# Keras: Custom output layer for multiple multi-class classifications

Hello, I’m quite new to machine learning and I want to build my first custom layer in Keras, using Python. I want to use a dataset of 103 dimensions to do classification task. The last fully connected layer of the model has 103 neurons (represented by 13 dots in the image). Groups of five dimensions of the former layer should be connected to three neurons of the output layer, so there will be 20 classifications. The neurons of the output layer represent "True" ("T" in the image), "indifferent" ("?") and "False" ("F"). The remaining three don’t need connections to the output layer.

How can I build this layer? And how can I make sure, that each of the 20 groups with three neurons gives probabilities that add up to 1? Can I apply the softmax activation function to each of the groups, for example?

Edit – This is my solution:

# define input and hidden layers. append them to list by calling the new layer with the last layer in the list
self.layers: list = [keras.layers.Input(shape=self.neurons)]
[self.layers.append(keras.layers.Dense(self.neurons, activation=self.activation_hidden_layers)(self.layers[-1])) for _ in range(num_hidden_layers)]
self.layers.append(keras.layers.Dense(self.neurons - self.dims_to_leave_out, activation=activation_hidden_layers)(self.layers[-1]))

# define multi-output layer by slicing the neurons from the last hidden layer
self.outputs: list = []
index_start: int = 0
for i in range(int((self.neurons - self.dims_to_leave_out)/self.neurons_per_output_layer)):
index_end: int = index_start + self.neurons_per_output_layer
self.outputs.append(keras.layers.Dense(self.output_dims_per_output_layer, activation=self.activation_output_layers)(self.layers[-1][:, index_start:index_end]))
index_start = index_end