Training a GAN model

Your team at PyBooks has made good progress in building the text generator using a Generative Adversarial Network (GAN). You have successfully defined the generator and discriminator networks. Now, it's time to train them. The final step is to generate some fake data and compare it with the real data to see how well your GAN has learned. We have used tensors as an input and the output would try to resemble the input tensors. The team at PyBooks can then use this synthetic data for text analysis as the features will have same relationship as text data.

The generator and discriminator have been initialized and saved to generator and discriminator, respectively.

The following variables have been initialized in the exercise:

seq_length = 5: Length of each synthetic data sequence
num_sequences = 100: Total number of sequences generated
num_epochs = 50: Number of complete passes through the dataset
print_every = 10: Output display frequency, showing results every 10 epochs

Cet exercice fait partie du cours

Deep Learning for Text with PyTorch

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Essayez cet exercice en complétant cet exemple de code.

# Define the loss function and optimizer
criterion = nn.____()
optimizer_gen = ____(generator.parameters(), lr=0.001)
optimizer_disc = ____(discriminator.parameters(), lr=0.001)

for epoch in range(num_epochs):
    for real_data in data:
      	# Unsqueezing real_data and prevent gradient recalculations
        real_data = real_data.____(0)
        noise = torch.rand((1, seq_length))
        fake_data = generator(noise)
        disc_real = discriminator(real_data)
        disc_fake = discriminator(fake_data.____())
        loss_disc = criterion(disc_real, torch.ones_like(disc_real)) + criterion(disc_fake, torch.zeros_like(disc_fake))
        optimizer_disc.zero_grad()
        loss_disc.backward()
        optimizer_disc.step()

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Cet exercice fait partie du cours

Deep Learning for Text with PyTorch

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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: Overview of Text Classification Exercise 2: Embedding in PyTorch Exercise 3: Categorizing text classification tasks Exercise 4: Convolutional neural networks for text classification Exercise 5: Build a CNN model for text Exercise 6: Train a CNN model for text Exercise 7: Testing the Sentiment Analysis CNN Model Exercise 8: Recurrent neural networks for text classification Exercise 9: Building an RNN model for text Exercise 10: Building an LSTM model for text Exercise 11: Building a GRU model for text Exercise 12: Evaluation metrics for text classification Exercise 13: Evaluating RNN classification models Exercise 14: Evaluating the model's performance Exercise 15: Comparing models

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

Exercice en cours

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