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 R_i × 2 matrix, where R_i 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.

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 S_i × 2 matrix, where S_i 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.

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.

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.

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.

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.