Fit the model to the tournament basketball data

Now that the model is compiled, you are ready to fit it to some data!

In this exercise, you'll use a dataset of scores from US College Basketball tournament games. Each row of the dataset has the team ids: team_1 and team_2, as integers. It also has the seed difference between the teams (seeds are assigned by the tournament committee and represent a ranking of how strong the teams are) and the score difference of the game (e.g. if team_1 wins by 5 points, the score difference is 5).

To fit the model, you provide a matrix of X variables (in this case one column: the seed difference) and a matrix of Y variables (in this case one column: the score difference).

The games_tourney DataFrame along with the compiled model object is available in your workspace.

Deze oefening maakt deel uit van de cursus

Advanced Deep Learning with Keras

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Oefeninstructies

Fit the model with seed_diff as the input variable and score_diff as the output variable.
Use 1 epoch, a batch size of 128, and a 10% validation split.

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Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Now fit the model
____(games_tourney_train[____], games_tourney_train[____],
          epochs=____,
          batch_size=____,
          validation_split=____,
          verbose=True)

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Deze oefening maakt deel uit van de cursus

Advanced Deep Learning with Keras

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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

Huidige oefening

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