The sections which follow, describe all of the functions, methods, and global variables which operate upon or are specifically related to the Neural Net Object type. Each function, method, and global variable has its own section. The sections are listed in alphabetic order.
Overview
The clear Method resets the specified NeuralNet object and randomizes all of the weights at each level of the net. Alternatively, the clear method may reset all of the weights to those specified in the weights vector supplied as an argument.
Type: Method
Syntax: (clear:aNeuralNet)
Syntax: (clear:aNeuralNet seed)
Syntax: (clear:aNeuralNet weightsVector)
When To Use
The clear method is used to reset a NeuralNet object and randomize its weights.
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Arguments Explanation
|
aNeuralNet |
The NeuralNet Object to be cleared |
|
Returns |
The NeuralNet Object cleared |
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Example1
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The clear method is used to reset a NeuralNet object and pseudo randomize its weights.
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Arguments Explanation
|
aNeuralNet |
The NeuralNet Object to be cleared |
|
seed |
The seed for the srandom function to be set before pseudo randomizing the weights. |
|
Returns |
The NeuralNet Object cleared |
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Example1
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When To Use
The clear method is used to reset the weights in a NeuralNet object to those values contained in the specified weights vector. The weights vector would have been obtained from a previous getWeights method.
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Arguments Explanation
|
aNeuralNet |
The NeuralNet Object to be cleared |
|
weightsVector |
The vector containing the new weights for this NeuralNet. |
|
Returns |
The NeuralNet Object has all its weights reset. |
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Example1
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Overview
The computeError Method computes the output error for the specified NeuralNet {aNeuralNet}. The output signal is compared to the specified target {target}, and the resulting least squares error is computed and placed in the error property of the NeuralNet.
The target must be a Number Vector of the same length or larger than specified in the previous makeNeuralNet (if larger, the target signals are taken from the right of the target vector). The output error is computed and placed in the error property of the NeuralNet. This method does NOT adjust weights in each hidden layer of the NeuralNet.
Type: Method
Syntax: (computeError:aNeuralNet target)
When To Use
The computeError method is used to compute the error.
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Arguments Explanation
|
aNeuralNet |
The NeuralNet whose input signal is to be propagated. |
|
target |
The target vector to be compared with the output signal. |
|
Returns |
The Object Identifier of the new NeuralLayer Object |
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Example1
(setq aNN (makeNeuralNet 6 1 binary:))
(clear:aNN)
(setq in (makeVector number: 6 .2 .1 .3 .5 .7 .22))
(setq target (makeVector number: 1 32))
(propagateForward:aNN in)
(setq error (computeError:aNN target))
Note1 The returned error is the sum of the squared errors for each output signal.
Note2 Input must be within the closed interval [0, 1].
Overview
The getWeights Method returns the weights of the specified NeuralNet object. The clear method may then be used later to reset all of the weights to those specified in the specified weights vector. In effect, the getWeights method saves the state of the NeuralNet object at that time.
Type: Method
Syntax: (getWeights:aNeuralNet)
When To Use
The getWeights method is used to save the sate of a NeuralNet object for later use.
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Arguments Explanation
|
aNeuralNet |
The NeuralNet Object whose state is to be saved |
|
Returns |
The weightsVector containing the weights of the NeuralNet object |
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Example1
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Overview
The makeNeuralNet Function creates a new NeuralNet object complete with all child NeuralLayer objects required. The makeNeuralNet function provides a time-saving short cut as opposed to using the (new NeuralNet: ) function call for the outer object and the (new NeuralLayer: ) function call for each of the input, hidden layer, and output neural layers.
Type: Function
Syntax: (makeNeuralNet inputs outputs filter)
Syntax: (makeNeuralNet inputs hidden outputs filter)
Syntax: (makeNeuralNet weightsVector)
The makeNeuralNet function is used to create a NeuralNet object easily. The number of hidden layer neurons is automatically set to (2 x inputs) + 1. This is the minimum number which allows the neural net to recognize any continuous function with the specified number of inputs.
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Arguments Explanation
|
inputs |
The number of cells in the input layer |
|
output |
The number of cells in the output layer . |
|
filter |
Must be one of the following valid output filter functions: continuous: Continuous unrestricted real number output sigmoid: Continuous output restricted within [0, 1] binary: Binary output (either 0 or 1) bipolar: Bipolar output (either -1 or 1) The default is continuous: The filter symbol identifies the output function used to determine the final output value from the NeuralNet object. |
|
Returns |
The Object Identifier of the new NeuralNet Object |
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Example1
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When To Use
The makeNeuralNet function is used to create a NeuralNet object easily. The number of hidden layer neurons is specified by the user.
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Arguments Explanation
|
inputs |
The number of cells in the input layer |
|
hidden |
The number of cells in the hidden layer |
|
output |
The number of cells in the output layer . |
|
filter |
Must be one of the following valid output filter functions: continuous: Continuous unrestricted real number output sigmoid: Continuous output restricted within [0, 1] binary: Binary output (either 0 or 1) bipolar: Bipolar output (either -1 or 1) The default is continuous: The filter symbol identifies the output function used to determine the final output value from the NeuralNet object. |
|
Returns |
The Object Identifier of the new NeuralNet Object |
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Example1
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When To Use
The makeNeuralNet function is used to recreate a NeuralNet object from the weightsVector returned from a previous getWeights method.
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Arguments Explanation
|
weightsVector |
The weightsVector returned from a previous getWeights method. |
|
Returns |
The recreated NeuralNet Object |
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Example1
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Overview
The propagateBackward Method propagates the output error for the specified NeuralNet {aNeuralNet}. The output signal is compared to the specified target {target}, and the resulting least squares error is propagated backward through each layer of the NeuralNet.
The target must be a Number Vector of the same length or larger than specified in the previous makeNeuralNet (if larger, the target signals are taken from the right of the target vector). The output error is propagated backward through the hidden layer finally producing adjusted weights in each layer of the NeuralNet.
Type: Method
Syntax: (propagateBackward:aNeuralNet target)
When To Use
The propagateBackward method is used to produce adjusted weights in the NeuralNet's layers.
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Arguments Explanation
|
aNeuralNet |
The NeuralNet whose input signal is to be propagated. |
|
target |
The target vector to be compared with the output signal. |
|
Returns |
The Object Identifier of the new NeuralLayer Object |
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Example1
(setq aNN (makeNeuralNet 6 1 binary:))
(clear:aNN)
(setq in (makeVector number: 6 .2 .1 .3 .5 .7 .22))
(setq target (makeVector number: 1 32))
(propagateForward:aNN in)
(propagateBackward:aNN target)
Overview
The propagateForward method propagates an input signal {inVector} forward in the specified neural net {aNeuralNet}. The input signal must be a Number Vector of the same length as specified in the previous makeNeuralNet. If the input signal is omitted, the current values in the input layer (aNeuralNet.input.output) are used as the input signal. The input signal is propagated forward through the hidden layer and producing new values in the output layer.
Type: Method
Syntax: (propagateForward:aNeuralNet inVector)
(propagateForward:aNeuralNet)
When To Use
The propagateForward method is used to produce new values in the NeuralNet's output layer.
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Arguments Explanation
|
aNeuralNet |
The NeuralNet whose input signal is to be propagated. |
|
inVector |
Optional Argument . The input signal to be propagated. |
|
Returns |
Propagates the input signal forward through the hidden layer producing new values and returns the Object Identifier of the NeuralNet Object. |
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Example1
(setq aNN (makeNeuralNet 6 1 continuous:))
(clear:aNN)
(setq in (makeVector number: 6 .2 .1 .3 .5 .7 .22))
(propagateForward:aNN in)
Note Input must be within the closed interval [0, 1].
Overview
The propagateUserError Method propagates the user error for the specified NeuralNet {aNeuralNet}. The output signal is assigned the specified user error {target}, and the resulting user supplied error is propagated backward through each layer of the NeuralNet.
The final error value is the sum of the squares of each user supplied error signal {target}. Each user supplied error signal should be positive (if the current output should be moved higher) or negative (if the current output should be moved lower). The magnitude of the error signal determines the magnitude of the output signal adjustment.
The target must be a Number Vector of the same length or larger than specified in the previous makeNeuralNet (if larger, the errors are taken from the right of the error vector). The user error is propagated backward through the hidden layer finally producing adjusted weights in each layer of the NeuralNet.
Type: Method
Syntax: (propagateUserError:aNeuralNet target)
When To Use
The propagateUserError method is used to produce adjusted weights in the NeuralNet's layers.
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Arguments Explanation
|
aNeuralNet |
The NeuralNet whose input signal is to be propagated. |
|
target |
The target vector to assign user errors to each output signal. |
|
Returns |
The Object Identifier of the new NeuralLayer Object |
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Example1
(setq aNN (makeNeuralNet 6 1 binary:))
(clear:aNN)
(setq in (makeVector number: 6 .2 .1 .3 .5 .7 .22))
(setq target (makeVector number: 1 32))
(propagateForward:aNN in)
(propagateUserError:aNN target)