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What I understand is, each input in a neural network is a feature.

However, what I don't understand is, when we need multiple outputs in a neural network.

For example, say, if we are classifying cats and dogs, only one output is enough. 0 = cat, 1 = dog.

When does a neural network have a single and when does it have multiple outputs?

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The output depends on what answer you want from the network. Think of the network as a function $f$ with weights $\theta$, that takes an input $x$ and gives some output $y$:

$$ f_\theta(X) \rightarrow y $$

The example you give (dog=1 or cat=0) is binary classification -- the answer from the network is "this looks more like a dog than a cat" if the output $y > 0.5$ or vice versa. So yes, $y$ is a scalar between 0 and 1.

If you had three or more classes e.g [dog, cat, banana] (multimomial classification), you want the network to answer how "confident" it is that the $x$ is a certain class. It will give a prediction for each class. In this example $y$ will be a vector of size 3, each element being a prediction for a class e.g y = $[dog=0.3, cat=0.01, banana=0.7]$

For a non-classification example: say a robot's action comprises the velocity $v$ and a joint angle $\phi$ (action = $[v, \phi]$). The robot's questions a neural network with "what action should I take?". The network's answer $y$ might looks like $ y = [v=3.0, \phi=23]$. So the size of the output will be dependant on the dimensions of the action. In this case we have a multiple-dimension output, one dimension for velocity, and one for the joint angle.

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