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The spatial relationships that you describe would correspond to features, and it's not clear that you need to use a neural network for detecting or discovering these features since you have just described them. Could you instead define a feature extractor that detects the correct patterns and returns you a vector of counts of feature occurrences across the ...


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There are two basic problems with your code: The functions sin(t) and cos(t) (both in numpy and tensorflow) take radians as inputs. Seeing Constant(90) in your code, and the learning rate of 1. I'm guessing that you assume that it is in degrees - that's incorrect. In your training data y_ta is not a rotation of y_in: y_in = np.array([np.sin(t), np.cos(t)])....


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