.. batch_norm_inference.rst: ################## BatchNormInference ################## .. code-block:: cpp BatchNormInference // Adjust input for mean and variance Description =========== Inputs ------ +---------------------+-------------------------+------------------------------+ | Name | Element Type | Shape | +=====================+=========================+==============================+ | input | real | :math:(\bullet, C, \ldots) | +---------------------+-------------------------+------------------------------+ | gamma | same as input | :math:(C) | +---------------------+-------------------------+------------------------------+ | beta | same as input | :math:(C) | +---------------------+-------------------------+------------------------------+ | mean | same as input | :math:(C) | +---------------------+-------------------------+------------------------------+ | variances | same as input | :math:(C) | +---------------------+-------------------------+------------------------------+ Attributes ---------- +------------------+--------------------+--------------------------------------------------------+ | Name | Type | Notes | +==================+====================+========================================================+ | epsilon | double | Small bias added to variance to avoid division by 0. | +------------------+--------------------+--------------------------------------------------------+ Outputs ------- +---------------------+-------------------------+-----------------------------+ | Name | Element Type | Shape | +=====================+=========================+=============================+ | normalized | same as gamma | Same as input | +---------------------+-------------------------+-----------------------------+ Mathematical Definition ======================= The axes of the input fall into two categories: positional and channel, with channel being axis 1. For each position, there are :math:C channel values, each normalized independently. Normalization of a channel sample is controlled by two values: * the mean :math:\mu, and * the variance :math:\sigma^2; and by two scaling attributes: :math:\gamma and :math:\beta. .. math:: \mathtt{normalized}_{\bullet, c, \ldots} = \frac{\mathtt{input}_{\bullet, c, \ldots}-\mu_c}{\sqrt{\sigma^2_c+\epsilon}}\gamma_c+\beta_c C++ Interface ============== .. doxygenclass:: ngraph::op::BatchNormInference :project: ngraph :members: