Exercise

Comparing activation functions

Comparing activation functions involves a bit of coding, but nothing you can't do!

You will try out different activation functions on the multi-label model you built for your farm irrigation machine in chapter 2. The function get_model('relu') returns a copy of this model and applies the 'relu' activation function to its hidden layer.

You will loop through several activation functions, generate a new model for each and train it. By storing the history callback in a dictionary you will be able to visualize which activation function performed best in the next exercise!

X_train, y_train, X_test, y_test are ready for you to use when training your models.

Instructions

100 XP
  • Fill up the activation functions array with relu,leaky_relu, sigmoid, and tanh.
  • Get a new model for each iteration with get_model() passing the current activation function as a parameter.
  • Fit your model providing the train and validation_data, use 20 epochs and set verbose to 0.