Summary and Schedule
This is a new lesson built with The Carpentries Workbench.
| Setup Instructions | Download files required for the lesson | |
| Duration: 00h 00m | 1. Introduction to Deep Learning |
What is machine learning and what is it used for? What is deep learning? How do I use a neural network for image classification? |
| Duration: 00h 10m | 2. Introduction to Image Data |
How much data do you need for Deep Learning? How do I prepare image data for use in a convolutional neural network (CNN)? How do I work with image data in python? Where can I find image data to train my model? :::::::::::::::::::::::::::::::::::::::::::::::: |
| Duration: 00h 22m | 3. Build a Convolutional Neural Network |
What is a (artificial) neural network (ANN)? How is a convolutional neural network (CNN) different from an ANN? What are the types of layers used to build a CNN? |
| Duration: 00h 34m | 4. Compile and Train (Fit) a Convolutional Neural Network |
How do you compile a convolutional neural network (CNN)? What is a loss function? What is an optimizer? How do you train (fit) a CNN? How do you evaluate a model during training? What is overfitting? |
| Duration: 00h 46m | 5. Evaluate a Convolutional Neural Network and Make Predictions (Classifications) |
How do you use a model to make a prediction? How do you measure model prediction accuracy? What can you do to improve model performance? What is a hyperparameter? |
| Duration: 00h 58m | 6. Share a Convolutional Neural Network and Next Steps |
How do I share my convolutional neural network (CNN)? Where can I find pre-trained models? Is Keras the best library to use? What is a GPU? What else can I do with a CNN? |
| Duration: 01h 10m | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.
This lesson is designed for Software Carpentry users who have completed Plotting and Programming in Python and want to jump straight into image classification. We recognize this jump is quite large and have done our best to provide the content and code to perform these types of analyses.
It uses the Anaconda package manager to install the required python packages, including the Spyder IDE.
These instructions are for setting up tensorflow in a CPU only environment.
Callout
Please note this lesson is designed to work with CPU only environments. This was an intentional decision to avoid the difficulties in setting up GPU environments. If you are an advanced user and choose to set up a GPU environment, you are on your own. We will not be able to troubleshoot any issues with GPU set up on the day of the workshop.
Software Setup
Install Python Using Anaconda
Python is a popular language for scientific computing, and a frequent choice for machine learning as well. Installing all of its scientific packages individually can be a bit difficult, however, so we recommend the installer Anaconda which includes most (but not all) of the software you need. Make sure you install the latest Python version 3.xx.
Also, please set up your python environment at least a day in advance of the workshop. If you encounter problems with the installation procedure, ask your workshop organizers via e-mail for assistance so you are ready to go as soon as the workshop begins.
Check out the Windows - Video tutorial or:
Open [https://www.anaconda.com/products/distribution] with your web browser.
Download the Python 3.xx installer for Windows.
Double-click the executable and install Python 3 using MOST of the default settings. The only exception is to check the Make Anaconda the default Python option.
Check out the Mac OS X - Video tutorial or:
Open [https://www.anaconda.com/products/distribution] with your web browser.
Download the Python 3 installer for Mac.
Install Python 3 using all of the defaults for installation.
Note the following installation steps require you to work from the shell. If you run into any difficulties, please request help before the workshop begins.
Open [https://www.anaconda.com/products/distribution] with your web browser.
Download the Python 3 installer for Linux.
-
Install Python 3 using all of the defaults for installation.
Open a terminal window.
Navigate to the folder where you downloaded the installer
Type
and press tab. The name of the file you just downloaded should appear.
Press enter.
Follow the text-only prompts. When the license agreement appears (a colon will be present at the bottom of the screen) hold the down arrow until the bottom of the text. Type
yesand press enter to approve the license. Press enter again to approve the default location for the files. Typeyesand press enter to prepend Anaconda to yourPATH(this makes the Anaconda distribution the default Python).
-
You may wish to initialise conda, so it configures the path and sets the required variables. To do this, execute the following command (if you have installed anaconda in your home directory):
$ ./anaconda3/bin/conda init
Download the scripts, files, and model outputs
Download workshop files
Download the scripts, files, and model outputs.
Create a project folder on your local device for this course and move the downloaded materials to the project folder. Extract all of the download contents here. Check your folder and file structures looks like this:
You can move the downloaded workshop zipped file to your home directory and unzip it using the unzip command.
For example:
[ace@rocky8-cnn-test ~]$ mv ./Downloads/intro-image-classification-cnn.zip .
[ace@rocky8-cnn-test ~]$ unzip intro-image-classification-cnn.zip
Archive: intro-image-classification-cnn.zip
creating: intro-image-classification-cnn/data/
inflating: intro-image-classification-cnn/data/Jabiru_TGS.JPG
inflating: intro-image-classification-cnn/data/model_dropout.keras
inflating: intro-image-classification-cnn/data/model_intro.keras
creating: intro-image-classification-cnn/files/
inflating: intro-image-classification-cnn/files/cnn_workshop_environment.yml
inflating: intro-image-classification-cnn/files/cnn_workshop_MACOS_environment.yml
creating: intro-image-classification-cnn/scripts/
inflating: intro-image-classification-cnn/scripts/01_intro.py
inflating: intro-image-classification-cnn/scripts/02_image.py
inflating: intro-image-classification-cnn/scripts/03_build_intro_model.py
inflating: intro-image-classification-cnn/scripts/04_fit_intro_model.py
inflating: intro-image-classification-cnn/scripts/04b_build_fit_dropout_model.py
inflating: intro-image-classification-cnn/scripts/05_predict_ep_best_model.py
inflating: intro-image-classification-cnn/scripts/05_step_9_tune_activation.py
inflating: intro-image-classification-cnn/scripts/05_step_9_tune_dropout.py
inflating: intro-image-classification-cnn/scripts/05_step_9_tune_optimizer.py
creating: intro-image-classification-cnn/scripts/fit_outputs/
[ace@rocky8-cnn-test ~]$ Callout
In Spyder, when you execute a script in its entirety (Run File F5), the working directory will automatically be set to the directory that contains that script file.
For this lesson, after launching Spyder, make sure to set the working directory to the ‘scripts’ folder included in the download. This will help to ensure that all of the scripts we provide run as intended.
Install the required packages
Conda is the package management system associated with Anaconda and runs on Windows, macOS and Linux.
Conda should already be available in your system once you installed Anaconda successfully. Conda thus works regardless of the operating system. Make sure you have an up-to-date version of Conda running. See these instructions for updating Conda if required.
Using Conda to install packages
Conda is the package management system associated with Anaconda and runs on Windows, macOS and Linux.
Conda should already be available in your system once you installed Anaconda successfully. Conda thus works regardless of the operating system. Make sure you have an up-to-date version of Conda running. See these instructions for updating Conda if required.
The easiest way to create a conda environment for this lesson is to use the Anaconda Prompt. You can search for “anaconda prompt” using the Windows search function (Windows Logo Key).
A terminal window will open with the title ‘Anaconda Prompt’:
Note the notation of the prompt inside the terminal window. The name inside the parentheses refers to which conda environment you are working inside of, and ‘base’ is the name given to the default environment included with every Anaconda distribution.
To create a new environment for this lesson, the command starts with
the conda keywords conda create. This command can be
followed by a name for the new environment and the package(s) to install
but to make things easier, inside the script download folder, we have
given you an environment.yml file to use instead. (See download link
below if you haven’t already.)
(base) C:\Users\Lab> conda env create --file cnn_workshop_environment.ymlIf the yml is not in your current directory, you can specify the full path to the file, eg:
(base) C:\Users\Lab> conda env create --file C:\Users\Lab\intro-image-classification-cnn\files\cnn_workshop_environment.ymlBe patient because it might take a while (15-20 min) for conda to work out all of the dependencies.
After the environment is created we tell Anaconda to use the new
environment with the conda keywords conda activate followed
by the environment name:
(base) C:\Users\Lab> conda activate cnn_workshop
(cnn_workshop) C:\Users\Lab>You will know you are in the right environment because the prompt changes from (base) to (cnn_workshop).
On the Mac, there is no Anaconda Prompt application, thus once the anaconda installation has been completed, simply start a terminal session and the session should look like:
Note the notation of the prompt inside the terminal window. The name inside the parentheses refers to which conda environment you are working inside of, and ‘base’ is the name given to the default environment included with every Anaconda distribution.
To create a new environment for this lesson, the command starts with
the conda keywords conda create. This command can be
followed by a name for the new environment and the package(s) to
install. To make things easier, inside the unziped
intro-image-classification-cnn folder, we have given you an
environment.yml file to use instead. (See download link above if you
haven’t already.)
(base) Mac$ conda env create --file cnn_workshop_environment.ymlIf the yml is not in your current directory, you can specify the full path to the file, eg:
(base) Mac$ conda env create --file intro-image-classification-cnn/files/cnn_workshop_environment.yml
Be patient because it might take a while (15-20 min) for conda to work out all of the dependencies.
If it installs correctly, you should get the following output on the screen.
done
#
# To activate this environment, use
#
# $ conda activate cnn_workshop
#
# To deactivate an active environment, use
#
# $ conda deactivate
After the environment is created we tell Anaconda to use the new
environment with the conda keywords conda activate followed
by the environment name:
(base) Mac:cnn-workshop ace$ conda activate cnn_workshop
(cnn_workshop) Mac:cnn-workshop ace$ You will know you are in the right environment because the prompt changes from (base) to (cnn_workshop).
Macos - Silicon (M1 and M2) - This sections needs to be tested and confirmed
After creating the MACOS environment with the yml, M1 and M2 computers (Apple Silicon) require an additional package to be installed.
TODO check if this is because of the integrated GPU and if so, can Intel version also use the graphics card and how?
(cnn_workshop_macos) C:\Users\Lab> pip install tensorflow-metalWithin Linux, there is no Anaconda Prompt application, thus once the anaconda installation has been completed, simply start a terminal session.
To create a new environment for this lesson, the command starts with
the conda keywords conda create. This command can be
followed by a name for the new environment and the package(s) to
install. To make things easier, inside the unziped
intro-image-classification-cnn folder, we have given you an
environment.yml file to use instead. (See download link above if you
haven’t already.)
$ conda env create --file cnn_workshop_environment.ymlIf the yml is not in your current directory, you can specify the full path to the file, eg:
$ conda env create --file intro-image-classification-cnn/files/cnn_workshop_environment.ymlBe patient because it might take a while (15-20 min) for conda to work out all of the dependencies.
Note, in the screen shots below, I haven’t initilised conda, so I had to give it the path to where anaconda was installed.
If it installs correctly, you should get the following output on the screen.
done
#
# To activate this environment, use
#
# $ conda activate cnn_workshop
#
# To deactivate an active environment, use
#
# $ conda deactivateNote you will need to initialise conda to be able to activate the conda enviroment.
Additonally, you will need to install scikeras and tensorflow within the conda environment to get everything working.
To do this, issue the following commands:
$ conda activate cnn_workshop
(base) $ pip install scikeras tensorflowStart Spyder
We teach this lesson using Python in Spyder (Scientific Python Development Environment), a free integrated development environment (IDE) included with Anaconda. Editing, interactive testing, debugging, and introspection tools are all included in Spyder.
To start Spyder, type the command spyder, making sure
you are still in the workshop environment:
(cnn_workshop) C:\Users\Lab> spyder
Callout
If you are using Linux, you might need to install qt5-qtbase and qt5-qtbase-gui for spyder to work.
On the Rocky 8 Distro, this can be done by issuing the command (as root):
yum install qt5-qtbase qt5-qtbase-guiCheck your setup
To check that all packages installed correctly, go to the interactive
IPython Console in Spyder (lower right hand side panel) and
type in the following commands:
PYTHON
import matplotlib
print('matplotlib version: ', matplotlib.__version__)
import numpy
print('numpy version: ', numpy.__version__)
import pandas
print('pandas version: ', pandas.__version__)
import seaborn
print('seaborn version: ', seaborn.__version__)
import sklearn
print('sklearn version: ', sklearn.__version__)
import scikeras
print('scikeras version: ', scikeras.__version__)
import tensorflow
print('Tensorflow version: ', tensorflow.__version__)Your package versions may vary from the screenshot below but this is similar to what your output will look like. The important thing is that there are no errors.
Set the working directory
There are a couple different ways to set the working directory in Spyder. The easiest way is to use the folder icon found on the upper right hand side of the toolbar. Click the folder icon and navigate to ‘…/intro-image-classification-cnn/scripts’ where ‘…’ is your project folder. Verify you are in the right place by selecting the ‘Files’ pane just below the toolbar and verify its contents.
Get the data
This lesson uses the CIFAR-10 image dataset that comes prepackaged with Keras. There are no additional steps needed to access the data.