expose renorm, renorm_clipping to layer_batch_normalization

Author: Jason George

R/layers-normalization.R

@@ -36,12 +36,12 @@
 #' - [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://arxiv.org/abs/1502.03167)
 #'   
 #' @export
-layer_batch_normalization <- function(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, 
-                                      input_shape = NULL,  batch_input_shape = NULL, batch_size = NULL, 
+layer_batch_normalization <- function(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, renorm = FALSE,
+                                      renorm_clipping = NULL, beta_constraint = NULL, gamma_constraint = NULL,
+                                      input_shape = NULL,  batch_input_shape = NULL, batch_size = NULL,
                                       dtype = NULL, name = NULL, trainable = NULL, weights = NULL) {
   create_layer(keras$layers$BatchNormalization, object, list(
     axis = as.integer(axis),
@@ -57,6 +57,8 @@ layer_batch_normalization <- function(object, axis = -1L, momentum = 0.99, epsil
     gamma_regularizer = gamma_regularizer,
     beta_constraint = beta_constraint,
     gamma_constraint = gamma_constraint,
+    renorm = renorm,
+    renorm_clipping = renorm_clipping,
     input_shape = normalize_shape(input_shape),
     batch_input_shape = normalize_shape(batch_input_shape),
     batch_size = as_nullable_integer(batch_size),