## DNN hybrid system training parameters
dnn_num_hidden_layers=3
dnn_input_dim=2000
dnn_output_dim=200
dnn_init_learning_rate=0.008
dnn_final_learning_rate=0.0008
dnn_mixup=5000
num_epochs=15
num_epochs_extra=5
num_iters_final=20

babel_type=limited

# Supervised tuning options
# To update only the last layer using only the supervised data after 
# semi-supervised training is done
do_supervised_tuning=true   
dnn_update_cpu_parallel_opts=(--minibatch-size 128 --num-jobs-nnet 8 --num-threads 16 \
    --parallel-opts "-pe smp 16" --cmd "queue.pl -l arch=*64 -l mem_free=2G,ram_free=1G")
dnn_update_gpu_parallel_opts=(--minibatch-size 512 --num-jobs-nnet 4 --num-threads 1 \
    --parallel-opts "-l gpu=1" --cmd "queue.pl -l arch=*64 -l mem_free=2G,ram_free=1G")

# Semi-supervised examples options
egs_gpu_opts=(--splice-width 4 --samples-per-iter 200000 --num-jobs-nnet 4 --cmd "queue.pl -l arch=*64 -l mem_free=2G,ram_free=1G")
egs_cpu_opts=(--splice-width 4 --samples-per-iter 200000 --num-jobs-nnet 8 --cmd "queue.pl -l arch=*64 -l mem_free=2G,ram_free=1G")
egs_io_opts="-tc 5"
weight_threshold=0.7