Tahmin modellerini değerlendirme

Değerlendirme zamanı! Önceki egzersizde eğittiğin aynı LSTM ağı, senin için birkaç epoch daha eğitildi ve net olarak hazır.

Görevin, onu bir test veri kümesi üzerinde Ortalama Kare Hatası (Mean Squared Error) metriğiyle değerlendirmek (torchmetrics zaten içe aktarılmış durumda). Bakalım model ne kadar iyi performans gösteriyor!

Bu egzersiz

PyTorch ile Orta Düzey Deep Learning

kursunun bir parçasıdır

Kursu Görüntüle

Egzersiz talimatları

Mean Squared Error metriğini tanımla ve mse değişkenine ata.
Girdi dizisini net'e geçir ve sonucu outputs'a atamadan önce sıkıştırmak için .squeeze() uygula.
Test metriğinin nihai değerini hesapla ve test_mse değişkenine ata.

Uygulamalı interaktif egzersiz

Bu örnek kodu tamamlayarak bu egzersizi bitirin.

# Define MSE metric
mse = ____

net.eval()
with torch.no_grad():
    for seqs, labels in dataloader_test:
        seqs = seqs.view(32, 96, 1)
        # Pass seqs to net and squeeze the result
        outputs = ____
        mse(outputs, labels)

# Compute final metric value
test_mse = ____
print(f"Test MSE: {test_mse}")

Kodu Düzenle ve Çalıştır

Bu egzersiz

PyTorch ile Orta Düzey Deep Learning

kursunun bir parçasıdır

IntermediárioNível de habilidade

4.8+

Kursa Ücretsiz Başlayın

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: Handling images with PyTorch Exercise 2: Image dataset Exercise 3: Data augmentation Exercise 4: Data augmentation in PyTorch Exercise 5: Convolutional Neural Networks Exercise 6: The convolutional layer Exercise 7: Building convolutional networks Exercise 8: Training image classifiers Exercise 9: Choosing augmentations Exercise 10: Dataset with augmentations Exercise 11: Image classifier training loop Exercise 12: Evaluating image classifiers Exercise 13: Multi-class model evaluation Exercise 14: Analyzing metrics per class

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: PyTorch ile dizileri işleme Exercise 2: Diziler oluşturma Exercise 3: Sıralı Veri Kümesi Exercise 4: Recurrent Neural Networks Exercise 5: Sıralı mimariler Exercise 6: Bir tahminleme RNN'i oluşturma Exercise 7: LSTM ve GRU hücreleri Exercise 8: RNN vs. LSTM vs. GRU Exercise 9: LSTM ağı Exercise 10: GRU ağı Exercise 11: RNN'leri eğitmek ve değerlendirmek Exercise 12: RNN eğitim döngüsü Exercise 13: Tahmin modellerini değerlendirme

Geçerli Egzersiz

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