Create formulas in Excel to calculate these outputs. Calculate the output of the output layer using the sigmoid function and the outputs of the hidden layer neurons:
| | Output | | --- | --- | | Neuron 1 | 0.7 | | Neuron 2 | 0.3 | | Bias | 0.2 |
For example, for Neuron 1:
To build a simple neural network in Excel, we'll use the following steps: Create a new Excel spreadsheet and prepare your input data. For this example, let's assume we're trying to predict the output of a simple XOR (exclusive OR) gate. Create a table with the following inputs:
| | Neuron 1 | Neuron 2 | Output | | --- | --- | --- | --- | | Input 1 | | | | | Input 2 | | | | | Bias | | | | build neural network with ms excel new
output = 1 / (1 + exp(-(weight1 * input1 + weight2 * input2 + bias)))
| | Neuron 1 | Neuron 2 | Output | | --- | --- | --- | --- | | Input 1 | 0.5 | 0.3 | | | Input 2 | 0.2 | 0.6 | | | Bias | 0.1 | 0.4 | | Calculate the output of each neuron in the hidden layer using the sigmoid function: Create formulas in Excel to calculate these outputs
| Input 1 | Input 2 | Output | | --- | --- | --- | | 0 | 0 | 0 | | 0 | 1 | 1 | | 1 | 0 | 1 | | 1 | 1 | 0 | Create a new table with the following structure: