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)
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