# Examples: sequence.in

This is a sequence prediction task. There are five bits in the input and six in the output. The input is a sequence of three different one-bit-on patterns, repeated three times. After the first two bits, the network just has to activate the last output unit. But once it has seen all three bits, it must predict the next bit in the sequence.

This task is very hard for a simple recurrent network, because the current input, or even the previous input, doesn't help the network much in its prediction. It must look back to the input before last. You will probably find that a basic SRN can't learn the task.

However, if you set the network's backpropTicks to 3 or more, it will learn the task. This causes the backpropagation phase to extend back through time the specified number of ticks. This helps the network learn that it must keep track of all three values to formulate its prediction.

Did the network generalize perfectly to the testing examples? If not, can you improve its generalization by encouraging binary hidden representations? Try giving the hidden layer an output cost type or raising the gain.

Last modified: Thu Nov 16 17:20:31 EST 2000