Multi-Label Image Classification in TensorFlow 2.0

The 2.2M parameters in MobileNet are frozen, but there are 1.3K trainable parameters in the dense layers. You need to apply the sigmoid activation function in the final neurons to ouput a probability score for each genre apart. By doing so, you are relying on multiple logistic regressions to train simultaneously inside the same model. Every final neuron will act as a seperate binary classifier for one single class, even though the features extracted are common to all final neurons. When generating predictions with this model, you should expect an independant probability score for each genre and that all probability scores do not necessarily sum up to 1. This is different from using a softmax layer in multi-class classification where the sum of probability scores in the output is equal to 1.