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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.

Este exercício faz parte do curso

Deep Learning for Text with PyTorch

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Instruções do exercício

  • 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.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# 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!')
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