Het gegevenstype instellen

In deze oefening laad je gegevens en stel je het type in. Let op: housing is beschikbaar en pandas is geïmporteerd als pd. Je gaat numpy en tensorflow importeren, en tensors definiëren die in tensorflow bruikbaar zijn met kolommen in housing met een opgegeven gegevenstype. Onthoud dat je bijvoorbeeld de kolom price uit housing kunt selecteren met housing['price'].

Deze oefening maakt deel uit van de cursus

Introductie tot TensorFlow in Python

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Oefeninstructies

Importeer numpy en tensorflow met hun standaardaliassen.
Gebruik een numpy-array om de tensor price het gegevenstype 32-bits drijvendekommagetal te geven.
Gebruik de tensorflow-functie cast() om de tensor waterfront het Booleaanse gegevenstype te geven.
Print eerst price en daarna waterfront. Vallen er je belangrijke verschillen op?

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import numpy and tensorflow with their standard aliases
____
____

# Use a numpy array to define price as a 32-bit float
price = np.____(housing['price'], np.____)

# Define waterfront as a Boolean using cast
waterfront = tf.____(housing['waterfront'], tf.____)

# Print price and waterfront
print(____)
print(____)

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Deze oefening maakt deel uit van de cursus

Introductie tot TensorFlow in Python

SkillTag.level.intermediateSkillTag.label

4.8+

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

Huidige oefening

Exercise 4: Verliesfuncties Exercise 5: Verliesfuncties in TensorFlow Exercise 6: De verliesfunctie aanpassen Exercise 7: Lineaire regressie Exercise 8: Een lineaire regressie opzetten 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!