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fix BatchNormNd docs #30096

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Jan 6, 2021
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fix BatchNormNd docs #30096

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12 changes: 6 additions & 6 deletions python/paddle/nn/layer/norm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -649,7 +649,7 @@ class BatchNorm1D(_BatchNormBase):
r"""
Applies Batch Normalization over a 2D or 3D input (a mini-batch of 1D inputswith additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .

When track_running_stats = False, the :math:`\\mu_{\\beta}`
When use_global_stats = False, the :math:`\\mu_{\\beta}`
and :math:`\\sigma_{\\beta}^{2}` are the statistics of one mini-batch.
Calculated as follows:

Expand All @@ -660,7 +660,7 @@ class BatchNorm1D(_BatchNormBase):
\\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\
\\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\

When track_running_stats = True, the :math:`\\mu_{\\beta}`
When use_global_stats = True, the :math:`\\mu_{\\beta}`
and :math:`\\sigma_{\\beta}^{2}` are not the statistics of one mini-batch.
They are global or running statistics (moving_mean and moving_variance). It usually got from the
pre-trained model. Calculated as follows:
Expand Down Expand Up @@ -739,7 +739,7 @@ class BatchNorm2D(_BatchNormBase):
r"""
Applies Batch Normalization over a 4D input (a mini-batch of 2D inputswith additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .

When track_running_stats = False, the :math:`\\mu_{\\beta}`
When use_global_stats = False, the :math:`\\mu_{\\beta}`
and :math:`\\sigma_{\\beta}^{2}` are the statistics of one mini-batch.
Calculated as follows:

Expand All @@ -750,7 +750,7 @@ class BatchNorm2D(_BatchNormBase):
\\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\
\\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\

When track_running_stats = True, the :math:`\\mu_{\\beta}`
When use_global_stats = True, the :math:`\\mu_{\\beta}`
and :math:`\\sigma_{\\beta}^{2}` are not the statistics of one mini-batch.
They are global or running statistics (moving_mean and moving_variance). It usually got from the
pre-trained model. Calculated as follows:
Expand Down Expand Up @@ -827,7 +827,7 @@ class BatchNorm3D(_BatchNormBase):
r"""
Applies Batch Normalization over a 5D input (a mini-batch of 3D inputswith additional channel dimension) as described in the paper Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift .

When track_running_stats = False, the :math:`\\mu_{\\beta}`
When use_global_stats = False, the :math:`\\mu_{\\beta}`
and :math:`\\sigma_{\\beta}^{2}` are the statistics of one mini-batch.
Calculated as follows:

Expand All @@ -838,7 +838,7 @@ class BatchNorm3D(_BatchNormBase):
\\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\
\\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\

When track_running_stats = True, the :math:`\\mu_{\\beta}`
When use_global_stats = True, the :math:`\\mu_{\\beta}`
and :math:`\\sigma_{\\beta}^{2}` are not the statistics of one mini-batch.
They are global or running statistics (moving_mean and moving_variance). It usually got from the
pre-trained model. Calculated as follows:
Expand Down