Metrics per klasse analyseren

Hoewel geaggregeerde metrics nuttige indicatoren zijn voor de prestaties van het model, is het vaak verhelderend om naar de metrics per klasse te kijken. Dit kan klassen onthullen waarop het model ondermaats presteert.

In deze oefening voer je de evaluatielus opnieuw uit om de precision van onze wolkenclassificator te krijgen, maar dit keer per klasse. Daarna koppel je deze scores aan de klassennamen om ze te interpreteren. Zoals gewoonlijk is Precision al voor je geïmporteerd. Succes!

Deze oefening maakt deel uit van de cursus

Gevorderde Deep Learning met PyTorch

Cursus bekijken

Oefeninstructies

Definieer een precision-metriek die geschikt is voor resultaten per klasse.
Bereken de precision per klasse door de dict-comprehension af te maken en te itereren over de .items() van het attribuut .class_to_idx van dataset_test.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# 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)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Gevorderde Deep Learning met PyTorch

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

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: Afbeeldingen verwerken met PyTorch Exercise 2: Afbeeldingsgegevensset Exercise 3: Data-augmentatie Exercise 4: Data-augmentatie in PyTorch Exercise 5: Convolutionele neurale netwerken Exercise 6: De convolutionele laag Exercise 7: Convolutionele netwerken bouwen Exercise 8: Afbeeldingsclassificatiemodellen trainen Exercise 9: Augmentaties kiezen Exercise 10: Gegevensset met augmentaties Exercise 11: Trainingslus voor beeldclassificatie Exercise 12: Beoordeling van beeldclassificatiemodellen Exercise 13: Evaluatie van een multiclass-model Exercise 14: Metrics per klasse analyseren

Huidige oefening

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