custom_layers.py 文件源码

def get_output_for(self, input, deterministic=False, **kwargs):
        """
        Parameters
        ----------
        input : tensor
            output from the previous layer
        deterministic : bool
            If true dropout and scaling is disabled, see notes
        """
        if deterministic or self.p == 0:
            return input
        else:
            # Using theano constant to prevent upcasting
            one = T.constant(1)

            retain_prob = one - self.p
            if self.rescale:
                input /= retain_prob

            mask = _srng.binomial(input.shape[:2], p=retain_prob,
                                      dtype=theano.config.floatX)
            axes = [0, 1] + (['x'] * (input.ndim - 2))
            mask = mask.dimshuffle(*axes)

            return input * mask