Fit the model and evaluate

Now that you've defined a new model, fit it to the regular season basketball data.

Use the model you fit in the previous exercise (which was trained on the regular season data) and evaluate the model on data for tournament games (games_tourney).

Deze oefening maakt deel uit van de cursus

Advanced Deep Learning with Keras

Cursus bekijken

Oefeninstructies

Fit the model to the games_season dataset, using 'team_1', 'team_2' and 'home' columns as inputs, and the 'score_diff' column as the target.
Fit the model using 1 epoch, 10% validation split and a batch size of 2048.
Evaluate the model on games_tourney, using the same inputs and outputs.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Fit the model to the games_season dataset
____([____, ____, ____],
          ____,
          epochs=____,
          verbose=____,
          validation_split=____,
          batch_size=____)

# Evaluate the model on the games_tourney dataset
print(____.____(____, ____, verbose=False))

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Advanced Deep Learning with Keras

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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

Huidige oefening

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