tf.compat.v1.layers.batch_normalization Functional interface for the batch normalization layer from_config(Ioffe et al., 2015). 
tf.compat.v1.layers.batch_normalization(
    inputs, axis=-1, momentum=0.99, epsilon=0.001, center=True, scale=True,
    beta_initializer=tf.zeros_initializer(),
    gamma_initializer=tf.ones_initializer(),
    moving_mean_initializer=tf.zeros_initializer(),
    moving_variance_initializer=tf.ones_initializer(), beta_regularizer=None,
    gamma_regularizer=None, beta_constraint=None, gamma_constraint=None,
    training=False, trainable=True, name=None, reuse=None, renorm=False,
    renorm_clipping=None, renorm_momentum=0.99, fused=None, virtual_batch_size=None,
    adjustment=None
)
 
Note: when training, the moving_mean and moving_variance need to be updated. By default the update ops are placed in tf.GraphKeys.UPDATE_OPS, so they need to be executed alongside the train_op. Also, be sure to add any batch_normalization ops before getting the update_ops collection. Otherwise, update_ops will be empty, and training/inference will not work properly. For example:

x_norm = tf.compat.v1.layers.batch_normalization(x, training=training)

# ...

update_ops = tf.compat.v1.get_collection(tf.GraphKeys.UPDATE_OPS)
train_op = optimizer.minimize(loss)
train_op = tf.group([train_op, update_ops])

 


 Arguments
  inputs   Tensor input.  
  axis   An int, the axis that should be normalized (typically the features axis). For instance, after a Convolution2D 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 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.  
  training   Either a Python boolean, or a TensorFlow boolean scalar tensor (e.g. a placeholder). Whether to return the output in training mode (normalized with statistics of the current batch) or in inference mode (normalized with moving statistics). NOTE: make sure to set this parameter correctly, or else your training/inference will not work properly.  
  trainable   Boolean, if True also add variables to the graph collection GraphKeys.TRAINABLE_VARIABLES (see tf.Variable).  
  name   String, the name of the layer.  
  reuse   Boolean, whether to reuse the weights of a previous layer by the same name.  
  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, inf, respectively.  
  renorm_momentum   Momentum used to update the moving means and standard deviations with renorm. Unlike momentum, this affects training and should be neither too small (which would add noise) nor too large (which would give stale estimates). Note that momentum is still applied to get the means and variances for inference.  
  fused   if None or True, use a faster, fused implementation if possible. If False, use the system recommended implementation.  
  virtual_batch_size   An int. By default, virtual_batch_size is None, which means batch normalization is performed across the whole batch. When virtual_batch_size is not None, instead perform "Ghost Batch Normalization", which creates virtual sub-batches which are each normalized separately (with shared gamma, beta, and moving statistics). Must divide the actual batch size during execution.  
  adjustment   A function taking the Tensor containing the (dynamic) shape of the input tensor and returning a pair (scale, bias) to apply to the normalized values (before gamma and beta), only during training. For example, if axis==-1, adjustment = lambda shape: ( tf.random.uniform(shape[-1:], 0.93, 1.07), tf.random.uniform(shape[-1:], -0.1, 0.1)) will scale the normalized value by up to 7% up or down, then shift the result by up to 0.1 (with independent scaling and bias for each feature but shared across all examples), and finally apply gamma and/or beta. If None, no adjustment is applied. Cannot be specified if virtual_batch_size is specified.   
 


 Returns   Output tensor.  

 


 Raises
  ValueError   if eager execution is enabled.    References: Batch Normalization - Accelerating Deep Network Training by Reducing Internal Covariate Shift: Ioffe et al., 2015 (pdf) Batch Renormalization - Towards Reducing Minibatch Dependence in Batch-Normalized Models: Ioffe, 2017 (pdf)