[GitHub] [beam] AnandInguva commented on a diff in pull request #21887: Add README documentation for scikit-learn MNIST example

[GitBox <gi...@apache.org>]

AnandInguva commented on code in PR #21887:
URL: https://github.com/apache/beam/pull/21887#discussion_r898117591


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sdks/python/apache_beam/examples/inference/README.md:
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@@ -235,3 +235,54 @@ He looked up and saw the sun and stars .;moon
 Each line has data separated by a semicolon ";".
 The first item is the sentence with the last word masked. The second item
 is the word that the model predicts for the mask.
+
+---
+## MNITST digit classification
+[`sklearn_mnist_classification.py`](./sklearn_mnist_classification.py) contains
+an implementation for a RunInference pipeline that performs image classification on handwritten digits from the [MNIST](https://en.wikipedia.org/wiki/MNIST_database) database.
+
+The pipeline reads rows of pixels corresponding to a digit, performs basic preprocessing, passes the pixels to the Scikit-learn implementation of RunInference, and then writes the predictions to a text file.
+
+### Dataset and model for language modeling
+- **Required**: A path to a file called `INPUT` that contains label and pixels to
+feed into the model. Each row should have elements that are comma-separated. The first element is the label. All subsuequent values are pixels from pixel0 to pixel784. It should look something like this:

Review Comment:
   It can be of any length.  the one you mentioned is of 28 x 28 image flattened. It could be 32 x 32 or 64 x 64 flattened as well. 
   
   We can say
   
   `All subsuequent elements would be pixel values.`



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