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Introduction to RNNs

bookChallenge: Building an LSTM for Sentiment Analysis

Task

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  1. Define the SentimentLSTM class, completing its __init__ method to set up the nn.Embedding, nn.LSTM, and nn.Linear layers, and implement its forward method to process input sequences.

  2. Instantiate the SentimentLSTM model, then define the nn.BCEWithLogitsLoss criterion and torch.optim.Adam optimizer.

  3. Implement the training and evaluation loops, including forward and backward passes, parameter updates, and accuracy calculation.

Solution

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SectionΒ 4. ChapterΒ 5
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bookChallenge: Building an LSTM for Sentiment Analysis

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Task

Swipe to start coding

  1. Define the SentimentLSTM class, completing its __init__ method to set up the nn.Embedding, nn.LSTM, and nn.Linear layers, and implement its forward method to process input sequences.

  2. Instantiate the SentimentLSTM model, then define the nn.BCEWithLogitsLoss criterion and torch.optim.Adam optimizer.

  3. Implement the training and evaluation loops, including forward and backward passes, parameter updates, and accuracy calculation.

Solution

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Everything was clear?

How can we improve it?

Thanks for your feedback!

close

Awesome!

Completion rate improved to 4.55
SectionΒ 4. ChapterΒ 5
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