Imposta una regressione lineare

Una regressione lineare univariata individua la relazione tra una singola caratteristica e il tensore target. In questo esercizio useremo la superficie del lotto e il prezzo di una proprietà. Come visto nel video, prenderemo i logaritmi naturali di entrambi i tensori, disponibili come price_log e size_log.

In questo esercizio definirai il modello e la funzione di perdita. Valuterai poi la funzione di perdita per due diversi valori di intercept e slope. Ricorda che i valori previsti sono dati da intercept + features*slope. Inoltre, nota che keras.losses.mse() è a tua disposizione. Inoltre, slope e intercept sono stati definiti come variabili.

Questo esercizio fa parte del corso

Introduzione a TensorFlow in Python

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Istruzioni dell'esercizio

Definisci una funzione che restituisca i valori previsti per una regressione lineare usando intercept, features e slope, senza usare add() o multiply().
Completa loss_function() aggiungendo le variabili del modello, intercept e slope, come argomenti.
Calcola l'errore quadratico medio usando targets e predictions.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Define a linear regression model
def linear_regression(intercept, slope, features = size_log):
	return ____

# Set loss_function() to take the variables as arguments
def loss_function(____, ____, features = size_log, targets = price_log):
	# Set the predicted values
	predictions = linear_regression(intercept, slope, features)
    
    # Return the mean squared error loss
	return keras.losses.____

# Compute the loss for different slope and intercept values
print(loss_function(0.1, 0.1).numpy())
print(loss_function(0.1, 0.5).numpy())

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Introduzione a TensorFlow in Python

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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 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: Dati di input Exercise 2: Caricare i dati con pandas Exercise 3: Impostare il tipo di dato Exercise 4: Funzioni di loss Exercise 5: Funzioni di perdita in TensorFlow Exercise 6: Modificare la funzione di loss Exercise 7: Regressione lineare Exercise 8: Imposta una regressione lineare

Esercizio in corso

Exercise 9: Addestra un modello lineare Exercise 10: Regressione lineare multipla Exercise 11: Addestramento in batch Exercise 12: Prepararsi al training in batch Exercise 13: Addestrare un modello lineare a lotti

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!