def get_net_parameters(opt):
net = DD()
net.model = opt.model
net.nL = opt.num_layers
net.nH = opt.num_heads
net.hSize = opt.hidden_dim
net.edpt = opt.embedding_dropout
net.adpt = opt.attention_dropout
net.rdpt = opt.residual_dropout
net.odpt = opt.output_dropout
net.pt = opt.pretrain
net.afn = opt.activation
# how to intialize parameters
# format is gauss+{}+{}.format(mean, std)
# n = the default initialization pytorch
net.init = opt.init
return net
def get_parameters(opt, exp_type="model"):
params = DD()
params.net = DD()
params.mle = 0
params.dataset = opt.dataset
params.net = get_net_parameters(opt)
params.train = get_training_parameters(opt)
params.model = params.net.model
params.exp = opt.exp
params.data = get_data_parameters(opt, params.exp, params.dataset)
params.eval = get_eval_parameters(opt, params.data.get("categories", None))
meta = DD()
params.trainer = opt.trainer
meta.iterations = int(opt.iterations)
meta.cycle = opt.cycle
params.cycle = opt.cycle
params.iters = int(opt.iterations)
global toy
toy = opt.toy
global do_gen
do_gen = opt.do_gen
global save
save = opt.save
global test_save
test_save = opt.test_save
global save_strategy
save_strategy = opt.save_strategy
print(params)
return params, meta