PyTorch DataLoader

Kerja bagus mendefinisikan kelas Dataset! WaterDataset yang baru saja Anda buat sekarang sudah dapat digunakan.

Langkah berikutnya dalam menyiapkan data pelatihan adalah membuat DataLoader. Sebuah DataLoader di PyTorch dapat dibuat dari Dataset untuk memuat data, membaginya menjadi batch, dan melakukan transformasi pada data jika diperlukan. Setelah itu, DataLoader akan menghasilkan sampel data yang siap untuk pelatihan.

Dalam latihan ini, Anda akan membangun DataLoader berdasarkan WaterDataset. Kelas DataLoader yang Anda perlukan sudah diimpor untuk Anda dari torch.utils.data. Mari kita mulai!

Latihan ini adalah bagian dari kursus

Deep Learning Lanjutan dengan PyTorch

Petunjuk latihan

Buat sebuah instance WaterDataset dari water_train.csv, lalu tetapkan ke dataset_train.
Buat dataloader_train berdasarkan dataset_train, dengan ukuran batch dua dan mengacak sampel.
Ambil satu batch fitur dan label dari DataLoader lalu cetak.

Latihan interaktif praktis

Cobalah latihan ini dengan menyelesaikan kode contoh berikut.

# Create an instance of the WaterDataset
dataset_train = ____(____)

# Create a DataLoader based on dataset_train
dataloader_train = ____(
    ____,
    batch_size=____,
    shuffle=____,
)

# Get a batch of features and labels
features, labels = ____
print(features, labels)

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 dan pemrograman berorientasi objek Exercise 2: Himpunan Data PyTorch Exercise 3: PyTorch DataLoader

Latihan Saat Ini

Exercise 4: Model PyTorch Exercise 5: Optimizer, pelatihan, dan evaluasi Exercise 6: Training loop Exercise 7: Optimizer Exercise 8: Evaluasi model Exercise 9: Gradien menghilang dan meledak Exercise 10: Inisialisasi dan aktivasi Exercise 11: Aktivasi: 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: 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