def __init__(self, input_size, outputclass):
super(stackedIndRNN_encoder, self).__init__()
hidden_size = args.hidden_size
self.DIs = nn.ModuleList()
denseinput = nn.Linear(input_size, hidden_size, bias=True)
self.DIs.append(denseinput)
for x in range(args.num_layers - 1):
denseinput = nn.Linear(hidden_size, hidden_size, bias=True)
self.DIs.append(denseinput)
self.BNs = nn.ModuleList()
for x in range(args.num_layers):
bn = Batch_norm_overtime(hidden_size, args.seq_len)
self.BNs.append(bn)
self.RNNs = nn.ModuleList()
rnn = IndRNN(hidden_size=hidden_size) #IndRNN
self.RNNs.append(rnn)
for x in range(args.num_layers - 1):
rnn = IndRNN(hidden_size=hidden_size) #IndRNN
self.RNNs.append(rnn)
self.lastfc = nn.Linear(hidden_size, outputclass, bias=True)
self.init_weights()
def __init__(self, outputclass):
super(stackedIndRNN_encoder, self).__init__()
self.embed = torch.nn.Embedding(outputclass, args.hidden_size)
self.DIs = nn.ModuleList()
denseinput = nn.Linear(args.hidden_size, args.hidden_size, bias=True)
self.DIs.append(denseinput)
for x in range(args.num_layers - 1):
denseinput = nn.Linear(args.hidden_size,
args.hidden_size,
bias=True)
self.DIs.append(denseinput)
self.BNs = nn.ModuleList()
for x in range(args.num_layers):
bn = Batch_norm_step(args.hidden_size, args.seq_len)
self.BNs.append(bn)
self.RNNs = nn.ModuleList()
for x in range(args.num_layers):
rnn = IndRNN(hidden_size=args.hidden_size) #IndRNN
self.RNNs.append(rnn)
self.lastfc = nn.Linear(args.hidden_size, outputclass)
self.init_weights()
self.outputclass = outputclass
def __init__(self, hidden_size, seq_len, bn_location='bn_before'):
super(IndRNNwithBN, self).__init__()
if bn_location == "bn_before":
self.add_module('norm1', BN(hidden_size, args.seq_len))
self.add_module('indrnn1', IndRNN(hidden_size))
if bn_location == "bn_after":
self.add_module('norm1', BN(hidden_size, args.seq_len))
if (bn_location != 'bn_before') and (bn_location != 'bn_after'):
print('Please select a batch normalization mode.')
assert 2 == 3
def __init__(self, hidden_size, seq_len,bn_location='bn_before'):
super(IndRNNwithBN, self).__init__()
if bn_location=="bn_before":
self.add_module('norm1', BN(hidden_size, args.seq_len))
self.add_module('indrnn1', IndRNN(hidden_size))
if bn_location=="bn_after":
self.add_module('norm1', BN(hidden_size, args.seq_len))
if (bn_location!='bn_before') and (bn_location!='bn_after'):
print('Please select a batch normalization mode.')
assert 2==3
if (bn_location!='bn_before'):
print('You are selecting the bn_before mode, where batch normalization is used before the recurrent connection.\
It generally provides a stable but worse results than the bn_after mode. So use bn_after first.')