Problem Scenario
Papers have been replaced by digital documents for many reasons. However, we still see a lot of papers in our daily life. Machines do not have an ability to understand what has been written on those physical papers. Converting handwritten characters to digital characters has been a tough problem in the past (and probably now). We cannot efficiently process those physical documents with computers unless we can convert them to digital documents.
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In this use case, we train the CNN model on MNIST dataset that consists of 70,000 images containing handwritten digits. Each image is 28 pixels by 28 pixels and contains one handwritten digit. We train the model on 60,000 images and keep 10,000 images for testing the model.
The live demo is available at our Machine Learning Showcase.
Objectives
- Use Remote Python Script Snap from ML Core Snap Pack to deploy a Python script to train Neural Networks model on Iris flower dataset.
- Test the model with a sample.
- Use Remote Python Script Snap from ML Core Snap Pack to deploy a Python script to host the model and schedule an Ultra Task to provide API.
- Test the API.
- Develop a demo with HTML and Javascript.
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The Remote Python Script Snap downloads the MNIST dataset (we use Keras library to get the dataset), train CNN model, and evaluate the model. Than, we format the model into JSON with JSON Formatter Snap and save the model on SnapLogic File System (SLFS) using File Writer Snap.
Python Script
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Below is a piece of the Python script from the Remote Python Script Snap used in this pipeline. The code is modified from official Keras example.
There are 3 main functions: snaplogic_init, snaplogic_process, and snaplogic_final. The first function, snaplogic_init, will be executed before consuming documents from the upstream snap. The second function, snaplogic_process, will be called on each of the incoming document. The last function, snaplogic_final, will be processed after all incoming documents have been consumed by snaplogic_process. In this case, Remote Python Script Snap does not have an input view, so snaplogic_process will not be executed.
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This pipeline is scheduled as an Ultra Task to provide a REST API that is accessible by external applications. The core snaps are File Reader, JSON Parser, and Remote Python Script that are the same from the Model Testing pipeline. The rest are for authentication, parameter extraction, and Cross-Origin Resource Sharing (CORS) handling.
Scheduling Ultra Task
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