Modellen evalueren

Er zijn twee getrainde modellen beschikbaar: large_model, met veel parameters, en small_model, met minder parameters. Beide modellen zijn getraind met train_features en train_labels, die voor je beschikbaar zijn. Er is ook een aparte testset met test_features en test_labels.

Je doel is om de relatieve modelprestaties te beoordelen en te bepalen of een van de modellen tekenen van overfitting vertoont. Dit doe je door large_model en small_model te evalueren op zowel de train- als de testset. Voor elk model kan dat met de methode .evaluate(x, y) om het verlies te berekenen voor features x en labels y. Daarna vergelijk je de vier verkregen verlieswaarden.

Deze oefening maakt deel uit van de cursus

Introductie tot TensorFlow in Python

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Oefeninstructies

Evalueer het kleine model met de traindata.
Evalueer het kleine model met de testdata.
Evalueer het grote model met de traindata.
Evalueer het grote model met de testdata.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Evaluate the small model using the train data
small_train = small_model.evaluate(____, ____)

# Evaluate the small model using the test data
small_test = ____

# Evaluate the large model using the train data
large_train = large_model.evaluate(____, ____)

# Evaluate the large model using the test data
large_test = ____

# Print losses
print('\n Small - Train: {}, Test: {}'.format(small_train, small_test))
print('Large - Train: {}, Test: {}'.format(large_train, large_test))

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot TensorFlow in Python

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Before you can build advanced models in TensorFlow 2, you will first need to understand the basics. In this chapter, you’ll learn how to define constants and variables, perform tensor addition and multiplication, and compute derivatives. Knowledge of linear algebra will be helpful, but not necessary.

Exercise 1: Constants and variables Exercise 2: Defining data as constants Exercise 3: Defining variables Exercise 4: Basic operations Exercise 5: Performing element-wise multiplication Exercise 6: Making predictions with matrix multiplication Exercise 7: Summing over tensor dimensions Exercise 8: Advanced operations Exercise 9: Reshaping tensors Exercise 10: Optimizing with gradients Exercise 11: Working with image data

In this chapter, you will learn how to build, solve, and make predictions with models in TensorFlow 2. You will focus on a simple class of models – the linear regression model – and will try to predict housing prices. By the end of the chapter, you will know how to load and manipulate data, construct loss functions, perform minimization, make predictions, and reduce resource use with batch training.

Exercise 1: Input data Exercise 2: Load data using pandas Exercise 3: Setting the data type Exercise 4: Loss functions Exercise 5: Loss functions in TensorFlow Exercise 6: Modifying the loss function Exercise 7: Linear regression Exercise 8: Set up a linear regression Exercise 9: Train a linear model Exercise 10: Multiple linear regression Exercise 11: Batch training Exercise 12: Preparing to batch train Exercise 13: Training a linear model in batches

The previous chapters taught you how to build models in TensorFlow 2. In this chapter, you will apply those same tools to build, train, and make predictions with neural networks. You will learn how to define dense layers, apply activation functions, select an optimizer, and apply regularization to reduce overfitting. You will take advantage of TensorFlow's flexibility by using both low-level linear algebra and high-level Keras API operations to define and train models.

Exercise 1: Dense layers Exercise 2: The linear algebra of dense layers Exercise 3: The low-level approach with multiple examples Exercise 4: Using the dense layer operation Exercise 5: Activation functions Exercise 6: Binary classification problems Exercise 7: Multiclass classification problems Exercise 8: Optimizers Exercise 9: The dangers of local minima Exercise 10: Avoiding local minima Exercise 11: Training a network in TensorFlow Exercise 12: Initialization in TensorFlow Exercise 13: Defining the model and loss function Exercise 14: Training neural networks with TensorFlow

In the final chapter, you'll use high-level APIs in TensorFlow 2 to train a sign language letter classifier. You will use both the sequential and functional Keras APIs to train, validate, make predictions with, and evaluate models. You will also learn how to use the Estimators API to streamline the model definition and training process, and to avoid errors.

Exercise 1: Neurale netwerken definiëren met Keras Exercise 2: Het sequentiële model in Keras Exercise 3: Een sequentieel model compileren Exercise 4: Een model met meerdere invoeren definiëren Exercise 5: Trainen en valideren met Keras Exercise 6: Trainen met Keras Exercise 7: Metrieken en validatie met Keras Exercise 8: Overfitting detecteren Exercise 9: Modellen evalueren

Huidige oefening

Exercise 10: Modellen trainen met de Estimators-API Exercise 11: Voorbereiden op trainen met Estimators Exercise 12: Estimators definiëren Exercise 13: Gefeliciteerd!