galaxy challenge constraints layer

gistfile1.py

class GalaxyDivNormLayer(nntools.layers.Layer):
    """
    rectification + divisive normalization
    """
    def __init__(self, input_layer):
        super(GalaxyDivNormLayer, self).__init__(input_layer)

        self.question_slices = [slice(0, 3), slice(3, 5), slice(5, 7), slice(7, 9), slice(9, 13), slice(13, 15),
                                slice(15, 18), slice(18, 25), slice(25, 28), slice(28, 31), slice(31, 37)]

        self.normalisation_mask = theano.shared(self.generate_normalisation_mask())
        # self.scaling_mask = theano.shared(self.generate_scaling_mask())

        # sequence of scaling steps to be undertaken.
        # First element is a slice indicating the values to be scaled. Second element is an index indicating the scale factor.
        # these have to happen IN ORDER else it doesn't work correctly.
        self.scaling_sequence = [
            (slice(3, 5), 1), # I: rescale Q2 by A1.2
            (slice(5, 13), 4), # II: rescale Q3, Q4, Q5 by A2.2
            (slice(15, 18), 0), # III: rescale Q7 by A1.1
            (slice(18, 25), 13), # IV: rescale Q8 by A6.1
            (slice(25, 28), 3), # V: rescale Q9 by A2.1
            (slice(28, 37), 7), # VI: rescale Q10, Q11 by A4.1
        ]

    def generate_normalisation_mask(self):
        """
        when the clipped input is multiplied by the normalisation mask, the normalisation denominators are generated.
        So then we can just divide the input by the normalisation constants (elementwise).
        """
        mask = np.zeros((37, 37), dtype=theano.config.floatX)
        for s in self.question_slices:
            mask[s, s] = 1.0
        return mask

    def get_output_for(self, input, normalize=True, *args, **kwargs):
        """
        Set normalize to False for the first few iterations to find a good region of
        parameter space. Normalization tends to complicate the initial steps.
        """
        if normalize:
            return self.weighted_answer_probabilities(input)
        else:
            input_clipped = T.clip(input, 0, 1) # clip on both sides,
            # any predictions over 1.0 are going to get normalised away anyway.
            return input_clipped

    def answer_probabilities(self, input):
        """
        normalise the answer groups for each question.
        """
        input_clipped = T.maximum(input, 0)
        normalization_denoms = T.dot(input_clipped, self.normalisation_mask) + 1e-12 # small constant to prevent division by 0
        input_normalized = input_clipped / normalization_denoms
        return input_normalized

    def weighted_answer_probabilities(self, input):
        probs = self.answer_probabilities(input)
        # go through the rescaling sequence in order (6 steps)
        for probs_slice, scale_idx in self.scaling_sequence:
            probs = T.set_subtensor(probs[:, probs_slice], probs[:, probs_slice] * probs[:, scale_idx].dimshuffle(0, 'x'))
        return probs