It consists of a simulator kernel, a graphical user interface based on X-Windows to interactively construct and visualize neural networks, and a compiler to generate large neural networks from a high level network description language. Applications of SNNS currently include printed character recognition, handwritten character recognition, recognition of machine parts, stock prize prediction, noise reduction in a telecom environment and texture analysis, among others.
We also give preliminary design decisions for a planned parallel version of SNNS on a massively parallel SIMD-computer with more than 16, processors MasPar MP which has been installed at our research institute recently.
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Grajski, C. Chen, C. Leave the File window open for the next task. Before loading any more files, however, take a look at the network that you just loaded.
The Display window should pop up and look like the following: This is a representation of an Artificial Neural Network arguably, not a very good one. Note a couple of things. First, each block is a called a node. Each node represents one simple neuron in the network. The integer value above each node is the node's id number. The number below the node is the node's current value. The size of the black square also represents the node's current value. Nodes with values close to 1 have large green squares, while nodes with values close to zero have large blue squares.
In this network, the two nodes at the bottom are input nodes, the node in the middle is the hidden node, and the node at the top is the output node. Therefore, the current input to the network is a 1. As this network has been trained to simulate the XOR function, having an output value of close to 0 for the inputs 1, 1 is correct.
What is missing from the visualization of a an ANN is the weights and links between the nodes. To show these parts of the network, we need to change the setup of the display.
Note that the buttons across from the links field are not highlighted. Go ahead and click on the first two of these buttons to activate the links and weight displays. Then click on the DONE button to effect the changes. The network Display window should now look like this: Note the connection lines and the weights that indicate their strength. Loading a Pattern File Now you have finished loading a network and displaying it, but the network has no data to process.
The first thing to do is to return to the File menu and click on the Patterns button so that all of the Pattern files in the directory are displayed in the file list.
The resulting picture should look like: Double-click on the file xor and then click on the LOAD button to load it into active memory. In the SNNS text window, it should tell you that it just loaded the pattern file. Conveniently, SNNS is able to have more than one pattern file in active memory at once, so long as the memory is available it will tell you if it's not. Now you have three pattern files in active memory, and, as you will see, you can choose which pattern set to use for your training set and which pattern to use for your testing set.
Looking at Patterns One of the important things to do when you are working with an ANN is to understand what it's doing with the inputs that you give it. In other words, you want to watch your network in action. Only then can you get a sense of what it's doing with the inputs. In order to control what patterns the network sees and execute the backpropagation training algorithm you need to open the Control window by clicking on the CONTROL button in the Manager window.
The control window will look like this: The editable fields on the left side of the window all deal with training parameters that you can set, and we will cover these a bit later. The various buttons also control various aspects of training and testing patterns. In the middle of the window you will notice the name of the pattern file you loaded last. The upper name indicates the training set that the network would use if you told it to start training right now. The lower name indicates the test set that the network will use to see how well it is generalizing its ability to solve the problem.
Your Control window should now look like the image above. Now what we would like to do is see how the network performs on each of the four patterns in xor [ 0 0 , 0 1 , 1 0 , 1 1 ].
The TEST button in the middle of the second row allows us to step through the current training set, apply each pattern to the network, and calculate the network's output.
Note that with the two patterns 1 1 and 0 0 the network outputs a value close to zero, while for the other two patterns the network outputs a value close to 1. Notice that the inputs are not clean 0 or 1 values, but in-between. Ask yourself if the output of the network makes sense given the inputs. In general, if the two are similar the output of the network should be close to zero.
Now you have successfully loaded a network, loaded several pattern sets, set the testing and training pattern sets, and stepped through a pattern set and tested the performance of the network on it. Note, you can do whatever you want to a network reset the weights, add nodes, delete nodes and these changes will only exist in active memory until you explicitly save the network. The first step in training a network is to initialize the network connection weights to small random values.
Go ahead and click on the INIT button. Notice that the numbers indicating the connection weights have changed. Now select the xor pattern as the training pattern and use the TEST button to step through and see how the network does. Notice that the network no longer gives appropriate answers for the various input patterns. Now, during the training process we are going to want to watch the performance of the network so that we know when to stop training to avoid overtraining.
The buttons with the arrows next to the words Scale X and Scale Y control the horizontal and vertical scale of the graph which you may need to change in order to get a good picture of what is going on. SNNS does not scale the graph automatically in the vertical direction.
Now go back to the Control window. The important buttons during training for a standard feed-forward backpropagation-trained network which is what we are working with are, for the most part, on the second line to the right of the word CYCLES. Get started with Microsoft Edge. Microsoft Edge was built to bring you the best of the web, with more control and more privacy browse. You don't need to download and install Internet Explorer 11 in Windows 8.
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