I want to build a neural network to learn a set of standard feature vectors. The set is thus of shape (N,100), where N is the number of samples. However, set of labels is of shape (Nx18) (e.g. each "label" is another array of 18 elements). I'm quite new to keras and neural nets, and I only know how to deal with the label when it is one dimensional (e.g. 0 or 1 in binary classification). How do I can deal with multi-dimensional output?
Thanks!
Maybe I don't completely understand the question but the simplest way would be to have an output layer with 18 neuron. Each neuron output one value, i.e. the output will be a vector of 18 values.
One possible way of doing this would be a feed-forward neural network with on hidden layer, e.g. containing 100 neurons. You will need the Dense layer in Keras for this.
nb_hidden = 100
model = Sequential()
model.add(Dense(input_dim = 100, output_dim = nb_hidden)
model.add(Dense(output_dim = 18, activation = 'softmax')
model.compile(loss='categorical_crossentropy', optimizer='adadelta')
Consider varying the number of hidden layers, the general network topology (e.g. include a Dropout layer) and the activation functions until you get a good result.
