Table Of Contents
Table Of Contents

mx.model.FeedForward.create

Description

Create a MXNet Feedforward neural net model with the specified training.

Usage

mx.model.FeedForward.create(symbol, X, y = NULL, ctx = NULL,

  begin.round = 1, num.round = 10, optimizer = "sgd",

  initializer = mx.init.uniform(0.01), eval.data = NULL,

  eval.metric = NULL, epoch.end.callback = NULL,

  batch.end.callback = NULL, array.batch.size = 128,

  array.layout = "auto", kvstore = "local", verbose = TRUE,

  arg.params = NULL, aux.params = NULL, input.names = NULL,

  output.names = NULL, fixed.param = NULL, allow.extra.params = FALSE,

  metric_cpu = TRUE, ...)

Arguments

Argument

Description

symbol

The symbolic configuration of the neural network.

X

mx.io.DataIter or R array/matrix.

The training data.

y

R array, optional label of the data.

This is only used when X is R array.

ctx

mx.context or list of mx.context, optional.

The devices used to perform training.

begin.round

integer (default=1).

The initial iteration over the training data to train the model.

num.round

integer (default=10).

The number of iterations over training data to train the model.

optimizer

string, default=”sgd”

The optimization method.

initializer

initializer object. default=mx.init.uniform(0.01).

The initialization scheme for parameters.

eval.data

mx.io.DataIter or list(data=R.array, label=R.array), optional.

The validation set used for validation evaluation during the progress

eval.metric

function, optional.

The evaluation function on the results.

epoch.end.callback

function, optional.

The callback when iteration ends.

batch.end.callback

function, optional.

The callback when one mini-batch iteration ends.

array.batch.size

integer (default=128).

The batch size used for R array training.

array.layout

can be “auto”, “colmajor”, “rowmajor”, (detault=auto).

The layout of array. “rowmajor” is only supported for two dimensional array. For matrix, “rowmajor” means dim(X) = c(nexample, nfeatures), “colmajor” means dim(X) = c(nfeatures, nexample) “auto” will auto detect the layout by match the feature size, and will report error when X is a square matrix to ask user to explicitly specify layout.

kvstore

string (default=”local”).

The parameter synchronization scheme in multiple devices.

verbose

logical (default=TRUE).

Specifies whether to print information on the iterations during training.

arg.params

list, optional.

Model parameter, list of name to NDArray of net’s weights.

aux.params

list, optional.

Model parameter, list of name to NDArray of net’s auxiliary states.

input.names

optional.

The names of the input symbols.

output.names

optional.

The names of the output symbols.

fixed.param

The parameters to be fixed during training. For these parameters, not gradients will be calculated and thus no space will be allocated for the gradient.

allow.extra.params

Whether allow extra parameters that are not needed by symbol.

If this is TRUE, no error will be thrown when arg_params or aux_params contain extra parameters that is not needed by the executor.

Value

model A trained mxnet model.