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## How do you find the output of a neuron?

Now, you can build a Neural Network and calculate it’s output based on some given input. As you can see, it’s very very easy….

- Multiply every incoming neuron by its corresponding weight.
- Add the values up.
- Add the bias term for the neuron in question.

## How do you find the net input to an output neuron?

The input to the output neuron y1 is 0*(-9)+1*9+1*(- 4.5)=4.5, and the output from the network Y1 is O(y1) = 1. The calculation for the input (1,1) goes. The input o1 to the left hidden neuron is 1*4+1*4+1*(-6) = 2. The output O1 from the left hidden neuron is then O(2) = 1.

## How are values computed in a neural network?

These values now serve as inputs for the output layer. The output-layer nodes are computed in the same way as the hidden-layer nodes, except that the values computed into the hidden-layer nodes are now used as inputs.

## How is the output of a neural network deterministic?

The demo neural network is deterministic in the sense that for a given set of input values and a given set of weights and bias values, the output values will always be the same. So, a neural network is really just a form of a function. Computing neural network output occurs in three phases.

## How many weights are in a demo neural network?

For the three-input, four-hidden, two-output demo neural network, there are a total of (3 * 4) + (4 * 2) + (4 + 2) = 20 + 6 = 26 weights. The demo neural network is deterministic in the sense that for a given set of input values and a given set of weights and bias values, the output values will always be the same.

## What is the first phase of a neural network?

The first phase is to deal with the raw input values. The second phase is to compute the values for the hidden-layer nodes. The third phase is to compute the values for the output-layer nodes. In this example, the demo does no processing of input, and simply copies raw input into the neural network input-layer nodes.