Inspecteer het model (I)

Nu je je model hebt getraind, gaan we er eens naar kijken. Je kunt de methode .get_weights() gebruiken om de gewichten van je model te bekijken.

De invoerlaag heeft 4 gewichten: 2 per invoer keer 2 per uitvoer.

De uitvoerlaag heeft 2 gewichten, één per uitvoer.

Deze oefening maakt deel uit van de cursus

Advanced Deep Learning met Keras

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Oefeninstructies

Print de gewichten van het model.
Print de kolomgemiddelden van de trainingsdata (games_tourney_train).

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Print the model's weights
print(____)

# Print the column means of the training data
print(____)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Advanced Deep Learning met 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 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: Modellen met twee outputs Exercise 2: Eenvoudig model met twee Resultaten Exercise 3: Train een model met twee outputs Exercise 4: Inspecteer het model (I)

Huidige oefening

Exercise 5: Evalueer het model Exercise 6: Eén model voor classificatie en regressie Exercise 7: Classificatie en regressie in één model Exercise 8: Compileer en train het model Exercise 9: Inspecteer het model (II)Exercise 10: Evalueren op nieuwe data met twee metriekwaarden Exercise 11: Afsluiting