Outputlagen

Outputlagen zijn gewoon Dense-lagen! Outputlagen worden gebruikt om de dimensie van de input terug te brengen naar de dimensie van de output. Je leert meer over outputdimensies in hoofdstuk 4, maar voor nu gebruik je steeds één output in je neurale netwerken, wat gelijkstaat aan Dense(1) of een dense laag met één unit.

Deze oefening maakt deel uit van de cursus

Advanced Deep Learning met Keras

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Oefeninstructies

Importeer de functies Input en Dense uit keras.layers.
Maak een invoerlaag met vorm 1.
Maak opnieuw een dense laag met 1 unit en geef input_tensor er direct aan door.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Load layers
____

# Input layer
input_tensor = ____(shape=(____,))

# Create a dense layer and connect the dense layer to the input_tensor in one step
# Note that we did this in 2 steps in the previous exercise, but are doing it in one step now
output_tensor = ____(____)(____)

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- en dense-lagen Exercise 2: Invoerlagen Exercise 3: Dense-lagen Exercise 4: Outputlagen

Huidige oefening

Exercise 5: Een model bouwen en compileren Exercise 6: Bouw een model Exercise 7: Een model compileren Exercise 8: Visualiseer een model Exercise 9: Een model trainen en evalueren Exercise 10: Train het model op de toernooi-basketbaldata Exercise 11: Evalueer het model op een testset

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: 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