Sunspot Prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "metric-expansion",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import tensorflow as tf\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "enclosed-marking",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = pd.read_csv('Sunspots.csv',index_col=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "interesting-mounting",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Monthly Mean Total Sunspot Number</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1749-01-31</td>\n",
       "      <td>96.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1749-02-28</td>\n",
       "      <td>104.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1749-03-31</td>\n",
       "      <td>116.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1749-04-30</td>\n",
       "      <td>92.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1749-05-31</td>\n",
       "      <td>141.7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Date  Monthly Mean Total Sunspot Number\n",
       "0  1749-01-31                               96.7\n",
       "1  1749-02-28                              104.3\n",
       "2  1749-03-31                              116.7\n",
       "3  1749-04-30                               92.8\n",
       "4  1749-05-31                              141.7"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "organized-bubble",
   "metadata": {},
   "outputs": [],
   "source": [
    "data['time_steps'] = range(0,len(data['Date']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "sexual-conclusion",
   "metadata": {},
   "outputs": [],
   "source": [
    "sunspots = [float(i) for i in data['Monthly Mean Total Sunspot Number']]\n",
    "time_step = [int(i) for i in data['time_steps']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "minute-membrane",
   "metadata": {},
   "outputs": [],
   "source": [
    "series = np.array(sunspots)\n",
    "time = np.array(time_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "hawaiian-karaoke",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(time,series)\n",
    "plt.xlabel('Time')\n",
    "plt.ylabel('Sunspots')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "three-parameter",
   "metadata": {},
   "outputs": [],
   "source": [
    "split_time = 2500\n",
    "window_size = 60\n",
    "batch_size = 250\n",
    "\n",
    "\n",
    "x_train = series[:split_time]\n",
    "time_train = time[:split_time]\n",
    "x_test = series[split_time:]\n",
    "time_test = time[split_time:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "embedded-testament",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_windows(series,window_size,batch_size,shuffle_buffer):\n",
    "    series = tf.expand_dims(series, axis=-1)   #reshaped only for conv1d\n",
    "    dataset = tf.data.Dataset.from_tensor_slices(series)\n",
    "    dataset = dataset.window(size=window_size+1,shift=1,drop_remainder=True)\n",
    "    dataset = dataset.flat_map(lambda window : window.batch(window_size+1))\n",
    "    dataset = dataset.shuffle(buffer_size=shuffle_buffer).map(lambda window : (window[:-1],window[-1:]))\n",
    "    dataset = dataset.batch(batch_size).prefetch(1)\n",
    "    return dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "bearing-wrist",
   "metadata": {},
   "outputs": [],
   "source": [
    "windowed_xTrain = create_windows(x_train,window_size,batch_size,split_time)\n",
    "windowed_xTest = create_windows(x_test,window_size,batch_size,split_time)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "fifth-moderator",
   "metadata": {},
   "outputs": [],
   "source": [
    "class mycallbacks(tf.keras.callbacks.Callback):\n",
    "    def on_epoch_end(self,epoch,logs={}):\n",
    "        if logs['loss'] < 13:\n",
    "            model.stop_training = True\n",
    "            print('\\nEnding training squence because the loss threshold has been reached')\n",
    "calls = mycallbacks()\n",
    "lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch : 1e-8 * 10 ** (epoch / 20))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "civil-decision",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv1D(filters=100,kernel_size=5,padding='causal',strides=1,activation='relu',input_shape=[None,1]),\n",
    "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(100,return_sequences=True)),\n",
    "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(100,return_sequences=True)),\n",
    "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(100,return_sequences=True)),\n",
    "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(100,return_sequences=True)),\n",
    "    tf.keras.layers.Dense(100,activation='relu'),\n",
    "    tf.keras.layers.Dense(100,activation='relu'),\n",
    "    tf.keras.layers.Dense(25,activation='relu'),\n",
    "    tf.keras.layers.Dense(1),\n",
    "    tf.keras.layers.Lambda(lambda x : x * 400)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "handmade-right",
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=tf.keras.losses.Huber(),optimizer=tf.keras.optimizers.SGD(learning_rate=1e-8,momentum=0.9))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "blessed-simple",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "history = model.fit(windowed_xTrain,epochs=100,verbose=0,callbacks=[lr_scheduler])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "italic-survey",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "lrs = 1e-8 * (10 ** (np.arange(100) / 20))\n",
    "loss = history.history['loss']\n",
    "\n",
    "plt.plot(lrs,loss)\n",
    "plt.xlabel('learning rate')\n",
    "plt.ylabel('loss')\n",
    "plt.ticklabel_format(axis='x', style='sci',scilimits=(1e-8,1e-3))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "numeric-discipline",
   "metadata": {},
   "outputs": [],
   "source": [
    "tf.keras.backend.clear_session()\n",
    "model = tf.keras.Sequential([\n",
    "    tf.keras.layers.Conv1D(filters=100,kernel_size=5,padding='causal',strides=1,activation='relu',input_shape=[None,1]),\n",
    "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(100,return_sequences=True)),\n",
    "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(100,return_sequences=True)),\n",
    "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(100,return_sequences=True)),\n",
    "    tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(100,return_sequences=True)),\n",
    "    tf.keras.layers.Dense(100,activation='relu'),\n",
    "    tf.keras.layers.Dense(100,activation='relu'),\n",
    "    tf.keras.layers.Dense(25,activation='relu'),\n",
    "    tf.keras.layers.Dense(1),\n",
    "    tf.keras.layers.Lambda(lambda x : x * 400)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "valued-mandate",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/500\n",
      "10/10 [==============================] - 6s 60ms/step - loss: 60.7817\n",
      "Epoch 2/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 39.9157\n",
      "Epoch 3/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 34.8753\n",
      "Epoch 4/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 33.6621\n",
      "Epoch 5/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 31.0824\n",
      "Epoch 6/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 31.6362\n",
      "Epoch 7/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 28.1596\n",
      "Epoch 8/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 27.5012\n",
      "Epoch 9/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 28.3890\n",
      "Epoch 10/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 26.8769\n",
      "Epoch 11/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 29.7923\n",
      "Epoch 12/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 27.3941\n",
      "Epoch 13/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 23.8668\n",
      "Epoch 14/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 48.4718\n",
      "Epoch 15/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 35.2813\n",
      "Epoch 16/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 29.5607\n",
      "Epoch 17/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 27.4556\n",
      "Epoch 18/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 28.0096\n",
      "Epoch 19/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 24.6126\n",
      "Epoch 20/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 29.7096\n",
      "Epoch 21/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 28.2621\n",
      "Epoch 22/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 26.1245\n",
      "Epoch 23/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 23.3243\n",
      "Epoch 24/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 28.1962\n",
      "Epoch 25/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 25.1446\n",
      "Epoch 26/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 23.8620\n",
      "Epoch 27/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 22.9752\n",
      "Epoch 28/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 22.8309\n",
      "Epoch 29/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 22.9283\n",
      "Epoch 30/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 22.1169\n",
      "Epoch 31/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 21.2798\n",
      "Epoch 32/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 20.7411\n",
      "Epoch 33/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 19.8557\n",
      "Epoch 34/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 18.7591\n",
      "Epoch 35/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 18.5189\n",
      "Epoch 36/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 20.1653\n",
      "Epoch 37/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 18.8107\n",
      "Epoch 38/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 18.4896\n",
      "Epoch 39/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 18.4850\n",
      "Epoch 40/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 21.2351\n",
      "Epoch 41/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 18.9510\n",
      "Epoch 42/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 19.4420\n",
      "Epoch 43/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 18.3374\n",
      "Epoch 44/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 19.5176\n",
      "Epoch 45/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 17.9749\n",
      "Epoch 46/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 19.9119\n",
      "Epoch 47/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 19.1477\n",
      "Epoch 48/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 18.4075\n",
      "Epoch 49/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 18.2453\n",
      "Epoch 50/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 17.3518\n",
      "Epoch 51/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 18.4197\n",
      "Epoch 52/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 18.4392\n",
      "Epoch 53/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 19.2287\n",
      "Epoch 54/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 17.0433\n",
      "Epoch 55/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 16.3319\n",
      "Epoch 56/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 19.0440\n",
      "Epoch 57/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 19.5120\n",
      "Epoch 58/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 17.4458\n",
      "Epoch 59/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 17.1264\n",
      "Epoch 60/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 18.6892\n",
      "Epoch 61/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 17.5151\n",
      "Epoch 62/500\n",
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      "Epoch 270/500\n",
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      "Epoch 274/500\n",
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      "Epoch 275/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 16.0726\n",
      "Epoch 276/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 14.5094\n",
      "Epoch 277/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 14.7844\n",
      "Epoch 278/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 14.4409\n",
      "Epoch 279/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 16.7452\n",
      "Epoch 280/500\n",
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      "Epoch 281/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 14.6136\n",
      "Epoch 282/500\n",
      "10/10 [==============================] - 1s 59ms/step - loss: 15.8352\n",
      "Epoch 283/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 15.5209\n",
      "Epoch 284/500\n",
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      "Epoch 285/500\n",
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      "Epoch 286/500\n",
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      "Epoch 287/500\n",
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      "Epoch 288/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 14.5148\n",
      "Epoch 289/500\n",
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      "Epoch 290/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 15.0077\n",
      "Epoch 291/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 14.2506\n",
      "Epoch 292/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 14.8500\n",
      "Epoch 293/500\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10/10 [==============================] - 1s 62ms/step - loss: 14.5818\n",
      "Epoch 294/500\n",
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      "Epoch 295/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 14.4095\n",
      "Epoch 296/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 14.8793\n",
      "Epoch 297/500\n",
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      "Epoch 298/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 15.0940\n",
      "Epoch 299/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 14.5154\n",
      "Epoch 300/500\n",
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      "10/10 [==============================] - 1s 62ms/step - loss: 13.9221\n",
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      "Epoch 309/500\n",
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      "Epoch 310/500\n",
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      "Epoch 311/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 15.3048\n",
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      "10/10 [==============================] - 1s 64ms/step - loss: 14.7785\n",
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      "Epoch 314/500\n",
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      "Epoch 315/500\n",
      "10/10 [==============================] - 1s 65ms/step - loss: 14.3702\n",
      "Epoch 316/500\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 14.5234\n",
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      "10/10 [==============================] - 1s 63ms/step - loss: 14.4087\n",
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      "Epoch 320/500\n",
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      "Epoch 321/500\n",
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      "Epoch 323/500\n",
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      "Epoch 324/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 16.1077\n",
      "Epoch 325/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 14.3298\n",
      "Epoch 326/500\n",
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      "Epoch 327/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 14.5374\n",
      "Epoch 328/500\n",
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      "Epoch 329/500\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 15.3196\n",
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      "10/10 [==============================] - 1s 62ms/step - loss: 13.5541\n",
      "Epoch 331/500\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 14.5872\n",
      "Epoch 332/500\n",
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      "Epoch 334/500\n",
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      "Epoch 335/500\n",
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      "Epoch 336/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 15.0336\n",
      "Epoch 337/500\n",
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      "Epoch 338/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 13.8285\n",
      "Epoch 339/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 13.5388\n",
      "Epoch 340/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 15.0480\n",
      "Epoch 341/500\n",
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      "Epoch 342/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 14.2270\n",
      "Epoch 343/500\n",
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      "Epoch 344/500\n",
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      "Epoch 345/500\n",
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      "Epoch 346/500\n",
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      "Epoch 347/500\n",
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      "Epoch 348/500\n",
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      "Epoch 349/500\n",
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      "Epoch 350/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 13.8624\n",
      "Epoch 351/500\n",
      "10/10 [==============================] - 1s 61ms/step - loss: 14.7374\n",
      "Epoch 352/500\n",
      "10/10 [==============================] - 1s 60ms/step - loss: 13.6460\n",
      "Epoch 353/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 14.6117\n",
      "Epoch 354/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 15.5957\n",
      "Epoch 355/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 14.2436\n",
      "Epoch 356/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.9511\n",
      "Epoch 357/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.7996\n",
      "Epoch 358/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 13.7993\n",
      "Epoch 359/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 13.7167\n",
      "Epoch 360/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 13.8374\n",
      "Epoch 361/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 14.7797\n",
      "Epoch 362/500\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 14.5563\n",
      "Epoch 363/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.8968\n",
      "Epoch 364/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.7295\n",
      "Epoch 365/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 13.6032\n",
      "Epoch 366/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.9726\n",
      "Epoch 367/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.3618\n",
      "Epoch 368/500\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 13.4488\n",
      "Epoch 369/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.5298\n",
      "Epoch 370/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.7844\n",
      "Epoch 371/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.6318\n",
      "Epoch 372/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.2014\n",
      "Epoch 373/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 13.5372\n",
      "Epoch 374/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.2627\n",
      "Epoch 375/500\n",
      "10/10 [==============================] - 1s 64ms/step - loss: 13.2759\n",
      "Epoch 376/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.1615\n",
      "Epoch 377/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.0871\n",
      "Epoch 378/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 13.4001\n",
      "Epoch 379/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 13.0019\n",
      "Epoch 380/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 13.8190\n",
      "Epoch 381/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 14.0023\n",
      "Epoch 382/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.8142\n",
      "Epoch 383/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 12.6407\n",
      "Epoch 384/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 12.5893\n",
      "Epoch 385/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 13.8327\n",
      "Epoch 386/500\n",
      "10/10 [==============================] - 1s 63ms/step - loss: 13.2194\n",
      "Epoch 387/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 13.7510\n",
      "Epoch 388/500\n",
      "10/10 [==============================] - 1s 62ms/step - loss: 13.1371\n",
      "\n",
      "Ending training squence because the loss threshold has been reached\n"
     ]
    }
   ],
   "source": [
    "model.compile(loss=tf.keras.losses.Huber(),optimizer=tf.keras.optimizers.SGD(learning_rate=1e-4,momentum=0.9))\n",
    "history = model.fit(windowed_xTrain,epochs=500,verbose=1,callbacks=[calls])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "early-context",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "loss = history.history['loss']\n",
    "total_epochs = len(loss)\n",
    "epochs = range(total_epochs)\n",
    "\n",
    "plt.plot(epochs,loss)\n",
    "plt.xlabel('Epochs')\n",
    "plt.ylabel('Loss')\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}