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  3. Introduction to Deep Learning in Python

Exercise

Changing optimization parameters

It's time to get your hands dirty with optimization. You'll now try optimizing a model at a very low learning rate, a very high learning rate, and a "just right" learning rate. You'll want to look at the results after running this exercise, remembering that a low value for the loss function is good.

For these exercises, we've pre-loaded the predictors and target values from your previous classification models (predicting who would survive on the Titanic). You'll want the optimization to start from scratch every time you change the learning rate, to give a fair comparison of how each learning rate did in your results. So we have created a function get_new_model() that creates an unoptimized model to optimize.

Instructions

100 XP
  • Import SGD from tensorflow.keras.optimizers.
  • Create a list of learning rates to try optimizing with called lr_to_test. The learning rates in it should be .000001, 0.01, and 1.
  • Using a for loop to iterate over lr_to_test:
    • Use the get_new_model() function to build a new, unoptimized model.
    • Create an optimizer called my_optimizer using the SGD() constructor with keyword argument lr=lr.
    • Compile your model. Set the optimizer parameter to be the SGD object you created above, and because this is a classification problem, use 'categorical_crossentropy' for the loss parameter.
    • Fit your model using the predictors and target.