I/O Layers
The I/O (Input / Output) layers represent interfaces between the
processing layers of a neural network and the external environment,
providing the net with the data needed for processing and/or training.
File Input
The file input layer allows data in a file to be applied to a network
for processing. Data for processing is expected as a number of rows of
semicolon-separated columns of values. For example, the following is a
set of three rows of four columns:
0.2;0.5;0.6;0.4
0.3;-0.35;0.23;0.29
0.7;0.99;0.56;0.4
Each value in a row will be made available as an output of the file
layer, and the rows will be processed sequentially by successive
processing steps of the network.
As some files may contain information additional to the required data,
the parameters firstRow, lastRow, firstCol and lastCol may be used to
define the range of useable data. The filename parameter specifies the
file that is to be read from.
URL Input
The URL input layer allows
data from a remote location to be applied to a network for processing.
The allowed protocols are http
and ftp. The data format is
the same as for the FileInput layer.
Excel Input
The Excel Input layer
permits data from an Excel file to be applied to a neural network for
processing. Its ‘sheet’ parameter allows the name of the sheet to be
chosen from which the input data is read.
Switch Input
The switch input allows the
choice of which input component is to be connected to the neural
network, choosing between all the input components attached to it. The
user, after having attached several input components to its input, can
set the ‘active input’ parameter with the name of the chosen component
that is to be connected to the net. The ‘default input’ parameter must
be filled with the name of the default component (the one activated when
the user selects the ‘Control->Reset Input Streams’ menu item).
The switch input component,
along with the output switch layer, permits dynamic changing of the
architecture of the neural network, changing the input and/or output
data layers attached to the neural network at any time. This is useful
to switch the input source, for instance, between the file containing
the training data set and the file containing the validation data set to
test the training of the neural network, as depicted in the following
screen shot:

Learning Switch
The learning switch is a special implementation of the Switch Input
component and can be used to attach both a training data set and a
validation data set to the neural net. In this way the user can test the
generalization property of a neural network using a different data set
to the one used during the training phase.
The training input data set can be attached by dragging an arrow from
the input component to the learning switch, while the validation input
data set can be attached simply by dragging an arrow from the red square
on top of the learning switch to the input component containing the
validation data set. To switch between them, simply change the value of
the 'validation' parameter shown in the Control Panel.
The following figure depicts the use of this component:

Warning: Because a validation data set will also be required for
the desired data, this component must be inserted both before the input
layer of the neural network and between the Teacher layer and the desired
input data sets.
File Output
The file output layer is
used to convert the results of a processing layer to a text file. The
filename parameter specifies the file that the results are to be written
to. Results are written in the same semicolon-separated form as file
input layers.
Excel Output
The Excel output layer is
used to write the results of a processing layer to an Excel formatted
file. The filename parameter specifies the file that the results are to
be written to. The ‘sheet’ parameter allows the name of the sheet to be
chosen, to which the input data is to be written.
Switch Output
The switch output permits
the choice of which output component is to be connected to the neural
network, choosing between all the output attached components. The user,
after having attached several components to its output, can set the
‘active output’ parameter with the name of the chosen component that is
to be connected to the net. The ‘default output’ parameter must be
filled with the name of the default component (which one activated when
the user selects the ‘Control->Reset Input Streams’ menu item).
Teacher
The Teacher layer allows the training of a neural net, using the
back-propagation learning algorithm. It calculates the difference
between the actual output of the net and the expected value from an
input source. It provides this difference to the output layer for the
training.
To train a net, add a Teacher component, connecting it to the output
layer of the net, and then connect an input layer component to it,
linked to a source containing the expected data (see figure).
