Just curious, if it is possible to capture inter-dependencies in hidden states by using CRF as middle layer i.e.Model: "sequential_1"
as shown below without introducing extra energies and other factors ?
Layer (type) Output Shape Param #
lstm_1 (LSTM) (None, None, 256) 358400
crf_1 (CRF) (None, None, 256) 131840
lstm_2 (LSTM) (None, None, 256) 525312
dense_1 (Dense) (None, None, 93) 23901
I need to use it as middle layer for LSTM not to lose its char-based generative ability, otherwise if you use as last layer it easily learns that you need to shift whole sentence to the left.
Just curious, if it is possible to capture inter-dependencies in hidden states by using CRF as middle layer i.e.Model: "sequential_1" as shown below without introducing extra energies and other factors ? Layer (type) Output Shape Param #
lstm_1 (LSTM) (None, None, 256) 358400
crf_1 (CRF) (None, None, 256) 131840
lstm_2 (LSTM) (None, None, 256) 525312
dense_1 (Dense) (None, None, 93) 23901
I need to use it as middle layer for LSTM not to lose its char-based generative ability, otherwise if you use as last layer it easily learns that you need to shift whole sentence to the left.
With Best Regards, Andrei Buin.