{
"nbformat": 4,
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"metadata": {
"colab": {
"name": "Notebook_5_CNNs_from_scratch",
"provenance": [],
"collapsed_sections": []
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "mY5WOZWrFI91",
"colab_type": "text"
},
"source": [
"# Neural Networks for Data Science Applications\n",
"## Lab session 5: Building CNNs from scratch\n",
"\n",
"**Contents of the lab session:**\n",
"+ Build flexible image pipelines in TF using tf.data.\n",
"+ Defining a CNN architecture from scratch.\n",
"+ Make code faster using `tf.function`."
]
},
{
"cell_type": "code",
"metadata": {
"id": "rnT0_RqFjcJr",
"colab_type": "code",
"outputId": "b5355bea-fe51-4eb5-865e-0cf9b740f993",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 156
}
},
"source": [
"# Remember to enable a GPU on Colab by:\n",
"# Runtime >> Change runtime type >> Hardware accelerator (before starting the VM).\n",
"!pip install -q tensorflow-gpu==2.0.0"
],
"execution_count": 1,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[K |████████████████████████████████| 380.8MB 44kB/s \n",
"\u001b[K |████████████████████████████████| 3.8MB 44.3MB/s \n",
"\u001b[K |████████████████████████████████| 450kB 45.4MB/s \n",
"\u001b[K |████████████████████████████████| 81kB 8.8MB/s \n",
"\u001b[31mERROR: tensorflow 1.15.0 has requirement tensorboard<1.16.0,>=1.15.0, but you'll have tensorboard 2.0.1 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: tensorflow 1.15.0 has requirement tensorflow-estimator==1.15.1, but you'll have tensorflow-estimator 2.0.1 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: tensorboard 2.0.1 has requirement grpcio>=1.24.3, but you'll have grpcio 1.15.0 which is incompatible.\u001b[0m\n",
"\u001b[31mERROR: google-colab 1.0.0 has requirement google-auth~=1.4.0, but you'll have google-auth 1.7.1 which is incompatible.\u001b[0m\n",
"\u001b[?25h"
],
"name": "stdout"
}
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "u3xJbtVsFsOj",
"colab_type": "text"
},
"source": [
"## About the task for this lab session"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "ANUF1kb7FjSZ",
"colab_type": "text"
},
"source": [
"The data can be downloaded as **Dataset 1** from here:\n",
"https://github.com/SullyChen/driving-datasets\n",
"\n",
"Direct Drive link:\n",
"https://drive.google.com/file/d/0B-KJCaaF7elleG1RbzVPZWV4Tlk/view?usp=sharing\n",
"\n",
"For each image (taken from a front-facing camera in a car), we want to predict the steering direction.)"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "nEuwwee9F5DR",
"colab_type": "text"
},
"source": [
"### Step 0 (ONLY COLAB): Load the Drive file\n",
"This part is only needed in a Colab environment for downloading the Drive file.\n",
"\n",
"*Note*: the download code can be found in Colab from the left menu, under Code Snippets."
]
},
{
"cell_type": "code",
"metadata": {
"id": "Xilk9BqHl5KG",
"colab_type": "code",
"colab": {}
},
"source": [
"# After this, restart the runtime to avoid authentication issues\n",
"!pip install -q -U PyDrive"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "goMDxq-hkNpP",
"colab_type": "code",
"colab": {}
},
"source": [
"# Import PyDrive and associated libraries.\n",
"# This only needs to be done once per notebook.\n",
"from pydrive.auth import GoogleAuth\n",
"from pydrive.drive import GoogleDrive\n",
"from google.colab import auth\n",
"from oauth2client.client import GoogleCredentials\n",
"\n",
"# Authenticate and create the PyDrive client.\n",
"# This only needs to be done once per notebook.\n",
"auth.authenticate_user()\n",
"gauth = GoogleAuth()\n",
"gauth.credentials = GoogleCredentials.get_application_default()\n",
"drive = GoogleDrive(gauth)"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "8O3QToxPkjbn",
"colab_type": "code",
"colab": {}
},
"source": [
"# Download the file based on its file ID.\n",
"# https://drive.google.com/file/d/0B-KJCaaF7elleG1RbzVPZWV4Tlk/view\n",
"file_id = '0B-KJCaaF7elleG1RbzVPZWV4Tlk'\n",
"downloaded = drive.CreateFile({'id': file_id})"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "LpjGMw7ioNfh",
"colab_type": "code",
"colab": {}
},
"source": [
"downloaded.GetContentFile('driving_dataset.zip')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "96X6DCWqpFFz",
"colab_type": "code",
"colab": {}
},
"source": [
"!unzip -q driving_dataset.zip"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "5AS0D0WtpY4m",
"colab_type": "code",
"colab": {}
},
"source": [
"# Explore the content! (Also from the left menu.)\n",
"# You can see that image information is provided in a single .txt file,\n",
"# making difficult the use of the ImageDataGenerator from tf.keras. \n",
"# For this reason, we'll make a more flexible pipeline with tf.data.\n",
"!ls driving_dataset"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "markdown",
"metadata": {
"id": "cy5FuAagGwpl",
"colab_type": "text"
},
"source": [
"### Step 1: Load the CSV file"
]
},
{
"cell_type": "code",
"metadata": {
"id": "XhRtM-rMp00M",
"colab_type": "code",
"colab": {}
},
"source": [
"import pandas as pd\n",
"files = pd.read_csv('driving_dataset/data.txt', \n",
" header=None, sep=' ')"
],
"execution_count": 0,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "A19KBQmOqEGj",
"colab_type": "code",
"outputId": "f6bef716-0686-4600-9d98-85da3459acf2",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 202
}
},
"source": [
"# First column is the file name, second column is the steering angle\n",
"files.head()"
],
"execution_count": 8,
"outputs": [
{
"output_type": "execute_result",
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