LSTM Gates: Forget, Input & Output Gates, the Candidate Value and the Cell State over Six Steps
This lesson is part of the course
In this lesson
You run a one-unit LSTM cell with given weights: compute the forget, input and output gates with sigmoids, the candidate with tanh, update the cell state c_t = f·c_(t−1) + i·g and the hidden state h_t = o·tanh(c_t), and carry both into the next step.
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Lessons in this course
- Sequences as Rows: Time Steps, Scaled Inputs, Words as Numbers & Why Order Matters Locked
- The Simple Recurrent Cell: Weighted Input, Recurrent Weight, Bias & tanh, Step by Step Locked
- Unrolling Through Time & Many-to-One: The Final Hidden State as a Summary and a Toy Sentiment Score Locked
- Vanishing & Exploding Gradients: Multiplying Step Factors Back Through Time Locked
- LSTM Gates: Forget, Input & Output Gates, the Candidate Value and the Cell State over Six Steps Locked
- GRU Cells: Update & Reset Gates, the New Hidden State and a Comparison with the LSTM Locked
- Parameter Counts & Cost: Weights per Gate, Simple vs GRU vs LSTM and a Size Table Locked
- Capstone: Next-Value Prediction with Given Trained Weights, MAE vs a Naive Forecast & a Chart Locked