Insieme di dati con due input

Costruire un modello con più input parte dalla creazione di un dataset personalizzato che sappia fornire tutti gli input al modello. In questo esercizio, creerai il dataset Omniglot che restituisce terne composte da:

L'immagine di un carattere da classificare,
Il vettore one-hot dell'alfabeto di lunghezza 30, con zeri ovunque tranne un singolo uno che indica l'ID dell'alfabeto da cui proviene il carattere,
L'etichetta target, un intero tra 0 e 963.

Ti vengono forniti samples, una lista di 3-uple che comprendono il percorso al file dell'immagine, il relativo vettore dell'alfabeto e l'etichetta target. Inoltre, sono già stati eseguiti i seguenti import, quindi mettiamoci al lavoro!

from PIL import Image
from torch.utils.data import DataLoader, Dataset
from torchvision import transforms

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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 OmniglotDataset(Dataset):
    def __init__(self, transform, samples):
        # Assign transform and samples to class attributes
        ____ = ____
        ____ = ____

Modifica ed esegui il codice

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 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: 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: Modelli con input multipli Exercise 2: Insieme di dati con due input

Esercizio in corso

Exercise 3: Modello con due input Exercise 4: Addestramento del modello con due input Exercise 5: Modelli multi-output Exercise 6: Dataset e DataLoader a due uscite Exercise 7: Architettura del modello a due output Exercise 8: Addestrare modelli multi-output Exercise 9: Valutazione dei modelli multi-output e pesatura delle loss Exercise 10: Valutazione di modelli multi-output Exercise 11: Ponderazione della loss Exercise 12: Riepilogo