HIDDEN LAYERS
 

 

 

 

 

 

 

 

 

 

 

 

 

The last unit on the right is the output layer because it is connected to the out­ put of the neural network. It is fully connected to all the units in the hidden layer. Most of the time, the neural network is being used to calculate a single value, so there is only one unit in the output layer and the value. We must map this value back to understand the output. For the network in Figure 7.6, we have to convert 0.49815 back into a value between $103,000 and $250,000. It corresponds to $176,228, which is quite close to the actual value of $171,000. In some implementations, the output layer uses a simple linear transfer function, so the output is a weighted linear combination of inputs. This eliminates the need to map the outputs. It is possible for the output layer to have more than one unit. For instance, a department store chain wants to predict the likelihood that customers will be purchasing products from various departments, such as women’s apparel, furniture, and entertainment. The stores want to use this information to plan promotions and direct target mailings.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

The next layer is called the hidden layer because it is connected neither to the inputs nor to the output of the network. Each unit in the hidden layer is typically fully connected to all the units in the input layer. Since this network contains standard units, the units in the hidden layer calculate their output by multiplying the value of each input by its corresponding weight, adding these up, and applying the transfer function. A neural network can have any number of hidden layers, but in general, one hidden layer is sufficient. The wider the layer (that is, the more units it contains) the greater the capacity of the network to recognize patterns. This greater capacity has a drawback, though, because the neural network can memorize patterns-of-one in the training examples. We want the network to generalize on the training set, not to memorize it. To achieve this, the hidden layer should not be too wide. each have an additional input coming down from the top. This is the constant input, sometimes called a bias, and is always set to 1. Like other inputs, it has a weight and is included in the combi­ nation function. The bias acts as a global offset that helps the network better understand patterns. The training phase adjusts the weights on constant inputs just as it does on the other weights in the network.