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Introduction to RNNs

bookChallenge: Time Series Forecasting with LSTM

Task

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  1. Define the TimeSeriesPredictor class, completing its __init__ method to set up the nn.LSTM and nn.Linear layers, and implement its forward method to process input sequences and output a prediction.

  2. Instantiate the TimeSeriesPredictor model, then define the nn.MSELoss criterion and torch.optim.Adam optimizer.

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

Solution

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SectionΒ 3. ChapterΒ 6
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bookChallenge: Time Series Forecasting with LSTM

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Task

Swipe to start coding

  1. Define the TimeSeriesPredictor class, completing its __init__ method to set up the nn.LSTM and nn.Linear layers, and implement its forward method to process input sequences and output a prediction.

  2. Instantiate the TimeSeriesPredictor model, then define the nn.MSELoss criterion and torch.optim.Adam optimizer.

  3. Implement the training and evaluation loops, including forward and backward passes, parameter updates, and loss 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 3. ChapterΒ 6
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