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.
About setting layer$trainable <- FALSE on a BatchNormalization layer:
The meaning of setting layer$trainable <- FALSE is to freeze the layer,
i.e. its internal state will not change during training:
its trainable weights will not be updated
during fit() or train_on_batch(), and its state updates will not be run.
Usually, this does not necessarily mean that the layer is run in inference
mode (which is normally controlled by the training argument that can
be passed when calling a layer). "Frozen state" and "inference mode"
are two separate concepts.
However, in the case of the BatchNormalization layer, setting
trainable <- FALSE on the layer means that the layer will be
subsequently run in inference mode (meaning that it will use
the moving mean and the moving variance to normalize the current batch,
rather than using the mean and variance of the current batch).
Note that:
Setting trainable on an model containing other layers will recursively
set the trainable value of all inner layers.
If the value of the trainable attribute is changed after calling
compile() on a model, the new value doesn't take effect for this model
until compile() is called again.
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,
...
)The return value depends on the value provided for the first argument.
If object is:
a keras_model_sequential(), then the layer is added to the sequential model
(which is modified in place). To enable piping, the sequential model is also
returned, invisibly.
a keras_input(), then the output tensor from calling layer(input) is returned.
NULL or missing, then a Layer instance is returned.
Object to compose the layer with. A tensor, array, or sequential model.
Integer, the axis that should be normalized
(typically the features axis). For instance, after a Conv2D layer
with data_format = "channels_first", use axis = 2.
Momentum for the moving average.
Small float added to variance to avoid dividing by zero.
If TRUE, add offset of beta to normalized tensor.
If FALSE, beta is ignored.
If TRUE, multiply by gamma. If FALSE, gamma is not used.
When the next layer is linear this can be disabled
since the scaling will be done by the next layer.
Initializer for the beta weight.
Initializer for the gamma weight.
Initializer for the moving mean.
Initializer for the moving variance.
Optional regularizer for the beta weight.
Optional regularizer for the gamma weight.
Optional constraint for the beta weight.
Optional constraint for the gamma weight.
Only applicable with the TensorFlow backend.
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.
Base layer keyword arguments (e.g. name and dtype).
inputs: Input tensor (of any rank).
training: R 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.
mask: Binary tensor of shape broadcastable to inputs tensor, with
TRUE values indicating the positions for which mean and variance
should be computed. Masked elements of the current inputs are not
taken into account for mean and variance computation during
training. Any prior unmasked element values will be taken into
account until their momentum expires.
Other normalization layers:
layer_group_normalization()
layer_layer_normalization()
layer_spectral_normalization()
layer_unit_normalization()
Other layers:
Layer()
layer_activation()
layer_activation_elu()
layer_activation_leaky_relu()
layer_activation_parametric_relu()
layer_activation_relu()
layer_activation_softmax()
layer_activity_regularization()
layer_add()
layer_additive_attention()
layer_alpha_dropout()
layer_attention()
layer_auto_contrast()
layer_average()
layer_average_pooling_1d()
layer_average_pooling_2d()
layer_average_pooling_3d()
layer_bidirectional()
layer_category_encoding()
layer_center_crop()
layer_concatenate()
layer_conv_1d()
layer_conv_1d_transpose()
layer_conv_2d()
layer_conv_2d_transpose()
layer_conv_3d()
layer_conv_3d_transpose()
layer_conv_lstm_1d()
layer_conv_lstm_2d()
layer_conv_lstm_3d()
layer_cropping_1d()
layer_cropping_2d()
layer_cropping_3d()
layer_dense()
layer_depthwise_conv_1d()
layer_depthwise_conv_2d()
layer_discretization()
layer_dot()
layer_dropout()
layer_einsum_dense()
layer_embedding()
layer_equalization()
layer_feature_space()
layer_flatten()
layer_flax_module_wrapper()
layer_gaussian_dropout()
layer_gaussian_noise()
layer_global_average_pooling_1d()
layer_global_average_pooling_2d()
layer_global_average_pooling_3d()
layer_global_max_pooling_1d()
layer_global_max_pooling_2d()
layer_global_max_pooling_3d()
layer_group_normalization()
layer_group_query_attention()
layer_gru()
layer_hashed_crossing()
layer_hashing()
layer_identity()
layer_integer_lookup()
layer_jax_model_wrapper()
layer_lambda()
layer_layer_normalization()
layer_lstm()
layer_masking()
layer_max_num_bounding_boxes()
layer_max_pooling_1d()
layer_max_pooling_2d()
layer_max_pooling_3d()
layer_maximum()
layer_mel_spectrogram()
layer_minimum()
layer_mix_up()
layer_multi_head_attention()
layer_multiply()
layer_normalization()
layer_permute()
layer_rand_augment()
layer_random_brightness()
layer_random_color_degeneration()
layer_random_color_jitter()
layer_random_contrast()
layer_random_crop()
layer_random_flip()
layer_random_grayscale()
layer_random_hue()
layer_random_posterization()
layer_random_rotation()
layer_random_saturation()
layer_random_sharpness()
layer_random_shear()
layer_random_translation()
layer_random_zoom()
layer_repeat_vector()
layer_rescaling()
layer_reshape()
layer_resizing()
layer_rnn()
layer_separable_conv_1d()
layer_separable_conv_2d()
layer_simple_rnn()
layer_solarization()
layer_spatial_dropout_1d()
layer_spatial_dropout_2d()
layer_spatial_dropout_3d()
layer_spectral_normalization()
layer_stft_spectrogram()
layer_string_lookup()
layer_subtract()
layer_text_vectorization()
layer_tfsm()
layer_time_distributed()
layer_torch_module_wrapper()
layer_unit_normalization()
layer_upsampling_1d()
layer_upsampling_2d()
layer_upsampling_3d()
layer_zero_padding_1d()
layer_zero_padding_2d()
layer_zero_padding_3d()
rnn_cell_gru()
rnn_cell_lstm()
rnn_cell_simple()
rnn_cells_stack()