# Multi-class classification configuration

1) What are the appropriate activation and loss functions for multi-class classification problem?

Is it so that:

• Up to 2 classes $$\rightarrow$$ Binary classification $$\rightarrow$$ Activation: Sigmoid $$\rightarrow$$ Loss: binary_crossentropy
• From 3 classes $$\rightarrow$$ Multi-class classification $$\rightarrow$$ Activation: Softmax $$\rightarrow$$ Loss: categorical_crossentropy

If so then...

• 'Dogs and cats' are binary classification but 'dogs, cats and horses' multi-class classification?

• If we have 2 classes then we can't output probability ranges for prediction results?

2) Is it so that for multi-class classification, the last dense layer must have a number of nodes equal to the number of classes?

In the following example there are two classes (dogs and cats) and last dense layer has one node (why not 2 nodes?). If we have 3 or 10 classes then last node should have 3 or 10 nodes?