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Network graph structure As described earlier, BACKPROPAGATION be apcan plied to any acyclic directed graph of sigmoid units Therefore, another design choice we face is how many units to include in the network and how to interconnect them The most common network structure is a layered network with feedforward connections from every unit in one layer to every unit in the next In the current design we chose this standard structure, using two layers of sigmoid units (one hidden layer and one output layer) It is common to use one or two layers of sigmoid units and, occasionally, three layers It is not common to use more layers than this because training times become very long and because networks with three layers of sigmoid units can already express a rich variety of target functions (see Section 462) Given our choice of a layered feedforward network with one hidden layer, how many hidden units should we include In the results reported in Figure 410, only three hidden units were used, yielding a test set accuracy of 90% In other experiments 30 hidden units were used, yielding a test set accuracy one to two percent higher Although the generalization accuracy varied only a small amount between these two experiments, the second experiment required significantly more training time Using 260 training images, the training time was approximately 1 hour on a Sun Sparc5 workstation for the 30 hidden unit network, compared to approximately 5 minutes for the 3 hidden unit network In many applications it has been found that some minimum number of hidden units is required in order to learn the target function accurately and that extra hidden units above this number do not dramatically affect generalization accuracy, provided cross-validation methods are used to determine how many gradient descent iterations should be performed If such methods are not used, then increasing the number of hidden units often increases the tendency to overfit the training data, thereby reducing generalization accuracy Other learning algorithm parameters In these learning experiments the learning rate r] was set to 03, and the momentum a! was set to 03 Lower values for both parameters produced roughly equivalent generalization accuracy, but longer training times If these values are set too high, training fails to converge to a network with acceptable error over the training set Full gradient descent was used in all these experiments (in contrast to the stochastic approximation to gradient descent in the algorithm of Table 42) Network weights in the output units were initialized to small random values However, input unit weights were initialized to zero, because this yields much more intelligible visualizations of the learned weights (see Figure 410), without any noticeable impact on generalization accuracy The. asp.net ean 13 EAN - 13 ASP . NET Control - EAN - 13 barcode generator with free ...
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Free download for .NET EAN 13 Barcode Generator trial package to create & generate EAN 13 barcodes in ASP . NET , WinForms applications using C# & VB. Player region code (mask) Listing 9-7 21 22 23 number of training iterations was selected by partitioning the available data into a training set and a separate validation set Gradient descent was used to minimize the error over the training set, and after every 50 gradient descent steps the performance of the network was evaluated over the validation set The final selected network was the one with the highest accuracy over the validation set See Section 465 for an explanation and justification of this procedure The final reported accuracy (e-g, 90% for the network in Figure 410) was measured over yet a third set of test examples that were not used in any way to influence training #import<UIKit/UIKith> #import "SecondViewControllerh" @interface FirstViewController : UIViewController { SecondViewController * second; } @property (nonatomic, retain) SecondViewController * second; - (IBAction)showDetails:(id)sender; @end asp.net ean 13 Reading barcode EAN 13 in asp . net , C# - CodeProject
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