Hi,
I understand that you are facing an issue in understanding the order of weights in an LSTM Layer with multiple units.As per the documentation, each LSTM unit is associated with “Input gate”, “Forget gate”, “Cell candidate” and “Output gate”.
The learnable weights mentioned in the documentation are with respect to the LSTM layer with multiple units, but not the individual LSTM unit. By checking the ” learnables” of the following example “dlnetwork” object with LSTM layer, we can interpret the learnable weights as explained in the documentation. The learnable weights are obtained by the concatenation of weight matrices associated with the “Input gate”, “Forget gate”, “Cell candidate” and “Output gate” of the individual LSTM cells in the LSTM layer.
inputSize = 1;
embeddingDimension = 100;
numWords = 2920
numClasses = numWords + 1;
layers = [
sequenceInputLayer(inputSize)
wordEmbeddingLayer(embeddingDimension,numWords)
lstmLayer(100)
dropoutLayer(0.2)
fullyConnectedLayer(numClasses)
softmaxLayer];
lgraph = layerGraph(layers);
dlnet = dlnetwork(lgraph);
Below is the above “dlnetwork” object “dlnet.Learnables”,
Please refer to the following documentation, for more information,
Hope this helps.
Regards,
Sanjana.
