CODEWITHSUNDEEP
neural-network
emmc_1247
emmc_1247

Reputation: 13

How does one calculate the target outputs of neurons in hidden layers of a neural network?

In a simple single-layer network, it is easy to calculate the target outputs of neurons, as they are identical to the target outputs of the network itself. However, in a multiple-layer network, I am not quite sure how to calculate the targets for each individual neuron in the hidden layers, because they do not necessarily have a direct connection to the final output and are most likely not given in the training data. How would one find these values?

I would not be surprised if I am missing something and am going about this incorrectly, but I would like to know nonetheless. Thanks in advance for any and all input.

Upvotes: 1

Views: 1028

Answers (1)

Carcigenicate
Carcigenicate

Reputation: 45806

Taken from this great guide on pg. 18:

  1. Calculate the Errors for the hidden layer neurons. Unlike the output layer we can’t calculate these directly (because we don’t have a Target), so we Back Propagate them from the output layer (hence the name of the algorithm). This is done by taking the Errors from the output neurons and running them back through the weights to get the hidden layer errors.

Or in other words, you don't. You propagate the activations from the input to the output, calculate the error of the output, then backpropagate the error from the output back to the input (thus the name of the algorithm).

In the unfortunate case that the link I posted goes down, it can be found by Googling "backpropagation algorithm 3".

Upvotes: 1

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