PyTorch-gegevensset

Tijd om je kennis van PyTorch Datasets op te frissen!

Voordat je met het trainen van een model kunt beginnen, moet je de data laden en in het juiste formaat aan het model doorgeven. In PyTorch gebeurt dit met Datasets en DataLoaders. Laten we beginnen met het bouwen van een PyTorch Dataset voor onze waterdrinkbaarheid-data.

In deze oefening definieer je een klasse WaterDataset om de data uit een CSV-bestand te laden. Hiervoor implementeer je de drie methoden die PyTorch van een Dataset verwacht:

.__init__() om de data te laden,
.__len__() om de grootte van de data terug te geven,
.__getitem()__ om features en label voor één sample op te halen.

De volgende imports die je nodig hebt, zijn al voor je gedaan:

import pandas as pd
from torch.utils.data import Dataset

Deze oefening maakt deel uit van de cursus

Gevorderde Deep Learning met PyTorch

Cursus bekijken

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

class WaterDataset(Dataset):
    def __init__(self, csv_path):
        super().__init__()
        # Load data to pandas DataFrame
        df = ____
        # Convert data to a NumPy array and assign to self.data
        ____ = ____.____

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Gevorderde Deep Learning met PyTorch

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus 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 en objectgeoriënteerd programmeren Exercise 2: PyTorch-gegevensset

Huidige oefening

Exercise 3: PyTorch DataLoader Exercise 4: PyTorch-model Exercise 5: Optimizers, training en evaluatie Exercise 6: Trainingslus Exercise 7: Optimizers Exercise 8: Modelevaluatie Exercise 9: Verkokende en exploderende gradiënten Exercise 10: Initialisatie en activatie Exercise 11: Activaties: ReLU vs. ELU Exercise 12: Batch-normalisatie

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