Data Mining Mehmed Kantardzic (good english books to read .txt) 📖

second, and so on, until the computation is finished with final output layer of nodes. In this pass, based on given input values of each learning sample, a network computes the corresponding output. Synaptic weights remain unaltered during this pass.

The second, backward pass, on the other hand, starts at the output layer, passing the error signal (the difference between the computed and the desired output value) leftward through the network, layer by layer, and recursively computing the local gradients δ for each neuron. This recursive process permits the synaptic weights of the network to undergo changes in accordance with the delta rule. For the neuron located at the output layer, δ is equal to the error signal of that neuron multiplied by the first derivative of its nonlinearity represented in the activation function. Based on local gradients δ, it is straightforward to compute Δw for each connection to the output nodes. Given the δ values for all neurons in the output layer, we use them in the previous layer before (usually the hidden layer) to compute modified local gradients for the nodes that are not the final, and again to correct Δw for input connections for this layer. The backward procedure is repeated until all layers are covered and all weight factors in the network are modified. Then, the backpropagation algorithm continues with a new training sample. When there are no more training samples, the first iteration of the learning process finishes. With the same samples, it is possible to go through a second, third, and sometimes hundreds of iterations until error energy Eav for the given iteration is small enough to stop the algorithm.

The backpropagation algorithm provides an “approximation” to the trajectory in weight space computed by the method of steepest descent. The smaller we make the learning rate parameter η, the smaller the changes to the synaptic weights in the network will be from one iteration to the next and the smoother will be the trajectory in weight space. This improvement, however, is attained at the cost of a slower rate of learning. If, on the other hand, we make η too large in order to speed up the learning process, the resulting large changes in the synaptic weights can cause the network to become unstable, and the solution will become oscillatory about a minimal point never reaching it.

A simple method of increasing the rate of learning yet avoiding the danger of instability is to modify the delta rule by including a momentum term:

where α is usually a positive number called momentum constant and Δwji(n − 1) is the correction of the weight factor for a previous (n − 1)th sample. α, in practice, is usually set to the value between 0.1 and 1. The addition of the momentum term smoothes the weight updating and tends to resist erratic weight changes because of gradient noise or high-spatial frequencies in the error surface. However, the use of momentum terms does not always seem to speed up training; it is more or less application-dependent. The momentum factor represents a method of averaging; rather than averaging derivatives, momentum averages the weight changes themselves. The idea behind momentum is apparent from its name: including some kind of inertia in weight corrections. The inclusion of the momentum term in the backpropagation algorithm has a stabilizing effect in cases where corrections in weight factors have a high oscillation and sign changes. The momentum term may also have the benefit of preventing the learning process from terminating in a shallow local minimum on the error surface.

Reflecting practical approaches to the problem of determining the optimal architecture of the network for a given task, the question about the values for three parameters, the number of hidden nodes (including the number of hidden layers), learning rate η, and momentum rate α, becomes very important. Usually the optimal architecture is determined experimentally, but some practical guidelines exist. If several networks with different numbers of hidden nodes give close results with respect to error criteria after the training, then the best network architecture is the one with smallest number of hidden nodes. Practically, that means starting the training process with networks that have a small number of hidden nodes, increasing this number, and then analyzing the resulting error in each case. If the error does not improve with the increasing number of hidden nodes, the latest analyzed network configuration can be selected as optimal. Optimal learning and momentum constants are also determined experimentally, but experience shows that the solution should be found with η about 0.1 and α about 0.5.

When the ANN is first set up, the initial weight factors must be given. The goal in choosing these values is to begin the learning process as fast as possible. The appropriate method is to take the initial weights as very small evenly distributed random numbers. That will cause the output values to be in mid-range regardless of the values of its inputs, and the learning process will converge much faster with every new iteration.

In backpropagation learning, we typically use the algorithm to compute the synaptic weights by using as many training samples as possible. The hope is that the neural network so designed will generalize the best. A network is said to generalize well when the input–output mapping computed by the network is correct for test data never used earlier in creating or training the network. In the MLP, if the number of hidden units is less that the number of inputs, the first layer performs a dimensionality reduction. Each hidden unit may be interpreted as defining a template. By analyzing these templates we can extract knowledge from a trained ANN. In this interpretation weights are defining relative importance in the templates. But the largest number of training samples and the largest number of learning iterations using these samples do not necessarily lead to the best generalization. Additional problems occur during the learning process, and they are briefly described through the following analysis.

The learning process using an