Infer MNIST Stream UI
This graph applies a neuronal network based on the tensorflow MNIST example below on a stream of images of handwritten numbers. It fetches the previously trained neuronal network model from the Data Pipeline repository
Tensorflow MNIST Example
.

The graph can be separated into three main components:
- Test image generation pipeline: Reads the images after generating and sends them to the evaluation operator.
- Recognition pipeline: Loads a trained model, evaluates the images via tensorflow API calls and generates a json result output.
- Stream UI pipeline: The Stream Recognition UI displays the json result and the image.
Prerequisites
- (Only if run in local mode) Operators in this graph use the following
libraries:
-
Python 2.7+
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TensorFlow 1.7.0
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SciPy 1.0+ & Pillow 5.0+
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Configure and Run the Graph
Follow the steps below to run the training example from the Data Pipeline UI:
- In the left panel, select the Graphs tab and navigate to com.sap.ml.tensorflow.evaluateMnistUI.
- In the tool bar, select Run (play button).
- The Status panel indicates if the graph is running.
- Open the Terminal UI to see the accuracy and model save progress.