WMT_LSTM_6.py

class LSTM(nn.Module):
    """
    input_size - will be 1 in this example since we have only 1 predictor (a sequence of previous values)
    hidden_size - Can be chosen to dictate how much hidden "long term memory" the network will have
    output_size - This will be equal to the prediciton_periods input to get_x_y_pairs
    """
    def __init__(self, input_size, hidden_size, output_size):
        super(LSTM, self).__init__()
        self.hidden_size = hidden_size
        
        self.lstm = nn.LSTM(input_size, hidden_size)
        
        self.linear = nn.Linear(hidden_size, output_size)
        
    def forward(self, x, hidden=None):
        if hidden==None:
            self.hidden = (torch.zeros(1,1,self.hidden_size),
                           torch.zeros(1,1,self.hidden_size))
        else:
            self.hidden = hidden
            
        """
        inputs need to be in the right shape as defined in documentation
        - https://pytorch.org/docs/stable/generated/torch.nn.LSTM.html
        
        lstm_out - will contain the hidden states from all times in the sequence
        self.hidden - will contain the current hidden state and cell state
        """
        lstm_out, self.hidden = self.lstm(x.view(len(x),1,-1), 
                                          self.hidden)
        
        predictions = self.linear(lstm_out.view(len(x), -1))
        
        return predictions[-1], self.hidden