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Make Your Own Neural Network in Python

A Three-Layer Example: Working on the Output Layer

Explore how to calculate the combined moderated inputs entering the final output layer of a three-layer neural network. Learn to apply weights, perform matrix multiplication, and use the sigmoid activation function to generate the network's final output values. This lesson helps you understand and implement the output computations essential for neural network learning and optimization.

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Input to the output layer

Let’s calculate the combined moderated input into the final layer X=W⋅IX = W \cdot I.

The inputs into this layer are the outputs from the second layer we just worked out, named OhiddenO_\text{hidden}. The weights are those for the links between the second and third layers, named Whidden_outputW_\text{hidden\_output}, not those we just used between the first and second. Therefore, we have:

Xoutput=Whidden_output⋅OhiddenX_{\text{output}} = W_{\text{hidden\_output}} \cdot O_{\text{hidden}}

So, working this out in ...