Multi-Label Classification with Deep Learning - AnalyticsWeek

We can demonstrate this using the Keras deep learning library. We will define a Multilayer Perceptron (MLP) model for the multi-label classification task defined in the previous section. Each sample has 10 inputs and three outputs; therefore, the network requires an input layer that expects 10 inputs specified via the "input_dim" argument in the first hidden layer and three nodes in the output layer. We will use the popular ReLU activation function in the hidden layer. The hidden layer has 20 nodes that were chosen after some trial and error.