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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])
Args | |
---|---|
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) |