layers.py 文件源码

def __init__(self, incomings, coeffs=Normal(std=0.01, mean=1.0), cropping=None, **kwargs):
        super(AdaptiveElemwiseSumLayer, self).__init__(incomings, T.add,
                                                       cropping=cropping, **kwargs)
        '''
        if isinstance(coeffs, list):
            if len(coeffs) != len(incomings):
                raise ValueError("Mismatch: got %d coeffs for %d incomings" %
                                 (len(coeffs), len(incomings)))
        else:
            coeffs = [coeffs] * len(incomings)
        '''
        self.coeffs = []
        for i in range(len(incomings)):
            coeff = theano.shared(np.float32(1.0), 'adacoeff{}'.format(i))
            self.coeffs.append(self.add_param(coeff, coeff.shape, trainable=True, scaling_param=True))