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NormLayer, axis option #843

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Dec 7, 2021
Merged

NormLayer, axis option #843

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9 changes: 7 additions & 2 deletions returnn/tf/layers/basic.py
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Original file line number Diff line number Diff line change
Expand Up @@ -817,16 +817,21 @@ class NormLayer(_ConcatInputLayer):
"""
layer_class = "norm"

def __init__(self, axes, param_shape=NotSpecified, scale=True, bias=True, epsilon=1e-6, **kwargs):
def __init__(self, axis=NotSpecified, axes=NotSpecified,
param_shape=NotSpecified, scale=True, bias=True, epsilon=1e-6, **kwargs):
"""
:param Dim|str|list[Dim|str] axes: axes over which the mean and variance are computed, e.g. "F" or "TF"
:param Dim|str|list[Dim|str] axis: axis or axes over which the mean and variance are computed, e.g. "F" or "TF"
:param Dim|str|list[Dim|str] axes: axis or axes over which the mean and variance are computed, e.g. "F" or "TF"
:param Dim|str|list[Dim|str]|tuple[Dim|str] param_shape: shape of the scale and bias parameters.
You can also refer to (static) axes of the input, such as the feature-dim.
This is also the default, i.e. a param-shape of [F], independent of the axes to normalize over.
:param bool scale: add trainable scale parameters
:param bool bias: add trainable bias parameters
:param float epsilon: epsilon for numerical stability
"""
if axis is not NotSpecified:
assert axes is NotSpecified
axes = axis
super(NormLayer, self).__init__(**kwargs)
assert not self.input_data.sparse
x = self.input_data.placeholder
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