Menganalisis metrik per kelas

Meskipun metrik teragregasi berguna sebagai indikator kinerja model, sering kali lebih informatif untuk melihat metrik per kelas. Ini dapat mengungkap kelas-kelas di mana model berkinerja kurang baik.

Dalam latihan ini, Anda akan menjalankan kembali loop evaluasi untuk memperoleh precision dari pengklasifikasi awan kita, tetapi kali ini per kelas. Lalu, Anda akan memetakan skor tersebut ke nama kelas untuk menafsirkannya. Seperti biasa, Precision sudah diimpor untuk Anda. Semoga berhasil!

Latihan ini adalah bagian dari kursus

Deep Learning Lanjutan dengan PyTorch

Petunjuk latihan

Definisikan metrik precision yang sesuai untuk hasil per kelas.
Hitung precision per kelas dengan melengkapi dict comprehension, mengiterasi .items() dari atribut .class_to_idx pada dataset_test.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# 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)

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 Exercise 14: Menganalisis metrik per kelas

Latihan Saat Ini

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