Time_series/LSTM_to_predict.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "727eea74",
   "metadata": {},
   "source": [
    "# Using LSTMs to predict time series\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "da902388",
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "BOjujz601HcS",
    "outputId": "b0a53dee-523b-4a27-b31d-2a1daed0df1c"
   },
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "from dataclasses import dataclass"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b505c20e",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_series(time, series, format=\"-\", start=0, end=None):\n",
    "    plt.plot(time[start:end], series[start:end], format)\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.ylabel(\"Value\")\n",
    "    plt.grid(False)\n",
    "\n",
    "def trend(time, slope=0):\n",
    "    return slope * time\n",
    "\n",
    "def seasonal_pattern(season_time):\n",
    "    \"\"\"Just an arbitrary pattern, you can change it if you wish\"\"\"\n",
    "    return np.where(season_time < 0.1,\n",
    "                    np.cos(season_time * 6 * np.pi),\n",
    "                    2 / np.exp(9 * season_time))\n",
    "\n",
    "def seasonality(time, period, amplitude=1, phase=0):\n",
    "    \"\"\"Repeats the same pattern at each period\"\"\"\n",
    "    season_time = ((time + phase) % period) / period\n",
    "    return amplitude * seasonal_pattern(season_time)\n",
    "\n",
    "def noise(time, noise_level=1, seed=None):\n",
    "    rnd = np.random.RandomState(seed)\n",
    "    return rnd.randn(len(time)) * noise_level"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "48aadcc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def generate_time_series():\n",
    "    # The time dimension or the x-coordinate of the time series\n",
    "    time = np.arange(4 * 365 + 1, dtype=\"float32\")\n",
    "\n",
    "    # Initial series is just a straight line with a y-intercept\n",
    "    y_intercept = 10\n",
    "    slope = 0.005\n",
    "    series = trend(time, slope) + y_intercept\n",
    "\n",
    "    # Adding seasonality\n",
    "    amplitude = 50\n",
    "    series += seasonality(time, period=365, amplitude=amplitude)\n",
    "\n",
    "    # Adding some noise\n",
    "    noise_level = 3\n",
    "    series += noise(time, noise_level, seed=51)\n",
    "    \n",
    "    return time, series\n",
    "\n",
    "\n",
    "# Save all \"global\" variables within the G class (G stands for global)\n",
    "@dataclass\n",
    "class G:\n",
    "    TIME, SERIES = generate_time_series()\n",
    "    SPLIT_TIME = 1100\n",
    "    WINDOW_SIZE = 20\n",
    "    BATCH_SIZE = 32\n",
    "    SHUFFLE_BUFFER_SIZE = 1000\n",
    "    \n",
    "\n",
    "# Plot the generated series\n",
    "plt.figure(figsize=(10, 6))\n",
    "plot_series(G.TIME, G.SERIES)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61d80b19",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the dataset\n",
    "def train_val_split(time, series, time_step=G.SPLIT_TIME):\n",
    "\n",
    "    time_train = time[:time_step]\n",
    "    series_train = series[:time_step]\n",
    "    time_valid = time[time_step:]\n",
    "    series_valid = series[time_step:]\n",
    "\n",
    "    return time_train, series_train, time_valid, series_valid\n",
    "\n",
    "time_train, series_train, time_valid, series_valid = train_val_split(G.TIME, G.SERIES)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "63d65bc2",
   "metadata": {
    "id": "4sTTIOCbyShY"
   },
   "outputs": [],
   "source": [
    "# Apply the transformation to the training set\n",
    "def windowed_dataset(series, window_size=G.WINDOW_SIZE, batch_size=G.BATCH_SIZE, shuffle_buffer=G.SHUFFLE_BUFFER_SIZE):\n",
    "    dataset = tf.data.Dataset.from_tensor_slices(series)\n",
    "    dataset = dataset.window(window_size + 1, shift=1, drop_remainder=True)\n",
    "    dataset = dataset.flat_map(lambda window: window.batch(window_size + 1))\n",
    "    dataset = dataset.shuffle(shuffle_buffer)\n",
    "    dataset = dataset.map(lambda window: (window[:-1], window[-1]))\n",
    "    dataset = dataset.batch(batch_size).prefetch(1)\n",
    "    return dataset\n",
    "\n",
    "dataset = windowed_dataset(series_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "ded09736",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_uncompiled_model():\n",
    "\n",
    "    tf.keras.backend.clear_session()\n",
    "    \n",
    "    model = tf.keras.models.Sequential([ \n",
    "        tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis = -1)),\n",
    "        tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(64, return_sequences = True)),\n",
    "        tf.keras.layers.Bidirectional(tf.keras.layers.LSTM(32)),\n",
    "        tf.keras.layers.Dense(1),\n",
    "        tf.keras.layers.Lambda(lambda x: x*100.0)\n",
    "    ]) \n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "df7f5667",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your current architecture is compatible with the windowed dataset! :)\n"
     ]
    }
   ],
   "source": [
    "uncompiled_model = create_uncompiled_model()\n",
    "\n",
    "try:\n",
    "    uncompiled_model.predict(dataset)\n",
    "except:\n",
    "    print(\"Your current architecture is incompatible with the windowed dataset, try adjusting it.\")\n",
    "else:\n",
    "    print(\"Your current architecture is compatible with the windowed dataset! :)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4b8878d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "def adjust_learning_rate():\n",
    "    \n",
    "    model = create_uncompiled_model()\n",
    "    \n",
    "    lr_schedule = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-6 * 10**(epoch / 20))\n",
    "    optimizer = tf.keras.optimizers.SGD(momentum = 0.8)\n",
    "\n",
    "    huber = tf.keras.losses.Huber()\n",
    "    model.compile(loss=huber,\n",
    "                  optimizer=optimizer, \n",
    "                  metrics=[\"mae\"]) \n",
    "\n",
    "    history = model.fit(dataset, epochs=100, callbacks=[lr_schedule])\n",
    "    \n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "1f0550ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "34/34 [==============================] - 7s 17ms/step - loss: 13.7924 - mae: 14.2806\n",
      "Epoch 2/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 5.3976 - mae: 5.8712\n",
      "Epoch 3/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 4.7213 - mae: 5.1882\n",
      "Epoch 4/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 4.4733 - mae: 4.9401\n",
      "Epoch 5/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 4.4023 - mae: 4.8691\n",
      "Epoch 6/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.2497 - mae: 4.7168\n",
      "Epoch 7/100\n",
      "34/34 [==============================] - 1s 19ms/step - loss: 4.1742 - mae: 4.6453\n",
      "Epoch 8/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 4.0353 - mae: 4.5093\n",
      "Epoch 9/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.9632 - mae: 4.4342\n",
      "Epoch 10/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 3.9457 - mae: 4.4152\n",
      "Epoch 11/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 3.7453 - mae: 4.2106\n",
      "Epoch 12/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.7511 - mae: 4.2223\n",
      "Epoch 13/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.6354 - mae: 4.1067\n",
      "Epoch 14/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.5619 - mae: 4.0314\n",
      "Epoch 15/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.5401 - mae: 4.0079\n",
      "Epoch 16/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.5169 - mae: 3.9839\n",
      "Epoch 17/100\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 3.5076 - mae: 3.9738\n",
      "Epoch 18/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.4311 - mae: 3.8970\n",
      "Epoch 19/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.3257 - mae: 3.7984\n",
      "Epoch 20/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 3.2089 - mae: 3.6766\n",
      "Epoch 21/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.3074 - mae: 3.7712\n",
      "Epoch 22/100\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 3.2807 - mae: 3.7474\n",
      "Epoch 23/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.2361 - mae: 3.7005\n",
      "Epoch 24/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.3468 - mae: 3.8162\n",
      "Epoch 25/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.0540 - mae: 3.5168\n",
      "Epoch 26/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.0375 - mae: 3.5022\n",
      "Epoch 27/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.1615 - mae: 3.6226\n",
      "Epoch 28/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.0695 - mae: 3.5314\n",
      "Epoch 29/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 3.1081 - mae: 3.5723\n",
      "Epoch 30/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 4.0205 - mae: 4.4970\n",
      "Epoch 31/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.2926 - mae: 3.7593\n",
      "Epoch 32/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.4537 - mae: 4.9339\n",
      "Epoch 33/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.3622 - mae: 3.8250\n",
      "Epoch 34/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.5259 - mae: 3.9921\n",
      "Epoch 35/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.1599 - mae: 3.6267\n",
      "Epoch 36/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.5121 - mae: 3.9839\n",
      "Epoch 37/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.9657 - mae: 4.4401\n",
      "Epoch 38/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 3.3860 - mae: 3.8522\n",
      "Epoch 39/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 4.7474 - mae: 5.2256\n",
      "Epoch 40/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 4.0367 - mae: 4.5100\n",
      "Epoch 41/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 6.1269 - mae: 6.6139\n",
      "Epoch 42/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 3.6659 - mae: 4.1348\n",
      "Epoch 43/100\n",
      "34/34 [==============================] - 1s 19ms/step - loss: 5.2190 - mae: 5.6988\n",
      "Epoch 44/100\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 8.1187 - mae: 8.6139\n",
      "Epoch 45/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 6.3253 - mae: 6.8103\n",
      "Epoch 46/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 7.1333 - mae: 7.6234\n",
      "Epoch 47/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 6.2041 - mae: 6.6881\n",
      "Epoch 48/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.0386 - mae: 4.5125\n",
      "Epoch 49/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 5.6012 - mae: 6.0851\n",
      "Epoch 50/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.7163 - mae: 5.1923\n",
      "Epoch 51/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 7.5375 - mae: 8.0312\n",
      "Epoch 52/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.9532 - mae: 5.4369\n",
      "Epoch 53/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 6.2693 - mae: 6.7560\n",
      "Epoch 54/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.7612 - mae: 4.2359\n",
      "Epoch 55/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.3807 - mae: 4.8536\n",
      "Epoch 56/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.9261 - mae: 4.3973\n",
      "Epoch 57/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.9088 - mae: 4.3828\n",
      "Epoch 58/100\n",
      "34/34 [==============================] - 1s 19ms/step - loss: 5.4784 - mae: 5.9616\n",
      "Epoch 59/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 4.5162 - mae: 4.9934\n",
      "Epoch 60/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 4.9750 - mae: 5.4561\n",
      "Epoch 61/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.9194 - mae: 4.3926\n",
      "Epoch 62/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.1279 - mae: 4.6060\n",
      "Epoch 63/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.7287 - mae: 5.2065\n",
      "Epoch 64/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 10.6652 - mae: 11.1587\n",
      "Epoch 65/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 7.2231 - mae: 7.7129\n",
      "Epoch 66/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 5.1461 - mae: 5.6271\n",
      "Epoch 67/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 7.1222 - mae: 7.6070\n",
      "Epoch 68/100\n",
      "34/34 [==============================] - 1s 19ms/step - loss: 5.8764 - mae: 6.3641\n",
      "Epoch 69/100\n",
      "34/34 [==============================] - 1s 19ms/step - loss: 8.4167 - mae: 8.9057\n",
      "Epoch 70/100\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 14.0264 - mae: 14.5208\n",
      "Epoch 71/100\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 16.9872 - mae: 17.4830\n",
      "Epoch 72/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 14.1965 - mae: 14.6923\n",
      "Epoch 73/100\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 19.5871 - mae: 20.0822\n",
      "Epoch 74/100\n",
      "34/34 [==============================] - 1s 19ms/step - loss: 20.0459 - mae: 20.5405\n",
      "Epoch 75/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 38.0102 - mae: 38.5077\n",
      "Epoch 76/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 24.0181 - mae: 24.5152\n",
      "Epoch 77/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 31.8738 - mae: 32.3715\n",
      "Epoch 78/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 36.7253 - mae: 37.2241\n",
      "Epoch 79/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 43.1763 - mae: 43.6751\n",
      "Epoch 80/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 46.3093 - mae: 46.8070\n",
      "Epoch 81/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 55.9475 - mae: 56.4475\n",
      "Epoch 82/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 54.6805 - mae: 55.1800\n",
      "Epoch 83/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 70.0656 - mae: 70.5656\n",
      "Epoch 84/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 61.5798 - mae: 62.0771\n",
      "Epoch 85/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 86.6093 - mae: 87.1093\n",
      "Epoch 86/100\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "34/34 [==============================] - 1s 17ms/step - loss: 97.2169 - mae: 97.7169\n",
      "Epoch 87/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 108.6137 - mae: 109.1137\n",
      "Epoch 88/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 122.1413 - mae: 122.6413\n",
      "Epoch 89/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 137.5522 - mae: 138.0522\n",
      "Epoch 90/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 153.5382 - mae: 154.0382\n",
      "Epoch 91/100\n",
      "34/34 [==============================] - 1s 19ms/step - loss: 172.9195 - mae: 173.4195\n",
      "Epoch 92/100\n",
      "34/34 [==============================] - 1s 19ms/step - loss: 193.6701 - mae: 194.1701\n",
      "Epoch 93/100\n",
      "34/34 [==============================] - 1s 19ms/step - loss: 217.8801 - mae: 218.3801\n",
      "Epoch 94/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 244.2989 - mae: 244.7989\n",
      "Epoch 95/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 274.4150 - mae: 274.9150\n",
      "Epoch 96/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 307.5704 - mae: 308.0704\n",
      "Epoch 97/100\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 345.8218 - mae: 346.3218\n",
      "Epoch 98/100\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 387.8124 - mae: 388.3124\n",
      "Epoch 99/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 435.2454 - mae: 435.7454\n",
      "Epoch 100/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 487.5956 - mae: 488.0956\n"
     ]
    }
   ],
   "source": [
    "\n",
    "lr_history = adjust_learning_rate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d1977dae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1e-06, 1.0, 0.0, 30.0)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "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.semilogx(lr_history.history[\"lr\"], lr_history.history[\"loss\"])\n",
    "plt.axis([1e-6, 1, 0, 30])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "8766c169",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model():\n",
    "\n",
    "    tf.random.set_seed(51)\n",
    "    \n",
    "    model = create_uncompiled_model()\n",
    "    \n",
    "    huber = tf.keras.losses.Huber()\n",
    "    optimizer = tf.keras.optimizers.SGD(learning_rate = 3e-5, momentum = 0.8)\n",
    "    model.compile(loss=huber,\n",
    "                  optimizer=optimizer,\n",
    "                  metrics=[\"mae\"])  \n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8cf3bc51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/40\n",
      "34/34 [==============================] - 8s 72ms/step - loss: 5.4697 - mae: 5.9509\n",
      "Epoch 2/40\n",
      "34/34 [==============================] - 2s 58ms/step - loss: 3.8927 - mae: 4.3617\n",
      "Epoch 3/40\n",
      "34/34 [==============================] - 2s 55ms/step - loss: 4.0127 - mae: 4.4819\n",
      "Epoch 4/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 4.0716 - mae: 4.5470\n",
      "Epoch 5/40\n",
      "34/34 [==============================] - 2s 53ms/step - loss: 3.4669 - mae: 3.9344\n",
      "Epoch 6/40\n",
      "34/34 [==============================] - 2s 54ms/step - loss: 3.2166 - mae: 3.6825\n",
      "Epoch 7/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 3.5147 - mae: 3.9866\n",
      "Epoch 8/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 3.1648 - mae: 3.6310\n",
      "Epoch 9/40\n",
      "34/34 [==============================] - 2s 54ms/step - loss: 3.9918 - mae: 4.4676\n",
      "Epoch 10/40\n",
      "34/34 [==============================] - 2s 54ms/step - loss: 3.0067 - mae: 3.4693\n",
      "Epoch 11/40\n",
      "34/34 [==============================] - 2s 54ms/step - loss: 2.8732 - mae: 3.3371\n",
      "Epoch 12/40\n",
      "34/34 [==============================] - 2s 54ms/step - loss: 2.9168 - mae: 3.3787\n",
      "Epoch 13/40\n",
      "34/34 [==============================] - 2s 54ms/step - loss: 2.9945 - mae: 3.4608\n",
      "Epoch 14/40\n",
      "34/34 [==============================] - 2s 54ms/step - loss: 2.9759 - mae: 3.4346\n",
      "Epoch 15/40\n",
      "34/34 [==============================] - 2s 54ms/step - loss: 3.0163 - mae: 3.4829\n",
      "Epoch 16/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 3.4817 - mae: 3.9536\n",
      "Epoch 17/40\n",
      "34/34 [==============================] - 2s 53ms/step - loss: 2.8706 - mae: 3.3363\n",
      "Epoch 18/40\n",
      "34/34 [==============================] - 2s 54ms/step - loss: 2.9245 - mae: 3.3868\n",
      "Epoch 19/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 3.0135 - mae: 3.4764\n",
      "Epoch 20/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 2.8459 - mae: 3.3083\n",
      "Epoch 21/40\n",
      "34/34 [==============================] - 2s 54ms/step - loss: 2.7448 - mae: 3.2065\n",
      "Epoch 22/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 3.0205 - mae: 3.4882\n",
      "Epoch 23/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 2.9051 - mae: 3.3632\n",
      "Epoch 24/40\n",
      "34/34 [==============================] - 2s 53ms/step - loss: 2.9105 - mae: 3.3760\n",
      "Epoch 25/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 3.1420 - mae: 3.6106\n",
      "Epoch 26/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 2.9085 - mae: 3.3691\n",
      "Epoch 27/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 2.8319 - mae: 3.2980\n",
      "Epoch 28/40\n",
      "34/34 [==============================] - 2s 53ms/step - loss: 3.0352 - mae: 3.5011\n",
      "Epoch 29/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 2.8922 - mae: 3.3580\n",
      "Epoch 30/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 2.7674 - mae: 3.2304\n",
      "Epoch 31/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 2.9547 - mae: 3.4169\n",
      "Epoch 32/40\n",
      "34/34 [==============================] - 2s 49ms/step - loss: 3.2035 - mae: 3.6748\n",
      "Epoch 33/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 3.3017 - mae: 3.7671\n",
      "Epoch 34/40\n",
      "34/34 [==============================] - 2s 54ms/step - loss: 3.2079 - mae: 3.6782\n",
      "Epoch 35/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 2.9512 - mae: 3.4163\n",
      "Epoch 36/40\n",
      "34/34 [==============================] - 2s 51ms/step - loss: 2.7522 - mae: 3.2151\n",
      "Epoch 37/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 2.8038 - mae: 3.2617\n",
      "Epoch 38/40\n",
      "34/34 [==============================] - 2s 54ms/step - loss: 2.8441 - mae: 3.3082\n",
      "Epoch 39/40\n",
      "34/34 [==============================] - 2s 54ms/step - loss: 2.7181 - mae: 3.1783\n",
      "Epoch 40/40\n",
      "34/34 [==============================] - 2s 52ms/step - loss: 2.7490 - mae: 3.2140\n"
     ]
    }
   ],
   "source": [
    "model = create_model()\n",
    "history = model.fit(dataset, epochs=40)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "58db2a59",
   "metadata": {},
   "outputs": [],
   "source": [
    "def compute_metrics(true_series, forecast):\n",
    "    \n",
    "    mse = tf.keras.metrics.mean_squared_error(true_series, forecast).numpy()\n",
    "    mae = tf.keras.metrics.mean_absolute_error(true_series, forecast).numpy()\n",
    "\n",
    "    return mse, mae"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "acknowledged-penny",
   "metadata": {},
   "outputs": [],
   "source": [
    "def model_forecast(model, series, window_size):\n",
    "    ds = tf.data.Dataset.from_tensor_slices(series)\n",
    "    ds = ds.window(window_size, shift=1, drop_remainder=True)\n",
    "    ds = ds.flat_map(lambda w: w.batch(window_size))\n",
    "    ds = ds.batch(32).prefetch(1)\n",
    "    forecast = model.predict(ds)\n",
    "    return forecast"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "neural-assurance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Compute the forecast for all the series\n",
    "rnn_forecast = model_forecast(model, G.SERIES, G.WINDOW_SIZE).squeeze()\n",
    "\n",
    "# Slice the forecast to get only the predictions for the validation set\n",
    "rnn_forecast = rnn_forecast[G.SPLIT_TIME - G.WINDOW_SIZE:-1]\n",
    "\n",
    "# Plot it\n",
    "plt.figure(figsize=(10, 6))\n",
    "\n",
    "plot_series(time_valid, series_valid)\n",
    "plot_series(time_valid, rnn_forecast)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "c951006f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 27.57, mae: 3.15 for forecast\n"
     ]
    }
   ],
   "source": [
    "mse, mae = compute_metrics(series_valid, rnn_forecast)\n",
    "\n",
    "print(f\"mse: {mse:.2f}, mae: {mae:.2f} for forecast\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "c243363b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as lstm_cell_1_layer_call_fn, lstm_cell_1_layer_call_and_return_conditional_losses, lstm_cell_2_layer_call_fn, lstm_cell_2_layer_call_and_return_conditional_losses, lstm_cell_4_layer_call_fn while saving (showing 5 of 20). These functions will not be directly callable after loading.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_model/my_model/assets\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:tensorflow:Assets written to: saved_model/my_model/assets\n",
      "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f526869b040> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
      "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f526869b910> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
      "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f52686a0eb0> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n",
      "WARNING:absl:<keras.layers.recurrent.LSTMCell object at 0x7f52686a8880> has the same name 'LSTMCell' as a built-in Keras object. Consider renaming <class 'keras.layers.recurrent.LSTMCell'> to avoid naming conflicts when loading with `tf.keras.models.load_model`. If renaming is not possible, pass the object in the `custom_objects` parameter of the load function.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "saved_model/\n",
      "saved_model/my_model/\n",
      "saved_model/my_model/keras_metadata.pb\n",
      "saved_model/my_model/variables/\n",
      "saved_model/my_model/variables/variables.data-00000-of-00001\n",
      "saved_model/my_model/variables/variables.index\n",
      "saved_model/my_model/saved_model.pb\n",
      "saved_model/my_model/assets/\n"
     ]
    }
   ],
   "source": [
    "# Save your model in the SavedModel format\n",
    "model.save('saved_model/my_model')\n",
    "\n",
    "# Compress the directory using tar\n",
    "! tar -czvf saved_model.tar.gz saved_model/"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}