I am currently struggling to understand how i should train my regression network using keras. I am not sure how I should pass my input data to the network.
Both the input data and the output data is stored as a list of numpy arrays.
Each input numpy array is a matrix which has (400 rows, x columns) Each output numpy array is a matrix which has (x number of rows, 13 columns)
So input dimension is 400 and output is 13. But how do I pass each of these sets within the list to the training?
# Multilayer Perceptron model = Sequential() # Feedforward model.add(Dense(3, input_dim=400, output_dim=13)) model.add(Activation('tanh')) model.add(Dense(1)) model.compile('sgd', 'mse')
I know that model.fit trains the model given the parameter, how it actually takes in the data seems a bit like magic to me, and how it knows that the columns of matrix A should be mapped the rows of matrix B this has to be done for the whole matrix, appended to the list.