Constant Index Standard linear error function. Tanh error function, usually better but can require a lower learning rate. Constant array consisting of the names for the activation function, so that the name of an activation function can be received by: Periodical cosinus activation function. Unable to allocate memory Unable to open configuration file for reading Unable to open configuration file for writing Unable to open train data file for reading Unable to open train data file for writing Error reading info from configuration file Error reading connections from configuration file Error reading neuron info from configuration file Error reading training data from file Unable to train with the selected activation function Unable to use the selected activation function Unable to use the selected training algorithm Index is out of bound No error Scaling parameters not present Irreconcilable differences between two struct fann_train_data structures Trying to take subset which is not within the training set Wrong version of configuration file Number of connections not equal to the number expected Fast (sigmoid like) activation function defined by David Elliott Fast (symmetric sigmoid like) activation function defined by David Elliott Standard linear error function. Constant array consisting of the names for the training error functions, so that the name of an error function can be received by: Tanh error function, usually better but can require a lower learning rate. Gaussian activation function. Symmetric gaussian activation function. Linear activation function. Bounded linear activation function. Bounded Linear activation function. Each layer only has connections to the next layer Each layer has connections to all following layers Constant array consisting of the names for the network types, so that the name of an network type can be received by: Sigmoid activation function. Stepwise linear approximation to sigmoid. Symmetric sigmoid activation function, aka. Periodical sinus activation function. Stop criteria is number of bits that fail. Stop criteria is Mean Square Error (MSE) value. Constant array consisting of the names for the training stop functions, so that the name of a stop function can be received by: Threshold activation function. Threshold activation function. Standard backpropagation algorithm, where the weights are updated after calculating the mean square error for the whole training set. Standard backpropagation algorithm, where the weights are updated after each training pattern. Constant array consisting of the names for the training algorithms, so that the name of an training function can be received by: A more advanced batch training algorithm which achieves good results for many problems. A more advanced batch training algorithm which achieves good results for many problems. Each layer only has connections to the next layer Each layer has connections to all following layers Stop criteria is number of bits that fail. Stop criteria is Mean Square Error (MSE) value. Standard backpropagation algorithm, where the weights are updated after calculating the mean square error for the whole training set. Standard backpropagation algorithm, where the weights are updated after each training pattern. A more advanced batch training algorithm which achieves good results for many problems. A more advanced batch training algorithm which achieves good results for many problems. |