CommencerCommencer gratuitement

Train a CNN model for text

Well done defining the TextClassificationCNN class. PyBooks now needs to train the model to optimize it for accurate sentiment analysis of book reviews.

The following packages have been imported for you: torch, torch.nn as nn, torch.nn.functional as F, torch.optim as optim.

An instance of TextClassificationCNN() with arguments vocab_size and embed_dim has also been loaded and saved as model.

Cet exercice fait partie du cours

Deep Learning for Text with PyTorch

Afficher le cours

Instructions

  • Define a loss function used for binary classification and save as criterion.
  • Zero the gradients at the start of the training loop.
  • Update the parameters at the end of the loop.

Exercice interactif pratique

Essayez cet exercice en complétant cet exemple de code.

# Define the loss function
criterion = nn.____()
optimizer = optim.SGD(model.parameters(), lr=0.1)

for epoch in range(10):
    for sentence, label in data:     
        # Clear the gradients
        model.____()
        sentence = torch.LongTensor([word_to_ix.get(w, 0) for w in sentence]).unsqueeze(0) 
        label = torch.LongTensor([int(label)])
        outputs = model(sentence)
        loss = criterion(outputs, label)
        loss.backward()
        # Update the parameters
        ____.____()
print('Training complete!')
Modifier et exécuter le code