RNN-Klassifikationsmodelle bewerten

Das Team von PyBooks möchte nun, dass du das von dir erstellte und mit dem Newsgroup-Datensatz ausgeführte RNN-Modell bewertest. Zur Erinnerung: Das Ziel war, die Artikel einer von drei Kategorien zuzuordnen:

rec.autos, sci.med und comp.graphics.

Das Modell wurde trainiert und du hast für jede Epoche den Loss ausgegeben.

Verwende torchmetrics, um verschiedene Metriken für dein Modell zu berechnen. Folgendes wurde für dich geladen: Accuracy, Precision, Recall, F1Score.

Eine Instanz von rnn_model, die in der vorherigen Übung trainiert wurde, ist ebenfalls für dich vorab geladen.

Diese Übung ist Teil des Kurses

Deep Learning für Text mit PyTorch

Anleitung zur Übung

Erstelle für jede Metrik eine Instanz für Mehrklassenklassifikation, mit num_classes gleich der Anzahl der Kategorien.
Erzeuge die Vorhersagen für das rnn_model mit den Testdaten X_test_seq.
Berechne die Metriken mit den vorhergesagten Klassen und den echten Labels.

Interaktive Übung

Vervollständige den Beispielcode, um diese Übung erfolgreich abzuschließen.

# Create an instance of the metrics
accuracy = Accuracy(task="multiclass", ____)
precision = Precision(____, num_classes=____)
recall = Recall(task=____, num_classes=____)
f1 = F1Score(____, ____)

# Generate the predictions
outputs = ____(X_test_seq)
_, predicted = ____.____(outputs, 1)

# Calculate the metrics
accuracy_score = accuracy(____, y_test_seq)
precision_score = precision(____, y_test_seq)
recall_score = recall(____, y_test_seq)
f1_score = f1(____, y_test_seq)
print("RNN Model - Accuracy: {}, Precision: {}, Recall: {}, F1 Score: {}".format(accuracy_score, precision_score, recall_score, f1_score))

Code bearbeiten und ausführen

Diese Übung ist Teil des Kurses

Deep Learning für Text mit PyTorch

Hohe SchwierigkeitSchwierigkeitsgrad

4.8+

Kurs kostenlos starten

This chapter introduces you to deep learning for text and its applications. Learn how to use PyTorch for text processing and get hands-on experience with techniques such as tokenization, stemming, stopword removal, and more. Understand the importance of encoding text data and implement encoding techniques using PyTorch. Finally, consolidate your knowledge by building a text processing pipeline combining these techniques.

Exercise 1: Introduction to preprocessing for text Exercise 2: Word frequency analysis Exercise 3: Preprocessing text Exercise 4: Encoding text data Exercise 5: One-hot encoded book titles Exercise 6: Bag-of-words for book titles Exercise 7: Applying TF-IDF to book descriptions Exercise 8: Introduction to building a text processing pipeline Exercise 9: Shakespearean language preprocessing pipeline Exercise 10: Shakespearean language encoder

Explore text classification and its role in Natural Language Processing (NLP). Apply your skills to implement word embeddings and develop both Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) for text classification using PyTorch, and understand how to evaluate your models using suitable metrics.

Exercise 1: Überblick über Textklassifikation Exercise 2: Embeddings in PyTorch Exercise 3: Textklassifizierungsaufgaben kategorisieren Exercise 4: Convolutional Neural Networks für die Textklassifizierung Exercise 5: Erstelle ein CNN-Modell für Text Exercise 6: Ein CNN-Modell für Text trainieren Exercise 7: Das Sentiment-Analyse-CNN-Modell testen Exercise 8: Rekurrente Neuronale Netze für die Textklassifikation Exercise 9: Ein RNN-Modell für Text bauen Exercise 10: Ein LSTM-Modell für Text aufbauen Exercise 11: Ein GRU-Modell für Text erstellen Exercise 12: Bewertungsmetriken für die Textklassifikation Exercise 13: RNN-Klassifikationsmodelle bewerten

Aktuelle Übung

Exercise 14: Leistung des Modells bewerten Exercise 15: Modelle vergleichen

Venture into the exciting world of text generation and its applications in NLP. Understand how to leverage Recurrent Neural Networks (RNNs), Generative Adversarial Networks (GANs), and pre-trained models for text generation tasks using PyTorch. Alongside, you'll learn to evaluate the performance of your models using relevant metrics.

Exercise 1: Introduction to text generation Exercise 2: Creating a RNN model for text generation Exercise 3: Text generation using RNN - Training and Generation Exercise 4: Generative adversarial networks for text generation Exercise 5: Building a generator and discriminator Exercise 6: Training a GAN model Exercise 7: Pre-trained models for text generation Exercise 8: Text completion with pre-trained GPT-2 models Exercise 9: Language translation with pretrained PyTorch model Exercise 10: Evaluation metrics for text generation Exercise 11: Evaluating pretrained text generation model Exercise 12: Understanding text generation metrics

Understand the concept of transfer learning and its application in text classification. Explore Transformers, their architecture, and how to use them for text classification and generation tasks. You will also delve into attention mechanisms and their role in text processing. Finally, understand the potential impacts of adversarial attacks on text classification models and learn how to protect your models.

Exercise 1: Transfer learning for text classification Exercise 2: Transfer learning using BERT Exercise 3: Evaluating the BERT model Exercise 4: Transformers for text processing Exercise 5: Creating a transformer model Exercise 6: Training and testing the Transformer model Exercise 7: Attention mechanisms for text processing Exercise 8: Creating a RNN model with attention Exercise 9: Training and testing the RNN model with attention Exercise 10: Adversarial attacks on text classification models Exercise 11: Adversarial attack classification Exercise 12: Safeguarding AI at PyBooks Exercise 13: Wrap-up