Dataset PyTorch

È il momento di ripassare i PyTorch Dataset!

Prima di avviare l’addestramento del modello, devi caricare i dati e passarli al modello nel formato corretto. In PyTorch, questo è gestito da Dataset e DataLoader. Iniziamo costruendo un Dataset PyTorch per i nostri dati sulla potabilità dell’acqua.

In questo esercizio definirai una classe chiamata WaterDataset per caricare i dati da un file CSV. Per farlo, dovrai implementare i tre metodi che PyTorch si aspetta da un Dataset:

.__init__() per caricare i dati,
.__len__() per restituire la dimensione dei dati,
.__getitem()__ per estrarre feature ed etichetta di un singolo campione.

I seguenti import necessari sono già stati eseguiti per te:

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

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Deep Learning intermedio con PyTorch

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Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

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
        ____ = ____.____

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Questo esercizio fa parte del corso

Deep Learning intermedio con PyTorch

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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 e programmazione orientata agli oggetti Exercise 2: Dataset PyTorch

Esercizio in corso

Exercise 3: PyTorch DataLoader Exercise 4: Modello PyTorch Exercise 5: Ottimizzatori, training e valutazione Exercise 6: Training loop Exercise 7: Ottimizzatori Exercise 8: Valutazione del modello Exercise 9: Gradienti che svaniscono ed esplodono Exercise 10: Inizializzazione e attivazione Exercise 11: Attivazioni: 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