Insieme di dati sequenziale

Ottimo lavoro nel costruire la funzione create_sequences()! È il momento di usarla per creare un insieme di dati di training per il tuo modello.

Proprio come per i dati tabellari e le immagini, i dati sequenziali vengono passati al modello nel modo più semplice tramite un Dataset e un DataLoader di torch. Per costruire un Dataset sequenziale, chiamerai create_sequences() per ottenere gli array NumPy con input e target e ne controllerai la forma. Poi li passerai a un TensorDataset per creare un vero Dataset di torch e ne verificherai la lunghezza.

La tua implementazione di create_sequences() e un DataFrame con i dati di training chiamato train_data sono già disponibili.

Questo esercizio fa parte del corso

Deep Learning intermedio con PyTorch

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Istruzioni dell'esercizio

Chiama create_sequences(), passandole il DataFrame di training e una lunghezza di sequenza pari a 24*4, assegnando il risultato a X_train, y_train.
Definisci dataset_train chiamando TensorDataset e passandole due argomenti, gli input e i target creati da create_sequences(), entrambi convertiti da array NumPy a tensori di float.

Esercizio pratico interattivo

Prova a risolvere questo esercizio completando il codice di esempio.

import torch
from torch.utils.data import TensorDataset

# Use create_sequences to create inputs and targets
X_train, y_train = ____
print(X_train.shape, y_train.shape)

# Create TensorDataset
dataset_train = ____(
    ____,
    ____,
)
print(len(dataset_train))

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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 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: Gestire le sequenze con PyTorch Exercise 2: Generare sequenze Exercise 3: Insieme di dati sequenziale

Esercizio in corso

Exercise 4: Reti neurali ricorrenti Exercise 5: Architetture sequenziali Exercise 6: Costruire un RNN per il forecasting Exercise 7: Celle LSTM e GRU Exercise 8: RNN vs. LSTM vs. GRU Exercise 9: Rete LSTM Exercise 10: Rete GRU Exercise 11: Addestrare e valutare le RNN Exercise 12: Ciclo di training RNN Exercise 13: Valutare i modelli di forecasting

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