Make an input layer for home vs. away

Now you will make an improvement to the model you used in the previous chapter for regular season games. You know there is a well-documented home-team advantage in basketball, so you will add a new input to your model to capture this effect.

This model will have three inputs: team_id_1, team_id_2, and home. The team IDs will be integers that you look up in your team strength model from the previous chapter, and home will be a binary variable, 1 if team_1 is playing at home, 0 if they are not.

The team_strength_model you used in the previous chapter has been loaded into your workspace. After applying it to each input, use a Concatenate layer to join the two team strengths and with the home vs away variable, and pass the result to a Dense layer.

This exercise is part of the course

Advanced Deep Learning with Keras

Exercise instructions

Create three inputs layers of shape 1, one each for team 1, team 2, and home vs away.
Lookup the team inputs in team_strength_model().
Concatenate the team strengths with the home input and pass to a Dense layer.

Hands-on interactive exercise

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

# Create an Input for each team
team_in_1 = ____(shape=(1,), name='Team-1-In')
team_in_2 = ____(shape=(1,), name='Team-2-In')

# Create an input for home vs away
home_in = ____(shape=(1,), name='Home-In')

# Lookup the team inputs in the team strength model
team_1_strength = ____(____)
team_2_strength = ____(____)

# Combine the team strengths with the home input using a Concatenate layer, then add a Dense layer
out = _____()([____, ____, home_in])
out = _____(____)(____)

Edit and Run Code

This exercise is part of the course

Advanced Deep Learning with Keras

IntermediateSkill Level

4.8+

Start Course for Free

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

Current Exercise

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