Ajuste um modelo com duas saídas

Agora que você definiu seu modelo de 2 saídas, ajuste-o aos dados do torneio. Eu dividi os dados em games_tourney_train and games_tourney_test, então usa o conjunto de treinamento para ajustar por enquanto.

Esse modelo vai usar as classificações pré-torneio, além das suas previsões pré-torneio do modelo da temporada regular que você criou antes neste curso.

Só pra lembrar, esse modelo vai prever os resultados de ambos os times.

Este exercicio faz parte do curso

Aprendizado profundo avançado com Keras

Instruções do exercicio

Ajuste o modelo ao conjunto de dados “ games_tourney_train ” usando 100 épocas e um tamanho de lote de 16384.
As colunas de entrada são 'seed_diff' e 'pred'.
As colunas de destino são 'score_1' e 'score_2'.

exercicio interativo prático

Tente este exercicio completando este código de exemplo.

# Fit the model
____(games_tourney_train[['____', '____']],
  		  games_tourney_train[['____', '____']],
  		  verbose=True,
  		  ____,
  		  ____)

Editar e Executar Código

Este exercicio faz parte do curso

Aprendizado profundo avançado com Keras

IntermediárioNível de habilidade

4.8+

Comece o curso gratuitamente

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: Modelos com duas saídas Exercise 2: Modelo simples de duas saídas Exercise 3: Ajuste um modelo com duas saídas

Exercicio Atual

Exercise 4: Dá uma olhada no modelo (I)Exercise 5: Avalie o modelo Exercise 6: Modelo único para classificação e regressão Exercise 7: Classificação e regressão em um modelo Exercise 8: Compile e ajuste o modelo Exercise 9: Dá uma olhada no modelo (II)Exercise 10: Avalie novos dados com duas métricas Exercise 11: Conclusão