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The next step is to train the autoencoder model. We call the. As for validation, we put images of 2s. Note that validation dataset will not be taken into account during backpropagation. I put it there so that we can see how the reconstruction errors between images of 1s and 2s diverge as training goes on. We can then plot the reconstruction error over epochs for both images of 1s good and 2s bad.


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It can be observed that error for 1s decreases over time while 2s stays high. This means that the autoencoder has learned to reconstruct good images that it has been trained with 1s , but not the bad ones 2s. The errors above are only point estimates. We need to see the whole distribution of reconstruction error in order to calculate the tolerance limits e. Part Average Testing Limits. From the chart below, we can see that indeed bad images have higher reconstruction error than good images. We can now calculate the Part Average Limit.

The resulting threshold is visualized below. We could visually see that images of 1s can be well reconstructed, while 2s are not. To further visualize how the decoder constructs images of 1s with different strokes and angles, we could look at the encoding produced in the bottleneck layer of the autoencoder model.


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We would like to see if there are any structures there. Since we have compressed the information into just a 2-neuron layer, we can easily represent each image as a 2-dimension vector and visualize all of them in a 2D plot. Perhaps the pattern looks more like a spectrum instead of cluster?

Indeed, we found that there are varying degrees of images of 1s in the embedding space. Going diagonally from the upper left to lower right quadrant, we see the various spectrum of angle.

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Meanwhile in the off-diagonal direction, we see thicker strokes as we get closer to the lower left quadrant. Nevertheless, I hope that this simple tutorial gives you a better insight of how one class learning and golden unit baselining with Part Average limit are quite similar in practice. I have provided a simple python code walkthrough in order to show aspects of one class learning that we usually analyze in practice. Disclaimer : Having worked with data from real factory floors, I can testify that the reality is much messier than this simplistic sample of image of 1s and 2s.

There are a lot of preprocessing that needs to be done. Moreover, not all of the data labels could be trusted — as these are annotated by humans, there could be errors due to inconsistency. Nonetheless, the fundamental concept stays the same. Sign in. Get started. Edward Elson Kosasih Follow. Towards Data Science Sharing concepts, ideas, and codes.

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