Een lineaire regressie opzetten

Een univariate lineaire regressie zoekt de relatie tussen één feature en de doeltensor. In deze oefening gebruiken we de kavelgrootte en prijs van een woning. Net zoals in de video nemen we de natuurlijke logaritmen van beide tensors, die beschikbaar zijn als price_log en size_log.

In deze oefening definieer je het model en de verliesfunctie. Vervolgens evalueer je de verliesfunctie voor twee verschillende waarden van intercept en slope. Onthoud dat de voorspelde waarden worden gegeven door intercept + features*slope. Let er daarnaast op dat keras.losses.mse() voor je beschikbaar is. Verder zijn slope en intercept gedefinieerd als variabelen.

Deze oefening maakt deel uit van de cursus

Introductie tot TensorFlow in Python

Cursus bekijken

Oefeninstructies

Definieer een functie die de voorspelde waarden voor een lineaire regressie retourneert met intercept, features en slope, zonder add() of multiply() te gebruiken.
Maak de loss_function() af door de modelvariabelen intercept en slope als argumenten toe te voegen.
Bereken de mean squared error met targets en predictions.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Define a linear regression model
def linear_regression(intercept, slope, features = size_log):
	return ____

# Set loss_function() to take the variables as arguments
def loss_function(____, ____, features = size_log, targets = price_log):
	# Set the predicted values
	predictions = linear_regression(intercept, slope, features)
    
    # Return the mean squared error loss
	return keras.losses.____

# Compute the loss for different slope and intercept values
print(loss_function(0.1, 0.1).numpy())
print(loss_function(0.1, 0.5).numpy())

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Introductie tot TensorFlow in Python

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Begin de cursus gratis

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: Invoergegevens Exercise 2: Gegevens laden met pandas Exercise 3: Het gegevenstype instellen Exercise 4: Verliesfuncties Exercise 5: Verliesfuncties in TensorFlow Exercise 6: De verliesfunctie aanpassen Exercise 7: Lineaire regressie Exercise 8: Een lineaire regressie opzetten

Huidige oefening

Exercise 9: Train een lineair model Exercise 10: Meervoudige lineaire regressie Exercise 11: Batchtraining Exercise 12: Voorbereiden op batchtraining Exercise 13: Een lineair model trainen 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: Defining neural networks with Keras Exercise 2: The sequential model in Keras Exercise 3: Compiling a sequential model Exercise 4: Defining a multiple input model Exercise 5: Training and validation with Keras Exercise 6: Training with Keras Exercise 7: Metrics and validation with Keras Exercise 8: Overfitting detection Exercise 9: Evaluating models Exercise 10: Training models with the Estimators API Exercise 11: Preparing to train with Estimators Exercise 12: Defining Estimators Exercise 13: Congratulations!