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import torch.nn as nn
import torch
class LSTMModel(nn.Module):
## constructor
def __init__(self, input_size, hidden_size, output_size, num_layers):
super(LSTMModel, self).__init__()
self.input_size = input_size
self.hidden_size = hidden_size
self.output_size = output_size
self.num_layers = num_layers
self.lstm = nn.LSTM(self.input_size, self.hidden_size, self.num_layers, batch_first=True)
self.fc = nn.Linear(self.hidden_size, self.output_size)
def forward(self,x, h0=None, c0=None):
# hidden and state vectors h0 and c0
if h0 is None or c0 is None:
h0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
c0 = torch.zeros(self.num_layers, x.size(0), self.hidden_size)
out, (hn, cn) = self.lstm(x, (h0, c0))
out = self.fc(out)
return out, (hn, cn)
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