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?