Machine Learning and Artificial Intelligence by Ameet V Joshi

Author:Ameet V Joshi
Language: eng
Format: epub, pdf
ISBN: 9783030266226
Publisher: Springer International Publishing

12.4.2.1 Forget Gate

At forget gate the input is combined with the previous output to generate a fraction between 0 and 1, that determines how much of the previous state need to be preserved (or in other words, how much of the state should be forgotten). This output is then multiplied with the previous state.

12.4.2.2 Input Gate

Input gate operates on the same signals as the forget gate, but here the objective is to decide which new information is going to enter the state of LSTM. The output of the input gate (again a fraction between 0 and 1) is multiplied with the output of tanh block that produces the new values that must be added to previous state. This gated vector is then added to previous state to generate current state.

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