From a12923c6aece5b665ddaa61abb9c1178c850da92 Mon Sep 17 00:00:00 2001 From: ooo oo <106524776+ooooo-create@users.noreply.github.com> Date: Wed, 1 Nov 2023 02:28:32 +0800 Subject: [PATCH] [fix] fix the math style in `paddle.static.nn.batch_norm` (#58514) --- python/paddle/static/nn/common.py | 24 ++++++++++++------------ 1 file changed, 12 insertions(+), 12 deletions(-) diff --git a/python/paddle/static/nn/common.py b/python/paddle/static/nn/common.py index 79a21683b2f764..50e6821d078833 100644 --- a/python/paddle/static/nn/common.py +++ b/python/paddle/static/nn/common.py @@ -2803,19 +2803,19 @@ def batch_norm( Internal Covariate Shift `_ for more details. - :math:input is the input features over a mini-batch. + :math:`input` is the input features over a mini-batch. .. math:: - \\mu_{\\beta} &\\gets \\frac{1}{m} \\sum_{i=1}^{m} x_i \\qquad &//\\ - \ mini-batch\ mean \\\\ - \\sigma_{\\beta}^{2} &\\gets \\frac{1}{m} \\sum_{i=1}^{m}(x_i - \\ - \\mu_{\\beta})^2 \\qquad &//\ mini-batch\ variance \\\\ - \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\ - \\sigma_{\\beta}^{2} + \\epsilon}} \\qquad &//\ normalize \\\\ - y_i &\\gets \\gamma \\hat{x_i} + \\beta \\qquad &//\ scale\ and\ shift + \mu_{\beta} &\gets \frac{1}{m} \sum_{i=1}^{m} x_i \qquad &// + \ mini-batch\ mean \\ + \sigma_{\beta}^{2} &\gets \frac{1}{m} \sum_{i=1}^{m}(x_i - + \mu_{\\beta})^2 \qquad &//\ mini-batch\ variance \\ + \hat{x_i} &\gets \frac{x_i - \mu_\beta} {\sqrt{ + \sigma_{\beta}^{2} + \epsilon}} \qquad &//\ normalize \\ + y_i &\gets \gamma \hat{x_i} + \beta \qquad &//\ scale\ and\ shift - moving\_mean = moving\_mean * momentum + mini-batch\_mean * (1. - momentum) \\\\ + moving\_mean = moving\_mean * momentum + mini-batch\_mean * (1. - momentum) \\ moving\_var = moving\_var * momentum + mini-batch\_var * (1. - momentum) @@ -2829,9 +2829,9 @@ def batch_norm( .. math:: - \\hat{x_i} &\\gets \\frac{x_i - \\mu_\\beta} {\\sqrt{\\ - \\sigma_{\\beta}^{2} + \\epsilon}} \\\\ - y_i &\\gets \\gamma \\hat{x_i} + \\beta + \hat{x_i} &\gets \frac{x_i - \mu_\beta} {\sqrt{ + \sigma_{\beta}^{2} + \epsilon}} \\ + y_i &\gets \gamma \hat{x_i} + \beta Note: if build_strategy.sync_batch_norm=True, the batch_norm in network will use