{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"**Chapter 15 – Processing Sequences Using RNNs and CNNs**"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"_This notebook contains all the sample code and solutions to the exercises in chapter 15._"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\n",
" \n",
" ![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\") \n",
" | \n",
" \n",
" ![](\"https://kaggle.com/static/images/open-in-kaggle.svg\") \n",
" | \n",
"
"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "dFXIv9qNpKzt",
"tags": []
},
"source": [
"# Setup"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "8IPbJEmZpKzu"
},
"source": [
"This project requires Python 3.7 or above:"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"id": "TFSU3FCOpKzu"
},
"outputs": [],
"source": [
"import sys\n",
"\n",
"assert sys.version_info >= (3, 7)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "GJtVEqxfpKzw"
},
"source": [
"And TensorFlow ≥ 2.8:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "0Piq5se2pKzx"
},
"outputs": [],
"source": [
"from packaging import version\n",
"import tensorflow as tf\n",
"\n",
"assert version.parse(tf.__version__) >= version.parse(\"2.8.0\")"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "DDaDoLQTpKzx"
},
"source": [
"As we did in earlier chapters, let's define the default font sizes to make the figures prettier:"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "8d4TH3NbpKzx"
},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"plt.rc('font', size=14)\n",
"plt.rc('axes', labelsize=14, titlesize=14)\n",
"plt.rc('legend', fontsize=14)\n",
"plt.rc('xtick', labelsize=10)\n",
"plt.rc('ytick', labelsize=10)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "RcoUIRsvpKzy"
},
"source": [
"And let's create the `images/rnn` folder (if it doesn't already exist), and define the `save_fig()` function which is used through this notebook to save the figures in high-res for the book:"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "PQFH5Y9PpKzy"
},
"outputs": [],
"source": [
"from pathlib import Path\n",
"\n",
"IMAGES_PATH = Path() / \"images\" / \"rnn\"\n",
"IMAGES_PATH.mkdir(parents=True, exist_ok=True)\n",
"\n",
"def save_fig(fig_id, tight_layout=True, fig_extension=\"png\", resolution=300):\n",
" path = IMAGES_PATH / f\"{fig_id}.{fig_extension}\"\n",
" if tight_layout:\n",
" plt.tight_layout()\n",
" plt.savefig(path, format=fig_extension, dpi=resolution)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "YTsawKlapKzy"
},
"source": [
"This chapter can be very slow without a GPU, so let's make sure there's one, or else issue a warning:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "Ekxzo6pOpKzy"
},
"outputs": [],
"source": [
"if not tf.config.list_physical_devices('GPU'):\n",
" print(\"No GPU was detected. Neural nets can be very slow without a GPU.\")\n",
" if \"google.colab\" in sys.modules:\n",
" print(\"Go to Runtime > Change runtime and select a GPU hardware \"\n",
" \"accelerator.\")\n",
" if \"kaggle_secrets\" in sys.modules:\n",
" print(\"Go to Settings > Accelerator and select GPU.\")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Basic RNNs"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Let's download the ridership data from the ageron/data project. It originally comes from Chicago's Transit Authority, and was downloaded from the [Chicago's Data Portal](https://homl.info/ridership)."
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Downloading data from https://github.com/ageron/data/raw/main/ridership.tgz\n",
"114688/108512 [===============================] - 0s 0us/step\n",
"122880/108512 [=================================] - 0s 0us/step\n"
]
},
{
"data": {
"text/plain": [
"'./datasets/ridership.tgz'"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"tf.keras.utils.get_file(\n",
" \"ridership.tgz\",\n",
" \"https://github.com/ageron/data/raw/main/ridership.tgz\",\n",
" cache_dir=\".\",\n",
" extract=True\n",
")"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"from pathlib import Path\n",
"\n",
"path = Path(\"datasets/ridership/CTA_-_Ridership_-_Daily_Boarding_Totals.csv\")\n",
"df = pd.read_csv(path, parse_dates=[\"service_date\"])\n",
"df.columns = [\"date\", \"day_type\", \"bus\", \"rail\", \"total\"] # shorter names\n",
"df = df.sort_values(\"date\").set_index(\"date\")\n",
"df = df.drop(\"total\", axis=1) # no need for total, it's just bus + rail\n",
"df = df.drop_duplicates() # remove duplicated months (2011-10 and 2014-07)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
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