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A Three-Layer Example: Working on the Hidden Layer

Explore how to compute the output of the hidden layer in a three-layer neural network by applying the sigmoid activation function. Understand the process of moderating inputs with weights and passing signals through each layer, which remains consistent regardless of the number of layers in the network.

Output of the hidden layer

Let’s visualize the combined moderated inputs into the second hidden layer.

Neural network with inputs to hidden layer
Neural network with inputs to hidden layer

So far, so good, but there’s more to do. We remember those nodes apply a sigmoid activation function to make the response to the signal more like those found in nature. So let’s do that:

Ohidden=sigmoid(Xhidden) O_\text{hidden} = \text{sigmoid}(X_\text{hidden})

The sigmoid function is applied to each element in XhiddenX_\text{hidden} to produce the matrix, which has the output of the middle hidden layer:

Ohidden=sigmoid[1.160.420.62 ...