Model met twee inputs en één output

Nu je twee inputs (team-id 1 en team-id 2) en een output (scoreverschil) hebt, kun je ze samenvoegen in een model. Zo kun je het later gebruiken om te fitten op data en te evalueren op nieuwe data.

Je model ziet eruit zoals in het volgende diagram:

Deze oefening maakt deel uit van de cursus

Advanced Deep Learning met Keras

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Oefeninstructies

Definieer een model met de twee teams als inputs en gebruik het scoreverschil als output.
Compileer het model met de optimizer 'adam' en de loss 'mean_absolute_error'.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Imports
from tensorflow.keras.layers import Subtract
from tensorflow.keras.models import Model

# Subtraction layer from previous exercise
score_diff = Subtract()([team_1_strength, team_2_strength])

# Create the model
model = ____([____, ____], ____)

# Compile the model
____(____, ____)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Advanced Deep Learning met Keras

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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: Categorie-embeddings Exercise 2: Team-lookup definiëren Exercise 3: Teammodel definiëren Exercise 4: Gedeelde lagen Exercise 5: Twee inputs definiëren Exercise 6: Zoek beide inputs op in hetzelfde model Exercise 7: Merge-lagen Exercise 8: Uitvoellaag met gedeelde laag Exercise 9: Model met twee inputs en één output

Huidige oefening

Exercise 10: Voorspellen met je model Exercise 11: Train het model op de trainingsdata van het reguliere seizoen Exercise 12: Evalueer het model op de toernooi-testdata

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