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What is Ct and ht in LSTM?
LSTM has three inputs(Ct-1, ht-1, Xt) and three outputs(Ct, ht, Ot). Inputs are previous hidden state(ht-1), cell state(Ct-1), and current input(Xt). The outputs are hidden state(ht), cell state(Ct), and current output(Ot). Seeing LSTM in these three broad parts makes it easy to understand the working of LSTM.
What is ht in LSTM?
Here,x t and h t are the input vector and output result to the memory cell at time t, respectively. h t is the value of the memory cell. i t , f t and o t are values of the input gate, the forget gate and the output gate at time t, respectively.
A key idea in LSTM is the (star)Gate. h is the hidden state, representing short term memory. C is the cell state, representing long term memory and x is the input. The gates perform only few matrices transformations, sigmoid and tanh activation in order to magically solve all the RNN problems.
Which is the key to understanding a LSTM network?
The key to LSTMs is the cell state, the horizontal line running through the top of the diagram. The cell state is kind of like a conveyor belt. It runs straight down the entire chain, with only some minor linear interactions. It’s very easy for information to just flow along it unchanged.
How are recurrent neural networks different from LSTMs?
Recurrent networks, on the other hand, take as their input not just the current input example they see, but also what they have perceived previously in time.
How are the repeating modules in a LSTM different?
LSTMs also have this chain like structure, but the repeating module has a different structure. Instead of having a single neural network layer, there are four, interacting in a very special way. The repeating module in an LSTM contains four interacting layers.
When was the first time I was introduced to LSTMs?
When I was first introduced to Long Short-Term Memory networks (LSTMs), it was hard to look past their complexity. I didn’t understand why they were designed the way they were designed, just that they worked.