Neural Network Subobject Properties
These properties define the details of a network's inputs, layers, outputs, targets, biases, and weights.
Inputs
These properties define the details of each ith network input.
net.inputs{1}.name
This property consists of a string defining the input name.
Network creation functions, such as feedforwardnet,
define this appropriately. But it can be set to any string as desired.
net.inputs{i}.feedbackInput (read only)
If this network is associated with an open-loop feedback output, then this property will indicate the index of that output. Otherwise it will be an empty matrix.
net.inputs{i}.processFcns
This property defines a row cell array of processing function names to be used by ith network input. The processing functions are applied to input values before the network uses them.
Side Effects. Whenever this property is altered, the input processParams are
set to default values for the given processing functions, processSettings, processedSize,
and processedRange are defined by applying the
process functions and parameters to exampleInput.
For a list of processing functions, type help nnprocess.
net.inputs{i}.processParams
This property holds a row cell array of processing function parameters to be used by ith network input. The processing parameters are applied by the processing functions to input values before the network uses them.
Side Effects. Whenever this property is altered, the input processSettings, processedSize,
and processedRange are defined by applying the
process functions and parameters to exampleInput.
net.inputs{i}.processSettings (read only)
This property holds a row cell array of processing function
settings to be used by ith network input. The processing
settings are found by applying the processing functions and parameters
to exampleInput and then used to provide consistent
results to new input values before the network uses them.
net.inputs{i}.processedRange (read only)
This property defines the range of exampleInput values
after they have been processed with processingFcns and processingParams.
net.inputs{i}.processedSize (read only)
This property defines the number of rows in the exampleInput values
after they have been processed with processingFcns and processingParams.
net.inputs{i}.range
This property defines the range of each element of the ith network input.
It can be set to any Ri ×
2 matrix, where Ri is the
number of elements in the input (net.inputs{i}.size),
and each element in column 1 is less than the element next to it in
column 2.
Each jth row defines the minimum and maximum values of the jth input element, in that order:
net.inputs{i}(j,:)
Uses. Some initialization functions use input ranges to find appropriate initial values for input weight matrices.
Side Effects. Whenever the number of rows in this property is altered, the
input size, processedSize, and processedRange change
to remain consistent. The sizes of any weights coming from this input
and the dimensions of the weight matrices also change.
net.inputs{i}.size
This property defines the number of elements in the ith network input. It can be set to 0 or a positive integer.
Side Effects. Whenever this property is altered, the input range, processedRange,
and processedSize are updated. Any associated input
weights change size accordingly.
net.inputs{i}.userdata
This property provides a place for users to add custom information to the ith network input.
Layers
These properties define the details of each ith network layer.
net.layers{i}.name
This property consists of a string defining the layer name.
Network creation functions, such as feedforwardnet,
define this appropriately. But it can be set to any string as desired.
net.layers{i}.dimensions
This property defines the physical dimensions of the ith layer's neurons. Being able to arrange a layer's neurons in a multidimensional manner is important for self-organizing maps.
It can be set to any row vector of 0 or positive integer elements,
where the product of all the elements becomes the number of neurons
in the layer (net.layers{i}.size).
Uses. Layer dimensions are used to calculate the neuron positions
within the layer (net.layers{i}.positions) using
the layer's topology function (net.layers{i}.topologyFcn).
Side Effects. Whenever this property is altered, the layer's size (net.layers{i}.size)
changes to remain consistent. The layer's neuron positions (net.layers{i}.positions)
and the distances between the neurons (net.layers{i}.distances)
are also updated.
net.layers{i}.distanceFcn
This property defines which of the
distance functions is used to calculate distances between
neurons in the ith layer from the neuron positions.
Neuron distances are used by self-organizing maps. It can be set to
the name of any distance function.
For a list of functions, type help nndistance.
Side Effects. Whenever this property is altered, the distances between the
layer's neurons (net.layers{i}.distances) are updated.
net.layers{i}.distances (read only)
This property defines the distances between neurons in the ith layer. These distances are used by self-organizing maps:
net.layers{i}.distances
It is always set to the result of applying the layer's distance
function (net.layers{i}.distanceFcn) to the positions
of the layer's neurons (net.layers{i}.positions).
net.layers{i}.initFcn
This property defines which of the layer initialization functions
are used to initialize the ith layer, if the network
initialization function (net.initFcn) is initlay. If the network initialization
is set to initlay, then the function
indicated by this property is used to initialize the layer's weights
and biases.
net.layers{i}.netInputFcn
This property defines which of the net input functions is used to calculate the ith layer's net input, given the layer's weighted inputs and bias during simulating and training.
For a list of functions, type help nnnetinput.
net.layers{i}.netInputParam
This property defines the parameters of the layer's net input
function. Call help on the current net input function
to get a description of each field:
help(net.layers{i}.netInputFcn)
net.layers{i}.positions (read only)
This property defines the positions of neurons in the ith layer. These positions are used by self-organizing maps.
It is always set to the result of applying the layer's topology
function (net.layers{i}.topologyFcn) to the positions
of the layer's dimensions (net.layers{i}.dimensions).
Plotting. Use plotsom to plot the
positions of a layer's neurons.
For instance, if the first-layer neurons of a network are arranged
with dimensions (net.layers{1}.dimensions) of [4
5], and the topology function (net.layers{1}.topologyFcn)
is hextop, the neurons' positions
can be plotted as follows:
plotsom(net.layers{1}.positions)
net.layers{i}.range (read only)
This property defines the output range of each neuron of the ith layer.
It is set to an Si ×
2 matrix, where Si is the
number of neurons in the layer (net.layers{i}.size),
and each element in column 1 is less than the element next to it in
column 2.
Each jth row defines the minimum and maximum
output values of the layer's transfer function net.layers{i}.transferFcn.
net.layers{i}.size
This property defines the number of neurons in the ith layer. It can be set to 0 or a positive integer.
Side Effects. Whenever this property is altered, the sizes of any input weights going to the layer
(net.inputWeights{i,:}.size), any layer weights going to the layer
(net.layerWeights{i,:}.size) or coming from the layer
(net.layerWeights{i,:}.size), and the layer's bias
(net.biases{i}.size), change.
The dimensions of the corresponding weight matrices (net.IW{i,:},
net.LW{i,:}, net.LW{:,i}), and biases
(net.b{i}) also change.
Changing this property also changes the size of the layer's output
(net.outputs{i}.size) and target
(net.targets{i}.size) if they exist.
Finally, when this property is altered, the dimensions of the layer's neurons
(net.layers{i}.dimension) are set to the same value. (This results
in a one-dimensional arrangement of neurons. If another arrangement is required, set the
dimensions property directly instead of using
size.)
net.layers{i}.topologyFcn
This property defines which of the topology functions are used
to calculate the ith layer's neuron positions (net.layers{i}.positions)
from the layer's dimensions (net.layers{i}.dimensions).
For a list of functions, type help nntopology.
Side Effects. Whenever this property is altered, the positions of the layer's
neurons (net.layers{i}.positions) are updated.
Use plotsom to plot the
positions of the layer neurons. For instance, if the first-layer neurons
of a network are arranged with dimensions (net.layers{1}.dimensions)
of [8 10] and the topology function (net.layers{1}.topologyFcn)
is randtop, the neuron positions
are arranged to resemble the following plot:
plotsom(net.layers{1}.positions)
net.layers{i}.transferFcn
This function defines which of the transfer functions is used to calculate the ith layer's output, given the layer's net input, during simulation and training.
For a list of functions, type help nntransfer.
net.layers{i}.transferParam
This property defines the parameters of the layer's transfer
function. Call help on the current transfer function
to get a description of what each field means:
help(net.layers{i}.transferFcn)
net.layers{i}.userdata
This property provides a place for users to add custom information to the ith network layer.
Outputs
net.outputs{i}.name
This property consists of a string defining the output name.
Network creation functions, such as feedforwardnet,
define this appropriately. But it can be set to any string as desired.
net.outputs{i}.feedbackInput
If the output implements open-loop feedback (net.outputs{i}.feedbackMode
= 'open'), then this property indicates the index of the
associated feedback input, otherwise it will be an empty matrix.
net.outputs{i}.feedbackDelay
This property defines the timestep difference between this output
and network inputs. Input-to-output network delays can be removed
and added with removedelay and adddelay functions resulting in this property
being incremented or decremented respectively. The difference in timing
between inputs and outputs is used by preparets to
properly format simulation and training data, and used by closeloop to add the correct number of
delays when closing an open-loop output, and openloop to
remove delays when opening a closed loop.
net.outputs{i}.feedbackMode
This property is set to the string 'none' for
non-feedback outputs. For feedback outputs it can either be set to 'open' or 'closed'.
If it is set to 'open', then the output will be
associated with a feedback input, with the property feedbackInput indicating
the input's index.
net.outputs{i}.processFcns
This property defines a row cell array of processing function names to be used by the ith network output. The processing functions are applied to target values before the network uses them, and applied in reverse to layer output values before being returned as network output values.
Side Effects. When you change this property, you also affect the following
settings: the output parameters processParams are
modified to the default values of the specified processing functions; processSettings, processedSize,
and processedRange are defined using the results
of applying the process functions and parameters to exampleOutput;
the ith layer size is updated to match the processedSize.
For a list of functions, type help nnprocess.
net.outputs{i}.processParams
This property holds a row cell array of processing function parameters to be used by ith network output on target values. The processing parameters are applied by the processing functions to input values before the network uses them.
Side Effects. Whenever this property is altered, the output processSettings, processedSize and processedRange are
defined by applying the process functions and parameters to exampleOutput.
The ith layer's size is also updated to match processedSize.
net.outputs{i}.processSettings (read only)
This property holds a row cell array of processing function
settings to be used by ith network output. The
processing settings are found by applying the processing functions
and parameters to exampleOutput and then used to
provide consistent results to new target values before the network
uses them. The processing settings are also applied in reverse to
layer output values before being returned by the network.
net.outputs{i}.processedRange (read only)
This property defines the range of exampleOutput values
after they have been processed with processingFcns and processingParams.
net.outputs{i}.processedSize (read only)
This property defines the number of rows in the exampleOutput values
after they have been processed with processingFcns and processingParams.
net.outputs{i}.size (read only)
This property defines the number of elements in the ith
layer's output. It is always set to the size of the ith
layer (net.layers{i}.size).
net.outputs{i}.userdata
This property provides a place for users to add custom information to the ith layer's output.
Biases
net.biases{i}.initFcn
This property defines the weight and bias initialization functions
used to set the ith layer's bias vector (net.b{i})
if the network initialization function is initlay and
the ith layer's initialization function is initwb.
net.biases{i}.learn
This property defines whether the ith bias vector is to be altered during training and adaption. It can be set to 0 or 1.
It enables or disables the bias's learning during calls to adapt and train.
net.biases{i}.learnFcn
This property defines which of the learning functions is used
to update the ith layer's bias vector (net.b{i})
during training, if the network training function is trainb, trainc,
or trainr, or during adaption,
if the network adapt function is trains.
For a list of functions, type help nnlearn.
Side Effects. Whenever this property is altered, the biases learning parameters
(net.biases{i}.learnParam) are set to contain the
fields and default values of the new function.
net.biases{i}.learnParam
This property defines the learning parameters and values for
the current learning function of the ith layer's
bias. The fields of this property depend on the current learning function.
Call help on the current learning function to get
a description of what each field means.
net.biases{i}.size (read only)
This property defines the size of the ith
layer's bias vector. It is always set to the size of the ith
layer (net.layers{i}.size).
net.biases{i}.userdata
This property provides a place for users to add custom information to the ith layer's bias.
Input Weights
net.inputWeights{i,j}.delays
This property defines a tapped delay line between the jth input and its weight to the ith layer. It must be set to a row vector of increasing values. The elements must be either 0 or positive integers.
Side Effects. Whenever this property is altered, the weight's size (net.inputWeights{i,j}.size)
and the dimensions of its weight matrix (net.IW{i,j})
are updated.
net.inputWeights{i,j}.initFcn
This property defines which of the Weight and Bias Initialization
Functions is used to initialize the weight matrix (net.IW{i,j})
going to the ith layer from the jth
input, if the network initialization function is initlay, and the ith
layer's initialization function is initwb.
This function can be set to the name of any weight initialization
function.
net.inputWeights{i,j}.initSettings (read only)
This property is set to values useful for initializing the weight
as part of the configuration process that occurs automatically the
first time a network is trained, or when the function configure is called on a network directly.
net.inputWeights{i,j}.learn
This property defines whether the weight matrix to the ith layer from the jth input is to be altered during training and adaption. It can be set to 0 or 1.
net.inputWeights{i,j}.learnFcn
This property defines which of the learning functions is used
to update the weight matrix (net.IW{i,j}) going
to the ith layer from the jth
input during training, if the network training function is trainb, trainc,
or trainr, or during adaption,
if the network adapt function is trains.
It can be set to the name of any weight learning function.
For a list of functions, type help nnlearn.
net.inputWeights{i,j}.learnParam
This property defines the learning parameters and values for the current learning function of the ith layer's weight coming from the jth input.
The fields of this property depend on the current learning function
(net.inputWeights{i,j}.learnFcn). Evaluate the
above reference to see the fields of the current learning function.
Call help on the current learning function
to get a description of what each field means.
net.inputWeights{i,j}.size (read only)
This property defines the dimensions of the ith
layer's weight matrix from the jth network input.
It is always set to a two-element row vector indicating the number
of rows and columns of the associated weight matrix (net.IW{i,j}).
The first element is equal to the size of the ith
layer (net.layers{i}.size). The second element
is equal to the product of the length of the weight's delay vectors
and the size of the jth input:
length(net.inputWeights{i,j}.delays) * net.inputs{j}.size
net.inputWeights{i,j}.userdata
This property provides a place for users to add custom information to the (i,j)th input weight.
net.inputWeights{i,j}.weightFcn
This property defines which of the weight functions is used to apply the ith layer's weight from the jth input to that input. It can be set to the name of any weight function. The weight function is used to transform layer inputs during simulation and training.
For a list of functions, type help nnweight.
net.inputWeights{i,j}.weightParam
This property defines the parameters of the layer's net input
function. Call help on the current net input function
to get a description of each field.
Layer Weights
net.layerWeights{i,j}.delays
This property defines a tapped delay line between the jth layer and its weight to the ith layer. It must be set to a row vector of increasing values. The elements must be either 0 or positive integers.
net.layerWeights{i,j}.initFcn
This property defines which of the weight and bias initialization
functions is used to initialize the weight matrix (net.LW{i,j})
going to the ith layer from the jth
layer, if the network initialization function is initlay, and the ith
layer's initialization function is initwb.
This function can be set to the name of any weight initialization
function.
net.layerWeights{i,j}.initSettings (read only)
This property is set to values useful for initializing the weight
as part of the configuration process that occurs automatically the
first time a network is trained, or when the function configure is called on a network directly.
net.layerWeights{i,j}.learn
This property defines whether the weight matrix to the ith layer from the jth layer is to be altered during training and adaption. It can be set to 0 or 1.
net.layerWeights{i,j}.learnFcn
This property defines which of the learning functions is used
to update the weight matrix (net.LW{i,j}) going
to the ith layer from the jth
layer during training, if the network training function is trainb, trainc,
or trainr, or during adaption,
if the network adapt function is trains.
It can be set to the name of any weight learning function.
For a list of functions, type help nnlearn.
net.layerWeights{i,j}.learnParam
This property defines the learning parameters fields and values
for the current learning function of the ith layer's
weight coming from the jth layer. The fields of
this property depend on the current learning function. Call help on
the current net input function to get a description of each field.
net.layerWeights{i,j}.size (read only)
This property defines the dimensions of the ith
layer's weight matrix from the jth layer. It is
always set to a two-element row vector indicating the number of rows
and columns of the associated weight matrix (net.LW{i,j}).
The first element is equal to the size of the ith
layer (net.layers{i}.size). The second element
is equal to the product of the length of the weight's delay vectors
and the size of the jth layer.
net.layerWeights{i,j}.userdata
This property provides a place for users to add custom information to the (i,j)th layer weight.
net.layerWeights{i,j}.weightFcn
This property defines which of the weight functions is used to apply the ith layer's weight from the jth layer to that layer's output. It can be set to the name of any weight function. The weight function is used to transform layer inputs when the network is simulated.
For a list of functions, type help nnweight.
net.layerWeights{i,j}.weightParam
This property defines the parameters of the layer's net input
function. Call help on the current net input function
to get a description of each field.
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