Unless I'm mistaken it seems like theano.tensor.sum(theano.tensor.exp(-10 * ws)) just encourages weights to become more positive. Why not use a L2 weight penalty like theano.tensor.sum(w**2) ? So the full loss would become: crossentropy_categorical_1hot(coding_dist, true_dist) + sum((layer.w**2).sum () for layer in l_layers)
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