Evaluasi model multi-kelas

Mari evaluasi pengklasifikasi awan kita dengan precision dan recall untuk melihat seberapa baik model mengklasifikasikan tujuh jenis awan. Dalam tugas klasifikasi multi-kelas, cara Anda merata-ratakan skor per kelas sangat penting. Ingat bahwa ada empat pendekatan:

Tanpa perataan, dan menganalisis hasil per kelas;
Micro-averaging, mengabaikan kelas dan menghitung metrik secara global;
Macro-averaging, menghitung metrik per kelas lalu merata-ratakannya;
Weighted-averaging, seperti macro tetapi rataan diberi bobot sesuai ukuran kelas.

Baik Precision maupun Recall sudah diimpor dari torchmetrics. Saatnya melihat seberapa baik kinerja model kita!

Latihan ini adalah bagian dari kursus

Deep Learning Lanjutan dengan PyTorch

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Define metrics
metric_precision = Precision(task=____, num_classes=____, average=____)
metric_recall = ____

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

precision = metric_precision.compute()
recall = metric_recall.compute()
print(f"Precision: {precision}")
print(f"Recall: {recall}")

Edit dan Jalankan Kode

Latihan ini adalah bagian dari kursus

Deep Learning Lanjutan dengan PyTorch

SkillTag.level.intermediateSkillTag.label

4.8+

Mulai Kursus 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: Menangani gambar dengan PyTorch Exercise 2: Himpunan data gambar Exercise 3: Augmentasi data Exercise 4: Augmentasi data di PyTorch Exercise 5: Convolutional Neural Networks Exercise 6: Layer konvolusi Exercise 7: Membangun jaringan konvolusional Exercise 8: Melatih pengklasifikasi gambar Exercise 9: Memilih augmentasi Exercise 10: Himpunan data dengan augmentasi Exercise 11: Loop pelatihan pengklasifikasi gambar Exercise 12: Mengevaluasi pengklasifikasi gambar Exercise 13: Evaluasi model multi-kelas

Latihan Saat Ini

Exercise 14: Menganalisis metrik per kelas

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