000], Loss: 0.0741\n",
      "Epoch [2014/10000], Loss: 0.0873\n",
      "Epoch [2015/10000], Loss: 0.0771\n",
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      "Epoch [2017/10000], Loss: 0.0882\n",
      "Epoch [2018/10000], Loss: 0.0772\n",
      "Epoch [2019/10000], Loss: 0.0776\n",
      "Epoch [2020/10000], Loss: 0.0803\n",
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      "Epoch [2023/10000], Loss: 0.0798\n",
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      "Epoch [2029/10000], Loss: 0.0714\n",
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      "Epoch [2031/10000], Loss: 0.0791\n",
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      "Epoch [2064/10000], Loss: 0.0768\n",
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      "Epoch [2088/10000], Loss: 0.0725\n",
      "Epoch [2089/10000], Loss: 0.0703\n",
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      "Epoch [2161/10000], Loss: 0.0768\n",
      "Epoch [2162/10000], Loss: 0.0755\n",
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      "Epoch [2171/10000], Loss: 0.0733\n",
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      "Epoch [2174/10000], Loss: 0.0848\n",
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      "Epoch [2176/10000], Loss: 0.0902\n",
      "Epoch [2177/10000], Loss: 0.0797\n",
      "Epoch [2178/10000], Loss: 0.0687\n",
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      "Epoch [2180/10000], Loss: 0.0727\n",
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      "Epoch [2191/10000], Loss: 0.0855\n",
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      "Epoch [2196/10000], Loss: 0.0778\n",
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      "Epoch [2201/10000], Loss: 0.0637\n",
      "Epoch [2202/10000], Loss: 0.0823\n",
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      "Epoch [2204/10000], Loss: 0.0776\n",
      "Epoch [2205/10000], Loss: 0.0685\n",
      "Epoch [2206/10000], Loss: 0.0703\n",
      "Epoch [2207/10000], Loss: 0.0694\n",
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      "Epoch [2209/10000], Loss: 0.0711\n",
      "Epoch [2210/10000], Loss: 0.0723\n",
      "Epoch [2211/10000], Loss: 0.0902\n",
      "Epoch [2212/10000], Loss: 0.0801\n",
      "Epoch [2213/10000], Loss: 0.0731\n",
      "Epoch [2214/10000], Loss: 0.0638\n",
      "Epoch [2215/10000], Loss: 0.0757\n",
      "Epoch [2216/10000], Loss: 0.0781\n",
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      "Epoch [2219/10000], Loss: 0.0776\n",
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      "Epoch [2223/10000], Loss: 0.0786\n",
      "Epoch [2224/10000], Loss: 0.0746\n",
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      "Epoch [2226/10000], Loss: 0.0658\n",
      "Epoch [2227/10000], Loss: 0.0745\n",
      "Epoch [2228/10000], Loss: 0.0699\n",
      "Epoch [2229/10000], Loss: 0.0713\n",
      "Epoch [2230/10000], Loss: 0.0782\n",
      "Epoch [2231/10000], Loss: 0.0795\n",
      "Epoch [2232/10000], Loss: 0.0774\n",
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      "Epoch [2234/10000], Loss: 0.0807\n",
      "Epoch [2235/10000], Loss: 0.0686\n",
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      "Epoch [2237/10000], Loss: 0.0791\n",
      "Epoch [2238/10000], Loss: 0.0798\n",
      "Epoch [2239/10000], Loss: 0.0778\n",
      "Epoch [2240/10000], Loss: 0.0660\n",
      "Epoch [2241/10000], Loss: 0.0711\n",
      "Epoch [2242/10000], Loss: 0.0698\n",
      "Epoch [2243/10000], Loss: 0.0647\n",
      "Epoch [2244/10000], Loss: 0.0773\n",
      "Epoch [2245/10000], Loss: 0.0665\n",
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      "Epoch [2251/10000], Loss: 0.0673\n",
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      "Epoch [2270/10000], Loss: 0.0661\n",
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      "Epoch [2301/10000], Loss: 0.0725\n",
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      "Epoch [2303/10000], Loss: 0.0847\n",
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      "Epoch [2310/10000], Loss: 0.0690\n",
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      "Epoch [2312/10000], Loss: 0.0785\n",
      "Epoch [2313/10000], Loss: 0.0631\n",
      "Epoch [2314/10000], Loss: 0.0684\n",
      "Epoch [2315/10000], Loss: 0.0839\n",
      "Epoch [2316/10000], Loss: 0.0666\n",
      "Epoch [2317/10000], Loss: 0.0787\n",
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      "Epoch [2321/10000], Loss: 0.0851\n",
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      "Epoch [2326/10000], Loss: 0.0687\n",
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      "Epoch [2330/10000], Loss: 0.0801\n",
      "Epoch [2331/10000], Loss: 0.0737\n",
      "Epoch [2332/10000], Loss: 0.0707\n",
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      "Epoch [2335/10000], Loss: 0.0882\n",
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      "Epoch [2378/10000], Loss: 0.0808\n",
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      "Epoch [2411/10000], Loss: 0.0731\n",
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      "Epoch [2432/10000], Loss: 0.0707\n",
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      "Epoch [2434/10000], Loss: 0.0658\n",
      "Epoch [2435/10000], Loss: 0.0696\n",
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      "Epoch [2460/10000], Loss: 0.0674\n",
      "Epoch [2461/10000], Loss: 0.0678\n",
      "Epoch [2462/10000], Loss: 0.0793\n",
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      "Epoch [2464/10000], Loss: 0.0688\n",
      "Epoch [2465/10000], Loss: 0.0640\n",
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      "Epoch [2469/10000], Loss: 0.0646\n",
      "Epoch [2470/10000], Loss: 0.0737\n",
      "Epoch [2471/10000], Loss: 0.0667\n",
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      "Epoch [2502/10000], Loss: 0.0692\n",
      "Epoch [2503/10000], Loss: 0.0653\n",
      "Epoch [2504/10000], Loss: 0.0755\n",
      "Epoch [2505/10000], Loss: 0.0710\n",
      "Epoch [2506/10000], Loss: 0.0617\n",
      "Epoch [2507/10000], Loss: 0.0686\n",
      "Epoch [2508/10000], Loss: 0.0697\n",
      "Epoch [2509/10000], Loss: 0.0735\n",
      "Epoch [2510/10000], Loss: 0.0688\n",
      "Epoch [2511/10000], Loss: 0.0648\n",
      "Epoch [2512/10000], Loss: 0.0728\n",
      "Epoch [2513/10000], Loss: 0.0751\n",
      "Epoch [2514/10000], Loss: 0.0623\n",
      "Epoch [2515/10000], Loss: 0.0684\n",
      "Epoch [2516/10000], Loss: 0.0661\n",
      "Epoch [2517/10000], Loss: 0.0632\n",
      "Epoch [2518/10000], Loss: 0.0586\n",
      "Epoch [2519/10000], Loss: 0.0739\n",
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      "Epoch [2521/10000], Loss: 0.0653\n",
      "Epoch [2522/10000], Loss: 0.0655\n",
      "Epoch [2523/10000], Loss: 0.0697\n",
      "Epoch [2524/10000], Loss: 0.0677\n",
      "Epoch [2525/10000], Loss: 0.0679\n",
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      "Epoch [2529/10000], Loss: 0.0786\n",
      "Epoch [2530/10000], Loss: 0.0619\n",
      "Epoch [2531/10000], Loss: 0.0722\n",
      "Epoch [2532/10000], Loss: 0.0566\n",
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      "Epoch [2560/10000], Loss: 0.0698\n",
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      "Epoch [2562/10000], Loss: 0.0792\n",
      "Epoch [2563/10000], Loss: 0.0658\n",
      "Epoch [2564/10000], Loss: 0.0628\n",
      "Epoch [2565/10000], Loss: 0.0691\n",
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      "Epoch [2586/10000], Loss: 0.0646\n",
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      "Epoch [2606/10000], Loss: 0.0697\n",
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      "Epoch [2612/10000], Loss: 0.0708\n",
      "Epoch [2613/10000], Loss: 0.0595\n",
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      "Epoch [2628/10000], Loss: 0.0659\n",
      "Epoch [2629/10000], Loss: 0.0706\n",
      "Epoch [2630/10000], Loss: 0.0549\n",
      "Epoch [2631/10000], Loss: 0.0617\n",
      "Epoch [2632/10000], Loss: 0.0573\n",
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      "Epoch [2637/10000], Loss: 0.0638\n",
      "Epoch [2638/10000], Loss: 0.0690\n",
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      "Epoch [2640/10000], Loss: 0.0677\n",
      "Epoch [2641/10000], Loss: 0.0631\n",
      "Epoch [2642/10000], Loss: 0.0737\n",
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      "Epoch [2658/10000], Loss: 0.0697\n",
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      "Epoch [2661/10000], Loss: 0.0556\n",
      "Epoch [2662/10000], Loss: 0.0631\n",
      "Epoch [2663/10000], Loss: 0.0628\n",
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      "Epoch [2668/10000], Loss: 0.0599\n",
      "Epoch [2669/10000], Loss: 0.0595\n",
      "Epoch [2670/10000], Loss: 0.0595\n",
      "Epoch [2671/10000], Loss: 0.0627\n",
      "Epoch [2672/10000], Loss: 0.0674\n",
      "Epoch [2673/10000], Loss: 0.0626\n",
      "Epoch [2674/10000], Loss: 0.0591\n",
      "Epoch [2675/10000], Loss: 0.0655\n",
      "Epoch [2676/10000], Loss: 0.0672\n",
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      "Epoch [2678/10000], Loss: 0.0673\n",
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      "Epoch [2693/10000], Loss: 0.0648\n",
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      "Epoch [2696/10000], Loss: 0.0659\n",
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      "Epoch [2702/10000], Loss: 0.0642\n",
      "Epoch [2703/10000], Loss: 0.0603\n",
      "Epoch [2704/10000], Loss: 0.0685\n",
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      "Epoch [2707/10000], Loss: 0.0568\n",
      "Epoch [2708/10000], Loss: 0.0696\n",
      "Epoch [2709/10000], Loss: 0.0571\n",
      "Epoch [2710/10000], Loss: 0.0698\n",
      "Epoch [2711/10000], Loss: 0.0687\n",
      "Epoch [2712/10000], Loss: 0.0599\n",
      "Epoch [2713/10000], Loss: 0.0651\n",
      "Epoch [2714/10000], Loss: 0.0580\n",
      "Epoch [2715/10000], Loss: 0.0531\n",
      "Epoch [2716/10000], Loss: 0.0587\n",
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      "Epoch [2719/10000], Loss: 0.0667\n",
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      "Epoch [2721/10000], Loss: 0.0603\n",
      "Epoch [2722/10000], Loss: 0.0622\n",
      "Epoch [2723/10000], Loss: 0.0628\n",
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      "Epoch [2729/10000], Loss: 0.0685\n",
      "Epoch [2730/10000], Loss: 0.0567\n",
      "Epoch [2731/10000], Loss: 0.0703\n",
      "Epoch [2732/10000], Loss: 0.0506\n",
      "Epoch [2733/10000], Loss: 0.0673\n",
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      "Epoch [3930/10000], Loss: 0.0392\n",
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      "Epoch [6813/10000], Loss: 0.0101\n",
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      "Epoch [6815/10000], Loss: 0.0092\n",
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      "Epoch [6817/10000], Loss: 0.0098\n",
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      "Epoch [6820/10000], Loss: 0.0093\n",
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      "Epoch [6930/10000], Loss: 0.0081\n",
      "Epoch [6931/10000], Loss: 0.0092\n",
      "Epoch [6932/10000], Loss: 0.0090\n",
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      "Epoch [7038/10000], Loss: 0.0087\n",
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      "Epoch [8606/10000], Loss: 0.0044\n",
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      "Epoch [8608/10000], Loss: 0.0037\n",
      "Epoch [8609/10000], Loss: 0.0041\n",
      "Epoch [8610/10000], Loss: 0.0036\n",
      "Epoch [8611/10000], Loss: 0.0040\n",
      "Epoch [8612/10000], Loss: 0.0044\n",
      "Epoch [8613/10000], Loss: 0.0047\n",
      "Epoch [8614/10000], Loss: 0.0041\n",
      "Epoch [8615/10000], Loss: 0.0045\n",
      "Epoch [8616/10000], Loss: 0.0044\n",
      "Epoch [8617/10000], Loss: 0.0043\n",
      "Epoch [8618/10000], Loss: 0.0047\n",
      "Epoch [8619/10000], Loss: 0.0044\n",
      "Epoch [8620/10000], Loss: 0.0045\n",
      "Epoch [8621/10000], Loss: 0.0043\n",
      "Epoch [8622/10000], Loss: 0.0039\n",
      "Epoch [8623/10000], Loss: 0.0043\n",
      "Epoch [8624/10000], Loss: 0.0043\n",
      "Epoch [8625/10000], Loss: 0.0041\n",
      "Epoch [8626/10000], Loss: 0.0046\n",
      "Epoch [8627/10000], Loss: 0.0041\n",
      "Epoch [8628/10000], Loss: 0.0042\n",
      "Epoch [8629/10000], Loss: 0.0048\n",
      "Epoch [8630/10000], Loss: 0.0042\n",
      "Epoch [8631/10000], Loss: 0.0048\n",
      "Epoch [8632/10000], Loss: 0.0042\n",
      "Epoch [8633/10000], Loss: 0.0042\n",
      "Epoch [8634/10000], Loss: 0.0048\n",
      "Epoch [8635/10000], Loss: 0.0039\n",
      "Epoch [8636/10000], Loss: 0.0039\n",
      "Epoch [8637/10000], Loss: 0.0039\n",
      "Epoch [8638/10000], Loss: 0.0045\n",
      "Epoch [8639/10000], Loss: 0.0046\n",
      "Epoch [8640/10000], Loss: 0.0041\n",
      "Epoch [8641/10000], Loss: 0.0045\n",
      "Epoch [8642/10000], Loss: 0.0043\n",
      "Epoch [8643/10000], Loss: 0.0049\n",
      "Epoch [8644/10000], Loss: 0.0040\n",
      "Epoch [8645/10000], Loss: 0.0040\n",
      "Epoch [8646/10000], Loss: 0.0049\n",
      "Epoch [8647/10000], Loss: 0.0046\n",
      "Epoch [8648/10000], Loss: 0.0043\n",
      "Epoch [8649/10000], Loss: 0.0043\n",
      "Epoch [8650/10000], Loss: 0.0043\n",
      "Epoch [8651/10000], Loss: 0.0046\n",
      "Epoch [8652/10000], Loss: 0.0043\n",
      "Epoch [8653/10000], Loss: 0.0044\n",
      "Epoch [8654/10000], Loss: 0.0046\n",
      "Epoch [8655/10000], Loss: 0.0043\n",
      "Epoch [8656/10000], Loss: 0.0046\n",
      "Epoch [8657/10000], Loss: 0.0039\n",
      "Epoch [8658/10000], Loss: 0.0043\n",
      "Epoch [8659/10000], Loss: 0.0044\n",
      "Epoch [8660/10000], Loss: 0.0051\n",
      "Epoch [8661/10000], Loss: 0.0039\n",
      "Epoch [8662/10000], Loss: 0.0040\n",
      "Epoch [8663/10000], Loss: 0.0044\n",
      "Epoch [8664/10000], Loss: 0.0040\n",
      "Epoch [8665/10000], Loss: 0.0046\n",
      "Epoch [8666/10000], Loss: 0.0046\n",
      "Epoch [8667/10000], Loss: 0.0045\n",
      "Epoch [8668/10000], Loss: 0.0043\n",
      "Epoch [8669/10000], Loss: 0.0041\n",
      "Epoch [8670/10000], Loss: 0.0039\n",
      "Epoch [8671/10000], Loss: 0.0044\n",
      "Epoch [8672/10000], Loss: 0.0045\n",
      "Epoch [8673/10000], Loss: 0.0040\n",
      "Epoch [8674/10000], Loss: 0.0044\n",
      "Epoch [8675/10000], Loss: 0.0042\n",
      "Epoch [8676/10000], Loss: 0.0045\n",
      "Epoch [8677/10000], Loss: 0.0043\n",
      "Epoch [8678/10000], Loss: 0.0043\n",
      "Epoch [8679/10000], Loss: 0.0046\n",
      "Epoch [8680/10000], Loss: 0.0040\n",
      "Epoch [8681/10000], Loss: 0.0042\n",
      "Epoch [8682/10000], Loss: 0.0047\n",
      "Epoch [8683/10000], Loss: 0.0040\n",
      "Epoch [8684/10000], Loss: 0.0049\n",
      "Epoch [8685/10000], Loss: 0.0048\n",
      "Epoch [8686/10000], Loss: 0.0043\n",
      "Epoch [8687/10000], Loss: 0.0042\n",
      "Epoch [8688/10000], Loss: 0.0054\n",
      "Epoch [8689/10000], Loss: 0.0041\n",
      "Epoch [8690/10000], Loss: 0.0044\n",
      "Epoch [8691/10000], Loss: 0.0040\n",
      "Epoch [8692/10000], Loss: 0.0039\n",
      "Epoch [8693/10000], Loss: 0.0046\n",
      "Epoch [8694/10000], Loss: 0.0041\n",
      "Epoch [8695/10000], Loss: 0.0042\n",
      "Epoch [8696/10000], Loss: 0.0042\n",
      "Epoch [8697/10000], Loss: 0.0042\n",
      "Epoch [8698/10000], Loss: 0.0042\n",
      "Epoch [8699/10000], Loss: 0.0041\n",
      "Epoch [8700/10000], Loss: 0.0044\n",
      "Epoch [8701/10000], Loss: 0.0040\n",
      "Epoch [8702/10000], Loss: 0.0044\n",
      "Epoch [8703/10000], Loss: 0.0041\n",
      "Epoch [8704/10000], Loss: 0.0042\n",
      "Epoch [8705/10000], Loss: 0.0039\n",
      "Epoch [8706/10000], Loss: 0.0042\n",
      "Epoch [8707/10000], Loss: 0.0040\n",
      "Epoch [8708/10000], Loss: 0.0044\n",
      "Epoch [8709/10000], Loss: 0.0043\n",
      "Epoch [8710/10000], Loss: 0.0041\n",
      "Epoch [8711/10000], Loss: 0.0040\n",
      "Epoch [8712/10000], Loss: 0.0038\n",
      "Epoch [8713/10000], Loss: 0.0042\n",
      "Epoch [8714/10000], Loss: 0.0038\n",
      "Epoch [8715/10000], Loss: 0.0042\n",
      "Epoch [8716/10000], Loss: 0.0040\n",
      "Epoch [8717/10000], Loss: 0.0044\n",
      "Epoch [8718/10000], Loss: 0.0042\n",
      "Epoch [8719/10000], Loss: 0.0042\n",
      "Epoch [8720/10000], Loss: 0.0041\n",
      "Epoch [8721/10000], Loss: 0.0040\n",
      "Epoch [8722/10000], Loss: 0.0045\n",
      "Epoch [8723/10000], Loss: 0.0043\n",
      "Epoch [8724/10000], Loss: 0.0037\n",
      "Epoch [8725/10000], Loss: 0.0046\n",
      "Epoch [8726/10000], Loss: 0.0039\n",
      "Epoch [8727/10000], Loss: 0.0040\n",
      "Epoch [8728/10000], Loss: 0.0041\n",
      "Epoch [8729/10000], Loss: 0.0049\n",
      "Epoch [8730/10000], Loss: 0.0043\n",
      "Epoch [8731/10000], Loss: 0.0036\n",
      "Epoch [8732/10000], Loss: 0.0042\n",
      "Epoch [8733/10000], Loss: 0.0047\n",
      "Epoch [8734/10000], Loss: 0.0040\n",
      "Epoch [8735/10000], Loss: 0.0047\n",
      "Epoch [8736/10000], Loss: 0.0043\n",
      "Epoch [8737/10000], Loss: 0.0042\n",
      "Epoch [8738/10000], Loss: 0.0042\n",
      "Epoch [8739/10000], Loss: 0.0044\n",
      "Epoch [8740/10000], Loss: 0.0043\n",
      "Epoch [8741/10000], Loss: 0.0049\n",
      "Epoch [8742/10000], Loss: 0.0039\n",
      "Epoch [8743/10000], Loss: 0.0043\n",
      "Epoch [8744/10000], Loss: 0.0037\n",
      "Epoch [8745/10000], Loss: 0.0044\n",
      "Epoch [8746/10000], Loss: 0.0039\n",
      "Epoch [8747/10000], Loss: 0.0044\n",
      "Epoch [8748/10000], Loss: 0.0040\n",
      "Epoch [8749/10000], Loss: 0.0041\n",
      "Epoch [8750/10000], Loss: 0.0040\n",
      "Epoch [8751/10000], Loss: 0.0041\n",
      "Epoch [8752/10000], Loss: 0.0044\n",
      "Epoch [8753/10000], Loss: 0.0042\n",
      "Epoch [8754/10000], Loss: 0.0041\n",
      "Epoch [8755/10000], Loss: 0.0041\n",
      "Epoch [8756/10000], Loss: 0.0038\n",
      "Epoch [8757/10000], Loss: 0.0041\n",
      "Epoch [8758/10000], Loss: 0.0040\n",
      "Epoch [8759/10000], Loss: 0.0042\n",
      "Epoch [8760/10000], Loss: 0.0036\n",
      "Epoch [8761/10000], Loss: 0.0039\n",
      "Epoch [8762/10000], Loss: 0.0040\n",
      "Epoch [8763/10000], Loss: 0.0040\n",
      "Epoch [8764/10000], Loss: 0.0040\n",
      "Epoch [8765/10000], Loss: 0.0040\n",
      "Epoch [8766/10000], Loss: 0.0040\n",
      "Epoch [8767/10000], Loss: 0.0037\n",
      "Epoch [8768/10000], Loss: 0.0038\n",
      "Epoch [8769/10000], Loss: 0.0040\n",
      "Epoch [8770/10000], Loss: 0.0042\n",
      "Epoch [8771/10000], Loss: 0.0040\n",
      "Epoch [8772/10000], Loss: 0.0043\n",
      "Epoch [8773/10000], Loss: 0.0043\n",
      "Epoch [8774/10000], Loss: 0.0041\n",
      "Epoch [8775/10000], Loss: 0.0041\n",
      "Epoch [8776/10000], Loss: 0.0044\n",
      "Epoch [8777/10000], Loss: 0.0039\n",
      "Epoch [8778/10000], Loss: 0.0048\n",
      "Epoch [8779/10000], Loss: 0.0038\n",
      "Epoch [8780/10000], Loss: 0.0046\n",
      "Epoch [8781/10000], Loss: 0.0043\n",
      "Epoch [8782/10000], Loss: 0.0043\n",
      "Epoch [8783/10000], Loss: 0.0040\n",
      "Epoch [8784/10000], Loss: 0.0042\n",
      "Epoch [8785/10000], Loss: 0.0041\n",
      "Epoch [8786/10000], Loss: 0.0041\n",
      "Epoch [8787/10000], Loss: 0.0044\n",
      "Epoch [8788/10000], Loss: 0.0043\n",
      "Epoch [8789/10000], Loss: 0.0039\n",
      "Epoch [8790/10000], Loss: 0.0046\n",
      "Epoch [8791/10000], Loss: 0.0038\n",
      "Epoch [8792/10000], Loss: 0.0043\n",
      "Epoch [8793/10000], Loss: 0.0040\n",
      "Epoch [8794/10000], Loss: 0.0040\n",
      "Epoch [8795/10000], Loss: 0.0043\n",
      "Epoch [8796/10000], Loss: 0.0041\n",
      "Epoch [8797/10000], Loss: 0.0040\n",
      "Epoch [8798/10000], Loss: 0.0040\n",
      "Epoch [8799/10000], Loss: 0.0039\n",
      "Epoch [8800/10000], Loss: 0.0041\n",
      "Epoch [8801/10000], Loss: 0.0039\n",
      "Epoch [8802/10000], Loss: 0.0036\n",
      "Epoch [8803/10000], Loss: 0.0043\n",
      "Epoch [8804/10000], Loss: 0.0041\n",
      "Epoch [8805/10000], Loss: 0.0036\n",
      "Epoch [8806/10000], Loss: 0.0041\n",
      "Epoch [8807/10000], Loss: 0.0042\n",
      "Epoch [8808/10000], Loss: 0.0043\n",
      "Epoch [8809/10000], Loss: 0.0039\n",
      "Epoch [8810/10000], Loss: 0.0042\n",
      "Epoch [8811/10000], Loss: 0.0042\n",
      "Epoch [8812/10000], Loss: 0.0044\n",
      "Epoch [8813/10000], Loss: 0.0044\n",
      "Epoch [8814/10000], Loss: 0.0043\n",
      "Epoch [8815/10000], Loss: 0.0041\n",
      "Epoch [8816/10000], Loss: 0.0036\n",
      "Epoch [8817/10000], Loss: 0.0035\n",
      "Epoch [8818/10000], Loss: 0.0043\n",
      "Epoch [8819/10000], Loss: 0.0043\n",
      "Epoch [8820/10000], Loss: 0.0037\n",
      "Epoch [8821/10000], Loss: 0.0043\n",
      "Epoch [8822/10000], Loss: 0.0037\n",
      "Epoch [8823/10000], Loss: 0.0036\n",
      "Epoch [8824/10000], Loss: 0.0041\n",
      "Epoch [8825/10000], Loss: 0.0039\n",
      "Epoch [8826/10000], Loss: 0.0042\n",
      "Epoch [8827/10000], Loss: 0.0040\n",
      "Epoch [8828/10000], Loss: 0.0038\n",
      "Epoch [8829/10000], Loss: 0.0040\n",
      "Epoch [8830/10000], Loss: 0.0039\n",
      "Epoch [8831/10000], Loss: 0.0046\n",
      "Epoch [8832/10000], Loss: 0.0038\n",
      "Epoch [8833/10000], Loss: 0.0038\n",
      "Epoch [8834/10000], Loss: 0.0038\n",
      "Epoch [8835/10000], Loss: 0.0037\n",
      "Epoch [8836/10000], Loss: 0.0041\n",
      "Epoch [8837/10000], Loss: 0.0040\n",
      "Epoch [8838/10000], Loss: 0.0043\n",
      "Epoch [8839/10000], Loss: 0.0044\n",
      "Epoch [8840/10000], Loss: 0.0039\n",
      "Epoch [8841/10000], Loss: 0.0039\n",
      "Epoch [8842/10000], Loss: 0.0038\n",
      "Epoch [8843/10000], Loss: 0.0036\n",
      "Epoch [8844/10000], Loss: 0.0040\n",
      "Epoch [8845/10000], Loss: 0.0036\n",
      "Epoch [8846/10000], Loss: 0.0037\n",
      "Epoch [8847/10000], Loss: 0.0043\n",
      "Epoch [8848/10000], Loss: 0.0045\n",
      "Epoch [8849/10000], Loss: 0.0045\n",
      "Epoch [8850/10000], Loss: 0.0042\n",
      "Epoch [8851/10000], Loss: 0.0037\n",
      "Epoch [8852/10000], Loss: 0.0039\n",
      "Epoch [8853/10000], Loss: 0.0037\n",
      "Epoch [8854/10000], Loss: 0.0040\n",
      "Epoch [8855/10000], Loss: 0.0041\n",
      "Epoch [8856/10000], Loss: 0.0039\n",
      "Epoch [8857/10000], Loss: 0.0037\n",
      "Epoch [8858/10000], Loss: 0.0038\n",
      "Epoch [8859/10000], Loss: 0.0043\n",
      "Epoch [8860/10000], Loss: 0.0040\n",
      "Epoch [8861/10000], Loss: 0.0039\n",
      "Epoch [8862/10000], Loss: 0.0036\n",
      "Epoch [8863/10000], Loss: 0.0042\n",
      "Epoch [8864/10000], Loss: 0.0038\n",
      "Epoch [8865/10000], Loss: 0.0045\n",
      "Epoch [8866/10000], Loss: 0.0037\n",
      "Epoch [8867/10000], Loss: 0.0043\n",
      "Epoch [8868/10000], Loss: 0.0039\n",
      "Epoch [8869/10000], Loss: 0.0040\n",
      "Epoch [8870/10000], Loss: 0.0037\n",
      "Epoch [8871/10000], Loss: 0.0041\n",
      "Epoch [8872/10000], Loss: 0.0041\n",
      "Epoch [8873/10000], Loss: 0.0038\n",
      "Epoch [8874/10000], Loss: 0.0041\n",
      "Epoch [8875/10000], Loss: 0.0042\n",
      "Epoch [8876/10000], Loss: 0.0041\n",
      "Epoch [8877/10000], Loss: 0.0042\n",
      "Epoch [8878/10000], Loss: 0.0043\n",
      "Epoch [8879/10000], Loss: 0.0037\n",
      "Epoch [8880/10000], Loss: 0.0039\n",
      "Epoch [8881/10000], Loss: 0.0044\n",
      "Epoch [8882/10000], Loss: 0.0039\n",
      "Epoch [8883/10000], Loss: 0.0037\n",
      "Epoch [8884/10000], Loss: 0.0040\n",
      "Epoch [8885/10000], Loss: 0.0041\n",
      "Epoch [8886/10000], Loss: 0.0037\n",
      "Epoch [8887/10000], Loss: 0.0038\n",
      "Epoch [8888/10000], Loss: 0.0037\n",
      "Epoch [8889/10000], Loss: 0.0038\n",
      "Epoch [8890/10000], Loss: 0.0044\n",
      "Epoch [8891/10000], Loss: 0.0039\n",
      "Epoch [8892/10000], Loss: 0.0042\n",
      "Epoch [8893/10000], Loss: 0.0040\n",
      "Epoch [8894/10000], Loss: 0.0037\n",
      "Epoch [8895/10000], Loss: 0.0036\n",
      "Epoch [8896/10000], Loss: 0.0048\n",
      "Epoch [8897/10000], Loss: 0.0041\n",
      "Epoch [8898/10000], Loss: 0.0040\n",
      "Epoch [8899/10000], Loss: 0.0038\n",
      "Epoch [8900/10000], Loss: 0.0040\n",
      "Epoch [8901/10000], Loss: 0.0046\n",
      "Epoch [8902/10000], Loss: 0.0035\n",
      "Epoch [8903/10000], Loss: 0.0039\n",
      "Epoch [8904/10000], Loss: 0.0037\n",
      "Epoch [8905/10000], Loss: 0.0039\n",
      "Epoch [8906/10000], Loss: 0.0043\n",
      "Epoch [8907/10000], Loss: 0.0043\n",
      "Epoch [8908/10000], Loss: 0.0042\n",
      "Epoch [8909/10000], Loss: 0.0037\n",
      "Epoch [8910/10000], Loss: 0.0041\n",
      "Epoch [8911/10000], Loss: 0.0037\n",
      "Epoch [8912/10000], Loss: 0.0038\n",
      "Epoch [8913/10000], Loss: 0.0038\n",
      "Epoch [8914/10000], Loss: 0.0038\n",
      "Epoch [8915/10000], Loss: 0.0038\n",
      "Epoch [8916/10000], Loss: 0.0040\n",
      "Epoch [8917/10000], Loss: 0.0043\n",
      "Epoch [8918/10000], Loss: 0.0035\n",
      "Epoch [8919/10000], Loss: 0.0035\n",
      "Epoch [8920/10000], Loss: 0.0038\n",
      "Epoch [8921/10000], Loss: 0.0045\n",
      "Epoch [8922/10000], Loss: 0.0036\n",
      "Epoch [8923/10000], Loss: 0.0040\n",
      "Epoch [8924/10000], Loss: 0.0039\n",
      "Epoch [8925/10000], Loss: 0.0038\n",
      "Epoch [8926/10000], Loss: 0.0043\n",
      "Epoch [8927/10000], Loss: 0.0040\n",
      "Epoch [8928/10000], Loss: 0.0038\n",
      "Epoch [8929/10000], Loss: 0.0040\n",
      "Epoch [8930/10000], Loss: 0.0042\n",
      "Epoch [8931/10000], Loss: 0.0038\n",
      "Epoch [8932/10000], Loss: 0.0039\n",
      "Epoch [8933/10000], Loss: 0.0044\n",
      "Epoch [8934/10000], Loss: 0.0039\n",
      "Epoch [8935/10000], Loss: 0.0040\n",
      "Epoch [8936/10000], Loss: 0.0044\n",
      "Epoch [8937/10000], Loss: 0.0039\n",
      "Epoch [8938/10000], Loss: 0.0041\n",
      "Epoch [8939/10000], Loss: 0.0035\n",
      "Epoch [8940/10000], Loss: 0.0036\n",
      "Epoch [8941/10000], Loss: 0.0039\n",
      "Epoch [8942/10000], Loss: 0.0036\n",
      "Epoch [8943/10000], Loss: 0.0036\n",
      "Epoch [8944/10000], Loss: 0.0041\n",
      "Epoch [8945/10000], Loss: 0.0037\n",
      "Epoch [8946/10000], Loss: 0.0036\n",
      "Epoch [8947/10000], Loss: 0.0037\n",
      "Epoch [8948/10000], Loss: 0.0038\n",
      "Epoch [8949/10000], Loss: 0.0040\n",
      "Epoch [8950/10000], Loss: 0.0039\n",
      "Epoch [8951/10000], Loss: 0.0040\n",
      "Epoch [8952/10000], Loss: 0.0038\n",
      "Epoch [8953/10000], Loss: 0.0041\n",
      "Epoch [8954/10000], Loss: 0.0041\n",
      "Epoch [8955/10000], Loss: 0.0037\n",
      "Epoch [8956/10000], Loss: 0.0037\n",
      "Epoch [8957/10000], Loss: 0.0038\n",
      "Epoch [8958/10000], Loss: 0.0039\n",
      "Epoch [8959/10000], Loss: 0.0040\n",
      "Epoch [8960/10000], Loss: 0.0039\n",
      "Epoch [8961/10000], Loss: 0.0040\n",
      "Epoch [8962/10000], Loss: 0.0039\n",
      "Epoch [8963/10000], Loss: 0.0037\n",
      "Epoch [8964/10000], Loss: 0.0036\n",
      "Epoch [8965/10000], Loss: 0.0037\n",
      "Epoch [8966/10000], Loss: 0.0038\n",
      "Epoch [8967/10000], Loss: 0.0041\n",
      "Epoch [8968/10000], Loss: 0.0038\n",
      "Epoch [8969/10000], Loss: 0.0037\n",
      "Epoch [8970/10000], Loss: 0.0039\n",
      "Epoch [8971/10000], Loss: 0.0038\n",
      "Epoch [8972/10000], Loss: 0.0039\n",
      "Epoch [8973/10000], Loss: 0.0040\n",
      "Epoch [8974/10000], Loss: 0.0045\n",
      "Epoch [8975/10000], Loss: 0.0038\n",
      "Epoch [8976/10000], Loss: 0.0041\n",
      "Epoch [8977/10000], Loss: 0.0043\n",
      "Epoch [8978/10000], Loss: 0.0036\n",
      "Epoch [8979/10000], Loss: 0.0038\n",
      "Epoch [8980/10000], Loss: 0.0038\n",
      "Epoch [8981/10000], Loss: 0.0042\n",
      "Epoch [8982/10000], Loss: 0.0039\n",
      "Epoch [8983/10000], Loss: 0.0038\n",
      "Epoch [8984/10000], Loss: 0.0038\n",
      "Epoch [8985/10000], Loss: 0.0038\n",
      "Epoch [8986/10000], Loss: 0.0039\n",
      "Epoch [8987/10000], Loss: 0.0041\n",
      "Epoch [8988/10000], Loss: 0.0037\n",
      "Epoch [8989/10000], Loss: 0.0040\n",
      "Epoch [8990/10000], Loss: 0.0033\n",
      "Epoch [8991/10000], Loss: 0.0035\n",
      "Epoch [8992/10000], Loss: 0.0041\n",
      "Epoch [8993/10000], Loss: 0.0042\n",
      "Epoch [8994/10000], Loss: 0.0036\n",
      "Epoch [8995/10000], Loss: 0.0038\n",
      "Epoch [8996/10000], Loss: 0.0044\n",
      "Epoch [8997/10000], Loss: 0.0039\n",
      "Epoch [8998/10000], Loss: 0.0035\n",
      "Epoch [8999/10000], Loss: 0.0037\n",
      "Epoch [9000/10000], Loss: 0.0042\n",
      "Epoch [9001/10000], Loss: 0.0036\n",
      "Epoch [9002/10000], Loss: 0.0035\n",
      "Epoch [9003/10000], Loss: 0.0041\n",
      "Epoch [9004/10000], Loss: 0.0038\n",
      "Epoch [9005/10000], Loss: 0.0040\n",
      "Epoch [9006/10000], Loss: 0.0040\n",
      "Epoch [9007/10000], Loss: 0.0038\n",
      "Epoch [9008/10000], Loss: 0.0035\n",
      "Epoch [9009/10000], Loss: 0.0039\n",
      "Epoch [9010/10000], Loss: 0.0043\n",
      "Epoch [9011/10000], Loss: 0.0037\n",
      "Epoch [9012/10000], Loss: 0.0036\n",
      "Epoch [9013/10000], Loss: 0.0040\n",
      "Epoch [9014/10000], Loss: 0.0038\n",
      "Epoch [9015/10000], Loss: 0.0036\n",
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      "Epoch [9017/10000], Loss: 0.0042\n",
      "Epoch [9018/10000], Loss: 0.0036\n",
      "Epoch [9019/10000], Loss: 0.0038\n",
      "Epoch [9020/10000], Loss: 0.0041\n",
      "Epoch [9021/10000], Loss: 0.0036\n",
      "Epoch [9022/10000], Loss: 0.0041\n",
      "Epoch [9023/10000], Loss: 0.0036\n",
      "Epoch [9024/10000], Loss: 0.0037\n",
      "Epoch [9025/10000], Loss: 0.0033\n",
      "Epoch [9026/10000], Loss: 0.0043\n",
      "Epoch [9027/10000], Loss: 0.0041\n",
      "Epoch [9028/10000], Loss: 0.0036\n",
      "Epoch [9029/10000], Loss: 0.0036\n",
      "Epoch [9030/10000], Loss: 0.0039\n",
      "Epoch [9031/10000], Loss: 0.0039\n",
      "Epoch [9032/10000], Loss: 0.0035\n",
      "Epoch [9033/10000], Loss: 0.0045\n",
      "Epoch [9034/10000], Loss: 0.0037\n",
      "Epoch [9035/10000], Loss: 0.0038\n",
      "Epoch [9036/10000], Loss: 0.0038\n",
      "Epoch [9037/10000], Loss: 0.0037\n",
      "Epoch [9038/10000], Loss: 0.0037\n",
      "Epoch [9039/10000], Loss: 0.0037\n",
      "Epoch [9040/10000], Loss: 0.0038\n",
      "Epoch [9041/10000], Loss: 0.0035\n",
      "Epoch [9042/10000], Loss: 0.0039\n",
      "Epoch [9043/10000], Loss: 0.0037\n",
      "Epoch [9044/10000], Loss: 0.0038\n",
      "Epoch [9045/10000], Loss: 0.0036\n",
      "Epoch [9046/10000], Loss: 0.0039\n",
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      "Epoch [9048/10000], Loss: 0.0037\n",
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      "Epoch [9050/10000], Loss: 0.0041\n",
      "Epoch [9051/10000], Loss: 0.0034\n",
      "Epoch [9052/10000], Loss: 0.0035\n",
      "Epoch [9053/10000], Loss: 0.0039\n",
      "Epoch [9054/10000], Loss: 0.0034\n",
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      "Epoch [9056/10000], Loss: 0.0040\n",
      "Epoch [9057/10000], Loss: 0.0038\n",
      "Epoch [9058/10000], Loss: 0.0035\n",
      "Epoch [9059/10000], Loss: 0.0037\n",
      "Epoch [9060/10000], Loss: 0.0040\n",
      "Epoch [9061/10000], Loss: 0.0044\n",
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      "Epoch [9063/10000], Loss: 0.0038\n",
      "Epoch [9064/10000], Loss: 0.0041\n",
      "Epoch [9065/10000], Loss: 0.0033\n",
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      "Epoch [9067/10000], Loss: 0.0034\n",
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      "Epoch [9069/10000], Loss: 0.0035\n",
      "Epoch [9070/10000], Loss: 0.0037\n",
      "Epoch [9071/10000], Loss: 0.0040\n",
      "Epoch [9072/10000], Loss: 0.0039\n",
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      "Epoch [9080/10000], Loss: 0.0033\n",
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      "Epoch [9086/10000], Loss: 0.0037\n",
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      "Epoch [9088/10000], Loss: 0.0034\n",
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      "Epoch [9090/10000], Loss: 0.0038\n",
      "Epoch [9091/10000], Loss: 0.0036\n",
      "Epoch [9092/10000], Loss: 0.0037\n",
      "Epoch [9093/10000], Loss: 0.0034\n",
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      "Epoch [9095/10000], Loss: 0.0040\n",
      "Epoch [9096/10000], Loss: 0.0036\n",
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      "Epoch [9098/10000], Loss: 0.0038\n",
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      "Epoch [9101/10000], Loss: 0.0037\n",
      "Epoch [9102/10000], Loss: 0.0038\n",
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      "Epoch [9104/10000], Loss: 0.0035\n",
      "Epoch [9105/10000], Loss: 0.0034\n",
      "Epoch [9106/10000], Loss: 0.0038\n",
      "Epoch [9107/10000], Loss: 0.0040\n",
      "Epoch [9108/10000], Loss: 0.0037\n",
      "Epoch [9109/10000], Loss: 0.0034\n",
      "Epoch [9110/10000], Loss: 0.0036\n",
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      "Epoch [9112/10000], Loss: 0.0036\n",
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      "Epoch [9117/10000], Loss: 0.0034\n",
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      "Epoch [9119/10000], Loss: 0.0036\n",
      "Epoch [9120/10000], Loss: 0.0038\n",
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      "Epoch [9123/10000], Loss: 0.0039\n",
      "Epoch [9124/10000], Loss: 0.0036\n",
      "Epoch [9125/10000], Loss: 0.0039\n",
      "Epoch [9126/10000], Loss: 0.0032\n",
      "Epoch [9127/10000], Loss: 0.0034\n",
      "Epoch [9128/10000], Loss: 0.0036\n",
      "Epoch [9129/10000], Loss: 0.0039\n",
      "Epoch [9130/10000], Loss: 0.0034\n",
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      "Epoch [9133/10000], Loss: 0.0034\n",
      "Epoch [9134/10000], Loss: 0.0037\n",
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      "Epoch [9137/10000], Loss: 0.0039\n",
      "Epoch [9138/10000], Loss: 0.0037\n",
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      "Epoch [9146/10000], Loss: 0.0039\n",
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      "Epoch [9152/10000], Loss: 0.0036\n",
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      "Epoch [9154/10000], Loss: 0.0037\n",
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      "Epoch [9156/10000], Loss: 0.0038\n",
      "Epoch [9157/10000], Loss: 0.0039\n",
      "Epoch [9158/10000], Loss: 0.0036\n",
      "Epoch [9159/10000], Loss: 0.0034\n",
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      "Epoch [9161/10000], Loss: 0.0043\n",
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      "Epoch [9163/10000], Loss: 0.0040\n",
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      "Epoch [9171/10000], Loss: 0.0034\n",
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      "Epoch [9188/10000], Loss: 0.0039\n",
      "Epoch [9189/10000], Loss: 0.0037\n",
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      "Epoch [9198/10000], Loss: 0.0037\n",
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      "Epoch [9209/10000], Loss: 0.0037\n",
      "Epoch [9210/10000], Loss: 0.0035\n",
      "Epoch [9211/10000], Loss: 0.0033\n",
      "Epoch [9212/10000], Loss: 0.0032\n",
      "Epoch [9213/10000], Loss: 0.0039\n",
      "Epoch [9214/10000], Loss: 0.0034\n",
      "Epoch [9215/10000], Loss: 0.0032\n",
      "Epoch [9216/10000], Loss: 0.0037\n",
      "Epoch [9217/10000], Loss: 0.0034\n",
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      "Epoch [9219/10000], Loss: 0.0034\n",
      "Epoch [9220/10000], Loss: 0.0036\n",
      "Epoch [9221/10000], Loss: 0.0033\n",
      "Epoch [9222/10000], Loss: 0.0039\n",
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      "Epoch [9226/10000], Loss: 0.0035\n",
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      "Epoch [9228/10000], Loss: 0.0037\n",
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      "Epoch [9230/10000], Loss: 0.0037\n",
      "Epoch [9231/10000], Loss: 0.0037\n",
      "Epoch [9232/10000], Loss: 0.0039\n",
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      "Epoch [9266/10000], Loss: 0.0038\n",
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      "Epoch [9277/10000], Loss: 0.0032\n",
      "Epoch [9278/10000], Loss: 0.0039\n",
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      "Epoch [9280/10000], Loss: 0.0036\n",
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      "Epoch [9287/10000], Loss: 0.0036\n",
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      "Epoch [9294/10000], Loss: 0.0040\n",
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      "Epoch [9299/10000], Loss: 0.0036\n",
      "Epoch [9300/10000], Loss: 0.0034\n",
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      "Epoch [9302/10000], Loss: 0.0035\n",
      "Epoch [9303/10000], Loss: 0.0038\n",
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      "Epoch [9308/10000], Loss: 0.0034\n",
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      "Epoch [9310/10000], Loss: 0.0034\n",
      "Epoch [9311/10000], Loss: 0.0032\n",
      "Epoch [9312/10000], Loss: 0.0035\n",
      "Epoch [9313/10000], Loss: 0.0034\n",
      "Epoch [9314/10000], Loss: 0.0036\n",
      "Epoch [9315/10000], Loss: 0.0032\n",
      "Epoch [9316/10000], Loss: 0.0033\n",
      "Epoch [9317/10000], Loss: 0.0039\n",
      "Epoch [9318/10000], Loss: 0.0036\n",
      "Epoch [9319/10000], Loss: 0.0031\n",
      "Epoch [9320/10000], Loss: 0.0036\n",
      "Epoch [9321/10000], Loss: 0.0033\n",
      "Epoch [9322/10000], Loss: 0.0037\n",
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      "Epoch [9328/10000], Loss: 0.0034\n",
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      "Epoch [9330/10000], Loss: 0.0031\n",
      "Epoch [9331/10000], Loss: 0.0034\n",
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      "Epoch [9333/10000], Loss: 0.0037\n",
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      "Epoch [9355/10000], Loss: 0.0039\n",
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      "Epoch [9357/10000], Loss: 0.0037\n",
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      "Epoch [9364/10000], Loss: 0.0037\n",
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      "Epoch [9380/10000], Loss: 0.0034\n",
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      "Epoch [9383/10000], Loss: 0.0037\n",
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      "Epoch [9411/10000], Loss: 0.0037\n",
      "Epoch [9412/10000], Loss: 0.0037\n",
      "Epoch [9413/10000], Loss: 0.0035\n",
      "Epoch [9414/10000], Loss: 0.0033\n",
      "Epoch [9415/10000], Loss: 0.0035\n",
      "Epoch [9416/10000], Loss: 0.0034\n",
      "Epoch [9417/10000], Loss: 0.0035\n",
      "Epoch [9418/10000], Loss: 0.0032\n",
      "Epoch [9419/10000], Loss: 0.0032\n",
      "Epoch [9420/10000], Loss: 0.0032\n",
      "Epoch [9421/10000], Loss: 0.0034\n",
      "Epoch [9422/10000], Loss: 0.0031\n",
      "Epoch [9423/10000], Loss: 0.0036\n",
      "Epoch [9424/10000], Loss: 0.0033\n",
      "Epoch [9425/10000], Loss: 0.0035\n",
      "Epoch [9426/10000], Loss: 0.0034\n",
      "Epoch [9427/10000], Loss: 0.0033\n",
      "Epoch [9428/10000], Loss: 0.0031\n",
      "Epoch [9429/10000], Loss: 0.0035\n",
      "Epoch [9430/10000], Loss: 0.0035\n",
      "Epoch [9431/10000], Loss: 0.0035\n",
      "Epoch [9432/10000], Loss: 0.0034\n",
      "Epoch [9433/10000], Loss: 0.0035\n",
      "Epoch [9434/10000], Loss: 0.0037\n",
      "Epoch [9435/10000], Loss: 0.0032\n",
      "Epoch [9436/10000], Loss: 0.0034\n",
      "Epoch [9437/10000], Loss: 0.0033\n",
      "Epoch [9438/10000], Loss: 0.0035\n",
      "Epoch [9439/10000], Loss: 0.0031\n",
      "Epoch [9440/10000], Loss: 0.0033\n",
      "Epoch [9441/10000], Loss: 0.0032\n",
      "Epoch [9442/10000], Loss: 0.0034\n",
      "Epoch [9443/10000], Loss: 0.0032\n",
      "Epoch [9444/10000], Loss: 0.0035\n",
      "Epoch [9445/10000], Loss: 0.0031\n",
      "Epoch [9446/10000], Loss: 0.0036\n",
      "Epoch [9447/10000], Loss: 0.0036\n",
      "Epoch [9448/10000], Loss: 0.0035\n",
      "Epoch [9449/10000], Loss: 0.0033\n",
      "Epoch [9450/10000], Loss: 0.0031\n",
      "Epoch [9451/10000], Loss: 0.0036\n",
      "Epoch [9452/10000], Loss: 0.0031\n",
      "Epoch [9453/10000], Loss: 0.0033\n",
      "Epoch [9454/10000], Loss: 0.0033\n",
      "Epoch [9455/10000], Loss: 0.0034\n",
      "Epoch [9456/10000], Loss: 0.0035\n",
      "Epoch [9457/10000], Loss: 0.0034\n",
      "Epoch [9458/10000], Loss: 0.0034\n",
      "Epoch [9459/10000], Loss: 0.0032\n",
      "Epoch [9460/10000], Loss: 0.0033\n",
      "Epoch [9461/10000], Loss: 0.0032\n",
      "Epoch [9462/10000], Loss: 0.0033\n",
      "Epoch [9463/10000], Loss: 0.0033\n",
      "Epoch [9464/10000], Loss: 0.0034\n",
      "Epoch [9465/10000], Loss: 0.0030\n",
      "Epoch [9466/10000], Loss: 0.0035\n",
      "Epoch [9467/10000], Loss: 0.0033\n",
      "Epoch [9468/10000], Loss: 0.0033\n",
      "Epoch [9469/10000], Loss: 0.0033\n",
      "Epoch [9470/10000], Loss: 0.0034\n",
      "Epoch [9471/10000], Loss: 0.0032\n",
      "Epoch [9472/10000], Loss: 0.0033\n",
      "Epoch [9473/10000], Loss: 0.0032\n",
      "Epoch [9474/10000], Loss: 0.0034\n",
      "Epoch [9475/10000], Loss: 0.0031\n",
      "Epoch [9476/10000], Loss: 0.0031\n",
      "Epoch [9477/10000], Loss: 0.0034\n",
      "Epoch [9478/10000], Loss: 0.0035\n",
      "Epoch [9479/10000], Loss: 0.0031\n",
      "Epoch [9480/10000], Loss: 0.0031\n",
      "Epoch [9481/10000], Loss: 0.0033\n",
      "Epoch [9482/10000], Loss: 0.0033\n",
      "Epoch [9483/10000], Loss: 0.0031\n",
      "Epoch [9484/10000], Loss: 0.0033\n",
      "Epoch [9485/10000], Loss: 0.0036\n",
      "Epoch [9486/10000], Loss: 0.0032\n",
      "Epoch [9487/10000], Loss: 0.0032\n",
      "Epoch [9488/10000], Loss: 0.0033\n",
      "Epoch [9489/10000], Loss: 0.0034\n",
      "Epoch [9490/10000], Loss: 0.0032\n",
      "Epoch [9491/10000], Loss: 0.0038\n",
      "Epoch [9492/10000], Loss: 0.0032\n",
      "Epoch [9493/10000], Loss: 0.0031\n",
      "Epoch [9494/10000], Loss: 0.0032\n",
      "Epoch [9495/10000], Loss: 0.0034\n",
      "Epoch [9496/10000], Loss: 0.0033\n",
      "Epoch [9497/10000], Loss: 0.0031\n",
      "Epoch [9498/10000], Loss: 0.0033\n",
      "Epoch [9499/10000], Loss: 0.0036\n",
      "Epoch [9500/10000], Loss: 0.0033\n",
      "Epoch [9501/10000], Loss: 0.0034\n",
      "Epoch [9502/10000], Loss: 0.0032\n",
      "Epoch [9503/10000], Loss: 0.0030\n",
      "Epoch [9504/10000], Loss: 0.0032\n",
      "Epoch [9505/10000], Loss: 0.0031\n",
      "Epoch [9506/10000], Loss: 0.0031\n",
      "Epoch [9507/10000], Loss: 0.0034\n",
      "Epoch [9508/10000], Loss: 0.0037\n",
      "Epoch [9509/10000], Loss: 0.0032\n",
      "Epoch [9510/10000], Loss: 0.0033\n",
      "Epoch [9511/10000], Loss: 0.0032\n",
      "Epoch [9512/10000], Loss: 0.0034\n",
      "Epoch [9513/10000], Loss: 0.0030\n",
      "Epoch [9514/10000], Loss: 0.0035\n",
      "Epoch [9515/10000], Loss: 0.0036\n",
      "Epoch [9516/10000], Loss: 0.0030\n",
      "Epoch [9517/10000], Loss: 0.0033\n",
      "Epoch [9518/10000], Loss: 0.0034\n",
      "Epoch [9519/10000], Loss: 0.0032\n",
      "Epoch [9520/10000], Loss: 0.0032\n",
      "Epoch [9521/10000], Loss: 0.0034\n",
      "Epoch [9522/10000], Loss: 0.0032\n",
      "Epoch [9523/10000], Loss: 0.0032\n",
      "Epoch [9524/10000], Loss: 0.0035\n",
      "Epoch [9525/10000], Loss: 0.0032\n",
      "Epoch [9526/10000], Loss: 0.0033\n",
      "Epoch [9527/10000], Loss: 0.0033\n",
      "Epoch [9528/10000], Loss: 0.0034\n",
      "Epoch [9529/10000], Loss: 0.0034\n",
      "Epoch [9530/10000], Loss: 0.0031\n",
      "Epoch [9531/10000], Loss: 0.0033\n",
      "Epoch [9532/10000], Loss: 0.0032\n",
      "Epoch [9533/10000], Loss: 0.0034\n",
      "Epoch [9534/10000], Loss: 0.0035\n",
      "Epoch [9535/10000], Loss: 0.0030\n",
      "Epoch [9536/10000], Loss: 0.0034\n",
      "Epoch [9537/10000], Loss: 0.0035\n",
      "Epoch [9538/10000], Loss: 0.0034\n",
      "Epoch [9539/10000], Loss: 0.0032\n",
      "Epoch [9540/10000], Loss: 0.0032\n",
      "Epoch [9541/10000], Loss: 0.0033\n",
      "Epoch [9542/10000], Loss: 0.0031\n",
      "Epoch [9543/10000], Loss: 0.0034\n",
      "Epoch [9544/10000], Loss: 0.0033\n",
      "Epoch [9545/10000], Loss: 0.0035\n",
      "Epoch [9546/10000], Loss: 0.0033\n",
      "Epoch [9547/10000], Loss: 0.0033\n",
      "Epoch [9548/10000], Loss: 0.0032\n",
      "Epoch [9549/10000], Loss: 0.0032\n",
      "Epoch [9550/10000], Loss: 0.0031\n",
      "Epoch [9551/10000], Loss: 0.0033\n",
      "Epoch [9552/10000], Loss: 0.0031\n",
      "Epoch [9553/10000], Loss: 0.0033\n",
      "Epoch [9554/10000], Loss: 0.0033\n",
      "Epoch [9555/10000], Loss: 0.0031\n",
      "Epoch [9556/10000], Loss: 0.0032\n",
      "Epoch [9557/10000], Loss: 0.0033\n",
      "Epoch [9558/10000], Loss: 0.0032\n",
      "Epoch [9559/10000], Loss: 0.0033\n",
      "Epoch [9560/10000], Loss: 0.0034\n",
      "Epoch [9561/10000], Loss: 0.0030\n",
      "Epoch [9562/10000], Loss: 0.0033\n",
      "Epoch [9563/10000], Loss: 0.0036\n",
      "Epoch [9564/10000], Loss: 0.0031\n",
      "Epoch [9565/10000], Loss: 0.0034\n",
      "Epoch [9566/10000], Loss: 0.0032\n",
      "Epoch [9567/10000], Loss: 0.0030\n",
      "Epoch [9568/10000], Loss: 0.0033\n",
      "Epoch [9569/10000], Loss: 0.0034\n",
      "Epoch [9570/10000], Loss: 0.0034\n",
      "Epoch [9571/10000], Loss: 0.0034\n",
      "Epoch [9572/10000], Loss: 0.0033\n",
      "Epoch [9573/10000], Loss: 0.0031\n",
      "Epoch [9574/10000], Loss: 0.0030\n",
      "Epoch [9575/10000], Loss: 0.0031\n",
      "Epoch [9576/10000], Loss: 0.0030\n",
      "Epoch [9577/10000], Loss: 0.0032\n",
      "Epoch [9578/10000], Loss: 0.0032\n",
      "Epoch [9579/10000], Loss: 0.0038\n",
      "Epoch [9580/10000], Loss: 0.0032\n",
      "Epoch [9581/10000], Loss: 0.0031\n",
      "Epoch [9582/10000], Loss: 0.0030\n",
      "Epoch [9583/10000], Loss: 0.0032\n",
      "Epoch [9584/10000], Loss: 0.0030\n",
      "Epoch [9585/10000], Loss: 0.0032\n",
      "Epoch [9586/10000], Loss: 0.0031\n",
      "Epoch [9587/10000], Loss: 0.0032\n",
      "Epoch [9588/10000], Loss: 0.0033\n",
      "Epoch [9589/10000], Loss: 0.0034\n",
      "Epoch [9590/10000], Loss: 0.0033\n",
      "Epoch [9591/10000], Loss: 0.0035\n",
      "Epoch [9592/10000], Loss: 0.0032\n",
      "Epoch [9593/10000], Loss: 0.0031\n",
      "Epoch [9594/10000], Loss: 0.0033\n",
      "Epoch [9595/10000], Loss: 0.0033\n",
      "Epoch [9596/10000], Loss: 0.0032\n",
      "Epoch [9597/10000], Loss: 0.0030\n",
      "Epoch [9598/10000], Loss: 0.0035\n",
      "Epoch [9599/10000], Loss: 0.0032\n",
      "Epoch [9600/10000], Loss: 0.0034\n",
      "Epoch [9601/10000], Loss: 0.0030\n",
      "Epoch [9602/10000], Loss: 0.0032\n",
      "Epoch [9603/10000], Loss: 0.0036\n",
      "Epoch [9604/10000], Loss: 0.0031\n",
      "Epoch [9605/10000], Loss: 0.0032\n",
      "Epoch [9606/10000], Loss: 0.0032\n",
      "Epoch [9607/10000], Loss: 0.0032\n",
      "Epoch [9608/10000], Loss: 0.0032\n",
      "Epoch [9609/10000], Loss: 0.0034\n",
      "Epoch [9610/10000], Loss: 0.0032\n",
      "Epoch [9611/10000], Loss: 0.0032\n",
      "Epoch [9612/10000], Loss: 0.0036\n",
      "Epoch [9613/10000], Loss: 0.0031\n",
      "Epoch [9614/10000], Loss: 0.0030\n",
      "Epoch [9615/10000], Loss: 0.0032\n",
      "Epoch [9616/10000], Loss: 0.0031\n",
      "Epoch [9617/10000], Loss: 0.0032\n",
      "Epoch [9618/10000], Loss: 0.0033\n",
      "Epoch [9619/10000], Loss: 0.0033\n",
      "Epoch [9620/10000], Loss: 0.0033\n",
      "Epoch [9621/10000], Loss: 0.0031\n",
      "Epoch [9622/10000], Loss: 0.0034\n",
      "Epoch [9623/10000], Loss: 0.0031\n",
      "Epoch [9624/10000], Loss: 0.0033\n",
      "Epoch [9625/10000], Loss: 0.0033\n",
      "Epoch [9626/10000], Loss: 0.0031\n",
      "Epoch [9627/10000], Loss: 0.0032\n",
      "Epoch [9628/10000], Loss: 0.0032\n",
      "Epoch [9629/10000], Loss: 0.0033\n",
      "Epoch [9630/10000], Loss: 0.0034\n",
      "Epoch [9631/10000], Loss: 0.0031\n",
      "Epoch [9632/10000], Loss: 0.0035\n",
      "Epoch [9633/10000], Loss: 0.0032\n",
      "Epoch [9634/10000], Loss: 0.0032\n",
      "Epoch [9635/10000], Loss: 0.0031\n",
      "Epoch [9636/10000], Loss: 0.0034\n",
      "Epoch [9637/10000], Loss: 0.0033\n",
      "Epoch [9638/10000], Loss: 0.0031\n",
      "Epoch [9639/10000], Loss: 0.0032\n",
      "Epoch [9640/10000], Loss: 0.0033\n",
      "Epoch [9641/10000], Loss: 0.0030\n",
      "Epoch [9642/10000], Loss: 0.0030\n",
      "Epoch [9643/10000], Loss: 0.0034\n",
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      "Epoch [9645/10000], Loss: 0.0031\n",
      "Epoch [9646/10000], Loss: 0.0031\n",
      "Epoch [9647/10000], Loss: 0.0032\n",
      "Epoch [9648/10000], Loss: 0.0029\n",
      "Epoch [9649/10000], Loss: 0.0036\n",
      "Epoch [9650/10000], Loss: 0.0032\n",
      "Epoch [9651/10000], Loss: 0.0033\n",
      "Epoch [9652/10000], Loss: 0.0031\n",
      "Epoch [9653/10000], Loss: 0.0030\n",
      "Epoch [9654/10000], Loss: 0.0029\n",
      "Epoch [9655/10000], Loss: 0.0032\n",
      "Epoch [9656/10000], Loss: 0.0030\n",
      "Epoch [9657/10000], Loss: 0.0030\n",
      "Epoch [9658/10000], Loss: 0.0030\n",
      "Epoch [9659/10000], Loss: 0.0032\n",
      "Epoch [9660/10000], Loss: 0.0032\n",
      "Epoch [9661/10000], Loss: 0.0030\n",
      "Epoch [9662/10000], Loss: 0.0030\n",
      "Epoch [9663/10000], Loss: 0.0031\n",
      "Epoch [9664/10000], Loss: 0.0032\n",
      "Epoch [9665/10000], Loss: 0.0030\n",
      "Epoch [9666/10000], Loss: 0.0030\n",
      "Epoch [9667/10000], Loss: 0.0031\n",
      "Epoch [9668/10000], Loss: 0.0030\n",
      "Epoch [9669/10000], Loss: 0.0028\n",
      "Epoch [9670/10000], Loss: 0.0034\n",
      "Epoch [9671/10000], Loss: 0.0034\n",
      "Epoch [9672/10000], Loss: 0.0030\n",
      "Epoch [9673/10000], Loss: 0.0033\n",
      "Epoch [9674/10000], Loss: 0.0032\n",
      "Epoch [9675/10000], Loss: 0.0033\n",
      "Epoch [9676/10000], Loss: 0.0033\n",
      "Epoch [9677/10000], Loss: 0.0030\n",
      "Epoch [9678/10000], Loss: 0.0032\n",
      "Epoch [9679/10000], Loss: 0.0031\n",
      "Epoch [9680/10000], Loss: 0.0030\n",
      "Epoch [9681/10000], Loss: 0.0034\n",
      "Epoch [9682/10000], Loss: 0.0032\n",
      "Epoch [9683/10000], Loss: 0.0032\n",
      "Epoch [9684/10000], Loss: 0.0030\n",
      "Epoch [9685/10000], Loss: 0.0031\n",
      "Epoch [9686/10000], Loss: 0.0035\n",
      "Epoch [9687/10000], Loss: 0.0032\n",
      "Epoch [9688/10000], Loss: 0.0031\n",
      "Epoch [9689/10000], Loss: 0.0030\n",
      "Epoch [9690/10000], Loss: 0.0033\n",
      "Epoch [9691/10000], Loss: 0.0036\n",
      "Epoch [9692/10000], Loss: 0.0029\n",
      "Epoch [9693/10000], Loss: 0.0032\n",
      "Epoch [9694/10000], Loss: 0.0031\n",
      "Epoch [9695/10000], Loss: 0.0032\n",
      "Epoch [9696/10000], Loss: 0.0030\n",
      "Epoch [9697/10000], Loss: 0.0028\n",
      "Epoch [9698/10000], Loss: 0.0030\n",
      "Epoch [9699/10000], Loss: 0.0031\n",
      "Epoch [9700/10000], Loss: 0.0030\n",
      "Epoch [9701/10000], Loss: 0.0031\n",
      "Epoch [9702/10000], Loss: 0.0030\n",
      "Epoch [9703/10000], Loss: 0.0032\n",
      "Epoch [9704/10000], Loss: 0.0032\n",
      "Epoch [9705/10000], Loss: 0.0030\n",
      "Epoch [9706/10000], Loss: 0.0029\n",
      "Epoch [9707/10000], Loss: 0.0030\n",
      "Epoch [9708/10000], Loss: 0.0031\n",
      "Epoch [9709/10000], Loss: 0.0031\n",
      "Epoch [9710/10000], Loss: 0.0030\n",
      "Epoch [9711/10000], Loss: 0.0031\n",
      "Epoch [9712/10000], Loss: 0.0032\n",
      "Epoch [9713/10000], Loss: 0.0034\n",
      "Epoch [9714/10000], Loss: 0.0031\n",
      "Epoch [9715/10000], Loss: 0.0030\n",
      "Epoch [9716/10000], Loss: 0.0030\n",
      "Epoch [9717/10000], Loss: 0.0033\n",
      "Epoch [9718/10000], Loss: 0.0033\n",
      "Epoch [9719/10000], Loss: 0.0031\n",
      "Epoch [9720/10000], Loss: 0.0033\n",
      "Epoch [9721/10000], Loss: 0.0032\n",
      "Epoch [9722/10000], Loss: 0.0032\n",
      "Epoch [9723/10000], Loss: 0.0033\n",
      "Epoch [9724/10000], Loss: 0.0031\n",
      "Epoch [9725/10000], Loss: 0.0032\n",
      "Epoch [9726/10000], Loss: 0.0030\n",
      "Epoch [9727/10000], Loss: 0.0030\n",
      "Epoch [9728/10000], Loss: 0.0031\n",
      "Epoch [9729/10000], Loss: 0.0032\n",
      "Epoch [9730/10000], Loss: 0.0030\n",
      "Epoch [9731/10000], Loss: 0.0031\n",
      "Epoch [9732/10000], Loss: 0.0032\n",
      "Epoch [9733/10000], Loss: 0.0031\n",
      "Epoch [9734/10000], Loss: 0.0030\n",
      "Epoch [9735/10000], Loss: 0.0030\n",
      "Epoch [9736/10000], Loss: 0.0030\n",
      "Epoch [9737/10000], Loss: 0.0030\n",
      "Epoch [9738/10000], Loss: 0.0031\n",
      "Epoch [9739/10000], Loss: 0.0033\n",
      "Epoch [9740/10000], Loss: 0.0031\n",
      "Epoch [9741/10000], Loss: 0.0030\n",
      "Epoch [9742/10000], Loss: 0.0032\n",
      "Epoch [9743/10000], Loss: 0.0031\n",
      "Epoch [9744/10000], Loss: 0.0032\n",
      "Epoch [9745/10000], Loss: 0.0035\n",
      "Epoch [9746/10000], Loss: 0.0032\n",
      "Epoch [9747/10000], Loss: 0.0031\n",
      "Epoch [9748/10000], Loss: 0.0031\n",
      "Epoch [9749/10000], Loss: 0.0031\n",
      "Epoch [9750/10000], Loss: 0.0032\n",
      "Epoch [9751/10000], Loss: 0.0031\n",
      "Epoch [9752/10000], Loss: 0.0030\n",
      "Epoch [9753/10000], Loss: 0.0032\n",
      "Epoch [9754/10000], Loss: 0.0031\n",
      "Epoch [9755/10000], Loss: 0.0031\n",
      "Epoch [9756/10000], Loss: 0.0029\n",
      "Epoch [9757/10000], Loss: 0.0028\n",
      "Epoch [9758/10000], Loss: 0.0032\n",
      "Epoch [9759/10000], Loss: 0.0032\n",
      "Epoch [9760/10000], Loss: 0.0032\n",
      "Epoch [9761/10000], Loss: 0.0034\n",
      "Epoch [9762/10000], Loss: 0.0028\n",
      "Epoch [9763/10000], Loss: 0.0031\n",
      "Epoch [9764/10000], Loss: 0.0030\n",
      "Epoch [9765/10000], Loss: 0.0033\n",
      "Epoch [9766/10000], Loss: 0.0033\n",
      "Epoch [9767/10000], Loss: 0.0031\n",
      "Epoch [9768/10000], Loss: 0.0029\n",
      "Epoch [9769/10000], Loss: 0.0029\n",
      "Epoch [9770/10000], Loss: 0.0031\n",
      "Epoch [9771/10000], Loss: 0.0029\n",
      "Epoch [9772/10000], Loss: 0.0031\n",
      "Epoch [9773/10000], Loss: 0.0032\n",
      "Epoch [9774/10000], Loss: 0.0031\n",
      "Epoch [9775/10000], Loss: 0.0032\n",
      "Epoch [9776/10000], Loss: 0.0032\n",
      "Epoch [9777/10000], Loss: 0.0030\n",
      "Epoch [9778/10000], Loss: 0.0033\n",
      "Epoch [9779/10000], Loss: 0.0031\n",
      "Epoch [9780/10000], Loss: 0.0030\n",
      "Epoch [9781/10000], Loss: 0.0028\n",
      "Epoch [9782/10000], Loss: 0.0033\n",
      "Epoch [9783/10000], Loss: 0.0031\n",
      "Epoch [9784/10000], Loss: 0.0029\n",
      "Epoch [9785/10000], Loss: 0.0031\n",
      "Epoch [9786/10000], Loss: 0.0031\n",
      "Epoch [9787/10000], Loss: 0.0033\n",
      "Epoch [9788/10000], Loss: 0.0030\n",
      "Epoch [9789/10000], Loss: 0.0029\n",
      "Epoch [9790/10000], Loss: 0.0031\n",
      "Epoch [9791/10000], Loss: 0.0031\n",
      "Epoch [9792/10000], Loss: 0.0032\n",
      "Epoch [9793/10000], Loss: 0.0030\n",
      "Epoch [9794/10000], Loss: 0.0029\n",
      "Epoch [9795/10000], Loss: 0.0029\n",
      "Epoch [9796/10000], Loss: 0.0029\n",
      "Epoch [9797/10000], Loss: 0.0033\n",
      "Epoch [9798/10000], Loss: 0.0030\n",
      "Epoch [9799/10000], Loss: 0.0031\n",
      "Epoch [9800/10000], Loss: 0.0033\n",
      "Epoch [9801/10000], Loss: 0.0032\n",
      "Epoch [9802/10000], Loss: 0.0029\n",
      "Epoch [9803/10000], Loss: 0.0032\n",
      "Epoch [9804/10000], Loss: 0.0032\n",
      "Epoch [9805/10000], Loss: 0.0031\n",
      "Epoch [9806/10000], Loss: 0.0031\n",
      "Epoch [9807/10000], Loss: 0.0033\n",
      "Epoch [9808/10000], Loss: 0.0031\n",
      "Epoch [9809/10000], Loss: 0.0032\n",
      "Epoch [9810/10000], Loss: 0.0031\n",
      "Epoch [9811/10000], Loss: 0.0030\n",
      "Epoch [9812/10000], Loss: 0.0032\n",
      "Epoch [9813/10000], Loss: 0.0031\n",
      "Epoch [9814/10000], Loss: 0.0030\n",
      "Epoch [9815/10000], Loss: 0.0031\n",
      "Epoch [9816/10000], Loss: 0.0029\n",
      "Epoch [9817/10000], Loss: 0.0033\n",
      "Epoch [9818/10000], Loss: 0.0030\n",
      "Epoch [9819/10000], Loss: 0.0030\n",
      "Epoch [9820/10000], Loss: 0.0033\n",
      "Epoch [9821/10000], Loss: 0.0031\n",
      "Epoch [9822/10000], Loss: 0.0032\n",
      "Epoch [9823/10000], Loss: 0.0030\n",
      "Epoch [9824/10000], Loss: 0.0029\n",
      "Epoch [9825/10000], Loss: 0.0030\n",
      "Epoch [9826/10000], Loss: 0.0030\n",
      "Epoch [9827/10000], Loss: 0.0031\n",
      "Epoch [9828/10000], Loss: 0.0031\n",
      "Epoch [9829/10000], Loss: 0.0032\n",
      "Epoch [9830/10000], Loss: 0.0029\n",
      "Epoch [9831/10000], Loss: 0.0031\n",
      "Epoch [9832/10000], Loss: 0.0031\n",
      "Epoch [9833/10000], Loss: 0.0029\n",
      "Epoch [9834/10000], Loss: 0.0029\n",
      "Epoch [9835/10000], Loss: 0.0031\n",
      "Epoch [9836/10000], Loss: 0.0033\n",
      "Epoch [9837/10000], Loss: 0.0030\n",
      "Epoch [9838/10000], Loss: 0.0031\n",
      "Epoch [9839/10000], Loss: 0.0031\n",
      "Epoch [9840/10000], Loss: 0.0031\n",
      "Epoch [9841/10000], Loss: 0.0032\n",
      "Epoch [9842/10000], Loss: 0.0029\n",
      "Epoch [9843/10000], Loss: 0.0034\n",
      "Epoch [9844/10000], Loss: 0.0031\n",
      "Epoch [9845/10000], Loss: 0.0032\n",
      "Epoch [9846/10000], Loss: 0.0032\n",
      "Epoch [9847/10000], Loss: 0.0032\n",
      "Epoch [9848/10000], Loss: 0.0030\n",
      "Epoch [9849/10000], Loss: 0.0033\n",
      "Epoch [9850/10000], Loss: 0.0031\n",
      "Epoch [9851/10000], Loss: 0.0030\n",
      "Epoch [9852/10000], Loss: 0.0034\n",
      "Epoch [9853/10000], Loss: 0.0032\n",
      "Epoch [9854/10000], Loss: 0.0029\n",
      "Epoch [9855/10000], Loss: 0.0029\n",
      "Epoch [9856/10000], Loss: 0.0029\n",
      "Epoch [9857/10000], Loss: 0.0031\n",
      "Epoch [9858/10000], Loss: 0.0031\n",
      "Epoch [9859/10000], Loss: 0.0030\n",
      "Epoch [9860/10000], Loss: 0.0032\n",
      "Epoch [9861/10000], Loss: 0.0031\n",
      "Epoch [9862/10000], Loss: 0.0030\n",
      "Epoch [9863/10000], Loss: 0.0031\n",
      "Epoch [9864/10000], Loss: 0.0032\n",
      "Epoch [9865/10000], Loss: 0.0029\n",
      "Epoch [9866/10000], Loss: 0.0031\n",
      "Epoch [9867/10000], Loss: 0.0030\n",
      "Epoch [9868/10000], Loss: 0.0030\n",
      "Epoch [9869/10000], Loss: 0.0032\n",
      "Epoch [9870/10000], Loss: 0.0032\n",
      "Epoch [9871/10000], Loss: 0.0029\n",
      "Epoch [9872/10000], Loss: 0.0031\n",
      "Epoch [9873/10000], Loss: 0.0030\n",
      "Epoch [9874/10000], Loss: 0.0028\n",
      "Epoch [9875/10000], Loss: 0.0031\n",
      "Epoch [9876/10000], Loss: 0.0032\n",
      "Epoch [9877/10000], Loss: 0.0030\n",
      "Epoch [9878/10000], Loss: 0.0030\n",
      "Epoch [9879/10000], Loss: 0.0029\n",
      "Epoch [9880/10000], Loss: 0.0033\n",
      "Epoch [9881/10000], Loss: 0.0030\n",
      "Epoch [9882/10000], Loss: 0.0030\n",
      "Epoch [9883/10000], Loss: 0.0030\n",
      "Epoch [9884/10000], Loss: 0.0032\n",
      "Epoch [9885/10000], Loss: 0.0029\n",
      "Epoch [9886/10000], Loss: 0.0031\n",
      "Epoch [9887/10000], Loss: 0.0031\n",
      "Epoch [9888/10000], Loss: 0.0030\n",
      "Epoch [9889/10000], Loss: 0.0031\n",
      "Epoch [9890/10000], Loss: 0.0030\n",
      "Epoch [9891/10000], Loss: 0.0029\n",
      "Epoch [9892/10000], Loss: 0.0030\n",
      "Epoch [9893/10000], Loss: 0.0028\n",
      "Epoch [9894/10000], Loss: 0.0031\n",
      "Epoch [9895/10000], Loss: 0.0029\n",
      "Epoch [9896/10000], Loss: 0.0031\n",
      "Epoch [9897/10000], Loss: 0.0032\n",
      "Epoch [9898/10000], Loss: 0.0028\n",
      "Epoch [9899/10000], Loss: 0.0028\n",
      "Epoch [9900/10000], Loss: 0.0030\n",
      "Epoch [9901/10000], Loss: 0.0029\n",
      "Epoch [9902/10000], Loss: 0.0032\n",
      "Epoch [9903/10000], Loss: 0.0029\n",
      "Epoch [9904/10000], Loss: 0.0028\n",
      "Epoch [9905/10000], Loss: 0.0031\n",
      "Epoch [9906/10000], Loss: 0.0030\n",
      "Epoch [9907/10000], Loss: 0.0029\n",
      "Epoch [9908/10000], Loss: 0.0028\n",
      "Epoch [9909/10000], Loss: 0.0029\n",
      "Epoch [9910/10000], Loss: 0.0029\n",
      "Epoch [9911/10000], Loss: 0.0030\n",
      "Epoch [9912/10000], Loss: 0.0030\n",
      "Epoch [9913/10000], Loss: 0.0028\n",
      "Epoch [9914/10000], Loss: 0.0031\n",
      "Epoch [9915/10000], Loss: 0.0030\n",
      "Epoch [9916/10000], Loss: 0.0031\n",
      "Epoch [9917/10000], Loss: 0.0031\n",
      "Epoch [9918/10000], Loss: 0.0031\n",
      "Epoch [9919/10000], Loss: 0.0031\n",
      "Epoch [9920/10000], Loss: 0.0031\n",
      "Epoch [9921/10000], Loss: 0.0030\n",
      "Epoch [9922/10000], Loss: 0.0031\n",
      "Epoch [9923/10000], Loss: 0.0030\n",
      "Epoch [9924/10000], Loss: 0.0031\n",
      "Epoch [9925/10000], Loss: 0.0027\n",
      "Epoch [9926/10000], Loss: 0.0031\n",
      "Epoch [9927/10000], Loss: 0.0030\n",
      "Epoch [9928/10000], Loss: 0.0030\n",
      "Epoch [9929/10000], Loss: 0.0029\n",
      "Epoch [9930/10000], Loss: 0.0032\n",
      "Epoch [9931/10000], Loss: 0.0031\n",
      "Epoch [9932/10000], Loss: 0.0028\n",
      "Epoch [9933/10000], Loss: 0.0029\n",
      "Epoch [9934/10000], Loss: 0.0029\n",
      "Epoch [9935/10000], Loss: 0.0030\n",
      "Epoch [9936/10000], Loss: 0.0031\n",
      "Epoch [9937/10000], Loss: 0.0030\n",
      "Epoch [9938/10000], Loss: 0.0029\n",
      "Epoch [9939/10000], Loss: 0.0029\n",
      "Epoch [9940/10000], Loss: 0.0032\n",
      "Epoch [9941/10000], Loss: 0.0030\n",
      "Epoch [9942/10000], Loss: 0.0033\n",
      "Epoch [9943/10000], Loss: 0.0031\n",
      "Epoch [9944/10000], Loss: 0.0030\n",
      "Epoch [9945/10000], Loss: 0.0031\n",
      "Epoch [9946/10000], Loss: 0.0031\n",
      "Epoch [9947/10000], Loss: 0.0032\n",
      "Epoch [9948/10000], Loss: 0.0030\n",
      "Epoch [9949/10000], Loss: 0.0028\n",
      "Epoch [9950/10000], Loss: 0.0030\n",
      "Epoch [9951/10000], Loss: 0.0029\n",
      "Epoch [9952/10000], Loss: 0.0031\n",
      "Epoch [9953/10000], Loss: 0.0029\n",
      "Epoch [9954/10000], Loss: 0.0033\n",
      "Epoch [9955/10000], Loss: 0.0030\n",
      "Epoch [9956/10000], Loss: 0.0030\n",
      "Epoch [9957/10000], Loss: 0.0030\n",
      "Epoch [9958/10000], Loss: 0.0030\n",
      "Epoch [9959/10000], Loss: 0.0030\n",
      "Epoch [9960/10000], Loss: 0.0030\n",
      "Epoch [9961/10000], Loss: 0.0030\n",
      "Epoch [9962/10000], Loss: 0.0030\n",
      "Epoch [9963/10000], Loss: 0.0033\n",
      "Epoch [9964/10000], Loss: 0.0029\n",
      "Epoch [9965/10000], Loss: 0.0031\n",
      "Epoch [9966/10000], Loss: 0.0029\n",
      "Epoch [9967/10000], Loss: 0.0030\n",
      "Epoch [9968/10000], Loss: 0.0030\n",
      "Epoch [9969/10000], Loss: 0.0031\n",
      "Epoch [9970/10000], Loss: 0.0031\n",
      "Epoch [9971/10000], Loss: 0.0031\n",
      "Epoch [9972/10000], Loss: 0.0029\n",
      "Epoch [9973/10000], Loss: 0.0030\n",
      "Epoch [9974/10000], Loss: 0.0028\n",
      "Epoch [9975/10000], Loss: 0.0030\n",
      "Epoch [9976/10000], Loss: 0.0030\n",
      "Epoch [9977/10000], Loss: 0.0031\n",
      "Epoch [9978/10000], Loss: 0.0030\n",
      "Epoch [9979/10000], Loss: 0.0030\n",
      "Epoch [9980/10000], Loss: 0.0030\n",
      "Epoch [9981/10000], Loss: 0.0032\n",
      "Epoch [9982/10000], Loss: 0.0027\n",
      "Epoch [9983/10000], Loss: 0.0030\n",
      "Epoch [9984/10000], Loss: 0.0030\n",
      "Epoch [9985/10000], Loss: 0.0028\n",
      "Epoch [9986/10000], Loss: 0.0029\n",
      "Epoch [9987/10000], Loss: 0.0027\n",
      "Epoch [9988/10000], Loss: 0.0032\n",
      "Epoch [9989/10000], Loss: 0.0031\n",
      "Epoch [9990/10000], Loss: 0.0031\n",
      "Epoch [9991/10000], Loss: 0.0028\n",
      "Epoch [9992/10000], Loss: 0.0029\n",
      "Epoch [9993/10000], Loss: 0.0030\n",
      "Epoch [9994/10000], Loss: 0.0029\n",
      "Epoch [9995/10000], Loss: 0.0030\n",
      "Epoch [9996/10000], Loss: 0.0029\n",
      "Epoch [9997/10000], Loss: 0.0031\n",
      "Epoch [9998/10000], Loss: 0.0030\n",
      "Epoch [9999/10000], Loss: 0.0030\n",
      "Epoch [10000/10000], Loss: 0.0031\n",
      "Eğitim tamamlandı\n"
     ]
    }
   ],
   "source": [
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model.to(device)\n",
    "X_train, y_train = X_train.to(device), y_train.to(device)\n",
    "num_epochs = 10000\n",
    "for epoch in range(num_epochs):\n",
    "    model.train()\n",
    "\n",
    "    optimizer.zero_grad()\n",
    "    outputs = model(X_train)  \n",
    "    outputs = outputs.squeeze()  \n",
    "    loss = criterion(outputs, y_train)  \n",
    "    loss.backward()\n",
    "    optimizer.step()\n",
    "\n",
    "    print(f'Epoch [{epoch+1}/{num_epochs}], Loss: {loss.item():.4f}')\n",
    "\n",
    "print(\"Eğitim tamamlandı\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import torch\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "X_test = X_test.to(device)\n",
    "y_test = y_test.to(device)\n",
    "model.eval()\n",
    "\n",
    "with torch.no_grad():\n",
    "    test_outputs = model(X_test).squeeze() \n",
    "    test_outputs = test_outputs.cpu().numpy()  \n",
    "    y_test_np = y_test.cpu().numpy()  \n",
    "\n",
    "\n",
    "plt.figure(figsize=(10,6))\n",
    "plt.plot(y_test_np, label='Gerçek Değerler', color='b')\n",
    "plt.plot(test_outputs, label='Tahmin Edilen Değerler', color='r')\n",
    "plt.title('Gerçek ve Tahmin Edilen Değerler')\n",
    "plt.xlabel('Zaman')\n",
    "plt.ylabel('Değer')\n",
    "plt.legend()\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}