Analizzare le metriche per classe

Le metriche aggregate sono utili per avere un'indicazione delle prestazioni del modello, ma spesso è ancora più informativo guardare le metriche per classe. Questo può rivelare classi su cui il modello performa peggio.

In questo esercizio, eseguirai di nuovo il ciclo di valutazione per ottenere la precision del nostro classificatore di nuvole, questa volta per singola classe. Poi mapperai questi punteggi ai nomi delle classi per interpretarli. Come al solito, Precision è già stato importato per te. Buon lavoro!

Questo esercizio fa parte del corso

Deep Learning intermedio con PyTorch

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Istruzioni dell'esercizio

Definisci una metrica di precision adatta a risultati per classe.
Calcola la precision per classe completando la dict comprehension, iterando su .items() dell'attributo .class_to_idx di dataset_test.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

# Define precision metric
metric_precision = Precision(
    ____, ____, ____
)

net.eval()
with torch.no_grad():
    for images, labels in dataloader_test:
        outputs = net(images)
        _, preds = torch.max(outputs, 1)
        metric_precision(preds, labels)
precision = metric_precision.compute()

# Get precision per class
precision_per_class = {
    k: ____[____].____
    for k, v 
    in ____
}
print(precision_per_class)

Modifica ed esegui il codice

Questo esercizio fa parte del corso

Deep Learning intermedio con PyTorch

IntermediárioNível de habilidade

4.8+

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Learn how to train neural networks in a robust way. In this chapter, you will use object-oriented programming to define PyTorch datasets and models and refresh your knowledge of training and evaluating neural networks. You will also get familiar with different optimizers and, finally, get to grips with various techniques that help mitigate the problems of unstable gradients so ubiquitous in neural nets training.

Exercise 1: PyTorch and object-oriented programming Exercise 2: PyTorch Dataset Exercise 3: PyTorch DataLoader Exercise 4: PyTorch Model Exercise 5: Optimizers, training, and evaluation Exercise 6: Training loop Exercise 7: Optimizers Exercise 8: Model evaluation Exercise 9: Vanishing and exploding gradients Exercise 10: Initialization and activation Exercise 11: Activations: ReLU vs. ELU Exercise 12: Batch Normalization

Train neural networks to solve image classification tasks. In this chapter, you will learn how to handle image data in PyTorch and get to grips with convolutional neural networks (CNNs). You will practice training and evaluating an image classifier while learning about how to improve the model performance with data augmentation.

Exercise 1: Gestire le immagini con PyTorch Exercise 2: Insieme di immagini (dataset)Exercise 3: Data augmentation Exercise 4: Data augmentation in PyTorch Exercise 5: Convolutional Neural Networks Exercise 6: Il livello convoluzionale Exercise 7: Costruire reti convoluzionali Exercise 8: Addestrare i classificatori di immagini Exercise 9: Scelta delle aumentazioni Exercise 10: Insieme di dati con augmentations Exercise 11: Ciclo di training del classificatore di immagini Exercise 12: Valutare i classificatori di immagini Exercise 13: Valutazione di un modello multi-classe Exercise 14: Analizzare le metriche per classe

Esercizio in corso

Build and train recurrent neural networks (RNNs) for processing sequential data such as time series, text, or audio. You will learn about the two most popular recurrent architectures, Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU) networks, as well as how to prepare sequential data for model training. You will practice your skills by training and evaluating a recurrent model for predicting electricity consumption.

Exercise 1: Handling sequences with PyTorch Exercise 2: Generating sequences Exercise 3: Sequential Dataset Exercise 4: Recurrent Neural Networks Exercise 5: Sequential architectures Exercise 6: Building a forecasting RNN Exercise 7: LSTM and GRU cells Exercise 8: RNN vs. LSTM vs. GRU Exercise 9: LSTM network Exercise 10: GRU network Exercise 11: Training and evaluating RNNs Exercise 12: RNN training loop Exercise 13: Evaluating forecasting models

Build multi-input and multi-output models, demonstrating how they can handle tasks requiring more than one input or generating multiple outputs. You will explore how to design and train these models using PyTorch and delve into the crucial topic of loss weighting in multi-output models. This involves understanding how to balance the importance of different tasks when training a model to perform multiple tasks simultaneously.

Exercise 1: Multi-input models Exercise 2: Two-input dataset Exercise 3: Two-input model Exercise 4: Training two-input model Exercise 5: Multi-output models Exercise 6: Two-output Dataset and DataLoader Exercise 7: Two-output model architecture Exercise 8: Training multi-output models Exercise 9: Evaluation of multi-output models and loss weighting Exercise 10: Multi-output model evaluation Exercise 11: Loss weighting Exercise 12: Wrap-up