Sınıf başına metrikleri analiz etme

Toplanmış metrikler modelin performansı için yararlı göstergeler olsa da, çoğu zaman metriklere sınıf bazında bakmak daha bilgilendiricidir. Bu, modelin zayıf kaldığı sınıfları ortaya çıkarabilir.

Bu egzersizde, değerlendirme döngüsünü tekrar çalıştırarak bulut sınıflandırıcımızın kesinliğini (precision) bu kez sınıf başına elde edeceksin. Ardından, bu puanları sınıf adlarıyla eşleyip yorumlayacaksın. Her zamanki gibi, Precision senin için içe aktarılmış durumda. İyi şanslar!

Bu egzersiz, kursun bir parçasıdır

PyTorch ile Orta Düzey Deep Learning

Kursa Göz Atın

Egzersiz talimatları

Sınıf bazlı sonuçlara uygun bir precision metriği tanımla.
dataset_test nesnesinin .class_to_idx özniteliğinin .items() üzerinde yineleme yaparak sözlük üretecini tamamla ve sınıf başına precision hesapla.

Uygulamalı etkileşimli egzersiz

Bu egzersizi bu örnek kodu tamamlayarak deneyin.

# 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)

Kodu Düzenle ve Çalıştır

Bu egzersiz, kursun bir parçasıdır

PyTorch ile Orta Düzey Deep Learning

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başla

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: PyTorch ile görüntülerle çalışmak Exercise 2: Görüntü veri kümesi Exercise 3: Veri artırma (data augmentation)Exercise 4: PyTorch'ta veri artırma (data augmentation)Exercise 5: Evrişimli Sinir Ağları Exercise 6: Evrişim katmanı Exercise 7: Evrişimsel ağlar oluşturma Exercise 8: Görüntü sınıflandırıcıları eğitme Exercise 9: Artırmaları seçme Exercise 10: Artırmalarla veri kümesi Exercise 11: Görüntü sınıflandırıcı eğitim döngüsü Exercise 12: Görüntü sınıflandırıcılarını değerlendirme Exercise 13: Çok sınıflı model değerlendirme Exercise 14: Sınıf başına metrikleri analiz etme

Geçerli egzersiz

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