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net.py
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net.py
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import torch as t
import torch.nn as nn
from torch.autograd import Variable
from configuration import config
import torch.nn.functional as F
class netLSTM(nn.Module):
def __init__(self):
super(netLSTM, self).__init__()
self.lstm = nn.LSTM(config.input_dim,
config.hid_dim,
config.num_layer,
batch_first=True,
dropout=config.drop_out)
self.fc2 = nn.Linear(config.hid_dim,
int(config.hid_dim/2))
self.fc3 = nn.Linear(int(config.hid_dim/2),
config.output_dim)
def forward(self, x, hs=None, use_gpu=config.use_gpu):
batch_size = x.size(0)
if hs is None:
h = Variable(t.zeros(config.num_layer,
batch_size,
config.hid_dim))
c = Variable(t.zeros(config.num_layer,
batch_size,
config.hid_dim))
hs = (h, c)
if use_gpu:
hs = (hs[0].cuda(), hs[1].cuda())
self.lstm.flatten_parameters()
out, hs_0 = self.lstm(x, hs)
out = out[:, -10:, :]
self.lstm.flatten_parameters()
out = out.contiguous()
out = out.view(-1, config.hid_dim)
out = F.relu(self.fc2(out))
out = self.fc3(out)
return out, hs_0
class netLSTM_withbn(nn.Module):
def __init__(self):
super(netLSTM_withbn, self).__init__()
self.lstm = nn.LSTM(config.input_dim,
config.hid_dim,
config.num_layer,
batch_first=True,
dropout=config.drop_out)
self.fc2 = nn.Linear(config.hid_dim,
int(config.hid_dim / 2))
self.fc3 = nn.Linear(int(config.hid_dim / 2),
config.output_dim)
self.bn = nn.BatchNorm1d(int(config.hid_dim / 2))
def forward(self, x, hs=None, use_gpu=config.use_gpu):
batch_size = x.size(0)
if hs is None:
h = Variable(t.zeros(config.num_layer,
batch_size,
config.hid_dim))
c = Variable(t.zeros(config.num_layer,
batch_size,
config.hid_dim))
hs = (h, c)
if use_gpu:
hs = (hs[0].cuda(), hs[1].cuda())
out, hs_0 = self.lstm(x, hs)
out = out.contiguous()
out = out.view(-1, config.hid_dim)
out = F.relu(self.bn(self.fc2(out)))
out = self.fc3(out)
return out, hs_0