layer_batch_normalization: Layer that normalizes its inputs

In keras: R Interface to 'Keras'

Layer that normalizes its inputs

Description

Layer that normalizes its inputs

Usage

layer_batch_normalization(
  object,
  axis = -1L,
  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 = NULL,
  gamma_regularizer = NULL,
  beta_constraint = NULL,
  gamma_constraint = NULL,
  synchronized = FALSE,
  ...
)

Arguments

object	Layer or model object
axis	Integer, 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.
synchronized	If TRUE, synchronizes the global batch statistics (mean and variance) for the layer across all devices at each training step in a distributed training strategy. If FALSE, each replica uses its own local batch statistics. Only relevant when used inside a tf$distribute strategy.
...	standard layer arguments.

Details

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 passing scale=FALSE to the constructor.

• beta is a learned offset factor (initialized as 0), which can be disabled by passing center=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.

When synchronized=TRUE is set and if this layer is used within a tf$distribute strategy, there will be an allreduce call to aggregate batch statistics across all replicas at every training step. Setting synchronized has no impact when the model is trained without specifying any distribution strategy.

Example usage:

strategy <- tf$distribute$MirroredStrategy()

with(strategy$scope(), {
  model <- keras_model_sequential()
  model %>%
    layer_dense(16) %>%
    layer_batch_normalization(synchronized=TRUE)
})

See Also

• https://www.tensorflow.org/api_docs/python/tf/keras/layers/BatchNormalization

• https://keras.io/api/layers

keras documentation built on May 29, 2024, 3:20 a.m.