One of the weights should have been 3.0 not 4.0, which then affects the rest of the calculations.
Here is the corrected section below. The corrected error is highlighted, and this then flows onto the rest of the calculations.
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Weight Update Worked Example
Let’s work through a couple of examples with numbers, just to see this weight update method
working.
The following network is the one we worked with before, but this time we’ve added example output values from the first hidden node o j=1 and the second hidden node o j=2 . These are just made up numbers to illustrate the method and aren’t worked out properly by feeding forward signals from the input layer.
The following network is the one we worked with before, but this time we’ve added example output values from the first hidden node o j=1 and the second hidden node o j=2 . These are just made up numbers to illustrate the method and aren’t worked out properly by feeding forward signals from the input layer.
We want to update the weight w 11 between the hidden and output layers, which currently has
the value 2.0.
Let’s write out the error slope again.
Let’s write out the error slope again.
Let’s do this bit by bit:
-
● The first bit ( t k o k ) is the error e 1 = 1.5, just as we saw before.
-
● The sum inside the sigmoid functions Σ j w jko j is (2.0 * 0.4) + (3.0 * 0.5) = 2.3.
-
● The sigmoid 1/(1 + e 2.3 ) is then 0.909. That middle expression is then 0.909 * (1 0.909)
= 0.083.
- ● The last part is simply o j which is oj =1 because we’re interested in the weight w 11 where j = 1. Here it is simply 0.4.
Multiplying all these three bits together and not forgetting the minus sign at the start gives us
0.04969.
If we have a learning rate of 0.1 that give is a change of (0.1 * 0.04969) = + 0.005. So the new w 11 is the original 2.0 plus 0.005 = 2.005.
This is quite a small change, but over many hundreds or thousands of iterations the weights will eventually settle down to a configuration so that the well trained neural network produces outputs that reflect the training examples.
If we have a learning rate of 0.1 that give is a change of (0.1 * 0.04969) = + 0.005. So the new w 11 is the original 2.0 plus 0.005 = 2.005.
This is quite a small change, but over many hundreds or thousands of iterations the weights will eventually settle down to a configuration so that the well trained neural network produces outputs that reflect the training examples.
