beta doc fixes (apache#13860)

R-package/R/context.R

@@ -22,7 +22,7 @@ init.context.default <- function() {
 
 #' Set/Get default context for array creation.
 #'
-#' @param new, optional takes \code{mx.cpu()} or \code{mx.gpu(id)}, new default ctx.
+#' @param new optional takes \code{mx.cpu()} or \code{mx.gpu(id)}, new default ctx.
 #' @return The default context.
 #'
 #' @export

R-package/R/model.R

@@ -562,7 +562,7 @@ mx.model.FeedForward.create <-
 #'
 #' @param model The MXNet Model.
 #' @param X The dataset to predict.
-#' @param ctx mx.cpu() or mx.gpu(i) The device used to generate the prediction.
+#' @param ctx mx.cpu() or mx.gpu(). The device used to generate the prediction.
 #' @param array.batch.size The batch size used in batching. Only used when X is R's array.
 #' @param array.layout can be "auto", "colmajor", "rowmajor", (detault=auto)
 #'     The layout of array. "rowmajor" is only supported for two dimensional array.

R-package/R/optimizer.R

@@ -21,7 +21,7 @@
 #' @param learning.rate float, default=0.01
 #'      The initial learning rate.
 #' @param momentum float, default=0
-#'      The momentumvalue
+#'      The momentum value
 #' @param wd float, default=0.0
 #'      L2 regularization coefficient add to all the weights.
 #' @param rescale.grad float, default=1.0

R-package/R/rnn.graph.R

@@ -195,7 +195,7 @@ gru.cell <- function(num_hidden, indata, prev.state, param, seqidx, layeridx, dr
 }
 
 
-#' unroll representation of RNN running on non CUDA device
+#' Unroll representation of RNN running on non CUDA device
 #' 
 #' @param config Either seq-to-one or one-to-one
 #' @param cell_type Type of RNN cell: either gru or lstm

src/operator/optimizer_op.cc

@@ -316,7 +316,7 @@ inline bool SGDStorageType(const nnvm::NodeAttrs& attrs,
 
 NNVM_REGISTER_OP(sgd_update)
 MXNET_ADD_SPARSE_OP_ALIAS(sgd_update)
-.describe(R"code(Update function for Stochastic Gradient Descent (SDG) optimizer.
+.describe(R"code(Update function for Stochastic Gradient Descent (SGD) optimizer.
 
 It updates the weights using::
 

src/operator/tensor/elemwise_unary_op_basic.cc

@@ -71,7 +71,7 @@ static bool IdentityAttrLikeRhsStorageType(const nnvm::NodeAttrs& attrs,
 
 // relu
 MXNET_OPERATOR_REGISTER_UNARY_WITH_RSP_CSR(relu, cpu, mshadow_op::relu)
-.describe(R"code(Computes rectified linear.
+.describe(R"code(Computes rectified linear activation.
 
 .. math::
    max(features, 0)