Addestrare un modello lineare a lotti

In questo esercizio alleneremo un modello di regressione lineare a lotti, ripartendo da dove ci eravamo fermati nell'esercizio precedente. Lo faremo scorrendo l'insieme di dati a lotti e aggiornando le variabili del modello, intercept e slope, dopo ogni passo. Questo approccio ci permette di addestrare con insiemi di dati troppo grandi per essere caricati interamente in memoria.

Nota che la funzione di perdita, loss_function(intercept, slope, targets, features), è già stata definita per te. Inoltre, keras è stato importato e numpy è disponibile come np. Le variabili allenabili devono essere inserite in var_list nell'ordine in cui compaiono come argomenti della funzione di perdita.

Questo esercizio fa parte del corso

Introduzione a TensorFlow in Python

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Istruzioni dell'esercizio

Usa l'ottimizzatore .Adam().
Carica i dati da 'kc_house_data.csv' a lotti con chunksize pari a 100.
Estrai la colonna price da batch, convertila in un array numpy di tipo float a 32 bit e assegnala a price_batch.
Completa la funzione di perdita, compila l'elenco delle variabili allenabili ed esegui la minimizzazione.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Initialize Adam optimizer
opt = keras.optimizers.____

# Load data in batches
for batch in pd.read_csv('____', ____=____):
	size_batch = np.array(batch['sqft_lot'], np.float32)

	# Extract the price values for the current batch
	price_batch = np.array(batch['____'], np.____)

	# Complete the loss, fill in the variable list, and minimize
	opt.minimize(lambda: loss_function(____, slope, price_batch, size_batch), var_list=[intercept, ____])

# Print trained parameters
print(intercept.numpy(), slope.numpy())

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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 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

Esercizio in corso

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!