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Models in Keras are defined as a sequence of layers.We create a Sequential model and add layers one at a time until we are happy with our network topology.There are eight input variables and one output variable (the last column).Once loaded we can split the dataset into input variables (X) and the output class variable (Y).random numbers), it is a good idea to set the random number seed.This is so that you can run the same code again and again and get the same result.We can specify the number of neurons in the layer as the first argument, the initialization method as the second argument as init and specify the activation function using the activation argument.
We are now ready to define our neural network model.
How do we know the number of layers and their types? There are heuristics that we can use and often the best network structure is found through a process of trial and error experimentation.
Generally, you need a network large enough to capture the structure of the problem if that helps at all.
Keras is a powerful easy-to-use Python library for developing and evaluating deep learning models.
It wraps the efficient numerical computation libraries Theano and Tensor Flow and allows you to define and train neural network models in a few short lines of code.