nn.py 文件源码

def __init__(self, incoming, num_kernels, dim_per_kernel=5, theta=lasagne.init.Normal(0.05),
                 log_weight_scale=lasagne.init.Constant(0.), b=lasagne.init.Constant(-1.), **kwargs):
        super(MinibatchLayer, self).__init__(incoming, **kwargs)
        self.num_kernels = num_kernels
        num_inputs = int(np.prod(self.input_shape[1:]))
        self.theta = self.add_param(theta, (num_inputs, num_kernels, dim_per_kernel), name="theta")
        self.log_weight_scale = self.add_param(log_weight_scale, (num_kernels, dim_per_kernel), name="log_weight_scale")
        self.W = self.theta * (T.exp(self.log_weight_scale)/T.sqrt(T.sum(T.square(self.theta),axis=0))).dimshuffle('x',0,1)
        self.b = self.add_param(b, (num_kernels,), name="b")