BNLayer.py 文件源码

def __init__(self, incoming,
                 gamma=init.Uniform([0.95, 1.05]),
                 beta=init.Constant(0.),
                 nonlinearity=nonlinearities.rectify,
                 epsilon=0.001,
                 **kwargs):
        super(BatchNormalizationLayer, self).__init__(incoming, **kwargs)
        if nonlinearity is None:
            self.nonlinearity = nonlinearities.identity
        else:
            self.nonlinearity = nonlinearity

        self.num_units = int(numpy.prod(self.input_shape[1:]))
        self.gamma = self.add_param(gamma, (self.num_units,), name="BatchNormalizationLayer:gamma", regularizable=True,
                                    gamma=True, trainable=True)
        self.beta = self.add_param(beta, (self.num_units,), name="BatchNormalizationLayer:beta", regularizable=False)
        self.epsilon = epsilon

        self.mean_inference = theano.shared(
            numpy.zeros((1, self.num_units), dtype=theano.config.floatX),
            borrow=True,
            broadcastable=(True, False))
        self.mean_inference.name = "shared:mean"

        self.variance_inference = theano.shared(
            numpy.zeros((1, self.num_units), dtype=theano.config.floatX),
            borrow=True,
            broadcastable=(True, False))
        self.variance_inference.name = "shared:variance"