|TensorFlow 2.0 version||View source on GitHub|
Base class of Batch normalization layer (Ioffe and Szegedy, 2014).
Normalize the activations of the previous layer at each batch, i.e. applies a transformation that maintains the mean activation close to 0 and the activation standard deviation close to 1.
axis: Integer, the axis that should be normalized (typically the features axis). For instance, after a
momentum: Momentum for the moving average.
epsilon: Small float added to variance to avoid dividing by zero.
center: If True, add offset of
betato normalized tensor. If False,
scale: If True, multiply by
gamma. If False,
gammais 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 (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 scalar
Tensorsused to clip the renorm correction. The correction
(r, d)is used as
corrected_value = normalized_value * r + d, with
rclipped to [rmin, rmax], and
dto [-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
momentumis still applied to get the means and variances for inference.
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.
trainable: Boolean, if
Truethe variables will be marked as trainable.
int. By default,
None, which means batch normalization is performed across the whole batch. When
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
Tensorcontaining 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.
inputs: Input tensor (of any rank).
training: Python boolean indicating whether the layer should behave in training mode or in inference mode.
training=True: The layer will normalize its inputs using the mean and variance of the current batch of inputs.
training=False: The layer will normalize its inputs using the mean and variance of its moving statistics, learned during training.
Arbitrary. Use the keyword argument
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Same shape as input.
__init__( 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 )