Addestrare reti neurali con TensorFlow

Nell'esercizio precedente hai definito un modello, model(w1, b1, w2, b2, features), e una funzione di perdita, loss_function(w1, b1, w2, b2, features, targets), entrambi disponibili anche in questo esercizio. Ora addestrerai il modello e poi valuterai le sue prestazioni prevedendo gli esiti di default in un set di test, composto da test_features e test_targets, anch'esso a tua disposizione. Le variabili addestrabili sono w1, b1, w2 e b2. Inoltre, sono già state importate le seguenti operazioni: keras.activations.relu() e keras.layers.Dropout().

Questo esercizio fa parte del corso

Introduzione a TensorFlow in Python

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Istruzioni dell'esercizio

Imposta l'ottimizzatore per eseguire la minimizzazione.
Aggiungi le quattro variabili addestrabili a var_list nell'ordine in cui compaiono come argomenti in loss_function().
Usa il modello e test_features per prevedere i valori di test_targets.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Train the model
for j in range(100):
    # Complete the optimizer
	opt.____(lambda: loss_function(w1, b1, w2, b2), 
                 var_list=[____, ____, ____, ____])

# Make predictions with model using test features
model_predictions = model(w1, b1, w2, b2, ____)

# Construct the confusion matrix
confusion_matrix(test_targets, model_predictions)

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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: 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: Livelli densi Exercise 2: L'algebra lineare dei livelli densi Exercise 3: L'approccio low-level con esempi multipli Exercise 4: Uso dell'operazione di livello denso Exercise 5: Funzioni di attivazione Exercise 6: Problemi di classificazione binaria Exercise 7: Problemi di classificazione multiclasse Exercise 8: Ottimizzatori Exercise 9: I pericoli dei minimi locali Exercise 10: Evitare i minimi locali Exercise 11: Addestrare una rete in TensorFlow Exercise 12: Inizializzazione in TensorFlow Exercise 13: Definire il modello e la funzione di perdita Exercise 14: Addestrare reti neurali con TensorFlow

Esercizio in corso

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!