Addestra un modello lineare

In questo esercizio riprendiamo da dove avevi lasciato nel precedente. L’intercetta e il coefficiente angolare, intercept e slope, sono stati definiti e inizializzati. Inoltre è stata definita una funzione, loss_function(intercept, slope), che calcola la loss usando i dati e le variabili del modello.

Ora definirai un’operazione di ottimizzazione come opt. Allenerai poi un modello lineare univariato minimizzando la loss per trovare i valori ottimali di intercept e slope. Nota che l’operazione opt proverà ad avvicinarsi all’ottimo a ogni passo, ma richiederà molti passi per trovarlo. Per questo, devi eseguire l’operazione ripetutamente.

Questo esercizio fa parte del corso

Introduzione a TensorFlow in Python

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Istruzioni dell'esercizio

Inizializza un ottimizzatore Adam come opt con learning rate pari a 0,5.
Applica il metodo .minimize() all’ottimizzatore.
Passa loss_function() con gli argomenti appropriati come funzione lambda a .minimize().
Fornisci l’elenco delle variabili da aggiornare in var_list.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Initialize an Adam optimizer
opt = keras.optimizers.____(0.5)

for j in range(100):
	# Apply minimize, pass the loss function, and supply the variables
	opt.____(lambda: ____(____, ____), var_list=[____, ____])

	# Print every 10th value of the loss
	if j % 10 == 0:
		print(loss_function(intercept, slope).numpy())

# Plot data and regression line
plot_results(intercept, slope)

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 Exercise 9: Addestra un modello lineare

Esercizio in corso

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!