SANN - Custom Neural Network - Quick Tab

Select the Quick tab of the SANN - Custom Neural Network dialog box to access the options described here. For information on the options that are common to all tabs (located at the top and on the lower-right side of the dialog box), see SANN - Custom Neural Network.

Network type. Use the options in this group box to specify the type of network (multilayer perceptron or radial basis function).

Multilayer perceptron (MLP). Select the Multilayer perceptron (MLP) option button to generate multilayer perceptron networks. The multilayer perceptron is the most common form of network. It requires iterative training and the networks are quite compact, execute quickly once trained, and in most problems yield better results than the other types of networks.

Radial basis function (RBF). Select the Radial basis function (RBF) option button to generate radial basis function networks. Radial basis function networks tend to be slower and larger than multilayer perceptron and often have inferior performance, but they can be trained faster than MLP for large data sets and linear output activation functions.

Error function. Specify the error function to be used in training a network.

Sum of squares. Select the Sum of squares option button to generate networks using the sum of squares error function. Note that this is the only error function available for regression type analyses.

Cross entropy. Select the Cross entropy option button to generate networks using cross entropy error functions. This error function assumes that the data is drawn from the exponential family of distributions (see Bishop 1995 for more details) and supports a direct probabilistic interpretation of the network outputs. Note that this error function is only available for classification problems. The option will be unavailable for regression type analyses. When the Cross entropy error function is selected, the Output neurons (in the Activation functions group box) will always be set to Softmax.

Activation functions. Use the options in this group box to select activation functions for the hidden and output neurons. The choice of the activation function, i.e., the precise mathematical function, is crucial in building a neural network model since it is directly related to the performance of the model. Generally, it is recommended that you choose the tanh and identity functions for the hidden and output neurons for multilayer perceptron networks (default settings) when the Sum of squares error function is used. For radial basis function networks, the Hidden units are automatically set to Gaussian; and the Output units are set to either Identity (when Sum of squares error function is used) or Softmax (when Cross entropy error function is used).

Hidden units. Use the Hidden units drop-down list to select the activation function for the hidden layer neurons. For multilayer perceptron networks, these include the identity function, hyperbolic tanh (recommended), logistic sigmoid, exponential, and sine activation functions. For radial basis functions networks, a Gaussian activation function is always used for hidden neurons.

Identity. Uses the identity function. With this function, the activation level is passed on directly as the output.

Tanh. Uses the hyperbolic tangent function (recommended). The hyperbolic tangent function (tanh) is a symmetric S-shaped (sigmoid) function, whose output lies in the range (-1, +1). Often performs better than the logistic sigmoid function because of its symmetry.

Logistic. Uses the logistic sigmoid function. This is an S-shaped (sigmoid) curve, with output in the range (0, 1).

Exponential. Uses the exponential activation function.

Sine. Uses the standard sine activation function.

Gaussian. Uses a Gaussian (or Normal) distribution. This is the only choice available for RBF neural networks.

Output units. Use the Output units drop-down list to select the activation functions for the hidden-output neurons. For multilayer perceptron networks, these include the identity function (recommended), hyperbolic tanh, logistic sigmoid, exponential, sine, and softmax activation functions. For Radial basis function (RBF) networks, the choice of Output units is dependent on the selected Error function. For RBF networks with Sum of squares error function, an Identity activation function is used. For RBF networks with Cross entropy error function, the Softmax activation function is always used.

Identity. Uses the identity function (recommended). With this function, the activation level is passed on directly as the output.

Tanh. Uses the hyperbolic tangent function. The hyperbolic tangent function (tanh) is a symmetric S-shaped (sigmoid) function, whose output lies in the range (-1, +1). Often performs better than the logistic sigmoid function because of its symmetry.

Logistic. Uses the logistic sigmoid function. This is an S-shaped (sigmoid) curve, with output in the range (0, 1).

Exp. Uses the negative exponential activation function.

Sine. Uses the standard sine activation function.

Softmax. Uses a specialized activation function for one-of-N encoded classification networks. It performs a normalized exponential (i.e., the outputs add up to 1). In combination with the cross entropy error function, it allows multilayer perceptron networks to be modified for class probability estimation (Bishop, 1995; Bridle, 1990).

Networks to train. Use this option to specify how many networks the Custom Neural Network (CNN) should train. The larger the number of networks trained, the more detailed is the search carried out by the CNN. It is recommended that you set the value for this option as large as possible depending on your hardware speed and resources. Although you can create one type of network type at a time, by training more than one network you can find multiple solutions provided by the same network. Furthermore, using the Results dialog, you can combine the predictions of these networks to create ensembles. Using predictions drawn from an ensemble of networks can generally yield better results compared to the predictions of the individual networks (see Bishop 1995).

No. of neurons. Specify the number of neurons in the hidden layer of the network. The more neurons the hidden layer contains, the more complex (flexible) it becomes.