LSTM3.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://medium.com/@jroessl/lstm-with-keras-4c4db55dd349"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 235,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(0,100,0.5) \n",
    "y = np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 237,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\albin\\AppData\\Roaming\\Python\\Python39\\site-packages\\IPython\\core\\interactiveshell.py:3442: DtypeWarning: Columns (6) have mixed types.Specify dtype option on import or set low_memory=False.\n",
      "  exec(code_obj, self.user_global_ns, self.user_ns)\n"
     ]
    }
   ],
   "source": [
    "og_df = pd.read_csv('data/Structured/all_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\albin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\pandas\\core\\frame.py:4901: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  return super().drop(\n",
      "C:\\Users\\albin\\AppData\\Roaming\\Python\\Python39\\site-packages\\IPython\\core\\interactiveshell.py:3382: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead.  To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  if await self.run_code(code, result, async_=asy):\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[133.95  26.68]\n",
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     ]
    }
   ],
   "source": [
    "\n",
    "df = og_df.dropna(axis=1, how='all')\n",
    "df.drop(['Summa gas/Diesel'], axis=1, inplace=True)\n",
    "\n",
    "df = df.fillna(method='ffill')\n",
    "df = df.fillna(method='bfill')\n",
    "for e in df.columns:\n",
    "    if df[e].nunique() == 1:\n",
    "        df = df.drop(e, axis=1)\n",
    "  \n",
    "first_column = df.pop('SE1')\n",
    "df.insert(1, 'SE1', first_column)\n",
    "df.drop(['Date'], axis=1, inplace=True)\n",
    "df = df.iloc[:,0:2]\n",
    "data = df.to_numpy()\n",
    "n_features = data.shape[1]\n",
    "print(data[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {},
   "outputs": [],
   "source": [
    "# scaler = MinMaxScaler(feature_range=(0, 1))\n",
    "# scaler = scaler.fit(data)\n",
    "# print('Min: %f, Max: %f' % (scaler.data_min_, scaler.data_max_))\n",
    "# # normalize the dataset and print\n",
    "# normalized = scaler.transform(data)\n",
    "# print(normalized)\n",
    "# # inverse transform and print\n",
    "# inversed = scaler.inverse_transform(normalized)\n",
    "# print(inversed)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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     ]
    }
   ],
   "source": [
    "look_back = 10\n",
    "def create_dataset(data, look_back=1):\n",
    "    dataX, dataY = [], []\n",
    "    for i in range(len(data)-look_back-1):\n",
    "        dataX.append(data[i:(i+look_back)])\n",
    "        dataY.append(data[i + look_back][0])\n",
    "    return np.array(dataX), np.array(dataY)\n",
    "\n",
    "x_train, y_train = create_dataset(data, look_back)\n",
    "print(x_train[0:10])\n",
    "print(y_train[0:10])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 241,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(202867, 10, 2)\n"
     ]
    }
   ],
   "source": [
    "print(x_train.shape)\n",
    "# x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
    "# print(x_train.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "model = tf.keras.models.Sequential([\n",
    "    tf.keras.layers.LSTM(32, return_sequences=True, input_shape=(look_back, n_features)),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Normalization(),\n",
    "    tf.keras.layers.LSTM(32),\n",
    "    tf.keras.layers.Dropout(0.2),\n",
    "    tf.keras.layers.Dense(1)\n",
    "])\n",
    "\n",
    "model.compile(optimizer='adam', loss='mse')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/200\n",
      "6340/6340 [==============================] - 94s 13ms/step - loss: 128835.3828\n",
      "Epoch 2/200\n",
      "6340/6340 [==============================] - 84s 13ms/step - loss: 71909.3516\n",
      "Epoch 3/200\n",
      "6340/6340 [==============================] - 104s 16ms/step - loss: 52050.0039\n",
      "Epoch 4/200\n",
      "6340/6340 [==============================] - 84s 13ms/step - loss: 44542.6992\n",
      "Epoch 5/200\n",
      "6340/6340 [==============================] - 84s 13ms/step - loss: 40563.2383\n",
      "Epoch 6/200\n",
      "6340/6340 [==============================] - 84s 13ms/step - loss: 37853.4883\n",
      "Epoch 7/200\n",
      " 618/6340 [=>............................] - ETA: 1:23 - loss: 33368.8203"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[243], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m model\u001b[39m.\u001b[39;49mfit(x_train, y_train, epochs\u001b[39m=\u001b[39;49m\u001b[39m200\u001b[39;49m, verbose\u001b[39m=\u001b[39;49m\u001b[39m1\u001b[39;49m)\n",
      "File \u001b[1;32mc:\\Users\\albin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\utils\\traceback_utils.py:65\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[1;34m(*args, **kwargs)\u001b[0m\n\u001b[0;32m     63\u001b[0m filtered_tb \u001b[39m=\u001b[39m \u001b[39mNone\u001b[39;00m\n\u001b[0;32m     64\u001b[0m \u001b[39mtry\u001b[39;00m:\n\u001b[1;32m---> 65\u001b[0m     \u001b[39mreturn\u001b[39;00m fn(\u001b[39m*\u001b[39margs, \u001b[39m*\u001b[39m\u001b[39m*\u001b[39mkwargs)\n\u001b[0;32m     66\u001b[0m \u001b[39mexcept\u001b[39;00m \u001b[39mException\u001b[39;00m \u001b[39mas\u001b[39;00m e:\n\u001b[0;32m     67\u001b[0m     filtered_tb \u001b[39m=\u001b[39m _process_traceback_frames(e\u001b[39m.\u001b[39m__traceback__)\n",
      "File \u001b[1;32mc:\\Users\\albin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\engine\\training.py:1656\u001b[0m, in \u001b[0;36mModel.fit\u001b[1;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[0;32m   1654\u001b[0m logs \u001b[39m=\u001b[39m tmp_logs\n\u001b[0;32m   1655\u001b[0m end_step \u001b[39m=\u001b[39m step \u001b[39m+\u001b[39m data_handler\u001b[39m.\u001b[39mstep_increment\n\u001b[1;32m-> 1656\u001b[0m callbacks\u001b[39m.\u001b[39;49mon_train_batch_end(end_step, logs)\n\u001b[0;32m   1657\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mstop_training:\n\u001b[0;32m   1658\u001b[0m     \u001b[39mbreak\u001b[39;00m\n",
      "File \u001b[1;32mc:\\Users\\albin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\callbacks.py:476\u001b[0m, in \u001b[0;36mCallbackList.on_train_batch_end\u001b[1;34m(self, batch, logs)\u001b[0m\n\u001b[0;32m    469\u001b[0m \u001b[39m\u001b[39m\u001b[39m\"\"\"Calls the `on_train_batch_end` methods of its callbacks.\u001b[39;00m\n\u001b[0;32m    470\u001b[0m \n\u001b[0;32m    471\u001b[0m \u001b[39mArgs:\u001b[39;00m\n\u001b[0;32m    472\u001b[0m \u001b[39m    batch: Integer, index of batch within the current epoch.\u001b[39;00m\n\u001b[0;32m    473\u001b[0m \u001b[39m    logs: Dict. Aggregated metric results up until this batch.\u001b[39;00m\n\u001b[0;32m    474\u001b[0m \u001b[39m\"\"\"\u001b[39;00m\n\u001b[0;32m    475\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_should_call_train_batch_hooks:\n\u001b[1;32m--> 476\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_batch_hook(ModeKeys\u001b[39m.\u001b[39;49mTRAIN, \u001b[39m\"\u001b[39;49m\u001b[39mend\u001b[39;49m\u001b[39m\"\u001b[39;49m, batch, logs\u001b[39m=\u001b[39;49mlogs)\n",
      "File \u001b[1;32mc:\\Users\\albin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\callbacks.py:323\u001b[0m, in \u001b[0;36mCallbackList._call_batch_hook\u001b[1;34m(self, mode, hook, batch, logs)\u001b[0m\n\u001b[0;32m    321\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_call_batch_begin_hook(mode, batch, logs)\n\u001b[0;32m    322\u001b[0m \u001b[39melif\u001b[39;00m hook \u001b[39m==\u001b[39m \u001b[39m\"\u001b[39m\u001b[39mend\u001b[39m\u001b[39m\"\u001b[39m:\n\u001b[1;32m--> 323\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_batch_end_hook(mode, batch, logs)\n\u001b[0;32m    324\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    325\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    326\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mUnrecognized hook: \u001b[39m\u001b[39m{\u001b[39;00mhook\u001b[39m}\u001b[39;00m\u001b[39m. \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    327\u001b[0m         \u001b[39m'\u001b[39m\u001b[39mExpected values are [\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mbegin\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m, \u001b[39m\u001b[39m\"\u001b[39m\u001b[39mend\u001b[39m\u001b[39m\"\u001b[39m\u001b[39m]\u001b[39m\u001b[39m'\u001b[39m\n\u001b[0;32m    328\u001b[0m     )\n",
      "File \u001b[1;32mc:\\Users\\albin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\callbacks.py:346\u001b[0m, in \u001b[0;36mCallbackList._call_batch_end_hook\u001b[1;34m(self, mode, batch, logs)\u001b[0m\n\u001b[0;32m    343\u001b[0m     batch_time \u001b[39m=\u001b[39m time\u001b[39m.\u001b[39mtime() \u001b[39m-\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_batch_start_time\n\u001b[0;32m    344\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_batch_times\u001b[39m.\u001b[39mappend(batch_time)\n\u001b[1;32m--> 346\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_call_batch_hook_helper(hook_name, batch, logs)\n\u001b[0;32m    348\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mlen\u001b[39m(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_batch_times) \u001b[39m>\u001b[39m\u001b[39m=\u001b[39m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_num_batches_for_timing_check:\n\u001b[0;32m    349\u001b[0m     end_hook_name \u001b[39m=\u001b[39m hook_name\n",
      "File \u001b[1;32mc:\\Users\\albin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\callbacks.py:394\u001b[0m, in \u001b[0;36mCallbackList._call_batch_hook_helper\u001b[1;34m(self, hook_name, batch, logs)\u001b[0m\n\u001b[0;32m    392\u001b[0m \u001b[39mfor\u001b[39;00m callback \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcallbacks:\n\u001b[0;32m    393\u001b[0m     hook \u001b[39m=\u001b[39m \u001b[39mgetattr\u001b[39m(callback, hook_name)\n\u001b[1;32m--> 394\u001b[0m     hook(batch, logs)\n\u001b[0;32m    396\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_check_timing:\n\u001b[0;32m    397\u001b[0m     \u001b[39mif\u001b[39;00m hook_name \u001b[39mnot\u001b[39;00m \u001b[39min\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_hook_times:\n",
      "File \u001b[1;32mc:\\Users\\albin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\callbacks.py:1094\u001b[0m, in \u001b[0;36mProgbarLogger.on_train_batch_end\u001b[1;34m(self, batch, logs)\u001b[0m\n\u001b[0;32m   1093\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39mon_train_batch_end\u001b[39m(\u001b[39mself\u001b[39m, batch, logs\u001b[39m=\u001b[39m\u001b[39mNone\u001b[39;00m):\n\u001b[1;32m-> 1094\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_batch_update_progbar(batch, logs)\n",
      "File \u001b[1;32mc:\\Users\\albin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\callbacks.py:1171\u001b[0m, in \u001b[0;36mProgbarLogger._batch_update_progbar\u001b[1;34m(self, batch, logs)\u001b[0m\n\u001b[0;32m   1168\u001b[0m \u001b[39mif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39m==\u001b[39m \u001b[39m1\u001b[39m:\n\u001b[0;32m   1169\u001b[0m     \u001b[39m# Only block async when verbose = 1.\u001b[39;00m\n\u001b[0;32m   1170\u001b[0m     logs \u001b[39m=\u001b[39m tf_utils\u001b[39m.\u001b[39msync_to_numpy_or_python_type(logs)\n\u001b[1;32m-> 1171\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mprogbar\u001b[39m.\u001b[39;49mupdate(\u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mseen, \u001b[39mlist\u001b[39;49m(logs\u001b[39m.\u001b[39;49mitems()), finalize\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)\n",
      "File \u001b[1;32mc:\\Users\\albin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\utils\\generic_utils.py:297\u001b[0m, in \u001b[0;36mProgbar.update\u001b[1;34m(self, current, values, finalize)\u001b[0m\n\u001b[0;32m    294\u001b[0m         info \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m\n\u001b[0;32m    296\u001b[0m     message \u001b[39m+\u001b[39m\u001b[39m=\u001b[39m info\n\u001b[1;32m--> 297\u001b[0m     io_utils\u001b[39m.\u001b[39;49mprint_msg(message, line_break\u001b[39m=\u001b[39;49m\u001b[39mFalse\u001b[39;49;00m)\n\u001b[0;32m    298\u001b[0m     message \u001b[39m=\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    300\u001b[0m \u001b[39melif\u001b[39;00m \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mverbose \u001b[39m==\u001b[39m \u001b[39m2\u001b[39m:\n",
      "File \u001b[1;32mc:\\Users\\albin\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\keras\\utils\\io_utils.py:79\u001b[0m, in \u001b[0;36mprint_msg\u001b[1;34m(message, line_break)\u001b[0m\n\u001b[0;32m     77\u001b[0m         sys\u001b[39m.\u001b[39mstdout\u001b[39m.\u001b[39mwrite(message \u001b[39m+\u001b[39m \u001b[39m\"\u001b[39m\u001b[39m\\n\u001b[39;00m\u001b[39m\"\u001b[39m)\n\u001b[0;32m     78\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m---> 79\u001b[0m         sys\u001b[39m.\u001b[39;49mstdout\u001b[39m.\u001b[39;49mwrite(message)\n\u001b[0;32m     80\u001b[0m     sys\u001b[39m.\u001b[39mstdout\u001b[39m.\u001b[39mflush()\n\u001b[0;32m     81\u001b[0m \u001b[39melse\u001b[39;00m:\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\ipykernel\\iostream.py:574\u001b[0m, in \u001b[0;36mOutStream.write\u001b[1;34m(self, string)\u001b[0m\n\u001b[0;32m    572\u001b[0m         \u001b[39mself\u001b[39m\u001b[39m.\u001b[39mpub_thread\u001b[39m.\u001b[39mschedule(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_flush)\n\u001b[0;32m    573\u001b[0m     \u001b[39melse\u001b[39;00m:\n\u001b[1;32m--> 574\u001b[0m         \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_schedule_flush()\n\u001b[0;32m    576\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39mlen\u001b[39m(string)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\ipykernel\\iostream.py:478\u001b[0m, in \u001b[0;36mOutStream._schedule_flush\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    475\u001b[0m \u001b[39mdef\u001b[39;00m \u001b[39m_schedule_in_thread\u001b[39m():\n\u001b[0;32m    476\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_io_loop\u001b[39m.\u001b[39mcall_later(\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mflush_interval, \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_flush)\n\u001b[1;32m--> 478\u001b[0m \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49mpub_thread\u001b[39m.\u001b[39;49mschedule(_schedule_in_thread)\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\ipykernel\\iostream.py:211\u001b[0m, in \u001b[0;36mIOPubThread.schedule\u001b[1;34m(self, f)\u001b[0m\n\u001b[0;32m    209\u001b[0m     \u001b[39mself\u001b[39m\u001b[39m.\u001b[39m_events\u001b[39m.\u001b[39mappend(f)\n\u001b[0;32m    210\u001b[0m     \u001b[39m# wake event thread (message content is ignored)\u001b[39;00m\n\u001b[1;32m--> 211\u001b[0m     \u001b[39mself\u001b[39;49m\u001b[39m.\u001b[39;49m_event_pipe\u001b[39m.\u001b[39;49msend(\u001b[39mb\u001b[39;49m\u001b[39m\"\u001b[39;49m\u001b[39m\"\u001b[39;49m)\n\u001b[0;32m    212\u001b[0m \u001b[39melse\u001b[39;00m:\n\u001b[0;32m    213\u001b[0m     f()\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\zmq\\sugar\\socket.py:688\u001b[0m, in \u001b[0;36mSocket.send\u001b[1;34m(self, data, flags, copy, track, routing_id, group)\u001b[0m\n\u001b[0;32m    681\u001b[0m         data \u001b[39m=\u001b[39m zmq\u001b[39m.\u001b[39mFrame(\n\u001b[0;32m    682\u001b[0m             data,\n\u001b[0;32m    683\u001b[0m             track\u001b[39m=\u001b[39mtrack,\n\u001b[0;32m    684\u001b[0m             copy\u001b[39m=\u001b[39mcopy \u001b[39mor\u001b[39;00m \u001b[39mNone\u001b[39;00m,\n\u001b[0;32m    685\u001b[0m             copy_threshold\u001b[39m=\u001b[39m\u001b[39mself\u001b[39m\u001b[39m.\u001b[39mcopy_threshold,\n\u001b[0;32m    686\u001b[0m         )\n\u001b[0;32m    687\u001b[0m     data\u001b[39m.\u001b[39mgroup \u001b[39m=\u001b[39m group\n\u001b[1;32m--> 688\u001b[0m \u001b[39mreturn\u001b[39;00m \u001b[39msuper\u001b[39;49m()\u001b[39m.\u001b[39;49msend(data, flags\u001b[39m=\u001b[39;49mflags, copy\u001b[39m=\u001b[39;49mcopy, track\u001b[39m=\u001b[39;49mtrack)\n",
      "File \u001b[1;32mzmq\\backend\\cython\\socket.pyx:742\u001b[0m, in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mzmq\\backend\\cython\\socket.pyx:789\u001b[0m, in \u001b[0;36mzmq.backend.cython.socket.Socket.send\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32mzmq\\backend\\cython\\socket.pyx:250\u001b[0m, in \u001b[0;36mzmq.backend.cython.socket._send_copy\u001b[1;34m()\u001b[0m\n",
      "File \u001b[1;32m~\\AppData\\Roaming\\Python\\Python39\\site-packages\\zmq\\backend\\cython\\checkrc.pxd:13\u001b[0m, in \u001b[0;36mzmq.backend.cython.checkrc._check_rc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyboardInterrupt\u001b[0m: "
     ]
    }
   ],
   "source": [
    "model.fit(x_train, y_train, epochs=200, verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "6/6 [==============================] - 1s 5ms/step\n"
     ]
    }
   ],
   "source": [
    "x_test = np.arange(0,100,0.5) \n",
    "y_test = np.sin(x_test)\n",
    "x_test, y_train = create_dataset(y_test, 10)\n",
    "x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1))\n",
    "predicted  = model.predict(x_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x1828025e040>]"
      ]
     },
     "execution_count": 209,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(predicted)"
   ]
  }
 ],
 "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": "3.9.1"
  },
  "orig_nbformat": 4,
  "vscode": {
   "interpreter": {
    "hash": "e8e86f11f8de651d2c5fce83c6190e2e2c0f42ae90d60e5c5b35a501d45892ca"
   }
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}