| | H1 | H2 | | --- | --- | --- | | X1 | 0.4 | 0.6 | | X2 | 0.3 | 0.2 | | Bias | 0.1 | 0.5 |
To "teach" the network, you must measure how far off its prediction is from the actual target ( ). Use for this calculation. Error Formula: Excel Implementation: =(Prediction_Cell - Actual_Cell)^2 4. Train the Network (Backpropagation) build neural network with ms excel full
: Repeat the summation and activation using hidden layer outputs as the new inputs. | | H1 | H2 | | --- | --- | --- | | X1 | 0
dE/dWeight_Input1_Hidden1 = -2 * (Actual Output - Predicted Output) * Hidden 1 * (1 - Hidden 1) * Input 1 Train the Network (Backpropagation) : Repeat the summation
Forward propagation is the process of moving data from the input to the output.
For Hidden Neuron 1 (H1), the derivative is: = (Error_from_output) * (Derivative_of_H1_activation) * (Weight_connecting_H1_to_Output)
To evaluate how well the network performed, calculate the Squared Error for the row: Formula for Error: =0.5 * ($C2 - a_o)^2