Ein lineares Modell trainieren

In dieser Übung machen wir dort weiter, wo die vorige aufgehört hat. Der Achsenabschnitt und die Steigung, intercept und slope, sind definiert und initialisiert. Außerdem ist eine Funktion loss_function(intercept, slope) definiert, die den Loss auf Basis der Daten und Modellvariablen berechnet.

Du definierst jetzt eine Optimierungsoperation als opt. Anschließend trainierst du ein univariates lineares Modell, indem du den Loss minimierst, um die optimalen Werte für intercept und slope zu finden. Beachte: Die Operation opt nähert sich mit jedem Schritt dem Optimum an, benötigt dafür aber viele Schritte. Du musst die Operation daher wiederholt ausführen.

Diese Übung ist Teil des Kurses

<Kurs>Einführung in TensorFlow mit Python</Kurs>

Übungsanweisungen

Initialisiere einen Adam-Optimizer als opt mit einer Lernrate von 0,5.
Wende die Methode .minimize() auf den Optimizer an.
Übergebe loss_function() mit den passenden Argumenten als Lambda-Funktion an .minimize().
Gib die Liste der zu aktualisierenden Variablen über var_list an.

Interaktive praktische Übung

Versuche dich an dieser Übung, indem du diesen Beispielcode vervollständigst.

# Initialize an Adam optimizer
opt = keras.optimizers.____(0.5)

for j in range(100):
	# Apply minimize, pass the loss function, and supply the variables
	opt.____(lambda: ____(____, ____), var_list=[____, ____])

	# Print every 10th value of the loss
	if j % 10 == 0:
		print(loss_function(intercept, slope).numpy())

# Plot data and regression line
plot_results(intercept, slope)

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

<Kurs>Einführung in TensorFlow mit Python</Kurs>

Mittlere SchwierigkeitSchwierigkeitsgrad

4.8+

Kurs kostenlos starten

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: Eingabedaten Exercise 2: Daten mit pandas laden Exercise 3: Den Datentyp festlegen Exercise 4: Verlustfunktionen Exercise 5: Loss-Funktionen in TensorFlow Exercise 6: Die Verlustfunktion anpassen Exercise 7: Lineare Regression Exercise 8: Lineare Regression einrichten Exercise 9: Ein lineares Modell trainieren

Aktuelle Übung

Exercise 10: Multiple lineare Regression Exercise 11: Batch-Training Exercise 12: Vorbereitung auf Batch-Training Exercise 13: Ein lineares Modell in Batches trainieren

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!