文件列表:

ACTIVATE.C (3872, 1996-01-17)
ART1.C (17979, 1996-01-17)
ART2.C (21821, 1996-01-17)
ART2TEST.DAT (23, 1996-01-17)
ARTTEST.DAT (111, 1996-01-17)
BPN.C (16796, 1996-01-17)
BUILDNET.C (4541, 1996-01-17)
CPN.C (19281, 1996-01-17)
EXEMPLAR.C (9089, 1996-01-17)
INITNET.C (2712, 1996-01-17)
LETTERS.DAT (3735, 1996-01-17)
NETSTRCT.H (2428, 1996-01-17)
PROPGATE.C (1734, 1996-01-17)
SHOWNET.C (2319, 1996-01-17)
UTILITY.C (1002, 1996-01-17)
XOR.DAT (179, 1996-01-17)

*********************************************************** Notice: This code is copyright (C) 1995 by David M. Skapura. It may be used as is, or modified to suit the requirements of a specific application without permission of the author. There are no royalty fees for use of this code, as long as the original author is credited as part of the final work. In exchange for this royalty free use, the user agrees that no guarantee or warantee is given or implied. ********************************************************** The code in the .c and .h files accompanying this test are used to create, train, save, and restore neural network models simulated on standard computer systems. Because we could not predict which computers might be used to simulate these networks, we have made the following assumptions in all of our models: 1. All input and output from the simulators is accomplished through data stored in files. Each simulator has its own functions to read and write data files, as required. 2. No attempt has been made to create a user interface for these simulators. Rather, the simulators are all implemented in ANSI C to improve portability, at the expense of the user interface. 3. Where possible, all simulators share common functions, such as those that create and connect layer structures in computer memory. 4. Each paradigm is defined by a file named ".c," where is the acronym for the network model. For example, the file "bpn.c" is the top level file for the backpropagation network paradigm, while "art1.c" contains the definition of the ART1 model. 5. In all paradigms, the layer is the central construct upon which the network model is built. The code will allow you to create and test networks of various sizes and configurations, but you are ultimately responsible for ensuring that the network topology is correct for your application. Also, because we could not predict your comfort level in using makefiles, the code for each network paradigm is designed to be #included as part of the top level file, instead of being compiled and linked separately. Each simulator has been compiled and tested on the example .dat files included with these code files. These example files are relativly small and easy to understand. The format for the .dat file is described in the file "exemplar.c", and the code that reads these exemplars is also contained in that file. Because we could not predict the specific example problems that you might want to try, these simulators are designed to create networks that are configured from commands that are processed in the top level function call for the paradigm. To use a network model to test a specific application, modify the code in the main() function in the paradigm file that you want to try. Define the size of the network you will need, set the parameters for the network appropriately, and tell the simulator where to look for the training exemplars. After training completes, the simulator always produces a network data file containing the weight values for the connections in the network after training. You can extend the function of these simulators to allow pattern matching after training by using the functions to create the network, then set the connection weights using the "restore" function defined for each network. The code for the simulator is also designed to try to simplify the process of developing a working network as much as possible. Capitalized symbols in the code are always shorthand notation to allow you to read and understand the code, while also allowing the c compiler to generate code as efficiently as possible. As an example, consider the main() function in the "bpn.c" file, which is shown below for clarification. void main () { bpn *n; int *layers; layers = define_layers (3, 2, 4, 1); n = build_bpn (layers); connect (LAYER[0], LAYER[1], COMPLETE, RANDOM); connect (LAYER[1], LAYER[2], COMPLETE, RANDOM); set_parameters (LAYER[1], DOT_PRODUCT, SIGMOID, 1.0, 0.6, 0.3); set_parameters (LAYER[2], DOT_PRODUCT, SIGMOID, 1.0, 0.6, 0.3); train_net (n, "xor.dat", 0.001, 10000); free (layers); destroy_bpn (n); } This function says, in English: Begin by creating a data structure to specify the configuration of the network. In this case, we are creating a 3 layer network, with 2 input units, 4 units on a single hidden layer, and 1 output unit. Build the neural network structure for this network, and assign the network pointer to the variable "n". Connect the input layer to the hidden layer, using random weights to initialize the connections. Connect the hidden layer to the output layer, using random weights to initialize the connections. Set the learning parameters for the hidden layer. In this example, use the vector inner product function to compute the input to the layer, use the sigmoid activation function to produce the output from the layer, use 1.0 as the activation modifier (tau) for the layer, 0.6 as the value for the learning rate, and 0.3 as the momentum value. Set the learning parameters for the output layer. Notice that, for both layers, calling set_parameters not only defines the activation function for the layer, but also installs the correct derivative function for the backpropagation computation (see activate.c and initnet.c for more details). Train the network, using exemplars contained in the file "xor.dat" Continue training until the global error goes below 0.001 or for 10,000 epochs, whichever occurs first. Once done with training, free the data structures and destroy the network. Notice that the network configuration does not get saved unless the network converges to a solution. You may want to change this functionality to save partially trained networks. Good luck, and may all of your networks converge. David ***********************************************************

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