Afbeeldingsgegevensset

Laten we beginnen met het bouwen van een Torch Dataset van afbeeldingen. Je gebruikt die om de data te verkennen en later om het in een model te voeren.

De trainingsdata voor de wolkenclassificatietaak staat in de volgende mapstructuur:

clouds_train
  - cirriform clouds
    - 539cd1c356e9c14749988a12fdf6c515.jpg
    - ...
  - clear sky
  - cumulonimbus clouds
  - cumulus clouds
  - high cumuliform clouds
  - stratiform clouds
  - stratocumulus clouds

Er zijn zeven mappen binnen clouds_train, elk met één wolkentype (of een heldere lucht). In elk van deze mappen staan de bijbehorende afbeeldingsbestanden.

De volgende imports zijn alvast voor je gedaan:

from torchvision.datasets import ImageFolder
from torchvision import transforms

Deze oefening maakt deel uit van de cursus

Gevorderde Deep Learning met PyTorch

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Oefeninstructies

Combineer twee transformaties en wijs ze toe aan train_transforms: eerst om de afbeelding om te zetten naar een tensor, en daarna om de afbeelding te schalen naar 128 bij 128.
Gebruik ImageFolder om dataset_train te definiëren. Geef het het pad naar de data ("clouds_train") en de eerder gedefinieerde transformaties mee.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Compose transformations
train_transforms = ____([
    transforms.____,
    transforms.____,
])

# Create Dataset using ImageFolder
dataset_train = ____(
    ____,
    transform=____,
)

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Gevorderde Deep Learning met PyTorch

SkillTag.level.intermediateSkillTag.label

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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: Afbeeldingen verwerken met PyTorch Exercise 2: Afbeeldingsgegevensset

Huidige oefening

Exercise 3: Data-augmentatie Exercise 4: Data-augmentatie in PyTorch Exercise 5: Convolutionele neurale netwerken Exercise 6: De convolutionele laag Exercise 7: Convolutionele netwerken bouwen Exercise 8: Afbeeldingsclassificatiemodellen trainen Exercise 9: Augmentaties kiezen Exercise 10: Gegevensset met augmentaties Exercise 11: Trainingslus voor beeldclassificatie Exercise 12: Beoordeling van beeldclassificatiemodellen Exercise 13: Evaluatie van een multiclass-model Exercise 14: Metrics per klasse analyseren

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