tf.compat.v1.layers.BatchNormalization

Batch Normalization layer from (Ioffe et al., 2015).

Inherits From: BatchNormalization, Layer, Layer, Module

Keras APIs handle BatchNormalization updates to the moving_mean and moving_variance as part of their fit() and evaluate() loops. However, if a custom training loop is used with an instance of Model, these updates need to be explicitly included. Here's a simple example of how it can be done:

  # model is an instance of Model that contains BatchNormalization layer.
  update_ops = model.get_updates_for(None) + model.get_updates_for(features)
  train_op = optimizer.minimize(loss)
  train_op = tf.group([train_op, update_ops])

axis An int or list of int, the axis or axes that should be normalized, typically the features axis/axes. For instance, after a Conv2D layer with data_format="channels_first", set axis=1. If a list of axes is provided, each axis in axis will be normalized simultaneously. Default is -1 which uses the last axis. Note: when using multi-axis batch norm, the beta, gamma, moving_mean, and moving_variance variables are the same rank as the input Tensor, with dimension size 1 in all reduced (non-axis) dimensions).
momentum Momentum for the moving average.
epsilon Small float added to variance to avoid dividing by zero.
center If True, add offset of beta to normalized tensor. If False, beta is ignored.
scale If True, multiply by gamma. If False, gamma is not used. When the next layer is linear (also e.g. nn.relu), this can be disabled since the scaling can be done by the next layer.
beta_initializer Initializer for the beta weight.
gamma_initializer Initializer for the gamma weight.
moving_mean_initializer Initializer for the moving mean.
moving_variance_initializer Initializer for the moving variance.
beta_regularizer Optional regularizer for the beta weight.
gamma_regularizer Optional regularizer for the gamma weight.
beta_constraint An optional projection function to be applied to the beta weight after being updated by an Optimizer (e.g. used to implement norm constraints or value constraints for layer weights). The function must take as input the unprojected variable and must return the projected variable (which must have the same shape). Constraints are not safe to use when doing asynchronous distributed training.
gamma_constraint An optional projection function to be applied to the gamma weight after being updated by an Optimizer.
renorm Whether to use Batch Renormalization (Ioffe, 2017). This adds extra variables during training. The inference is the same for either value of this parameter.
renorm_clipping A dictionary that may map keys 'rmax', 'rmin', 'dmax' to scalar Tensors used to clip the renorm correction. The correction (r, d) is used as corrected_value = normalized_value * r + d, with r clipped to [rmin, rmax], and d to [-dmax, dmax]. Missing rmax, rmin, dmax are set to inf, 0, i