4. Sequences, Time Serirs and Prediction/C4W3_Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "727eea74",
   "metadata": {},
   "source": [
    "# Week 3: Using RNNs to predict time series\n",
    "\n",
    "Welcome! In the previous assignment you used a vanilla deep neural network to create forecasts for generated time series. This time you will be using Tensorflow's layers for processing sequence data such as Recurrent layers or LSTMs to see how these two approaches compare.\n",
    "\n",
    "Let's get started!"
   ]
  },
  {
   "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": "markdown",
   "id": "74720e74",
   "metadata": {},
   "source": [
    "## Generating the data\n",
    "\n",
    "The next cell includes a bunch of helper functions to generate and plot the time series:"
   ]
  },
  {
   "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": "markdown",
   "id": "f06d883a",
   "metadata": {},
   "source": [
    "You will be generating the same time series data as in last week's assignment.\n",
    "\n",
    "**Notice that this time all the generation is done within a function and global variables are saved within a dataclass. This is done to avoid using global scope as it was done in during the first week of the course.**\n",
    "\n",
    "If you haven't used dataclasses before, they are just Python classes that provide a convenient syntax for storing data. You can read more about them in the [docs](https://docs.python.org/3/library/dataclasses.html).\n",
    "\n"
   ]
  },
  {
   "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": "markdown",
   "id": "153e8341",
   "metadata": {},
   "source": [
    "## Processing the data\n",
    "\n",
    "Since you already coded the `train_val_split` and `windowed_dataset` functions during past week's assignments, this time they are provided for you:\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "61d80b19",
   "metadata": {},
   "outputs": [],
   "source": [
    "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",
    "\n",
    "# Split the dataset\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": [
    "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",
    "# Apply the transformation to the training set\n",
    "dataset = windowed_dataset(series_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f13c3746",
   "metadata": {},
   "source": [
    "## Defining the model architecture\n",
    "\n",
    "Now that you have a function that will process the data before it is fed into your neural network for training, it is time to define you layer architecture. Unlike previous weeks or courses in which you define your layers and compile the model in the same function, here you will first need to complete the `create_uncompiled_model` function below. \n",
    "\n",
    "This is done so you can reuse your model's layers for the learning rate adjusting and the actual training.\n",
    "\n",
    "Hint:\n",
    "- Fill in the `Lambda` layers at the beginning and end of the network with the correct lamda functions.\n",
    "- You should use `SimpleRNN` or `Bidirectional(LSTM)` as intermediate layers.\n",
    "- The last layer of the network (before the last `Lambda`) should be a `Dense` layer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "ded09736",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_uncompiled_model():\n",
    "\n",
    "    ### START CODE HERE\n",
    "    \n",
    "    model = tf.keras.models.Sequential([ \n",
    "        tf.keras.layers.Lambda(lambda x: tf.expand_dims(x, axis=-1), input_shape=[None]),\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",
    "    ### END CODE HERE\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "df7f5667",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your current architecture is compatible with the windowed dataset! :)\n"
     ]
    }
   ],
   "source": [
    "# Test your uncompiled model\n",
    "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": "markdown",
   "id": "871fcb6b",
   "metadata": {},
   "source": [
    "## Adjusting the learning rate - (Optional Exercise)\n",
    "\n",
    "As you saw in the lecture you can leverage Tensorflow's callbacks to dinamically vary the learning rate during training. This can be helpful to get a better sense of which learning rate better acommodates to the problem at hand.\n",
    "\n",
    "**Notice that this is only changing the learning rate during the training process to give you an idea of what a reasonable learning rate is and should not be confused with selecting the best learning rate, this is known as hyperparameter optimization and it is outside the scope of this course.**\n",
    "\n",
    "For the optimizers you can try out:\n",
    "- [`tf.keras.optimizers.Adam`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/Adam)\n",
    "- [`tf.keras.optimizers.SGD`](https://www.tensorflow.org/api_docs/python/tf/keras/optimizers/SGD) with a momentum of 0.9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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",
    "    \n",
    "    ### START CODE HERE\n",
    "    \n",
    "    # Select your optimizer\n",
    "    optimizer = tf.keras.optimizers.SGD(lr=1e-6, momentum=0.9)\n",
    "    \n",
    "    # Compile the model passing in the appropriate loss\n",
    "    model.compile(loss=tf.keras.losses.Huber(),\n",
    "                  optimizer=optimizer, \n",
    "                  metrics=[\"mae\"]) \n",
    "    \n",
    "    ### END CODE HERE\n",
    "    \n",
    "    history = model.fit(dataset, epochs=100, callbacks=[lr_schedule])\n",
    "    \n",
    "    return history"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1f0550ca",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "34/34 [==============================] - 3s 24ms/step - loss: 15.3377 - mae: 15.8286 - lr: 1.0000e-06\n",
      "Epoch 2/100\n",
      "34/34 [==============================] - 1s 19ms/step - loss: 5.4254 - mae: 5.8999 - lr: 1.1220e-06\n",
      "Epoch 3/100\n",
      "34/34 [==============================] - 1s 19ms/step - loss: 4.6105 - mae: 5.0764 - lr: 1.2589e-06\n",
      "Epoch 4/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 4.3123 - mae: 4.7766 - lr: 1.4125e-06\n",
      "Epoch 5/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.0758 - mae: 4.5427 - lr: 1.5849e-06\n",
      "Epoch 6/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.9884 - mae: 4.4555 - lr: 1.7783e-06\n",
      "Epoch 7/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.7692 - mae: 4.2372 - lr: 1.9953e-06\n",
      "Epoch 8/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.6189 - mae: 4.0889 - lr: 2.2387e-06\n",
      "Epoch 9/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.5341 - mae: 4.0040 - lr: 2.5119e-06\n",
      "Epoch 10/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 3.3786 - mae: 3.8463 - lr: 2.8184e-06\n",
      "Epoch 11/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.3069 - mae: 3.7745 - lr: 3.1623e-06\n",
      "Epoch 12/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.2238 - mae: 3.6870 - lr: 3.5481e-06\n",
      "Epoch 13/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.0878 - mae: 3.5553 - lr: 3.9811e-06\n",
      "Epoch 14/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 2.9859 - mae: 3.4486 - lr: 4.4668e-06\n",
      "Epoch 15/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.0391 - mae: 3.5037 - lr: 5.0119e-06\n",
      "Epoch 16/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 2.9416 - mae: 3.4092 - lr: 5.6234e-06\n",
      "Epoch 17/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 2.9032 - mae: 3.3662 - lr: 6.3096e-06\n",
      "Epoch 18/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 2.9409 - mae: 3.4095 - lr: 7.0795e-06\n",
      "Epoch 19/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.2524 - mae: 3.7231 - lr: 7.9433e-06\n",
      "Epoch 20/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 3.0886 - mae: 3.5594 - lr: 8.9125e-06\n",
      "Epoch 21/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 2.7462 - mae: 3.2082 - lr: 1.0000e-05\n",
      "Epoch 22/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 2.8374 - mae: 3.3001 - lr: 1.1220e-05\n",
      "Epoch 23/100\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 2.8871 - mae: 3.3534 - lr: 1.2589e-05\n",
      "Epoch 24/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.0763 - mae: 3.5407 - lr: 1.4125e-05\n",
      "Epoch 25/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 2.7592 - mae: 3.2201 - lr: 1.5849e-05\n",
      "Epoch 26/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 2.9797 - mae: 3.4439 - lr: 1.7783e-05\n",
      "Epoch 27/100\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 3.4425 - mae: 3.9170 - lr: 1.9953e-05\n",
      "Epoch 28/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.3031 - mae: 3.7753 - lr: 2.2387e-05\n",
      "Epoch 29/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.2070 - mae: 3.6769 - lr: 2.5119e-05\n",
      "Epoch 30/100\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 2.8981 - mae: 3.3692 - lr: 2.8184e-05\n",
      "Epoch 31/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.6503 - mae: 5.1310 - lr: 3.1623e-05\n",
      "Epoch 32/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.2824 - mae: 3.7535 - lr: 3.5481e-05\n",
      "Epoch 33/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.6586 - mae: 4.1324 - lr: 3.9811e-05\n",
      "Epoch 34/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.5553 - mae: 4.0282 - lr: 4.4668e-05\n",
      "Epoch 35/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.3532 - mae: 4.8342 - lr: 5.0119e-05\n",
      "Epoch 36/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 2.8920 - mae: 3.3525 - lr: 5.6234e-05\n",
      "Epoch 37/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 6.0628 - mae: 6.5486 - lr: 6.3096e-05\n",
      "Epoch 38/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.0801 - mae: 4.5525 - lr: 7.0795e-05\n",
      "Epoch 39/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 8.6578 - mae: 9.1439 - lr: 7.9433e-05\n",
      "Epoch 40/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 11.5626 - mae: 12.0565 - lr: 8.9125e-05\n",
      "Epoch 41/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 6.3295 - mae: 6.8175 - lr: 1.0000e-04\n",
      "Epoch 42/100\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 5.7910 - mae: 6.2748 - lr: 1.1220e-04\n",
      "Epoch 43/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 7.9882 - mae: 8.4778 - lr: 1.2589e-04\n",
      "Epoch 44/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 8.3751 - mae: 8.8635 - lr: 1.4125e-04\n",
      "Epoch 45/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 7.3434 - mae: 7.8353 - lr: 1.5849e-04\n",
      "Epoch 46/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 12.0321 - mae: 12.5267 - lr: 1.7783e-04\n",
      "Epoch 47/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 7.3756 - mae: 7.8606 - lr: 1.9953e-04\n",
      "Epoch 48/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.6992 - mae: 5.1787 - lr: 2.2387e-04\n",
      "Epoch 49/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.8659 - mae: 4.3411 - lr: 2.5119e-04\n",
      "Epoch 50/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 3.8323 - mae: 4.3026 - lr: 2.8184e-04\n",
      "Epoch 51/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.5729 - mae: 5.0549 - lr: 3.1623e-04\n",
      "Epoch 52/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 5.5871 - mae: 6.0684 - lr: 3.5481e-04\n",
      "Epoch 53/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 4.5686 - mae: 5.0427 - lr: 3.9811e-04\n",
      "Epoch 54/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 4.1060 - mae: 4.5824 - lr: 4.4668e-04\n",
      "Epoch 55/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 5.1004 - mae: 5.5814 - lr: 5.0119e-04\n",
      "Epoch 56/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 5.6340 - mae: 6.1183 - lr: 5.6234e-04\n",
      "Epoch 57/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 5.7722 - mae: 6.2561 - lr: 6.3096e-04\n",
      "Epoch 58/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 5.0715 - mae: 5.5471 - lr: 7.0795e-04\n",
      "Epoch 59/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 4.3015 - mae: 4.7772 - lr: 7.9433e-04\n",
      "Epoch 60/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 9.3904 - mae: 9.8784 - lr: 8.9125e-04\n",
      "Epoch 61/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 5.9558 - mae: 6.4329 - lr: 0.0010\n",
      "Epoch 62/100\n",
      "34/34 [==============================] - 1s 18ms/step - loss: 7.2462 - mae: 7.7300 - lr: 0.0011\n",
      "Epoch 63/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 14.2619 - mae: 14.7562 - lr: 0.0013\n",
      "Epoch 64/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 7.2498 - mae: 7.7344 - lr: 0.0014\n",
      "Epoch 65/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 7.7342 - mae: 8.2210 - lr: 0.0016\n",
      "Epoch 66/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 12.2043 - mae: 12.6919 - lr: 0.0018\n",
      "Epoch 67/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 11.7222 - mae: 12.2109 - lr: 0.0020\n",
      "Epoch 68/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 13.2247 - mae: 13.7162 - lr: 0.0022\n",
      "Epoch 69/100\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 14.5479 - mae: 15.0399 - lr: 0.0025\n",
      "Epoch 70/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 17.4596 - mae: 17.9528 - lr: 0.0028\n",
      "Epoch 71/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 19.2903 - mae: 19.7852 - lr: 0.0032\n",
      "Epoch 72/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 34.0415 - mae: 34.5388 - lr: 0.0035\n",
      "Epoch 73/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 30.3372 - mae: 30.8337 - lr: 0.0040\n",
      "Epoch 74/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 60.5412 - mae: 61.0388 - lr: 0.0045\n",
      "Epoch 75/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 29.7525 - mae: 30.2479 - lr: 0.0050\n",
      "Epoch 76/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 24.2824 - mae: 24.7799 - lr: 0.0056\n",
      "Epoch 77/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 32.8916 - mae: 33.3890 - lr: 0.0063\n",
      "Epoch 78/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 49.7256 - mae: 50.2238 - lr: 0.0071\n",
      "Epoch 79/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 41.9856 - mae: 42.4838 - lr: 0.0079\n",
      "Epoch 80/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 85.6473 - mae: 86.1473 - lr: 0.0089\n",
      "Epoch 81/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 96.0283 - mae: 96.5283 - lr: 0.0100\n",
      "Epoch 82/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 81.4594 - mae: 81.9589 - lr: 0.0112\n",
      "Epoch 83/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 118.9559 - mae: 119.4553 - lr: 0.0126\n",
      "Epoch 84/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 91.2979 - mae: 91.7968 - lr: 0.0141\n",
      "Epoch 85/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 77.1159 - mae: 77.6139 - lr: 0.0158\n",
      "Epoch 86/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 180.3801 - mae: 180.8783 - lr: 0.0178\n",
      "Epoch 87/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 256.6303 - mae: 257.1303 - lr: 0.0200\n",
      "Epoch 88/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 232.7661 - mae: 233.2661 - lr: 0.0224\n",
      "Epoch 89/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 108.1996 - mae: 108.6990 - lr: 0.0251\n",
      "Epoch 90/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 374.1383 - mae: 374.6378 - lr: 0.0282\n",
      "Epoch 91/100\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 316.9807 - mae: 317.4802 - lr: 0.0316\n",
      "Epoch 92/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 342.8324 - mae: 343.3324 - lr: 0.0355\n",
      "Epoch 93/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 383.1915 - mae: 383.6915 - lr: 0.0398\n",
      "Epoch 94/100\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 692.7543 - mae: 693.2543 - lr: 0.0447\n",
      "Epoch 95/100\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 753.5186 - mae: 754.0186 - lr: 0.0501\n",
      "Epoch 96/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 865.6870 - mae: 866.1862 - lr: 0.0562\n",
      "Epoch 97/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 879.6528 - mae: 880.1528 - lr: 0.0631\n",
      "Epoch 98/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 693.8512 - mae: 694.3512 - lr: 0.0708\n",
      "Epoch 99/100\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 660.7539 - mae: 661.2539 - lr: 0.0794\n",
      "Epoch 100/100\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 739.6763 - mae: 740.1763 - lr: 0.0891\n"
     ]
    }
   ],
   "source": [
    "# Run the training with dynamic LR\n",
    "lr_history = adjust_learning_rate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "d1977dae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1e-06, 1.0, 0.0, 30.0)"
      ]
     },
     "execution_count": 25,
     "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": [
    "# Plot the loss for every LR\n",
    "plt.semilogx(lr_history.history[\"lr\"], lr_history.history[\"loss\"])\n",
    "plt.axis([1e-6, 1, 0, 30])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1971fd3a",
   "metadata": {},
   "source": [
    "## Compiling the model\n",
    "\n",
    "Now that you have trained the model while varying the learning rate, it is time to do the actual training that will be used to forecast the time series. For this complete the `create_model` function below.\n",
    "\n",
    "Notice that you are reusing the architecture you defined in the `create_uncompiled_model` earlier. Now you only need to compile this model using the appropriate loss, optimizer (and learning rate).\n",
    "\n",
    "Hint:\n",
    "- The training should be really quick so if you notice that each epoch is taking more than a few seconds, consider trying a different architecture.\n",
    "\n",
    "\n",
    "- If after the first epoch you get an output like this: `loss: nan - mae: nan` it is very likely that your network is suffering from exploding gradients. This is a common problem if you used `SGD` as optimizer and set a learning rate that is too high. **If you encounter this problem consider lowering the learning rate or using Adam with the default learning rate.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "8766c169",
   "metadata": {},
   "outputs": [],
   "source": [
    "def create_model():\n",
    "\n",
    "    tf.random.set_seed(51)\n",
    "    \n",
    "    model = create_uncompiled_model()\n",
    "\n",
    "    ### START CODE HERE\n",
    "\n",
    "    model.compile(loss=\"mse\",\n",
    "                  optimizer=tf.keras.optimizers.SGD(lr=1e-8, momentum=0.9),\n",
    "                  metrics=[\"mae\"])  \n",
    "    \n",
    "    ### END CODE HERE\n",
    "\n",
    "    return model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "8cf3bc51",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "34/34 [==============================] - 4s 35ms/step - loss: 208.0979 - mae: 11.9987\n",
      "Epoch 2/50\n",
      "34/34 [==============================] - 1s 26ms/step - loss: 87.3049 - mae: 6.2821\n",
      "Epoch 3/50\n",
      "34/34 [==============================] - 1s 24ms/step - loss: 63.5173 - mae: 5.2124\n",
      "Epoch 4/50\n",
      "34/34 [==============================] - 1s 23ms/step - loss: 58.5890 - mae: 4.9160\n",
      "Epoch 5/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 57.4780 - mae: 4.8961 0s - loss: 56.7606 - mae: 4.88\n",
      "Epoch 6/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 56.8718 - mae: 4.8686\n",
      "Epoch 7/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 55.3730 - mae: 4.8102\n",
      "Epoch 8/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 54.8111 - mae: 4.7638\n",
      "Epoch 9/50\n",
      "34/34 [==============================] - 1s 23ms/step - loss: 54.0558 - mae: 4.7163\n",
      "Epoch 10/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 53.4040 - mae: 4.7200\n",
      "Epoch 11/50\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 52.6941 - mae: 4.6592\n",
      "Epoch 12/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 52.4487 - mae: 4.6880\n",
      "Epoch 13/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 52.0094 - mae: 4.6992\n",
      "Epoch 14/50\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 51.0428 - mae: 4.6009\n",
      "Epoch 15/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 50.6057 - mae: 4.5803\n",
      "Epoch 16/50\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 50.4814 - mae: 4.5568\n",
      "Epoch 17/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 49.7546 - mae: 4.5346\n",
      "Epoch 18/50\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 49.3778 - mae: 4.5169\n",
      "Epoch 19/50\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 49.0421 - mae: 4.5215\n",
      "Epoch 20/50\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 49.4050 - mae: 4.5259\n",
      "Epoch 21/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 48.0901 - mae: 4.4597\n",
      "Epoch 22/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 47.9634 - mae: 4.4803\n",
      "Epoch 23/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 47.5192 - mae: 4.4605\n",
      "Epoch 24/50\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 47.0014 - mae: 4.4129\n",
      "Epoch 25/50\n",
      "34/34 [==============================] - 1s 23ms/step - loss: 46.8753 - mae: 4.4124\n",
      "Epoch 26/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 46.2704 - mae: 4.3768\n",
      "Epoch 27/50\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 46.0098 - mae: 4.3559\n",
      "Epoch 28/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 45.6236 - mae: 4.3510\n",
      "Epoch 29/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 45.2593 - mae: 4.3326\n",
      "Epoch 30/50\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 45.2134 - mae: 4.3309\n",
      "Epoch 31/50\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 44.9578 - mae: 4.3451\n",
      "Epoch 32/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 44.3625 - mae: 4.2737\n",
      "Epoch 33/50\n",
      "34/34 [==============================] - 1s 22ms/step - loss: 44.1234 - mae: 4.2599\n",
      "Epoch 34/50\n",
      "34/34 [==============================] - 1s 21ms/step - loss: 44.2660 - mae: 4.2766\n",
      "Epoch 35/50\n",
      "34/34 [==============================] - 1s 20ms/step - loss: 43.8921 - mae: 4.2674\n",
      "Epoch 36/50\n",
      "34/34 [==============================] - 1s 19ms/step - loss: 43.5409 - mae: 4.2487\n",
      "Epoch 37/50\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 43.3760 - mae: 4.2425\n",
      "Epoch 38/50\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 42.8332 - mae: 4.2066\n",
      "Epoch 39/50\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 42.7791 - mae: 4.2036\n",
      "Epoch 40/50\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 42.6085 - mae: 4.2087\n",
      "Epoch 41/50\n",
      "34/34 [==============================] - 1s 15ms/step - loss: 42.2542 - mae: 4.1829\n",
      "Epoch 42/50\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 42.1472 - mae: 4.1836\n",
      "Epoch 43/50\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 42.1467 - mae: 4.1851\n",
      "Epoch 44/50\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 41.8770 - mae: 4.1698\n",
      "Epoch 45/50\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 41.5759 - mae: 4.1492\n",
      "Epoch 46/50\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 41.2441 - mae: 4.1350\n",
      "Epoch 47/50\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 41.2438 - mae: 4.1508\n",
      "Epoch 48/50\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 41.1652 - mae: 4.1444\n",
      "Epoch 49/50\n",
      "34/34 [==============================] - 1s 16ms/step - loss: 40.7686 - mae: 4.0913\n",
      "Epoch 50/50\n",
      "34/34 [==============================] - 1s 17ms/step - loss: 40.7003 - mae: 4.1169\n"
     ]
    }
   ],
   "source": [
    "# Save an instance of the model\n",
    "model = create_model()\n",
    "\n",
    "# Train it\n",
    "history = model.fit(dataset, epochs=50)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "96aeabb6",
   "metadata": {},
   "source": [
    "## Evaluating the forecast\n",
    "\n",
    "Now it is time to evaluate the performance of the forecast. For this you can use the `compute_metrics` function that you coded in a previous assignment:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "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": "markdown",
   "id": "5aa1e989",
   "metadata": {},
   "source": [
    "At this point only the model that will perform the forecast is ready but you still need to compute the actual forecast. \n",
    "\n",
    "## Faster model forecasts\n",
    "\n",
    "In the previous week you used a for loop to compute the forecasts for every point in the sequence. This approach is valid but there is a more efficient way of doing the same thing by using batches of data. The code to implement this is provided in the `model_forecast` below. Notice that the code is very similar to the one in the `windowed_dataset` function with the differences that:\n",
    "\n",
    "- The dataset is windowed using `window_size` rather than `window_size + 1`\n",
    "- No shuffle should be used\n",
    "- No need to split the data into features and labels\n",
    "- A model is used to predict batches of the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "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": 30,
   "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": "markdown",
   "id": "e212c8a9",
   "metadata": {},
   "source": [
    "**Expected Output:**\n",
    "\n",
    "A series similar to this one:\n",
    "\n",
    "<div>\n",
    "<img src=\"images/expected.png\" width=\"500\"/>\n",
    "</div>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c951006f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mse: 37.86, mae: 4.20 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": "markdown",
   "id": "66d1d7cd",
   "metadata": {},
   "source": [
    "**To pass this assignment your forecast should achieve an MAE of 4.5 or less.**\n",
    "\n",
    "- If your forecast didn't achieve this threshold try re-training your model with a different architecture (you will need to re-run both `create_uncompiled_model` and `create_model` functions) or tweaking the optimizer's parameters.\n",
    "\n",
    "\n",
    "- If your forecast did achieve this threshold run the following cell to save your model in a `tar` file which will be used for grading and after doing so, submit your assigment for grading.\n",
    "\n",
    "\n",
    "- This environment includes a dummy `SavedModel` directory which contains a dummy model trained for one epoch. **To replace this file with your actual model you need to run the next cell before submitting for grading.**\n",
    "\n",
    "\n",
    "- Unlike last week, this time the model is saved using the `SavedModel` format. This is done because the HDF5 format does not fully support `Lambda` layers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "c243363b",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:absl:Found untraced functions such as lstm_cell_34_layer_call_fn, lstm_cell_34_layer_call_and_return_conditional_losses, lstm_cell_35_layer_call_fn, lstm_cell_35_layer_call_and_return_conditional_losses, lstm_cell_34_layer_call_fn while saving (showing 5 of 10). 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 0x7f4c4c6b85e0> 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 0x7f4c2c672190> 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/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6afab213",
   "metadata": {},
   "source": [
    "**Congratulations on finishing this week's assignment!**\n",
    "\n",
    "You have successfully implemented a neural network capable of forecasting time series leveraging Tensorflow's layers for sequence modelling such as `RNNs` and `LSTMs`! **This resulted in a forecast that matches (or even surpasses) the one from last week while training for half of the epochs.**\n",
    "\n",
    "**Keep it up!**"
   ]
  }
 ],
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