Back propagation Neural Networks

In both Project, you are reading the input (x vector) and the output (y vector) from a file.

Below is the structure of the input file:

1) First line: M, L, N where M is number of Input Nodes, L is number of Hidden Nodes and N is number of Output Nodes.

2) Second line: K, the number of training examples, each line has length M+N values, first M values are X vector and last N values are output values.

3) K lines follow as described.

An example of input file:

3 2 2

3

1 1 1.5 2 2

-1 2.25 0.5 -0.5 1.2

1 1 1 1 2

Above is a file that describes:

1. Network with 3 input nodes, 2 hidden and 2 output.
2. Training is 3 examples.
3. Second example has training example X [1 1 1.5] and output.

There is a normalization step and it is done by computing, for each numeric  x-data column value v, v' = (v - mean) / std dev. This  technique is sometimes called Gaussian normalization.