def __init__(self,
input_size,
hidden_size,
num_layers,
dropout_rate=0,
dropout_output=False,
rnn_type=nn.LSTM,
concat_layers=False,
use_tanh=1,
bidirectional=True,
res_net=False,
get_all_layers=False): # 训练都不padding 这里还要改一下
super(StackedBRNN, self).__init__()
self.dropout_output = dropout_output
self.dropout_rate = dropout_rate
self.num_layers = num_layers
self.res_net = res_net
self.concat_layers = concat_layers
self.get_all_layers = get_all_layers
self.rnns = nn.ModuleList()
for i in range(num_layers):
input_size = input_size if i == 0 else 2 * hidden_size
#self.rnns.append(rnn_type(input_size, hidden_size,
# num_layers=1,
# bidirectional=True))
self.rnns.append(
MF.SRUCell(input_size,
hidden_size,
dropout=dropout_rate,
rnn_dropout=dropout_rate,
use_tanh=use_tanh,
bidirectional=bidirectional))
def __init__(self,
input_size,
hidden_size,
num_layers,
dropout_rate=0,
dropout_output=False,
rnn_type=nn.LSTM,
concat_layers=False,
padding=False):
super(StackedBRNN, self).__init__()
self.padding = padding
self.dropout_output = dropout_output
self.dropout_rate = dropout_rate
self.num_layers = num_layers
self.concat_layers = concat_layers
self.rnns = nn.ModuleList()
self.lns = nn.ModuleList()
for i in range(num_layers):
input_size = input_size if i == 0 else 2 * hidden_size
#self.rnns.append(rnn_type(input_size, hidden_size,
# num_layers=1,
# bidirectional=True))
self.rnns.append(
MF.SRUCell(input_size,
hidden_size,
dropout=dropout_rate,
rnn_dropout=dropout_rate,
use_tanh=1,
bidirectional=True))
self.lns.append(LayerNorm(d_hid=2 * hidden_size))
def __init__(self,
input_size,
hidden_size,
num_layers=1,
dropout=0,
bidirectional=True,
batch_first=True):
super(SRU, self).__init__()
self.sru = custom_nn.SRUCell(n_in=input_size,
n_out=hidden_size,
dropout=dropout,
rnn_dropout=0,
use_tanh=1,
use_selu=0,
bidirectional=bidirectional,
layer_norm=False,
rescale=False)