Compilare un modello sequenziale

In questo esercizio, lavorerai alla classificazione delle lettere dal dataset Sign Language MNIST; userai però un'architettura di rete diversa rispetto all'esercizio precedente. Ci saranno meno livelli, ma più nodi. Applicherai anche il dropout per prevenire l'overfitting. Infine, compilerai il modello per usare l'optimizer adam e la loss categorical_crossentropy. Userai anche un metodo in keras per riassumere l'architettura del modello. Nota che keras è stato importato da tensorflow e un modello sequenziale keras è già stato definito come model.

Questo esercizio fa parte del corso

Introduzione a TensorFlow in Python

Visualizza il corso

Istruzioni dell'esercizio

Nel primo livello denso, imposta il numero di nodi a 16, l'attivazione a sigmoid e input_shape a (784,).
Applica il dropout con un tasso del 25% all'output del primo livello.
Imposta il livello di output come denso, con 4 nodi, e usa una funzione di attivazione softmax.
Compila il modello usando l'optimizer adam e la funzione di loss categorical_crossentropy.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Define the first dense layer
model.add(keras.layers.Dense(____, ____, ____))

# Apply dropout to the first layer's output
model.add(keras.layers.____(0.25))

# Define the output layer
____

# Compile the model
model.compile('____', loss='____')

# Print a model summary
print(model.summary())

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Introduzione a TensorFlow in Python

IntermediárioNível de habilidade

4.8+

Inizia il corso gratis

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: 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: Definire reti neurali con Keras Exercise 2: Il modello sequenziale in Keras Exercise 3: Compilare un modello sequenziale

Esercizio in corso

Exercise 4: Definire un modello con input multipli Exercise 5: Training e validazione con Keras Exercise 6: Addestramento con Keras Exercise 7: Metriche e validazione con Keras Exercise 8: Rilevamento dell'overfitting Exercise 9: Valutare i modelli Exercise 10: Addestrare modelli con l'API Estimators Exercise 11: Prepararti all'addestramento con Estimators Exercise 12: Definire gli Estimator Exercise 13: Congratulazioni!