Defining data as constants

Throughout this course, we will use tensorflow version 2.6.0 and will exclusively import the submodules needed to complete each exercise. This will usually be done for you, but you will do it in this exercise by importing constant from tensorflow.

After you have imported constant, you will use it to transform a numpy array, credit_numpy, into a tensorflow constant, credit_constant. This array contains feature columns from a dataset on credit card holders and is previewed in the image below. We will return to this dataset in later chapters.

Note that tensorflow 2 allows you to use data as either a numpy array or a tensorflow constant object. Using a constant will ensure that any operations performed with that object are done in tensorflow.

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

Cet exercice fait partie du cours

Introduction to TensorFlow in Python

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Instructions

Import the constant submodule from the tensorflow module.
Convert the credit_numpy array into a constant object in tensorflow. Do not set the data type.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# 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)

Modifier et exécuter le code

Cet exercice fait partie du cours

Introduction to TensorFlow in Python

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4.8+

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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

Exercice en cours

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: 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!