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For each layer except the input layer, the value of the current neuron is calculated by taking the linear combination of the values output by the neurons of the previous layer, where the weight determines the contribution of a neuron in the previous layer to current neuron (as equation (1) shown). Obtaining the linear combination result z, a non-linear squashing function is used to constrain the output into a restricted range (as equation (2) shown). Typically, sigmoid function or tanh function are used.
http://bit.ly/14OItNY19om4bo
For each step of feed-forward, the calculated results are propagated one layer close to the output layer.
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