Gegevens definiëren als constanten

In deze cursus gebruiken we tensorflow versie 2.6.0 en importeren we uitsluitend de submodules die je nodig hebt om elke oefening te voltooien. Meestal is dat al voor je gedaan, maar in deze oefening doe je het zelf door constant uit tensorflow te importeren.

Nadat je constant hebt geïmporteerd, gebruik je het om een numpy-array, credit_numpy, om te zetten in een tensorflow-constante, credit_constant. Deze array bevat featurekolommen uit een gegevensset over creditcardhouders en is te zien in de onderstaande afbeelding. We keren in latere hoofdstukken terug naar deze gegevensset.

Merk op dat tensorflow 2 je laat werken met gegevens als een numpy-array of als een tensorflow-object van het type constant. Door een constant te gebruiken, zorg je ervoor dat alle bewerkingen met dat object in tensorflow worden uitgevoerd.

This image shows four feature columns from a dataset on credit card default: education, marriage, age, and bill amount.

Deze oefening maakt deel uit van de cursus

Introductie tot TensorFlow in Python

Cursus bekijken

Oefeninstructies

Importeer de submodule constant uit de module tensorflow.
Zet de array credit_numpy om in een constant-object in tensorflow. Stel het gegevenstype niet in.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import constant from TensorFlow
from tensorflow import ____

# Convert the credit_numpy array into a tensorflow constant
credit_constant = constant(____)

# Print constant datatype
print('\n The datatype is:', credit_constant.dtype)

# Print constant shape
print('\n The shape is:', credit_constant.shape)

Code bewerken en uitvoeren

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: Constanten en variabelen Exercise 2: Gegevens definiëren als constanten

Huidige oefening

Exercise 3: Variabelen definiëren Exercise 4: Basisbewerkingen Exercise 5: Elementgewijze vermenigvuldiging uitvoeren Exercise 6: Voorspellingen maken met matrixvermenigvuldiging Exercise 7: Optellen over tensordimensies Exercise 8: Geavanceerde bewerkingen Exercise 9: Tensors reshapen Exercise 10: Optimaliseren met gradiënten Exercise 11: Werken met afbeeldingsdata

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: 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!