Create and train a PyTorch model for digit classification
Introduction
Prepare a PyTorch development environment
Create a PyTorch model for MNIST
About PyTorch model training
Perform training and save the model
Use the model for inference
Understand inference on Android
Create an Android application
Prepare Test Data
Run the Application
Optimizing neural network models in PyTorch
Create an optimized PyTorch model for MNIST
Run optimization
Update the Android application
Review
Next Steps
Create and train a PyTorch model for digit classification
Introduction
Prepare a PyTorch development environment
Create a PyTorch model for MNIST
About PyTorch model training
Perform training and save the model
Use the model for inference
Understand inference on Android
Create an Android application
Prepare Test Data
Run the Application
Optimizing neural network models in PyTorch
Create an optimized PyTorch model for MNIST
Run optimization
Update the Android application
Review
Next Steps
You are now ready to run the Android application. You can use an emulator or a physical device.
The screenshots below show an emulator.
To run the app in Android Studio using an emulator, follow these steps:
- Configure the Emulator:
- Go to Tools > Device Manager (or click the Device Manager icon on the toolbar).
- Click Create Device to set up a new virtual device (if you haven’t done so already).
- Choose a device model, such as Pixel 4, and click Next.
- Select a system image, such as Android 11, API level 30, and click Next.
- Review the settings and click Finish to create the emulator.
- Run the App:
- Make sure the emulator is selected in the device dropdown menu in the toolbar (next to the “Run” button).
- Click the Run button (a green triangle). Android Studio will build the app, install it on the emulator, and launch it.
- View the App on the Emulator: Once the app is installed, it will automatically open on the emulator screen, allowing you to interact with it as if it were running on a real device.
Once the application is started, click the Load Image button. It will load a randomly selected image. Then, click Run Inference to recognize the digit. The application will display the predicted label and the inference time as shown below:
In the next step you will learn how to further optimize the model.