R6590/lstm2.py
5979591645acmaster

lstm2.py
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			import torch
			import torch.nn as nn
			from torch.autograd import Variable

			class LSTMmodel(nn.Module):
			'''
			Class representing the LSTM model
			'''
			def __init__(self):
			super(LSTMmodel,self).__init__()

			#self.inputlinear = nn.Linear(1, 64)
			self.lstm1 = nn.LSTMCell(1, 128)
			self.lstm2 = nn.LSTMCell(128, 128)
			self.outputlinear = nn.Linear(128, 1)

			def forward(self, input, future = 0):
			outputs = []
			h_t = torch.zeros(input.size(0), 128, dtype=torch.double)
			c_t = torch.zeros(input.size(0), 128, dtype=torch.double)
			h_t2 = torch.zeros(input.size(0), 128, dtype=torch.double)
			c_t2 = torch.zeros(input.size(0), 128, dtype=torch.double)

			for i, input_t in enumerate(input.chunk(input.size(1), dim=1)):
			h_t, c_t = self.lstm1(input_t, (h_t, c_t))
			h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
			output = self.linear(h_t2)
			outputs += [output]
			for i in range(future):# if we should predict the future
			h_t, c_t = self.lstm1(output, (h_t, c_t))
			h_t2, c_t2 = self.lstm2(h_t, (h_t2, c_t2))
			output = self.linear(h_t2)
			outputs += [output]
			outputs = torch.stack(outputs, 1).squeeze(2)
			return outputs