Compile and fit the model

Now that you have a model with 2 outputs, compile it with 2 loss functions: mean absolute error (MAE) for 'score_diff' and binary cross-entropy (also known as logloss) for 'won'. Then fit the model with 'seed_diff' and 'pred' as inputs. For outputs, predict 'score_diff' and 'won'.

This model can use the scores of the games to make sure that close games (small score diff) have lower win probabilities than blowouts (large score diff).

The regression problem is easier than the classification problem because MAE punishes the model less for a loss due to random chance. For example, if score_diff is -1 and won is 0, that means team_1 had some bad luck and lost by a single free throw. The data for the easy problem helps the model find a solution to the hard problem.

This exercise is part of the course

Advanced Deep Learning with Keras

Exercise instructions

Import Adam from keras.optimizers.
Compile the model with 2 losses: 'mean_absolute_error' and 'binary_crossentropy', and use the Adam optimizer with a learning rate of 0.01.
Fit the model with 'seed_diff' and 'pred' columns as the inputs and 'score_diff' and 'won' columns as the targets.
Use 10 epochs and a batch size of 16384.

Hands-on interactive exercise

Have a go at this exercise by completing this sample code.

# Import the Adam optimizer
____

# Compile the model with 2 losses and the Adam optimzer with a higher learning rate
model.____(loss=['____', '____'], optimizer=____)

# Fit the model to the tournament training data, with 2 inputs and 2 outputs
model.____(games_tourney_train[['____', '____']],
          [games_tourney_train[['____']], games_tourney_train[['____']]],
          epochs=____,
          verbose=True,
          batch_size=____)

Edit and Run Code

This exercise is part of the course

Advanced Deep Learning with Keras

IntermediateSkill Level

4.8+

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In this chapter, you'll become familiar with the basics of the Keras functional API. You'll build a simple functional network using functional building blocks, fit it to data, and make predictions.

Exercise 1: Keras input and dense layers Exercise 2: Input layers Exercise 3: Dense layers Exercise 4: Output layers Exercise 5: Build and compile a model Exercise 6: Build a model Exercise 7: Compile a model Exercise 8: Visualize a model Exercise 9: Fit and evaluate a model Exercise 10: Fit the model to the tournament basketball data Exercise 11: Evaluate the model on a test set

In this chapter, you will build two-input networks that use categorical embeddings to represent high-cardinality data, shared layers to specify re-usable building blocks, and merge layers to join multiple inputs to a single output. By the end of this chapter, you will have the foundational building blocks for designing neural networks with complex data flows.

Exercise 1: Category embeddings Exercise 2: Define team lookup Exercise 3: Define team model Exercise 4: Shared layers Exercise 5: Defining two inputs Exercise 6: Lookup both inputs in the same model Exercise 7: Merge layers Exercise 8: Output layer using shared layer Exercise 9: Model using two inputs and one output Exercise 10: Predict from your model Exercise 11: Fit the model to the regular season training data Exercise 12: Evaluate the model on the tournament test data

In this chapter, you will extend your 2-input model to 3 inputs, and learn how to use Keras' summary and plot functions to understand the parameters and topology of your neural networks. By the end of the chapter, you will understand how to extend a 2-input model to 3 inputs and beyond.

Exercise 1: Three-input models Exercise 2: Make an input layer for home vs. away Exercise 3: Make a model and compile it Exercise 4: Fit the model and evaluate Exercise 5: Summarizing and plotting models Exercise 6: Model summaries Exercise 7: Plotting models Exercise 8: Stacking models Exercise 9: Add the model predictions to the tournament data Exercise 10: Create an input layer with multiple columns Exercise 11: Fit the model Exercise 12: Evaluate the model

In this chapter, you will build neural networks with multiple outputs, which can be used to solve regression problems with multiple targets. You will also build a model that solves a regression problem and a classification problem simultaneously.

Exercise 1: Two-output models Exercise 2: Simple two-output model Exercise 3: Fit a model with two outputs Exercise 4: Inspect the model (I)Exercise 5: Evaluate the model Exercise 6: Single model for classification and regression Exercise 7: Classification and regression in one model Exercise 8: Compile and fit the model

Current Exercise

Exercise 9: Inspect the model (II)Exercise 10: Evaluate on new data with two metrics Exercise 11: Wrap-up