Class BatchNormalization
Inherits From: BatchNormalization
, Layer
Defined in tensorflow/python/layers/normalization.py
.
Batch Normalization layer from http://arxiv.org/abs/1502.03167.
"Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift"
Sergey Ioffe, Christian Szegedy
Arguments:
axis
: Anint
or list ofint
, the axis or axes that should be normalized, typically the features axis/axes. For instance, after aConv2D
layer withdata_format="channels_first"
, setaxis=1
. If a list of axes is provided, each axis inaxis
will be normalized simultaneously. Default is-1
which uses the last axis. Note: when using multi-axis batch norm, thebeta
,gamma
,moving_mean
, andmoving_variance
variables are the same rank as the input Tensor, with dimension size 1 in all reduced (non-axis) dimensions).momentum
: Momentum for the moving average.epsilon
: Small float added to variance to avoid dividing by zero.center
: If True, add offset ofbeta
to normalized tensor. If False,beta
is ignored.scale
: If True, multiply bygamma
. 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 thebeta
weight after being updated by anOptimizer
(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 thegamma
weight after being updated by anOptimizer
.renorm
: Whether to use Batch Renormalization (https://arxiv.org/abs/1702.03275). 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 scalarTensors
used to clip the renorm correction. The correction(r, d)
is used ascorrected_value = normalized_value * r + d
, withr
clipped to [rmin, rmax], andd
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. Unlikemomentum
, this affects training and should be neither too small (which would add noise) nor too large (which would give stale estimates). Note thatmomentum
is still applied to get the means and variances for inference.fused
: ifNone
orTrue
, use a faster, fused implementation if possible. IfFalse
, use the system recommended implementation.trainable
: Boolean, ifTrue
also add variables to the graph collectionGraphKeys.TRAINABLE_VARIABLES
(see tf.Variable).virtual_batch_size
: Anint
. By default,virtual_batch_size
isNone
, which means batch normalization is performed across the whole batch. Whenvirtual_batch_size
is notNone
, 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 theTensor
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. IfNone
, no adjustment is applied. Cannot be specified if virtual_batch_size is specified.name
: A string, the name of the layer.
__init__
__init__(
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,
renorm=False,
renorm_clipping=None,
renorm_momentum=0.99,
fused=None,
trainable=True,
virtual_batch_size=None,
adjustment=None,
name=None,
**kwargs
)
Properties
activity_regularizer
Optional regularizer function for the output of this layer.
dtype
graph
input
Retrieves the input tensor(s) of a layer.
Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer.
Returns:
Input tensor or list of input tensors.
Raises:
AttributeError
: if the layer is connected to more than one incoming layers.
Raises:
RuntimeError
: If called in Eager mode.AttributeError
: If no inbound nodes are found.
input_mask
Retrieves the input mask tensor(s) of a layer.
Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.
Returns:
Input mask tensor (potentially None) or list of input mask tensors.
Raises:
AttributeError
: if the layer is connected to more than one incoming layers.
input_shape
Retrieves the input shape(s) of a layer.
Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer, or if all inputs have the same shape.
Returns:
Input shape, as an integer shape tuple (or list of shape tuples, one tuple per input tensor).
Raises:
AttributeError
: if the layer has no defined input_shape.RuntimeError
: if called in Eager mode.
losses
Losses which are associated with this Layer
.
Variable regularization tensors are created when this property is accessed,
so it is eager safe: accessing losses
under a tf.GradientTape
will
propagate gradients back to the corresponding variables.
Returns:
A list of tensors.
name
non_trainable_variables
non_trainable_weights
output
Retrieves the output tensor(s) of a layer.
Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer.
Returns:
Output tensor or list of output tensors.
Raises:
AttributeError
: if the layer is connected to more than one incoming layers.RuntimeError
: if called in Eager mode.
output_mask
Retrieves the output mask tensor(s) of a layer.
Only applicable if the layer has exactly one inbound node, i.e. if it is connected to one incoming layer.
Returns:
Output mask tensor (potentially None) or list of output mask tensors.
Raises:
AttributeError
: if the layer is connected to more than one incoming layers.
output_shape
Retrieves the output shape(s) of a layer.
Only applicable if the layer has one output, or if all outputs have the same shape.
Returns:
Output shape, as an integer shape tuple (or list of shape tuples, one tuple per output tensor).
Raises:
AttributeError
: if the layer has no defined output shape.RuntimeError
: if called in Eager mode.
scope_name
trainable_variables
trainable_weights
updates
variables
Returns the list of all layer variables/weights.
Alias of self.weights
.
Returns:
A list of variables.
weights
Returns the list of all layer variables/weights.
Returns:
A list of variables.
Methods
tf.layers.BatchNormalization.__call__
__call__(
inputs,
*args,
**kwargs
)
Wraps call
, applying pre- and post-processing steps.
Arguments:
inputs
: input tensor(s).*args
: additional positional arguments to be passed toself.call
.**kwargs
: additional keyword arguments to be passed toself.call
. Note: kwargscope
is reserved for use by the layer.
Returns:
Output tensor(s).
Raises:
ValueError
: if the layer'scall
method returns None (an invalid value).
tf.layers.BatchNormalization.__deepcopy__
__deepcopy__(memo)
tf.layers.BatchNormalization.__setattr__
__setattr__(
name,
value
)
Implement setattr(self, name, value).
tf.layers.BatchNormalization.apply
apply(
inputs,
*args,
**kwargs
)
Apply the layer on a input.
This is an alias of self.__call__
.
Arguments:
inputs
: Input tensor(s).*args
: additional positional arguments to be passed toself.call
.**kwargs
: additional keyword arguments to be passed toself.call
.
Returns:
Output tensor(s).
tf.layers.BatchNormalization.build
build(input_shape)
Creates the variables of the layer (optional, for subclass implementers).
This is a method that implementers of subclasses of Layer
or Model
can override if they need a state-creation step in-between
layer instantiation and layer call.
This is typically used to create the weights of Layer
subclasses.
Arguments:
input_shape
: Instance ofTensorShape
, or list of instances ofTensorShape
if the layer expects a list of inputs (one instance per input).
tf.layers.BatchNormalization.compute_mask
compute_mask(
inputs,
mask=None
)
Computes an output mask tensor.
Arguments:
inputs
: Tensor or list of tensors.mask
: Tensor or list of tensors.
Returns:
None or a tensor (or list of tensors, one per output tensor of the layer).
tf.layers.BatchNormalization.compute_output_shape
compute_output_shape(input_shape)
Computes the output shape of the layer.
Assumes that the layer will be built to match that input shape provided.
Arguments:
input_shape
: Shape tuple (tuple of integers) or list of shape tuples (one per output tensor of the layer). Shape tuples can include None for free dimensions, instead of an integer.
Returns:
An input shape tuple.
tf.layers.BatchNormalization.count_params
count_params()
Count the total number of scalars composing the weights.
Returns:
An integer count.
Raises:
ValueError
: if the layer isn't yet built (in which case its weights aren't yet defined).
tf.layers.BatchNormalization.from_config
from_config(
cls,
config
)
Creates a layer from its config.
This method is the reverse of get_config
,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights
).
Arguments:
config
: A Python dictionary, typically the output of get_config.
Returns:
A layer instance.
tf.layers.BatchNormalization.get_config
get_config()
Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by Network
(one layer of abstraction above).
Returns:
Python dictionary.
tf.layers.BatchNormalization.get_input_at
get_input_at(node_index)
Retrieves the input tensor(s) of a layer at a given node.
Arguments:
node_index
: Integer, index of the node from which to retrieve the attribute. E.g.node_index=0
will correspond to the first time the layer was called.
Returns:
A tensor (or list of tensors if the layer has multiple inputs).
Raises:
RuntimeError
: If called in Eager mode.
tf.layers.BatchNormalization.get_input_mask_at
get_input_mask_at(node_index)
Retrieves the input mask tensor(s) of a layer at a given node.
Arguments:
node_index
: Integer, index of the node from which to retrieve the attribute. E.g.node_index=0
will correspond to the first time the layer was called.
Returns:
A mask tensor (or list of tensors if the layer has multiple inputs).
tf.layers.BatchNormalization.get_input_shape_at
get_input_shape_at(node_index)
Retrieves the input shape(s) of a layer at a given node.
Arguments:
node_index
: Integer, index of the node from which to retrieve the attribute. E.g.node_index=0
will correspond to the first time the layer was called.
Returns:
A shape tuple (or list of shape tuples if the layer has multiple inputs).
Raises:
RuntimeError
: If called in Eager mode.
tf.layers.BatchNormalization.get_losses_for
get_losses_for(inputs)
Retrieves losses relevant to a specific set of inputs.
Arguments:
inputs
: Input tensor or list/tuple of input tensors.
Returns:
List of loss tensors of the layer that depend on inputs
.
Raises:
RuntimeError
: If called in Eager mode.
tf.layers.BatchNormalization.get_output_at
get_output_at(node_index)
Retrieves the output tensor(s) of a layer at a given node.
Arguments:
node_index
: Integer, index of the node from which to retrieve the attribute. E.g.node_index=0
will correspond to the first time the layer was called.
Returns:
A tensor (or list of tensors if the layer has multiple outputs).
Raises:
RuntimeError
: If called in Eager mode.
tf.layers.BatchNormalization.get_output_mask_at
get_output_mask_at(node_index)
Retrieves the output mask tensor(s) of a layer at a given node.
Arguments:
node_index
: Integer, index of the node from which to retrieve the attribute. E.g.node_index=0
will correspond to the first time the layer was called.
Returns:
A mask tensor (or list of tensors if the layer has multiple outputs).
tf.layers.BatchNormalization.get_output_shape_at
get_output_shape_at(node_index)
Retrieves the output shape(s) of a layer at a given node.
Arguments:
node_index
: Integer, index of the node from which to retrieve the attribute. E.g.node_index=0
will correspond to the first time the layer was called.
Returns:
A shape tuple (or list of shape tuples if the layer has multiple outputs).
Raises:
RuntimeError
: If called in Eager mode.
tf.layers.BatchNormalization.get_updates_for
get_updates_for(inputs)
Retrieves updates relevant to a specific set of inputs.
Arguments:
inputs
: Input tensor or list/tuple of input tensors.
Returns:
List of update ops of the layer that depend on inputs
.
Raises:
RuntimeError
: If called in Eager mode.
tf.layers.BatchNormalization.get_weights
get_weights()
Returns the current weights of the layer.
Returns:
Weights values as a list of numpy arrays.
tf.layers.BatchNormalization.set_weights
set_weights(weights)
Sets the weights of the layer, from Numpy arrays.
Arguments:
weights
: a list of Numpy arrays. The number of arrays and their shape must match number of the dimensions of the weights of the layer (i.e. it should match the output ofget_weights
).
Raises:
ValueError
: If the provided weights list does not match the layer's specifications.