Function Index Save the entire network to a configuration file. Saves the entire network to a configuration file. Save the training structure to a file, with the format as specified in read_train_from_file Saves the training structure to a fixed point data file. Scale data in input vector before feed it to ann based on previously calculated parameters. Scales the inputs in the training data to the specified range. Scale data in output vector before feed it to ann based on previously calculated parameters. Scales the outputs in the training data to the specified range. Scale input and output data based on previously calculated parameters. Scales the inputs and outputs in the training data to the specified range. Set the activation function for neuron number neuron in layer number layer, counting the input layer as layer 0. Set the activation function for all of the hidden layers. Set the activation function for all the neurons in the layer number layer, counting the input layer as layer 0. Set the activation function for the output layer. Set the activation steepness for neuron number neuron in layer number layer, counting the input layer as layer 0. Set the steepness of the activation steepness in all of the hidden layers. Set the activation steepness all of the neurons in layer number layer, counting the input layer as layer 0. Set the steepness of the activation steepness in the output layer. Set the bit fail limit used during training. Sets the callback function for use during training. Sets the array of cascade candidate activation functions. Sets the array of cascade candidate activation steepnesses. Sets the cascade candidate change fraction. Sets the candidate limit. Sets the number of cascade candidate stagnation epochs. Sets the max candidate epochs. Sets the maximum out epochs. Sets the number of candidate groups. Sets the cascade output change fraction. Sets the number of cascade output stagnation epochs. Sets the weight multiplier. Change where errors are logged to. Calculate scaling parameters for future use based on training data. Set the learning momentum. Set the learning rate. Calculate scaling parameters for future use based on training data. Sets the quickprop decay factor. Sets the quickprop mu factor. The decrease factor is a value smaller than 1, which is used to decrease the step-size during RPROP training. The maximum step-size is a positive number determining how large the maximum step-size may be. The minimum step-size is a small positive number determining how small the minimum step-size may be. The increase factor used during RPROP training. Calculate scaling parameters for future use based on training data. Set the training data to the input and output data provided. Set the error function used during training. Set the stop function used during training. Set the training algorithm. Set a connection in the network. Set connections in the network. Shuffles training data, randomizing the order. Changes the training data to a subset, starting at position pos and length elements forward. |