Twee inputs definiëren

In deze oefening definieer je twee invoerlagen voor de twee teams in je model. Zo kun je later in het model aangeven hoe de gegevens van elk team verschillend worden gebruikt.

Deze oefening maakt deel uit van de cursus

Advanced Deep Learning met Keras

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Oefeninstructies

Maak een invoerlaag voor team 1. Onthoud dat onze invoerdimensie 1 is.
Geef de invoer de naam "Team-1-In", zodat je deze later kunt onderscheiden van team 2.
Maak een invoerlaag voor team 2 met de naam "Team-2-In".

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Load the input layer from tensorflow.keras.layers
from tensorflow.keras.layers import Input

# Input layer for team 1
team_in_1 = ____

# Separate input layer for team 2
team_in_2 = ____

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

Huidige oefening

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 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