Making predictions with matrix multiplication

In later chapters, you will learn to train linear regression models. This process will yield a vector of parameters that can be multiplied by the input data to generate predictions. In this exercise, you will use input data, features, and a target vector, bill, which are taken from a credit card dataset we will use later in the course.

\(features = \begin{bmatrix} 2 & 24 \\ 2 & 26 \\ 2 & 57 \\ 1 & 37 \end{bmatrix}\), \(bill = \begin{bmatrix} 3913 \\ 2682 \\ 8617 \\ 64400 \end{bmatrix}\), \(params = \begin{bmatrix} 1000 \\ 150 \end{bmatrix}\)

The matrix of input data, features, contains two columns: education level and age. The target vector, bill, is the size of the credit card borrower's bill.

Since we have not trained the model, you will enter a guess for the values of the parameter vector, params. You will then use matmul() to perform matrix multiplication of features by params to generate predictions, billpred, which you will compare with bill. Note that we have imported matmul() and constant().

Cet exercice fait partie du cours

Introduction to TensorFlow in Python

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Instructions

Define features, params, and bill as constants.
Compute the predicted value vector, billpred, by multiplying the input data, features, by the parameters, params. Use matrix multiplication, rather than the element-wise product.
Define error as the targets, bill, minus the predicted values, billpred.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Define features, params, and bill as constants
features = ____([[2, 24], [2, 26], [2, 57], [1, 37]])
params = ____([[1000], [150]])
bill = ____([[3913], [2682], [8617], [64400]])

# Compute billpred using features and params
billpred = ____

# Compute and print the error
error = ____ - ____
print(error.numpy())

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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 Exercise 3: Defining variables Exercise 4: Basic operations Exercise 5: Performing element-wise multiplication Exercise 6: Making predictions with matrix multiplication

Exercice en cours

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!