Normalize and scale inputs or activations.

Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.

Batch normalization differs from other layers in several key aspects:

1) Adding BatchNormalization with training=True to a model causes the result of one example to depend on the contents of all other examples in a minibatch. Be careful when padding batches or masking examples, as these can change the minibatch statistics and affect other examples.

2) Updates to the weights (moving statistics) are based on the forward pass of a model rather than the result of gradient computations.

3) When performing inference using a model containing batch normalization, it is generally (though not always) desirable to use accumulated statistics rather than mini-batch statistics. This is accomplished by passing training=False when calling the model, or using model.predict.

axis Integer, the axis that should be normalized (typically the features axis). For instance, after a Conv2D layer with data_format="channels_first", set axis=1 in BatchNormalization.
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 will 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 Optional constraint for the beta weight.
gamma_constraint Optional constraint for the gamma weight.
renorm Whether to use Batch Renormalization. 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</