Contents
How many units should my LSTM have?
The number of units in each layer of the stack can vary. For example in translate.py from Tensorflow it can be configured to 1024, 512 or virtually any number. The best range can be found via cross validation. But I have seen both 1000 and 500 number of units in each layer of the stack.
How many inputs does LSTM have?
3 input
Tips for LSTM Input The meaning of the 3 input dimensions are: samples, time steps, and features. The LSTM input layer is defined by the input_shape argument on the first hidden layer.
How are units connected in a LSTM cell?
In that sense the units do not share information. However, the connections do mean that on each time step, input data and hidden state plus output from last output from all other units in the cell are combined are used in calculations.
How are inputs and outputs combined in LSTM?
However, the connections do mean that on each time step, input data and hidden state plus output from last output from all other units in the cell are combined are used in calculations. Any cell unit can base its new internal state plus its output on the values of all other outputs and internal states from other units in the cell.
How are neurons in a LSTM cell dependent?
Consequently each neuron in LSTM cell is dependent to the input of the current time-step and the output of the adjacent nodes of the previous time-steps. About the third question, the input size will be equal to the number of features of the input for each time-step. The number of the outputs depends on your task as I referred to at first.
How are LSTM units and cells related in keras?
In keras.layers.LSTM (units, activation=’tanh’,….), the units refers to the dimensionality or length of the hidden state or the length of the activation vector passed on the next LSTM cell/unit – the next LSTM cell/unit is the “green picture above with the gates etc from http://colah.github.io/posts/2015-08-Understanding-LSTMs/