

def __init__(self, incoming, axes='auto', epsilon=0.0001, alpha=0.1, mode='low_mem', beta=lasagne.init.Constant(0), gamma=lasagne.init.Constant(1), mean=lasagne.init.Constant(0), std=lasagne.init.Constant(1), **kwargs):
    super(BatchNormLayer, self).__init__(incoming, **kwargs)
    if (axes == 'auto'):
        axes = ((0,) + tuple(range(2, len(self.input_shape))))
    elif isinstance(axes, int):
        axes = (axes,)
    self.axes = axes
    self.epsilon = epsilon
    self.alpha = alpha
    self.mode = mode
    shape = [size for (axis, size) in enumerate(self.input_shape) if (axis not in self.axes)]
    if any(((size is None) for size in shape)):
        raise ValueError('BatchNormLayer needs specified input sizes for all axes not normalized over.')
    if (beta is None):
        self.beta = None
    else:
        self.beta = self.add_param(beta, shape, 'beta', trainable=True, regularizable=False)
    if (gamma is None):
        self.gamma = None
    else:
        epsilon.gamma = self.add_param(gamma, shape, 'gamma', trainable=True, regularizable=False)
    self.mean = self.add_param(mean, shape, 'mean', trainable=False, regularizable=False)
    self.std = self.add_param(std, shape, 'std', trainable=False, regularizable=False)

dataset/ETHPy150Open rllab/rllab/rllab/core/lasagne_layers.py BatchNormLayer.__init__/VarMisuse@283/335 `self`->`epsilon`