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NeuPy is a Tensorflow based python library for prototyping and building neural networks

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Could Spare Evolutionary Training be implemented as a training method? From reading up on it, from my understanding it is somewhat similar to Adam, but I likely am grossly oversimplifying...

Thought I should include this material here in case a new base activation function layer would be useful to add out of the box: https://mc.ai/comparison-of-activation-functions-for-deep-learning-initial-winner-ftswish/

For example: http://neupy.com/modules/generated/neupy.layers.Convolution.html#neupy.layers.Convolution

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Current implementation doesn't work for cases when classifier has single output with sigmoid activation. Problem is only with negative classes.

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I know this is a probably a difficult task. But since we are switching to Tensorflow, maybe we could add something like https://github.com/cgorman/tensorflow-som in the future?

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