Neural Network Lab Test Instructions

You may use this as a test for debugging if you wish.

1- Construct a neural network with two hidden layers.

2- Write a special method to initialize the weights with the values shown in this diagram, instead of with small random values:

3- Load this simple training dataset:

@RELATION neural_net_test
@ATTRIBUTE x1 continuous
@ATTRIBUTE x2 continuous
@ATTRIBUTE y1 continuous
@ATTRIBUTE y2 continuous
@DATA
0.3,0.7,0.1,1.0

4- Write a method that will print the weights of your network. (Relative to the figure above, the weights should be printed in a top-to-bottom, left-to-right order.) If you want exact comparisons, print your double-precision floating point values to 10 decimal places. (In C++, call "cout.precision(10);" before printing the values.)

5- Perform three epochs of training. (Since the dataset only contains one pattern, an epoch is just one pattern presentation.) Use a learning rate of 0.1, and a momentum term of 0. Before each pattern presentation, print the weights. After you forward-propagate the input vector, print the predicted output vector. After you back-propagate the error, print the error values assigned to each network unit.

6- Compare your printed values with these. Your values should match to at least 10 decimal places. If your numbers are off, you have a bug.

Weights:                                                                 
    0.1, 0.2, -0.1,                                                      
    -0.2, 0.3, -0.3,                                                     

    0.1, -0.2, -0.3, 
    0.2, -0.1, 0.3,  

    0.2, -0.1, 0.3, 
    0.1, -0.2, -0.3 
Input vector: 0.3, 0.7
Target output: 0.1, 1 
Forward propagating...
Predicted output: 0.5802212893, 0.4591181386
Back propagating...
Error values:
    0.0006498826879, -0.001076309464
    -0.003775554904, -0.01849687375
    -0.1169648797, 0.1343164751
Descending gradient...
Weights:
    0.1000649883, 0.2000194965, -0.09995450821,
    -0.2001076309, 0.2999677107, -0.3000753417,

    0.09962244451, -0.200197267, -0.3001588284,
    0.1981503126, -0.1009664336, 0.2992218814,

    0.188303512, -0.1054666053, 0.2933556532,
    0.1134316475, -0.1937224306, -0.2923699726
Input vector: 0.3, 0.7
Target output: 0.1, 1
Forward propagating...
Predicted output: 0.5757806352, 0.4643173074
Back propagating...
Error values:
    0.0006201909322, -0.001060640521
    -0.003374132411, -0.01792982084
    -0.1162128911, 0.1332386127
Descending gradient...
Weights:
    0.1001270074, 0.2000381022, -0.09991109485,
    -0.200213695, 0.2999358915, -0.3001495865,

    0.09928503127, -0.200373569, -0.300300756,
    0.1963573305, -0.1019032854, 0.2984676916,

    0.1766822229, -0.1108965022, 0.2867617159,
    0.1267555088, -0.1874970289, -0.2848099931