def layer_norm(x, w, b, e=1e-5):
sizes = x.get_output_shape()[1:]
u = auto.mean(x, len(sizes), True)
s = auto.mean(auto.square(x - u), len(sizes), True)
y = (x - u) / auto.sqrt(s + e)
y = y * w + b
return y
def _to_tensor(self):
input = self.model_inputs[0].zvalue
assert len(self.onnx_attr['axes']) == 1, "we only support axes with 1 elements for now"
axes = self.onnx_attr['axes'][0]
keepdims = True if self.onnx_attr['keepdims'] == 1 else False
return autograd.mean(input, axis=int(axes), keepDims=keepdims)