Using TensorFlow for things other than machine learning

LaplacianTensorFlow provides a very convenient dataflow graph framework for not just machine learning applications but really anything where data goes through a number of processing stages. The great thing about using TensorFlow for this is that all the GPU and scaling capabilities are potentially available, along with a Python API for added convenience.

To test this out, I created a simple Python script to act as an image processor that can be inserted into a Jpeg video stream using MQTT as a way of moving the data around. The collection of scripts to generate, process and display the video can be found here. The script uses TensorFlow to shrink each frame in the stream by a factor of two (using average pooling) and then performing simple edge detection using a discrete Laplacian, implemented with a 2-D convolution. Jpeg encoding and decoding is also performed using TensorFlow functions.

The frame rate tops out at around 17 frames per second on my i7-2700K/GTX 970 machine (video source frame rate was 30 frames per second). I am guessing that there is a finite startup latency in TensorFlow – it’s no doubt highly inefficient to run the graph with one image at a time.

There’s no rocket science here and the functionality is trivial. However, it is interesting to think how else TensorFlow can be used. Given the incredible interest and the likelihood of dedicated hardware acceleration one day, there might be considerable value in mapping problems onto TensorFlow graphs.

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