Contents
What is true about the forget gate in Lstms?
The remember vector is usually called the forget gate. The output of the forget gate tells the cell state which information to forget by multiplying 0 to a position in the matrix. If the output of the forget gate is 1, the information is kept in the cell state.
How does a GRU work?
How do GRUs work? As mentioned above, GRUs are improved version of standard recurrent neural network. To solve the vanishing gradient problem of a standard RNN, GRU uses, so-called, update gate and reset gate. Basically, these are two vectors which decide what information should be passed to the output.
Why do we need to understand why LSTMs work?
In order to understand why LSTMs work, and get an intuitive understanding of the statistical complexity behind the model that allows it to fit to a variety of data samples, I strongly believe that it’s necessary to understand the mathematical operations that go on behind the cell, so here we go! Great, big complex diagram.
What are the normalizing equations in the LSTM?
Before we jump into the specific gates and all the math behind them, I need to point out that there are two types of normalizing equations that are being used in the LSTM. The first is the sigmoid function (represented with a lower-case sigma), and the second is the tanh function.
The sigmoid layer decides which part of the cell state will be present in the output whereas tanh layer shifts the output in the range of [-1,1]. The results of the two layers undergo point-wise multiplication to produce the output ht of the cell.
What makes up a cell in a LSTM?
This entire rectangle is called an LSTM “cell”. It is analogous to the circle from the previous RNN diagram. These are the parts that make up the LSTM cell: The Gates: “Forget” or also known as “Remember”, “Input”, and “Output” There is usually a lot of confusion between the “Cell State” and the “Hidden State”.