From e6975624536d0fabbe933e47ff42f655398fbbfc Mon Sep 17 00:00:00 2001
From: jjallaire <jj@rstudio.com>
Date: Fri, 14 Jul 2017 11:50:46 -0400
Subject: [PATCH] rename and export call_layer to create_layer

---
 NAMESPACE                    |  1 +
 R/layer-wrappers.R           |  4 ++--
 R/layers-activations.R       | 10 +++++-----
 R/layers-convolutional.R     | 32 +++++++++++++++----------------
 R/layers-core.R              | 37 ++++++++++++++++++++++++------------
 R/layers-dropout.R           |  8 ++++----
 R/layers-embedding.R         |  2 +-
 R/layers-locally-connected.R |  4 ++--
 R/layers-noise.R             |  6 +++---
 R/layers-normalization.R     |  2 +-
 R/layers-pooling.R           | 24 +++++++++++------------
 R/layers-recurrent.R         |  6 +++---
 man/create_layer.Rd          | 27 ++++++++++++++++++++++++++
 13 files changed, 102 insertions(+), 61 deletions(-)
 create mode 100644 man/create_layer.Rd

diff --git a/NAMESPACE b/NAMESPACE
index 318e1ac0d..24743ee10 100644
--- a/NAMESPACE
+++ b/NAMESPACE
@@ -40,6 +40,7 @@ export(constraint_minmaxnorm)
 export(constraint_nonneg)
 export(constraint_unitnorm)
 export(count_params)
+export(create_layer)
 export(dataset_boston_housing)
 export(dataset_cifar10)
 export(dataset_cifar100)
diff --git a/R/layer-wrappers.R b/R/layer-wrappers.R
index 7c7f0e316..be4e7b6bd 100644
--- a/R/layer-wrappers.R
+++ b/R/layer-wrappers.R
@@ -21,7 +21,7 @@ time_distributed <- function(object, layer, input_shape = NULL,
                              batch_input_shape = NULL, batch_size = NULL, dtype = NULL, 
                              name = NULL, trainable = NULL, weights = NULL) {
 
-  call_layer(keras$layers$TimeDistributed, object, list(
+  create_layer(keras$layers$TimeDistributed, object, list(
     layer = layer,
     input_shape = normalize_shape(input_shape),
     batch_input_shape = normalize_shape(batch_input_shape),
@@ -51,7 +51,7 @@ bidirectional <- function(object, layer, merge_mode = "concat", input_shape = NU
                           batch_input_shape = NULL, batch_size = NULL, dtype = NULL, 
                           name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$Bidirectional, object, list(
+  create_layer(keras$layers$Bidirectional, object, list(
     layer = layer,
     merge_mode = merge_mode,
     input_shape = normalize_shape(input_shape),
diff --git a/R/layers-activations.R b/R/layers-activations.R
index 3a5c0fba5..3ca0fc757 100644
--- a/R/layers-activations.R
+++ b/R/layers-activations.R
@@ -15,7 +15,7 @@ layer_activation <- function(object, activation, input_shape = NULL,
                              batch_input_shape = NULL, batch_size = NULL, dtype = NULL, 
                              name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$Activation, object, list(
+  create_layer(keras$layers$Activation, object, list(
     activation = activation,
     input_shape = normalize_shape(input_shape),
     batch_input_shape = normalize_shape(batch_input_shape),
@@ -47,7 +47,7 @@ layer_activation_leaky_relu <- function(object, alpha = 0.3, input_shape = NULL,
                                         dtype = NULL, name = NULL, trainable = NULL, 
                                         weights = NULL) {
   
-  call_layer(keras$layers$LeakyReLU, object, list(
+  create_layer(keras$layers$LeakyReLU, object, list(
     alpha = alpha,
     input_shape = normalize_shape(input_shape),
     batch_input_shape = normalize_shape(batch_input_shape),
@@ -105,7 +105,7 @@ layer_activation_parametric_relu <- function(object, alpha_initializer = "zeros"
   args$weights <- weights
   
   # call layer
-  call_layer(keras$layers$PReLU, object, args)
+  create_layer(keras$layers$PReLU, object, args)
 }
 
 
@@ -126,7 +126,7 @@ layer_activation_thresholded_relu <- function(object, theta = 1.0, input_shape =
                                               dtype = NULL, name = NULL, trainable = NULL, 
                                               weights = NULL) {
   
-  call_layer(keras$layers$ThresholdedReLU, object, list(
+  create_layer(keras$layers$ThresholdedReLU, object, list(
     theta = theta,
     input_shape = normalize_shape(input_shape),
     batch_input_shape = normalize_shape(batch_input_shape),
@@ -158,7 +158,7 @@ layer_activation_elu <- function(object, alpha = 1.0, input_shape = NULL,
                                  batch_input_shape = NULL, batch_size = NULL, dtype = NULL, 
                                  name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$ELU, object, list(
+  create_layer(keras$layers$ELU, object, list(
     alpha = alpha,
     input_shape = normalize_shape(input_shape),
     batch_input_shape = normalize_shape(batch_input_shape),
diff --git a/R/layers-convolutional.R b/R/layers-convolutional.R
index 312043465..17cc5bf15 100644
--- a/R/layers-convolutional.R
+++ b/R/layers-convolutional.R
@@ -56,7 +56,7 @@ layer_conv_1d <- function(object, filters, kernel_size, strides = 1L, padding =
                           batch_input_shape = NULL, batch_size = NULL, dtype = NULL, 
                           name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$Conv1D, object, list(
+  create_layer(keras$layers$Conv1D, object, list(
     filters = as.integer(filters),
     kernel_size = as_integer_tuple(kernel_size),
     strides = as_integer_tuple(strides),
@@ -138,7 +138,7 @@ layer_conv_2d <- function(object, filters, kernel_size, strides = c(1L, 1L), pad
                           batch_input_shape = NULL, batch_size = NULL, dtype = NULL, 
                           name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$Conv2D, object, list(
+  create_layer(keras$layers$Conv2D, object, list(
     filters = as.integer(filters),
     kernel_size = as_integer_tuple(kernel_size),
     strides = as_integer_tuple(strides),
@@ -225,7 +225,7 @@ layer_conv_3d <- function(object, filters, kernel_size, strides = c(1L, 1L, 1L),
                           batch_input_shape = NULL, batch_size = NULL, dtype = NULL, 
                           name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$Conv3D, object, list(
+  create_layer(keras$layers$Conv3D, object, list(
     filters = as.integer(filters),
     kernel_size = as_integer_tuple(kernel_size),
     strides = as_integer_tuple(strides),
@@ -300,7 +300,7 @@ layer_conv_2d_transpose <- function(object, filters, kernel_size, strides = c(1L
                                     batch_input_shape = NULL, batch_size = NULL, dtype = NULL, 
                                     name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$Conv2DTranspose, object, list(
+  create_layer(keras$layers$Conv2DTranspose, object, list(
     filters = as.integer(filters),
     kernel_size = as_integer_tuple(kernel_size),
     strides = as_integer_tuple(strides),
@@ -387,7 +387,7 @@ layer_conv_3d_transpose <- function(object, filters, kernel_size, strides = c(1,
                                     kernel_constraint = NULL, bias_constraint = NULL, input_shape = NULL,
                                     batch_input_shape = NULL, batch_size = NULL, dtype = NULL, 
                                     name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$Conv3DTranspose, object, list(
+  create_layer(keras$layers$Conv3DTranspose, object, list(
     filters = as.integer(filters),
     kernel_size = as_integer_tuple(kernel_size),
     strides = as_integer_tuple(strides),
@@ -469,7 +469,7 @@ layer_separable_conv_2d <- function(object, filters, kernel_size, strides = c(1L
                                     depthwise_constraint = NULL, pointwise_constraint = NULL, bias_constraint = NULL,
                                     batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$SeparableConv2D, object, list(
+  create_layer(keras$layers$SeparableConv2D, object, list(
     filters = as.integer(filters),
     kernel_size = as_integer_tuple(kernel_size),
     strides = as_integer_tuple(strides),
@@ -516,7 +516,7 @@ layer_separable_conv_2d <- function(object, filters, kernel_size, strides = c(1L
 layer_upsampling_1d <- function(object, size = 2L,
                                 batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$UpSampling1D, object, list(
+  create_layer(keras$layers$UpSampling1D, object, list(
     size = as.integer(size),
     batch_size = as_nullable_integer(batch_size),
     name = name,
@@ -552,7 +552,7 @@ layer_upsampling_1d <- function(object, size = 2L,
 layer_upsampling_2d <- function(object, size = c(2L, 2L), data_format = NULL,
                                 batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$UpSampling2D, object, list(
+  create_layer(keras$layers$UpSampling2D, object, list(
     size = as.integer(size),
     data_format = data_format,
     batch_size = as_nullable_integer(batch_size),
@@ -598,7 +598,7 @@ layer_upsampling_2d <- function(object, size = c(2L, 2L), data_format = NULL,
 layer_upsampling_3d <- function(object, size= c(2L, 2L, 2L), data_format = NULL,
                                 batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$UpSampling3D, object, list(
+  create_layer(keras$layers$UpSampling3D, object, list(
     size = as.integer(size),
     data_format = data_format,
     batch_size = as_nullable_integer(batch_size),
@@ -628,7 +628,7 @@ layer_upsampling_3d <- function(object, size= c(2L, 2L, 2L), data_format = NULL,
 #' @export
 layer_zero_padding_1d <- function(object, padding = 1L,
                                   batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$ZeroPadding1D, object, list(
+  create_layer(keras$layers$ZeroPadding1D, object, list(
     padding = as.integer(padding),
     batch_size = as_nullable_integer(batch_size),
     name = name,
@@ -667,7 +667,7 @@ layer_zero_padding_1d <- function(object, padding = 1L,
 layer_zero_padding_2d <- function(object, padding = c(1L, 1L), data_format = NULL,
                                   batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
 
-  call_layer(keras$layers$ZeroPadding2D, object, list(
+  create_layer(keras$layers$ZeroPadding2D, object, list(
     padding = normalize_padding(padding, 2L),
     data_format = data_format,
     batch_size = as_nullable_integer(batch_size),
@@ -714,7 +714,7 @@ layer_zero_padding_2d <- function(object, padding = c(1L, 1L), data_format = NUL
 layer_zero_padding_3d <- function(object,  padding = c(1L, 1L, 1L), data_format = NULL,
                                   batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$ZeroPadding3D, object, list(
+  create_layer(keras$layers$ZeroPadding3D, object, list(
     padding = normalize_padding(padding, 3L),
     data_format = data_format,
     batch_size = as_nullable_integer(batch_size),
@@ -745,7 +745,7 @@ layer_zero_padding_3d <- function(object,  padding = c(1L, 1L, 1L), data_format
 #' @export
 layer_cropping_1d <- function(object, cropping = c(1L, 1L),
                               batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$Cropping1D, object, list(
+  create_layer(keras$layers$Cropping1D, object, list(
     cropping = as.integer(cropping),
     batch_size = as_nullable_integer(batch_size),
     name = name,
@@ -783,7 +783,7 @@ layer_cropping_1d <- function(object, cropping = c(1L, 1L),
 layer_cropping_2d <- function(object, cropping = list(c(0L, 0L), c(0L, 0L)), data_format = NULL,
                               batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$Cropping2D, object, list(
+  create_layer(keras$layers$Cropping2D, object, list(
     cropping = normalize_cropping(cropping, 2L),
     data_format = data_format,
     batch_size = as_nullable_integer(batch_size),
@@ -834,7 +834,7 @@ layer_cropping_2d <- function(object, cropping = list(c(0L, 0L), c(0L, 0L)), dat
 #' @export
 layer_cropping_3d <- function(object, cropping = list(c(1L, 1L), c(1L, 1L), c(1L, 1L)), data_format = NULL,
                               batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$Cropping3D, object, list(
+  create_layer(keras$layers$Cropping3D, object, list(
     cropping = normalize_cropping(cropping, 3L),
     data_format = data_format,
     batch_size = as_nullable_integer(batch_size),
@@ -931,7 +931,7 @@ layer_conv_lstm_2d <- function(object, filters, kernel_size, strides = c(1L, 1L)
                                return_sequences = FALSE, go_backwards = FALSE, stateful = FALSE, dropout = 0.0, recurrent_dropout = 0.0,
                                batch_size = NULL, name = NULL, trainable = NULL, weights = NULL, input_shape = NULL) {
   
-  call_layer(keras$layers$ConvLSTM2D, object, list(
+  create_layer(keras$layers$ConvLSTM2D, object, list(
     filters = as.integer(filters),
     kernel_size = as_integer_tuple(kernel_size),
     strides = as_integer_tuple(strides),
diff --git a/R/layers-core.R b/R/layers-core.R
index 05ff39ebf..3a77955a6 100644
--- a/R/layers-core.R
+++ b/R/layers-core.R
@@ -98,7 +98,7 @@ layer_dense <- function(object, units, activation = NULL, use_bias = TRUE,
                         name = NULL, trainable = NULL, weights = NULL
                         ) {
   
-  call_layer(keras$layers$Dense, object, list(
+  create_layer(keras$layers$Dense, object, list(
     units = as.integer(units),
     activation = activation,
     use_bias = use_bias,
@@ -141,7 +141,7 @@ layer_reshape <- function(object, target_shape, input_shape = NULL,
                           batch_input_shape = NULL, batch_size = NULL, dtype = NULL, 
                           name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$Reshape, object, list(
+  create_layer(keras$layers$Reshape, object, list(
     target_shape = normalize_shape(target_shape),
     input_shape = normalize_shape(input_shape),
     batch_input_shape = normalize_shape(batch_input_shape),
@@ -179,7 +179,7 @@ layer_permute <- function(object, dims, input_shape = NULL,
                           batch_input_shape = NULL, batch_size = NULL, dtype = NULL, 
                           name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$Permute, object, list(
+  create_layer(keras$layers$Permute, object, list(
     dims = as_integer_tuple(dims),
     input_shape = normalize_shape(input_shape),
     batch_input_shape = normalize_shape(batch_input_shape),
@@ -208,7 +208,7 @@ layer_permute <- function(object, dims, input_shape = NULL,
 layer_repeat_vector <- function(object, n, 
                                 batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$RepeatVector, object, list(
+  create_layer(keras$layers$RepeatVector, object, list(
     n = as.integer(n),
     batch_size = as_nullable_integer(batch_size),
     name = name,
@@ -259,7 +259,7 @@ layer_lambda <- function(object, f, output_shape = NULL, mask = NULL, arguments
   if (identical(backend()$backend(), "theano"))
     args$output_shape = as_integer_tuple(output_shape)
   
-  call_layer(keras$layers$Lambda, object, args)
+  create_layer(keras$layers$Lambda, object, args)
   
 }
 
@@ -285,7 +285,7 @@ layer_activity_regularization <- function(object, l1 = 0.0, l2 = 0.0, input_shap
                                           dtype = NULL, name = NULL, trainable = NULL, 
                                           weights = NULL) {
   
-  call_layer(keras$layers$ActivityRegularization, object, list(
+  create_layer(keras$layers$ActivityRegularization, object, list(
     l1 = l1,
     l2 = l2,
     input_shape = normalize_shape(input_shape),
@@ -318,7 +318,7 @@ layer_masking <- function(object, mask_value = 0.0, input_shape = NULL,
                           batch_input_shape = NULL, batch_size = NULL, dtype = NULL, 
                           name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$Masking, object, list(
+  create_layer(keras$layers$Masking, object, list(
     mask_value = mask_value,
     input_shape = normalize_shape(input_shape),
     batch_input_shape = normalize_shape(batch_input_shape),
@@ -345,7 +345,7 @@ layer_masking <- function(object, mask_value = 0.0, input_shape = NULL,
 layer_flatten <- function(object, 
                           batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$Flatten, object, list())
+  create_layer(keras$layers$Flatten, object, list())
   
 }
 
@@ -398,8 +398,21 @@ normalize_shape <- function(shape) {
 }
 
 
-# Helper function to call a layer
-call_layer <- function(layer_function, object, args) {
+#' Create a Keras Layer
+#' 
+#' @param layer_class Python layer class or R6 class of type KerasLayer
+#' @param object Object to compose layer with. This is either a 
+#' [keras_sequential_model()] to add the layer to, or another Layer which
+#' this layer will call.
+#' @param args Arguments to layer constructor function 
+#' 
+#' @return A Keras layer
+#' 
+#' @note The `object` parameter can be missing, in which case the 
+#' layer is created without a connection to an existing graph.
+#' 
+#' @export
+create_layer <- function(layer_class, object, args) {
   
   # remove kwargs that are null
   args$input_shape <- args$input_shape
@@ -410,8 +423,8 @@ call_layer <- function(layer_function, object, args) {
   args$trainable <- args$trainable
   args$weights <- args$weights
   
-  # call function
-  layer <- do.call(layer_function, args)
+  # create layer from class
+  layer <- do.call(layer_class, args)
   
   # compose if we have an x
   if (missing(object) || is.null(object))
diff --git a/R/layers-dropout.R b/R/layers-dropout.R
index 67a768074..b10fe11bb 100644
--- a/R/layers-dropout.R
+++ b/R/layers-dropout.R
@@ -21,7 +21,7 @@
 layer_dropout <- function(object, rate, noise_shape = NULL, seed = NULL, 
                           batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$Dropout, object, list(
+  create_layer(keras$layers$Dropout, object, list(
     rate = rate,
     noise_shape = normalize_shape(noise_shape),
     seed = seed,
@@ -58,7 +58,7 @@ layer_dropout <- function(object, rate, noise_shape = NULL, seed = NULL,
 layer_spatial_dropout_1d <- function(object, rate, 
                                      batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$SpatialDropout1D, object, list(
+  create_layer(keras$layers$SpatialDropout1D, object, list(
     rate = rate,
     batch_size = as_nullable_integer(batch_size),
     name = name,
@@ -102,7 +102,7 @@ layer_spatial_dropout_1d <- function(object, rate,
 layer_spatial_dropout_2d <- function(object, rate, data_format = NULL, 
                                      batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$SpatialDropout2D, object, list(
+  create_layer(keras$layers$SpatialDropout2D, object, list(
     rate = rate,
     data_format = data_format,
     batch_size = as_nullable_integer(batch_size),
@@ -148,7 +148,7 @@ layer_spatial_dropout_2d <- function(object, rate, data_format = NULL,
 layer_spatial_dropout_3d <- function(object, rate, data_format = NULL, 
                                      batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$SpatialDropout3D, object, list(
+  create_layer(keras$layers$SpatialDropout3D, object, list(
     rate = rate,
     data_format = data_format,
     batch_size = as_nullable_integer(batch_size),
diff --git a/R/layers-embedding.R b/R/layers-embedding.R
index dad2f689b..32f46e24c 100644
--- a/R/layers-embedding.R
+++ b/R/layers-embedding.R
@@ -39,7 +39,7 @@
 layer_embedding <- function(object, input_dim, output_dim, embeddings_initializer = "uniform", embeddings_regularizer = NULL, 
                             activity_regularizer = NULL, embeddings_constraint = NULL, mask_zero = FALSE, input_length = NULL, 
                             batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$Embedding, object, list(
+  create_layer(keras$layers$Embedding, object, list(
     input_dim = as.integer(input_dim),
     output_dim = as.integer(output_dim),
     embeddings_initializer = embeddings_initializer,
diff --git a/R/layers-locally-connected.R b/R/layers-locally-connected.R
index 95460c615..105473015 100644
--- a/R/layers-locally-connected.R
+++ b/R/layers-locally-connected.R
@@ -30,7 +30,7 @@ layer_locally_connected_1d <- function(object, filters, kernel_size, strides = 1
                                        bias_initializer = "zeros", kernel_regularizer = NULL, bias_regularizer = NULL, 
                                        activity_regularizer = NULL, kernel_constraint = NULL, bias_constraint = NULL, 
                                        batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$LocallyConnected1D, object, list(
+  create_layer(keras$layers$LocallyConnected1D, object, list(
     filters = as.integer(filters),
     kernel_size = as_integer_tuple(kernel_size),
     strides = as_integer_tuple(strides),
@@ -96,7 +96,7 @@ layer_locally_connected_2d <- function(object, filters, kernel_size, strides = c
                                        bias_initializer = "zeros", kernel_regularizer = NULL, bias_regularizer = NULL, 
                                        activity_regularizer = NULL, kernel_constraint = NULL, bias_constraint = NULL, 
                                        batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$LocallyConnected2D, object, list(
+  create_layer(keras$layers$LocallyConnected2D, object, list(
     filters = as.integer(filters),
     kernel_size = as_integer_tuple(kernel_size),
     strides = as_integer_tuple(strides),
diff --git a/R/layers-noise.R b/R/layers-noise.R
index 8c2740c88..e6e2b090c 100644
--- a/R/layers-noise.R
+++ b/R/layers-noise.R
@@ -23,7 +23,7 @@
 layer_gaussian_noise <- function(object, stddev, input_shape = NULL,
                                  batch_input_shape = NULL, batch_size = NULL, dtype = NULL, 
                                  name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$GaussianNoise, object, list(
+  create_layer(keras$layers$GaussianNoise, object, list(
     stddev = stddev,
     input_shape = normalize_shape(input_shape),
     batch_input_shape = normalize_shape(batch_input_shape),
@@ -59,7 +59,7 @@ layer_gaussian_noise <- function(object, stddev, input_shape = NULL,
 layer_gaussian_dropout <- function(object, rate, input_shape = NULL,
                                    batch_input_shape = NULL, batch_size = NULL, dtype = NULL, 
                                    name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$GaussianDropout, object, list(
+  create_layer(keras$layers$GaussianDropout, object, list(
     rate = rate,
     input_shape = normalize_shape(input_shape),
     batch_input_shape = normalize_shape(batch_input_shape),
@@ -102,7 +102,7 @@ layer_gaussian_dropout <- function(object, rate, input_shape = NULL,
 #'
 #' @export
 layer_alpha_dropout <- function(object, rate, noise_shape = NULL, seed = NULL) {
-  call_layer(keras$layers$AlphaDropout, object, list(
+  create_layer(keras$layers$AlphaDropout, object, list(
     rate = rate,
     noise_shape = noise_shape,
     seed = as_nullable_integer(seed)
diff --git a/R/layers-normalization.R b/R/layers-normalization.R
index dc6718f92..0068e81a7 100644
--- a/R/layers-normalization.R
+++ b/R/layers-normalization.R
@@ -43,7 +43,7 @@ layer_batch_normalization <- function(object, axis = -1L, momentum = 0.99, epsil
                                       beta_constraint = NULL, gamma_constraint = NULL, 
                                       input_shape = NULL,  batch_input_shape = NULL, batch_size = NULL, 
                                       dtype = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$BatchNormalization, object, list(
+  create_layer(keras$layers$BatchNormalization, object, list(
     axis = as.integer(axis),
     momentum = momentum,
     epsilon = epsilon,
diff --git a/R/layers-pooling.R b/R/layers-pooling.R
index 08f906b16..3a84e6998 100644
--- a/R/layers-pooling.R
+++ b/R/layers-pooling.R
@@ -20,7 +20,7 @@
 layer_max_pooling_1d <- function(object, pool_size = 2L, strides = NULL, padding = "valid", 
                                  batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$MaxPooling1D, object, list(
+  create_layer(keras$layers$MaxPooling1D, object, list(
     pool_size = as.integer(pool_size),
     strides = as_nullable_integer(strides),
     padding = padding,
@@ -60,7 +60,7 @@ layer_max_pooling_1d <- function(object, pool_size = 2L, strides = NULL, padding
 layer_max_pooling_2d <- function(object, pool_size = c(2L, 2L), strides = NULL, padding = "valid", data_format = NULL, 
                                  batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$MaxPooling2D, object, list(
+  create_layer(keras$layers$MaxPooling2D, object, list(
     pool_size = as.integer(pool_size),
     strides = as_nullable_integer(strides),
     padding = padding,
@@ -106,7 +106,7 @@ layer_max_pooling_2d <- function(object, pool_size = c(2L, 2L), strides = NULL,
 layer_max_pooling_3d <- function(object, pool_size = c(2L, 2L, 2L), strides = NULL, padding = "valid", data_format = NULL, 
                                  batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$MaxPooling3D, object, list(
+  create_layer(keras$layers$MaxPooling3D, object, list(
     pool_size = as.integer(pool_size),
     strides = as_nullable_integer(strides),
     padding = padding,
@@ -140,7 +140,7 @@ layer_max_pooling_3d <- function(object, pool_size = c(2L, 2L, 2L), strides = NU
 layer_average_pooling_1d <- function(object, pool_size = 2L, strides = NULL, padding = "valid", 
                                      batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$AveragePooling1D, object, list(
+  create_layer(keras$layers$AveragePooling1D, object, list(
     pool_size = as.integer(pool_size),
     strides = as_nullable_integer(strides),
     padding = padding,
@@ -179,7 +179,7 @@ layer_average_pooling_1d <- function(object, pool_size = 2L, strides = NULL, pad
 layer_average_pooling_2d <- function(object, pool_size = c(2L, 2L), strides = NULL, padding = "valid", data_format = NULL, 
                                      batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$AveragePooling2D, object, list(
+  create_layer(keras$layers$AveragePooling2D, object, list(
     pool_size = as.integer(pool_size),
     strides = as_nullable_integer(strides),
     padding = padding,
@@ -223,7 +223,7 @@ layer_average_pooling_2d <- function(object, pool_size = c(2L, 2L), strides = NU
 #' @export
 layer_average_pooling_3d <- function(object, pool_size = c(2L, 2L, 2L), strides = NULL, padding = "valid", data_format = NULL, 
                                      batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$AveragePooling3D, object, list(
+  create_layer(keras$layers$AveragePooling3D, object, list(
     pool_size = as.integer(pool_size),
     strides = as_nullable_integer(strides),
     padding = padding,
@@ -252,7 +252,7 @@ layer_average_pooling_3d <- function(object, pool_size = c(2L, 2L, 2L), strides
 #' @export
 layer_global_max_pooling_1d <- function(object, 
                                         batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$GlobalMaxPooling1D, object, list(
+  create_layer(keras$layers$GlobalMaxPooling1D, object, list(
     batch_size = as_nullable_integer(batch_size),
     name = name,
     trainable = trainable,
@@ -275,7 +275,7 @@ layer_global_max_pooling_1d <- function(object,
 #' @export
 layer_global_average_pooling_1d <- function(object, 
                                             batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$GlobalAveragePooling1D, object, list(
+  create_layer(keras$layers$GlobalAveragePooling1D, object, list(
     batch_size = as_nullable_integer(batch_size),
     name = name,
     trainable = trainable,
@@ -300,7 +300,7 @@ layer_global_average_pooling_1d <- function(object,
 #' @export
 layer_global_max_pooling_2d <- function(object, data_format = NULL, 
                                         batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$GlobalMaxPooling2D, object, list(
+  create_layer(keras$layers$GlobalMaxPooling2D, object, list(
     data_format = data_format,
     batch_size = as_nullable_integer(batch_size),
     name = name,
@@ -325,7 +325,7 @@ layer_global_max_pooling_2d <- function(object, data_format = NULL,
 #' @export
 layer_global_average_pooling_2d <- function(object, data_format = NULL, 
                                             batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$GlobalAveragePooling2D, object, list(
+  create_layer(keras$layers$GlobalAveragePooling2D, object, list(
     data_format = data_format,
     batch_size = as_nullable_integer(batch_size),
     name = name,
@@ -359,7 +359,7 @@ layer_global_average_pooling_2d <- function(object, data_format = NULL,
 #' @export
 layer_global_max_pooling_3d <- function(object, data_format = NULL, 
                                         batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$GlobalMaxPooling3D, object, list(
+  create_layer(keras$layers$GlobalMaxPooling3D, object, list(
     data_format = data_format,
     batch_size = as_nullable_integer(batch_size),
     name = name,
@@ -392,7 +392,7 @@ layer_global_max_pooling_3d <- function(object, data_format = NULL,
 #' @export
 layer_global_average_pooling_3d <- function(object, data_format = NULL, 
                                             batch_size = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$GlobalAveragePooling3D, object, list(
+  create_layer(keras$layers$GlobalAveragePooling3D, object, list(
     data_format = data_format,
     batch_size = as_nullable_integer(batch_size),
     name = name,
diff --git a/R/layers-recurrent.R b/R/layers-recurrent.R
index 83487b5fb..a3c2629ae 100644
--- a/R/layers-recurrent.R
+++ b/R/layers-recurrent.R
@@ -61,7 +61,7 @@ layer_simple_rnn <- function(object, units, activation = "tanh", use_bias = TRUE
                              kernel_constraint = NULL, recurrent_constraint = NULL, bias_constraint = NULL, 
                              dropout = 0.0, recurrent_dropout = 0.0, input_shape = NULL, batch_input_shape = NULL, batch_size = NULL, 
                              dtype = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$SimpleRNN, object, list(
+  create_layer(keras$layers$SimpleRNN, object, list(
     units = as.integer(units),
     activation = activation,
     use_bias = use_bias,
@@ -120,7 +120,7 @@ layer_gru <- function(object, units, activation = "tanh", recurrent_activation =
                       kernel_constraint = NULL, recurrent_constraint = NULL, bias_constraint = NULL, 
                       dropout = 0.0, recurrent_dropout = 0.0, input_shape = NULL, batch_input_shape = NULL, batch_size = NULL, 
                       dtype = NULL, name = NULL, trainable = NULL, weights = NULL) {
-  call_layer(keras$layers$GRU, object, list(
+  create_layer(keras$layers$GRU, object, list(
     units = as.integer(units),
     activation = activation,
     recurrent_activation = recurrent_activation,
@@ -183,7 +183,7 @@ layer_lstm <- function(object, units, activation = "tanh", recurrent_activation
                        dropout = 0.0, recurrent_dropout = 0.0, input_shape = NULL, batch_input_shape = NULL, batch_size = NULL, 
                        dtype = NULL, name = NULL, trainable = NULL, weights = NULL) {
   
-  call_layer(keras$layers$LSTM, object, list(
+  create_layer(keras$layers$LSTM, object, list(
     units = as.integer(units),
     activation = activation,
     recurrent_activation = recurrent_activation,
diff --git a/man/create_layer.Rd b/man/create_layer.Rd
new file mode 100644
index 000000000..98b64ac3e
--- /dev/null
+++ b/man/create_layer.Rd
@@ -0,0 +1,27 @@
+% Generated by roxygen2: do not edit by hand
+% Please edit documentation in R/layers-core.R
+\name{create_layer}
+\alias{create_layer}
+\title{Create a Keras Layer}
+\usage{
+create_layer(layer_class, object, args)
+}
+\arguments{
+\item{layer_class}{Python layer class or R6 class of type KerasLayer}
+
+\item{object}{Object to compose layer with. This is either a
+\code{\link[=keras_sequential_model]{keras_sequential_model()}} to add the layer to, or another Layer which
+this layer will call.}
+
+\item{args}{Arguments to layer constructor function}
+}
+\value{
+A Keras layer
+}
+\description{
+Create a Keras Layer
+}
+\note{
+The \code{object} parameter can be missing, in which case the
+layer is created without a connection to an existing graph.
+}