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Layer that normalizes its inputs.
tf.compat.v1.keras.layers.BatchNormalization(
axis=-1,
momentum=0.99,
epsilon=0.001,
center=True,
scale=True,
beta_initializer='zeros',
gamma_initializer='ones',
moving_mean_initializer='zeros',
moving_variance_initializer='ones',
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
)
Batch normalization applies a transformation that maintains the mean output close to 0 and the output standard deviation close to 1.
Importantly, batch normalization works differently during training and during inference.
During training (i.e. when using fit()
or when calling the layer/model
with the argument training=True
), the layer normalizes its output using
the mean and standard deviation of the current batch of inputs. That is to
say, for each channel being normalized, the layer returns
gamma * (batch - mean(batch)) / sqrt(var(batch) + epsilon) + beta
, where:
epsilon
is small constant (configurable as part of the constructor arguments)gamma
is a learned scaling factor (initialized as 1), which can be disabled by passingscale=False
to the constructor.beta
is a learned offset factor (initialized as 0), which can be disabled by passingcenter=False
to the constructor.
During inference (i.e. when using evaluate()
or predict()
) or when
calling the layer/model with the argument training=False
(which is the
default), the layer normalizes its output using a moving average of the
mean and standard deviation of the batches it has seen during training. That
is to say, it returns
gamma * (batch - self.moving_mean) / sqrt(self.moving_var + epsilon) + beta
.
self.moving_mean
and self.moving_var
are non-trainable variables that
are updated each time the layer in called in training mode, as such:
moving_mean = moving_mean * momentum + mean(batch) * (1 - momentum)
moving_var = moving_var * momentum + var(batch) * (1 - momentum)
As such, the layer will only normalize its inputs during inference after having been trained on data that has similar statistics as the inference data.
Args | |
---|---|
axis
|
Integer or a list of integers, the axis that should be normalized
(typically the features axis). For instance, after a Conv2D 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 will 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
|
Optional constraint for the beta weight. |
gamma_constraint
|
Optional constraint for the gamma weight. |
renorm
|
Whether to use Batch Renormalization. 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 True , use a faster, fused implementation, or raise a ValueError
if the fused implementation cannot be used. If None , use the faster
implementation if possible. If False, do not used the fused
implementation.
Note that in TensorFlow 1.x, the meaning of fused=True is different: if
False , the layer uses the system-recommended implementation.
|
trainable
|
Boolean, if True the variables will be marked as trainable.
|
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.
|
Input shape: Arbitrary. Use the keyword argument input_shape
(tuple of
integers, does not include the samples axis) when using this layer as the
first layer in a model.
Output shape: Same shape as input.
Reference | |
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