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.compat.v1.zeros_initializer(),
    gamma_initializer=tf.compat.v1.ones_initializer(),
    moving_mean_initializer=tf.compat.v1.zeros_initializer(),
    moving_variance_initializer=tf.compat.v1.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
)

Migrate to TF2

This API is a legacy api that is only compatible with eager execution and tf.function if you combine it with tf.compat.v1.keras.utils.track_tf1_style_variables

Please refer to tf.layers model mapping section of the migration guide to learn how to use your TensorFlow v1 model in TF2 with 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.

Before:

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

After:

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 -

Description

  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])

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.

Output tensor.

ValueError if eager execution is enabled.

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)