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.

Questo esercizio fa parte del corso

Introduction to Deep Learning in Python

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Istruzioni dell'esercizio

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.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Import the SGD optimizer
____

# Create list of learning rates: lr_to_test
lr_to_test = ____

# Loop over learning rates
for lr in lr_to_test:
    print('\n\nTesting model with learning rate: %f\n'%lr )
    
    # Build new model to test, unaffected by previous models
    model = ____
    
    # Create SGD optimizer with specified learning rate: my_optimizer
    my_optimizer = ____
    
    # Compile the model
    ____
    
    # Fit the model
    ____

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