Model (Tensorflow backend)¶
These are optional controllable parameters for the CGRUnits inside a MDGRU block.
periodic convolution on input x (dtype=bool)
--periodic_convolution_x False
periodic convolution on input h (dtype=bool)
--periodic_convolution_h False
use Bernoulli distribution (insted of Gaussian) for dropconnect (dtype=bool)
--use_bernoulli False
stride (dtype=int)
--strides None
use dropconnect on input x (dtype=bool)
--use_dropconnect_x True
use dropconnect on input h (dtype=bool)
--use_dropconnect_h True
don’t use average pooling (dtype=bool)
--no_avg_pooling True
filter size for input x (dtype=int)
--filter_size_x 7
filter size for input h (dtype=int)
--filter_size_h 7
use static RNN (dtype=bool)
--use_static_rnn False
add batch normalization at the input x in gate (dtype=bool)
--add_x_bn False
add batch normalization at the input h in candidate (dtype=bool)
--add_h_bn False
add batch normalization at the candidates input and state (dtype=bool)
--add_e_bn False
add residual learning to the input x of each cgru (dtype=bool)
--resgrux False
add residual learning to the input h of each cgru (dtype=bool)
--resgruh False
move the reset gate to the location the original GRU applies it at (dtype=bool)
--put_r_back False
apply dropconnect on the candidate weights as well (dtype=bool)
--use_dropconnect_on_state False