Output Layer

Definition

The output layer is the final layer of a neural network.

It produces the network's output values.

Basic structure:

input layer -> hidden layers -> output layer

The output layer turns internal representation into final output.

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Output Depends on the Task

Different tasks need different output structures.

Examples:

one number
several numbers
a score for each class
a probability-like value

The output layer must match what the task requires.

The form of the output layer depends on the problem.

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Output Values Are Still Computed

The output layer does not magically know the answer.

It applies the same kind of computation:

previous layer values
-> weights and bias
-> output values

The final values are produced by parameterized transformations.

The output is computed from the representation before it.

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Output vs Loss

The output layer produces a prediction or result.

Loss computation evaluates that result.

network output -> compare with target -> loss

The output layer is part of the model.

The loss function measures how good the model output was.

Output is what the model produces; loss measures its error.

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Final Summary

• The output layer is the final layer of a network.
• It produces the final values for the task.
• Its shape depends on the problem.
• Output values are computed from previous layer values.
• Output is separate from loss.
• Loss evaluates the output.