De prestaties van het model evalueren

Het PyBooks-team boekt vooruitgang met de boekaanbevelingsengine. Het modelleeringteam heeft twee verschillende modellen voor je klaargezet voor de boekaanbevelingsengine van PyBooks. Eén model is gebaseerd op LSTM (lstm_model) en het andere gebruikt een GRU (gru_model). Jij krijgt de taak om deze modellen te evalueren en te vergelijken.

De testlabels zijn y_test en de voorspellingen van het model zijn y_pred_lstm voor lstm_model en y_pred_gru voor gru_model.

Deze oefening maakt deel uit van de cursus

Deep Learning voor tekst met PyTorch

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Oefeninstructies

Definieer accuracy, precision, recall en F1 voor multi-class-classificatie door num_classes en task op te geven.
Bereken en print de accuracy, precision, recall en F1-score voor lstm_model.
Bereken op dezelfde manier de evaluatiemetrics voor gru_model.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Create an instance of the metrics
accuracy = ____(task=____, num_classes=3)
precision = ____(task=____, num_classes=3)
recall = ____(task=____, num_classes=3)
f1 = ____(task=____, num_classes=3)

# Calculate metrics for the LSTM model
accuracy_1 = accuracy(____, ____)
precision_1 = precision(____, ____)
recall_1 = recall(____, ____)
f1_1 = f1(____, ____)
print("LSTM Model - Accuracy: {}, Precision: {}, Recall: {}, F1 Score: {}".format(accuracy_1, precision_1, recall_1, f1_1))

# Calculate metrics for the GRU model
accuracy_2 = accuracy(____, ____)
precision_2 = precision(____, ____)
recall_2 = recall(____, ____)
f1_2 = f1(____, ____)
print("GRU Model - Accuracy: {}, Precision: {}, Recall: {}, F1 Score: {}".format(accuracy_2, precision_2, recall_2, f1_2))

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Deep Learning voor tekst met PyTorch

SkillTag.level.advancedSkillTag.label

4.8+

Begin de cursus gratis

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: Overzicht van tekstclassificatie Exercise 2: Embedding in PyTorch Exercise 3: Tekstclassificatietaken categoriseren Exercise 4: Convolutionele neurale netwerken voor tekstclassificatie Exercise 5: Bouw een CNN-model voor tekst Exercise 6: Train een CNN-model voor tekst Exercise 7: Het Sentimentanalyse-CNN-model testen Exercise 8: Recurrent neural networks voor tekstclassificatie Exercise 9: Een RNN-model voor tekst bouwen Exercise 10: Een LSTM-model voor tekst bouwen Exercise 11: Een GRU-model bouwen voor tekst Exercise 12: Evaluatiemetrics voor tekstclassificatie Exercise 13: RNN-classificatiemodellen evalueren Exercise 14: De prestaties van het model evalueren

Huidige oefening

Exercise 15: Modellen vergelijken

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