RNN time series forecasting.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import tensorflow as tf\n",
    "keras = tf.keras\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_series(time, series, format=\"-\", start=0, end=None, label=None):\n",
    "    plt.plot(time[start:end], series[start:end], format, label=label)\n",
    "    plt.xlabel(\"Time\")\n",
    "    plt.ylabel(\"Value\")\n",
    "    if label:\n",
    "        plt.legend(fontsize=14)\n",
    "    plt.grid(True)\n",
    "    \n",
    "def trend(time, slope=0):\n",
    "    return slope * time\n",
    "  \n",
    "def seasonal_pattern(season_time):\n",
    "    return np.where(season_time < 0.4,\n",
    "                    np.sin(season_time * 2 * np.pi),\n",
    "                    1 / np.exp(3 * season_time))\n",
    "\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",
    "  \n",
    "def white_noise(time, noise_level=1, seed=None):\n",
    "    rnd = np.random.RandomState(seed)\n",
    "    return rnd.randn(len(time)) * noise_level\n",
    "  \n",
    "  \n",
    "def window_dataset(series, window_size, batch_size=32,\n",
    "                   shuffle_buffer=1000):\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",
    "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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "time = np.arange(4 * 365 + 1)\n",
    "\n",
    "slope = 0.05\n",
    "baseline = 10\n",
    "amplitude = 40\n",
    "series = baseline + trend(time, slope) + seasonality(time, period=365, amplitude=amplitude)\n",
    "\n",
    "noise_level = 5\n",
    "noise = white_noise(time, noise_level, seed=42)\n",
    "\n",
    "series += noise\n",
    "\n",
    "plt.figure(figsize=(10, 6))\n",
    "plot_series(time, series)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "split_time = 1000\n",
    "time_train = time[:split_time]\n",
    "x_train = series[:split_time]\n",
    "time_valid = time[split_time:]\n",
    "x_valid = series[split_time:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>RNN</b>\n",
    "\n",
    "- Very powerful \n",
    "- Requires Data\n",
    "- Tricky to train (somewhat unstable gradiet)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Are Neural networks that contains a recurrent layer\n",
    "- can sequencially process a sequence of input\n",
    "- Three dimensional input\n",
    "![Alt text](explanation_images/rnns.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Single memory cell(Small NN) used repeatdly to compute the outputs.\n",
    "- Single memory cell could be:\n",
    "    - Simple Dense Layer\n",
    "    - Complex: LSTM cell, GRU cell\n",
    " \n",
    "Only single cell, in the diagram it's the same cell\n",
    "\n",
    "- X0: input\n",
    "- Y0 hat: current output\n",
    "- H0: state or context factor, it's fed to the memory cell \n",
    "\n",
    "![Alt text](explanation_images/singlecCell.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- 4*3 becaue the batch size is 4, the # of steps (30) and the # of uits is 3\n",
    "- H0 is just a copy of Y0... H1 is a copy of Y1\n",
    "\n",
    "![Alt text](explanation_images/recurrentLayer.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- this is ignorong all the output except the last one\n",
    "- will handle any sequences of any length  \n",
    "\n",
    "![Alt text](explanation_images/seqToVector.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The more steps the deeper it is\n",
    "- training could be slow\n",
    "![Alt text](explanation_images/bptt.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "window_size = 30\n",
    "train_set = window_dataset(x_train, window_size, batch_size=128)\n",
    "valid_set = window_dataset(x_valid, window_size, batch_size=128)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = keras.models.Sequential([\n",
    "    keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1),\n",
    "                        input_shape=[None]),\n",
    "    keras.layers.SimpleRNN(100, return_sequences=True),\n",
    "    keras.layers.SimpleRNN(100),\n",
    "    keras.layers.Dense(1),\n",
    "    keras.layers.Lambda(lambda x: x * 200.0)\n",
    "])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "lr_schedule = keras.callbacks.LearningRateScheduler(\n",
    "    lambda epoch: 1e-7 * 10 **(epoch/20)\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "optimizer = keras.optimizers.SGD(learning_rate=1e-7, momentum=0.9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "model.compile(loss=keras.losses.Huber(),\n",
    "              optimizer=optimizer,\n",
    "              metrics=['mae'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "8/8 [==============================] - 4s 228ms/step - loss: 45.3921 - mae: 45.8882 - lr: 1.0000e-07\n",
      "Epoch 2/100\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 23.3522 - mae: 23.8494 - lr: 1.1220e-07\n",
      "Epoch 3/100\n",
      "8/8 [==============================] - 0s 24ms/step - loss: 20.7985 - mae: 21.2951 - lr: 1.2589e-07\n",
      "Epoch 4/100\n",
      "8/8 [==============================] - 0s 24ms/step - loss: 13.0945 - mae: 13.5859 - lr: 1.4125e-07\n",
      "Epoch 5/100\n",
      "8/8 [==============================] - 0s 34ms/step - loss: 7.5664 - mae: 8.0497 - lr: 1.5849e-07\n",
      "Epoch 6/100\n",
      "8/8 [==============================] - 0s 24ms/step - loss: 6.2879 - mae: 6.7717 - lr: 1.7783e-07\n",
      "Epoch 7/100\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 5.9785 - mae: 6.4626 - lr: 1.9953e-07\n",
      "Epoch 8/100\n",
      "8/8 [==============================] - 0s 23ms/step - loss: 5.4062 - mae: 5.8892 - lr: 2.2387e-07\n",
      "Epoch 9/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 5.2683 - mae: 5.7475 - lr: 2.5119e-07\n",
      "Epoch 10/100\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 5.0691 - mae: 5.5502 - lr: 2.8184e-07\n",
      "Epoch 11/100\n",
      "8/8 [==============================] - 0s 33ms/step - loss: 4.8187 - mae: 5.3008 - lr: 3.1623e-07\n",
      "Epoch 12/100\n",
      "8/8 [==============================] - 0s 23ms/step - loss: 4.7123 - mae: 5.1924 - lr: 3.5481e-07\n",
      "Epoch 13/100\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 4.5753 - mae: 5.0548 - lr: 3.9811e-07\n",
      "Epoch 14/100\n",
      "8/8 [==============================] - 0s 23ms/step - loss: 4.5342 - mae: 5.0127 - lr: 4.4668e-07\n",
      "Epoch 15/100\n",
      "8/8 [==============================] - 0s 23ms/step - loss: 4.6237 - mae: 5.1014 - lr: 5.0119e-07\n",
      "Epoch 16/100\n",
      "8/8 [==============================] - 0s 24ms/step - loss: 4.5715 - mae: 5.0484 - lr: 5.6234e-07\n",
      "Epoch 17/100\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 4.4537 - mae: 4.9351 - lr: 6.3096e-07\n",
      "Epoch 18/100\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 4.6084 - mae: 5.0881 - lr: 7.0795e-07\n",
      "Epoch 19/100\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 4.4029 - mae: 4.8795 - lr: 7.9433e-07\n",
      "Epoch 20/100\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 4.2563 - mae: 4.7317 - lr: 8.9125e-07\n",
      "Epoch 21/100\n",
      "8/8 [==============================] - 0s 23ms/step - loss: 4.4251 - mae: 4.9010 - lr: 1.0000e-06\n",
      "Epoch 22/100\n",
      "8/8 [==============================] - 0s 23ms/step - loss: 4.3079 - mae: 4.7858 - lr: 1.1220e-06\n",
      "Epoch 23/100\n",
      "8/8 [==============================] - 0s 23ms/step - loss: 4.6001 - mae: 5.0792 - lr: 1.2589e-06\n",
      "Epoch 24/100\n",
      "8/8 [==============================] - 0s 24ms/step - loss: 4.8859 - mae: 5.3690 - lr: 1.4125e-06\n",
      "Epoch 25/100\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 4.8048 - mae: 5.2832 - lr: 1.5849e-06\n",
      "Epoch 26/100\n",
      "8/8 [==============================] - 0s 32ms/step - loss: 4.4523 - mae: 4.9271 - lr: 1.7783e-06\n",
      "Epoch 27/100\n",
      "8/8 [==============================] - 0s 34ms/step - loss: 5.1567 - mae: 5.6328 - lr: 1.9953e-06\n",
      "Epoch 28/100\n",
      "8/8 [==============================] - 0s 32ms/step - loss: 5.3439 - mae: 5.8296 - lr: 2.2387e-06\n",
      "Epoch 29/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 5.3134 - mae: 5.7972 - lr: 2.5119e-06\n",
      "Epoch 30/100\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 7.6526 - mae: 8.1428 - lr: 2.8184e-06\n",
      "Epoch 31/100\n",
      "8/8 [==============================] - 0s 22ms/step - loss: 7.8577 - mae: 8.3483 - lr: 3.1623e-06\n",
      "Epoch 32/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 6.8058 - mae: 7.2952 - lr: 3.5481e-06\n",
      "Epoch 33/100\n",
      "8/8 [==============================] - 0s 23ms/step - loss: 6.9537 - mae: 7.4412 - lr: 3.9811e-06\n",
      "Epoch 34/100\n",
      "8/8 [==============================] - 0s 24ms/step - loss: 8.2234 - mae: 8.7110 - lr: 4.4668e-06\n",
      "Epoch 35/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 8.3507 - mae: 8.8421 - lr: 5.0119e-06\n",
      "Epoch 36/100\n",
      "8/8 [==============================] - 0s 32ms/step - loss: 10.0856 - mae: 10.5780 - lr: 5.6234e-06\n",
      "Epoch 37/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 8.2736 - mae: 8.7583 - lr: 6.3096e-06\n",
      "Epoch 38/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 10.5775 - mae: 11.0724 - lr: 7.0795e-06\n",
      "Epoch 39/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 10.5003 - mae: 10.9901 - lr: 7.9433e-06\n",
      "Epoch 40/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 22.2372 - mae: 22.7304 - lr: 8.9125e-06\n",
      "Epoch 41/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 46.8455 - mae: 47.3446 - lr: 1.0000e-05\n",
      "Epoch 42/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 49.2789 - mae: 49.7784 - lr: 1.1220e-05\n",
      "Epoch 43/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 20.3290 - mae: 20.8233 - lr: 1.2589e-05\n",
      "Epoch 44/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 22.0921 - mae: 22.5888 - lr: 1.4125e-05\n",
      "Epoch 45/100\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 16.3601 - mae: 16.8521 - lr: 1.5849e-05\n",
      "Epoch 46/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 15.1325 - mae: 15.6258 - lr: 1.7783e-05\n",
      "Epoch 47/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 17.8693 - mae: 18.3677 - lr: 1.9953e-05\n",
      "Epoch 48/100\n",
      "8/8 [==============================] - 0s 25ms/step - loss: 19.2866 - mae: 19.7847 - lr: 2.2387e-05\n",
      "Epoch 49/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 48.7019 - mae: 49.1998 - lr: 2.5119e-05\n",
      "Epoch 50/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 75.6480 - mae: 76.1446 - lr: 2.8184e-05\n",
      "Epoch 51/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 48.0764 - mae: 48.5747 - lr: 3.1623e-05\n",
      "Epoch 52/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 54.2391 - mae: 54.7389 - lr: 3.5481e-05\n",
      "Epoch 53/100\n",
      "8/8 [==============================] - 0s 32ms/step - loss: 55.2430 - mae: 55.7430 - lr: 3.9811e-05\n",
      "Epoch 54/100\n",
      "8/8 [==============================] - 0s 33ms/step - loss: 60.7516 - mae: 61.2516 - lr: 4.4668e-05\n",
      "Epoch 55/100\n",
      "8/8 [==============================] - 0s 30ms/step - loss: 65.0888 - mae: 65.5888 - lr: 5.0119e-05\n",
      "Epoch 56/100\n",
      "8/8 [==============================] - 0s 30ms/step - loss: 69.7034 - mae: 70.2034 - lr: 5.6234e-05\n",
      "Epoch 57/100\n",
      "8/8 [==============================] - 0s 34ms/step - loss: 74.5589 - mae: 75.0589 - lr: 6.3096e-05\n",
      "Epoch 58/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 81.6226 - mae: 82.1226 - lr: 7.0795e-05\n",
      "Epoch 59/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 87.3946 - mae: 87.8946 - lr: 7.9433e-05\n",
      "Epoch 60/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 88.3123 - mae: 88.8123 - lr: 8.9125e-05\n",
      "Epoch 61/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 92.8028 - mae: 93.3028 - lr: 1.0000e-04\n",
      "Epoch 62/100\n",
      "8/8 [==============================] - 0s 29ms/step - loss: 117.0368 - mae: 117.5368 - lr: 1.1220e-04\n",
      "Epoch 63/100\n",
      "8/8 [==============================] - 0s 34ms/step - loss: 137.8309 - mae: 138.3309 - lr: 1.2589e-04\n",
      "Epoch 64/100\n",
      "8/8 [==============================] - 0s 30ms/step - loss: 136.9706 - mae: 137.4706 - lr: 1.4125e-04\n",
      "Epoch 65/100\n",
      "8/8 [==============================] - 0s 32ms/step - loss: 193.2016 - mae: 193.7016 - lr: 1.5849e-04\n",
      "Epoch 66/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 235.7962 - mae: 236.2962 - lr: 1.7783e-04\n",
      "Epoch 67/100\n",
      "8/8 [==============================] - 0s 30ms/step - loss: 274.0317 - mae: 274.5317 - lr: 1.9953e-04\n",
      "Epoch 68/100\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 341.0200 - mae: 341.5200 - lr: 2.2387e-04\n",
      "Epoch 69/100\n",
      "8/8 [==============================] - 0s 29ms/step - loss: 422.9315 - mae: 423.4315 - lr: 2.5119e-04\n",
      "Epoch 70/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 489.5974 - mae: 490.0974 - lr: 2.8184e-04\n",
      "Epoch 71/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 611.5299 - mae: 612.0299 - lr: 3.1623e-04\n",
      "Epoch 72/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 671.4094 - mae: 671.9094 - lr: 3.5481e-04\n",
      "Epoch 73/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 756.6519 - mae: 757.1519 - lr: 3.9811e-04\n",
      "Epoch 74/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 895.9949 - mae: 896.4949 - lr: 4.4668e-04\n",
      "Epoch 75/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 1023.0147 - mae: 1023.5147 - lr: 5.0119e-04\n",
      "Epoch 76/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 1159.9044 - mae: 1160.4044 - lr: 5.6234e-04\n",
      "Epoch 77/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 1326.6934 - mae: 1327.1934 - lr: 6.3096e-04\n",
      "Epoch 78/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 1447.4110 - mae: 1447.9110 - lr: 7.0795e-04\n",
      "Epoch 79/100\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 1622.5078 - mae: 1623.0078 - lr: 7.9433e-04\n",
      "Epoch 80/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 1814.3561 - mae: 1814.8561 - lr: 8.9125e-04\n",
      "Epoch 81/100\n",
      "8/8 [==============================] - 0s 34ms/step - loss: 2076.9370 - mae: 2077.4370 - lr: 0.0010\n",
      "Epoch 82/100\n",
      "8/8 [==============================] - 0s 31ms/step - loss: 2368.7693 - mae: 2369.2693 - lr: 0.0011\n",
      "Epoch 83/100\n",
      "8/8 [==============================] - 0s 38ms/step - loss: 2612.6216 - mae: 2613.1216 - lr: 0.0013\n",
      "Epoch 84/100\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 2941.8726 - mae: 2942.3726 - lr: 0.0014\n",
      "Epoch 85/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 3315.3425 - mae: 3315.8425 - lr: 0.0016\n",
      "Epoch 86/100\n",
      "8/8 [==============================] - 0s 29ms/step - loss: 3750.6113 - mae: 3751.1113 - lr: 0.0018\n",
      "Epoch 87/100\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 4296.3711 - mae: 4296.8711 - lr: 0.0020\n",
      "Epoch 88/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 4802.7739 - mae: 4803.2739 - lr: 0.0022\n",
      "Epoch 89/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 5386.5811 - mae: 5387.0811 - lr: 0.0025\n",
      "Epoch 90/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 6036.9917 - mae: 6037.4917 - lr: 0.0028\n",
      "Epoch 91/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 6781.3721 - mae: 6781.8721 - lr: 0.0032\n",
      "Epoch 92/100\n",
      "8/8 [==============================] - 0s 26ms/step - loss: 7549.1523 - mae: 7549.6523 - lr: 0.0035\n",
      "Epoch 93/100\n",
      "8/8 [==============================] - 0s 32ms/step - loss: 8464.5898 - mae: 8465.0898 - lr: 0.0040\n",
      "Epoch 94/100\n",
      "8/8 [==============================] - 0s 29ms/step - loss: 9468.2832 - mae: 9468.7832 - lr: 0.0045\n",
      "Epoch 95/100\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 10516.2695 - mae: 10516.7695 - lr: 0.0050\n",
      "Epoch 96/100\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 11748.5518 - mae: 11749.0518 - lr: 0.0056\n",
      "Epoch 97/100\n",
      "8/8 [==============================] - 0s 27ms/step - loss: 13094.5264 - mae: 13095.0264 - lr: 0.0063\n",
      "Epoch 98/100\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 14600.4668 - mae: 14600.9668 - lr: 0.0071\n",
      "Epoch 99/100\n",
      "8/8 [==============================] - 0s 28ms/step - loss: 16765.8691 - mae: 16766.3691 - lr: 0.0079\n",
      "Epoch 100/100\n",
      "8/8 [==============================] - 0s 30ms/step - loss: 18200.2266 - mae: 18200.7266 - lr: 0.0089\n"
     ]
    }
   ],
   "source": [
    "history = model.fit(train_set, epochs=100, callbacks=[lr_schedule])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "early_stoping = keras.callbacks.EarlyStopping(patience=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "model_checkpoint = keras.callbacks.ModelCheckpoint(\"model.keras\", save_best_only=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "history = model.fit(train_set, epochs=100, validation_data=valid_set, callbacks=[early_stoping, model_checkpoint])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- to speed things up\n",
    "- Sequence to sequnce RNN\n",
    "\n",
    "![Alt text](explanation_images/seq2seq.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Stateless and stateful RNNs</b>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<b>Long Short Term Cell</b>\n",
    "\n",
    "- for longer sequences (rembers more)\n",
    "- it has another state factor c\n",
    "\n",
    "- forget gate (sigmoid + dense): when it multiply by 0 so it erases it.\n",
    "- input gate (sigmoid + dense): decides when it's time to rember sth. (addition)\n",
    "- output gate (sigmoid + dense): it learns which it should output next\n",
    "\n",
    "![Alt text](explanation_images/lstm.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- generic is slower becaue it will run a lot of optimizers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "keras.src.layers.rnn.lstm.LSTM"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "keras.layers.RNN(keras.layers.LSTMCell(100))\n",
    "keras.layers.LSTM"
   ]
  }
 ],
 "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": "3.11.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}