message: Fix the formatting of BatchNormalization equations.
diff: @@ -144,23 +144,23 @@ class BatchNormalizationBase(Layer):
  Normalization equations:
  Consider the intermediate activations \(x\) of a mini-batch of size
  - \(m\):
  + \\(m\\):
  We can compute the mean and variance of the batch
  - \({\mu_B} = \frac{1}{m} \sum_{i=1}^{m} {x_i}\)
  + \\({\mu_B} = \frac{1}{m} \sum_{i=1}^{m} {x_i}\\)
  - \({\sigma_B^2} = \frac{1}{m} \sum_{i=1}^{m} ({x_i} - {\mu_B})^2\)
  + \\({\sigma_B^2} = \frac{1}{m} \sum_{i=1}^{m} ({x_i} - {\mu_B})^2\\)
  - and then compute a normalized \(x\), including a small factor
  - \({\epsilon}\) for numerical stability.
  + and then compute a normalized \\(x\\), including a small factor
  + \\({\epsilon}\\) for numerical stability.
  - \(\hat{x_i} = \frac{x_i - \mu_B}{\sqrt{\sigma_B^2 + \epsilon}}\)
  + \\(\hat{x_i} = \frac{x_i - \mu_B}{\sqrt{\sigma_B^2 + \epsilon}}\\)
  - And finally \(\hat{x}\) is linearly transformed by \({\gamma}\)
  - and \({\beta}\), which are learned parameters:
  + And finally \\(\hat{x}\) is linearly transformed by \({\gamma}\\)
  + and \\({\beta}\\), which are learned parameters:
  - \({y_i} = {\gamma * \hat{x_i} + \beta}\)
  + \\({y_i} = {\gamma * \hat{x_i} + \beta}\\)
  References:
  - [Batch Normalization: Accelerating Deep Network Training by Reducing
  