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Functional interface for the batch normalization layer from_config(Ioffe et al., 2015).

Migrate to TF2

This API is not compatible with eager execution or tf.function.

Please refer to tf.layers section of the migration guide to migrate a TensorFlow v1 model to Keras. The corresponding TensorFlow v2 layer is tf.keras.layers.BatchNormalization.

The batch updating pattern with tf.control_dependencies(tf.GraphKeys.UPDATE_OPS) should not be used in native TF2. Consult the tf.keras.layers.BatchNormalization documentation for further information.

Structural Mapping to Native TF2

None of the supported arguments have changed name.


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


To migrate code using TF1 functional layers use the Keras Functional API:

 x = tf.keras.Input(shape=(28, 28, 1),)
 y = tf.keras.layers.BatchNormalization()(x)
 model = tf.keras.Model(x, y)

How to Map Arguments

TF1 Arg Name TF2 Arg Name Note
name name Layer base class
trainable trainable Layer base class
axis axis -
momentum momentum -
epsilon epsilon -
center center -
scale scale -
beta_initializer beta_initializer -
gamma_initializer gamma_initializer -
moving_mean_initializer moving_mean_initializer -
beta_regularizer `beta_regularizer' -
gamma_regularizer `gamma_regularizer' -
beta_constraint `beta_constraint' -
gamma_constraint `gamma_constraint' -
renorm Not supported -
renorm_clipping Not supported -
renorm_momentum Not supported -
fused Not supported -
virtual_batch_size Not supported -
adjustment Not supported -


Used in the notebooks

Used in the guide
  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 =[train_op, update_ops])

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.